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|
f6e2737840 | ||
|
b9fdb9f701 | ||
|
e97b83bdbb | ||
|
51f81efb02 | ||
|
bfa14db2cb | ||
|
c3bd113a0b | ||
|
f4b78e73a4 | ||
|
501d4e9cf1 | ||
|
5e1f4f7464 | ||
|
5ca4230524 | ||
|
eb5eb8aa11 | ||
|
3662a274e2 | ||
|
ade40aa1a0 | ||
|
3ec2eb8bf1 | ||
|
21766a0898 | ||
|
0d834b9394 | ||
|
425eab3464 | ||
|
9beeef6267 | ||
|
6127d2ff1b | ||
|
c92ec3a925 | ||
|
ee3d63b6be | ||
|
e79b7db4b4 | ||
|
b921a52071 | ||
|
44c0e6b993 | ||
|
3bc8ee998d | ||
|
7f62300f7d | ||
|
fccc39834a | ||
|
d261bec1ec | ||
|
1fa777c1d7 | ||
|
2aaee73633 | ||
|
a5c2b5ed89 | ||
|
bbb1e35ea2 | ||
|
b0ae92d605 | ||
|
34f6d66742 | ||
|
125d5c8d96 | ||
|
2ab2bce74d | ||
|
c5d4c87c02 | ||
|
4e0cf7d4ed | ||
|
a9f0e7d536 | ||
|
f774a8d24e | ||
|
81e0723d65 | ||
|
b331ca784a | ||
|
8114959e7e | ||
|
cd14e7e8fd | ||
|
35b4104daf | ||
|
f7b38c4841 | ||
|
0f6862ef30 | ||
|
6cd7bf9f86 | ||
|
3ffe2e768b | ||
|
9e1f49c4e5 | ||
|
8bec3a2aa1 | ||
|
6c0566f937 | ||
|
3bd898b6ce | ||
|
876da12599 | ||
|
0c8825b2be | ||
|
1742c04bab | ||
|
d6fdfde9d7 | ||
|
4005cd66e0 | ||
|
4a3d05b657 |
4
.eslintignore
Normal file
4
.eslintignore
Normal file
@ -0,0 +1,4 @@
|
|||||||
|
extensions
|
||||||
|
extensions-disabled
|
||||||
|
repositories
|
||||||
|
venv
|
91
.eslintrc.js
Normal file
91
.eslintrc.js
Normal file
@ -0,0 +1,91 @@
|
|||||||
|
/* global module */
|
||||||
|
module.exports = {
|
||||||
|
env: {
|
||||||
|
browser: true,
|
||||||
|
es2021: true,
|
||||||
|
},
|
||||||
|
extends: "eslint:recommended",
|
||||||
|
parserOptions: {
|
||||||
|
ecmaVersion: "latest",
|
||||||
|
},
|
||||||
|
rules: {
|
||||||
|
"arrow-spacing": "error",
|
||||||
|
"block-spacing": "error",
|
||||||
|
"brace-style": "error",
|
||||||
|
"comma-dangle": ["error", "only-multiline"],
|
||||||
|
"comma-spacing": "error",
|
||||||
|
"comma-style": ["error", "last"],
|
||||||
|
"curly": ["error", "multi-line", "consistent"],
|
||||||
|
"eol-last": "error",
|
||||||
|
"func-call-spacing": "error",
|
||||||
|
"function-call-argument-newline": ["error", "consistent"],
|
||||||
|
"function-paren-newline": ["error", "consistent"],
|
||||||
|
"indent": ["error", 4],
|
||||||
|
"key-spacing": "error",
|
||||||
|
"keyword-spacing": "error",
|
||||||
|
"linebreak-style": ["error", "unix"],
|
||||||
|
"no-extra-semi": "error",
|
||||||
|
"no-mixed-spaces-and-tabs": "error",
|
||||||
|
"no-multi-spaces": "error",
|
||||||
|
"no-redeclare": ["error", {builtinGlobals: false}],
|
||||||
|
"no-trailing-spaces": "error",
|
||||||
|
"no-unused-vars": "off",
|
||||||
|
"no-whitespace-before-property": "error",
|
||||||
|
"object-curly-newline": ["error", {consistent: true, multiline: true}],
|
||||||
|
"object-curly-spacing": ["error", "never"],
|
||||||
|
"operator-linebreak": ["error", "after"],
|
||||||
|
"quote-props": ["error", "consistent-as-needed"],
|
||||||
|
"semi": ["error", "always"],
|
||||||
|
"semi-spacing": "error",
|
||||||
|
"semi-style": ["error", "last"],
|
||||||
|
"space-before-blocks": "error",
|
||||||
|
"space-before-function-paren": ["error", "never"],
|
||||||
|
"space-in-parens": ["error", "never"],
|
||||||
|
"space-infix-ops": "error",
|
||||||
|
"space-unary-ops": "error",
|
||||||
|
"switch-colon-spacing": "error",
|
||||||
|
"template-curly-spacing": ["error", "never"],
|
||||||
|
"unicode-bom": "error",
|
||||||
|
},
|
||||||
|
globals: {
|
||||||
|
//script.js
|
||||||
|
gradioApp: "readonly",
|
||||||
|
executeCallbacks: "readonly",
|
||||||
|
onAfterUiUpdate: "readonly",
|
||||||
|
onOptionsChanged: "readonly",
|
||||||
|
onUiLoaded: "readonly",
|
||||||
|
onUiUpdate: "readonly",
|
||||||
|
uiCurrentTab: "writable",
|
||||||
|
uiElementInSight: "readonly",
|
||||||
|
uiElementIsVisible: "readonly",
|
||||||
|
//ui.js
|
||||||
|
opts: "writable",
|
||||||
|
all_gallery_buttons: "readonly",
|
||||||
|
selected_gallery_button: "readonly",
|
||||||
|
selected_gallery_index: "readonly",
|
||||||
|
switch_to_txt2img: "readonly",
|
||||||
|
switch_to_img2img_tab: "readonly",
|
||||||
|
switch_to_img2img: "readonly",
|
||||||
|
switch_to_sketch: "readonly",
|
||||||
|
switch_to_inpaint: "readonly",
|
||||||
|
switch_to_inpaint_sketch: "readonly",
|
||||||
|
switch_to_extras: "readonly",
|
||||||
|
get_tab_index: "readonly",
|
||||||
|
create_submit_args: "readonly",
|
||||||
|
restart_reload: "readonly",
|
||||||
|
updateInput: "readonly",
|
||||||
|
//extraNetworks.js
|
||||||
|
requestGet: "readonly",
|
||||||
|
popup: "readonly",
|
||||||
|
// from python
|
||||||
|
localization: "readonly",
|
||||||
|
// progrssbar.js
|
||||||
|
randomId: "readonly",
|
||||||
|
requestProgress: "readonly",
|
||||||
|
// imageviewer.js
|
||||||
|
modalPrevImage: "readonly",
|
||||||
|
modalNextImage: "readonly",
|
||||||
|
// token-counters.js
|
||||||
|
setupTokenCounters: "readonly",
|
||||||
|
}
|
||||||
|
};
|
2
.git-blame-ignore-revs
Normal file
2
.git-blame-ignore-revs
Normal file
@ -0,0 +1,2 @@
|
|||||||
|
# Apply ESlint
|
||||||
|
9c54b78d9dde5601e916f308d9a9d6953ec39430
|
42
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
42
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
@ -43,10 +43,19 @@ body:
|
|||||||
- type: input
|
- type: input
|
||||||
id: commit
|
id: commit
|
||||||
attributes:
|
attributes:
|
||||||
label: Commit where the problem happens
|
label: Version or Commit where the problem happens
|
||||||
description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
|
description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
|
||||||
validations:
|
validations:
|
||||||
required: true
|
required: true
|
||||||
|
- type: dropdown
|
||||||
|
id: py-version
|
||||||
|
attributes:
|
||||||
|
label: What Python version are you running on ?
|
||||||
|
multiple: false
|
||||||
|
options:
|
||||||
|
- Python 3.10.x
|
||||||
|
- Python 3.11.x (above, no supported yet)
|
||||||
|
- Python 3.9.x (below, no recommended)
|
||||||
- type: dropdown
|
- type: dropdown
|
||||||
id: platforms
|
id: platforms
|
||||||
attributes:
|
attributes:
|
||||||
@ -59,6 +68,35 @@ body:
|
|||||||
- iOS
|
- iOS
|
||||||
- Android
|
- Android
|
||||||
- Other/Cloud
|
- Other/Cloud
|
||||||
|
- type: dropdown
|
||||||
|
id: device
|
||||||
|
attributes:
|
||||||
|
label: What device are you running WebUI on?
|
||||||
|
multiple: true
|
||||||
|
options:
|
||||||
|
- Nvidia GPUs (RTX 20 above)
|
||||||
|
- Nvidia GPUs (GTX 16 below)
|
||||||
|
- AMD GPUs (RX 6000 above)
|
||||||
|
- AMD GPUs (RX 5000 below)
|
||||||
|
- CPU
|
||||||
|
- Other GPUs
|
||||||
|
- type: dropdown
|
||||||
|
id: cross_attention_opt
|
||||||
|
attributes:
|
||||||
|
label: Cross attention optimization
|
||||||
|
description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
|
||||||
|
multiple: false
|
||||||
|
options:
|
||||||
|
- Automatic
|
||||||
|
- xformers
|
||||||
|
- sdp-no-mem
|
||||||
|
- sdp
|
||||||
|
- Doggettx
|
||||||
|
- V1
|
||||||
|
- InvokeAI
|
||||||
|
- "None "
|
||||||
|
validations:
|
||||||
|
required: true
|
||||||
- type: dropdown
|
- type: dropdown
|
||||||
id: browsers
|
id: browsers
|
||||||
attributes:
|
attributes:
|
||||||
|
2
.github/ISSUE_TEMPLATE/config.yml
vendored
2
.github/ISSUE_TEMPLATE/config.yml
vendored
@ -1,5 +1,5 @@
|
|||||||
blank_issues_enabled: false
|
blank_issues_enabled: false
|
||||||
contact_links:
|
contact_links:
|
||||||
- name: WebUI Community Support
|
- name: WebUI Community Support
|
||||||
url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
|
url: https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
|
||||||
about: Please ask and answer questions here.
|
about: Please ask and answer questions here.
|
||||||
|
33
.github/pull_request_template.md
vendored
33
.github/pull_request_template.md
vendored
@ -1,28 +1,15 @@
|
|||||||
# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
|
## Description
|
||||||
|
|
||||||
If you have a large change, pay special attention to this paragraph:
|
* a simple description of what you're trying to accomplish
|
||||||
|
* a summary of changes in code
|
||||||
|
* which issues it fixes, if any
|
||||||
|
|
||||||
> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
|
## Screenshots/videos:
|
||||||
|
|
||||||
Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
|
|
||||||
|
|
||||||
**Describe what this pull request is trying to achieve.**
|
## Checklist:
|
||||||
|
|
||||||
A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
|
- [ ] I have read [contributing wiki page](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
|
||||||
|
- [ ] I have performed a self-review of my own code
|
||||||
**Additional notes and description of your changes**
|
- [ ] My code follows the [style guidelines](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
|
||||||
|
- [ ] My code passes [tests](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)
|
||||||
More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
|
|
||||||
|
|
||||||
**Environment this was tested in**
|
|
||||||
|
|
||||||
List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
|
|
||||||
- OS: [e.g. Windows, Linux]
|
|
||||||
- Browser: [e.g. chrome, safari]
|
|
||||||
- Graphics card: [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
|
|
||||||
|
|
||||||
**Screenshots or videos of your changes**
|
|
||||||
|
|
||||||
If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
|
|
||||||
|
|
||||||
This is **required** for anything that touches the user interface.
|
|
||||||
|
55
.github/workflows/on_pull_request.yaml
vendored
55
.github/workflows/on_pull_request.yaml
vendored
@ -1,39 +1,38 @@
|
|||||||
# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
|
name: Linter
|
||||||
name: Run Linting/Formatting on Pull Requests
|
|
||||||
|
|
||||||
on:
|
on:
|
||||||
- push
|
- push
|
||||||
- pull_request
|
- pull_request
|
||||||
# See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
|
|
||||||
# if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
|
|
||||||
# pull_request:
|
|
||||||
# branches:
|
|
||||||
# - master
|
|
||||||
# branches-ignore:
|
|
||||||
# - development
|
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
lint:
|
lint-python:
|
||||||
|
name: ruff
|
||||||
runs-on: ubuntu-latest
|
runs-on: ubuntu-latest
|
||||||
|
if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
|
||||||
steps:
|
steps:
|
||||||
- name: Checkout Code
|
- name: Checkout Code
|
||||||
uses: actions/checkout@v3
|
uses: actions/checkout@v3
|
||||||
- name: Set up Python 3.10
|
- uses: actions/setup-python@v4
|
||||||
uses: actions/setup-python@v4
|
|
||||||
with:
|
with:
|
||||||
python-version: 3.10.6
|
python-version: 3.11
|
||||||
cache: pip
|
# NB: there's no cache: pip here since we're not installing anything
|
||||||
cache-dependency-path: |
|
# from the requirements.txt file(s) in the repository; it's faster
|
||||||
**/requirements*txt
|
# not to have GHA download an (at the time of writing) 4 GB cache
|
||||||
- name: Install PyLint
|
# of PyTorch and other dependencies.
|
||||||
run: |
|
- name: Install Ruff
|
||||||
python -m pip install --upgrade pip
|
run: pip install ruff==0.0.272
|
||||||
pip install pylint
|
- name: Run Ruff
|
||||||
# This lets PyLint check to see if it can resolve imports
|
run: ruff .
|
||||||
- name: Install dependencies
|
lint-js:
|
||||||
run: |
|
name: eslint
|
||||||
export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
|
runs-on: ubuntu-latest
|
||||||
python launch.py
|
if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
|
||||||
- name: Analysing the code with pylint
|
steps:
|
||||||
run: |
|
- name: Checkout Code
|
||||||
pylint $(git ls-files '*.py')
|
uses: actions/checkout@v3
|
||||||
|
- name: Install Node.js
|
||||||
|
uses: actions/setup-node@v3
|
||||||
|
with:
|
||||||
|
node-version: 18
|
||||||
|
- run: npm i --ci
|
||||||
|
- run: npm run lint
|
||||||
|
58
.github/workflows/run_tests.yaml
vendored
58
.github/workflows/run_tests.yaml
vendored
@ -1,4 +1,4 @@
|
|||||||
name: Run basic features tests on CPU with empty SD model
|
name: Tests
|
||||||
|
|
||||||
on:
|
on:
|
||||||
- push
|
- push
|
||||||
@ -6,7 +6,9 @@ on:
|
|||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
test:
|
test:
|
||||||
|
name: tests on CPU with empty model
|
||||||
runs-on: ubuntu-latest
|
runs-on: ubuntu-latest
|
||||||
|
if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
|
||||||
steps:
|
steps:
|
||||||
- name: Checkout Code
|
- name: Checkout Code
|
||||||
uses: actions/checkout@v3
|
uses: actions/checkout@v3
|
||||||
@ -17,13 +19,55 @@ jobs:
|
|||||||
cache: pip
|
cache: pip
|
||||||
cache-dependency-path: |
|
cache-dependency-path: |
|
||||||
**/requirements*txt
|
**/requirements*txt
|
||||||
|
launch.py
|
||||||
|
- name: Install test dependencies
|
||||||
|
run: pip install wait-for-it -r requirements-test.txt
|
||||||
|
env:
|
||||||
|
PIP_DISABLE_PIP_VERSION_CHECK: "1"
|
||||||
|
PIP_PROGRESS_BAR: "off"
|
||||||
|
- name: Setup environment
|
||||||
|
run: python launch.py --skip-torch-cuda-test --exit
|
||||||
|
env:
|
||||||
|
PIP_DISABLE_PIP_VERSION_CHECK: "1"
|
||||||
|
PIP_PROGRESS_BAR: "off"
|
||||||
|
TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
|
||||||
|
WEBUI_LAUNCH_LIVE_OUTPUT: "1"
|
||||||
|
PYTHONUNBUFFERED: "1"
|
||||||
|
- name: Start test server
|
||||||
|
run: >
|
||||||
|
python -m coverage run
|
||||||
|
--data-file=.coverage.server
|
||||||
|
launch.py
|
||||||
|
--skip-prepare-environment
|
||||||
|
--skip-torch-cuda-test
|
||||||
|
--test-server
|
||||||
|
--do-not-download-clip
|
||||||
|
--no-half
|
||||||
|
--disable-opt-split-attention
|
||||||
|
--use-cpu all
|
||||||
|
--api-server-stop
|
||||||
|
2>&1 | tee output.txt &
|
||||||
- name: Run tests
|
- name: Run tests
|
||||||
run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
|
run: |
|
||||||
- name: Upload main app stdout-stderr
|
wait-for-it --service 127.0.0.1:7860 -t 600
|
||||||
|
python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
|
||||||
|
- name: Kill test server
|
||||||
|
if: always()
|
||||||
|
run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
|
||||||
|
- name: Show coverage
|
||||||
|
run: |
|
||||||
|
python -m coverage combine .coverage*
|
||||||
|
python -m coverage report -i
|
||||||
|
python -m coverage html -i
|
||||||
|
- name: Upload main app output
|
||||||
uses: actions/upload-artifact@v3
|
uses: actions/upload-artifact@v3
|
||||||
if: always()
|
if: always()
|
||||||
with:
|
with:
|
||||||
name: stdout-stderr
|
name: output
|
||||||
path: |
|
path: output.txt
|
||||||
test/stdout.txt
|
- name: Upload coverage HTML
|
||||||
test/stderr.txt
|
uses: actions/upload-artifact@v3
|
||||||
|
if: always()
|
||||||
|
with:
|
||||||
|
name: htmlcov
|
||||||
|
path: htmlcov
|
||||||
|
19
.github/workflows/warns_merge_master.yml
vendored
Normal file
19
.github/workflows/warns_merge_master.yml
vendored
Normal file
@ -0,0 +1,19 @@
|
|||||||
|
name: Pull requests can't target master branch
|
||||||
|
|
||||||
|
"on":
|
||||||
|
pull_request:
|
||||||
|
types:
|
||||||
|
- opened
|
||||||
|
- synchronize
|
||||||
|
- reopened
|
||||||
|
branches:
|
||||||
|
- master
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
check:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
steps:
|
||||||
|
- name: Warning marge into master
|
||||||
|
run: |
|
||||||
|
echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch."
|
||||||
|
exit 1
|
6
.gitignore
vendored
6
.gitignore
vendored
@ -32,4 +32,8 @@ notification.mp3
|
|||||||
/extensions
|
/extensions
|
||||||
/test/stdout.txt
|
/test/stdout.txt
|
||||||
/test/stderr.txt
|
/test/stderr.txt
|
||||||
/cache.json
|
/cache.json*
|
||||||
|
/config_states/
|
||||||
|
/node_modules
|
||||||
|
/package-lock.json
|
||||||
|
/.coverage*
|
||||||
|
352
CHANGELOG.md
Normal file
352
CHANGELOG.md
Normal file
@ -0,0 +1,352 @@
|
|||||||
|
## 1.5.1
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* support parsing text encoder blocks in some new LoRAs
|
||||||
|
* delete scale checker script due to user demand
|
||||||
|
|
||||||
|
### Extensions and API:
|
||||||
|
* add postprocess_batch_list script callback
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix TI training for SD1
|
||||||
|
* fix reload altclip model error
|
||||||
|
* prepend the pythonpath instead of overriding it
|
||||||
|
* fix typo in SD_WEBUI_RESTARTING
|
||||||
|
* if txt2img/img2img raises an exception, finally call state.end()
|
||||||
|
* fix composable diffusion weight parsing
|
||||||
|
* restyle Startup profile for black users
|
||||||
|
* fix webui not launching with --nowebui
|
||||||
|
* catch exception for non git extensions
|
||||||
|
* fix some options missing from /sdapi/v1/options
|
||||||
|
* fix for extension update status always saying "unknown"
|
||||||
|
* fix display of extra network cards that have `<>` in the name
|
||||||
|
* update lora extension to work with python 3.8
|
||||||
|
|
||||||
|
|
||||||
|
## 1.5.0
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* SD XL support
|
||||||
|
* user metadata system for custom networks
|
||||||
|
* extended Lora metadata editor: set activation text, default weight, view tags, training info
|
||||||
|
* Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
|
||||||
|
* show github stars for extenstions
|
||||||
|
* img2img batch mode can read extra stuff from png info
|
||||||
|
* img2img batch works with subdirectories
|
||||||
|
* hotkeys to move prompt elements: alt+left/right
|
||||||
|
* restyle time taken/VRAM display
|
||||||
|
* add textual inversion hashes to infotext
|
||||||
|
* optimization: cache git extension repo information
|
||||||
|
* move generate button next to the generated picture for mobile clients
|
||||||
|
* hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
|
||||||
|
* skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* checkbox to check/uncheck all extensions in the Installed tab
|
||||||
|
* add gradio user to infotext and to filename patterns
|
||||||
|
* allow gif for extra network previews
|
||||||
|
* add options to change colors in grid
|
||||||
|
* use natural sort for items in extra networks
|
||||||
|
* Mac: use empty_cache() from torch 2 to clear VRAM
|
||||||
|
* added automatic support for installing the right libraries for Navi3 (AMD)
|
||||||
|
* add option SWIN_torch_compile to accelerate SwinIR upscale
|
||||||
|
* suppress printing TI embedding info at start to console by default
|
||||||
|
* speedup extra networks listing
|
||||||
|
* added `[none]` filename token.
|
||||||
|
* removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
|
||||||
|
* add always_discard_next_to_last_sigma option to XYZ plot
|
||||||
|
* automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
|
||||||
|
|
||||||
|
### Extensions and API:
|
||||||
|
* api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
|
||||||
|
* allow Script to have custom metaclass
|
||||||
|
* add model exists status check /sdapi/v1/options
|
||||||
|
* rename --add-stop-route to --api-server-stop
|
||||||
|
* add `before_hr` script callback
|
||||||
|
* add callback `after_extra_networks_activate`
|
||||||
|
* disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
|
||||||
|
* return http 404 when thumb file not found
|
||||||
|
* allow replacing extensions index with environment variable
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix for catch errors when retrieving extension index #11290
|
||||||
|
* fix very slow loading speed of .safetensors files when reading from network drives
|
||||||
|
* API cache cleanup
|
||||||
|
* fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
|
||||||
|
* fix warning of 'has_mps' deprecated from PyTorch
|
||||||
|
* fix problem with extra network saving images as previews losing generation info
|
||||||
|
* fix throwing exception when trying to resize image with I;16 mode
|
||||||
|
* fix for #11534: canvas zoom and pan extension hijacking shortcut keys
|
||||||
|
* fixed launch script to be runnable from any directory
|
||||||
|
* don't add "Seed Resize: -1x-1" to API image metadata
|
||||||
|
* correctly remove end parenthesis with ctrl+up/down
|
||||||
|
* fixing --subpath on newer gradio version
|
||||||
|
* fix: check fill size none zero when resize (fixes #11425)
|
||||||
|
* use submit and blur for quick settings textbox
|
||||||
|
* save img2img batch with images.save_image()
|
||||||
|
* prevent running preload.py for disabled extensions
|
||||||
|
* fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
|
||||||
|
|
||||||
|
|
||||||
|
## 1.4.1
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* add queue lock for refresh-checkpoints
|
||||||
|
|
||||||
|
## 1.4.0
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* zoom controls for inpainting
|
||||||
|
* run basic torch calculation at startup in parallel to reduce the performance impact of first generation
|
||||||
|
* option to pad prompt/neg prompt to be same length
|
||||||
|
* remove taming_transformers dependency
|
||||||
|
* custom k-diffusion scheduler settings
|
||||||
|
* add an option to show selected settings in main txt2img/img2img UI
|
||||||
|
* sysinfo tab in settings
|
||||||
|
* infer styles from prompts when pasting params into the UI
|
||||||
|
* an option to control the behavior of the above
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* bump Gradio to 3.32.0
|
||||||
|
* bump xformers to 0.0.20
|
||||||
|
* Add option to disable token counters
|
||||||
|
* tooltip fixes & optimizations
|
||||||
|
* make it possible to configure filename for the zip download
|
||||||
|
* `[vae_filename]` pattern for filenames
|
||||||
|
* Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
|
||||||
|
* change UI reorder setting to multiselect
|
||||||
|
* read version info form CHANGELOG.md if git version info is not available
|
||||||
|
* link footer API to Wiki when API is not active
|
||||||
|
* persistent conds cache (opt-in optimization)
|
||||||
|
|
||||||
|
### Extensions:
|
||||||
|
* After installing extensions, webui properly restarts the process rather than reloads the UI
|
||||||
|
* Added VAE listing to web API. Via: /sdapi/v1/sd-vae
|
||||||
|
* custom unet support
|
||||||
|
* Add onAfterUiUpdate callback
|
||||||
|
* refactor EmbeddingDatabase.register_embedding() to allow unregistering
|
||||||
|
* add before_process callback for scripts
|
||||||
|
* add ability for alwayson scripts to specify section and let user reorder those sections
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* Fix dragging text to prompt
|
||||||
|
* fix incorrect quoting for infotext values with colon in them
|
||||||
|
* fix "hires. fix" prompt sharing same labels with txt2img_prompt
|
||||||
|
* Fix s_min_uncond default type int
|
||||||
|
* Fix for #10643 (Inpainting mask sometimes not working)
|
||||||
|
* fix bad styling for thumbs view in extra networks #10639
|
||||||
|
* fix for empty list of optimizations #10605
|
||||||
|
* small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
|
||||||
|
* fix --ui-debug-mode exit
|
||||||
|
* patch GitPython to not use leaky persistent processes
|
||||||
|
* fix duplicate Cross attention optimization after UI reload
|
||||||
|
* torch.cuda.is_available() check for SdOptimizationXformers
|
||||||
|
* fix hires fix using wrong conds in second pass if using Loras.
|
||||||
|
* handle exception when parsing generation parameters from png info
|
||||||
|
* fix upcast attention dtype error
|
||||||
|
* forcing Torch Version to 1.13.1 for RX 5000 series GPUs
|
||||||
|
* split mask blur into X and Y components, patch Outpainting MK2 accordingly
|
||||||
|
* don't die when a LoRA is a broken symlink
|
||||||
|
* allow activation of Generate Forever during generation
|
||||||
|
|
||||||
|
|
||||||
|
## 1.3.2
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix files served out of tmp directory even if they are saved to disk
|
||||||
|
* fix postprocessing overwriting parameters
|
||||||
|
|
||||||
|
## 1.3.1
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* revert default cross attention optimization to Doggettx
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix bug: LoRA don't apply on dropdown list sd_lora
|
||||||
|
* fix png info always added even if setting is not enabled
|
||||||
|
* fix some fields not applying in xyz plot
|
||||||
|
* fix "hires. fix" prompt sharing same labels with txt2img_prompt
|
||||||
|
* fix lora hashes not being added properly to infotex if there is only one lora
|
||||||
|
* fix --use-cpu failing to work properly at startup
|
||||||
|
* make --disable-opt-split-attention command line option work again
|
||||||
|
|
||||||
|
## 1.3.0
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* add UI to edit defaults
|
||||||
|
* token merging (via dbolya/tomesd)
|
||||||
|
* settings tab rework: add a lot of additional explanations and links
|
||||||
|
* load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
|
||||||
|
* update extensions table: show branch, show date in separate column, and show version from tags if available
|
||||||
|
* TAESD - another option for cheap live previews
|
||||||
|
* allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
|
||||||
|
* calculate hashes for Lora
|
||||||
|
* add lora hashes to infotext
|
||||||
|
* when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
|
||||||
|
* select cross attention optimization from UI
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* bump Gradio to 3.31.0
|
||||||
|
* bump PyTorch to 2.0.1 for macOS and Linux AMD
|
||||||
|
* allow setting defaults for elements in extensions' tabs
|
||||||
|
* allow selecting file type for live previews
|
||||||
|
* show "Loading..." for extra networks when displaying for the first time
|
||||||
|
* suppress ENSD infotext for samplers that don't use it
|
||||||
|
* clientside optimizations
|
||||||
|
* add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
|
||||||
|
* allow whitespace in styles.csv
|
||||||
|
* add option to reorder tabs
|
||||||
|
* move some functionality (swap resolution and set seed to -1) to client
|
||||||
|
* option to specify editor height for img2img
|
||||||
|
* button to copy image resolution into img2img width/height sliders
|
||||||
|
* switch from pyngrok to ngrok-py
|
||||||
|
* lazy-load images in extra networks UI
|
||||||
|
* set "Navigate image viewer with gamepad" option to false by default, by request
|
||||||
|
* change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
|
||||||
|
* allow hiding buttons in ui-config.json
|
||||||
|
|
||||||
|
### Extensions:
|
||||||
|
* add /sdapi/v1/script-info api
|
||||||
|
* use Ruff to lint Python code
|
||||||
|
* use ESlint to lint Javascript code
|
||||||
|
* add/modify CFG callbacks for Self-Attention Guidance extension
|
||||||
|
* add command and endpoint for graceful server stopping
|
||||||
|
* add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
|
||||||
|
* rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
|
||||||
|
* add /sdapi/v1/refresh-loras api checkpoint post request
|
||||||
|
* tests overhaul
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix an issue preventing the program from starting if the user specifies a bad Gradio theme
|
||||||
|
* fix broken prompts from file script
|
||||||
|
* fix symlink scanning for extra networks
|
||||||
|
* fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
|
||||||
|
* allow web UI to be ran fully offline
|
||||||
|
* fix inability to run with --freeze-settings
|
||||||
|
* fix inability to merge checkpoint without adding metadata
|
||||||
|
* fix extra networks' save preview image not adding infotext for jpeg/webm
|
||||||
|
* remove blinking effect from text in hires fix and scale resolution preview
|
||||||
|
* make links to `http://<...>.git` extensions work in the extension tab
|
||||||
|
* fix bug with webui hanging at startup due to hanging git process
|
||||||
|
|
||||||
|
|
||||||
|
## 1.2.1
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* add an option to always refer to LoRA by filenames
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
|
||||||
|
* fix upscalers disappearing after the user reloads UI
|
||||||
|
* allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
|
||||||
|
* allow web UI to be ran fully offline
|
||||||
|
* fix localizations not working
|
||||||
|
* fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
|
||||||
|
|
||||||
|
## 1.2.0
|
||||||
|
|
||||||
|
### Features:
|
||||||
|
* do not wait for Stable Diffusion model to load at startup
|
||||||
|
* add filename patterns: `[denoising]`
|
||||||
|
* directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
|
||||||
|
* LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
|
||||||
|
* LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
|
||||||
|
* LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
|
||||||
|
* LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
|
||||||
|
* add version to infotext, footer and console output when starting
|
||||||
|
* add links to wiki for filename pattern settings
|
||||||
|
* add extended info for quicksettings setting and use multiselect input instead of a text field
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* bump Gradio to 3.29.0
|
||||||
|
* bump PyTorch to 2.0.1
|
||||||
|
* `--subpath` option for gradio for use with reverse proxy
|
||||||
|
* Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
|
||||||
|
* do not apply localizations if there are none (possible frontend optimization)
|
||||||
|
* add extra `None` option for VAE in XYZ plot
|
||||||
|
* print error to console when batch processing in img2img fails
|
||||||
|
* create HTML for extra network pages only on demand
|
||||||
|
* allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
|
||||||
|
* put infotext options into their own category in settings tab
|
||||||
|
* do not show licenses page when user selects Show all pages in settings
|
||||||
|
|
||||||
|
### Extensions:
|
||||||
|
* tooltip localization support
|
||||||
|
* add API method to get LoRA models with prompt
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* re-add `/docs` endpoint
|
||||||
|
* fix gamepad navigation
|
||||||
|
* make the lightbox fullscreen image function properly
|
||||||
|
* fix squished thumbnails in extras tab
|
||||||
|
* keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
|
||||||
|
* fix webui showing the same image if you configure the generation to always save results into same file
|
||||||
|
* fix bug with upscalers not working properly
|
||||||
|
* fix MPS on PyTorch 2.0.1, Intel Macs
|
||||||
|
* make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
|
||||||
|
* prevent Reload UI button/link from reloading the page when it's not yet ready
|
||||||
|
* fix prompts from file script failing to read contents from a drag/drop file
|
||||||
|
|
||||||
|
|
||||||
|
## 1.1.1
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
|
||||||
|
|
||||||
|
## 1.1.0
|
||||||
|
### Features:
|
||||||
|
* switch to PyTorch 2.0.0 (except for AMD GPUs)
|
||||||
|
* visual improvements to custom code scripts
|
||||||
|
* add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
|
||||||
|
* add support for saving init images in img2img, and record their hashes in infotext for reproducability
|
||||||
|
* automatically select current word when adjusting weight with ctrl+up/down
|
||||||
|
* add dropdowns for X/Y/Z plot
|
||||||
|
* add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
|
||||||
|
* support Gradio's theme API
|
||||||
|
* use TCMalloc on Linux by default; possible fix for memory leaks
|
||||||
|
* add optimization option to remove negative conditioning at low sigma values #9177
|
||||||
|
* embed model merge metadata in .safetensors file
|
||||||
|
* extension settings backup/restore feature #9169
|
||||||
|
* add "resize by" and "resize to" tabs to img2img
|
||||||
|
* add option "keep original size" to textual inversion images preprocess
|
||||||
|
* image viewer scrolling via analog stick
|
||||||
|
* button to restore the progress from session lost / tab reload
|
||||||
|
|
||||||
|
### Minor:
|
||||||
|
* bump Gradio to 3.28.1
|
||||||
|
* change "scale to" to sliders in Extras tab
|
||||||
|
* add labels to tool buttons to make it possible to hide them
|
||||||
|
* add tiled inference support for ScuNET
|
||||||
|
* add branch support for extension installation
|
||||||
|
* change Linux installation script to install into current directory rather than `/home/username`
|
||||||
|
* sort textual inversion embeddings by name (case-insensitive)
|
||||||
|
* allow styles.csv to be symlinked or mounted in docker
|
||||||
|
* remove the "do not add watermark to images" option
|
||||||
|
* make selected tab configurable with UI config
|
||||||
|
* make the extra networks UI fixed height and scrollable
|
||||||
|
* add `disable_tls_verify` arg for use with self-signed certs
|
||||||
|
|
||||||
|
### Extensions:
|
||||||
|
* add reload callback
|
||||||
|
* add `is_hr_pass` field for processing
|
||||||
|
|
||||||
|
### Bug Fixes:
|
||||||
|
* fix broken batch image processing on 'Extras/Batch Process' tab
|
||||||
|
* add "None" option to extra networks dropdowns
|
||||||
|
* fix FileExistsError for CLIP Interrogator
|
||||||
|
* fix /sdapi/v1/txt2img endpoint not working on Linux #9319
|
||||||
|
* fix disappearing live previews and progressbar during slow tasks
|
||||||
|
* fix fullscreen image view not working properly in some cases
|
||||||
|
* prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
|
||||||
|
* fix prompt schedule for second order samplers
|
||||||
|
* fix image mask/composite for weird resolutions #9628
|
||||||
|
* use correct images for previews when using AND (see #9491)
|
||||||
|
* one broken image in img2img batch won't stop all processing
|
||||||
|
* fix image orientation bug in train/preprocess
|
||||||
|
* fix Ngrok recreating tunnels every reload
|
||||||
|
* fix `--realesrgan-models-path` and `--ldsr-models-path` not working
|
||||||
|
* fix `--skip-install` not working
|
||||||
|
* use SAMPLE file format in Outpainting Mk2 & Poorman
|
||||||
|
* do not fail all LoRAs if some have failed to load when making a picture
|
||||||
|
|
||||||
|
## 1.0.0
|
||||||
|
* everything
|
@ -2,7 +2,7 @@
|
|||||||
|
|
||||||
# if you were managing a localization and were removed from this file, this is because
|
# if you were managing a localization and were removed from this file, this is because
|
||||||
# the intended way to do localizations now is via extensions. See:
|
# the intended way to do localizations now is via extensions. See:
|
||||||
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
|
# https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
|
||||||
# Make a repo with your localization and since you are still listed as a collaborator
|
# Make a repo with your localization and since you are still listed as a collaborator
|
||||||
# you can add it to the wiki page yourself. This change is because some people complained
|
# you can add it to the wiki page yourself. This change is because some people complained
|
||||||
# the git commit log is cluttered with things unrelated to almost everyone and
|
# the git commit log is cluttered with things unrelated to almost everyone and
|
||||||
|
96
README.md
96
README.md
@ -4,7 +4,7 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
![](screenshot.png)
|
![](screenshot.png)
|
||||||
|
|
||||||
## Features
|
## Features
|
||||||
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
|
[Detailed feature showcase with images](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
|
||||||
- Original txt2img and img2img modes
|
- Original txt2img and img2img modes
|
||||||
- One click install and run script (but you still must install python and git)
|
- One click install and run script (but you still must install python and git)
|
||||||
- Outpainting
|
- Outpainting
|
||||||
@ -15,7 +15,7 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
- Attention, specify parts of text that the model should pay more attention to
|
- Attention, specify parts of text that the model should pay more attention to
|
||||||
- a man in a `((tuxedo))` - will pay more attention to tuxedo
|
- a man in a `((tuxedo))` - will pay more attention to tuxedo
|
||||||
- a man in a `(tuxedo:1.21)` - alternative syntax
|
- a man in a `(tuxedo:1.21)` - alternative syntax
|
||||||
- select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
|
- select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
|
||||||
- Loopback, run img2img processing multiple times
|
- Loopback, run img2img processing multiple times
|
||||||
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
|
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
|
||||||
- Textual Inversion
|
- Textual Inversion
|
||||||
@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
- CodeFormer, face restoration tool as an alternative to GFPGAN
|
- CodeFormer, face restoration tool as an alternative to GFPGAN
|
||||||
- RealESRGAN, neural network upscaler
|
- RealESRGAN, neural network upscaler
|
||||||
- ESRGAN, neural network upscaler with a lot of third party models
|
- ESRGAN, neural network upscaler with a lot of third party models
|
||||||
- SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
|
- SwinIR and Swin2SR ([see here](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
|
||||||
- LDSR, Latent diffusion super resolution upscaling
|
- LDSR, Latent diffusion super resolution upscaling
|
||||||
- Resizing aspect ratio options
|
- Resizing aspect ratio options
|
||||||
- Sampling method selection
|
- Sampling method selection
|
||||||
@ -63,14 +63,14 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
|
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
|
||||||
- Reloading checkpoints on the fly
|
- Reloading checkpoints on the fly
|
||||||
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
|
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
|
||||||
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
|
- [Custom scripts](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
|
||||||
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
|
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
|
||||||
- separate prompts using uppercase `AND`
|
- separate prompts using uppercase `AND`
|
||||||
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
|
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
|
||||||
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
|
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
|
||||||
- DeepDanbooru integration, creates danbooru style tags for anime prompts
|
- DeepDanbooru integration, creates danbooru style tags for anime prompts
|
||||||
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
|
- [xformers](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
|
||||||
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
|
- via extension: [History tab](https://ghproxy.com/https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
|
||||||
- Generate forever option
|
- Generate forever option
|
||||||
- Training tab
|
- Training tab
|
||||||
- hypernetworks and embeddings options
|
- hypernetworks and embeddings options
|
||||||
@ -82,10 +82,10 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
- Can select to load a different VAE from settings screen
|
- Can select to load a different VAE from settings screen
|
||||||
- Estimated completion time in progress bar
|
- Estimated completion time in progress bar
|
||||||
- API
|
- API
|
||||||
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
|
- Support for dedicated [inpainting model](https://ghproxy.com/https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
|
||||||
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
|
- via extension: [Aesthetic Gradients](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://ghproxy.com/https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://ghproxy.com/https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
|
||||||
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
|
- [Stable Diffusion 2.0](https://ghproxy.com/https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
|
||||||
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
|
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
|
||||||
- Now without any bad letters!
|
- Now without any bad letters!
|
||||||
- Load checkpoints in safetensors format
|
- Load checkpoints in safetensors format
|
||||||
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
|
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
|
||||||
@ -93,16 +93,22 @@ A browser interface based on Gradio library for Stable Diffusion.
|
|||||||
- Reorder elements in the UI from settings screen
|
- Reorder elements in the UI from settings screen
|
||||||
|
|
||||||
## Installation and Running
|
## Installation and Running
|
||||||
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
|
Make sure the required [dependencies](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
|
||||||
|
|
||||||
Alternatively, use online services (like Google Colab):
|
Alternatively, use online services (like Google Colab):
|
||||||
|
|
||||||
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
|
- [List of Online Services](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
|
||||||
|
|
||||||
|
### Installation on Windows 10/11 with NVidia-GPUs using release package
|
||||||
|
1. Download `sd.webui.zip` from [v1.0.0-pre](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
|
||||||
|
2. Run `update.bat`.
|
||||||
|
3. Run `run.bat`.
|
||||||
|
> For more details see [Install-and-Run-on-NVidia-GPUs](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
|
||||||
|
|
||||||
### Automatic Installation on Windows
|
### Automatic Installation on Windows
|
||||||
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
|
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
|
||||||
2. Install [git](https://git-scm.com/download/win).
|
2. Install [git](https://git-scm.com/download/win).
|
||||||
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
|
3. Download the stable-diffusion-webui repository, for example by running `git clone https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
|
||||||
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
|
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
|
||||||
|
|
||||||
### Automatic Installation on Linux
|
### Automatic Installation on Linux
|
||||||
@ -115,47 +121,53 @@ sudo dnf install wget git python3
|
|||||||
# Arch-based:
|
# Arch-based:
|
||||||
sudo pacman -S wget git python3
|
sudo pacman -S wget git python3
|
||||||
```
|
```
|
||||||
2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
|
2. Navigate to the directory you would like the webui to be installed and execute the following command:
|
||||||
```bash
|
```bash
|
||||||
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
|
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
|
||||||
```
|
```
|
||||||
3. Run `webui.sh`.
|
3. Run `webui.sh`.
|
||||||
|
4. Check `webui-user.sh` for options.
|
||||||
### Installation on Apple Silicon
|
### Installation on Apple Silicon
|
||||||
|
|
||||||
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
|
Find the instructions [here](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
|
||||||
|
|
||||||
## Contributing
|
## Contributing
|
||||||
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
|
Here's how to add code to this repo: [Contributing](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
|
||||||
|
|
||||||
## Documentation
|
## Documentation
|
||||||
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
|
|
||||||
|
The documentation was moved from this README over to the project's [wiki](https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
|
||||||
|
|
||||||
|
For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://ghproxy.com/https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
|
||||||
|
|
||||||
## Credits
|
## Credits
|
||||||
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
|
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
|
||||||
|
|
||||||
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
|
- Stable Diffusion - https://ghproxy.com/https://github.com/CompVis/stable-diffusion, https://ghproxy.com/https://github.com/CompVis/taming-transformers
|
||||||
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
|
- k-diffusion - https://ghproxy.com/https://github.com/crowsonkb/k-diffusion.git
|
||||||
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
|
- GFPGAN - https://ghproxy.com/https://github.com/TencentARC/GFPGAN.git
|
||||||
- CodeFormer - https://github.com/sczhou/CodeFormer
|
- CodeFormer - https://ghproxy.com/https://github.com/sczhou/CodeFormer
|
||||||
- ESRGAN - https://github.com/xinntao/ESRGAN
|
- ESRGAN - https://ghproxy.com/https://github.com/xinntao/ESRGAN
|
||||||
- SwinIR - https://github.com/JingyunLiang/SwinIR
|
- SwinIR - https://ghproxy.com/https://github.com/JingyunLiang/SwinIR
|
||||||
- Swin2SR - https://github.com/mv-lab/swin2sr
|
- Swin2SR - https://ghproxy.com/https://github.com/mv-lab/swin2sr
|
||||||
- LDSR - https://github.com/Hafiidz/latent-diffusion
|
- LDSR - https://ghproxy.com/https://github.com/Hafiidz/latent-diffusion
|
||||||
- MiDaS - https://github.com/isl-org/MiDaS
|
- MiDaS - https://ghproxy.com/https://github.com/isl-org/MiDaS
|
||||||
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
|
- Ideas for optimizations - https://ghproxy.com/https://github.com/basujindal/stable-diffusion
|
||||||
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
|
- Cross Attention layer optimization - Doggettx - https://ghproxy.com/https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
|
||||||
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
|
- Cross Attention layer optimization - InvokeAI, lstein - https://ghproxy.com/https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
|
||||||
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
|
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://ghproxy.com/https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://ghproxy.com/https://github.com/AminRezaei0x443/memory-efficient-attention)
|
||||||
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
|
- Textual Inversion - Rinon Gal - https://ghproxy.com/https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
|
||||||
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
|
- Idea for SD upscale - https://ghproxy.com/https://github.com/jquesnelle/txt2imghd
|
||||||
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
|
- Noise generation for outpainting mk2 - https://ghproxy.com/https://github.com/parlance-zz/g-diffuser-bot
|
||||||
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
|
- CLIP interrogator idea and borrowing some code - https://ghproxy.com/https://github.com/pharmapsychotic/clip-interrogator
|
||||||
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
|
- Idea for Composable Diffusion - https://ghproxy.com/https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
|
||||||
- xformers - https://github.com/facebookresearch/xformers
|
- xformers - https://ghproxy.com/https://github.com/facebookresearch/xformers
|
||||||
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
|
- DeepDanbooru - interrogator for anime diffusers https://ghproxy.com/https://github.com/KichangKim/DeepDanbooru
|
||||||
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
|
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://ghproxy.com/https://github.com/Birch-san/diffusers-play/tree/92feee6)
|
||||||
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
|
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://ghproxy.com/https://github.com/timothybrooks/instruct-pix2pix
|
||||||
- Security advice - RyotaK
|
- Security advice - RyotaK
|
||||||
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
|
- UniPC sampler - Wenliang Zhao - https://ghproxy.com/https://github.com/wl-zhao/UniPC
|
||||||
|
- TAESD - Ollin Boer Bohan - https://ghproxy.com/https://github.com/madebyollin/taesd
|
||||||
|
- LyCORIS - KohakuBlueleaf
|
||||||
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
|
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
|
||||||
- (You)
|
- (You)
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
|
# File modified by authors of InstructPix2Pix from original (https://ghproxy.com/https://github.com/CompVis/stable-diffusion).
|
||||||
# See more details in LICENSE.
|
# See more details in LICENSE.
|
||||||
|
|
||||||
model:
|
model:
|
||||||
|
@ -4,8 +4,8 @@ channels:
|
|||||||
- defaults
|
- defaults
|
||||||
dependencies:
|
dependencies:
|
||||||
- python=3.10
|
- python=3.10
|
||||||
- pip=22.2.2
|
- pip=23.0
|
||||||
- cudatoolkit=11.3
|
- cudatoolkit=11.8
|
||||||
- pytorch=1.12.1
|
- pytorch=2.0
|
||||||
- torchvision=0.13.1
|
- torchvision=0.15
|
||||||
- numpy=1.23.1
|
- numpy=1.23
|
||||||
|
@ -12,7 +12,7 @@ import safetensors.torch
|
|||||||
|
|
||||||
from ldm.models.diffusion.ddim import DDIMSampler
|
from ldm.models.diffusion.ddim import DDIMSampler
|
||||||
from ldm.util import instantiate_from_config, ismap
|
from ldm.util import instantiate_from_config, ismap
|
||||||
from modules import shared, sd_hijack
|
from modules import shared, sd_hijack, devices
|
||||||
|
|
||||||
cached_ldsr_model: torch.nn.Module = None
|
cached_ldsr_model: torch.nn.Module = None
|
||||||
|
|
||||||
@ -88,7 +88,7 @@ class LDSR:
|
|||||||
|
|
||||||
x_t = None
|
x_t = None
|
||||||
logs = None
|
logs = None
|
||||||
for n in range(n_runs):
|
for _ in range(n_runs):
|
||||||
if custom_shape is not None:
|
if custom_shape is not None:
|
||||||
x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
|
x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
|
||||||
x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
|
x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
|
||||||
@ -110,11 +110,9 @@ class LDSR:
|
|||||||
diffusion_steps = int(steps)
|
diffusion_steps = int(steps)
|
||||||
eta = 1.0
|
eta = 1.0
|
||||||
|
|
||||||
down_sample_method = 'Lanczos'
|
|
||||||
|
|
||||||
gc.collect()
|
gc.collect()
|
||||||
if torch.cuda.is_available:
|
devices.torch_gc()
|
||||||
torch.cuda.empty_cache()
|
|
||||||
|
|
||||||
im_og = image
|
im_og = image
|
||||||
width_og, height_og = im_og.size
|
width_og, height_og = im_og.size
|
||||||
@ -131,11 +129,11 @@ class LDSR:
|
|||||||
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
|
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
|
||||||
else:
|
else:
|
||||||
print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
|
print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
|
||||||
|
|
||||||
# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
|
# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
|
||||||
pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
|
pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
|
||||||
im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
|
im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
|
||||||
|
|
||||||
logs = self.run(model["model"], im_padded, diffusion_steps, eta)
|
logs = self.run(model["model"], im_padded, diffusion_steps, eta)
|
||||||
|
|
||||||
sample = logs["sample"]
|
sample = logs["sample"]
|
||||||
@ -151,14 +149,13 @@ class LDSR:
|
|||||||
|
|
||||||
del model
|
del model
|
||||||
gc.collect()
|
gc.collect()
|
||||||
if torch.cuda.is_available:
|
devices.torch_gc()
|
||||||
torch.cuda.empty_cache()
|
|
||||||
|
|
||||||
return a
|
return a
|
||||||
|
|
||||||
|
|
||||||
def get_cond(selected_path):
|
def get_cond(selected_path):
|
||||||
example = dict()
|
example = {}
|
||||||
up_f = 4
|
up_f = 4
|
||||||
c = selected_path.convert('RGB')
|
c = selected_path.convert('RGB')
|
||||||
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
|
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
|
||||||
@ -196,7 +193,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s
|
|||||||
@torch.no_grad()
|
@torch.no_grad()
|
||||||
def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
|
def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
|
||||||
corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
|
corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
|
||||||
log = dict()
|
log = {}
|
||||||
|
|
||||||
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
|
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
|
||||||
return_first_stage_outputs=True,
|
return_first_stage_outputs=True,
|
||||||
@ -244,7 +241,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
|
|||||||
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
|
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
|
||||||
log["sample_noquant"] = x_sample_noquant
|
log["sample_noquant"] = x_sample_noquant
|
||||||
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
|
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
|
||||||
except:
|
except Exception:
|
||||||
pass
|
pass
|
||||||
|
|
||||||
log["sample"] = x_sample
|
log["sample"] = x_sample
|
||||||
|
@ -1,13 +1,11 @@
|
|||||||
import os
|
import os
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
from basicsr.utils.download_util import load_file_from_url
|
|
||||||
|
|
||||||
|
from modules.modelloader import load_file_from_url
|
||||||
from modules.upscaler import Upscaler, UpscalerData
|
from modules.upscaler import Upscaler, UpscalerData
|
||||||
from ldsr_model_arch import LDSR
|
from ldsr_model_arch import LDSR
|
||||||
from modules import shared, script_callbacks
|
from modules import shared, script_callbacks, errors
|
||||||
import sd_hijack_autoencoder, sd_hijack_ddpm_v1
|
import sd_hijack_autoencoder # noqa: F401
|
||||||
|
import sd_hijack_ddpm_v1 # noqa: F401
|
||||||
|
|
||||||
|
|
||||||
class UpscalerLDSR(Upscaler):
|
class UpscalerLDSR(Upscaler):
|
||||||
@ -25,35 +23,36 @@ class UpscalerLDSR(Upscaler):
|
|||||||
yaml_path = os.path.join(self.model_path, "project.yaml")
|
yaml_path = os.path.join(self.model_path, "project.yaml")
|
||||||
old_model_path = os.path.join(self.model_path, "model.pth")
|
old_model_path = os.path.join(self.model_path, "model.pth")
|
||||||
new_model_path = os.path.join(self.model_path, "model.ckpt")
|
new_model_path = os.path.join(self.model_path, "model.ckpt")
|
||||||
safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
|
|
||||||
|
local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
|
||||||
|
local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
|
||||||
|
local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
|
||||||
|
local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
|
||||||
|
|
||||||
if os.path.exists(yaml_path):
|
if os.path.exists(yaml_path):
|
||||||
statinfo = os.stat(yaml_path)
|
statinfo = os.stat(yaml_path)
|
||||||
if statinfo.st_size >= 10485760:
|
if statinfo.st_size >= 10485760:
|
||||||
print("Removing invalid LDSR YAML file.")
|
print("Removing invalid LDSR YAML file.")
|
||||||
os.remove(yaml_path)
|
os.remove(yaml_path)
|
||||||
|
|
||||||
if os.path.exists(old_model_path):
|
if os.path.exists(old_model_path):
|
||||||
print("Renaming model from model.pth to model.ckpt")
|
print("Renaming model from model.pth to model.ckpt")
|
||||||
os.rename(old_model_path, new_model_path)
|
os.rename(old_model_path, new_model_path)
|
||||||
if os.path.exists(safetensors_model_path):
|
|
||||||
model = safetensors_model_path
|
if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
|
||||||
|
model = local_safetensors_path
|
||||||
else:
|
else:
|
||||||
model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
|
model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
|
||||||
file_name="model.ckpt", progress=True)
|
|
||||||
yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
|
|
||||||
file_name="project.yaml", progress=True)
|
|
||||||
|
|
||||||
try:
|
yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
|
||||||
return LDSR(model, yaml)
|
|
||||||
|
|
||||||
except Exception:
|
return LDSR(model, yaml)
|
||||||
print("Error importing LDSR:", file=sys.stderr)
|
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
return None
|
|
||||||
|
|
||||||
def do_upscale(self, img, path):
|
def do_upscale(self, img, path):
|
||||||
ldsr = self.load_model(path)
|
try:
|
||||||
if ldsr is None:
|
ldsr = self.load_model(path)
|
||||||
print("NO LDSR!")
|
except Exception:
|
||||||
|
errors.report(f"Failed loading LDSR model {path}", exc_info=True)
|
||||||
return img
|
return img
|
||||||
ddim_steps = shared.opts.ldsr_steps
|
ddim_steps = shared.opts.ldsr_steps
|
||||||
return ldsr.super_resolution(img, ddim_steps, self.scale)
|
return ldsr.super_resolution(img, ddim_steps, self.scale)
|
||||||
|
@ -1,16 +1,21 @@
|
|||||||
# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
|
# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
|
||||||
# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
|
# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
|
||||||
# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
|
# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
|
||||||
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
import pytorch_lightning as pl
|
import pytorch_lightning as pl
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
from contextlib import contextmanager
|
from contextlib import contextmanager
|
||||||
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
|
|
||||||
|
from torch.optim.lr_scheduler import LambdaLR
|
||||||
|
|
||||||
|
from ldm.modules.ema import LitEma
|
||||||
|
from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
|
||||||
from ldm.modules.diffusionmodules.model import Encoder, Decoder
|
from ldm.modules.diffusionmodules.model import Encoder, Decoder
|
||||||
from ldm.util import instantiate_from_config
|
from ldm.util import instantiate_from_config
|
||||||
|
|
||||||
import ldm.models.autoencoder
|
import ldm.models.autoencoder
|
||||||
|
from packaging import version
|
||||||
|
|
||||||
class VQModel(pl.LightningModule):
|
class VQModel(pl.LightningModule):
|
||||||
def __init__(self,
|
def __init__(self,
|
||||||
@ -19,7 +24,7 @@ class VQModel(pl.LightningModule):
|
|||||||
n_embed,
|
n_embed,
|
||||||
embed_dim,
|
embed_dim,
|
||||||
ckpt_path=None,
|
ckpt_path=None,
|
||||||
ignore_keys=[],
|
ignore_keys=None,
|
||||||
image_key="image",
|
image_key="image",
|
||||||
colorize_nlabels=None,
|
colorize_nlabels=None,
|
||||||
monitor=None,
|
monitor=None,
|
||||||
@ -57,7 +62,7 @@ class VQModel(pl.LightningModule):
|
|||||||
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
||||||
|
|
||||||
if ckpt_path is not None:
|
if ckpt_path is not None:
|
||||||
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
|
||||||
self.scheduler_config = scheduler_config
|
self.scheduler_config = scheduler_config
|
||||||
self.lr_g_factor = lr_g_factor
|
self.lr_g_factor = lr_g_factor
|
||||||
|
|
||||||
@ -76,18 +81,19 @@ class VQModel(pl.LightningModule):
|
|||||||
if context is not None:
|
if context is not None:
|
||||||
print(f"{context}: Restored training weights")
|
print(f"{context}: Restored training weights")
|
||||||
|
|
||||||
def init_from_ckpt(self, path, ignore_keys=list()):
|
def init_from_ckpt(self, path, ignore_keys=None):
|
||||||
sd = torch.load(path, map_location="cpu")["state_dict"]
|
sd = torch.load(path, map_location="cpu")["state_dict"]
|
||||||
keys = list(sd.keys())
|
keys = list(sd.keys())
|
||||||
for k in keys:
|
for k in keys:
|
||||||
for ik in ignore_keys:
|
for ik in ignore_keys or []:
|
||||||
if k.startswith(ik):
|
if k.startswith(ik):
|
||||||
print("Deleting key {} from state_dict.".format(k))
|
print("Deleting key {} from state_dict.".format(k))
|
||||||
del sd[k]
|
del sd[k]
|
||||||
missing, unexpected = self.load_state_dict(sd, strict=False)
|
missing, unexpected = self.load_state_dict(sd, strict=False)
|
||||||
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
||||||
if len(missing) > 0:
|
if missing:
|
||||||
print(f"Missing Keys: {missing}")
|
print(f"Missing Keys: {missing}")
|
||||||
|
if unexpected:
|
||||||
print(f"Unexpected Keys: {unexpected}")
|
print(f"Unexpected Keys: {unexpected}")
|
||||||
|
|
||||||
def on_train_batch_end(self, *args, **kwargs):
|
def on_train_batch_end(self, *args, **kwargs):
|
||||||
@ -141,7 +147,7 @@ class VQModel(pl.LightningModule):
|
|||||||
return x
|
return x
|
||||||
|
|
||||||
def training_step(self, batch, batch_idx, optimizer_idx):
|
def training_step(self, batch, batch_idx, optimizer_idx):
|
||||||
# https://github.com/pytorch/pytorch/issues/37142
|
# https://ghproxy.com/https://github.com/pytorch/pytorch/issues/37142
|
||||||
# try not to fool the heuristics
|
# try not to fool the heuristics
|
||||||
x = self.get_input(batch, self.image_key)
|
x = self.get_input(batch, self.image_key)
|
||||||
xrec, qloss, ind = self(x, return_pred_indices=True)
|
xrec, qloss, ind = self(x, return_pred_indices=True)
|
||||||
@ -165,7 +171,7 @@ class VQModel(pl.LightningModule):
|
|||||||
def validation_step(self, batch, batch_idx):
|
def validation_step(self, batch, batch_idx):
|
||||||
log_dict = self._validation_step(batch, batch_idx)
|
log_dict = self._validation_step(batch, batch_idx)
|
||||||
with self.ema_scope():
|
with self.ema_scope():
|
||||||
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
|
self._validation_step(batch, batch_idx, suffix="_ema")
|
||||||
return log_dict
|
return log_dict
|
||||||
|
|
||||||
def _validation_step(self, batch, batch_idx, suffix=""):
|
def _validation_step(self, batch, batch_idx, suffix=""):
|
||||||
@ -232,7 +238,7 @@ class VQModel(pl.LightningModule):
|
|||||||
return self.decoder.conv_out.weight
|
return self.decoder.conv_out.weight
|
||||||
|
|
||||||
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
|
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
|
||||||
log = dict()
|
log = {}
|
||||||
x = self.get_input(batch, self.image_key)
|
x = self.get_input(batch, self.image_key)
|
||||||
x = x.to(self.device)
|
x = x.to(self.device)
|
||||||
if only_inputs:
|
if only_inputs:
|
||||||
@ -249,7 +255,8 @@ class VQModel(pl.LightningModule):
|
|||||||
if plot_ema:
|
if plot_ema:
|
||||||
with self.ema_scope():
|
with self.ema_scope():
|
||||||
xrec_ema, _ = self(x)
|
xrec_ema, _ = self(x)
|
||||||
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
|
if x.shape[1] > 3:
|
||||||
|
xrec_ema = self.to_rgb(xrec_ema)
|
||||||
log["reconstructions_ema"] = xrec_ema
|
log["reconstructions_ema"] = xrec_ema
|
||||||
return log
|
return log
|
||||||
|
|
||||||
@ -264,7 +271,7 @@ class VQModel(pl.LightningModule):
|
|||||||
|
|
||||||
class VQModelInterface(VQModel):
|
class VQModelInterface(VQModel):
|
||||||
def __init__(self, embed_dim, *args, **kwargs):
|
def __init__(self, embed_dim, *args, **kwargs):
|
||||||
super().__init__(embed_dim=embed_dim, *args, **kwargs)
|
super().__init__(*args, embed_dim=embed_dim, **kwargs)
|
||||||
self.embed_dim = embed_dim
|
self.embed_dim = embed_dim
|
||||||
|
|
||||||
def encode(self, x):
|
def encode(self, x):
|
||||||
@ -282,5 +289,5 @@ class VQModelInterface(VQModel):
|
|||||||
dec = self.decoder(quant)
|
dec = self.decoder(quant)
|
||||||
return dec
|
return dec
|
||||||
|
|
||||||
setattr(ldm.models.autoencoder, "VQModel", VQModel)
|
ldm.models.autoencoder.VQModel = VQModel
|
||||||
setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
|
ldm.models.autoencoder.VQModelInterface = VQModelInterface
|
||||||
|
@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
|
|||||||
beta_schedule="linear",
|
beta_schedule="linear",
|
||||||
loss_type="l2",
|
loss_type="l2",
|
||||||
ckpt_path=None,
|
ckpt_path=None,
|
||||||
ignore_keys=[],
|
ignore_keys=None,
|
||||||
load_only_unet=False,
|
load_only_unet=False,
|
||||||
monitor="val/loss",
|
monitor="val/loss",
|
||||||
use_ema=True,
|
use_ema=True,
|
||||||
@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
|
|||||||
if monitor is not None:
|
if monitor is not None:
|
||||||
self.monitor = monitor
|
self.monitor = monitor
|
||||||
if ckpt_path is not None:
|
if ckpt_path is not None:
|
||||||
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
|
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
|
||||||
|
|
||||||
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
|
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
|
||||||
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
|
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
|
||||||
@ -182,22 +182,22 @@ class DDPMV1(pl.LightningModule):
|
|||||||
if context is not None:
|
if context is not None:
|
||||||
print(f"{context}: Restored training weights")
|
print(f"{context}: Restored training weights")
|
||||||
|
|
||||||
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
|
def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
|
||||||
sd = torch.load(path, map_location="cpu")
|
sd = torch.load(path, map_location="cpu")
|
||||||
if "state_dict" in list(sd.keys()):
|
if "state_dict" in list(sd.keys()):
|
||||||
sd = sd["state_dict"]
|
sd = sd["state_dict"]
|
||||||
keys = list(sd.keys())
|
keys = list(sd.keys())
|
||||||
for k in keys:
|
for k in keys:
|
||||||
for ik in ignore_keys:
|
for ik in ignore_keys or []:
|
||||||
if k.startswith(ik):
|
if k.startswith(ik):
|
||||||
print("Deleting key {} from state_dict.".format(k))
|
print("Deleting key {} from state_dict.".format(k))
|
||||||
del sd[k]
|
del sd[k]
|
||||||
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
|
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
|
||||||
sd, strict=False)
|
sd, strict=False)
|
||||||
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
||||||
if len(missing) > 0:
|
if missing:
|
||||||
print(f"Missing Keys: {missing}")
|
print(f"Missing Keys: {missing}")
|
||||||
if len(unexpected) > 0:
|
if unexpected:
|
||||||
print(f"Unexpected Keys: {unexpected}")
|
print(f"Unexpected Keys: {unexpected}")
|
||||||
|
|
||||||
def q_mean_variance(self, x_start, t):
|
def q_mean_variance(self, x_start, t):
|
||||||
@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):
|
|||||||
|
|
||||||
@torch.no_grad()
|
@torch.no_grad()
|
||||||
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
|
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
|
||||||
log = dict()
|
log = {}
|
||||||
x = self.get_input(batch, self.first_stage_key)
|
x = self.get_input(batch, self.first_stage_key)
|
||||||
N = min(x.shape[0], N)
|
N = min(x.shape[0], N)
|
||||||
n_row = min(x.shape[0], n_row)
|
n_row = min(x.shape[0], n_row)
|
||||||
@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
|
|||||||
log["inputs"] = x
|
log["inputs"] = x
|
||||||
|
|
||||||
# get diffusion row
|
# get diffusion row
|
||||||
diffusion_row = list()
|
diffusion_row = []
|
||||||
x_start = x[:n_row]
|
x_start = x[:n_row]
|
||||||
|
|
||||||
for t in range(self.num_timesteps):
|
for t in range(self.num_timesteps):
|
||||||
@ -444,13 +444,13 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
conditioning_key = None
|
conditioning_key = None
|
||||||
ckpt_path = kwargs.pop("ckpt_path", None)
|
ckpt_path = kwargs.pop("ckpt_path", None)
|
||||||
ignore_keys = kwargs.pop("ignore_keys", [])
|
ignore_keys = kwargs.pop("ignore_keys", [])
|
||||||
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
|
super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
|
||||||
self.concat_mode = concat_mode
|
self.concat_mode = concat_mode
|
||||||
self.cond_stage_trainable = cond_stage_trainable
|
self.cond_stage_trainable = cond_stage_trainable
|
||||||
self.cond_stage_key = cond_stage_key
|
self.cond_stage_key = cond_stage_key
|
||||||
try:
|
try:
|
||||||
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
||||||
except:
|
except Exception:
|
||||||
self.num_downs = 0
|
self.num_downs = 0
|
||||||
if not scale_by_std:
|
if not scale_by_std:
|
||||||
self.scale_factor = scale_factor
|
self.scale_factor = scale_factor
|
||||||
@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
self.instantiate_cond_stage(cond_stage_config)
|
self.instantiate_cond_stage(cond_stage_config)
|
||||||
self.cond_stage_forward = cond_stage_forward
|
self.cond_stage_forward = cond_stage_forward
|
||||||
self.clip_denoised = False
|
self.clip_denoised = False
|
||||||
self.bbox_tokenizer = None
|
self.bbox_tokenizer = None
|
||||||
|
|
||||||
self.restarted_from_ckpt = False
|
self.restarted_from_ckpt = False
|
||||||
if ckpt_path is not None:
|
if ckpt_path is not None:
|
||||||
@ -792,7 +792,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
||||||
|
|
||||||
# 2. apply model loop over last dim
|
# 2. apply model loop over last dim
|
||||||
if isinstance(self.first_stage_model, VQModelInterface):
|
if isinstance(self.first_stage_model, VQModelInterface):
|
||||||
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
||||||
force_not_quantize=predict_cids or force_not_quantize)
|
force_not_quantize=predict_cids or force_not_quantize)
|
||||||
for i in range(z.shape[-1])]
|
for i in range(z.shape[-1])]
|
||||||
@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
|
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
|
||||||
return self.p_losses(x, c, t, *args, **kwargs)
|
return self.p_losses(x, c, t, *args, **kwargs)
|
||||||
|
|
||||||
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
|
|
||||||
def rescale_bbox(bbox):
|
|
||||||
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
|
|
||||||
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
|
|
||||||
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
|
|
||||||
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
|
|
||||||
return x0, y0, w, h
|
|
||||||
|
|
||||||
return [rescale_bbox(b) for b in bboxes]
|
|
||||||
|
|
||||||
def apply_model(self, x_noisy, t, cond, return_ids=False):
|
def apply_model(self, x_noisy, t, cond, return_ids=False):
|
||||||
|
|
||||||
if isinstance(cond, dict):
|
if isinstance(cond, dict):
|
||||||
@ -900,7 +890,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
if hasattr(self, "split_input_params"):
|
if hasattr(self, "split_input_params"):
|
||||||
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
||||||
assert not return_ids
|
assert not return_ids
|
||||||
ks = self.split_input_params["ks"] # eg. (128, 128)
|
ks = self.split_input_params["ks"] # eg. (128, 128)
|
||||||
stride = self.split_input_params["stride"] # eg. (64, 64)
|
stride = self.split_input_params["stride"] # eg. (64, 64)
|
||||||
|
|
||||||
@ -1126,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
if cond is not None:
|
if cond is not None:
|
||||||
if isinstance(cond, dict):
|
if isinstance(cond, dict):
|
||||||
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
||||||
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
[x[:batch_size] for x in cond[key]] for key in cond}
|
||||||
else:
|
else:
|
||||||
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
||||||
|
|
||||||
@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
if i % log_every_t == 0 or i == timesteps - 1:
|
if i % log_every_t == 0 or i == timesteps - 1:
|
||||||
intermediates.append(x0_partial)
|
intermediates.append(x0_partial)
|
||||||
if callback: callback(i)
|
if callback:
|
||||||
if img_callback: img_callback(img, i)
|
callback(i)
|
||||||
|
if img_callback:
|
||||||
|
img_callback(img, i)
|
||||||
return img, intermediates
|
return img, intermediates
|
||||||
|
|
||||||
@torch.no_grad()
|
@torch.no_grad()
|
||||||
@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
if i % log_every_t == 0 or i == timesteps - 1:
|
if i % log_every_t == 0 or i == timesteps - 1:
|
||||||
intermediates.append(img)
|
intermediates.append(img)
|
||||||
if callback: callback(i)
|
if callback:
|
||||||
if img_callback: img_callback(img, i)
|
callback(i)
|
||||||
|
if img_callback:
|
||||||
|
img_callback(img, i)
|
||||||
|
|
||||||
if return_intermediates:
|
if return_intermediates:
|
||||||
return img, intermediates
|
return img, intermediates
|
||||||
@ -1221,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
if cond is not None:
|
if cond is not None:
|
||||||
if isinstance(cond, dict):
|
if isinstance(cond, dict):
|
||||||
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
||||||
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
[x[:batch_size] for x in cond[key]] for key in cond}
|
||||||
else:
|
else:
|
||||||
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
||||||
return self.p_sample_loop(cond,
|
return self.p_sample_loop(cond,
|
||||||
@ -1253,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
use_ddim = ddim_steps is not None
|
use_ddim = ddim_steps is not None
|
||||||
|
|
||||||
log = dict()
|
log = {}
|
||||||
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
|
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
|
||||||
return_first_stage_outputs=True,
|
return_first_stage_outputs=True,
|
||||||
force_c_encode=True,
|
force_c_encode=True,
|
||||||
@ -1280,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
if plot_diffusion_rows:
|
if plot_diffusion_rows:
|
||||||
# get diffusion row
|
# get diffusion row
|
||||||
diffusion_row = list()
|
diffusion_row = []
|
||||||
z_start = z[:n_row]
|
z_start = z[:n_row]
|
||||||
for t in range(self.num_timesteps):
|
for t in range(self.num_timesteps):
|
||||||
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
||||||
@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):
|
|||||||
|
|
||||||
if inpaint:
|
if inpaint:
|
||||||
# make a simple center square
|
# make a simple center square
|
||||||
b, h, w = z.shape[0], z.shape[2], z.shape[3]
|
h, w = z.shape[2], z.shape[3]
|
||||||
mask = torch.ones(N, h, w).to(self.device)
|
mask = torch.ones(N, h, w).to(self.device)
|
||||||
# zeros will be filled in
|
# zeros will be filled in
|
||||||
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
|
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
|
||||||
@ -1424,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
|
|||||||
# TODO: move all layout-specific hacks to this class
|
# TODO: move all layout-specific hacks to this class
|
||||||
def __init__(self, cond_stage_key, *args, **kwargs):
|
def __init__(self, cond_stage_key, *args, **kwargs):
|
||||||
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
|
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
|
||||||
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
|
super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
|
||||||
|
|
||||||
def log_images(self, batch, N=8, *args, **kwargs):
|
def log_images(self, batch, N=8, *args, **kwargs):
|
||||||
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
|
logs = super().log_images(*args, batch=batch, N=N, **kwargs)
|
||||||
|
|
||||||
key = 'train' if self.training else 'validation'
|
key = 'train' if self.training else 'validation'
|
||||||
dset = self.trainer.datamodule.datasets[key]
|
dset = self.trainer.datamodule.datasets[key]
|
||||||
@ -1443,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
|
|||||||
logs['bbox_image'] = cond_img
|
logs['bbox_image'] = cond_img
|
||||||
return logs
|
return logs
|
||||||
|
|
||||||
setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
|
ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
|
||||||
setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
|
ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
|
||||||
setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
|
ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
|
||||||
setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
|
ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
|
||||||
|
147
extensions-builtin/LDSR/vqvae_quantize.py
Normal file
147
extensions-builtin/LDSR/vqvae_quantize.py
Normal file
@ -0,0 +1,147 @@
|
|||||||
|
# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
|
||||||
|
# where the license is as follows:
|
||||||
|
#
|
||||||
|
# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
|
||||||
|
#
|
||||||
|
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
# of this software and associated documentation files (the "Software"), to deal
|
||||||
|
# in the Software without restriction, including without limitation the rights
|
||||||
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
|
# copies of the Software, and to permit persons to whom the Software is
|
||||||
|
# furnished to do so, subject to the following conditions:
|
||||||
|
#
|
||||||
|
# The above copyright notice and this permission notice shall be included in all
|
||||||
|
# copies or substantial portions of the Software.
|
||||||
|
#
|
||||||
|
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||||
|
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
||||||
|
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
|
||||||
|
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
|
||||||
|
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
|
||||||
|
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
|
||||||
|
# OR OTHER DEALINGS IN THE SOFTWARE./
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import numpy as np
|
||||||
|
from einops import rearrange
|
||||||
|
|
||||||
|
|
||||||
|
class VectorQuantizer2(nn.Module):
|
||||||
|
"""
|
||||||
|
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
|
||||||
|
avoids costly matrix multiplications and allows for post-hoc remapping of indices.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# NOTE: due to a bug the beta term was applied to the wrong term. for
|
||||||
|
# backwards compatibility we use the buggy version by default, but you can
|
||||||
|
# specify legacy=False to fix it.
|
||||||
|
def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
|
||||||
|
sane_index_shape=False, legacy=True):
|
||||||
|
super().__init__()
|
||||||
|
self.n_e = n_e
|
||||||
|
self.e_dim = e_dim
|
||||||
|
self.beta = beta
|
||||||
|
self.legacy = legacy
|
||||||
|
|
||||||
|
self.embedding = nn.Embedding(self.n_e, self.e_dim)
|
||||||
|
self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
|
||||||
|
|
||||||
|
self.remap = remap
|
||||||
|
if self.remap is not None:
|
||||||
|
self.register_buffer("used", torch.tensor(np.load(self.remap)))
|
||||||
|
self.re_embed = self.used.shape[0]
|
||||||
|
self.unknown_index = unknown_index # "random" or "extra" or integer
|
||||||
|
if self.unknown_index == "extra":
|
||||||
|
self.unknown_index = self.re_embed
|
||||||
|
self.re_embed = self.re_embed + 1
|
||||||
|
print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
|
||||||
|
f"Using {self.unknown_index} for unknown indices.")
|
||||||
|
else:
|
||||||
|
self.re_embed = n_e
|
||||||
|
|
||||||
|
self.sane_index_shape = sane_index_shape
|
||||||
|
|
||||||
|
def remap_to_used(self, inds):
|
||||||
|
ishape = inds.shape
|
||||||
|
assert len(ishape) > 1
|
||||||
|
inds = inds.reshape(ishape[0], -1)
|
||||||
|
used = self.used.to(inds)
|
||||||
|
match = (inds[:, :, None] == used[None, None, ...]).long()
|
||||||
|
new = match.argmax(-1)
|
||||||
|
unknown = match.sum(2) < 1
|
||||||
|
if self.unknown_index == "random":
|
||||||
|
new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
|
||||||
|
else:
|
||||||
|
new[unknown] = self.unknown_index
|
||||||
|
return new.reshape(ishape)
|
||||||
|
|
||||||
|
def unmap_to_all(self, inds):
|
||||||
|
ishape = inds.shape
|
||||||
|
assert len(ishape) > 1
|
||||||
|
inds = inds.reshape(ishape[0], -1)
|
||||||
|
used = self.used.to(inds)
|
||||||
|
if self.re_embed > self.used.shape[0]: # extra token
|
||||||
|
inds[inds >= self.used.shape[0]] = 0 # simply set to zero
|
||||||
|
back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
|
||||||
|
return back.reshape(ishape)
|
||||||
|
|
||||||
|
def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
|
||||||
|
assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
|
||||||
|
assert rescale_logits is False, "Only for interface compatible with Gumbel"
|
||||||
|
assert return_logits is False, "Only for interface compatible with Gumbel"
|
||||||
|
# reshape z -> (batch, height, width, channel) and flatten
|
||||||
|
z = rearrange(z, 'b c h w -> b h w c').contiguous()
|
||||||
|
z_flattened = z.view(-1, self.e_dim)
|
||||||
|
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
||||||
|
|
||||||
|
d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
|
||||||
|
torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
|
||||||
|
torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
|
||||||
|
|
||||||
|
min_encoding_indices = torch.argmin(d, dim=1)
|
||||||
|
z_q = self.embedding(min_encoding_indices).view(z.shape)
|
||||||
|
perplexity = None
|
||||||
|
min_encodings = None
|
||||||
|
|
||||||
|
# compute loss for embedding
|
||||||
|
if not self.legacy:
|
||||||
|
loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
|
||||||
|
torch.mean((z_q - z.detach()) ** 2)
|
||||||
|
else:
|
||||||
|
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
|
||||||
|
torch.mean((z_q - z.detach()) ** 2)
|
||||||
|
|
||||||
|
# preserve gradients
|
||||||
|
z_q = z + (z_q - z).detach()
|
||||||
|
|
||||||
|
# reshape back to match original input shape
|
||||||
|
z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
|
||||||
|
|
||||||
|
if self.remap is not None:
|
||||||
|
min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
|
||||||
|
min_encoding_indices = self.remap_to_used(min_encoding_indices)
|
||||||
|
min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
|
||||||
|
|
||||||
|
if self.sane_index_shape:
|
||||||
|
min_encoding_indices = min_encoding_indices.reshape(
|
||||||
|
z_q.shape[0], z_q.shape[2], z_q.shape[3])
|
||||||
|
|
||||||
|
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
|
||||||
|
|
||||||
|
def get_codebook_entry(self, indices, shape):
|
||||||
|
# shape specifying (batch, height, width, channel)
|
||||||
|
if self.remap is not None:
|
||||||
|
indices = indices.reshape(shape[0], -1) # add batch axis
|
||||||
|
indices = self.unmap_to_all(indices)
|
||||||
|
indices = indices.reshape(-1) # flatten again
|
||||||
|
|
||||||
|
# get quantized latent vectors
|
||||||
|
z_q = self.embedding(indices)
|
||||||
|
|
||||||
|
if shape is not None:
|
||||||
|
z_q = z_q.view(shape)
|
||||||
|
# reshape back to match original input shape
|
||||||
|
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
||||||
|
|
||||||
|
return z_q
|
@ -1,5 +1,6 @@
|
|||||||
from modules import extra_networks, shared
|
from modules import extra_networks, shared
|
||||||
import lora
|
import networks
|
||||||
|
|
||||||
|
|
||||||
class ExtraNetworkLora(extra_networks.ExtraNetwork):
|
class ExtraNetworkLora(extra_networks.ExtraNetwork):
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
@ -8,19 +9,51 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
|
|||||||
def activate(self, p, params_list):
|
def activate(self, p, params_list):
|
||||||
additional = shared.opts.sd_lora
|
additional = shared.opts.sd_lora
|
||||||
|
|
||||||
if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
|
if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
|
||||||
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
|
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
|
||||||
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
||||||
|
|
||||||
names = []
|
names = []
|
||||||
multipliers = []
|
te_multipliers = []
|
||||||
|
unet_multipliers = []
|
||||||
|
dyn_dims = []
|
||||||
for params in params_list:
|
for params in params_list:
|
||||||
assert len(params.items) > 0
|
assert params.items
|
||||||
|
|
||||||
names.append(params.items[0])
|
names.append(params.positional[0])
|
||||||
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
|
|
||||||
|
|
||||||
lora.load_loras(names, multipliers)
|
te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
|
||||||
|
te_multiplier = float(params.named.get("te", te_multiplier))
|
||||||
|
|
||||||
|
unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
|
||||||
|
unet_multiplier = float(params.named.get("unet", unet_multiplier))
|
||||||
|
|
||||||
|
dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
|
||||||
|
dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
|
||||||
|
|
||||||
|
te_multipliers.append(te_multiplier)
|
||||||
|
unet_multipliers.append(unet_multiplier)
|
||||||
|
dyn_dims.append(dyn_dim)
|
||||||
|
|
||||||
|
networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
|
||||||
|
|
||||||
|
if shared.opts.lora_add_hashes_to_infotext:
|
||||||
|
network_hashes = []
|
||||||
|
for item in networks.loaded_networks:
|
||||||
|
shorthash = item.network_on_disk.shorthash
|
||||||
|
if not shorthash:
|
||||||
|
continue
|
||||||
|
|
||||||
|
alias = item.mentioned_name
|
||||||
|
if not alias:
|
||||||
|
continue
|
||||||
|
|
||||||
|
alias = alias.replace(":", "").replace(",", "")
|
||||||
|
|
||||||
|
network_hashes.append(f"{alias}: {shorthash}")
|
||||||
|
|
||||||
|
if network_hashes:
|
||||||
|
p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
|
||||||
|
|
||||||
def deactivate(self, p):
|
def deactivate(self, p):
|
||||||
pass
|
pass
|
||||||
|
@ -1,362 +1,9 @@
|
|||||||
import glob
|
import networks
|
||||||
import os
|
|
||||||
import re
|
|
||||||
import torch
|
|
||||||
from typing import Union
|
|
||||||
|
|
||||||
from modules import shared, devices, sd_models, errors
|
list_available_loras = networks.list_available_networks
|
||||||
|
|
||||||
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
|
available_loras = networks.available_networks
|
||||||
|
available_lora_aliases = networks.available_network_aliases
|
||||||
re_digits = re.compile(r"\d+")
|
available_lora_hash_lookup = networks.available_network_hash_lookup
|
||||||
re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
|
forbidden_lora_aliases = networks.forbidden_network_aliases
|
||||||
re_compiled = {}
|
loaded_loras = networks.loaded_networks
|
||||||
|
|
||||||
suffix_conversion = {
|
|
||||||
"attentions": {},
|
|
||||||
"resnets": {
|
|
||||||
"conv1": "in_layers_2",
|
|
||||||
"conv2": "out_layers_3",
|
|
||||||
"time_emb_proj": "emb_layers_1",
|
|
||||||
"conv_shortcut": "skip_connection",
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def convert_diffusers_name_to_compvis(key, is_sd2):
|
|
||||||
def match(match_list, regex_text):
|
|
||||||
regex = re_compiled.get(regex_text)
|
|
||||||
if regex is None:
|
|
||||||
regex = re.compile(regex_text)
|
|
||||||
re_compiled[regex_text] = regex
|
|
||||||
|
|
||||||
r = re.match(regex, key)
|
|
||||||
if not r:
|
|
||||||
return False
|
|
||||||
|
|
||||||
match_list.clear()
|
|
||||||
match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
|
|
||||||
return True
|
|
||||||
|
|
||||||
m = []
|
|
||||||
|
|
||||||
if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
|
|
||||||
suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
|
|
||||||
return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
|
|
||||||
|
|
||||||
if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
|
|
||||||
suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
|
|
||||||
return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
|
|
||||||
|
|
||||||
if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
|
|
||||||
suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
|
|
||||||
return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
|
|
||||||
|
|
||||||
if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
|
|
||||||
return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
|
|
||||||
|
|
||||||
if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
|
|
||||||
return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
|
|
||||||
|
|
||||||
if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
|
|
||||||
if is_sd2:
|
|
||||||
if 'mlp_fc1' in m[1]:
|
|
||||||
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
|
|
||||||
elif 'mlp_fc2' in m[1]:
|
|
||||||
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
|
|
||||||
else:
|
|
||||||
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
|
|
||||||
|
|
||||||
return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
|
|
||||||
|
|
||||||
return key
|
|
||||||
|
|
||||||
|
|
||||||
class LoraOnDisk:
|
|
||||||
def __init__(self, name, filename):
|
|
||||||
self.name = name
|
|
||||||
self.filename = filename
|
|
||||||
self.metadata = {}
|
|
||||||
|
|
||||||
_, ext = os.path.splitext(filename)
|
|
||||||
if ext.lower() == ".safetensors":
|
|
||||||
try:
|
|
||||||
self.metadata = sd_models.read_metadata_from_safetensors(filename)
|
|
||||||
except Exception as e:
|
|
||||||
errors.display(e, f"reading lora {filename}")
|
|
||||||
|
|
||||||
if self.metadata:
|
|
||||||
m = {}
|
|
||||||
for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
|
|
||||||
m[k] = v
|
|
||||||
|
|
||||||
self.metadata = m
|
|
||||||
|
|
||||||
self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
|
|
||||||
|
|
||||||
|
|
||||||
class LoraModule:
|
|
||||||
def __init__(self, name):
|
|
||||||
self.name = name
|
|
||||||
self.multiplier = 1.0
|
|
||||||
self.modules = {}
|
|
||||||
self.mtime = None
|
|
||||||
|
|
||||||
|
|
||||||
class LoraUpDownModule:
|
|
||||||
def __init__(self):
|
|
||||||
self.up = None
|
|
||||||
self.down = None
|
|
||||||
self.alpha = None
|
|
||||||
|
|
||||||
|
|
||||||
def assign_lora_names_to_compvis_modules(sd_model):
|
|
||||||
lora_layer_mapping = {}
|
|
||||||
|
|
||||||
for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
|
|
||||||
lora_name = name.replace(".", "_")
|
|
||||||
lora_layer_mapping[lora_name] = module
|
|
||||||
module.lora_layer_name = lora_name
|
|
||||||
|
|
||||||
for name, module in shared.sd_model.model.named_modules():
|
|
||||||
lora_name = name.replace(".", "_")
|
|
||||||
lora_layer_mapping[lora_name] = module
|
|
||||||
module.lora_layer_name = lora_name
|
|
||||||
|
|
||||||
sd_model.lora_layer_mapping = lora_layer_mapping
|
|
||||||
|
|
||||||
|
|
||||||
def load_lora(name, filename):
|
|
||||||
lora = LoraModule(name)
|
|
||||||
lora.mtime = os.path.getmtime(filename)
|
|
||||||
|
|
||||||
sd = sd_models.read_state_dict(filename)
|
|
||||||
|
|
||||||
keys_failed_to_match = {}
|
|
||||||
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
|
|
||||||
|
|
||||||
for key_diffusers, weight in sd.items():
|
|
||||||
key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
|
|
||||||
key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
|
|
||||||
|
|
||||||
sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
|
|
||||||
|
|
||||||
if sd_module is None:
|
|
||||||
m = re_x_proj.match(key)
|
|
||||||
if m:
|
|
||||||
sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
|
|
||||||
|
|
||||||
if sd_module is None:
|
|
||||||
keys_failed_to_match[key_diffusers] = key
|
|
||||||
continue
|
|
||||||
|
|
||||||
lora_module = lora.modules.get(key, None)
|
|
||||||
if lora_module is None:
|
|
||||||
lora_module = LoraUpDownModule()
|
|
||||||
lora.modules[key] = lora_module
|
|
||||||
|
|
||||||
if lora_key == "alpha":
|
|
||||||
lora_module.alpha = weight.item()
|
|
||||||
continue
|
|
||||||
|
|
||||||
if type(sd_module) == torch.nn.Linear:
|
|
||||||
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
|
|
||||||
elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
|
|
||||||
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
|
|
||||||
elif type(sd_module) == torch.nn.MultiheadAttention:
|
|
||||||
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
|
|
||||||
elif type(sd_module) == torch.nn.Conv2d:
|
|
||||||
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
|
|
||||||
else:
|
|
||||||
print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
|
|
||||||
continue
|
|
||||||
assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
|
|
||||||
|
|
||||||
with torch.no_grad():
|
|
||||||
module.weight.copy_(weight)
|
|
||||||
|
|
||||||
module.to(device=devices.cpu, dtype=devices.dtype)
|
|
||||||
|
|
||||||
if lora_key == "lora_up.weight":
|
|
||||||
lora_module.up = module
|
|
||||||
elif lora_key == "lora_down.weight":
|
|
||||||
lora_module.down = module
|
|
||||||
else:
|
|
||||||
assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
|
|
||||||
|
|
||||||
if len(keys_failed_to_match) > 0:
|
|
||||||
print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
|
|
||||||
|
|
||||||
return lora
|
|
||||||
|
|
||||||
|
|
||||||
def load_loras(names, multipliers=None):
|
|
||||||
already_loaded = {}
|
|
||||||
|
|
||||||
for lora in loaded_loras:
|
|
||||||
if lora.name in names:
|
|
||||||
already_loaded[lora.name] = lora
|
|
||||||
|
|
||||||
loaded_loras.clear()
|
|
||||||
|
|
||||||
loras_on_disk = [available_loras.get(name, None) for name in names]
|
|
||||||
if any([x is None for x in loras_on_disk]):
|
|
||||||
list_available_loras()
|
|
||||||
|
|
||||||
loras_on_disk = [available_loras.get(name, None) for name in names]
|
|
||||||
|
|
||||||
for i, name in enumerate(names):
|
|
||||||
lora = already_loaded.get(name, None)
|
|
||||||
|
|
||||||
lora_on_disk = loras_on_disk[i]
|
|
||||||
if lora_on_disk is not None:
|
|
||||||
if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
|
|
||||||
lora = load_lora(name, lora_on_disk.filename)
|
|
||||||
|
|
||||||
if lora is None:
|
|
||||||
print(f"Couldn't find Lora with name {name}")
|
|
||||||
continue
|
|
||||||
|
|
||||||
lora.multiplier = multipliers[i] if multipliers else 1.0
|
|
||||||
loaded_loras.append(lora)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_calc_updown(lora, module, target):
|
|
||||||
with torch.no_grad():
|
|
||||||
up = module.up.weight.to(target.device, dtype=target.dtype)
|
|
||||||
down = module.down.weight.to(target.device, dtype=target.dtype)
|
|
||||||
|
|
||||||
if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
|
|
||||||
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
|
|
||||||
else:
|
|
||||||
updown = up @ down
|
|
||||||
|
|
||||||
updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
|
|
||||||
|
|
||||||
return updown
|
|
||||||
|
|
||||||
|
|
||||||
def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
|
|
||||||
"""
|
|
||||||
Applies the currently selected set of Loras to the weights of torch layer self.
|
|
||||||
If weights already have this particular set of loras applied, does nothing.
|
|
||||||
If not, restores orginal weights from backup and alters weights according to loras.
|
|
||||||
"""
|
|
||||||
|
|
||||||
lora_layer_name = getattr(self, 'lora_layer_name', None)
|
|
||||||
if lora_layer_name is None:
|
|
||||||
return
|
|
||||||
|
|
||||||
current_names = getattr(self, "lora_current_names", ())
|
|
||||||
wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
|
|
||||||
|
|
||||||
weights_backup = getattr(self, "lora_weights_backup", None)
|
|
||||||
if weights_backup is None:
|
|
||||||
if isinstance(self, torch.nn.MultiheadAttention):
|
|
||||||
weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
|
|
||||||
else:
|
|
||||||
weights_backup = self.weight.to(devices.cpu, copy=True)
|
|
||||||
|
|
||||||
self.lora_weights_backup = weights_backup
|
|
||||||
|
|
||||||
if current_names != wanted_names:
|
|
||||||
if weights_backup is not None:
|
|
||||||
if isinstance(self, torch.nn.MultiheadAttention):
|
|
||||||
self.in_proj_weight.copy_(weights_backup[0])
|
|
||||||
self.out_proj.weight.copy_(weights_backup[1])
|
|
||||||
else:
|
|
||||||
self.weight.copy_(weights_backup)
|
|
||||||
|
|
||||||
for lora in loaded_loras:
|
|
||||||
module = lora.modules.get(lora_layer_name, None)
|
|
||||||
if module is not None and hasattr(self, 'weight'):
|
|
||||||
self.weight += lora_calc_updown(lora, module, self.weight)
|
|
||||||
continue
|
|
||||||
|
|
||||||
module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
|
|
||||||
module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
|
|
||||||
module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
|
|
||||||
module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
|
|
||||||
|
|
||||||
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
|
|
||||||
updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
|
|
||||||
updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
|
|
||||||
updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
|
|
||||||
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
|
|
||||||
|
|
||||||
self.in_proj_weight += updown_qkv
|
|
||||||
self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
|
|
||||||
continue
|
|
||||||
|
|
||||||
if module is None:
|
|
||||||
continue
|
|
||||||
|
|
||||||
print(f'failed to calculate lora weights for layer {lora_layer_name}')
|
|
||||||
|
|
||||||
setattr(self, "lora_current_names", wanted_names)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
|
|
||||||
setattr(self, "lora_current_names", ())
|
|
||||||
setattr(self, "lora_weights_backup", None)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_Linear_forward(self, input):
|
|
||||||
lora_apply_weights(self)
|
|
||||||
|
|
||||||
return torch.nn.Linear_forward_before_lora(self, input)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_Linear_load_state_dict(self, *args, **kwargs):
|
|
||||||
lora_reset_cached_weight(self)
|
|
||||||
|
|
||||||
return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_Conv2d_forward(self, input):
|
|
||||||
lora_apply_weights(self)
|
|
||||||
|
|
||||||
return torch.nn.Conv2d_forward_before_lora(self, input)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_Conv2d_load_state_dict(self, *args, **kwargs):
|
|
||||||
lora_reset_cached_weight(self)
|
|
||||||
|
|
||||||
return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_MultiheadAttention_forward(self, *args, **kwargs):
|
|
||||||
lora_apply_weights(self)
|
|
||||||
|
|
||||||
return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
|
|
||||||
lora_reset_cached_weight(self)
|
|
||||||
|
|
||||||
return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
|
|
||||||
|
|
||||||
|
|
||||||
def list_available_loras():
|
|
||||||
available_loras.clear()
|
|
||||||
|
|
||||||
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
|
|
||||||
|
|
||||||
candidates = \
|
|
||||||
glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
|
|
||||||
glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
|
|
||||||
glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
|
|
||||||
|
|
||||||
for filename in sorted(candidates, key=str.lower):
|
|
||||||
if os.path.isdir(filename):
|
|
||||||
continue
|
|
||||||
|
|
||||||
name = os.path.splitext(os.path.basename(filename))[0]
|
|
||||||
|
|
||||||
available_loras[name] = LoraOnDisk(name, filename)
|
|
||||||
|
|
||||||
|
|
||||||
available_loras = {}
|
|
||||||
loaded_loras = []
|
|
||||||
|
|
||||||
list_available_loras()
|
|
||||||
|
21
extensions-builtin/Lora/lyco_helpers.py
Normal file
21
extensions-builtin/Lora/lyco_helpers.py
Normal file
@ -0,0 +1,21 @@
|
|||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
def make_weight_cp(t, wa, wb):
|
||||||
|
temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
|
||||||
|
return torch.einsum('i j k l, i r -> r j k l', temp, wa)
|
||||||
|
|
||||||
|
|
||||||
|
def rebuild_conventional(up, down, shape, dyn_dim=None):
|
||||||
|
up = up.reshape(up.size(0), -1)
|
||||||
|
down = down.reshape(down.size(0), -1)
|
||||||
|
if dyn_dim is not None:
|
||||||
|
up = up[:, :dyn_dim]
|
||||||
|
down = down[:dyn_dim, :]
|
||||||
|
return (up @ down).reshape(shape)
|
||||||
|
|
||||||
|
|
||||||
|
def rebuild_cp_decomposition(up, down, mid):
|
||||||
|
up = up.reshape(up.size(0), -1)
|
||||||
|
down = down.reshape(down.size(0), -1)
|
||||||
|
return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
|
155
extensions-builtin/Lora/network.py
Normal file
155
extensions-builtin/Lora/network.py
Normal file
@ -0,0 +1,155 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
import os
|
||||||
|
from collections import namedtuple
|
||||||
|
import enum
|
||||||
|
|
||||||
|
from modules import sd_models, cache, errors, hashes, shared
|
||||||
|
|
||||||
|
NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
|
||||||
|
|
||||||
|
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
|
||||||
|
|
||||||
|
|
||||||
|
class SdVersion(enum.Enum):
|
||||||
|
Unknown = 1
|
||||||
|
SD1 = 2
|
||||||
|
SD2 = 3
|
||||||
|
SDXL = 4
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkOnDisk:
|
||||||
|
def __init__(self, name, filename):
|
||||||
|
self.name = name
|
||||||
|
self.filename = filename
|
||||||
|
self.metadata = {}
|
||||||
|
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
|
||||||
|
|
||||||
|
def read_metadata():
|
||||||
|
metadata = sd_models.read_metadata_from_safetensors(filename)
|
||||||
|
metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text
|
||||||
|
|
||||||
|
return metadata
|
||||||
|
|
||||||
|
if self.is_safetensors:
|
||||||
|
try:
|
||||||
|
self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
|
||||||
|
except Exception as e:
|
||||||
|
errors.display(e, f"reading lora {filename}")
|
||||||
|
|
||||||
|
if self.metadata:
|
||||||
|
m = {}
|
||||||
|
for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
|
||||||
|
m[k] = v
|
||||||
|
|
||||||
|
self.metadata = m
|
||||||
|
|
||||||
|
self.alias = self.metadata.get('ss_output_name', self.name)
|
||||||
|
|
||||||
|
self.hash = None
|
||||||
|
self.shorthash = None
|
||||||
|
self.set_hash(
|
||||||
|
self.metadata.get('sshs_model_hash') or
|
||||||
|
hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
|
||||||
|
''
|
||||||
|
)
|
||||||
|
|
||||||
|
self.sd_version = self.detect_version()
|
||||||
|
|
||||||
|
def detect_version(self):
|
||||||
|
if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
|
||||||
|
return SdVersion.SDXL
|
||||||
|
elif str(self.metadata.get('ss_v2', "")) == "True":
|
||||||
|
return SdVersion.SD2
|
||||||
|
elif len(self.metadata):
|
||||||
|
return SdVersion.SD1
|
||||||
|
|
||||||
|
return SdVersion.Unknown
|
||||||
|
|
||||||
|
def set_hash(self, v):
|
||||||
|
self.hash = v
|
||||||
|
self.shorthash = self.hash[0:12]
|
||||||
|
|
||||||
|
if self.shorthash:
|
||||||
|
import networks
|
||||||
|
networks.available_network_hash_lookup[self.shorthash] = self
|
||||||
|
|
||||||
|
def read_hash(self):
|
||||||
|
if not self.hash:
|
||||||
|
self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
|
||||||
|
|
||||||
|
def get_alias(self):
|
||||||
|
import networks
|
||||||
|
if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
|
||||||
|
return self.name
|
||||||
|
else:
|
||||||
|
return self.alias
|
||||||
|
|
||||||
|
|
||||||
|
class Network: # LoraModule
|
||||||
|
def __init__(self, name, network_on_disk: NetworkOnDisk):
|
||||||
|
self.name = name
|
||||||
|
self.network_on_disk = network_on_disk
|
||||||
|
self.te_multiplier = 1.0
|
||||||
|
self.unet_multiplier = 1.0
|
||||||
|
self.dyn_dim = None
|
||||||
|
self.modules = {}
|
||||||
|
self.mtime = None
|
||||||
|
|
||||||
|
self.mentioned_name = None
|
||||||
|
"""the text that was used to add the network to prompt - can be either name or an alias"""
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleType:
|
||||||
|
def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModule:
|
||||||
|
def __init__(self, net: Network, weights: NetworkWeights):
|
||||||
|
self.network = net
|
||||||
|
self.network_key = weights.network_key
|
||||||
|
self.sd_key = weights.sd_key
|
||||||
|
self.sd_module = weights.sd_module
|
||||||
|
|
||||||
|
if hasattr(self.sd_module, 'weight'):
|
||||||
|
self.shape = self.sd_module.weight.shape
|
||||||
|
|
||||||
|
self.dim = None
|
||||||
|
self.bias = weights.w.get("bias")
|
||||||
|
self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
|
||||||
|
self.scale = weights.w["scale"].item() if "scale" in weights.w else None
|
||||||
|
|
||||||
|
def multiplier(self):
|
||||||
|
if 'transformer' in self.sd_key[:20]:
|
||||||
|
return self.network.te_multiplier
|
||||||
|
else:
|
||||||
|
return self.network.unet_multiplier
|
||||||
|
|
||||||
|
def calc_scale(self):
|
||||||
|
if self.scale is not None:
|
||||||
|
return self.scale
|
||||||
|
if self.dim is not None and self.alpha is not None:
|
||||||
|
return self.alpha / self.dim
|
||||||
|
|
||||||
|
return 1.0
|
||||||
|
|
||||||
|
def finalize_updown(self, updown, orig_weight, output_shape):
|
||||||
|
if self.bias is not None:
|
||||||
|
updown = updown.reshape(self.bias.shape)
|
||||||
|
updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
updown = updown.reshape(output_shape)
|
||||||
|
|
||||||
|
if len(output_shape) == 4:
|
||||||
|
updown = updown.reshape(output_shape)
|
||||||
|
|
||||||
|
if orig_weight.size().numel() == updown.size().numel():
|
||||||
|
updown = updown.reshape(orig_weight.shape)
|
||||||
|
|
||||||
|
return updown * self.calc_scale() * self.multiplier()
|
||||||
|
|
||||||
|
def calc_updown(self, target):
|
||||||
|
raise NotImplementedError()
|
||||||
|
|
||||||
|
def forward(self, x, y):
|
||||||
|
raise NotImplementedError()
|
||||||
|
|
22
extensions-builtin/Lora/network_full.py
Normal file
22
extensions-builtin/Lora/network_full.py
Normal file
@ -0,0 +1,22 @@
|
|||||||
|
import network
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleTypeFull(network.ModuleType):
|
||||||
|
def create_module(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
if all(x in weights.w for x in ["diff"]):
|
||||||
|
return NetworkModuleFull(net, weights)
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModuleFull(network.NetworkModule):
|
||||||
|
def __init__(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
super().__init__(net, weights)
|
||||||
|
|
||||||
|
self.weight = weights.w.get("diff")
|
||||||
|
|
||||||
|
def calc_updown(self, orig_weight):
|
||||||
|
output_shape = self.weight.shape
|
||||||
|
updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
|
||||||
|
return self.finalize_updown(updown, orig_weight, output_shape)
|
55
extensions-builtin/Lora/network_hada.py
Normal file
55
extensions-builtin/Lora/network_hada.py
Normal file
@ -0,0 +1,55 @@
|
|||||||
|
import lyco_helpers
|
||||||
|
import network
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleTypeHada(network.ModuleType):
|
||||||
|
def create_module(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
|
||||||
|
return NetworkModuleHada(net, weights)
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModuleHada(network.NetworkModule):
|
||||||
|
def __init__(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
super().__init__(net, weights)
|
||||||
|
|
||||||
|
if hasattr(self.sd_module, 'weight'):
|
||||||
|
self.shape = self.sd_module.weight.shape
|
||||||
|
|
||||||
|
self.w1a = weights.w["hada_w1_a"]
|
||||||
|
self.w1b = weights.w["hada_w1_b"]
|
||||||
|
self.dim = self.w1b.shape[0]
|
||||||
|
self.w2a = weights.w["hada_w2_a"]
|
||||||
|
self.w2b = weights.w["hada_w2_b"]
|
||||||
|
|
||||||
|
self.t1 = weights.w.get("hada_t1")
|
||||||
|
self.t2 = weights.w.get("hada_t2")
|
||||||
|
|
||||||
|
def calc_updown(self, orig_weight):
|
||||||
|
w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
|
||||||
|
output_shape = [w1a.size(0), w1b.size(1)]
|
||||||
|
|
||||||
|
if self.t1 is not None:
|
||||||
|
output_shape = [w1a.size(1), w1b.size(1)]
|
||||||
|
t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
|
||||||
|
output_shape += t1.shape[2:]
|
||||||
|
else:
|
||||||
|
if len(w1b.shape) == 4:
|
||||||
|
output_shape += w1b.shape[2:]
|
||||||
|
updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
|
||||||
|
|
||||||
|
if self.t2 is not None:
|
||||||
|
t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
|
||||||
|
else:
|
||||||
|
updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
|
||||||
|
|
||||||
|
updown = updown1 * updown2
|
||||||
|
|
||||||
|
return self.finalize_updown(updown, orig_weight, output_shape)
|
30
extensions-builtin/Lora/network_ia3.py
Normal file
30
extensions-builtin/Lora/network_ia3.py
Normal file
@ -0,0 +1,30 @@
|
|||||||
|
import network
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleTypeIa3(network.ModuleType):
|
||||||
|
def create_module(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
if all(x in weights.w for x in ["weight"]):
|
||||||
|
return NetworkModuleIa3(net, weights)
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModuleIa3(network.NetworkModule):
|
||||||
|
def __init__(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
super().__init__(net, weights)
|
||||||
|
|
||||||
|
self.w = weights.w["weight"]
|
||||||
|
self.on_input = weights.w["on_input"].item()
|
||||||
|
|
||||||
|
def calc_updown(self, orig_weight):
|
||||||
|
w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
|
||||||
|
output_shape = [w.size(0), orig_weight.size(1)]
|
||||||
|
if self.on_input:
|
||||||
|
output_shape.reverse()
|
||||||
|
else:
|
||||||
|
w = w.reshape(-1, 1)
|
||||||
|
|
||||||
|
updown = orig_weight * w
|
||||||
|
|
||||||
|
return self.finalize_updown(updown, orig_weight, output_shape)
|
64
extensions-builtin/Lora/network_lokr.py
Normal file
64
extensions-builtin/Lora/network_lokr.py
Normal file
@ -0,0 +1,64 @@
|
|||||||
|
import torch
|
||||||
|
|
||||||
|
import lyco_helpers
|
||||||
|
import network
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleTypeLokr(network.ModuleType):
|
||||||
|
def create_module(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
|
||||||
|
has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
|
||||||
|
if has_1 and has_2:
|
||||||
|
return NetworkModuleLokr(net, weights)
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def make_kron(orig_shape, w1, w2):
|
||||||
|
if len(w2.shape) == 4:
|
||||||
|
w1 = w1.unsqueeze(2).unsqueeze(2)
|
||||||
|
w2 = w2.contiguous()
|
||||||
|
return torch.kron(w1, w2).reshape(orig_shape)
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModuleLokr(network.NetworkModule):
|
||||||
|
def __init__(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
super().__init__(net, weights)
|
||||||
|
|
||||||
|
self.w1 = weights.w.get("lokr_w1")
|
||||||
|
self.w1a = weights.w.get("lokr_w1_a")
|
||||||
|
self.w1b = weights.w.get("lokr_w1_b")
|
||||||
|
self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
|
||||||
|
self.w2 = weights.w.get("lokr_w2")
|
||||||
|
self.w2a = weights.w.get("lokr_w2_a")
|
||||||
|
self.w2b = weights.w.get("lokr_w2_b")
|
||||||
|
self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
|
||||||
|
self.t2 = weights.w.get("lokr_t2")
|
||||||
|
|
||||||
|
def calc_updown(self, orig_weight):
|
||||||
|
if self.w1 is not None:
|
||||||
|
w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
else:
|
||||||
|
w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w1 = w1a @ w1b
|
||||||
|
|
||||||
|
if self.w2 is not None:
|
||||||
|
w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
elif self.t2 is None:
|
||||||
|
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2 = w2a @ w2b
|
||||||
|
else:
|
||||||
|
t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
|
||||||
|
|
||||||
|
output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
|
||||||
|
if len(orig_weight.shape) == 4:
|
||||||
|
output_shape = orig_weight.shape
|
||||||
|
|
||||||
|
updown = make_kron(output_shape, w1, w2)
|
||||||
|
|
||||||
|
return self.finalize_updown(updown, orig_weight, output_shape)
|
86
extensions-builtin/Lora/network_lora.py
Normal file
86
extensions-builtin/Lora/network_lora.py
Normal file
@ -0,0 +1,86 @@
|
|||||||
|
import torch
|
||||||
|
|
||||||
|
import lyco_helpers
|
||||||
|
import network
|
||||||
|
from modules import devices
|
||||||
|
|
||||||
|
|
||||||
|
class ModuleTypeLora(network.ModuleType):
|
||||||
|
def create_module(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
|
||||||
|
return NetworkModuleLora(net, weights)
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
class NetworkModuleLora(network.NetworkModule):
|
||||||
|
def __init__(self, net: network.Network, weights: network.NetworkWeights):
|
||||||
|
super().__init__(net, weights)
|
||||||
|
|
||||||
|
self.up_model = self.create_module(weights.w, "lora_up.weight")
|
||||||
|
self.down_model = self.create_module(weights.w, "lora_down.weight")
|
||||||
|
self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
|
||||||
|
|
||||||
|
self.dim = weights.w["lora_down.weight"].shape[0]
|
||||||
|
|
||||||
|
def create_module(self, weights, key, none_ok=False):
|
||||||
|
weight = weights.get(key)
|
||||||
|
|
||||||
|
if weight is None and none_ok:
|
||||||
|
return None
|
||||||
|
|
||||||
|
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
|
||||||
|
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
|
||||||
|
|
||||||
|
if is_linear:
|
||||||
|
weight = weight.reshape(weight.shape[0], -1)
|
||||||
|
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
|
||||||
|
elif is_conv and key == "lora_down.weight" or key == "dyn_up":
|
||||||
|
if len(weight.shape) == 2:
|
||||||
|
weight = weight.reshape(weight.shape[0], -1, 1, 1)
|
||||||
|
|
||||||
|
if weight.shape[2] != 1 or weight.shape[3] != 1:
|
||||||
|
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
|
||||||
|
else:
|
||||||
|
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
|
||||||
|
elif is_conv and key == "lora_mid.weight":
|
||||||
|
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
|
||||||
|
elif is_conv and key == "lora_up.weight" or key == "dyn_down":
|
||||||
|
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
|
||||||
|
else:
|
||||||
|
raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
if weight.shape != module.weight.shape:
|
||||||
|
weight = weight.reshape(module.weight.shape)
|
||||||
|
module.weight.copy_(weight)
|
||||||
|
|
||||||
|
module.to(device=devices.cpu, dtype=devices.dtype)
|
||||||
|
module.weight.requires_grad_(False)
|
||||||
|
|
||||||
|
return module
|
||||||
|
|
||||||
|
def calc_updown(self, orig_weight):
|
||||||
|
up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
|
||||||
|
output_shape = [up.size(0), down.size(1)]
|
||||||
|
if self.mid_model is not None:
|
||||||
|
# cp-decomposition
|
||||||
|
mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
|
||||||
|
updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
|
||||||
|
output_shape += mid.shape[2:]
|
||||||
|
else:
|
||||||
|
if len(down.shape) == 4:
|
||||||
|
output_shape += down.shape[2:]
|
||||||
|
updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
|
||||||
|
|
||||||
|
return self.finalize_updown(updown, orig_weight, output_shape)
|
||||||
|
|
||||||
|
def forward(self, x, y):
|
||||||
|
self.up_model.to(device=devices.device)
|
||||||
|
self.down_model.to(device=devices.device)
|
||||||
|
|
||||||
|
return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
|
||||||
|
|
||||||
|
|
468
extensions-builtin/Lora/networks.py
Normal file
468
extensions-builtin/Lora/networks.py
Normal file
@ -0,0 +1,468 @@
|
|||||||
|
import os
|
||||||
|
import re
|
||||||
|
|
||||||
|
import network
|
||||||
|
import network_lora
|
||||||
|
import network_hada
|
||||||
|
import network_ia3
|
||||||
|
import network_lokr
|
||||||
|
import network_full
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from typing import Union
|
||||||
|
|
||||||
|
from modules import shared, devices, sd_models, errors, scripts, sd_hijack
|
||||||
|
|
||||||
|
module_types = [
|
||||||
|
network_lora.ModuleTypeLora(),
|
||||||
|
network_hada.ModuleTypeHada(),
|
||||||
|
network_ia3.ModuleTypeIa3(),
|
||||||
|
network_lokr.ModuleTypeLokr(),
|
||||||
|
network_full.ModuleTypeFull(),
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
re_digits = re.compile(r"\d+")
|
||||||
|
re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
|
||||||
|
re_compiled = {}
|
||||||
|
|
||||||
|
suffix_conversion = {
|
||||||
|
"attentions": {},
|
||||||
|
"resnets": {
|
||||||
|
"conv1": "in_layers_2",
|
||||||
|
"conv2": "out_layers_3",
|
||||||
|
"time_emb_proj": "emb_layers_1",
|
||||||
|
"conv_shortcut": "skip_connection",
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def convert_diffusers_name_to_compvis(key, is_sd2):
|
||||||
|
def match(match_list, regex_text):
|
||||||
|
regex = re_compiled.get(regex_text)
|
||||||
|
if regex is None:
|
||||||
|
regex = re.compile(regex_text)
|
||||||
|
re_compiled[regex_text] = regex
|
||||||
|
|
||||||
|
r = re.match(regex, key)
|
||||||
|
if not r:
|
||||||
|
return False
|
||||||
|
|
||||||
|
match_list.clear()
|
||||||
|
match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
|
||||||
|
return True
|
||||||
|
|
||||||
|
m = []
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_conv_in(.*)"):
|
||||||
|
return f'diffusion_model_input_blocks_0_0{m[0]}'
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_conv_out(.*)"):
|
||||||
|
return f'diffusion_model_out_2{m[0]}'
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
|
||||||
|
return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
|
||||||
|
suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
|
||||||
|
return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
|
||||||
|
suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
|
||||||
|
return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
|
||||||
|
suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
|
||||||
|
return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
|
||||||
|
return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
|
||||||
|
|
||||||
|
if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
|
||||||
|
return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
|
||||||
|
|
||||||
|
if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
|
||||||
|
if is_sd2:
|
||||||
|
if 'mlp_fc1' in m[1]:
|
||||||
|
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
|
||||||
|
elif 'mlp_fc2' in m[1]:
|
||||||
|
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
|
||||||
|
else:
|
||||||
|
return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
|
||||||
|
|
||||||
|
return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
|
||||||
|
|
||||||
|
if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
|
||||||
|
if 'mlp_fc1' in m[1]:
|
||||||
|
return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
|
||||||
|
elif 'mlp_fc2' in m[1]:
|
||||||
|
return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
|
||||||
|
else:
|
||||||
|
return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
|
||||||
|
|
||||||
|
return key
|
||||||
|
|
||||||
|
|
||||||
|
def assign_network_names_to_compvis_modules(sd_model):
|
||||||
|
network_layer_mapping = {}
|
||||||
|
|
||||||
|
if shared.sd_model.is_sdxl:
|
||||||
|
for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
|
||||||
|
if not hasattr(embedder, 'wrapped'):
|
||||||
|
continue
|
||||||
|
|
||||||
|
for name, module in embedder.wrapped.named_modules():
|
||||||
|
network_name = f'{i}_{name.replace(".", "_")}'
|
||||||
|
network_layer_mapping[network_name] = module
|
||||||
|
module.network_layer_name = network_name
|
||||||
|
else:
|
||||||
|
for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
|
||||||
|
network_name = name.replace(".", "_")
|
||||||
|
network_layer_mapping[network_name] = module
|
||||||
|
module.network_layer_name = network_name
|
||||||
|
|
||||||
|
for name, module in shared.sd_model.model.named_modules():
|
||||||
|
network_name = name.replace(".", "_")
|
||||||
|
network_layer_mapping[network_name] = module
|
||||||
|
module.network_layer_name = network_name
|
||||||
|
|
||||||
|
sd_model.network_layer_mapping = network_layer_mapping
|
||||||
|
|
||||||
|
|
||||||
|
def load_network(name, network_on_disk):
|
||||||
|
net = network.Network(name, network_on_disk)
|
||||||
|
net.mtime = os.path.getmtime(network_on_disk.filename)
|
||||||
|
|
||||||
|
sd = sd_models.read_state_dict(network_on_disk.filename)
|
||||||
|
|
||||||
|
# this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
|
||||||
|
if not hasattr(shared.sd_model, 'network_layer_mapping'):
|
||||||
|
assign_network_names_to_compvis_modules(shared.sd_model)
|
||||||
|
|
||||||
|
keys_failed_to_match = {}
|
||||||
|
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
|
||||||
|
|
||||||
|
matched_networks = {}
|
||||||
|
|
||||||
|
for key_network, weight in sd.items():
|
||||||
|
key_network_without_network_parts, network_part = key_network.split(".", 1)
|
||||||
|
|
||||||
|
key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
|
||||||
|
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
|
||||||
|
|
||||||
|
if sd_module is None:
|
||||||
|
m = re_x_proj.match(key)
|
||||||
|
if m:
|
||||||
|
sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
|
||||||
|
|
||||||
|
# SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
|
||||||
|
if sd_module is None and "lora_unet" in key_network_without_network_parts:
|
||||||
|
key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
|
||||||
|
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
|
||||||
|
elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
|
||||||
|
key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
|
||||||
|
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
|
||||||
|
|
||||||
|
# some SD1 Loras also have correct compvis keys
|
||||||
|
if sd_module is None:
|
||||||
|
key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
|
||||||
|
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
|
||||||
|
|
||||||
|
if sd_module is None:
|
||||||
|
keys_failed_to_match[key_network] = key
|
||||||
|
continue
|
||||||
|
|
||||||
|
if key not in matched_networks:
|
||||||
|
matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
|
||||||
|
|
||||||
|
matched_networks[key].w[network_part] = weight
|
||||||
|
|
||||||
|
for key, weights in matched_networks.items():
|
||||||
|
net_module = None
|
||||||
|
for nettype in module_types:
|
||||||
|
net_module = nettype.create_module(net, weights)
|
||||||
|
if net_module is not None:
|
||||||
|
break
|
||||||
|
|
||||||
|
if net_module is None:
|
||||||
|
raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
|
||||||
|
|
||||||
|
net.modules[key] = net_module
|
||||||
|
|
||||||
|
if keys_failed_to_match:
|
||||||
|
print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
|
||||||
|
|
||||||
|
return net
|
||||||
|
|
||||||
|
|
||||||
|
def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
|
||||||
|
already_loaded = {}
|
||||||
|
|
||||||
|
for net in loaded_networks:
|
||||||
|
if net.name in names:
|
||||||
|
already_loaded[net.name] = net
|
||||||
|
|
||||||
|
loaded_networks.clear()
|
||||||
|
|
||||||
|
networks_on_disk = [available_network_aliases.get(name, None) for name in names]
|
||||||
|
if any(x is None for x in networks_on_disk):
|
||||||
|
list_available_networks()
|
||||||
|
|
||||||
|
networks_on_disk = [available_network_aliases.get(name, None) for name in names]
|
||||||
|
|
||||||
|
failed_to_load_networks = []
|
||||||
|
|
||||||
|
for i, name in enumerate(names):
|
||||||
|
net = already_loaded.get(name, None)
|
||||||
|
|
||||||
|
network_on_disk = networks_on_disk[i]
|
||||||
|
|
||||||
|
if network_on_disk is not None:
|
||||||
|
if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
|
||||||
|
try:
|
||||||
|
net = load_network(name, network_on_disk)
|
||||||
|
except Exception as e:
|
||||||
|
errors.display(e, f"loading network {network_on_disk.filename}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
net.mentioned_name = name
|
||||||
|
|
||||||
|
network_on_disk.read_hash()
|
||||||
|
|
||||||
|
if net is None:
|
||||||
|
failed_to_load_networks.append(name)
|
||||||
|
print(f"Couldn't find network with name {name}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
|
||||||
|
net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
|
||||||
|
net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
|
||||||
|
loaded_networks.append(net)
|
||||||
|
|
||||||
|
if failed_to_load_networks:
|
||||||
|
sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
|
||||||
|
|
||||||
|
|
||||||
|
def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
|
||||||
|
weights_backup = getattr(self, "network_weights_backup", None)
|
||||||
|
|
||||||
|
if weights_backup is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
if isinstance(self, torch.nn.MultiheadAttention):
|
||||||
|
self.in_proj_weight.copy_(weights_backup[0])
|
||||||
|
self.out_proj.weight.copy_(weights_backup[1])
|
||||||
|
else:
|
||||||
|
self.weight.copy_(weights_backup)
|
||||||
|
|
||||||
|
|
||||||
|
def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
|
||||||
|
"""
|
||||||
|
Applies the currently selected set of networks to the weights of torch layer self.
|
||||||
|
If weights already have this particular set of networks applied, does nothing.
|
||||||
|
If not, restores orginal weights from backup and alters weights according to networks.
|
||||||
|
"""
|
||||||
|
|
||||||
|
network_layer_name = getattr(self, 'network_layer_name', None)
|
||||||
|
if network_layer_name is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
current_names = getattr(self, "network_current_names", ())
|
||||||
|
wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
|
||||||
|
|
||||||
|
weights_backup = getattr(self, "network_weights_backup", None)
|
||||||
|
if weights_backup is None:
|
||||||
|
if isinstance(self, torch.nn.MultiheadAttention):
|
||||||
|
weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
|
||||||
|
else:
|
||||||
|
weights_backup = self.weight.to(devices.cpu, copy=True)
|
||||||
|
|
||||||
|
self.network_weights_backup = weights_backup
|
||||||
|
|
||||||
|
if current_names != wanted_names:
|
||||||
|
network_restore_weights_from_backup(self)
|
||||||
|
|
||||||
|
for net in loaded_networks:
|
||||||
|
module = net.modules.get(network_layer_name, None)
|
||||||
|
if module is not None and hasattr(self, 'weight'):
|
||||||
|
with torch.no_grad():
|
||||||
|
updown = module.calc_updown(self.weight)
|
||||||
|
|
||||||
|
if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
|
||||||
|
# inpainting model. zero pad updown to make channel[1] 4 to 9
|
||||||
|
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
|
||||||
|
|
||||||
|
self.weight += updown
|
||||||
|
continue
|
||||||
|
|
||||||
|
module_q = net.modules.get(network_layer_name + "_q_proj", None)
|
||||||
|
module_k = net.modules.get(network_layer_name + "_k_proj", None)
|
||||||
|
module_v = net.modules.get(network_layer_name + "_v_proj", None)
|
||||||
|
module_out = net.modules.get(network_layer_name + "_out_proj", None)
|
||||||
|
|
||||||
|
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
|
||||||
|
with torch.no_grad():
|
||||||
|
updown_q = module_q.calc_updown(self.in_proj_weight)
|
||||||
|
updown_k = module_k.calc_updown(self.in_proj_weight)
|
||||||
|
updown_v = module_v.calc_updown(self.in_proj_weight)
|
||||||
|
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
|
||||||
|
updown_out = module_out.calc_updown(self.out_proj.weight)
|
||||||
|
|
||||||
|
self.in_proj_weight += updown_qkv
|
||||||
|
self.out_proj.weight += updown_out
|
||||||
|
continue
|
||||||
|
|
||||||
|
if module is None:
|
||||||
|
continue
|
||||||
|
|
||||||
|
print(f'failed to calculate network weights for layer {network_layer_name}')
|
||||||
|
|
||||||
|
self.network_current_names = wanted_names
|
||||||
|
|
||||||
|
|
||||||
|
def network_forward(module, input, original_forward):
|
||||||
|
"""
|
||||||
|
Old way of applying Lora by executing operations during layer's forward.
|
||||||
|
Stacking many loras this way results in big performance degradation.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if len(loaded_networks) == 0:
|
||||||
|
return original_forward(module, input)
|
||||||
|
|
||||||
|
input = devices.cond_cast_unet(input)
|
||||||
|
|
||||||
|
network_restore_weights_from_backup(module)
|
||||||
|
network_reset_cached_weight(module)
|
||||||
|
|
||||||
|
y = original_forward(module, input)
|
||||||
|
|
||||||
|
network_layer_name = getattr(module, 'network_layer_name', None)
|
||||||
|
for lora in loaded_networks:
|
||||||
|
module = lora.modules.get(network_layer_name, None)
|
||||||
|
if module is None:
|
||||||
|
continue
|
||||||
|
|
||||||
|
y = module.forward(y, input)
|
||||||
|
|
||||||
|
return y
|
||||||
|
|
||||||
|
|
||||||
|
def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
|
||||||
|
self.network_current_names = ()
|
||||||
|
self.network_weights_backup = None
|
||||||
|
|
||||||
|
|
||||||
|
def network_Linear_forward(self, input):
|
||||||
|
if shared.opts.lora_functional:
|
||||||
|
return network_forward(self, input, torch.nn.Linear_forward_before_network)
|
||||||
|
|
||||||
|
network_apply_weights(self)
|
||||||
|
|
||||||
|
return torch.nn.Linear_forward_before_network(self, input)
|
||||||
|
|
||||||
|
|
||||||
|
def network_Linear_load_state_dict(self, *args, **kwargs):
|
||||||
|
network_reset_cached_weight(self)
|
||||||
|
|
||||||
|
return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def network_Conv2d_forward(self, input):
|
||||||
|
if shared.opts.lora_functional:
|
||||||
|
return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
|
||||||
|
|
||||||
|
network_apply_weights(self)
|
||||||
|
|
||||||
|
return torch.nn.Conv2d_forward_before_network(self, input)
|
||||||
|
|
||||||
|
|
||||||
|
def network_Conv2d_load_state_dict(self, *args, **kwargs):
|
||||||
|
network_reset_cached_weight(self)
|
||||||
|
|
||||||
|
return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def network_MultiheadAttention_forward(self, *args, **kwargs):
|
||||||
|
network_apply_weights(self)
|
||||||
|
|
||||||
|
return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
|
||||||
|
network_reset_cached_weight(self)
|
||||||
|
|
||||||
|
return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def list_available_networks():
|
||||||
|
available_networks.clear()
|
||||||
|
available_network_aliases.clear()
|
||||||
|
forbidden_network_aliases.clear()
|
||||||
|
available_network_hash_lookup.clear()
|
||||||
|
forbidden_network_aliases.update({"none": 1, "Addams": 1})
|
||||||
|
|
||||||
|
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
|
||||||
|
|
||||||
|
candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
|
||||||
|
candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
|
||||||
|
for filename in candidates:
|
||||||
|
if os.path.isdir(filename):
|
||||||
|
continue
|
||||||
|
|
||||||
|
name = os.path.splitext(os.path.basename(filename))[0]
|
||||||
|
try:
|
||||||
|
entry = network.NetworkOnDisk(name, filename)
|
||||||
|
except OSError: # should catch FileNotFoundError and PermissionError etc.
|
||||||
|
errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
|
||||||
|
continue
|
||||||
|
|
||||||
|
available_networks[name] = entry
|
||||||
|
|
||||||
|
if entry.alias in available_network_aliases:
|
||||||
|
forbidden_network_aliases[entry.alias.lower()] = 1
|
||||||
|
|
||||||
|
available_network_aliases[name] = entry
|
||||||
|
available_network_aliases[entry.alias] = entry
|
||||||
|
|
||||||
|
|
||||||
|
re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
|
||||||
|
|
||||||
|
|
||||||
|
def infotext_pasted(infotext, params):
|
||||||
|
if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
|
||||||
|
return # if the other extension is active, it will handle those fields, no need to do anything
|
||||||
|
|
||||||
|
added = []
|
||||||
|
|
||||||
|
for k in params:
|
||||||
|
if not k.startswith("AddNet Model "):
|
||||||
|
continue
|
||||||
|
|
||||||
|
num = k[13:]
|
||||||
|
|
||||||
|
if params.get("AddNet Module " + num) != "LoRA":
|
||||||
|
continue
|
||||||
|
|
||||||
|
name = params.get("AddNet Model " + num)
|
||||||
|
if name is None:
|
||||||
|
continue
|
||||||
|
|
||||||
|
m = re_network_name.match(name)
|
||||||
|
if m:
|
||||||
|
name = m.group(1)
|
||||||
|
|
||||||
|
multiplier = params.get("AddNet Weight A " + num, "1.0")
|
||||||
|
|
||||||
|
added.append(f"<lora:{name}:{multiplier}>")
|
||||||
|
|
||||||
|
if added:
|
||||||
|
params["Prompt"] += "\n" + "".join(added)
|
||||||
|
|
||||||
|
|
||||||
|
available_networks = {}
|
||||||
|
available_network_aliases = {}
|
||||||
|
loaded_networks = []
|
||||||
|
available_network_hash_lookup = {}
|
||||||
|
forbidden_network_aliases = {}
|
||||||
|
|
||||||
|
list_available_networks()
|
@ -4,3 +4,4 @@ from modules import paths
|
|||||||
|
|
||||||
def preload(parser):
|
def preload(parser):
|
||||||
parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
|
parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
|
||||||
|
parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
|
||||||
|
@ -1,56 +1,123 @@
|
|||||||
|
import re
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
import gradio as gr
|
import gradio as gr
|
||||||
|
from fastapi import FastAPI
|
||||||
|
|
||||||
import lora
|
import network
|
||||||
|
import networks
|
||||||
|
import lora # noqa:F401
|
||||||
import extra_networks_lora
|
import extra_networks_lora
|
||||||
import ui_extra_networks_lora
|
import ui_extra_networks_lora
|
||||||
from modules import script_callbacks, ui_extra_networks, extra_networks, shared
|
from modules import script_callbacks, ui_extra_networks, extra_networks, shared
|
||||||
|
|
||||||
|
|
||||||
def unload():
|
def unload():
|
||||||
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
|
torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
|
||||||
torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
|
torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
|
||||||
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
|
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
|
||||||
torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
|
torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
|
||||||
torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
|
torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
|
||||||
torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
|
torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
|
||||||
|
|
||||||
|
|
||||||
def before_ui():
|
def before_ui():
|
||||||
ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
|
ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
|
||||||
extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
|
|
||||||
|
extra_network = extra_networks_lora.ExtraNetworkLora()
|
||||||
|
extra_networks.register_extra_network(extra_network)
|
||||||
|
extra_networks.register_extra_network_alias(extra_network, "lyco")
|
||||||
|
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'Linear_forward_before_lora'):
|
if not hasattr(torch.nn, 'Linear_forward_before_network'):
|
||||||
torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
|
torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
|
if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
|
||||||
torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
|
torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
|
if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
|
||||||
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
|
torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
|
if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
|
||||||
torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
|
torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
|
if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
|
||||||
torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
|
torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
|
||||||
|
|
||||||
if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
|
if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
|
||||||
torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
|
torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
|
||||||
|
|
||||||
torch.nn.Linear.forward = lora.lora_Linear_forward
|
torch.nn.Linear.forward = networks.network_Linear_forward
|
||||||
torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
|
torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
|
||||||
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
|
torch.nn.Conv2d.forward = networks.network_Conv2d_forward
|
||||||
torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
|
torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
|
||||||
torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
|
torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
|
||||||
torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
|
torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
|
||||||
|
|
||||||
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
|
script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
|
||||||
script_callbacks.on_script_unloaded(unload)
|
script_callbacks.on_script_unloaded(unload)
|
||||||
script_callbacks.on_before_ui(before_ui)
|
script_callbacks.on_before_ui(before_ui)
|
||||||
|
script_callbacks.on_infotext_pasted(networks.infotext_pasted)
|
||||||
|
|
||||||
|
|
||||||
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
|
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
|
||||||
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
|
"sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
|
||||||
|
"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
|
||||||
|
"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
|
||||||
|
"lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
|
||||||
|
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
|
||||||
}))
|
}))
|
||||||
|
|
||||||
|
|
||||||
|
shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
|
||||||
|
"lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
|
||||||
|
}))
|
||||||
|
|
||||||
|
|
||||||
|
def create_lora_json(obj: network.NetworkOnDisk):
|
||||||
|
return {
|
||||||
|
"name": obj.name,
|
||||||
|
"alias": obj.alias,
|
||||||
|
"path": obj.filename,
|
||||||
|
"metadata": obj.metadata,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def api_networks(_: gr.Blocks, app: FastAPI):
|
||||||
|
@app.get("/sdapi/v1/loras")
|
||||||
|
async def get_loras():
|
||||||
|
return [create_lora_json(obj) for obj in networks.available_networks.values()]
|
||||||
|
|
||||||
|
@app.post("/sdapi/v1/refresh-loras")
|
||||||
|
async def refresh_loras():
|
||||||
|
return networks.list_available_networks()
|
||||||
|
|
||||||
|
|
||||||
|
script_callbacks.on_app_started(api_networks)
|
||||||
|
|
||||||
|
re_lora = re.compile("<lora:([^:]+):")
|
||||||
|
|
||||||
|
|
||||||
|
def infotext_pasted(infotext, d):
|
||||||
|
hashes = d.get("Lora hashes")
|
||||||
|
if not hashes:
|
||||||
|
return
|
||||||
|
|
||||||
|
hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
|
||||||
|
hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
|
||||||
|
|
||||||
|
def network_replacement(m):
|
||||||
|
alias = m.group(1)
|
||||||
|
shorthash = hashes.get(alias)
|
||||||
|
if shorthash is None:
|
||||||
|
return m.group(0)
|
||||||
|
|
||||||
|
network_on_disk = networks.available_network_hash_lookup.get(shorthash)
|
||||||
|
if network_on_disk is None:
|
||||||
|
return m.group(0)
|
||||||
|
|
||||||
|
return f'<lora:{network_on_disk.get_alias()}:'
|
||||||
|
|
||||||
|
d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
|
||||||
|
|
||||||
|
|
||||||
|
script_callbacks.on_infotext_pasted(infotext_pasted)
|
||||||
|
216
extensions-builtin/Lora/ui_edit_user_metadata.py
Normal file
216
extensions-builtin/Lora/ui_edit_user_metadata.py
Normal file
@ -0,0 +1,216 @@
|
|||||||
|
import datetime
|
||||||
|
import html
|
||||||
|
import random
|
||||||
|
|
||||||
|
import gradio as gr
|
||||||
|
import re
|
||||||
|
|
||||||
|
from modules import ui_extra_networks_user_metadata
|
||||||
|
|
||||||
|
|
||||||
|
def is_non_comma_tagset(tags):
|
||||||
|
average_tag_length = sum(len(x) for x in tags.keys()) / len(tags)
|
||||||
|
|
||||||
|
return average_tag_length >= 16
|
||||||
|
|
||||||
|
|
||||||
|
re_word = re.compile(r"[-_\w']+")
|
||||||
|
re_comma = re.compile(r" *, *")
|
||||||
|
|
||||||
|
|
||||||
|
def build_tags(metadata):
|
||||||
|
tags = {}
|
||||||
|
|
||||||
|
for _, tags_dict in metadata.get("ss_tag_frequency", {}).items():
|
||||||
|
for tag, tag_count in tags_dict.items():
|
||||||
|
tag = tag.strip()
|
||||||
|
tags[tag] = tags.get(tag, 0) + int(tag_count)
|
||||||
|
|
||||||
|
if tags and is_non_comma_tagset(tags):
|
||||||
|
new_tags = {}
|
||||||
|
|
||||||
|
for text, text_count in tags.items():
|
||||||
|
for word in re.findall(re_word, text):
|
||||||
|
if len(word) < 3:
|
||||||
|
continue
|
||||||
|
|
||||||
|
new_tags[word] = new_tags.get(word, 0) + text_count
|
||||||
|
|
||||||
|
tags = new_tags
|
||||||
|
|
||||||
|
ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True)
|
||||||
|
|
||||||
|
return [(tag, tags[tag]) for tag in ordered_tags]
|
||||||
|
|
||||||
|
|
||||||
|
class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
|
||||||
|
def __init__(self, ui, tabname, page):
|
||||||
|
super().__init__(ui, tabname, page)
|
||||||
|
|
||||||
|
self.select_sd_version = None
|
||||||
|
|
||||||
|
self.taginfo = None
|
||||||
|
self.edit_activation_text = None
|
||||||
|
self.slider_preferred_weight = None
|
||||||
|
self.edit_notes = None
|
||||||
|
|
||||||
|
def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
|
||||||
|
user_metadata = self.get_user_metadata(name)
|
||||||
|
user_metadata["description"] = desc
|
||||||
|
user_metadata["sd version"] = sd_version
|
||||||
|
user_metadata["activation text"] = activation_text
|
||||||
|
user_metadata["preferred weight"] = preferred_weight
|
||||||
|
user_metadata["notes"] = notes
|
||||||
|
|
||||||
|
self.write_user_metadata(name, user_metadata)
|
||||||
|
|
||||||
|
def get_metadata_table(self, name):
|
||||||
|
table = super().get_metadata_table(name)
|
||||||
|
item = self.page.items.get(name, {})
|
||||||
|
metadata = item.get("metadata") or {}
|
||||||
|
|
||||||
|
keys = {
|
||||||
|
'ss_sd_model_name': "Model:",
|
||||||
|
'ss_clip_skip': "Clip skip:",
|
||||||
|
'ss_network_module': "Kohya module:",
|
||||||
|
}
|
||||||
|
|
||||||
|
for key, label in keys.items():
|
||||||
|
value = metadata.get(key, None)
|
||||||
|
if value is not None and str(value) != "None":
|
||||||
|
table.append((label, html.escape(value)))
|
||||||
|
|
||||||
|
ss_training_started_at = metadata.get('ss_training_started_at')
|
||||||
|
if ss_training_started_at:
|
||||||
|
table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
|
||||||
|
|
||||||
|
ss_bucket_info = metadata.get("ss_bucket_info")
|
||||||
|
if ss_bucket_info and "buckets" in ss_bucket_info:
|
||||||
|
resolutions = {}
|
||||||
|
for _, bucket in ss_bucket_info["buckets"].items():
|
||||||
|
resolution = bucket["resolution"]
|
||||||
|
resolution = f'{resolution[1]}x{resolution[0]}'
|
||||||
|
|
||||||
|
resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"])
|
||||||
|
|
||||||
|
resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True)
|
||||||
|
resolutions_text = html.escape(", ".join(resolutions_list[0:4]))
|
||||||
|
if len(resolutions) > 4:
|
||||||
|
resolutions_text += ", ..."
|
||||||
|
resolutions_text = f"<span title='{html.escape(', '.join(resolutions_list))}'>{resolutions_text}</span>"
|
||||||
|
|
||||||
|
table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text))
|
||||||
|
|
||||||
|
image_count = 0
|
||||||
|
for _, params in metadata.get("ss_dataset_dirs", {}).items():
|
||||||
|
image_count += int(params.get("img_count", 0))
|
||||||
|
|
||||||
|
if image_count:
|
||||||
|
table.append(("Dataset size:", image_count))
|
||||||
|
|
||||||
|
return table
|
||||||
|
|
||||||
|
def put_values_into_components(self, name):
|
||||||
|
user_metadata = self.get_user_metadata(name)
|
||||||
|
values = super().put_values_into_components(name)
|
||||||
|
|
||||||
|
item = self.page.items.get(name, {})
|
||||||
|
metadata = item.get("metadata") or {}
|
||||||
|
|
||||||
|
tags = build_tags(metadata)
|
||||||
|
gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
|
||||||
|
|
||||||
|
return [
|
||||||
|
*values[0:5],
|
||||||
|
item.get("sd_version", "Unknown"),
|
||||||
|
gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
|
||||||
|
user_metadata.get('activation text', ''),
|
||||||
|
float(user_metadata.get('preferred weight', 0.0)),
|
||||||
|
gr.update(visible=True if tags else False),
|
||||||
|
gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
|
||||||
|
]
|
||||||
|
|
||||||
|
def generate_random_prompt(self, name):
|
||||||
|
item = self.page.items.get(name, {})
|
||||||
|
metadata = item.get("metadata") or {}
|
||||||
|
tags = build_tags(metadata)
|
||||||
|
|
||||||
|
return self.generate_random_prompt_from_tags(tags)
|
||||||
|
|
||||||
|
def generate_random_prompt_from_tags(self, tags):
|
||||||
|
max_count = None
|
||||||
|
res = []
|
||||||
|
for tag, count in tags:
|
||||||
|
if not max_count:
|
||||||
|
max_count = count
|
||||||
|
|
||||||
|
v = random.random() * max_count
|
||||||
|
if count > v:
|
||||||
|
res.append(tag)
|
||||||
|
|
||||||
|
return ", ".join(sorted(res))
|
||||||
|
|
||||||
|
def create_extra_default_items_in_left_column(self):
|
||||||
|
|
||||||
|
# this would be a lot better as gr.Radio but I can't make it work
|
||||||
|
self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
|
||||||
|
|
||||||
|
def create_editor(self):
|
||||||
|
self.create_default_editor_elems()
|
||||||
|
|
||||||
|
self.taginfo = gr.HighlightedText(label="Training dataset tags")
|
||||||
|
self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
|
||||||
|
self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
|
||||||
|
|
||||||
|
with gr.Row() as row_random_prompt:
|
||||||
|
with gr.Column(scale=8):
|
||||||
|
random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
|
||||||
|
|
||||||
|
with gr.Column(scale=1, min_width=120):
|
||||||
|
generate_random_prompt = gr.Button('Generate').style(full_width=True, size="lg")
|
||||||
|
|
||||||
|
self.edit_notes = gr.TextArea(label='Notes', lines=4)
|
||||||
|
|
||||||
|
generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False)
|
||||||
|
|
||||||
|
def select_tag(activation_text, evt: gr.SelectData):
|
||||||
|
tag = evt.value[0]
|
||||||
|
|
||||||
|
words = re.split(re_comma, activation_text)
|
||||||
|
if tag in words:
|
||||||
|
words = [x for x in words if x != tag and x.strip()]
|
||||||
|
return ", ".join(words)
|
||||||
|
|
||||||
|
return activation_text + ", " + tag if activation_text else tag
|
||||||
|
|
||||||
|
self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False)
|
||||||
|
|
||||||
|
self.create_default_buttons()
|
||||||
|
|
||||||
|
viewed_components = [
|
||||||
|
self.edit_name,
|
||||||
|
self.edit_description,
|
||||||
|
self.html_filedata,
|
||||||
|
self.html_preview,
|
||||||
|
self.edit_notes,
|
||||||
|
self.select_sd_version,
|
||||||
|
self.taginfo,
|
||||||
|
self.edit_activation_text,
|
||||||
|
self.slider_preferred_weight,
|
||||||
|
row_random_prompt,
|
||||||
|
random_prompt,
|
||||||
|
]
|
||||||
|
|
||||||
|
self.button_edit\
|
||||||
|
.click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
|
||||||
|
.then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
|
||||||
|
|
||||||
|
edited_components = [
|
||||||
|
self.edit_description,
|
||||||
|
self.select_sd_version,
|
||||||
|
self.edit_activation_text,
|
||||||
|
self.slider_preferred_weight,
|
||||||
|
self.edit_notes,
|
||||||
|
]
|
||||||
|
|
||||||
|
self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
|
@ -1,8 +1,11 @@
|
|||||||
import json
|
|
||||||
import os
|
import os
|
||||||
import lora
|
|
||||||
|
import network
|
||||||
|
import networks
|
||||||
|
|
||||||
from modules import shared, ui_extra_networks
|
from modules import shared, ui_extra_networks
|
||||||
|
from modules.ui_extra_networks import quote_js
|
||||||
|
from ui_edit_user_metadata import LoraUserMetadataEditor
|
||||||
|
|
||||||
|
|
||||||
class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
|
class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
|
||||||
@ -10,22 +13,66 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
|
|||||||
super().__init__('Lora')
|
super().__init__('Lora')
|
||||||
|
|
||||||
def refresh(self):
|
def refresh(self):
|
||||||
lora.list_available_loras()
|
networks.list_available_networks()
|
||||||
|
|
||||||
|
def create_item(self, name, index=None, enable_filter=True):
|
||||||
|
lora_on_disk = networks.available_networks.get(name)
|
||||||
|
|
||||||
|
path, ext = os.path.splitext(lora_on_disk.filename)
|
||||||
|
|
||||||
|
alias = lora_on_disk.get_alias()
|
||||||
|
|
||||||
|
item = {
|
||||||
|
"name": name,
|
||||||
|
"filename": lora_on_disk.filename,
|
||||||
|
"preview": self.find_preview(path),
|
||||||
|
"description": self.find_description(path),
|
||||||
|
"search_term": self.search_terms_from_path(lora_on_disk.filename),
|
||||||
|
"local_preview": f"{path}.{shared.opts.samples_format}",
|
||||||
|
"metadata": lora_on_disk.metadata,
|
||||||
|
"sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
|
||||||
|
"sd_version": lora_on_disk.sd_version.name,
|
||||||
|
}
|
||||||
|
|
||||||
|
self.read_user_metadata(item)
|
||||||
|
activation_text = item["user_metadata"].get("activation text")
|
||||||
|
preferred_weight = item["user_metadata"].get("preferred weight", 0.0)
|
||||||
|
item["prompt"] = quote_js(f"<lora:{alias}:") + " + " + (str(preferred_weight) if preferred_weight else "opts.extra_networks_default_multiplier") + " + " + quote_js(">")
|
||||||
|
|
||||||
|
if activation_text:
|
||||||
|
item["prompt"] += " + " + quote_js(" " + activation_text)
|
||||||
|
|
||||||
|
sd_version = item["user_metadata"].get("sd version")
|
||||||
|
if sd_version in network.SdVersion.__members__:
|
||||||
|
item["sd_version"] = sd_version
|
||||||
|
sd_version = network.SdVersion[sd_version]
|
||||||
|
else:
|
||||||
|
sd_version = lora_on_disk.sd_version
|
||||||
|
|
||||||
|
if shared.opts.lora_show_all or not enable_filter:
|
||||||
|
pass
|
||||||
|
elif sd_version == network.SdVersion.Unknown:
|
||||||
|
model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
|
||||||
|
if model_version.name in shared.opts.lora_hide_unknown_for_versions:
|
||||||
|
return None
|
||||||
|
elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
|
||||||
|
return None
|
||||||
|
elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
|
||||||
|
return None
|
||||||
|
elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
|
||||||
|
return None
|
||||||
|
|
||||||
|
return item
|
||||||
|
|
||||||
def list_items(self):
|
def list_items(self):
|
||||||
for name, lora_on_disk in lora.available_loras.items():
|
for index, name in enumerate(networks.available_networks):
|
||||||
path, ext = os.path.splitext(lora_on_disk.filename)
|
item = self.create_item(name, index)
|
||||||
yield {
|
|
||||||
"name": name,
|
if item is not None:
|
||||||
"filename": path,
|
yield item
|
||||||
"preview": self.find_preview(path),
|
|
||||||
"description": self.find_description(path),
|
|
||||||
"search_term": self.search_terms_from_path(lora_on_disk.filename),
|
|
||||||
"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
|
|
||||||
"local_preview": f"{path}.{shared.opts.samples_format}",
|
|
||||||
"metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
|
|
||||||
}
|
|
||||||
|
|
||||||
def allowed_directories_for_previews(self):
|
def allowed_directories_for_previews(self):
|
||||||
return [shared.cmd_opts.lora_dir]
|
return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
|
||||||
|
|
||||||
|
def create_user_metadata_editor(self, ui, tabname):
|
||||||
|
return LoraUserMetadataEditor(ui, tabname, self)
|
||||||
|
@ -1,15 +1,16 @@
|
|||||||
import os.path
|
|
||||||
import sys
|
import sys
|
||||||
import traceback
|
|
||||||
|
|
||||||
import PIL.Image
|
import PIL.Image
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
from basicsr.utils.download_util import load_file_from_url
|
from tqdm import tqdm
|
||||||
|
|
||||||
import modules.upscaler
|
import modules.upscaler
|
||||||
from modules import devices, modelloader
|
from modules import devices, modelloader, script_callbacks, errors
|
||||||
from scunet_model_arch import SCUNet as net
|
from scunet_model_arch import SCUNet
|
||||||
|
|
||||||
|
from modules.modelloader import load_file_from_url
|
||||||
|
from modules.shared import opts
|
||||||
|
|
||||||
|
|
||||||
class UpscalerScuNET(modules.upscaler.Upscaler):
|
class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||||
@ -17,15 +18,15 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
|
|||||||
self.name = "ScuNET"
|
self.name = "ScuNET"
|
||||||
self.model_name = "ScuNET GAN"
|
self.model_name = "ScuNET GAN"
|
||||||
self.model_name2 = "ScuNET PSNR"
|
self.model_name2 = "ScuNET PSNR"
|
||||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
self.model_url = "https://ghproxy.com/https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
||||||
self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
|
self.model_url2 = "https://ghproxy.com/https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
|
||||||
self.user_path = dirname
|
self.user_path = dirname
|
||||||
super().__init__()
|
super().__init__()
|
||||||
model_paths = self.find_models(ext_filter=[".pth"])
|
model_paths = self.find_models(ext_filter=[".pth"])
|
||||||
scalers = []
|
scalers = []
|
||||||
add_model2 = True
|
add_model2 = True
|
||||||
for file in model_paths:
|
for file in model_paths:
|
||||||
if "http" in file:
|
if file.startswith("http"):
|
||||||
name = self.model_name
|
name = self.model_name
|
||||||
else:
|
else:
|
||||||
name = modelloader.friendly_name(file)
|
name = modelloader.friendly_name(file)
|
||||||
@ -35,53 +36,109 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
|
|||||||
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
||||||
scalers.append(scaler_data)
|
scalers.append(scaler_data)
|
||||||
except Exception:
|
except Exception:
|
||||||
print(f"Error loading ScuNET model: {file}", file=sys.stderr)
|
errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
if add_model2:
|
if add_model2:
|
||||||
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
|
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
|
||||||
scalers.append(scaler_data2)
|
scalers.append(scaler_data2)
|
||||||
self.scalers = scalers
|
self.scalers = scalers
|
||||||
|
|
||||||
def do_upscale(self, img: PIL.Image, selected_file):
|
@staticmethod
|
||||||
torch.cuda.empty_cache()
|
@torch.no_grad()
|
||||||
|
def tiled_inference(img, model):
|
||||||
|
# test the image tile by tile
|
||||||
|
h, w = img.shape[2:]
|
||||||
|
tile = opts.SCUNET_tile
|
||||||
|
tile_overlap = opts.SCUNET_tile_overlap
|
||||||
|
if tile == 0:
|
||||||
|
return model(img)
|
||||||
|
|
||||||
model = self.load_model(selected_file)
|
device = devices.get_device_for('scunet')
|
||||||
if model is None:
|
assert tile % 8 == 0, "tile size should be a multiple of window_size"
|
||||||
|
sf = 1
|
||||||
|
|
||||||
|
stride = tile - tile_overlap
|
||||||
|
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
|
||||||
|
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
|
||||||
|
E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
|
||||||
|
W = torch.zeros_like(E, dtype=devices.dtype, device=device)
|
||||||
|
|
||||||
|
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
|
||||||
|
for h_idx in h_idx_list:
|
||||||
|
|
||||||
|
for w_idx in w_idx_list:
|
||||||
|
|
||||||
|
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
|
||||||
|
|
||||||
|
out_patch = model(in_patch)
|
||||||
|
out_patch_mask = torch.ones_like(out_patch)
|
||||||
|
|
||||||
|
E[
|
||||||
|
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
|
||||||
|
].add_(out_patch)
|
||||||
|
W[
|
||||||
|
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
|
||||||
|
].add_(out_patch_mask)
|
||||||
|
pbar.update(1)
|
||||||
|
output = E.div_(W)
|
||||||
|
|
||||||
|
return output
|
||||||
|
|
||||||
|
def do_upscale(self, img: PIL.Image.Image, selected_file):
|
||||||
|
|
||||||
|
devices.torch_gc()
|
||||||
|
|
||||||
|
try:
|
||||||
|
model = self.load_model(selected_file)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
|
||||||
return img
|
return img
|
||||||
|
|
||||||
device = devices.get_device_for('scunet')
|
device = devices.get_device_for('scunet')
|
||||||
img = np.array(img)
|
tile = opts.SCUNET_tile
|
||||||
img = img[:, :, ::-1]
|
h, w = img.height, img.width
|
||||||
img = np.moveaxis(img, 2, 0) / 255
|
np_img = np.array(img)
|
||||||
img = torch.from_numpy(img).float()
|
np_img = np_img[:, :, ::-1] # RGB to BGR
|
||||||
img = img.unsqueeze(0).to(device)
|
np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
|
||||||
|
torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
|
||||||
|
|
||||||
with torch.no_grad():
|
if tile > h or tile > w:
|
||||||
output = model(img)
|
_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
|
||||||
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
|
_img[:, :, :h, :w] = torch_img # pad image
|
||||||
output = 255. * np.moveaxis(output, 0, 2)
|
torch_img = _img
|
||||||
output = output.astype(np.uint8)
|
|
||||||
output = output[:, :, ::-1]
|
torch_output = self.tiled_inference(torch_img, model).squeeze(0)
|
||||||
torch.cuda.empty_cache()
|
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
|
||||||
return PIL.Image.fromarray(output, 'RGB')
|
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
|
||||||
|
del torch_img, torch_output
|
||||||
|
devices.torch_gc()
|
||||||
|
|
||||||
|
output = np_output.transpose((1, 2, 0)) # CHW to HWC
|
||||||
|
output = output[:, :, ::-1] # BGR to RGB
|
||||||
|
return PIL.Image.fromarray((output * 255).astype(np.uint8))
|
||||||
|
|
||||||
def load_model(self, path: str):
|
def load_model(self, path: str):
|
||||||
device = devices.get_device_for('scunet')
|
device = devices.get_device_for('scunet')
|
||||||
if "http" in path:
|
if path.startswith("http"):
|
||||||
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
|
# TODO: this doesn't use `path` at all?
|
||||||
progress=True)
|
filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
|
||||||
else:
|
else:
|
||||||
filename = path
|
filename = path
|
||||||
if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
|
model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
|
||||||
print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
|
|
||||||
return None
|
|
||||||
|
|
||||||
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
|
|
||||||
model.load_state_dict(torch.load(filename), strict=True)
|
model.load_state_dict(torch.load(filename), strict=True)
|
||||||
model.eval()
|
model.eval()
|
||||||
for k, v in model.named_parameters():
|
for _, v in model.named_parameters():
|
||||||
v.requires_grad = False
|
v.requires_grad = False
|
||||||
model = model.to(device)
|
model = model.to(device)
|
||||||
|
|
||||||
return model
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def on_ui_settings():
|
||||||
|
import gradio as gr
|
||||||
|
from modules import shared
|
||||||
|
|
||||||
|
shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
|
||||||
|
shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
|
||||||
|
|
||||||
|
|
||||||
|
script_callbacks.on_ui_settings(on_ui_settings)
|
||||||
|
@ -61,7 +61,9 @@ class WMSA(nn.Module):
|
|||||||
Returns:
|
Returns:
|
||||||
output: tensor shape [b h w c]
|
output: tensor shape [b h w c]
|
||||||
"""
|
"""
|
||||||
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
|
if self.type != 'W':
|
||||||
|
x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
|
||||||
|
|
||||||
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
|
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
|
||||||
h_windows = x.size(1)
|
h_windows = x.size(1)
|
||||||
w_windows = x.size(2)
|
w_windows = x.size(2)
|
||||||
@ -85,8 +87,9 @@ class WMSA(nn.Module):
|
|||||||
output = self.linear(output)
|
output = self.linear(output)
|
||||||
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
|
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
|
||||||
|
|
||||||
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
|
if self.type != 'W':
|
||||||
dims=(1, 2))
|
output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
|
||||||
|
|
||||||
return output
|
return output
|
||||||
|
|
||||||
def relative_embedding(self):
|
def relative_embedding(self):
|
||||||
@ -262,4 +265,4 @@ class SCUNet(nn.Module):
|
|||||||
nn.init.constant_(m.bias, 0)
|
nn.init.constant_(m.bias, 0)
|
||||||
elif isinstance(m, nn.LayerNorm):
|
elif isinstance(m, nn.LayerNorm):
|
||||||
nn.init.constant_(m.bias, 0)
|
nn.init.constant_(m.bias, 0)
|
||||||
nn.init.constant_(m.weight, 1.0)
|
nn.init.constant_(m.weight, 1.0)
|
||||||
|
@ -1,35 +1,35 @@
|
|||||||
import contextlib
|
import sys
|
||||||
import os
|
import platform
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
from basicsr.utils.download_util import load_file_from_url
|
|
||||||
from tqdm import tqdm
|
from tqdm import tqdm
|
||||||
|
|
||||||
from modules import modelloader, devices, script_callbacks, shared
|
from modules import modelloader, devices, script_callbacks, shared
|
||||||
from modules.shared import cmd_opts, opts, state
|
from modules.shared import opts, state
|
||||||
from swinir_model_arch import SwinIR as net
|
from swinir_model_arch import SwinIR
|
||||||
from swinir_model_arch_v2 import Swin2SR as net2
|
from swinir_model_arch_v2 import Swin2SR
|
||||||
from modules.upscaler import Upscaler, UpscalerData
|
from modules.upscaler import Upscaler, UpscalerData
|
||||||
|
|
||||||
|
SWINIR_MODEL_URL = "https://ghproxy.com/https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
|
||||||
|
|
||||||
device_swinir = devices.get_device_for('swinir')
|
device_swinir = devices.get_device_for('swinir')
|
||||||
|
|
||||||
|
|
||||||
class UpscalerSwinIR(Upscaler):
|
class UpscalerSwinIR(Upscaler):
|
||||||
def __init__(self, dirname):
|
def __init__(self, dirname):
|
||||||
|
self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
|
||||||
|
self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
|
||||||
self.name = "SwinIR"
|
self.name = "SwinIR"
|
||||||
self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
|
self.model_url = SWINIR_MODEL_URL
|
||||||
"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
|
|
||||||
"-L_x4_GAN.pth "
|
|
||||||
self.model_name = "SwinIR 4x"
|
self.model_name = "SwinIR 4x"
|
||||||
self.user_path = dirname
|
self.user_path = dirname
|
||||||
super().__init__()
|
super().__init__()
|
||||||
scalers = []
|
scalers = []
|
||||||
model_files = self.find_models(ext_filter=[".pt", ".pth"])
|
model_files = self.find_models(ext_filter=[".pt", ".pth"])
|
||||||
for model in model_files:
|
for model in model_files:
|
||||||
if "http" in model:
|
if model.startswith("http"):
|
||||||
name = self.model_name
|
name = self.model_name
|
||||||
else:
|
else:
|
||||||
name = modelloader.friendly_name(model)
|
name = modelloader.friendly_name(model)
|
||||||
@ -38,42 +38,54 @@ class UpscalerSwinIR(Upscaler):
|
|||||||
self.scalers = scalers
|
self.scalers = scalers
|
||||||
|
|
||||||
def do_upscale(self, img, model_file):
|
def do_upscale(self, img, model_file):
|
||||||
model = self.load_model(model_file)
|
use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
|
||||||
if model is None:
|
and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
|
||||||
return img
|
current_config = (model_file, opts.SWIN_tile)
|
||||||
model = model.to(device_swinir, dtype=devices.dtype)
|
|
||||||
|
if use_compile and self._cached_model_config == current_config:
|
||||||
|
model = self._cached_model
|
||||||
|
else:
|
||||||
|
self._cached_model = None
|
||||||
|
try:
|
||||||
|
model = self.load_model(model_file)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
|
||||||
|
return img
|
||||||
|
model = model.to(device_swinir, dtype=devices.dtype)
|
||||||
|
if use_compile:
|
||||||
|
model = torch.compile(model)
|
||||||
|
self._cached_model = model
|
||||||
|
self._cached_model_config = current_config
|
||||||
img = upscale(img, model)
|
img = upscale(img, model)
|
||||||
try:
|
devices.torch_gc()
|
||||||
torch.cuda.empty_cache()
|
|
||||||
except:
|
|
||||||
pass
|
|
||||||
return img
|
return img
|
||||||
|
|
||||||
def load_model(self, path, scale=4):
|
def load_model(self, path, scale=4):
|
||||||
if "http" in path:
|
if path.startswith("http"):
|
||||||
dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
|
filename = modelloader.load_file_from_url(
|
||||||
filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
|
url=path,
|
||||||
|
model_dir=self.model_download_path,
|
||||||
|
file_name=f"{self.model_name.replace(' ', '_')}.pth",
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
filename = path
|
filename = path
|
||||||
if filename is None or not os.path.exists(filename):
|
|
||||||
return None
|
|
||||||
if filename.endswith(".v2.pth"):
|
if filename.endswith(".v2.pth"):
|
||||||
model = net2(
|
model = Swin2SR(
|
||||||
upscale=scale,
|
upscale=scale,
|
||||||
in_chans=3,
|
in_chans=3,
|
||||||
img_size=64,
|
img_size=64,
|
||||||
window_size=8,
|
window_size=8,
|
||||||
img_range=1.0,
|
img_range=1.0,
|
||||||
depths=[6, 6, 6, 6, 6, 6],
|
depths=[6, 6, 6, 6, 6, 6],
|
||||||
embed_dim=180,
|
embed_dim=180,
|
||||||
num_heads=[6, 6, 6, 6, 6, 6],
|
num_heads=[6, 6, 6, 6, 6, 6],
|
||||||
mlp_ratio=2,
|
mlp_ratio=2,
|
||||||
upsampler="nearest+conv",
|
upsampler="nearest+conv",
|
||||||
resi_connection="1conv",
|
resi_connection="1conv",
|
||||||
)
|
)
|
||||||
params = None
|
params = None
|
||||||
else:
|
else:
|
||||||
model = net(
|
model = SwinIR(
|
||||||
upscale=scale,
|
upscale=scale,
|
||||||
in_chans=3,
|
in_chans=3,
|
||||||
img_size=64,
|
img_size=64,
|
||||||
@ -151,7 +163,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
|
|||||||
for w_idx in w_idx_list:
|
for w_idx in w_idx_list:
|
||||||
if state.interrupted or state.skipped:
|
if state.interrupted or state.skipped:
|
||||||
break
|
break
|
||||||
|
|
||||||
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
|
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
|
||||||
out_patch = model(in_patch)
|
out_patch = model(in_patch)
|
||||||
out_patch_mask = torch.ones_like(out_patch)
|
out_patch_mask = torch.ones_like(out_patch)
|
||||||
@ -173,6 +185,8 @@ def on_ui_settings():
|
|||||||
|
|
||||||
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
|
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
|
||||||
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
|
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
|
||||||
|
if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
|
||||||
|
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
|
||||||
|
|
||||||
|
|
||||||
script_callbacks.on_ui_settings(on_ui_settings)
|
script_callbacks.on_ui_settings(on_ui_settings)
|
||||||
|
@ -644,7 +644,7 @@ class SwinIR(nn.Module):
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
||||||
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
|
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
|
||||||
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
||||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||||||
@ -805,7 +805,7 @@ class SwinIR(nn.Module):
|
|||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
H, W = x.shape[2:]
|
H, W = x.shape[2:]
|
||||||
x = self.check_image_size(x)
|
x = self.check_image_size(x)
|
||||||
|
|
||||||
self.mean = self.mean.type_as(x)
|
self.mean = self.mean.type_as(x)
|
||||||
x = (x - self.mean) * self.img_range
|
x = (x - self.mean) * self.img_range
|
||||||
|
|
||||||
@ -844,7 +844,7 @@ class SwinIR(nn.Module):
|
|||||||
H, W = self.patches_resolution
|
H, W = self.patches_resolution
|
||||||
flops += H * W * 3 * self.embed_dim * 9
|
flops += H * W * 3 * self.embed_dim * 9
|
||||||
flops += self.patch_embed.flops()
|
flops += self.patch_embed.flops()
|
||||||
for i, layer in enumerate(self.layers):
|
for layer in self.layers:
|
||||||
flops += layer.flops()
|
flops += layer.flops()
|
||||||
flops += H * W * 3 * self.embed_dim * self.embed_dim
|
flops += H * W * 3 * self.embed_dim * self.embed_dim
|
||||||
flops += self.upsample.flops()
|
flops += self.upsample.flops()
|
||||||
|
@ -74,7 +74,7 @@ class WindowAttention(nn.Module):
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
|
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
|
||||||
pretrained_window_size=[0, 0]):
|
pretrained_window_size=(0, 0)):
|
||||||
|
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.dim = dim
|
self.dim = dim
|
||||||
@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module):
|
|||||||
attn_mask = None
|
attn_mask = None
|
||||||
|
|
||||||
self.register_buffer("attn_mask", attn_mask)
|
self.register_buffer("attn_mask", attn_mask)
|
||||||
|
|
||||||
def calculate_mask(self, x_size):
|
def calculate_mask(self, x_size):
|
||||||
# calculate attention mask for SW-MSA
|
# calculate attention mask for SW-MSA
|
||||||
H, W = x_size
|
H, W = x_size
|
||||||
@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module):
|
|||||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||||
|
|
||||||
return attn_mask
|
return attn_mask
|
||||||
|
|
||||||
def forward(self, x, x_size):
|
def forward(self, x, x_size):
|
||||||
H, W = x_size
|
H, W = x_size
|
||||||
@ -288,7 +288,7 @@ class SwinTransformerBlock(nn.Module):
|
|||||||
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
||||||
else:
|
else:
|
||||||
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
|
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
|
||||||
|
|
||||||
# merge windows
|
# merge windows
|
||||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||||
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
||||||
@ -369,7 +369,7 @@ class PatchMerging(nn.Module):
|
|||||||
H, W = self.input_resolution
|
H, W = self.input_resolution
|
||||||
flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
|
flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
|
||||||
flops += H * W * self.dim // 2
|
flops += H * W * self.dim // 2
|
||||||
return flops
|
return flops
|
||||||
|
|
||||||
class BasicLayer(nn.Module):
|
class BasicLayer(nn.Module):
|
||||||
""" A basic Swin Transformer layer for one stage.
|
""" A basic Swin Transformer layer for one stage.
|
||||||
@ -447,7 +447,7 @@ class BasicLayer(nn.Module):
|
|||||||
nn.init.constant_(blk.norm1.weight, 0)
|
nn.init.constant_(blk.norm1.weight, 0)
|
||||||
nn.init.constant_(blk.norm2.bias, 0)
|
nn.init.constant_(blk.norm2.bias, 0)
|
||||||
nn.init.constant_(blk.norm2.weight, 0)
|
nn.init.constant_(blk.norm2.weight, 0)
|
||||||
|
|
||||||
class PatchEmbed(nn.Module):
|
class PatchEmbed(nn.Module):
|
||||||
r""" Image to Patch Embedding
|
r""" Image to Patch Embedding
|
||||||
Args:
|
Args:
|
||||||
@ -492,7 +492,7 @@ class PatchEmbed(nn.Module):
|
|||||||
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
|
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
|
||||||
if self.norm is not None:
|
if self.norm is not None:
|
||||||
flops += Ho * Wo * self.embed_dim
|
flops += Ho * Wo * self.embed_dim
|
||||||
return flops
|
return flops
|
||||||
|
|
||||||
class RSTB(nn.Module):
|
class RSTB(nn.Module):
|
||||||
"""Residual Swin Transformer Block (RSTB).
|
"""Residual Swin Transformer Block (RSTB).
|
||||||
@ -531,7 +531,7 @@ class RSTB(nn.Module):
|
|||||||
num_heads=num_heads,
|
num_heads=num_heads,
|
||||||
window_size=window_size,
|
window_size=window_size,
|
||||||
mlp_ratio=mlp_ratio,
|
mlp_ratio=mlp_ratio,
|
||||||
qkv_bias=qkv_bias,
|
qkv_bias=qkv_bias,
|
||||||
drop=drop, attn_drop=attn_drop,
|
drop=drop, attn_drop=attn_drop,
|
||||||
drop_path=drop_path,
|
drop_path=drop_path,
|
||||||
norm_layer=norm_layer,
|
norm_layer=norm_layer,
|
||||||
@ -622,7 +622,7 @@ class Upsample(nn.Sequential):
|
|||||||
else:
|
else:
|
||||||
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
||||||
super(Upsample, self).__init__(*m)
|
super(Upsample, self).__init__(*m)
|
||||||
|
|
||||||
class Upsample_hf(nn.Sequential):
|
class Upsample_hf(nn.Sequential):
|
||||||
"""Upsample module.
|
"""Upsample module.
|
||||||
|
|
||||||
@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential):
|
|||||||
m.append(nn.PixelShuffle(3))
|
m.append(nn.PixelShuffle(3))
|
||||||
else:
|
else:
|
||||||
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
|
||||||
super(Upsample_hf, self).__init__(*m)
|
super(Upsample_hf, self).__init__(*m)
|
||||||
|
|
||||||
|
|
||||||
class UpsampleOneStep(nn.Sequential):
|
class UpsampleOneStep(nn.Sequential):
|
||||||
@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential):
|
|||||||
H, W = self.input_resolution
|
H, W = self.input_resolution
|
||||||
flops = H * W * self.num_feat * 3 * 9
|
flops = H * W * self.num_feat * 3 * 9
|
||||||
return flops
|
return flops
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class Swin2SR(nn.Module):
|
class Swin2SR(nn.Module):
|
||||||
r""" Swin2SR
|
r""" Swin2SR
|
||||||
@ -698,8 +698,8 @@ class Swin2SR(nn.Module):
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
def __init__(self, img_size=64, patch_size=1, in_chans=3,
|
||||||
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
|
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
|
||||||
window_size=7, mlp_ratio=4., qkv_bias=True,
|
window_size=7, mlp_ratio=4., qkv_bias=True,
|
||||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||||||
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
|
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
|
||||||
@ -764,7 +764,7 @@ class Swin2SR(nn.Module):
|
|||||||
num_heads=num_heads[i_layer],
|
num_heads=num_heads[i_layer],
|
||||||
window_size=window_size,
|
window_size=window_size,
|
||||||
mlp_ratio=self.mlp_ratio,
|
mlp_ratio=self.mlp_ratio,
|
||||||
qkv_bias=qkv_bias,
|
qkv_bias=qkv_bias,
|
||||||
drop=drop_rate, attn_drop=attn_drop_rate,
|
drop=drop_rate, attn_drop=attn_drop_rate,
|
||||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
||||||
norm_layer=norm_layer,
|
norm_layer=norm_layer,
|
||||||
@ -776,7 +776,7 @@ class Swin2SR(nn.Module):
|
|||||||
|
|
||||||
)
|
)
|
||||||
self.layers.append(layer)
|
self.layers.append(layer)
|
||||||
|
|
||||||
if self.upsampler == 'pixelshuffle_hf':
|
if self.upsampler == 'pixelshuffle_hf':
|
||||||
self.layers_hf = nn.ModuleList()
|
self.layers_hf = nn.ModuleList()
|
||||||
for i_layer in range(self.num_layers):
|
for i_layer in range(self.num_layers):
|
||||||
@ -787,7 +787,7 @@ class Swin2SR(nn.Module):
|
|||||||
num_heads=num_heads[i_layer],
|
num_heads=num_heads[i_layer],
|
||||||
window_size=window_size,
|
window_size=window_size,
|
||||||
mlp_ratio=self.mlp_ratio,
|
mlp_ratio=self.mlp_ratio,
|
||||||
qkv_bias=qkv_bias,
|
qkv_bias=qkv_bias,
|
||||||
drop=drop_rate, attn_drop=attn_drop_rate,
|
drop=drop_rate, attn_drop=attn_drop_rate,
|
||||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
|
||||||
norm_layer=norm_layer,
|
norm_layer=norm_layer,
|
||||||
@ -799,7 +799,7 @@ class Swin2SR(nn.Module):
|
|||||||
|
|
||||||
)
|
)
|
||||||
self.layers_hf.append(layer)
|
self.layers_hf.append(layer)
|
||||||
|
|
||||||
self.norm = norm_layer(self.num_features)
|
self.norm = norm_layer(self.num_features)
|
||||||
|
|
||||||
# build the last conv layer in deep feature extraction
|
# build the last conv layer in deep feature extraction
|
||||||
@ -829,10 +829,10 @@ class Swin2SR(nn.Module):
|
|||||||
self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
||||||
self.conv_after_aux = nn.Sequential(
|
self.conv_after_aux = nn.Sequential(
|
||||||
nn.Conv2d(3, num_feat, 3, 1, 1),
|
nn.Conv2d(3, num_feat, 3, 1, 1),
|
||||||
nn.LeakyReLU(inplace=True))
|
nn.LeakyReLU(inplace=True))
|
||||||
self.upsample = Upsample(upscale, num_feat)
|
self.upsample = Upsample(upscale, num_feat)
|
||||||
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
||||||
|
|
||||||
elif self.upsampler == 'pixelshuffle_hf':
|
elif self.upsampler == 'pixelshuffle_hf':
|
||||||
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
||||||
nn.LeakyReLU(inplace=True))
|
nn.LeakyReLU(inplace=True))
|
||||||
@ -846,7 +846,7 @@ class Swin2SR(nn.Module):
|
|||||||
nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
|
||||||
nn.LeakyReLU(inplace=True))
|
nn.LeakyReLU(inplace=True))
|
||||||
self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
|
||||||
|
|
||||||
elif self.upsampler == 'pixelshuffledirect':
|
elif self.upsampler == 'pixelshuffledirect':
|
||||||
# for lightweight SR (to save parameters)
|
# for lightweight SR (to save parameters)
|
||||||
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
|
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
|
||||||
@ -905,7 +905,7 @@ class Swin2SR(nn.Module):
|
|||||||
x = self.patch_unembed(x, x_size)
|
x = self.patch_unembed(x, x_size)
|
||||||
|
|
||||||
return x
|
return x
|
||||||
|
|
||||||
def forward_features_hf(self, x):
|
def forward_features_hf(self, x):
|
||||||
x_size = (x.shape[2], x.shape[3])
|
x_size = (x.shape[2], x.shape[3])
|
||||||
x = self.patch_embed(x)
|
x = self.patch_embed(x)
|
||||||
@ -919,7 +919,7 @@ class Swin2SR(nn.Module):
|
|||||||
x = self.norm(x) # B L C
|
x = self.norm(x) # B L C
|
||||||
x = self.patch_unembed(x, x_size)
|
x = self.patch_unembed(x, x_size)
|
||||||
|
|
||||||
return x
|
return x
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
H, W = x.shape[2:]
|
H, W = x.shape[2:]
|
||||||
@ -951,7 +951,7 @@ class Swin2SR(nn.Module):
|
|||||||
x = self.conv_after_body(self.forward_features(x)) + x
|
x = self.conv_after_body(self.forward_features(x)) + x
|
||||||
x_before = self.conv_before_upsample(x)
|
x_before = self.conv_before_upsample(x)
|
||||||
x_out = self.conv_last(self.upsample(x_before))
|
x_out = self.conv_last(self.upsample(x_before))
|
||||||
|
|
||||||
x_hf = self.conv_first_hf(x_before)
|
x_hf = self.conv_first_hf(x_before)
|
||||||
x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
|
x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
|
||||||
x_hf = self.conv_before_upsample_hf(x_hf)
|
x_hf = self.conv_before_upsample_hf(x_hf)
|
||||||
@ -977,15 +977,15 @@ class Swin2SR(nn.Module):
|
|||||||
x_first = self.conv_first(x)
|
x_first = self.conv_first(x)
|
||||||
res = self.conv_after_body(self.forward_features(x_first)) + x_first
|
res = self.conv_after_body(self.forward_features(x_first)) + x_first
|
||||||
x = x + self.conv_last(res)
|
x = x + self.conv_last(res)
|
||||||
|
|
||||||
x = x / self.img_range + self.mean
|
x = x / self.img_range + self.mean
|
||||||
if self.upsampler == "pixelshuffle_aux":
|
if self.upsampler == "pixelshuffle_aux":
|
||||||
return x[:, :, :H*self.upscale, :W*self.upscale], aux
|
return x[:, :, :H*self.upscale, :W*self.upscale], aux
|
||||||
|
|
||||||
elif self.upsampler == "pixelshuffle_hf":
|
elif self.upsampler == "pixelshuffle_hf":
|
||||||
x_out = x_out / self.img_range + self.mean
|
x_out = x_out / self.img_range + self.mean
|
||||||
return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
|
return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
|
||||||
|
|
||||||
else:
|
else:
|
||||||
return x[:, :, :H*self.upscale, :W*self.upscale]
|
return x[:, :, :H*self.upscale, :W*self.upscale]
|
||||||
|
|
||||||
@ -994,7 +994,7 @@ class Swin2SR(nn.Module):
|
|||||||
H, W = self.patches_resolution
|
H, W = self.patches_resolution
|
||||||
flops += H * W * 3 * self.embed_dim * 9
|
flops += H * W * 3 * self.embed_dim * 9
|
||||||
flops += self.patch_embed.flops()
|
flops += self.patch_embed.flops()
|
||||||
for i, layer in enumerate(self.layers):
|
for layer in self.layers:
|
||||||
flops += layer.flops()
|
flops += layer.flops()
|
||||||
flops += H * W * 3 * self.embed_dim * self.embed_dim
|
flops += H * W * 3 * self.embed_dim * self.embed_dim
|
||||||
flops += self.upsample.flops()
|
flops += self.upsample.flops()
|
||||||
@ -1014,4 +1014,4 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
x = torch.randn((1, 3, height, width))
|
x = torch.randn((1, 3, height, width))
|
||||||
x = model(x)
|
x = model(x)
|
||||||
print(x.shape)
|
print(x.shape)
|
||||||
|
776
extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
Normal file
776
extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
Normal file
@ -0,0 +1,776 @@
|
|||||||
|
onUiLoaded(async() => {
|
||||||
|
const elementIDs = {
|
||||||
|
img2imgTabs: "#mode_img2img .tab-nav",
|
||||||
|
inpaint: "#img2maskimg",
|
||||||
|
inpaintSketch: "#inpaint_sketch",
|
||||||
|
rangeGroup: "#img2img_column_size",
|
||||||
|
sketch: "#img2img_sketch"
|
||||||
|
};
|
||||||
|
const tabNameToElementId = {
|
||||||
|
"Inpaint sketch": elementIDs.inpaintSketch,
|
||||||
|
"Inpaint": elementIDs.inpaint,
|
||||||
|
"Sketch": elementIDs.sketch
|
||||||
|
};
|
||||||
|
|
||||||
|
// Helper functions
|
||||||
|
// Get active tab
|
||||||
|
function getActiveTab(elements, all = false) {
|
||||||
|
const tabs = elements.img2imgTabs.querySelectorAll("button");
|
||||||
|
|
||||||
|
if (all) return tabs;
|
||||||
|
|
||||||
|
for (let tab of tabs) {
|
||||||
|
if (tab.classList.contains("selected")) {
|
||||||
|
return tab;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Get tab ID
|
||||||
|
function getTabId(elements) {
|
||||||
|
const activeTab = getActiveTab(elements);
|
||||||
|
return tabNameToElementId[activeTab.innerText];
|
||||||
|
}
|
||||||
|
|
||||||
|
// Wait until opts loaded
|
||||||
|
async function waitForOpts() {
|
||||||
|
for (;;) {
|
||||||
|
if (window.opts && Object.keys(window.opts).length) {
|
||||||
|
return window.opts;
|
||||||
|
}
|
||||||
|
await new Promise(resolve => setTimeout(resolve, 100));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Function for defining the "Ctrl", "Shift" and "Alt" keys
|
||||||
|
function isModifierKey(event, key) {
|
||||||
|
switch (key) {
|
||||||
|
case "Ctrl":
|
||||||
|
return event.ctrlKey;
|
||||||
|
case "Shift":
|
||||||
|
return event.shiftKey;
|
||||||
|
case "Alt":
|
||||||
|
return event.altKey;
|
||||||
|
default:
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check if hotkey is valid
|
||||||
|
function isValidHotkey(value) {
|
||||||
|
const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
|
||||||
|
return (
|
||||||
|
(typeof value === "string" &&
|
||||||
|
value.length === 1 &&
|
||||||
|
/[a-z]/i.test(value)) ||
|
||||||
|
specialKeys.includes(value)
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Normalize hotkey
|
||||||
|
function normalizeHotkey(hotkey) {
|
||||||
|
return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Format hotkey for display
|
||||||
|
function formatHotkeyForDisplay(hotkey) {
|
||||||
|
return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Create hotkey configuration with the provided options
|
||||||
|
function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
|
||||||
|
const result = {}; // Resulting hotkey configuration
|
||||||
|
const usedKeys = new Set(); // Set of used hotkeys
|
||||||
|
|
||||||
|
// Iterate through defaultHotkeysConfig keys
|
||||||
|
for (const key in defaultHotkeysConfig) {
|
||||||
|
const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
|
||||||
|
const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
|
||||||
|
|
||||||
|
// Apply appropriate value for undefined, boolean, or object userValue
|
||||||
|
if (
|
||||||
|
userValue === undefined ||
|
||||||
|
typeof userValue === "boolean" ||
|
||||||
|
typeof userValue === "object" ||
|
||||||
|
userValue === "disable"
|
||||||
|
) {
|
||||||
|
result[key] =
|
||||||
|
userValue === undefined ? defaultValue : userValue;
|
||||||
|
} else if (isValidHotkey(userValue)) {
|
||||||
|
const normalizedUserValue = normalizeHotkey(userValue);
|
||||||
|
|
||||||
|
// Check for conflicting hotkeys
|
||||||
|
if (!usedKeys.has(normalizedUserValue)) {
|
||||||
|
usedKeys.add(normalizedUserValue);
|
||||||
|
result[key] = normalizedUserValue;
|
||||||
|
} else {
|
||||||
|
console.error(
|
||||||
|
`Hotkey: ${formatHotkeyForDisplay(
|
||||||
|
userValue
|
||||||
|
)} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
|
||||||
|
defaultValue
|
||||||
|
)}`
|
||||||
|
);
|
||||||
|
result[key] = defaultValue;
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
console.error(
|
||||||
|
`Hotkey: ${formatHotkeyForDisplay(
|
||||||
|
userValue
|
||||||
|
)} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
|
||||||
|
defaultValue
|
||||||
|
)}`
|
||||||
|
);
|
||||||
|
result[key] = defaultValue;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Disables functions in the config object based on the provided list of function names
|
||||||
|
function disableFunctions(config, disabledFunctions) {
|
||||||
|
// Bind the hasOwnProperty method to the functionMap object to avoid errors
|
||||||
|
const hasOwnProperty =
|
||||||
|
Object.prototype.hasOwnProperty.bind(functionMap);
|
||||||
|
|
||||||
|
// Loop through the disabledFunctions array and disable the corresponding functions in the config object
|
||||||
|
disabledFunctions.forEach(funcName => {
|
||||||
|
if (hasOwnProperty(funcName)) {
|
||||||
|
const key = functionMap[funcName];
|
||||||
|
config[key] = "disable";
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
// Return the updated config object
|
||||||
|
return config;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
|
||||||
|
* If the image display property is set to 'none', the mask breaks. To fix this, the function
|
||||||
|
* temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
|
||||||
|
* to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
|
||||||
|
* very long images.
|
||||||
|
*/
|
||||||
|
function restoreImgRedMask(elements) {
|
||||||
|
const mainTabId = getTabId(elements);
|
||||||
|
|
||||||
|
if (!mainTabId) return;
|
||||||
|
|
||||||
|
const mainTab = gradioApp().querySelector(mainTabId);
|
||||||
|
const img = mainTab.querySelector("img");
|
||||||
|
const imageARPreview = gradioApp().querySelector("#imageARPreview");
|
||||||
|
|
||||||
|
if (!img || !imageARPreview) return;
|
||||||
|
|
||||||
|
imageARPreview.style.transform = "";
|
||||||
|
if (parseFloat(mainTab.style.width) > 865) {
|
||||||
|
const transformString = mainTab.style.transform;
|
||||||
|
const scaleMatch = transformString.match(
|
||||||
|
/scale\(([-+]?[0-9]*\.?[0-9]+)\)/
|
||||||
|
);
|
||||||
|
let zoom = 1; // default zoom
|
||||||
|
|
||||||
|
if (scaleMatch && scaleMatch[1]) {
|
||||||
|
zoom = Number(scaleMatch[1]);
|
||||||
|
}
|
||||||
|
|
||||||
|
imageARPreview.style.transformOrigin = "0 0";
|
||||||
|
imageARPreview.style.transform = `scale(${zoom})`;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (img.style.display !== "none") return;
|
||||||
|
|
||||||
|
img.style.display = "block";
|
||||||
|
|
||||||
|
setTimeout(() => {
|
||||||
|
img.style.display = "none";
|
||||||
|
}, 400);
|
||||||
|
}
|
||||||
|
|
||||||
|
const hotkeysConfigOpts = await waitForOpts();
|
||||||
|
|
||||||
|
// Default config
|
||||||
|
const defaultHotkeysConfig = {
|
||||||
|
canvas_hotkey_zoom: "Alt",
|
||||||
|
canvas_hotkey_adjust: "Ctrl",
|
||||||
|
canvas_hotkey_reset: "KeyR",
|
||||||
|
canvas_hotkey_fullscreen: "KeyS",
|
||||||
|
canvas_hotkey_move: "KeyF",
|
||||||
|
canvas_hotkey_overlap: "KeyO",
|
||||||
|
canvas_disabled_functions: [],
|
||||||
|
canvas_show_tooltip: true,
|
||||||
|
canvas_blur_prompt: false
|
||||||
|
};
|
||||||
|
|
||||||
|
const functionMap = {
|
||||||
|
"Zoom": "canvas_hotkey_zoom",
|
||||||
|
"Adjust brush size": "canvas_hotkey_adjust",
|
||||||
|
"Moving canvas": "canvas_hotkey_move",
|
||||||
|
"Fullscreen": "canvas_hotkey_fullscreen",
|
||||||
|
"Reset Zoom": "canvas_hotkey_reset",
|
||||||
|
"Overlap": "canvas_hotkey_overlap"
|
||||||
|
};
|
||||||
|
|
||||||
|
// Loading the configuration from opts
|
||||||
|
const preHotkeysConfig = createHotkeyConfig(
|
||||||
|
defaultHotkeysConfig,
|
||||||
|
hotkeysConfigOpts
|
||||||
|
);
|
||||||
|
|
||||||
|
// Disable functions that are not needed by the user
|
||||||
|
const hotkeysConfig = disableFunctions(
|
||||||
|
preHotkeysConfig,
|
||||||
|
preHotkeysConfig.canvas_disabled_functions
|
||||||
|
);
|
||||||
|
|
||||||
|
let isMoving = false;
|
||||||
|
let mouseX, mouseY;
|
||||||
|
let activeElement;
|
||||||
|
|
||||||
|
const elements = Object.fromEntries(
|
||||||
|
Object.keys(elementIDs).map(id => [
|
||||||
|
id,
|
||||||
|
gradioApp().querySelector(elementIDs[id])
|
||||||
|
])
|
||||||
|
);
|
||||||
|
const elemData = {};
|
||||||
|
|
||||||
|
// Apply functionality to the range inputs. Restore redmask and correct for long images.
|
||||||
|
const rangeInputs = elements.rangeGroup ?
|
||||||
|
Array.from(elements.rangeGroup.querySelectorAll("input")) :
|
||||||
|
[
|
||||||
|
gradioApp().querySelector("#img2img_width input[type='range']"),
|
||||||
|
gradioApp().querySelector("#img2img_height input[type='range']")
|
||||||
|
];
|
||||||
|
|
||||||
|
for (const input of rangeInputs) {
|
||||||
|
input?.addEventListener("input", () => restoreImgRedMask(elements));
|
||||||
|
}
|
||||||
|
|
||||||
|
function applyZoomAndPan(elemId) {
|
||||||
|
const targetElement = gradioApp().querySelector(elemId);
|
||||||
|
|
||||||
|
if (!targetElement) {
|
||||||
|
console.log("Element not found");
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
targetElement.style.transformOrigin = "0 0";
|
||||||
|
|
||||||
|
elemData[elemId] = {
|
||||||
|
zoom: 1,
|
||||||
|
panX: 0,
|
||||||
|
panY: 0
|
||||||
|
};
|
||||||
|
let fullScreenMode = false;
|
||||||
|
|
||||||
|
// Create tooltip
|
||||||
|
function createTooltip() {
|
||||||
|
const toolTipElemnt =
|
||||||
|
targetElement.querySelector(".image-container");
|
||||||
|
const tooltip = document.createElement("div");
|
||||||
|
tooltip.className = "canvas-tooltip";
|
||||||
|
|
||||||
|
// Creating an item of information
|
||||||
|
const info = document.createElement("i");
|
||||||
|
info.className = "canvas-tooltip-info";
|
||||||
|
info.textContent = "";
|
||||||
|
|
||||||
|
// Create a container for the contents of the tooltip
|
||||||
|
const tooltipContent = document.createElement("div");
|
||||||
|
tooltipContent.className = "canvas-tooltip-content";
|
||||||
|
|
||||||
|
// Define an array with hotkey information and their actions
|
||||||
|
const hotkeysInfo = [
|
||||||
|
{
|
||||||
|
configKey: "canvas_hotkey_zoom",
|
||||||
|
action: "Zoom canvas",
|
||||||
|
keySuffix: " + wheel"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
configKey: "canvas_hotkey_adjust",
|
||||||
|
action: "Adjust brush size",
|
||||||
|
keySuffix: " + wheel"
|
||||||
|
},
|
||||||
|
{configKey: "canvas_hotkey_reset", action: "Reset zoom"},
|
||||||
|
{
|
||||||
|
configKey: "canvas_hotkey_fullscreen",
|
||||||
|
action: "Fullscreen mode"
|
||||||
|
},
|
||||||
|
{configKey: "canvas_hotkey_move", action: "Move canvas"},
|
||||||
|
{configKey: "canvas_hotkey_overlap", action: "Overlap"}
|
||||||
|
];
|
||||||
|
|
||||||
|
// Create hotkeys array with disabled property based on the config values
|
||||||
|
const hotkeys = hotkeysInfo.map(info => {
|
||||||
|
const configValue = hotkeysConfig[info.configKey];
|
||||||
|
const key = info.keySuffix ?
|
||||||
|
`${configValue}${info.keySuffix}` :
|
||||||
|
configValue.charAt(configValue.length - 1);
|
||||||
|
return {
|
||||||
|
key,
|
||||||
|
action: info.action,
|
||||||
|
disabled: configValue === "disable"
|
||||||
|
};
|
||||||
|
});
|
||||||
|
|
||||||
|
for (const hotkey of hotkeys) {
|
||||||
|
if (hotkey.disabled) {
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
|
||||||
|
const p = document.createElement("p");
|
||||||
|
p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
|
||||||
|
tooltipContent.appendChild(p);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Add information and content elements to the tooltip element
|
||||||
|
tooltip.appendChild(info);
|
||||||
|
tooltip.appendChild(tooltipContent);
|
||||||
|
|
||||||
|
// Add a hint element to the target element
|
||||||
|
toolTipElemnt.appendChild(tooltip);
|
||||||
|
}
|
||||||
|
|
||||||
|
//Show tool tip if setting enable
|
||||||
|
if (hotkeysConfig.canvas_show_tooltip) {
|
||||||
|
createTooltip();
|
||||||
|
}
|
||||||
|
|
||||||
|
// In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
|
||||||
|
function fixCanvas() {
|
||||||
|
const activeTab = getActiveTab(elements).textContent.trim();
|
||||||
|
|
||||||
|
if (activeTab !== "img2img") {
|
||||||
|
const img = targetElement.querySelector(`${elemId} img`);
|
||||||
|
|
||||||
|
if (img && img.style.display !== "none") {
|
||||||
|
img.style.display = "none";
|
||||||
|
img.style.visibility = "hidden";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Reset the zoom level and pan position of the target element to their initial values
|
||||||
|
function resetZoom() {
|
||||||
|
elemData[elemId] = {
|
||||||
|
zoomLevel: 1,
|
||||||
|
panX: 0,
|
||||||
|
panY: 0
|
||||||
|
};
|
||||||
|
|
||||||
|
fixCanvas();
|
||||||
|
targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
|
||||||
|
|
||||||
|
const canvas = gradioApp().querySelector(
|
||||||
|
`${elemId} canvas[key="interface"]`
|
||||||
|
);
|
||||||
|
|
||||||
|
toggleOverlap("off");
|
||||||
|
fullScreenMode = false;
|
||||||
|
|
||||||
|
if (
|
||||||
|
canvas &&
|
||||||
|
parseFloat(canvas.style.width) > 865 &&
|
||||||
|
parseFloat(targetElement.style.width) > 865
|
||||||
|
) {
|
||||||
|
fitToElement();
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
targetElement.style.width = "";
|
||||||
|
if (canvas) {
|
||||||
|
targetElement.style.height = canvas.style.height;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
|
||||||
|
function toggleOverlap(forced = "") {
|
||||||
|
const zIndex1 = "0";
|
||||||
|
const zIndex2 = "998";
|
||||||
|
|
||||||
|
targetElement.style.zIndex =
|
||||||
|
targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
|
||||||
|
|
||||||
|
if (forced === "off") {
|
||||||
|
targetElement.style.zIndex = zIndex1;
|
||||||
|
} else if (forced === "on") {
|
||||||
|
targetElement.style.zIndex = zIndex2;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Adjust the brush size based on the deltaY value from a mouse wheel event
|
||||||
|
function adjustBrushSize(
|
||||||
|
elemId,
|
||||||
|
deltaY,
|
||||||
|
withoutValue = false,
|
||||||
|
percentage = 5
|
||||||
|
) {
|
||||||
|
const input =
|
||||||
|
gradioApp().querySelector(
|
||||||
|
`${elemId} input[aria-label='Brush radius']`
|
||||||
|
) ||
|
||||||
|
gradioApp().querySelector(
|
||||||
|
`${elemId} button[aria-label="Use brush"]`
|
||||||
|
);
|
||||||
|
|
||||||
|
if (input) {
|
||||||
|
input.click();
|
||||||
|
if (!withoutValue) {
|
||||||
|
const maxValue =
|
||||||
|
parseFloat(input.getAttribute("max")) || 100;
|
||||||
|
const changeAmount = maxValue * (percentage / 100);
|
||||||
|
const newValue =
|
||||||
|
parseFloat(input.value) +
|
||||||
|
(deltaY > 0 ? -changeAmount : changeAmount);
|
||||||
|
input.value = Math.min(Math.max(newValue, 0), maxValue);
|
||||||
|
input.dispatchEvent(new Event("change"));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Reset zoom when uploading a new image
|
||||||
|
const fileInput = gradioApp().querySelector(
|
||||||
|
`${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
|
||||||
|
);
|
||||||
|
fileInput.addEventListener("click", resetZoom);
|
||||||
|
|
||||||
|
// Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
|
||||||
|
function updateZoom(newZoomLevel, mouseX, mouseY) {
|
||||||
|
newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
|
||||||
|
|
||||||
|
elemData[elemId].panX +=
|
||||||
|
mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
|
||||||
|
elemData[elemId].panY +=
|
||||||
|
mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;
|
||||||
|
|
||||||
|
targetElement.style.transformOrigin = "0 0";
|
||||||
|
targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
|
||||||
|
|
||||||
|
toggleOverlap("on");
|
||||||
|
return newZoomLevel;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Change the zoom level based on user interaction
|
||||||
|
function changeZoomLevel(operation, e) {
|
||||||
|
if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
|
||||||
|
e.preventDefault();
|
||||||
|
|
||||||
|
let zoomPosX, zoomPosY;
|
||||||
|
let delta = 0.2;
|
||||||
|
if (elemData[elemId].zoomLevel > 7) {
|
||||||
|
delta = 0.9;
|
||||||
|
} else if (elemData[elemId].zoomLevel > 2) {
|
||||||
|
delta = 0.6;
|
||||||
|
}
|
||||||
|
|
||||||
|
zoomPosX = e.clientX;
|
||||||
|
zoomPosY = e.clientY;
|
||||||
|
|
||||||
|
fullScreenMode = false;
|
||||||
|
elemData[elemId].zoomLevel = updateZoom(
|
||||||
|
elemData[elemId].zoomLevel +
|
||||||
|
(operation === "+" ? delta : -delta),
|
||||||
|
zoomPosX - targetElement.getBoundingClientRect().left,
|
||||||
|
zoomPosY - targetElement.getBoundingClientRect().top
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* This function fits the target element to the screen by calculating
|
||||||
|
* the required scale and offsets. It also updates the global variables
|
||||||
|
* zoomLevel, panX, and panY to reflect the new state.
|
||||||
|
*/
|
||||||
|
|
||||||
|
function fitToElement() {
|
||||||
|
//Reset Zoom
|
||||||
|
targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
|
||||||
|
|
||||||
|
// Get element and screen dimensions
|
||||||
|
const elementWidth = targetElement.offsetWidth;
|
||||||
|
const elementHeight = targetElement.offsetHeight;
|
||||||
|
const parentElement = targetElement.parentElement;
|
||||||
|
const screenWidth = parentElement.clientWidth;
|
||||||
|
const screenHeight = parentElement.clientHeight;
|
||||||
|
|
||||||
|
// Get element's coordinates relative to the parent element
|
||||||
|
const elementRect = targetElement.getBoundingClientRect();
|
||||||
|
const parentRect = parentElement.getBoundingClientRect();
|
||||||
|
const elementX = elementRect.x - parentRect.x;
|
||||||
|
|
||||||
|
// Calculate scale and offsets
|
||||||
|
const scaleX = screenWidth / elementWidth;
|
||||||
|
const scaleY = screenHeight / elementHeight;
|
||||||
|
const scale = Math.min(scaleX, scaleY);
|
||||||
|
|
||||||
|
const transformOrigin =
|
||||||
|
window.getComputedStyle(targetElement).transformOrigin;
|
||||||
|
const [originX, originY] = transformOrigin.split(" ");
|
||||||
|
const originXValue = parseFloat(originX);
|
||||||
|
const originYValue = parseFloat(originY);
|
||||||
|
|
||||||
|
const offsetX =
|
||||||
|
(screenWidth - elementWidth * scale) / 2 -
|
||||||
|
originXValue * (1 - scale);
|
||||||
|
const offsetY =
|
||||||
|
(screenHeight - elementHeight * scale) / 2.5 -
|
||||||
|
originYValue * (1 - scale);
|
||||||
|
|
||||||
|
// Apply scale and offsets to the element
|
||||||
|
targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
|
||||||
|
|
||||||
|
// Update global variables
|
||||||
|
elemData[elemId].zoomLevel = scale;
|
||||||
|
elemData[elemId].panX = offsetX;
|
||||||
|
elemData[elemId].panY = offsetY;
|
||||||
|
|
||||||
|
fullScreenMode = false;
|
||||||
|
toggleOverlap("off");
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* This function fits the target element to the screen by calculating
|
||||||
|
* the required scale and offsets. It also updates the global variables
|
||||||
|
* zoomLevel, panX, and panY to reflect the new state.
|
||||||
|
*/
|
||||||
|
|
||||||
|
// Fullscreen mode
|
||||||
|
function fitToScreen() {
|
||||||
|
const canvas = gradioApp().querySelector(
|
||||||
|
`${elemId} canvas[key="interface"]`
|
||||||
|
);
|
||||||
|
|
||||||
|
if (!canvas) return;
|
||||||
|
|
||||||
|
if (canvas.offsetWidth > 862) {
|
||||||
|
targetElement.style.width = canvas.offsetWidth + "px";
|
||||||
|
}
|
||||||
|
|
||||||
|
if (fullScreenMode) {
|
||||||
|
resetZoom();
|
||||||
|
fullScreenMode = false;
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
//Reset Zoom
|
||||||
|
targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
|
||||||
|
|
||||||
|
// Get scrollbar width to right-align the image
|
||||||
|
const scrollbarWidth =
|
||||||
|
window.innerWidth - document.documentElement.clientWidth;
|
||||||
|
|
||||||
|
// Get element and screen dimensions
|
||||||
|
const elementWidth = targetElement.offsetWidth;
|
||||||
|
const elementHeight = targetElement.offsetHeight;
|
||||||
|
const screenWidth = window.innerWidth - scrollbarWidth;
|
||||||
|
const screenHeight = window.innerHeight;
|
||||||
|
|
||||||
|
// Get element's coordinates relative to the page
|
||||||
|
const elementRect = targetElement.getBoundingClientRect();
|
||||||
|
const elementY = elementRect.y;
|
||||||
|
const elementX = elementRect.x;
|
||||||
|
|
||||||
|
// Calculate scale and offsets
|
||||||
|
const scaleX = screenWidth / elementWidth;
|
||||||
|
const scaleY = screenHeight / elementHeight;
|
||||||
|
const scale = Math.min(scaleX, scaleY);
|
||||||
|
|
||||||
|
// Get the current transformOrigin
|
||||||
|
const computedStyle = window.getComputedStyle(targetElement);
|
||||||
|
const transformOrigin = computedStyle.transformOrigin;
|
||||||
|
const [originX, originY] = transformOrigin.split(" ");
|
||||||
|
const originXValue = parseFloat(originX);
|
||||||
|
const originYValue = parseFloat(originY);
|
||||||
|
|
||||||
|
// Calculate offsets with respect to the transformOrigin
|
||||||
|
const offsetX =
|
||||||
|
(screenWidth - elementWidth * scale) / 2 -
|
||||||
|
elementX -
|
||||||
|
originXValue * (1 - scale);
|
||||||
|
const offsetY =
|
||||||
|
(screenHeight - elementHeight * scale) / 2 -
|
||||||
|
elementY -
|
||||||
|
originYValue * (1 - scale);
|
||||||
|
|
||||||
|
// Apply scale and offsets to the element
|
||||||
|
targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
|
||||||
|
|
||||||
|
// Update global variables
|
||||||
|
elemData[elemId].zoomLevel = scale;
|
||||||
|
elemData[elemId].panX = offsetX;
|
||||||
|
elemData[elemId].panY = offsetY;
|
||||||
|
|
||||||
|
fullScreenMode = true;
|
||||||
|
toggleOverlap("on");
|
||||||
|
}
|
||||||
|
|
||||||
|
// Handle keydown events
|
||||||
|
function handleKeyDown(event) {
|
||||||
|
// Disable key locks to make pasting from the buffer work correctly
|
||||||
|
if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// before activating shortcut, ensure user is not actively typing in an input field
|
||||||
|
if (!hotkeysConfig.canvas_blur_prompt) {
|
||||||
|
if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
const hotkeyActions = {
|
||||||
|
[hotkeysConfig.canvas_hotkey_reset]: resetZoom,
|
||||||
|
[hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
|
||||||
|
[hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
|
||||||
|
};
|
||||||
|
|
||||||
|
const action = hotkeyActions[event.code];
|
||||||
|
if (action) {
|
||||||
|
event.preventDefault();
|
||||||
|
action(event);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (
|
||||||
|
isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
|
||||||
|
isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
|
||||||
|
) {
|
||||||
|
event.preventDefault();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Get Mouse position
|
||||||
|
function getMousePosition(e) {
|
||||||
|
mouseX = e.offsetX;
|
||||||
|
mouseY = e.offsetY;
|
||||||
|
}
|
||||||
|
|
||||||
|
targetElement.addEventListener("mousemove", getMousePosition);
|
||||||
|
|
||||||
|
// Handle events only inside the targetElement
|
||||||
|
let isKeyDownHandlerAttached = false;
|
||||||
|
|
||||||
|
function handleMouseMove() {
|
||||||
|
if (!isKeyDownHandlerAttached) {
|
||||||
|
document.addEventListener("keydown", handleKeyDown);
|
||||||
|
isKeyDownHandlerAttached = true;
|
||||||
|
|
||||||
|
activeElement = elemId;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
function handleMouseLeave() {
|
||||||
|
if (isKeyDownHandlerAttached) {
|
||||||
|
document.removeEventListener("keydown", handleKeyDown);
|
||||||
|
isKeyDownHandlerAttached = false;
|
||||||
|
|
||||||
|
activeElement = null;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Add mouse event handlers
|
||||||
|
targetElement.addEventListener("mousemove", handleMouseMove);
|
||||||
|
targetElement.addEventListener("mouseleave", handleMouseLeave);
|
||||||
|
|
||||||
|
// Reset zoom when click on another tab
|
||||||
|
elements.img2imgTabs.addEventListener("click", resetZoom);
|
||||||
|
elements.img2imgTabs.addEventListener("click", () => {
|
||||||
|
// targetElement.style.width = "";
|
||||||
|
if (parseInt(targetElement.style.width) > 865) {
|
||||||
|
setTimeout(fitToElement, 0);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
targetElement.addEventListener("wheel", e => {
|
||||||
|
// change zoom level
|
||||||
|
const operation = e.deltaY > 0 ? "-" : "+";
|
||||||
|
changeZoomLevel(operation, e);
|
||||||
|
|
||||||
|
// Handle brush size adjustment with ctrl key pressed
|
||||||
|
if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
|
||||||
|
e.preventDefault();
|
||||||
|
|
||||||
|
// Increase or decrease brush size based on scroll direction
|
||||||
|
adjustBrushSize(elemId, e.deltaY);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
// Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
|
||||||
|
function handleMoveKeyDown(e) {
|
||||||
|
|
||||||
|
// Disable key locks to make pasting from the buffer work correctly
|
||||||
|
if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
// before activating shortcut, ensure user is not actively typing in an input field
|
||||||
|
if (!hotkeysConfig.canvas_blur_prompt) {
|
||||||
|
if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if (e.code === hotkeysConfig.canvas_hotkey_move) {
|
||||||
|
if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
|
||||||
|
e.preventDefault();
|
||||||
|
document.activeElement.blur();
|
||||||
|
isMoving = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
function handleMoveKeyUp(e) {
|
||||||
|
if (e.code === hotkeysConfig.canvas_hotkey_move) {
|
||||||
|
isMoving = false;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
document.addEventListener("keydown", handleMoveKeyDown);
|
||||||
|
document.addEventListener("keyup", handleMoveKeyUp);
|
||||||
|
|
||||||
|
// Detect zoom level and update the pan speed.
|
||||||
|
function updatePanPosition(movementX, movementY) {
|
||||||
|
let panSpeed = 2;
|
||||||
|
|
||||||
|
if (elemData[elemId].zoomLevel > 8) {
|
||||||
|
panSpeed = 3.5;
|
||||||
|
}
|
||||||
|
|
||||||
|
elemData[elemId].panX += movementX * panSpeed;
|
||||||
|
elemData[elemId].panY += movementY * panSpeed;
|
||||||
|
|
||||||
|
// Delayed redraw of an element
|
||||||
|
requestAnimationFrame(() => {
|
||||||
|
targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;
|
||||||
|
toggleOverlap("on");
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
function handleMoveByKey(e) {
|
||||||
|
if (isMoving && elemId === activeElement) {
|
||||||
|
updatePanPosition(e.movementX, e.movementY);
|
||||||
|
targetElement.style.pointerEvents = "none";
|
||||||
|
} else {
|
||||||
|
targetElement.style.pointerEvents = "auto";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Prevents sticking to the mouse
|
||||||
|
window.onblur = function() {
|
||||||
|
isMoving = false;
|
||||||
|
};
|
||||||
|
|
||||||
|
gradioApp().addEventListener("mousemove", handleMoveByKey);
|
||||||
|
}
|
||||||
|
|
||||||
|
applyZoomAndPan(elementIDs.sketch);
|
||||||
|
applyZoomAndPan(elementIDs.inpaint);
|
||||||
|
applyZoomAndPan(elementIDs.inpaintSketch);
|
||||||
|
|
||||||
|
// Make the function global so that other extensions can take advantage of this solution
|
||||||
|
window.applyZoomAndPan = applyZoomAndPan;
|
||||||
|
});
|
@ -0,0 +1,14 @@
|
|||||||
|
import gradio as gr
|
||||||
|
from modules import shared
|
||||||
|
|
||||||
|
shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
|
||||||
|
"canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
|
||||||
|
"canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
|
||||||
|
"canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
|
||||||
|
"canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
|
||||||
|
"canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
|
||||||
|
"canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
|
||||||
|
"canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
|
||||||
|
"canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
|
||||||
|
"canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
|
||||||
|
}))
|
63
extensions-builtin/canvas-zoom-and-pan/style.css
Normal file
63
extensions-builtin/canvas-zoom-and-pan/style.css
Normal file
@ -0,0 +1,63 @@
|
|||||||
|
.canvas-tooltip-info {
|
||||||
|
position: absolute;
|
||||||
|
top: 10px;
|
||||||
|
left: 10px;
|
||||||
|
cursor: help;
|
||||||
|
background-color: rgba(0, 0, 0, 0.3);
|
||||||
|
width: 20px;
|
||||||
|
height: 20px;
|
||||||
|
border-radius: 50%;
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
justify-content: center;
|
||||||
|
flex-direction: column;
|
||||||
|
|
||||||
|
z-index: 100;
|
||||||
|
}
|
||||||
|
|
||||||
|
.canvas-tooltip-info::after {
|
||||||
|
content: '';
|
||||||
|
display: block;
|
||||||
|
width: 2px;
|
||||||
|
height: 7px;
|
||||||
|
background-color: white;
|
||||||
|
margin-top: 2px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.canvas-tooltip-info::before {
|
||||||
|
content: '';
|
||||||
|
display: block;
|
||||||
|
width: 2px;
|
||||||
|
height: 2px;
|
||||||
|
background-color: white;
|
||||||
|
}
|
||||||
|
|
||||||
|
.canvas-tooltip-content {
|
||||||
|
display: none;
|
||||||
|
background-color: #f9f9f9;
|
||||||
|
color: #333;
|
||||||
|
border: 1px solid #ddd;
|
||||||
|
padding: 15px;
|
||||||
|
position: absolute;
|
||||||
|
top: 40px;
|
||||||
|
left: 10px;
|
||||||
|
width: 250px;
|
||||||
|
font-size: 16px;
|
||||||
|
opacity: 0;
|
||||||
|
border-radius: 8px;
|
||||||
|
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
|
||||||
|
|
||||||
|
z-index: 100;
|
||||||
|
}
|
||||||
|
|
||||||
|
.canvas-tooltip:hover .canvas-tooltip-content {
|
||||||
|
display: block;
|
||||||
|
animation: fadeIn 0.5s;
|
||||||
|
opacity: 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
@keyframes fadeIn {
|
||||||
|
from {opacity: 0;}
|
||||||
|
to {opacity: 1;}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,48 @@
|
|||||||
|
import gradio as gr
|
||||||
|
from modules import scripts, shared, ui_components, ui_settings
|
||||||
|
from modules.ui_components import FormColumn
|
||||||
|
|
||||||
|
|
||||||
|
class ExtraOptionsSection(scripts.Script):
|
||||||
|
section = "extra_options"
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.comps = None
|
||||||
|
self.setting_names = None
|
||||||
|
|
||||||
|
def title(self):
|
||||||
|
return "Extra options"
|
||||||
|
|
||||||
|
def show(self, is_img2img):
|
||||||
|
return scripts.AlwaysVisible
|
||||||
|
|
||||||
|
def ui(self, is_img2img):
|
||||||
|
self.comps = []
|
||||||
|
self.setting_names = []
|
||||||
|
|
||||||
|
with gr.Blocks() as interface:
|
||||||
|
with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
|
||||||
|
for setting_name in shared.opts.extra_options:
|
||||||
|
with FormColumn():
|
||||||
|
comp = ui_settings.create_setting_component(setting_name)
|
||||||
|
|
||||||
|
self.comps.append(comp)
|
||||||
|
self.setting_names.append(setting_name)
|
||||||
|
|
||||||
|
def get_settings_values():
|
||||||
|
return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
|
||||||
|
|
||||||
|
interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
|
||||||
|
|
||||||
|
return self.comps
|
||||||
|
|
||||||
|
def before_process(self, p, *args):
|
||||||
|
for name, value in zip(self.setting_names, args):
|
||||||
|
if name not in p.override_settings:
|
||||||
|
p.override_settings[name] = value
|
||||||
|
|
||||||
|
|
||||||
|
shared.options_templates.update(shared.options_section(('ui', "User interface"), {
|
||||||
|
"extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_restart(),
|
||||||
|
"extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion")
|
||||||
|
}))
|
26
extensions-builtin/mobile/javascript/mobile.js
Normal file
26
extensions-builtin/mobile/javascript/mobile.js
Normal file
@ -0,0 +1,26 @@
|
|||||||
|
var isSetupForMobile = false;
|
||||||
|
|
||||||
|
function isMobile() {
|
||||||
|
for (var tab of ["txt2img", "img2img"]) {
|
||||||
|
var imageTab = gradioApp().getElementById(tab + '_results');
|
||||||
|
if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) {
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
|
function reportWindowSize() {
|
||||||
|
var currentlyMobile = isMobile();
|
||||||
|
if (currentlyMobile == isSetupForMobile) return;
|
||||||
|
isSetupForMobile = currentlyMobile;
|
||||||
|
|
||||||
|
for (var tab of ["txt2img", "img2img"]) {
|
||||||
|
var button = gradioApp().getElementById(tab + '_generate_box');
|
||||||
|
var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
|
||||||
|
target.insertBefore(button, target.firstElementChild);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
window.addEventListener("resize", reportWindowSize);
|
@ -1,103 +1,42 @@
|
|||||||
// Stable Diffusion WebUI - Bracket checker
|
// Stable Diffusion WebUI - Bracket checker
|
||||||
// Version 1.0
|
// By Hingashi no Florin/Bwin4L & @akx
|
||||||
// By Hingashi no Florin/Bwin4L
|
|
||||||
// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
|
// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
|
||||||
// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
|
// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
|
||||||
|
|
||||||
function checkBrackets(evt, textArea, counterElt) {
|
function checkBrackets(textArea, counterElt) {
|
||||||
errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n';
|
var counts = {};
|
||||||
errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
|
(textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
|
||||||
errorStringCurly = '{...} - Different number of opening and closing curly brackets detected.\n';
|
counts[bracket] = (counts[bracket] || 0) + 1;
|
||||||
|
|
||||||
openBracketRegExp = /\(/g;
|
|
||||||
closeBracketRegExp = /\)/g;
|
|
||||||
|
|
||||||
openSquareBracketRegExp = /\[/g;
|
|
||||||
closeSquareBracketRegExp = /\]/g;
|
|
||||||
|
|
||||||
openCurlyBracketRegExp = /\{/g;
|
|
||||||
closeCurlyBracketRegExp = /\}/g;
|
|
||||||
|
|
||||||
totalOpenBracketMatches = 0;
|
|
||||||
totalCloseBracketMatches = 0;
|
|
||||||
totalOpenSquareBracketMatches = 0;
|
|
||||||
totalCloseSquareBracketMatches = 0;
|
|
||||||
totalOpenCurlyBracketMatches = 0;
|
|
||||||
totalCloseCurlyBracketMatches = 0;
|
|
||||||
|
|
||||||
openBracketMatches = textArea.value.match(openBracketRegExp);
|
|
||||||
if(openBracketMatches) {
|
|
||||||
totalOpenBracketMatches = openBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
closeBracketMatches = textArea.value.match(closeBracketRegExp);
|
|
||||||
if(closeBracketMatches) {
|
|
||||||
totalCloseBracketMatches = closeBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
|
|
||||||
if(openSquareBracketMatches) {
|
|
||||||
totalOpenSquareBracketMatches = openSquareBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
|
|
||||||
if(closeSquareBracketMatches) {
|
|
||||||
totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
|
|
||||||
if(openCurlyBracketMatches) {
|
|
||||||
totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
|
|
||||||
if(closeCurlyBracketMatches) {
|
|
||||||
totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
if(totalOpenBracketMatches != totalCloseBracketMatches) {
|
|
||||||
if(!counterElt.title.includes(errorStringParen)) {
|
|
||||||
counterElt.title += errorStringParen;
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
counterElt.title = counterElt.title.replace(errorStringParen, '');
|
|
||||||
}
|
|
||||||
|
|
||||||
if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
|
|
||||||
if(!counterElt.title.includes(errorStringSquare)) {
|
|
||||||
counterElt.title += errorStringSquare;
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
counterElt.title = counterElt.title.replace(errorStringSquare, '');
|
|
||||||
}
|
|
||||||
|
|
||||||
if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
|
|
||||||
if(!counterElt.title.includes(errorStringCurly)) {
|
|
||||||
counterElt.title += errorStringCurly;
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
counterElt.title = counterElt.title.replace(errorStringCurly, '');
|
|
||||||
}
|
|
||||||
|
|
||||||
if(counterElt.title != '') {
|
|
||||||
counterElt.classList.add('error');
|
|
||||||
} else {
|
|
||||||
counterElt.classList.remove('error');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function setupBracketChecking(id_prompt, id_counter){
|
|
||||||
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
|
|
||||||
var counter = gradioApp().getElementById(id_counter)
|
|
||||||
|
|
||||||
textarea.addEventListener("input", function(evt){
|
|
||||||
checkBrackets(evt, textarea, counter)
|
|
||||||
});
|
});
|
||||||
|
var errors = [];
|
||||||
|
|
||||||
|
function checkPair(open, close, kind) {
|
||||||
|
if (counts[open] !== counts[close]) {
|
||||||
|
errors.push(
|
||||||
|
`${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
checkPair('(', ')', 'round brackets');
|
||||||
|
checkPair('[', ']', 'square brackets');
|
||||||
|
checkPair('{', '}', 'curly brackets');
|
||||||
|
counterElt.title = errors.join('\n');
|
||||||
|
counterElt.classList.toggle('error', errors.length !== 0);
|
||||||
}
|
}
|
||||||
|
|
||||||
onUiLoaded(function(){
|
function setupBracketChecking(id_prompt, id_counter) {
|
||||||
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
|
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
|
||||||
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
|
var counter = gradioApp().getElementById(id_counter);
|
||||||
setupBracketChecking('img2img_prompt', 'img2img_token_counter')
|
|
||||||
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
|
if (textarea && counter) {
|
||||||
})
|
textarea.addEventListener("input", () => checkBrackets(textarea, counter));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
onUiLoaded(function() {
|
||||||
|
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
|
||||||
|
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
|
||||||
|
setupBracketChecking('img2img_prompt', 'img2img_token_counter');
|
||||||
|
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
|
||||||
|
});
|
||||||
|
@ -1,15 +1,14 @@
|
|||||||
<div class='card' style={style} onclick={card_clicked}>
|
<div class='card' style={style} onclick={card_clicked} data-name="{name}" {sort_keys}>
|
||||||
{metadata_button}
|
{background_image}
|
||||||
|
<div class="button-row">
|
||||||
|
{metadata_button}
|
||||||
|
{edit_button}
|
||||||
|
</div>
|
||||||
<div class='actions'>
|
<div class='actions'>
|
||||||
<div class='additional'>
|
<div class='additional'>
|
||||||
<ul>
|
<span style="display:none" class='search_term{search_only}'>{search_term}</span>
|
||||||
<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
|
|
||||||
</ul>
|
|
||||||
<span style="display:none" class='search_term'>{search_term}</span>
|
|
||||||
</div>
|
</div>
|
||||||
<span class='name'>{name}</span>
|
<span class='name'>{name}</span>
|
||||||
<span class='description'>{description}</span>
|
<span class='description'>{description}</span>
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
|
@ -1,10 +1,12 @@
|
|||||||
<div>
|
<div>
|
||||||
<a href="/docs">API</a>
|
<a href="{api_docs}">API</a>
|
||||||
•
|
•
|
||||||
<a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
|
<a href="https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
|
||||||
•
|
•
|
||||||
<a href="https://gradio.app">Gradio</a>
|
<a href="https://gradio.app">Gradio</a>
|
||||||
•
|
•
|
||||||
|
<a href="#" onclick="showProfile('./internal/profile-startup'); return false;">Startup profile</a>
|
||||||
|
•
|
||||||
<a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
|
<a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
|
||||||
</div>
|
</div>
|
||||||
<br />
|
<br />
|
||||||
|
@ -1,7 +0,0 @@
|
|||||||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
|
|
||||||
<filter id='shadow' color-interpolation-filters="sRGB">
|
|
||||||
<feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
|
|
||||||
<feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
|
|
||||||
</filter>
|
|
||||||
<path style="filter:url(#shadow);" fill="#FFFFFF" d="M13.18 19C13.35 19.72 13.64 20.39 14.03 21H5C3.9 21 3 20.11 3 19V5C3 3.9 3.9 3 5 3H19C20.11 3 21 3.9 21 5V11.18C20.5 11.07 20 11 19.5 11C19.33 11 19.17 11 19 11.03V5H5V19H13.18M11.21 15.83L9.25 13.47L6.5 17H13.03C13.14 15.54 13.73 14.22 14.64 13.19L13.96 12.29L11.21 15.83M19 13.5V12L16.75 14.25L19 16.5V15C20.38 15 21.5 16.12 21.5 17.5C21.5 17.9 21.41 18.28 21.24 18.62L22.33 19.71C22.75 19.08 23 18.32 23 17.5C23 15.29 21.21 13.5 19 13.5M19 20C17.62 20 16.5 18.88 16.5 17.5C16.5 17.1 16.59 16.72 16.76 16.38L15.67 15.29C15.25 15.92 15 16.68 15 17.5C15 19.71 16.79 21.5 19 21.5V23L21.25 20.75L19 18.5V20Z" />
|
|
||||||
</svg>
|
|
Before Width: | Height: | Size: 989 B |
@ -4,7 +4,7 @@
|
|||||||
#licenses pre { margin: 1em 0 2em 0;}
|
#licenses pre { margin: 1em 0 2em 0;}
|
||||||
</style>
|
</style>
|
||||||
|
|
||||||
<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
|
||||||
<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
|
<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
|
||||||
<pre>
|
<pre>
|
||||||
S-Lab License 1.0
|
S-Lab License 1.0
|
||||||
@ -45,7 +45,7 @@ please contact the contributor(s) of the work.
|
|||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
|
|
||||||
<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
|
||||||
<small>Code for architecture and reading models copied.</small>
|
<small>Code for architecture and reading models copied.</small>
|
||||||
<pre>
|
<pre>
|
||||||
MIT License
|
MIT License
|
||||||
@ -71,7 +71,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
|
||||||
<small>Some code is copied to support ESRGAN models.</small>
|
<small>Some code is copied to support ESRGAN models.</small>
|
||||||
<pre>
|
<pre>
|
||||||
BSD 3-Clause License
|
BSD 3-Clause License
|
||||||
@ -105,7 +105,7 @@ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|||||||
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
|
||||||
<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
|
<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
|
||||||
<pre>
|
<pre>
|
||||||
MIT License
|
MIT License
|
||||||
@ -131,7 +131,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
|
||||||
<small>Code added by contirubtors, most likely copied from this repository.</small>
|
<small>Code added by contirubtors, most likely copied from this repository.</small>
|
||||||
<pre>
|
<pre>
|
||||||
MIT License
|
MIT License
|
||||||
@ -157,7 +157,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
|
||||||
<small>Some small amounts of code borrowed and reworked.</small>
|
<small>Some small amounts of code borrowed and reworked.</small>
|
||||||
<pre>
|
<pre>
|
||||||
MIT License
|
MIT License
|
||||||
@ -183,7 +183,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
|
||||||
<small>Code added by contributors, most likely copied from this repository.</small>
|
<small>Code added by contributors, most likely copied from this repository.</small>
|
||||||
|
|
||||||
<pre>
|
<pre>
|
||||||
@ -390,7 +390,7 @@ SOFTWARE.
|
|||||||
limitations under the License.
|
limitations under the License.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
|
||||||
<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
|
<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
|
||||||
<pre>
|
<pre>
|
||||||
MIT License
|
MIT License
|
||||||
@ -417,7 +417,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|||||||
SOFTWARE.
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
|
||||||
<small>Some small amounts of code borrowed and reworked.</small>
|
<small>Some small amounts of code borrowed and reworked.</small>
|
||||||
<pre>
|
<pre>
|
||||||
Copyright 2023 The HuggingFace Team. All rights reserved.
|
Copyright 2023 The HuggingFace Team. All rights reserved.
|
||||||
@ -637,7 +637,7 @@ SOFTWARE.
|
|||||||
limitations under the License.
|
limitations under the License.
|
||||||
</pre>
|
</pre>
|
||||||
|
|
||||||
<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
|
<h2><a href="https://ghproxy.com/https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
|
||||||
<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
|
<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
|
||||||
<pre>
|
<pre>
|
||||||
The MIT License (MIT)
|
The MIT License (MIT)
|
||||||
@ -661,4 +661,30 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|||||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
||||||
THE SOFTWARE.
|
THE SOFTWARE.
|
||||||
|
</pre>
|
||||||
|
|
||||||
|
<h2><a href="https://ghproxy.com/https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2>
|
||||||
|
<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small>
|
||||||
|
<pre>
|
||||||
|
MIT License
|
||||||
|
|
||||||
|
Copyright (c) 2023 Ollin Boer Bohan
|
||||||
|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
|
in the Software without restriction, including without limitation the rights
|
||||||
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
|
copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all
|
||||||
|
copies or substantial portions of the Software.
|
||||||
|
|
||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
|
SOFTWARE.
|
||||||
</pre>
|
</pre>
|
@ -1,116 +1,113 @@
|
|||||||
|
|
||||||
let currentWidth = null;
|
let currentWidth = null;
|
||||||
let currentHeight = null;
|
let currentHeight = null;
|
||||||
let arFrameTimeout = setTimeout(function(){},0);
|
let arFrameTimeout = setTimeout(function() {}, 0);
|
||||||
|
|
||||||
function dimensionChange(e, is_width, is_height){
|
function dimensionChange(e, is_width, is_height) {
|
||||||
|
|
||||||
if(is_width){
|
if (is_width) {
|
||||||
currentWidth = e.target.value*1.0
|
currentWidth = e.target.value * 1.0;
|
||||||
}
|
}
|
||||||
if(is_height){
|
if (is_height) {
|
||||||
currentHeight = e.target.value*1.0
|
currentHeight = e.target.value * 1.0;
|
||||||
}
|
}
|
||||||
|
|
||||||
var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
|
var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
|
||||||
|
|
||||||
if(!inImg2img){
|
if (!inImg2img) {
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
var targetElement = null;
|
var targetElement = null;
|
||||||
|
|
||||||
var tabIndex = get_tab_index('mode_img2img')
|
var tabIndex = get_tab_index('mode_img2img');
|
||||||
if(tabIndex == 0){ // img2img
|
if (tabIndex == 0) { // img2img
|
||||||
targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
|
targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
|
||||||
} else if(tabIndex == 1){ //Sketch
|
} else if (tabIndex == 1) { //Sketch
|
||||||
targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
|
targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
|
||||||
} else if(tabIndex == 2){ // Inpaint
|
} else if (tabIndex == 2) { // Inpaint
|
||||||
targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
|
targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
|
||||||
} else if(tabIndex == 3){ // Inpaint sketch
|
} else if (tabIndex == 3) { // Inpaint sketch
|
||||||
targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
|
targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
if(targetElement){
|
if (targetElement) {
|
||||||
|
|
||||||
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
|
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
|
||||||
if(!arPreviewRect){
|
if (!arPreviewRect) {
|
||||||
arPreviewRect = document.createElement('div')
|
arPreviewRect = document.createElement('div');
|
||||||
arPreviewRect.id = "imageARPreview";
|
arPreviewRect.id = "imageARPreview";
|
||||||
gradioApp().appendChild(arPreviewRect)
|
gradioApp().appendChild(arPreviewRect);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
var viewportOffset = targetElement.getBoundingClientRect();
|
var viewportOffset = targetElement.getBoundingClientRect();
|
||||||
|
|
||||||
viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
|
var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
|
||||||
|
|
||||||
scaledx = targetElement.naturalWidth*viewportscale
|
var scaledx = targetElement.naturalWidth * viewportscale;
|
||||||
scaledy = targetElement.naturalHeight*viewportscale
|
var scaledy = targetElement.naturalHeight * viewportscale;
|
||||||
|
|
||||||
cleintRectTop = (viewportOffset.top+window.scrollY)
|
var cleintRectTop = (viewportOffset.top + window.scrollY);
|
||||||
cleintRectLeft = (viewportOffset.left+window.scrollX)
|
var cleintRectLeft = (viewportOffset.left + window.scrollX);
|
||||||
cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
|
var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
|
||||||
cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
|
var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
|
||||||
|
|
||||||
viewRectTop = cleintRectCentreY-(scaledy/2)
|
var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
|
||||||
viewRectLeft = cleintRectCentreX-(scaledx/2)
|
var arscaledx = currentWidth * arscale;
|
||||||
arRectWidth = scaledx
|
var arscaledy = currentHeight * arscale;
|
||||||
arRectHeight = scaledy
|
|
||||||
|
var arRectTop = cleintRectCentreY - (arscaledy / 2);
|
||||||
arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight )
|
var arRectLeft = cleintRectCentreX - (arscaledx / 2);
|
||||||
arscaledx = currentWidth*arscale
|
var arRectWidth = arscaledx;
|
||||||
arscaledy = currentHeight*arscale
|
var arRectHeight = arscaledy;
|
||||||
|
|
||||||
arRectTop = cleintRectCentreY-(arscaledy/2)
|
arPreviewRect.style.top = arRectTop + 'px';
|
||||||
arRectLeft = cleintRectCentreX-(arscaledx/2)
|
arPreviewRect.style.left = arRectLeft + 'px';
|
||||||
arRectWidth = arscaledx
|
arPreviewRect.style.width = arRectWidth + 'px';
|
||||||
arRectHeight = arscaledy
|
arPreviewRect.style.height = arRectHeight + 'px';
|
||||||
|
|
||||||
arPreviewRect.style.top = arRectTop+'px';
|
clearTimeout(arFrameTimeout);
|
||||||
arPreviewRect.style.left = arRectLeft+'px';
|
arFrameTimeout = setTimeout(function() {
|
||||||
arPreviewRect.style.width = arRectWidth+'px';
|
arPreviewRect.style.display = 'none';
|
||||||
arPreviewRect.style.height = arRectHeight+'px';
|
}, 2000);
|
||||||
|
|
||||||
clearTimeout(arFrameTimeout);
|
arPreviewRect.style.display = 'block';
|
||||||
arFrameTimeout = setTimeout(function(){
|
|
||||||
arPreviewRect.style.display = 'none';
|
}
|
||||||
},2000);
|
|
||||||
|
}
|
||||||
arPreviewRect.style.display = 'block';
|
|
||||||
|
|
||||||
}
|
onAfterUiUpdate(function() {
|
||||||
|
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
|
||||||
}
|
if (arPreviewRect) {
|
||||||
|
arPreviewRect.style.display = 'none';
|
||||||
|
}
|
||||||
onUiUpdate(function(){
|
var tabImg2img = gradioApp().querySelector("#tab_img2img");
|
||||||
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
|
if (tabImg2img) {
|
||||||
if(arPreviewRect){
|
var inImg2img = tabImg2img.style.display == "block";
|
||||||
arPreviewRect.style.display = 'none';
|
if (inImg2img) {
|
||||||
}
|
let inputs = gradioApp().querySelectorAll('input');
|
||||||
var tabImg2img = gradioApp().querySelector("#tab_img2img");
|
inputs.forEach(function(e) {
|
||||||
if (tabImg2img) {
|
var is_width = e.parentElement.id == "img2img_width";
|
||||||
var inImg2img = tabImg2img.style.display == "block";
|
var is_height = e.parentElement.id == "img2img_height";
|
||||||
if(inImg2img){
|
|
||||||
let inputs = gradioApp().querySelectorAll('input');
|
if ((is_width || is_height) && !e.classList.contains('scrollwatch')) {
|
||||||
inputs.forEach(function(e){
|
e.addEventListener('input', function(e) {
|
||||||
var is_width = e.parentElement.id == "img2img_width"
|
dimensionChange(e, is_width, is_height);
|
||||||
var is_height = e.parentElement.id == "img2img_height"
|
});
|
||||||
|
e.classList.add('scrollwatch');
|
||||||
if((is_width || is_height) && !e.classList.contains('scrollwatch')){
|
}
|
||||||
e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
|
if (is_width) {
|
||||||
e.classList.add('scrollwatch')
|
currentWidth = e.value * 1.0;
|
||||||
}
|
}
|
||||||
if(is_width){
|
if (is_height) {
|
||||||
currentWidth = e.value*1.0
|
currentHeight = e.value * 1.0;
|
||||||
}
|
}
|
||||||
if(is_height){
|
});
|
||||||
currentHeight = e.value*1.0
|
}
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
}
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
@ -1,177 +1,176 @@
|
|||||||
|
|
||||||
contextMenuInit = function(){
|
var contextMenuInit = function() {
|
||||||
let eventListenerApplied=false;
|
let eventListenerApplied = false;
|
||||||
let menuSpecs = new Map();
|
let menuSpecs = new Map();
|
||||||
|
|
||||||
const uid = function(){
|
const uid = function() {
|
||||||
return Date.now().toString(36) + Math.random().toString(36).substr(2);
|
return Date.now().toString(36) + Math.random().toString(36).substring(2);
|
||||||
}
|
};
|
||||||
|
|
||||||
function showContextMenu(event,element,menuEntries){
|
function showContextMenu(event, element, menuEntries) {
|
||||||
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
|
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
|
||||||
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
|
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
|
||||||
|
|
||||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||||
if(oldMenu){
|
if (oldMenu) {
|
||||||
oldMenu.remove()
|
oldMenu.remove();
|
||||||
}
|
}
|
||||||
|
|
||||||
let tabButton = uiCurrentTab
|
let baseStyle = window.getComputedStyle(uiCurrentTab);
|
||||||
let baseStyle = window.getComputedStyle(tabButton)
|
|
||||||
|
const contextMenu = document.createElement('nav');
|
||||||
const contextMenu = document.createElement('nav')
|
contextMenu.id = "context-menu";
|
||||||
contextMenu.id = "context-menu"
|
contextMenu.style.background = baseStyle.background;
|
||||||
contextMenu.style.background = baseStyle.background
|
contextMenu.style.color = baseStyle.color;
|
||||||
contextMenu.style.color = baseStyle.color
|
contextMenu.style.fontFamily = baseStyle.fontFamily;
|
||||||
contextMenu.style.fontFamily = baseStyle.fontFamily
|
contextMenu.style.top = posy + 'px';
|
||||||
contextMenu.style.top = posy+'px'
|
contextMenu.style.left = posx + 'px';
|
||||||
contextMenu.style.left = posx+'px'
|
|
||||||
|
|
||||||
|
|
||||||
|
const contextMenuList = document.createElement('ul');
|
||||||
const contextMenuList = document.createElement('ul')
|
contextMenuList.className = 'context-menu-items';
|
||||||
contextMenuList.className = 'context-menu-items';
|
contextMenu.append(contextMenuList);
|
||||||
contextMenu.append(contextMenuList);
|
|
||||||
|
menuEntries.forEach(function(entry) {
|
||||||
menuEntries.forEach(function(entry){
|
let contextMenuEntry = document.createElement('a');
|
||||||
let contextMenuEntry = document.createElement('a')
|
contextMenuEntry.innerHTML = entry['name'];
|
||||||
contextMenuEntry.innerHTML = entry['name']
|
contextMenuEntry.addEventListener("click", function() {
|
||||||
contextMenuEntry.addEventListener("click", function(e) {
|
entry['func']();
|
||||||
entry['func']();
|
});
|
||||||
})
|
contextMenuList.append(contextMenuEntry);
|
||||||
contextMenuList.append(contextMenuEntry);
|
|
||||||
|
});
|
||||||
})
|
|
||||||
|
gradioApp().appendChild(contextMenu);
|
||||||
gradioApp().appendChild(contextMenu)
|
|
||||||
|
let menuWidth = contextMenu.offsetWidth + 4;
|
||||||
let menuWidth = contextMenu.offsetWidth + 4;
|
let menuHeight = contextMenu.offsetHeight + 4;
|
||||||
let menuHeight = contextMenu.offsetHeight + 4;
|
|
||||||
|
let windowWidth = window.innerWidth;
|
||||||
let windowWidth = window.innerWidth;
|
let windowHeight = window.innerHeight;
|
||||||
let windowHeight = window.innerHeight;
|
|
||||||
|
if ((windowWidth - posx) < menuWidth) {
|
||||||
if ( (windowWidth - posx) < menuWidth ) {
|
contextMenu.style.left = windowWidth - menuWidth + "px";
|
||||||
contextMenu.style.left = windowWidth - menuWidth + "px";
|
}
|
||||||
}
|
|
||||||
|
if ((windowHeight - posy) < menuHeight) {
|
||||||
if ( (windowHeight - posy) < menuHeight ) {
|
contextMenu.style.top = windowHeight - menuHeight + "px";
|
||||||
contextMenu.style.top = windowHeight - menuHeight + "px";
|
}
|
||||||
}
|
|
||||||
|
}
|
||||||
}
|
|
||||||
|
function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
|
||||||
function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
|
|
||||||
|
var currentItems = menuSpecs.get(targetElementSelector);
|
||||||
currentItems = menuSpecs.get(targetElementSelector)
|
|
||||||
|
if (!currentItems) {
|
||||||
if(!currentItems){
|
currentItems = [];
|
||||||
currentItems = []
|
menuSpecs.set(targetElementSelector, currentItems);
|
||||||
menuSpecs.set(targetElementSelector,currentItems);
|
}
|
||||||
}
|
let newItem = {
|
||||||
let newItem = {'id':targetElementSelector+'_'+uid(),
|
id: targetElementSelector + '_' + uid(),
|
||||||
'name':entryName,
|
name: entryName,
|
||||||
'func':entryFunction,
|
func: entryFunction,
|
||||||
'isNew':true}
|
isNew: true
|
||||||
|
};
|
||||||
currentItems.push(newItem)
|
|
||||||
return newItem['id']
|
currentItems.push(newItem);
|
||||||
}
|
return newItem['id'];
|
||||||
|
}
|
||||||
function removeContextMenuOption(uid){
|
|
||||||
menuSpecs.forEach(function(v,k) {
|
function removeContextMenuOption(uid) {
|
||||||
let index = -1
|
menuSpecs.forEach(function(v) {
|
||||||
v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
|
let index = -1;
|
||||||
if(index>=0){
|
v.forEach(function(e, ei) {
|
||||||
v.splice(index, 1);
|
if (e['id'] == uid) {
|
||||||
}
|
index = ei;
|
||||||
})
|
}
|
||||||
}
|
});
|
||||||
|
if (index >= 0) {
|
||||||
function addContextMenuEventListener(){
|
v.splice(index, 1);
|
||||||
if(eventListenerApplied){
|
}
|
||||||
return;
|
});
|
||||||
}
|
}
|
||||||
gradioApp().addEventListener("click", function(e) {
|
|
||||||
let source = e.composedPath()[0]
|
function addContextMenuEventListener() {
|
||||||
if(source.id && source.id.indexOf('check_progress')>-1){
|
if (eventListenerApplied) {
|
||||||
return
|
return;
|
||||||
}
|
}
|
||||||
|
gradioApp().addEventListener("click", function(e) {
|
||||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
if (!e.isTrusted) {
|
||||||
if(oldMenu){
|
return;
|
||||||
oldMenu.remove()
|
}
|
||||||
}
|
|
||||||
});
|
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||||
gradioApp().addEventListener("contextmenu", function(e) {
|
if (oldMenu) {
|
||||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
oldMenu.remove();
|
||||||
if(oldMenu){
|
}
|
||||||
oldMenu.remove()
|
});
|
||||||
}
|
gradioApp().addEventListener("contextmenu", function(e) {
|
||||||
menuSpecs.forEach(function(v,k) {
|
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||||
if(e.composedPath()[0].matches(k)){
|
if (oldMenu) {
|
||||||
showContextMenu(e,e.composedPath()[0],v)
|
oldMenu.remove();
|
||||||
e.preventDefault()
|
}
|
||||||
return
|
menuSpecs.forEach(function(v, k) {
|
||||||
}
|
if (e.composedPath()[0].matches(k)) {
|
||||||
})
|
showContextMenu(e, e.composedPath()[0], v);
|
||||||
});
|
e.preventDefault();
|
||||||
eventListenerApplied=true
|
}
|
||||||
|
});
|
||||||
}
|
});
|
||||||
|
eventListenerApplied = true;
|
||||||
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
|
|
||||||
}
|
}
|
||||||
|
|
||||||
initResponse = contextMenuInit();
|
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];
|
||||||
appendContextMenuOption = initResponse[0];
|
};
|
||||||
removeContextMenuOption = initResponse[1];
|
|
||||||
addContextMenuEventListener = initResponse[2];
|
var initResponse = contextMenuInit();
|
||||||
|
var appendContextMenuOption = initResponse[0];
|
||||||
(function(){
|
var removeContextMenuOption = initResponse[1];
|
||||||
//Start example Context Menu Items
|
var addContextMenuEventListener = initResponse[2];
|
||||||
let generateOnRepeat = function(genbuttonid,interruptbuttonid){
|
|
||||||
let genbutton = gradioApp().querySelector(genbuttonid);
|
(function() {
|
||||||
let interruptbutton = gradioApp().querySelector(interruptbuttonid);
|
//Start example Context Menu Items
|
||||||
if(!interruptbutton.offsetParent){
|
let generateOnRepeat = function(genbuttonid, interruptbuttonid) {
|
||||||
genbutton.click();
|
let genbutton = gradioApp().querySelector(genbuttonid);
|
||||||
}
|
let interruptbutton = gradioApp().querySelector(interruptbuttonid);
|
||||||
clearInterval(window.generateOnRepeatInterval)
|
if (!interruptbutton.offsetParent) {
|
||||||
window.generateOnRepeatInterval = setInterval(function(){
|
genbutton.click();
|
||||||
if(!interruptbutton.offsetParent){
|
}
|
||||||
genbutton.click();
|
clearInterval(window.generateOnRepeatInterval);
|
||||||
}
|
window.generateOnRepeatInterval = setInterval(function() {
|
||||||
},
|
if (!interruptbutton.offsetParent) {
|
||||||
500)
|
genbutton.click();
|
||||||
}
|
}
|
||||||
|
},
|
||||||
appendContextMenuOption('#txt2img_generate','Generate forever',function(){
|
500);
|
||||||
generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
|
};
|
||||||
})
|
|
||||||
appendContextMenuOption('#img2img_generate','Generate forever',function(){
|
let generateOnRepeat_txt2img = function() {
|
||||||
generateOnRepeat('#img2img_generate','#img2img_interrupt');
|
generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
|
||||||
})
|
};
|
||||||
|
|
||||||
let cancelGenerateForever = function(){
|
let generateOnRepeat_img2img = function() {
|
||||||
clearInterval(window.generateOnRepeatInterval)
|
generateOnRepeat('#img2img_generate', '#img2img_interrupt');
|
||||||
}
|
};
|
||||||
|
|
||||||
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
|
appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img);
|
||||||
appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
|
appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img);
|
||||||
appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
|
appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img);
|
||||||
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
|
appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img);
|
||||||
|
|
||||||
appendContextMenuOption('#roll','Roll three',
|
let cancelGenerateForever = function() {
|
||||||
function(){
|
clearInterval(window.generateOnRepeatInterval);
|
||||||
let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
|
};
|
||||||
setTimeout(function(){rollbutton.click()},100)
|
|
||||||
setTimeout(function(){rollbutton.click()},200)
|
appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
|
||||||
setTimeout(function(){rollbutton.click()},300)
|
appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever);
|
||||||
}
|
appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
|
||||||
)
|
appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever);
|
||||||
})();
|
|
||||||
//End example Context Menu Items
|
})();
|
||||||
|
//End example Context Menu Items
|
||||||
onUiUpdate(function(){
|
|
||||||
addContextMenuEventListener()
|
onAfterUiUpdate(addContextMenuEventListener);
|
||||||
});
|
|
||||||
|
101
javascript/dragdrop.js
vendored
101
javascript/dragdrop.js
vendored
@ -1,11 +1,11 @@
|
|||||||
// allows drag-dropping files into gradio image elements, and also pasting images from clipboard
|
// allows drag-dropping files into gradio image elements, and also pasting images from clipboard
|
||||||
|
|
||||||
function isValidImageList( files ) {
|
function isValidImageList(files) {
|
||||||
return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
|
return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
|
||||||
}
|
}
|
||||||
|
|
||||||
function dropReplaceImage( imgWrap, files ) {
|
function dropReplaceImage(imgWrap, files) {
|
||||||
if ( ! isValidImageList( files ) ) {
|
if (!isValidImageList(files)) {
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -14,46 +14,61 @@ function dropReplaceImage( imgWrap, files ) {
|
|||||||
imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
|
imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
|
||||||
const callback = () => {
|
const callback = () => {
|
||||||
const fileInput = imgWrap.querySelector('input[type="file"]');
|
const fileInput = imgWrap.querySelector('input[type="file"]');
|
||||||
if ( fileInput ) {
|
if (fileInput) {
|
||||||
if ( files.length === 0 ) {
|
if (files.length === 0) {
|
||||||
files = new DataTransfer();
|
files = new DataTransfer();
|
||||||
files.items.add(tmpFile);
|
files.items.add(tmpFile);
|
||||||
fileInput.files = files.files;
|
fileInput.files = files.files;
|
||||||
} else {
|
} else {
|
||||||
fileInput.files = files;
|
fileInput.files = files;
|
||||||
}
|
}
|
||||||
fileInput.dispatchEvent(new Event('change'));
|
fileInput.dispatchEvent(new Event('change'));
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
if ( imgWrap.closest('#pnginfo_image') ) {
|
if (imgWrap.closest('#pnginfo_image')) {
|
||||||
// special treatment for PNG Info tab, wait for fetch request to finish
|
// special treatment for PNG Info tab, wait for fetch request to finish
|
||||||
const oldFetch = window.fetch;
|
const oldFetch = window.fetch;
|
||||||
window.fetch = async (input, options) => {
|
window.fetch = async(input, options) => {
|
||||||
const response = await oldFetch(input, options);
|
const response = await oldFetch(input, options);
|
||||||
if ( 'api/predict/' === input ) {
|
if ('api/predict/' === input) {
|
||||||
const content = await response.text();
|
const content = await response.text();
|
||||||
window.fetch = oldFetch;
|
window.fetch = oldFetch;
|
||||||
window.requestAnimationFrame( () => callback() );
|
window.requestAnimationFrame(() => callback());
|
||||||
return new Response(content, {
|
return new Response(content, {
|
||||||
status: response.status,
|
status: response.status,
|
||||||
statusText: response.statusText,
|
statusText: response.statusText,
|
||||||
headers: response.headers
|
headers: response.headers
|
||||||
})
|
});
|
||||||
}
|
}
|
||||||
return response;
|
return response;
|
||||||
};
|
};
|
||||||
} else {
|
} else {
|
||||||
window.requestAnimationFrame( () => callback() );
|
window.requestAnimationFrame(() => callback());
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
function eventHasFiles(e) {
|
||||||
|
if (!e.dataTransfer || !e.dataTransfer.files) return false;
|
||||||
|
if (e.dataTransfer.files.length > 0) return true;
|
||||||
|
if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true;
|
||||||
|
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
|
function dragDropTargetIsPrompt(target) {
|
||||||
|
if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true;
|
||||||
|
if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true;
|
||||||
|
return false;
|
||||||
|
}
|
||||||
|
|
||||||
window.document.addEventListener('dragover', e => {
|
window.document.addEventListener('dragover', e => {
|
||||||
const target = e.composedPath()[0];
|
const target = e.composedPath()[0];
|
||||||
const imgWrap = target.closest('[data-testid="image"]');
|
if (!eventHasFiles(e)) return;
|
||||||
if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
|
|
||||||
return;
|
var targetImage = target.closest('[data-testid="image"]');
|
||||||
}
|
if (!dragDropTargetIsPrompt(target) && !targetImage) return;
|
||||||
|
|
||||||
e.stopPropagation();
|
e.stopPropagation();
|
||||||
e.preventDefault();
|
e.preventDefault();
|
||||||
e.dataTransfer.dropEffect = 'copy';
|
e.dataTransfer.dropEffect = 'copy';
|
||||||
@ -61,37 +76,55 @@ window.document.addEventListener('dragover', e => {
|
|||||||
|
|
||||||
window.document.addEventListener('drop', e => {
|
window.document.addEventListener('drop', e => {
|
||||||
const target = e.composedPath()[0];
|
const target = e.composedPath()[0];
|
||||||
if (target.placeholder.indexOf("Prompt") == -1) {
|
if (!eventHasFiles(e)) return;
|
||||||
|
|
||||||
|
if (dragDropTargetIsPrompt(target)) {
|
||||||
|
e.stopPropagation();
|
||||||
|
e.preventDefault();
|
||||||
|
|
||||||
|
let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
|
||||||
|
|
||||||
|
const imgParent = gradioApp().getElementById(prompt_target);
|
||||||
|
const files = e.dataTransfer.files;
|
||||||
|
const fileInput = imgParent.querySelector('input[type="file"]');
|
||||||
|
if (fileInput) {
|
||||||
|
fileInput.files = files;
|
||||||
|
fileInput.dispatchEvent(new Event('change'));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
var targetImage = target.closest('[data-testid="image"]');
|
||||||
|
if (targetImage) {
|
||||||
|
e.stopPropagation();
|
||||||
|
e.preventDefault();
|
||||||
|
const files = e.dataTransfer.files;
|
||||||
|
dropReplaceImage(targetImage, files);
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
const imgWrap = target.closest('[data-testid="image"]');
|
|
||||||
if ( !imgWrap ) {
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
e.stopPropagation();
|
|
||||||
e.preventDefault();
|
|
||||||
const files = e.dataTransfer.files;
|
|
||||||
dropReplaceImage( imgWrap, files );
|
|
||||||
});
|
});
|
||||||
|
|
||||||
window.addEventListener('paste', e => {
|
window.addEventListener('paste', e => {
|
||||||
const files = e.clipboardData.files;
|
const files = e.clipboardData.files;
|
||||||
if ( ! isValidImageList( files ) ) {
|
if (!isValidImageList(files)) {
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
|
const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
|
||||||
.filter(el => uiElementIsVisible(el));
|
.filter(el => uiElementIsVisible(el))
|
||||||
if ( ! visibleImageFields.length ) {
|
.sort((a, b) => uiElementInSight(b) - uiElementInSight(a));
|
||||||
|
|
||||||
|
|
||||||
|
if (!visibleImageFields.length) {
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
const firstFreeImageField = visibleImageFields
|
const firstFreeImageField = visibleImageFields
|
||||||
.filter(el => el.querySelector('input[type=file]'))?.[0];
|
.filter(el => el.querySelector('input[type=file]'))?.[0];
|
||||||
|
|
||||||
dropReplaceImage(
|
dropReplaceImage(
|
||||||
firstFreeImageField ?
|
firstFreeImageField ?
|
||||||
firstFreeImageField :
|
firstFreeImageField :
|
||||||
visibleImageFields[visibleImageFields.length - 1]
|
visibleImageFields[visibleImageFields.length - 1]
|
||||||
, files );
|
, files
|
||||||
|
);
|
||||||
});
|
});
|
||||||
|
@ -1,96 +1,121 @@
|
|||||||
function keyupEditAttention(event){
|
function keyupEditAttention(event) {
|
||||||
let target = event.originalTarget || event.composedPath()[0];
|
let target = event.originalTarget || event.composedPath()[0];
|
||||||
if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
|
if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
|
||||||
if (! (event.metaKey || event.ctrlKey)) return;
|
if (!(event.metaKey || event.ctrlKey)) return;
|
||||||
|
|
||||||
let isPlus = event.key == "ArrowUp"
|
let isPlus = event.key == "ArrowUp";
|
||||||
let isMinus = event.key == "ArrowDown"
|
let isMinus = event.key == "ArrowDown";
|
||||||
if (!isPlus && !isMinus) return;
|
if (!isPlus && !isMinus) return;
|
||||||
|
|
||||||
let selectionStart = target.selectionStart;
|
let selectionStart = target.selectionStart;
|
||||||
let selectionEnd = target.selectionEnd;
|
let selectionEnd = target.selectionEnd;
|
||||||
let text = target.value;
|
let text = target.value;
|
||||||
|
|
||||||
function selectCurrentParenthesisBlock(OPEN, CLOSE){
|
function selectCurrentParenthesisBlock(OPEN, CLOSE) {
|
||||||
if (selectionStart !== selectionEnd) return false;
|
if (selectionStart !== selectionEnd) return false;
|
||||||
|
|
||||||
// Find opening parenthesis around current cursor
|
// Find opening parenthesis around current cursor
|
||||||
const before = text.substring(0, selectionStart);
|
const before = text.substring(0, selectionStart);
|
||||||
let beforeParen = before.lastIndexOf(OPEN);
|
let beforeParen = before.lastIndexOf(OPEN);
|
||||||
if (beforeParen == -1) return false;
|
if (beforeParen == -1) return false;
|
||||||
let beforeParenClose = before.lastIndexOf(CLOSE);
|
let beforeParenClose = before.lastIndexOf(CLOSE);
|
||||||
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
|
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
|
||||||
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
|
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
|
||||||
beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
|
beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
|
||||||
}
|
}
|
||||||
|
|
||||||
// Find closing parenthesis around current cursor
|
// Find closing parenthesis around current cursor
|
||||||
const after = text.substring(selectionStart);
|
const after = text.substring(selectionStart);
|
||||||
let afterParen = after.indexOf(CLOSE);
|
let afterParen = after.indexOf(CLOSE);
|
||||||
if (afterParen == -1) return false;
|
if (afterParen == -1) return false;
|
||||||
let afterParenOpen = after.indexOf(OPEN);
|
let afterParenOpen = after.indexOf(OPEN);
|
||||||
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
|
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
|
||||||
afterParen = after.indexOf(CLOSE, afterParen + 1);
|
afterParen = after.indexOf(CLOSE, afterParen + 1);
|
||||||
afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
|
afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
|
||||||
}
|
}
|
||||||
if (beforeParen === -1 || afterParen === -1) return false;
|
if (beforeParen === -1 || afterParen === -1) return false;
|
||||||
|
|
||||||
// Set the selection to the text between the parenthesis
|
// Set the selection to the text between the parenthesis
|
||||||
const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
|
const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
|
||||||
const lastColon = parenContent.lastIndexOf(":");
|
const lastColon = parenContent.lastIndexOf(":");
|
||||||
selectionStart = beforeParen + 1;
|
selectionStart = beforeParen + 1;
|
||||||
selectionEnd = selectionStart + lastColon;
|
selectionEnd = selectionStart + lastColon;
|
||||||
target.setSelectionRange(selectionStart, selectionEnd);
|
target.setSelectionRange(selectionStart, selectionEnd);
|
||||||
return true;
|
return true;
|
||||||
}
|
}
|
||||||
|
|
||||||
// If the user hasn't selected anything, let's select their current parenthesis block
|
function selectCurrentWord() {
|
||||||
if(! selectCurrentParenthesisBlock('<', '>')){
|
if (selectionStart !== selectionEnd) return false;
|
||||||
selectCurrentParenthesisBlock('(', ')')
|
const delimiters = opts.keyedit_delimiters + " \r\n\t";
|
||||||
}
|
|
||||||
|
// seek backward until to find beggining
|
||||||
event.preventDefault();
|
while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
|
||||||
|
selectionStart--;
|
||||||
closeCharacter = ')'
|
}
|
||||||
delta = opts.keyedit_precision_attention
|
|
||||||
|
// seek forward to find end
|
||||||
if (selectionStart > 0 && text[selectionStart - 1] == '<'){
|
while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
|
||||||
closeCharacter = '>'
|
selectionEnd++;
|
||||||
delta = opts.keyedit_precision_extra
|
}
|
||||||
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
|
|
||||||
|
target.setSelectionRange(selectionStart, selectionEnd);
|
||||||
// do not include spaces at the end
|
return true;
|
||||||
while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
|
}
|
||||||
selectionEnd -= 1;
|
|
||||||
}
|
// If the user hasn't selected anything, let's select their current parenthesis block or word
|
||||||
if(selectionStart == selectionEnd){
|
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
|
||||||
return
|
selectCurrentWord();
|
||||||
}
|
}
|
||||||
|
|
||||||
text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
|
event.preventDefault();
|
||||||
|
|
||||||
selectionStart += 1;
|
var closeCharacter = ')';
|
||||||
selectionEnd += 1;
|
var delta = opts.keyedit_precision_attention;
|
||||||
}
|
|
||||||
|
if (selectionStart > 0 && text[selectionStart - 1] == '<') {
|
||||||
end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
|
closeCharacter = '>';
|
||||||
weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
|
delta = opts.keyedit_precision_extra;
|
||||||
if (isNaN(weight)) return;
|
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
|
||||||
|
|
||||||
weight += isPlus ? delta : -delta;
|
// do not include spaces at the end
|
||||||
weight = parseFloat(weight.toPrecision(12));
|
while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
|
||||||
if(String(weight).length == 1) weight += ".0"
|
selectionEnd -= 1;
|
||||||
|
}
|
||||||
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
|
if (selectionStart == selectionEnd) {
|
||||||
|
return;
|
||||||
target.focus();
|
}
|
||||||
target.value = text;
|
|
||||||
target.selectionStart = selectionStart;
|
text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
|
||||||
target.selectionEnd = selectionEnd;
|
|
||||||
|
selectionStart += 1;
|
||||||
updateInput(target)
|
selectionEnd += 1;
|
||||||
}
|
}
|
||||||
|
|
||||||
addEventListener('keydown', (event) => {
|
var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
|
||||||
keyupEditAttention(event);
|
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
|
||||||
});
|
if (isNaN(weight)) return;
|
||||||
|
|
||||||
|
weight += isPlus ? delta : -delta;
|
||||||
|
weight = parseFloat(weight.toPrecision(12));
|
||||||
|
if (String(weight).length == 1) weight += ".0";
|
||||||
|
|
||||||
|
if (closeCharacter == ')' && weight == 1) {
|
||||||
|
var endParenPos = text.substring(selectionEnd).indexOf(')');
|
||||||
|
text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1);
|
||||||
|
selectionStart--;
|
||||||
|
selectionEnd--;
|
||||||
|
} else {
|
||||||
|
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
|
||||||
|
}
|
||||||
|
|
||||||
|
target.focus();
|
||||||
|
target.value = text;
|
||||||
|
target.selectionStart = selectionStart;
|
||||||
|
target.selectionEnd = selectionEnd;
|
||||||
|
|
||||||
|
updateInput(target);
|
||||||
|
}
|
||||||
|
|
||||||
|
addEventListener('keydown', (event) => {
|
||||||
|
keyupEditAttention(event);
|
||||||
|
});
|
||||||
|
41
javascript/edit-order.js
Normal file
41
javascript/edit-order.js
Normal file
@ -0,0 +1,41 @@
|
|||||||
|
/* alt+left/right moves text in prompt */
|
||||||
|
|
||||||
|
function keyupEditOrder(event) {
|
||||||
|
if (!opts.keyedit_move) return;
|
||||||
|
|
||||||
|
let target = event.originalTarget || event.composedPath()[0];
|
||||||
|
if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
|
||||||
|
if (!event.altKey) return;
|
||||||
|
|
||||||
|
let isLeft = event.key == "ArrowLeft";
|
||||||
|
let isRight = event.key == "ArrowRight";
|
||||||
|
if (!isLeft && !isRight) return;
|
||||||
|
event.preventDefault();
|
||||||
|
|
||||||
|
let selectionStart = target.selectionStart;
|
||||||
|
let selectionEnd = target.selectionEnd;
|
||||||
|
let text = target.value;
|
||||||
|
let items = text.split(",");
|
||||||
|
let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length;
|
||||||
|
let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length;
|
||||||
|
let range = indexEnd - indexStart + 1;
|
||||||
|
|
||||||
|
if (isLeft && indexStart > 0) {
|
||||||
|
items.splice(indexStart - 1, 0, ...items.splice(indexStart, range));
|
||||||
|
target.value = items.join();
|
||||||
|
target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1);
|
||||||
|
target.selectionEnd = items.slice(0, indexEnd).join().length;
|
||||||
|
} else if (isRight && indexEnd < items.length - 1) {
|
||||||
|
items.splice(indexStart + 1, 0, ...items.splice(indexStart, range));
|
||||||
|
target.value = items.join();
|
||||||
|
target.selectionStart = items.slice(0, indexStart + 1).join().length + 1;
|
||||||
|
target.selectionEnd = items.slice(0, indexEnd + 2).join().length;
|
||||||
|
}
|
||||||
|
|
||||||
|
event.preventDefault();
|
||||||
|
updateInput(target);
|
||||||
|
}
|
||||||
|
|
||||||
|
addEventListener('keydown', (event) => {
|
||||||
|
keyupEditOrder(event);
|
||||||
|
});
|
@ -1,49 +1,92 @@
|
|||||||
|
|
||||||
function extensions_apply(_, _, disable_all){
|
function extensions_apply(_disabled_list, _update_list, disable_all) {
|
||||||
var disable = []
|
var disable = [];
|
||||||
var update = []
|
var update = [];
|
||||||
|
|
||||||
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
|
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
|
||||||
if(x.name.startsWith("enable_") && ! x.checked)
|
if (x.name.startsWith("enable_") && !x.checked) {
|
||||||
disable.push(x.name.substr(7))
|
disable.push(x.name.substring(7));
|
||||||
|
}
|
||||||
if(x.name.startsWith("update_") && x.checked)
|
|
||||||
update.push(x.name.substr(7))
|
if (x.name.startsWith("update_") && x.checked) {
|
||||||
})
|
update.push(x.name.substring(7));
|
||||||
|
}
|
||||||
restart_reload()
|
});
|
||||||
|
|
||||||
return [JSON.stringify(disable), JSON.stringify(update), disable_all]
|
restart_reload();
|
||||||
}
|
|
||||||
|
return [JSON.stringify(disable), JSON.stringify(update), disable_all];
|
||||||
function extensions_check(_, _){
|
}
|
||||||
var disable = []
|
|
||||||
|
function extensions_check() {
|
||||||
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
|
var disable = [];
|
||||||
if(x.name.startsWith("enable_") && ! x.checked)
|
|
||||||
disable.push(x.name.substr(7))
|
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
|
||||||
})
|
if (x.name.startsWith("enable_") && !x.checked) {
|
||||||
|
disable.push(x.name.substring(7));
|
||||||
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
|
}
|
||||||
x.innerHTML = "Loading..."
|
});
|
||||||
})
|
|
||||||
|
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
|
||||||
|
x.innerHTML = "Loading...";
|
||||||
var id = randomId()
|
});
|
||||||
requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
|
|
||||||
|
|
||||||
})
|
var id = randomId();
|
||||||
|
requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
|
||||||
return [id, JSON.stringify(disable)]
|
|
||||||
}
|
});
|
||||||
|
|
||||||
function install_extension_from_index(button, url){
|
return [id, JSON.stringify(disable)];
|
||||||
button.disabled = "disabled"
|
}
|
||||||
button.value = "Installing..."
|
|
||||||
|
function install_extension_from_index(button, url) {
|
||||||
textarea = gradioApp().querySelector('#extension_to_install textarea')
|
button.disabled = "disabled";
|
||||||
textarea.value = url
|
button.value = "Installing...";
|
||||||
updateInput(textarea)
|
|
||||||
|
var textarea = gradioApp().querySelector('#extension_to_install textarea');
|
||||||
gradioApp().querySelector('#install_extension_button').click()
|
textarea.value = url;
|
||||||
}
|
updateInput(textarea);
|
||||||
|
|
||||||
|
gradioApp().querySelector('#install_extension_button').click();
|
||||||
|
}
|
||||||
|
|
||||||
|
function config_state_confirm_restore(_, config_state_name, config_restore_type) {
|
||||||
|
if (config_state_name == "Current") {
|
||||||
|
return [false, config_state_name, config_restore_type];
|
||||||
|
}
|
||||||
|
let restored = "";
|
||||||
|
if (config_restore_type == "extensions") {
|
||||||
|
restored = "all saved extension versions";
|
||||||
|
} else if (config_restore_type == "webui") {
|
||||||
|
restored = "the webui version";
|
||||||
|
} else {
|
||||||
|
restored = "the webui version and all saved extension versions";
|
||||||
|
}
|
||||||
|
let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
|
||||||
|
if (confirmed) {
|
||||||
|
restart_reload();
|
||||||
|
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
|
||||||
|
x.innerHTML = "Loading...";
|
||||||
|
});
|
||||||
|
}
|
||||||
|
return [confirmed, config_state_name, config_restore_type];
|
||||||
|
}
|
||||||
|
|
||||||
|
function toggle_all_extensions(event) {
|
||||||
|
gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) {
|
||||||
|
checkbox_el.checked = event.target.checked;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
function toggle_extension() {
|
||||||
|
let all_extensions_toggled = true;
|
||||||
|
for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) {
|
||||||
|
if (!checkbox_el.checked) {
|
||||||
|
all_extensions_toggled = false;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled;
|
||||||
|
}
|
||||||
|
@ -1,179 +1,313 @@
|
|||||||
|
function setupExtraNetworksForTab(tabname) {
|
||||||
function setupExtraNetworksForTab(tabname){
|
gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
|
||||||
gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
|
|
||||||
|
var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
|
||||||
var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
|
var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
|
||||||
var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
|
var sort = gradioApp().getElementById(tabname + '_extra_sort');
|
||||||
var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
|
var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
|
||||||
|
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
|
||||||
search.classList.add('search')
|
|
||||||
tabs.appendChild(search)
|
search.classList.add('search');
|
||||||
tabs.appendChild(refresh)
|
sort.classList.add('sort');
|
||||||
|
sortOrder.classList.add('sortorder');
|
||||||
search.addEventListener("input", function(evt){
|
sort.dataset.sortkey = 'sortDefault';
|
||||||
searchTerm = search.value.toLowerCase()
|
tabs.appendChild(search);
|
||||||
|
tabs.appendChild(sort);
|
||||||
gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
|
tabs.appendChild(sortOrder);
|
||||||
text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
|
tabs.appendChild(refresh);
|
||||||
elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
|
|
||||||
})
|
var applyFilter = function() {
|
||||||
});
|
var searchTerm = search.value.toLowerCase();
|
||||||
}
|
|
||||||
|
gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
|
||||||
var activePromptTextarea = {};
|
var searchOnly = elem.querySelector('.search_only');
|
||||||
|
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase();
|
||||||
function setupExtraNetworks(){
|
|
||||||
setupExtraNetworksForTab('txt2img')
|
var visible = text.indexOf(searchTerm) != -1;
|
||||||
setupExtraNetworksForTab('img2img')
|
|
||||||
|
if (searchOnly && searchTerm.length < 4) {
|
||||||
function registerPrompt(tabname, id){
|
visible = false;
|
||||||
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
|
}
|
||||||
|
|
||||||
if (! activePromptTextarea[tabname]){
|
elem.style.display = visible ? "" : "none";
|
||||||
activePromptTextarea[tabname] = textarea
|
});
|
||||||
}
|
};
|
||||||
|
|
||||||
textarea.addEventListener("focus", function(){
|
var applySort = function() {
|
||||||
activePromptTextarea[tabname] = textarea;
|
var reverse = sortOrder.classList.contains("sortReverse");
|
||||||
});
|
var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
|
||||||
}
|
sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
|
||||||
|
var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
|
||||||
registerPrompt('txt2img', 'txt2img_prompt')
|
if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
|
||||||
registerPrompt('txt2img', 'txt2img_neg_prompt')
|
return;
|
||||||
registerPrompt('img2img', 'img2img_prompt')
|
}
|
||||||
registerPrompt('img2img', 'img2img_neg_prompt')
|
|
||||||
}
|
sort.dataset.sortkey = sortKeyStore;
|
||||||
|
|
||||||
onUiLoaded(setupExtraNetworks)
|
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
|
||||||
|
cards.forEach(function(card) {
|
||||||
var re_extranet = /<([^:]+:[^:]+):[\d\.]+>/;
|
card.originalParentElement = card.parentElement;
|
||||||
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
|
});
|
||||||
|
var sortedCards = Array.from(cards);
|
||||||
function tryToRemoveExtraNetworkFromPrompt(textarea, text){
|
sortedCards.sort(function(cardA, cardB) {
|
||||||
var m = text.match(re_extranet)
|
var a = cardA.dataset[sortKey];
|
||||||
if(! m) return false
|
var b = cardB.dataset[sortKey];
|
||||||
|
if (!isNaN(a) && !isNaN(b)) {
|
||||||
var partToSearch = m[1]
|
return parseInt(a) - parseInt(b);
|
||||||
var replaced = false
|
}
|
||||||
var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
|
|
||||||
m = found.match(re_extranet);
|
return (a < b ? -1 : (a > b ? 1 : 0));
|
||||||
if(m[1] == partToSearch){
|
});
|
||||||
replaced = true;
|
if (reverse) {
|
||||||
return ""
|
sortedCards.reverse();
|
||||||
}
|
}
|
||||||
return found;
|
cards.forEach(function(card) {
|
||||||
})
|
card.remove();
|
||||||
|
});
|
||||||
if(replaced){
|
sortedCards.forEach(function(card) {
|
||||||
textarea.value = newTextareaText
|
card.originalParentElement.appendChild(card);
|
||||||
return true;
|
});
|
||||||
}
|
};
|
||||||
|
|
||||||
return false
|
search.addEventListener("input", applyFilter);
|
||||||
}
|
applyFilter();
|
||||||
|
["change", "blur", "click"].forEach(function(evt) {
|
||||||
function cardClicked(tabname, textToAdd, allowNegativePrompt){
|
sort.querySelector("input").addEventListener(evt, applySort);
|
||||||
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
|
});
|
||||||
|
sortOrder.addEventListener("click", function() {
|
||||||
if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
|
sortOrder.classList.toggle("sortReverse");
|
||||||
textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
|
applySort();
|
||||||
}
|
});
|
||||||
|
|
||||||
updateInput(textarea)
|
extraNetworksApplyFilter[tabname] = applyFilter;
|
||||||
}
|
}
|
||||||
|
|
||||||
function saveCardPreview(event, tabname, filename){
|
function applyExtraNetworkFilter(tabname) {
|
||||||
var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea')
|
setTimeout(extraNetworksApplyFilter[tabname], 1);
|
||||||
var button = gradioApp().getElementById(tabname + '_save_preview')
|
}
|
||||||
|
|
||||||
textarea.value = filename
|
var extraNetworksApplyFilter = {};
|
||||||
updateInput(textarea)
|
var activePromptTextarea = {};
|
||||||
|
|
||||||
button.click()
|
function setupExtraNetworks() {
|
||||||
|
setupExtraNetworksForTab('txt2img');
|
||||||
event.stopPropagation()
|
setupExtraNetworksForTab('img2img');
|
||||||
event.preventDefault()
|
|
||||||
}
|
function registerPrompt(tabname, id) {
|
||||||
|
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
|
||||||
function extraNetworksSearchButton(tabs_id, event){
|
|
||||||
searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
|
if (!activePromptTextarea[tabname]) {
|
||||||
button = event.target
|
activePromptTextarea[tabname] = textarea;
|
||||||
text = button.classList.contains("search-all") ? "" : button.textContent.trim()
|
}
|
||||||
|
|
||||||
searchTextarea.value = text
|
textarea.addEventListener("focus", function() {
|
||||||
updateInput(searchTextarea)
|
activePromptTextarea[tabname] = textarea;
|
||||||
}
|
});
|
||||||
|
}
|
||||||
var globalPopup = null;
|
|
||||||
var globalPopupInner = null;
|
registerPrompt('txt2img', 'txt2img_prompt');
|
||||||
function popup(contents){
|
registerPrompt('txt2img', 'txt2img_neg_prompt');
|
||||||
if(! globalPopup){
|
registerPrompt('img2img', 'img2img_prompt');
|
||||||
globalPopup = document.createElement('div')
|
registerPrompt('img2img', 'img2img_neg_prompt');
|
||||||
globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
|
}
|
||||||
globalPopup.classList.add('global-popup');
|
|
||||||
|
onUiLoaded(setupExtraNetworks);
|
||||||
var close = document.createElement('div')
|
|
||||||
close.classList.add('global-popup-close');
|
var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
|
||||||
close.onclick = function(){ globalPopup.style.display = "none"; };
|
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
|
||||||
close.title = "Close";
|
|
||||||
globalPopup.appendChild(close)
|
function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
|
||||||
|
var m = text.match(re_extranet);
|
||||||
globalPopupInner = document.createElement('div')
|
var replaced = false;
|
||||||
globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
|
var newTextareaText;
|
||||||
globalPopupInner.classList.add('global-popup-inner');
|
if (m) {
|
||||||
globalPopup.appendChild(globalPopupInner)
|
var extraTextAfterNet = m[2];
|
||||||
|
var partToSearch = m[1];
|
||||||
gradioApp().appendChild(globalPopup);
|
var foundAtPosition = -1;
|
||||||
}
|
newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) {
|
||||||
|
m = found.match(re_extranet);
|
||||||
globalPopupInner.innerHTML = '';
|
if (m[1] == partToSearch) {
|
||||||
globalPopupInner.appendChild(contents);
|
replaced = true;
|
||||||
|
foundAtPosition = pos;
|
||||||
globalPopup.style.display = "flex";
|
return "";
|
||||||
}
|
}
|
||||||
|
return found;
|
||||||
function extraNetworksShowMetadata(text){
|
});
|
||||||
elem = document.createElement('pre')
|
|
||||||
elem.classList.add('popup-metadata');
|
if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
|
||||||
elem.textContent = text;
|
newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
|
||||||
|
}
|
||||||
popup(elem);
|
} else {
|
||||||
}
|
newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
|
||||||
|
if (found == text) {
|
||||||
function requestGet(url, data, handler, errorHandler){
|
replaced = true;
|
||||||
var xhr = new XMLHttpRequest();
|
return "";
|
||||||
var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
|
}
|
||||||
xhr.open("GET", url + "?" + args, true);
|
return found;
|
||||||
|
});
|
||||||
xhr.onreadystatechange = function () {
|
}
|
||||||
if (xhr.readyState === 4) {
|
|
||||||
if (xhr.status === 200) {
|
if (replaced) {
|
||||||
try {
|
textarea.value = newTextareaText;
|
||||||
var js = JSON.parse(xhr.responseText);
|
return true;
|
||||||
handler(js)
|
}
|
||||||
} catch (error) {
|
|
||||||
console.error(error);
|
return false;
|
||||||
errorHandler()
|
}
|
||||||
}
|
|
||||||
} else{
|
function cardClicked(tabname, textToAdd, allowNegativePrompt) {
|
||||||
errorHandler()
|
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
|
||||||
}
|
|
||||||
}
|
if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
|
||||||
};
|
textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
|
||||||
var js = JSON.stringify(data);
|
}
|
||||||
xhr.send(js);
|
|
||||||
}
|
updateInput(textarea);
|
||||||
|
}
|
||||||
function extraNetworksRequestMetadata(event, extraPage, cardName){
|
|
||||||
showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
|
function saveCardPreview(event, tabname, filename) {
|
||||||
|
var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea');
|
||||||
requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
|
var button = gradioApp().getElementById(tabname + '_save_preview');
|
||||||
if(data && data.metadata){
|
|
||||||
extraNetworksShowMetadata(data.metadata)
|
textarea.value = filename;
|
||||||
} else{
|
updateInput(textarea);
|
||||||
showError()
|
|
||||||
}
|
button.click();
|
||||||
}, showError)
|
|
||||||
|
event.stopPropagation();
|
||||||
event.stopPropagation()
|
event.preventDefault();
|
||||||
}
|
}
|
||||||
|
|
||||||
|
function extraNetworksSearchButton(tabs_id, event) {
|
||||||
|
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea');
|
||||||
|
var button = event.target;
|
||||||
|
var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
|
||||||
|
|
||||||
|
searchTextarea.value = text;
|
||||||
|
updateInput(searchTextarea);
|
||||||
|
}
|
||||||
|
|
||||||
|
var globalPopup = null;
|
||||||
|
var globalPopupInner = null;
|
||||||
|
function closePopup() {
|
||||||
|
if (!globalPopup) return;
|
||||||
|
|
||||||
|
globalPopup.style.display = "none";
|
||||||
|
}
|
||||||
|
function popup(contents) {
|
||||||
|
if (!globalPopup) {
|
||||||
|
globalPopup = document.createElement('div');
|
||||||
|
globalPopup.onclick = closePopup;
|
||||||
|
globalPopup.classList.add('global-popup');
|
||||||
|
|
||||||
|
var close = document.createElement('div');
|
||||||
|
close.classList.add('global-popup-close');
|
||||||
|
close.onclick = closePopup;
|
||||||
|
close.title = "Close";
|
||||||
|
globalPopup.appendChild(close);
|
||||||
|
|
||||||
|
globalPopupInner = document.createElement('div');
|
||||||
|
globalPopupInner.onclick = function(event) {
|
||||||
|
event.stopPropagation(); return false;
|
||||||
|
};
|
||||||
|
globalPopupInner.classList.add('global-popup-inner');
|
||||||
|
globalPopup.appendChild(globalPopupInner);
|
||||||
|
|
||||||
|
gradioApp().querySelector('.main').appendChild(globalPopup);
|
||||||
|
}
|
||||||
|
|
||||||
|
globalPopupInner.innerHTML = '';
|
||||||
|
globalPopupInner.appendChild(contents);
|
||||||
|
|
||||||
|
globalPopup.style.display = "flex";
|
||||||
|
}
|
||||||
|
|
||||||
|
function extraNetworksShowMetadata(text) {
|
||||||
|
var elem = document.createElement('pre');
|
||||||
|
elem.classList.add('popup-metadata');
|
||||||
|
elem.textContent = text;
|
||||||
|
|
||||||
|
popup(elem);
|
||||||
|
}
|
||||||
|
|
||||||
|
function requestGet(url, data, handler, errorHandler) {
|
||||||
|
var xhr = new XMLHttpRequest();
|
||||||
|
var args = Object.keys(data).map(function(k) {
|
||||||
|
return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]);
|
||||||
|
}).join('&');
|
||||||
|
xhr.open("GET", url + "?" + args, true);
|
||||||
|
|
||||||
|
xhr.onreadystatechange = function() {
|
||||||
|
if (xhr.readyState === 4) {
|
||||||
|
if (xhr.status === 200) {
|
||||||
|
try {
|
||||||
|
var js = JSON.parse(xhr.responseText);
|
||||||
|
handler(js);
|
||||||
|
} catch (error) {
|
||||||
|
console.error(error);
|
||||||
|
errorHandler();
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
errorHandler();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
var js = JSON.stringify(data);
|
||||||
|
xhr.send(js);
|
||||||
|
}
|
||||||
|
|
||||||
|
function extraNetworksRequestMetadata(event, extraPage, cardName) {
|
||||||
|
var showError = function() {
|
||||||
|
extraNetworksShowMetadata("there was an error getting metadata");
|
||||||
|
};
|
||||||
|
|
||||||
|
requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) {
|
||||||
|
if (data && data.metadata) {
|
||||||
|
extraNetworksShowMetadata(data.metadata);
|
||||||
|
} else {
|
||||||
|
showError();
|
||||||
|
}
|
||||||
|
}, showError);
|
||||||
|
|
||||||
|
event.stopPropagation();
|
||||||
|
}
|
||||||
|
|
||||||
|
var extraPageUserMetadataEditors = {};
|
||||||
|
|
||||||
|
function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
|
||||||
|
var id = tabname + '_' + extraPage + '_edit_user_metadata';
|
||||||
|
|
||||||
|
var editor = extraPageUserMetadataEditors[id];
|
||||||
|
if (!editor) {
|
||||||
|
editor = {};
|
||||||
|
editor.page = gradioApp().getElementById(id);
|
||||||
|
editor.nameTextarea = gradioApp().querySelector("#" + id + "_name" + ' textarea');
|
||||||
|
editor.button = gradioApp().querySelector("#" + id + "_button");
|
||||||
|
extraPageUserMetadataEditors[id] = editor;
|
||||||
|
}
|
||||||
|
|
||||||
|
editor.nameTextarea.value = cardName;
|
||||||
|
updateInput(editor.nameTextarea);
|
||||||
|
|
||||||
|
editor.button.click();
|
||||||
|
|
||||||
|
popup(editor.page);
|
||||||
|
|
||||||
|
event.stopPropagation();
|
||||||
|
}
|
||||||
|
|
||||||
|
function extraNetworksRefreshSingleCard(page, tabname, name) {
|
||||||
|
requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
|
||||||
|
if (data && data.html) {
|
||||||
|
var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
|
||||||
|
|
||||||
|
var newDiv = document.createElement('DIV');
|
||||||
|
newDiv.innerHTML = data.html;
|
||||||
|
var newCard = newDiv.firstElementChild;
|
||||||
|
|
||||||
|
newCard.style = '';
|
||||||
|
card.parentElement.insertBefore(newCard, card);
|
||||||
|
card.parentElement.removeChild(card);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
@ -1,33 +1,35 @@
|
|||||||
// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
|
// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
|
||||||
|
|
||||||
let txt2img_gallery, img2img_gallery, modal = undefined;
|
let txt2img_gallery, img2img_gallery, modal = undefined;
|
||||||
onUiUpdate(function(){
|
onAfterUiUpdate(function() {
|
||||||
if (!txt2img_gallery) {
|
if (!txt2img_gallery) {
|
||||||
txt2img_gallery = attachGalleryListeners("txt2img")
|
txt2img_gallery = attachGalleryListeners("txt2img");
|
||||||
}
|
}
|
||||||
if (!img2img_gallery) {
|
if (!img2img_gallery) {
|
||||||
img2img_gallery = attachGalleryListeners("img2img")
|
img2img_gallery = attachGalleryListeners("img2img");
|
||||||
}
|
}
|
||||||
if (!modal) {
|
if (!modal) {
|
||||||
modal = gradioApp().getElementById('lightboxModal')
|
modal = gradioApp().getElementById('lightboxModal');
|
||||||
modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
|
modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']});
|
||||||
}
|
}
|
||||||
});
|
});
|
||||||
|
|
||||||
let modalObserver = new MutationObserver(function(mutations) {
|
let modalObserver = new MutationObserver(function(mutations) {
|
||||||
mutations.forEach(function(mutationRecord) {
|
mutations.forEach(function(mutationRecord) {
|
||||||
let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
|
let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText;
|
||||||
if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
|
if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) {
|
||||||
gradioApp().getElementById(selectedTab+"_generation_info_button").click()
|
gradioApp().getElementById(selectedTab + "_generation_info_button")?.click();
|
||||||
});
|
}
|
||||||
|
});
|
||||||
});
|
});
|
||||||
|
|
||||||
function attachGalleryListeners(tab_name) {
|
function attachGalleryListeners(tab_name) {
|
||||||
gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
|
var gallery = gradioApp().querySelector('#' + tab_name + '_gallery');
|
||||||
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
|
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click());
|
||||||
gallery?.addEventListener('keydown', (e) => {
|
gallery?.addEventListener('keydown', (e) => {
|
||||||
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
|
if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow
|
||||||
gradioApp().getElementById(tab_name+"_generation_info_button").click()
|
gradioApp().getElementById(tab_name + "_generation_info_button").click();
|
||||||
});
|
}
|
||||||
return gallery;
|
});
|
||||||
|
return gallery;
|
||||||
}
|
}
|
||||||
|
@ -1,20 +1,21 @@
|
|||||||
// mouseover tooltips for various UI elements
|
// mouseover tooltips for various UI elements
|
||||||
|
|
||||||
titles = {
|
var titles = {
|
||||||
"Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
|
"Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
|
||||||
"Sampling method": "Which algorithm to use to produce the image",
|
"Sampling method": "Which algorithm to use to produce the image",
|
||||||
"GFPGAN": "Restore low quality faces using GFPGAN neural network",
|
"GFPGAN": "Restore low quality faces using GFPGAN neural network",
|
||||||
"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
|
"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
|
||||||
"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
|
"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
|
||||||
"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
|
"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
|
||||||
"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
|
"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
|
||||||
|
|
||||||
"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
|
"\u{1F4D0}": "Auto detect size from img2img",
|
||||||
"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
|
"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
|
||||||
|
"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
|
||||||
"CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
|
"CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
|
||||||
"Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
|
"Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
|
||||||
"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
|
"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
|
||||||
"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
|
"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized",
|
||||||
"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
|
"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
|
||||||
"\u{1f4c2}": "Open images output directory",
|
"\u{1f4c2}": "Open images output directory",
|
||||||
"\u{1f4be}": "Save style",
|
"\u{1f4be}": "Save style",
|
||||||
@ -22,6 +23,7 @@ titles = {
|
|||||||
"\u{1f4cb}": "Apply selected styles to current prompt",
|
"\u{1f4cb}": "Apply selected styles to current prompt",
|
||||||
"\u{1f4d2}": "Paste available values into the field",
|
"\u{1f4d2}": "Paste available values into the field",
|
||||||
"\u{1f3b4}": "Show/hide extra networks",
|
"\u{1f3b4}": "Show/hide extra networks",
|
||||||
|
"\u{1f300}": "Restore progress",
|
||||||
|
|
||||||
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
|
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
|
||||||
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
|
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
|
||||||
@ -39,7 +41,7 @@ titles = {
|
|||||||
"Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
|
"Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
|
||||||
|
|
||||||
"Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
|
"Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
|
||||||
|
|
||||||
"Skip": "Stop processing current image and continue processing.",
|
"Skip": "Stop processing current image and continue processing.",
|
||||||
"Interrupt": "Stop processing images and return any results accumulated so far.",
|
"Interrupt": "Stop processing images and return any results accumulated so far.",
|
||||||
"Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
|
"Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
|
||||||
@ -65,8 +67,8 @@ titles = {
|
|||||||
|
|
||||||
"Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
|
"Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
|
||||||
|
|
||||||
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
|
"Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.",
|
||||||
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
|
"Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.",
|
||||||
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
|
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
|
||||||
|
|
||||||
"Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
|
"Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
|
||||||
@ -82,10 +84,7 @@ titles = {
|
|||||||
"Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
|
"Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
|
||||||
"Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
|
"Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
|
||||||
|
|
||||||
"vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
|
|
||||||
|
|
||||||
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
|
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
|
||||||
"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
|
|
||||||
|
|
||||||
"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
|
"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
|
||||||
"Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
|
"Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
|
||||||
@ -96,7 +95,7 @@ titles = {
|
|||||||
"Add difference": "Result = A + (B - C) * M",
|
"Add difference": "Result = A + (B - C) * M",
|
||||||
"No interpolation": "Result = A",
|
"No interpolation": "Result = A",
|
||||||
|
|
||||||
"Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
|
"Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
|
||||||
"Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
|
"Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
|
||||||
|
|
||||||
"Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
|
"Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
|
||||||
@ -109,39 +108,85 @@ titles = {
|
|||||||
"Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
|
"Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
|
||||||
"Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
|
"Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
|
||||||
"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
|
"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
|
||||||
"Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
|
|
||||||
"Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
|
"Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
|
||||||
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
|
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.",
|
||||||
|
"Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
|
||||||
|
};
|
||||||
|
|
||||||
|
function updateTooltip(element) {
|
||||||
|
if (element.title) return; // already has a title
|
||||||
|
|
||||||
|
let text = element.textContent;
|
||||||
|
let tooltip = localization[titles[text]] || titles[text];
|
||||||
|
|
||||||
|
if (!tooltip) {
|
||||||
|
let value = element.value;
|
||||||
|
if (value) tooltip = localization[titles[value]] || titles[value];
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!tooltip) {
|
||||||
|
// Gradio dropdown options have `data-value`.
|
||||||
|
let dataValue = element.dataset.value;
|
||||||
|
if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue];
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!tooltip) {
|
||||||
|
for (const c of element.classList) {
|
||||||
|
if (c in titles) {
|
||||||
|
tooltip = localization[titles[c]] || titles[c];
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (tooltip) {
|
||||||
|
element.title = tooltip;
|
||||||
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Nodes to check for adding tooltips.
|
||||||
|
const tooltipCheckNodes = new Set();
|
||||||
|
// Timer for debouncing tooltip check.
|
||||||
|
let tooltipCheckTimer = null;
|
||||||
|
|
||||||
onUiUpdate(function(){
|
function processTooltipCheckNodes() {
|
||||||
gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
|
for (const node of tooltipCheckNodes) {
|
||||||
tooltip = titles[span.textContent];
|
updateTooltip(node);
|
||||||
|
}
|
||||||
|
tooltipCheckNodes.clear();
|
||||||
|
}
|
||||||
|
|
||||||
if(!tooltip){
|
onUiUpdate(function(mutationRecords) {
|
||||||
tooltip = titles[span.value];
|
for (const record of mutationRecords) {
|
||||||
}
|
if (record.type === "childList" && record.target.classList.contains("options")) {
|
||||||
|
// This smells like a Gradio dropdown menu having changed,
|
||||||
if(!tooltip){
|
// so let's enqueue an update for the input element that shows the current value.
|
||||||
for (const c of span.classList) {
|
let wrap = record.target.parentNode;
|
||||||
if (c in titles) {
|
let input = wrap?.querySelector("input");
|
||||||
tooltip = titles[c];
|
if (input) {
|
||||||
break;
|
input.title = ""; // So we'll even have a chance to update it.
|
||||||
}
|
tooltipCheckNodes.add(input);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
for (const node of record.addedNodes) {
|
||||||
if(tooltip){
|
if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) {
|
||||||
span.title = tooltip;
|
if (!node.title) {
|
||||||
}
|
if (
|
||||||
})
|
node.tagName === "SPAN" ||
|
||||||
|
node.tagName === "BUTTON" ||
|
||||||
gradioApp().querySelectorAll('select').forEach(function(select){
|
node.tagName === "P" ||
|
||||||
if (select.onchange != null) return;
|
node.tagName === "INPUT" ||
|
||||||
|
(node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item
|
||||||
select.onchange = function(){
|
) {
|
||||||
select.title = titles[select.value] || "";
|
tooltipCheckNodes.add(node);
|
||||||
}
|
}
|
||||||
})
|
}
|
||||||
})
|
node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if (tooltipCheckNodes.size) {
|
||||||
|
clearTimeout(tooltipCheckTimer);
|
||||||
|
tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
@ -1,22 +1,18 @@
|
|||||||
|
|
||||||
function setInactive(elem, inactive){
|
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) {
|
||||||
if(inactive){
|
function setInactive(elem, inactive) {
|
||||||
elem.classList.add('inactive')
|
elem.classList.toggle('inactive', !!inactive);
|
||||||
} else{
|
}
|
||||||
elem.classList.remove('inactive')
|
|
||||||
}
|
var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale');
|
||||||
}
|
var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x');
|
||||||
|
var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y');
|
||||||
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
|
|
||||||
hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
|
gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : "";
|
||||||
hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
|
|
||||||
hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
|
setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0);
|
||||||
|
setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0);
|
||||||
gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
|
setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0);
|
||||||
|
|
||||||
setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
|
return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y];
|
||||||
setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
|
}
|
||||||
setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
|
|
||||||
|
|
||||||
return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
|
|
||||||
}
|
|
||||||
|
@ -1,21 +1,19 @@
|
|||||||
/**
|
/**
|
||||||
* temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
|
* temporary fix for https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
|
||||||
* @see https://github.com/gradio-app/gradio/issues/1721
|
* @see https://ghproxy.com/https://github.com/gradio-app/gradio/issues/1721
|
||||||
*/
|
*/
|
||||||
window.addEventListener( 'resize', () => imageMaskResize());
|
|
||||||
function imageMaskResize() {
|
function imageMaskResize() {
|
||||||
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
|
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
|
||||||
if ( ! canvases.length ) {
|
if (!canvases.length) {
|
||||||
canvases_fixed = false;
|
window.removeEventListener('resize', imageMaskResize);
|
||||||
window.removeEventListener( 'resize', imageMaskResize );
|
return;
|
||||||
return;
|
|
||||||
}
|
}
|
||||||
|
|
||||||
const wrapper = canvases[0].closest('.touch-none');
|
const wrapper = canvases[0].closest('.touch-none');
|
||||||
const previewImage = wrapper.previousElementSibling;
|
const previewImage = wrapper.previousElementSibling;
|
||||||
|
|
||||||
if ( ! previewImage.complete ) {
|
if (!previewImage.complete) {
|
||||||
previewImage.addEventListener( 'load', () => imageMaskResize());
|
previewImage.addEventListener('load', imageMaskResize);
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -24,22 +22,22 @@ function imageMaskResize() {
|
|||||||
const nw = previewImage.naturalWidth;
|
const nw = previewImage.naturalWidth;
|
||||||
const nh = previewImage.naturalHeight;
|
const nh = previewImage.naturalHeight;
|
||||||
const portrait = nh > nw;
|
const portrait = nh > nw;
|
||||||
const factor = portrait;
|
|
||||||
|
|
||||||
const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
|
const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw);
|
||||||
const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
|
const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh);
|
||||||
|
|
||||||
wrapper.style.width = `${wW}px`;
|
wrapper.style.width = `${wW}px`;
|
||||||
wrapper.style.height = `${wH}px`;
|
wrapper.style.height = `${wH}px`;
|
||||||
wrapper.style.left = `0px`;
|
wrapper.style.left = `0px`;
|
||||||
wrapper.style.top = `0px`;
|
wrapper.style.top = `0px`;
|
||||||
|
|
||||||
canvases.forEach( c => {
|
canvases.forEach(c => {
|
||||||
c.style.width = c.style.height = '';
|
c.style.width = c.style.height = '';
|
||||||
c.style.maxWidth = '100%';
|
c.style.maxWidth = '100%';
|
||||||
c.style.maxHeight = '100%';
|
c.style.maxHeight = '100%';
|
||||||
c.style.objectFit = 'contain';
|
c.style.objectFit = 'contain';
|
||||||
});
|
});
|
||||||
}
|
}
|
||||||
|
|
||||||
onUiUpdate(() => imageMaskResize());
|
onAfterUiUpdate(imageMaskResize);
|
||||||
|
window.addEventListener('resize', imageMaskResize);
|
||||||
|
@ -1,19 +0,0 @@
|
|||||||
window.onload = (function(){
|
|
||||||
window.addEventListener('drop', e => {
|
|
||||||
const target = e.composedPath()[0];
|
|
||||||
const idx = selected_gallery_index();
|
|
||||||
if (target.placeholder.indexOf("Prompt") == -1) return;
|
|
||||||
|
|
||||||
let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
|
|
||||||
|
|
||||||
e.stopPropagation();
|
|
||||||
e.preventDefault();
|
|
||||||
const imgParent = gradioApp().getElementById(prompt_target);
|
|
||||||
const files = e.dataTransfer.files;
|
|
||||||
const fileInput = imgParent.querySelector('input[type="file"]');
|
|
||||||
if ( fileInput ) {
|
|
||||||
fileInput.files = files;
|
|
||||||
fileInput.dispatchEvent(new Event('change'));
|
|
||||||
}
|
|
||||||
});
|
|
||||||
});
|
|
@ -5,24 +5,24 @@ function closeModal() {
|
|||||||
|
|
||||||
function showModal(event) {
|
function showModal(event) {
|
||||||
const source = event.target || event.srcElement;
|
const source = event.target || event.srcElement;
|
||||||
const modalImage = gradioApp().getElementById("modalImage")
|
const modalImage = gradioApp().getElementById("modalImage");
|
||||||
const lb = gradioApp().getElementById("lightboxModal")
|
const lb = gradioApp().getElementById("lightboxModal");
|
||||||
modalImage.src = source.src
|
modalImage.src = source.src;
|
||||||
if (modalImage.style.display === 'none') {
|
if (modalImage.style.display === 'none') {
|
||||||
lb.style.setProperty('background-image', 'url(' + source.src + ')');
|
lb.style.setProperty('background-image', 'url(' + source.src + ')');
|
||||||
}
|
}
|
||||||
lb.style.display = "flex";
|
lb.style.display = "flex";
|
||||||
lb.focus()
|
lb.focus();
|
||||||
|
|
||||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
|
const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
|
||||||
const tabImg2Img = gradioApp().getElementById("tab_img2img")
|
const tabImg2Img = gradioApp().getElementById("tab_img2img");
|
||||||
// show the save button in modal only on txt2img or img2img tabs
|
// show the save button in modal only on txt2img or img2img tabs
|
||||||
if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
|
if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
|
||||||
gradioApp().getElementById("modal_save").style.display = "inline"
|
gradioApp().getElementById("modal_save").style.display = "inline";
|
||||||
} else {
|
} else {
|
||||||
gradioApp().getElementById("modal_save").style.display = "none"
|
gradioApp().getElementById("modal_save").style.display = "none";
|
||||||
}
|
}
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function negmod(n, m) {
|
function negmod(n, m) {
|
||||||
@ -30,14 +30,15 @@ function negmod(n, m) {
|
|||||||
}
|
}
|
||||||
|
|
||||||
function updateOnBackgroundChange() {
|
function updateOnBackgroundChange() {
|
||||||
const modalImage = gradioApp().getElementById("modalImage")
|
const modalImage = gradioApp().getElementById("modalImage");
|
||||||
if (modalImage && modalImage.offsetParent) {
|
if (modalImage && modalImage.offsetParent) {
|
||||||
let currentButton = selected_gallery_button();
|
let currentButton = selected_gallery_button();
|
||||||
|
|
||||||
if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
|
if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
|
||||||
modalImage.src = currentButton.children[0].src;
|
modalImage.src = currentButton.children[0].src;
|
||||||
if (modalImage.style.display === 'none') {
|
if (modalImage.style.display === 'none') {
|
||||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
const modal = gradioApp().getElementById("lightboxModal");
|
||||||
|
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@ -49,112 +50,109 @@ function modalImageSwitch(offset) {
|
|||||||
if (galleryButtons.length > 1) {
|
if (galleryButtons.length > 1) {
|
||||||
var currentButton = selected_gallery_button();
|
var currentButton = selected_gallery_button();
|
||||||
|
|
||||||
var result = -1
|
var result = -1;
|
||||||
galleryButtons.forEach(function(v, i) {
|
galleryButtons.forEach(function(v, i) {
|
||||||
if (v == currentButton) {
|
if (v == currentButton) {
|
||||||
result = i
|
result = i;
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
|
|
||||||
if (result != -1) {
|
if (result != -1) {
|
||||||
nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
|
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
|
||||||
nextButton.click()
|
nextButton.click();
|
||||||
const modalImage = gradioApp().getElementById("modalImage");
|
const modalImage = gradioApp().getElementById("modalImage");
|
||||||
const modal = gradioApp().getElementById("lightboxModal");
|
const modal = gradioApp().getElementById("lightboxModal");
|
||||||
modalImage.src = nextButton.children[0].src;
|
modalImage.src = nextButton.children[0].src;
|
||||||
if (modalImage.style.display === 'none') {
|
if (modalImage.style.display === 'none') {
|
||||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||||
}
|
}
|
||||||
setTimeout(function() {
|
setTimeout(function() {
|
||||||
modal.focus()
|
modal.focus();
|
||||||
}, 10)
|
}, 10);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
function saveImage(){
|
function saveImage() {
|
||||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
|
const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
|
||||||
const tabImg2Img = gradioApp().getElementById("tab_img2img")
|
const tabImg2Img = gradioApp().getElementById("tab_img2img");
|
||||||
const saveTxt2Img = "save_txt2img"
|
const saveTxt2Img = "save_txt2img";
|
||||||
const saveImg2Img = "save_img2img"
|
const saveImg2Img = "save_img2img";
|
||||||
if (tabTxt2Img.style.display != "none") {
|
if (tabTxt2Img.style.display != "none") {
|
||||||
gradioApp().getElementById(saveTxt2Img).click()
|
gradioApp().getElementById(saveTxt2Img).click();
|
||||||
} else if (tabImg2Img.style.display != "none") {
|
} else if (tabImg2Img.style.display != "none") {
|
||||||
gradioApp().getElementById(saveImg2Img).click()
|
gradioApp().getElementById(saveImg2Img).click();
|
||||||
} else {
|
} else {
|
||||||
console.error("missing implementation for saving modal of this type")
|
console.error("missing implementation for saving modal of this type");
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalSaveImage(event) {
|
function modalSaveImage(event) {
|
||||||
saveImage()
|
saveImage();
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalNextImage(event) {
|
function modalNextImage(event) {
|
||||||
modalImageSwitch(1)
|
modalImageSwitch(1);
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalPrevImage(event) {
|
function modalPrevImage(event) {
|
||||||
modalImageSwitch(-1)
|
modalImageSwitch(-1);
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalKeyHandler(event) {
|
function modalKeyHandler(event) {
|
||||||
switch (event.key) {
|
switch (event.key) {
|
||||||
case "s":
|
case "s":
|
||||||
saveImage()
|
saveImage();
|
||||||
break;
|
break;
|
||||||
case "ArrowLeft":
|
case "ArrowLeft":
|
||||||
modalPrevImage(event)
|
modalPrevImage(event);
|
||||||
break;
|
break;
|
||||||
case "ArrowRight":
|
case "ArrowRight":
|
||||||
modalNextImage(event)
|
modalNextImage(event);
|
||||||
break;
|
break;
|
||||||
case "Escape":
|
case "Escape":
|
||||||
closeModal();
|
closeModal();
|
||||||
break;
|
break;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
function setupImageForLightbox(e) {
|
function setupImageForLightbox(e) {
|
||||||
if (e.dataset.modded)
|
if (e.dataset.modded) {
|
||||||
return;
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
e.dataset.modded = true;
|
e.dataset.modded = true;
|
||||||
e.style.cursor='pointer'
|
e.style.cursor = 'pointer';
|
||||||
e.style.userSelect='none'
|
e.style.userSelect = 'none';
|
||||||
|
|
||||||
var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
|
var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
|
||||||
|
|
||||||
// For Firefox, listening on click first switched to next image then shows the lightbox.
|
// For Firefox, listening on click first switched to next image then shows the lightbox.
|
||||||
// If you know how to fix this without switching to mousedown event, please.
|
// If you know how to fix this without switching to mousedown event, please.
|
||||||
// For other browsers the event is click to make it possiblr to drag picture.
|
// For other browsers the event is click to make it possiblr to drag picture.
|
||||||
var event = isFirefox ? 'mousedown' : 'click'
|
var event = isFirefox ? 'mousedown' : 'click';
|
||||||
|
|
||||||
e.addEventListener(event, function (evt) {
|
e.addEventListener(event, function(evt) {
|
||||||
if(!opts.js_modal_lightbox || evt.button != 0) return;
|
if (!opts.js_modal_lightbox || evt.button != 0) return;
|
||||||
|
|
||||||
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
|
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);
|
||||||
evt.preventDefault()
|
evt.preventDefault();
|
||||||
showModal(evt)
|
showModal(evt);
|
||||||
}, true);
|
}, true);
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalZoomSet(modalImage, enable) {
|
function modalZoomSet(modalImage, enable) {
|
||||||
if (enable) {
|
if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
|
||||||
modalImage.classList.add('modalImageFullscreen');
|
|
||||||
} else {
|
|
||||||
modalImage.classList.remove('modalImageFullscreen');
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalZoomToggle(event) {
|
function modalZoomToggle(event) {
|
||||||
modalImage = gradioApp().getElementById("modalImage");
|
var modalImage = gradioApp().getElementById("modalImage");
|
||||||
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
|
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function modalTileImageToggle(event) {
|
function modalTileImageToggle(event) {
|
||||||
@ -163,96 +161,93 @@ function modalTileImageToggle(event) {
|
|||||||
const isTiling = modalImage.style.display === 'none';
|
const isTiling = modalImage.style.display === 'none';
|
||||||
if (isTiling) {
|
if (isTiling) {
|
||||||
modalImage.style.display = 'block';
|
modalImage.style.display = 'block';
|
||||||
modal.style.setProperty('background-image', 'none')
|
modal.style.setProperty('background-image', 'none');
|
||||||
} else {
|
} else {
|
||||||
modalImage.style.display = 'none';
|
modalImage.style.display = 'none';
|
||||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||||
}
|
}
|
||||||
|
|
||||||
event.stopPropagation()
|
event.stopPropagation();
|
||||||
}
|
}
|
||||||
|
|
||||||
function galleryImageHandler(e) {
|
onAfterUiUpdate(function() {
|
||||||
//if (e && e.parentElement.tagName == 'BUTTON') {
|
var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img');
|
||||||
e.onclick = showGalleryImage;
|
|
||||||
//}
|
|
||||||
}
|
|
||||||
|
|
||||||
onUiUpdate(function() {
|
|
||||||
fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
|
|
||||||
if (fullImg_preview != null) {
|
if (fullImg_preview != null) {
|
||||||
fullImg_preview.forEach(setupImageForLightbox);
|
fullImg_preview.forEach(setupImageForLightbox);
|
||||||
}
|
}
|
||||||
updateOnBackgroundChange();
|
updateOnBackgroundChange();
|
||||||
})
|
});
|
||||||
|
|
||||||
document.addEventListener("DOMContentLoaded", function() {
|
document.addEventListener("DOMContentLoaded", function() {
|
||||||
//const modalFragment = document.createDocumentFragment();
|
//const modalFragment = document.createDocumentFragment();
|
||||||
const modal = document.createElement('div')
|
const modal = document.createElement('div');
|
||||||
modal.onclick = closeModal;
|
modal.onclick = closeModal;
|
||||||
modal.id = "lightboxModal";
|
modal.id = "lightboxModal";
|
||||||
modal.tabIndex = 0
|
modal.tabIndex = 0;
|
||||||
modal.addEventListener('keydown', modalKeyHandler, true)
|
modal.addEventListener('keydown', modalKeyHandler, true);
|
||||||
|
|
||||||
const modalControls = document.createElement('div')
|
const modalControls = document.createElement('div');
|
||||||
modalControls.className = 'modalControls gradio-container';
|
modalControls.className = 'modalControls gradio-container';
|
||||||
modal.append(modalControls);
|
modal.append(modalControls);
|
||||||
|
|
||||||
const modalZoom = document.createElement('span')
|
const modalZoom = document.createElement('span');
|
||||||
modalZoom.className = 'modalZoom cursor';
|
modalZoom.className = 'modalZoom cursor';
|
||||||
modalZoom.innerHTML = '⤡'
|
modalZoom.innerHTML = '⤡';
|
||||||
modalZoom.addEventListener('click', modalZoomToggle, true)
|
modalZoom.addEventListener('click', modalZoomToggle, true);
|
||||||
modalZoom.title = "Toggle zoomed view";
|
modalZoom.title = "Toggle zoomed view";
|
||||||
modalControls.appendChild(modalZoom)
|
modalControls.appendChild(modalZoom);
|
||||||
|
|
||||||
const modalTileImage = document.createElement('span')
|
const modalTileImage = document.createElement('span');
|
||||||
modalTileImage.className = 'modalTileImage cursor';
|
modalTileImage.className = 'modalTileImage cursor';
|
||||||
modalTileImage.innerHTML = '⊞'
|
modalTileImage.innerHTML = '⊞';
|
||||||
modalTileImage.addEventListener('click', modalTileImageToggle, true)
|
modalTileImage.addEventListener('click', modalTileImageToggle, true);
|
||||||
modalTileImage.title = "Preview tiling";
|
modalTileImage.title = "Preview tiling";
|
||||||
modalControls.appendChild(modalTileImage)
|
modalControls.appendChild(modalTileImage);
|
||||||
|
|
||||||
const modalSave = document.createElement("span")
|
const modalSave = document.createElement("span");
|
||||||
modalSave.className = "modalSave cursor"
|
modalSave.className = "modalSave cursor";
|
||||||
modalSave.id = "modal_save"
|
modalSave.id = "modal_save";
|
||||||
modalSave.innerHTML = "🖫"
|
modalSave.innerHTML = "🖫";
|
||||||
modalSave.addEventListener("click", modalSaveImage, true)
|
modalSave.addEventListener("click", modalSaveImage, true);
|
||||||
modalSave.title = "Save Image(s)"
|
modalSave.title = "Save Image(s)";
|
||||||
modalControls.appendChild(modalSave)
|
modalControls.appendChild(modalSave);
|
||||||
|
|
||||||
const modalClose = document.createElement('span')
|
const modalClose = document.createElement('span');
|
||||||
modalClose.className = 'modalClose cursor';
|
modalClose.className = 'modalClose cursor';
|
||||||
modalClose.innerHTML = '×'
|
modalClose.innerHTML = '×';
|
||||||
modalClose.onclick = closeModal;
|
modalClose.onclick = closeModal;
|
||||||
modalClose.title = "Close image viewer";
|
modalClose.title = "Close image viewer";
|
||||||
modalControls.appendChild(modalClose)
|
modalControls.appendChild(modalClose);
|
||||||
|
|
||||||
const modalImage = document.createElement('img')
|
const modalImage = document.createElement('img');
|
||||||
modalImage.id = 'modalImage';
|
modalImage.id = 'modalImage';
|
||||||
modalImage.onclick = closeModal;
|
modalImage.onclick = closeModal;
|
||||||
modalImage.tabIndex = 0
|
modalImage.tabIndex = 0;
|
||||||
modalImage.addEventListener('keydown', modalKeyHandler, true)
|
modalImage.addEventListener('keydown', modalKeyHandler, true);
|
||||||
modal.appendChild(modalImage)
|
modal.appendChild(modalImage);
|
||||||
|
|
||||||
const modalPrev = document.createElement('a')
|
const modalPrev = document.createElement('a');
|
||||||
modalPrev.className = 'modalPrev';
|
modalPrev.className = 'modalPrev';
|
||||||
modalPrev.innerHTML = '❮'
|
modalPrev.innerHTML = '❮';
|
||||||
modalPrev.tabIndex = 0
|
modalPrev.tabIndex = 0;
|
||||||
modalPrev.addEventListener('click', modalPrevImage, true);
|
modalPrev.addEventListener('click', modalPrevImage, true);
|
||||||
modalPrev.addEventListener('keydown', modalKeyHandler, true)
|
modalPrev.addEventListener('keydown', modalKeyHandler, true);
|
||||||
modal.appendChild(modalPrev)
|
modal.appendChild(modalPrev);
|
||||||
|
|
||||||
const modalNext = document.createElement('a')
|
const modalNext = document.createElement('a');
|
||||||
modalNext.className = 'modalNext';
|
modalNext.className = 'modalNext';
|
||||||
modalNext.innerHTML = '❯'
|
modalNext.innerHTML = '❯';
|
||||||
modalNext.tabIndex = 0
|
modalNext.tabIndex = 0;
|
||||||
modalNext.addEventListener('click', modalNextImage, true);
|
modalNext.addEventListener('click', modalNextImage, true);
|
||||||
modalNext.addEventListener('keydown', modalKeyHandler, true)
|
modalNext.addEventListener('keydown', modalKeyHandler, true);
|
||||||
|
|
||||||
modal.appendChild(modalNext)
|
modal.appendChild(modalNext);
|
||||||
|
|
||||||
gradioApp().appendChild(modal)
|
|
||||||
|
|
||||||
|
try {
|
||||||
|
gradioApp().appendChild(modal);
|
||||||
|
} catch (e) {
|
||||||
|
gradioApp().body.appendChild(modal);
|
||||||
|
}
|
||||||
|
|
||||||
document.body.appendChild(modal);
|
document.body.appendChild(modal);
|
||||||
|
|
||||||
|
63
javascript/imageviewerGamepad.js
Normal file
63
javascript/imageviewerGamepad.js
Normal file
@ -0,0 +1,63 @@
|
|||||||
|
let gamepads = [];
|
||||||
|
|
||||||
|
window.addEventListener('gamepadconnected', (e) => {
|
||||||
|
const index = e.gamepad.index;
|
||||||
|
let isWaiting = false;
|
||||||
|
gamepads[index] = setInterval(async() => {
|
||||||
|
if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
|
||||||
|
const gamepad = navigator.getGamepads()[index];
|
||||||
|
const xValue = gamepad.axes[0];
|
||||||
|
if (xValue <= -0.3) {
|
||||||
|
modalPrevImage(e);
|
||||||
|
isWaiting = true;
|
||||||
|
} else if (xValue >= 0.3) {
|
||||||
|
modalNextImage(e);
|
||||||
|
isWaiting = true;
|
||||||
|
}
|
||||||
|
if (isWaiting) {
|
||||||
|
await sleepUntil(() => {
|
||||||
|
const xValue = navigator.getGamepads()[index].axes[0];
|
||||||
|
if (xValue < 0.3 && xValue > -0.3) {
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
}, opts.js_modal_lightbox_gamepad_repeat);
|
||||||
|
isWaiting = false;
|
||||||
|
}
|
||||||
|
}, 10);
|
||||||
|
});
|
||||||
|
|
||||||
|
window.addEventListener('gamepaddisconnected', (e) => {
|
||||||
|
clearInterval(gamepads[e.gamepad.index]);
|
||||||
|
});
|
||||||
|
|
||||||
|
/*
|
||||||
|
Primarily for vr controller type pointer devices.
|
||||||
|
I use the wheel event because there's currently no way to do it properly with web xr.
|
||||||
|
*/
|
||||||
|
let isScrolling = false;
|
||||||
|
window.addEventListener('wheel', (e) => {
|
||||||
|
if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
|
||||||
|
isScrolling = true;
|
||||||
|
|
||||||
|
if (e.deltaX <= -0.6) {
|
||||||
|
modalPrevImage(e);
|
||||||
|
} else if (e.deltaX >= 0.6) {
|
||||||
|
modalNextImage(e);
|
||||||
|
}
|
||||||
|
|
||||||
|
setTimeout(() => {
|
||||||
|
isScrolling = false;
|
||||||
|
}, opts.js_modal_lightbox_gamepad_repeat);
|
||||||
|
});
|
||||||
|
|
||||||
|
function sleepUntil(f, timeout) {
|
||||||
|
return new Promise((resolve) => {
|
||||||
|
const timeStart = new Date();
|
||||||
|
const wait = setInterval(function() {
|
||||||
|
if (f() || new Date() - timeStart > timeout) {
|
||||||
|
clearInterval(wait);
|
||||||
|
resolve();
|
||||||
|
}
|
||||||
|
}, 20);
|
||||||
|
});
|
||||||
|
}
|
@ -1,165 +1,176 @@
|
|||||||
|
|
||||||
// localization = {} -- the dict with translations is created by the backend
|
// localization = {} -- the dict with translations is created by the backend
|
||||||
|
|
||||||
ignore_ids_for_localization={
|
var ignore_ids_for_localization = {
|
||||||
setting_sd_hypernetwork: 'OPTION',
|
setting_sd_hypernetwork: 'OPTION',
|
||||||
setting_sd_model_checkpoint: 'OPTION',
|
setting_sd_model_checkpoint: 'OPTION',
|
||||||
setting_realesrgan_enabled_models: 'OPTION',
|
modelmerger_primary_model_name: 'OPTION',
|
||||||
modelmerger_primary_model_name: 'OPTION',
|
modelmerger_secondary_model_name: 'OPTION',
|
||||||
modelmerger_secondary_model_name: 'OPTION',
|
modelmerger_tertiary_model_name: 'OPTION',
|
||||||
modelmerger_tertiary_model_name: 'OPTION',
|
train_embedding: 'OPTION',
|
||||||
train_embedding: 'OPTION',
|
train_hypernetwork: 'OPTION',
|
||||||
train_hypernetwork: 'OPTION',
|
txt2img_styles: 'OPTION',
|
||||||
txt2img_styles: 'OPTION',
|
img2img_styles: 'OPTION',
|
||||||
img2img_styles: 'OPTION',
|
setting_random_artist_categories: 'SPAN',
|
||||||
setting_random_artist_categories: 'SPAN',
|
setting_face_restoration_model: 'SPAN',
|
||||||
setting_face_restoration_model: 'SPAN',
|
setting_realesrgan_enabled_models: 'SPAN',
|
||||||
setting_realesrgan_enabled_models: 'SPAN',
|
extras_upscaler_1: 'SPAN',
|
||||||
extras_upscaler_1: 'SPAN',
|
extras_upscaler_2: 'SPAN',
|
||||||
extras_upscaler_2: 'SPAN',
|
};
|
||||||
}
|
|
||||||
|
var re_num = /^[.\d]+$/;
|
||||||
re_num = /^[\.\d]+$/
|
var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
|
||||||
re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
|
|
||||||
|
var original_lines = {};
|
||||||
original_lines = {}
|
var translated_lines = {};
|
||||||
translated_lines = {}
|
|
||||||
|
function hasLocalization() {
|
||||||
function textNodesUnder(el){
|
return window.localization && Object.keys(window.localization).length > 0;
|
||||||
var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
|
}
|
||||||
while(n=walk.nextNode()) a.push(n);
|
|
||||||
return a;
|
function textNodesUnder(el) {
|
||||||
}
|
var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
|
||||||
|
while ((n = walk.nextNode())) a.push(n);
|
||||||
function canBeTranslated(node, text){
|
return a;
|
||||||
if(! text) return false;
|
}
|
||||||
if(! node.parentElement) return false;
|
|
||||||
|
function canBeTranslated(node, text) {
|
||||||
parentType = node.parentElement.nodeName
|
if (!text) return false;
|
||||||
if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
|
if (!node.parentElement) return false;
|
||||||
|
|
||||||
if (parentType=='OPTION' || parentType=='SPAN'){
|
var parentType = node.parentElement.nodeName;
|
||||||
pnode = node
|
if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
|
||||||
for(var level=0; level<4; level++){
|
|
||||||
pnode = pnode.parentElement
|
if (parentType == 'OPTION' || parentType == 'SPAN') {
|
||||||
if(! pnode) break;
|
var pnode = node;
|
||||||
|
for (var level = 0; level < 4; level++) {
|
||||||
if(ignore_ids_for_localization[pnode.id] == parentType) return false;
|
pnode = pnode.parentElement;
|
||||||
}
|
if (!pnode) break;
|
||||||
}
|
|
||||||
|
if (ignore_ids_for_localization[pnode.id] == parentType) return false;
|
||||||
if(re_num.test(text)) return false;
|
}
|
||||||
if(re_emoji.test(text)) return false;
|
}
|
||||||
return true
|
|
||||||
}
|
if (re_num.test(text)) return false;
|
||||||
|
if (re_emoji.test(text)) return false;
|
||||||
function getTranslation(text){
|
return true;
|
||||||
if(! text) return undefined
|
}
|
||||||
|
|
||||||
if(translated_lines[text] === undefined){
|
function getTranslation(text) {
|
||||||
original_lines[text] = 1
|
if (!text) return undefined;
|
||||||
}
|
|
||||||
|
if (translated_lines[text] === undefined) {
|
||||||
tl = localization[text]
|
original_lines[text] = 1;
|
||||||
if(tl !== undefined){
|
}
|
||||||
translated_lines[tl] = 1
|
|
||||||
}
|
var tl = localization[text];
|
||||||
|
if (tl !== undefined) {
|
||||||
return tl
|
translated_lines[tl] = 1;
|
||||||
}
|
}
|
||||||
|
|
||||||
function processTextNode(node){
|
return tl;
|
||||||
text = node.textContent.trim()
|
}
|
||||||
|
|
||||||
if(! canBeTranslated(node, text)) return
|
function processTextNode(node) {
|
||||||
|
var text = node.textContent.trim();
|
||||||
tl = getTranslation(text)
|
|
||||||
if(tl !== undefined){
|
if (!canBeTranslated(node, text)) return;
|
||||||
node.textContent = tl
|
|
||||||
}
|
var tl = getTranslation(text);
|
||||||
}
|
if (tl !== undefined) {
|
||||||
|
node.textContent = tl;
|
||||||
function processNode(node){
|
}
|
||||||
if(node.nodeType == 3){
|
}
|
||||||
processTextNode(node)
|
|
||||||
return
|
function processNode(node) {
|
||||||
}
|
if (node.nodeType == 3) {
|
||||||
|
processTextNode(node);
|
||||||
if(node.title){
|
return;
|
||||||
tl = getTranslation(node.title)
|
}
|
||||||
if(tl !== undefined){
|
|
||||||
node.title = tl
|
if (node.title) {
|
||||||
}
|
let tl = getTranslation(node.title);
|
||||||
}
|
if (tl !== undefined) {
|
||||||
|
node.title = tl;
|
||||||
if(node.placeholder){
|
}
|
||||||
tl = getTranslation(node.placeholder)
|
}
|
||||||
if(tl !== undefined){
|
|
||||||
node.placeholder = tl
|
if (node.placeholder) {
|
||||||
}
|
let tl = getTranslation(node.placeholder);
|
||||||
}
|
if (tl !== undefined) {
|
||||||
|
node.placeholder = tl;
|
||||||
textNodesUnder(node).forEach(function(node){
|
}
|
||||||
processTextNode(node)
|
}
|
||||||
})
|
|
||||||
}
|
textNodesUnder(node).forEach(function(node) {
|
||||||
|
processTextNode(node);
|
||||||
function dumpTranslations(){
|
});
|
||||||
dumped = {}
|
}
|
||||||
if (localization.rtl) {
|
|
||||||
dumped.rtl = true
|
function dumpTranslations() {
|
||||||
}
|
if (!hasLocalization()) {
|
||||||
|
// If we don't have any localization,
|
||||||
Object.keys(original_lines).forEach(function(text){
|
// we will not have traversed the app to find
|
||||||
if(dumped[text] !== undefined) return
|
// original_lines, so do that now.
|
||||||
|
processNode(gradioApp());
|
||||||
dumped[text] = localization[text] || text
|
}
|
||||||
})
|
var dumped = {};
|
||||||
|
if (localization.rtl) {
|
||||||
return dumped
|
dumped.rtl = true;
|
||||||
}
|
}
|
||||||
|
|
||||||
onUiUpdate(function(m){
|
for (const text in original_lines) {
|
||||||
m.forEach(function(mutation){
|
if (dumped[text] !== undefined) continue;
|
||||||
mutation.addedNodes.forEach(function(node){
|
dumped[text] = localization[text] || text;
|
||||||
processNode(node)
|
}
|
||||||
})
|
|
||||||
});
|
return dumped;
|
||||||
})
|
}
|
||||||
|
|
||||||
|
function download_localization() {
|
||||||
document.addEventListener("DOMContentLoaded", function() {
|
var text = JSON.stringify(dumpTranslations(), null, 4);
|
||||||
processNode(gradioApp())
|
|
||||||
|
var element = document.createElement('a');
|
||||||
if (localization.rtl) { // if the language is from right to left,
|
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
|
||||||
(new MutationObserver((mutations, observer) => { // wait for the style to load
|
element.setAttribute('download', "localization.json");
|
||||||
mutations.forEach(mutation => {
|
element.style.display = 'none';
|
||||||
mutation.addedNodes.forEach(node => {
|
document.body.appendChild(element);
|
||||||
if (node.tagName === 'STYLE') {
|
|
||||||
observer.disconnect();
|
element.click();
|
||||||
|
|
||||||
for (const x of node.sheet.rules) { // find all rtl media rules
|
document.body.removeChild(element);
|
||||||
if (Array.from(x.media || []).includes('rtl')) {
|
}
|
||||||
x.media.appendMedium('all'); // enable them
|
|
||||||
}
|
document.addEventListener("DOMContentLoaded", function() {
|
||||||
}
|
if (!hasLocalization()) {
|
||||||
}
|
return;
|
||||||
})
|
}
|
||||||
});
|
|
||||||
})).observe(gradioApp(), { childList: true });
|
onUiUpdate(function(m) {
|
||||||
}
|
m.forEach(function(mutation) {
|
||||||
})
|
mutation.addedNodes.forEach(function(node) {
|
||||||
|
processNode(node);
|
||||||
function download_localization() {
|
});
|
||||||
text = JSON.stringify(dumpTranslations(), null, 4)
|
});
|
||||||
|
});
|
||||||
var element = document.createElement('a');
|
|
||||||
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
|
processNode(gradioApp());
|
||||||
element.setAttribute('download', "localization.json");
|
|
||||||
element.style.display = 'none';
|
if (localization.rtl) { // if the language is from right to left,
|
||||||
document.body.appendChild(element);
|
(new MutationObserver((mutations, observer) => { // wait for the style to load
|
||||||
|
mutations.forEach(mutation => {
|
||||||
element.click();
|
mutation.addedNodes.forEach(node => {
|
||||||
|
if (node.tagName === 'STYLE') {
|
||||||
document.body.removeChild(element);
|
observer.disconnect();
|
||||||
}
|
|
||||||
|
for (const x of node.sheet.rules) { // find all rtl media rules
|
||||||
|
if (Array.from(x.media || []).includes('rtl')) {
|
||||||
|
x.media.appendMedium('all'); // enable them
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
});
|
||||||
|
});
|
||||||
|
})).observe(gradioApp(), {childList: true});
|
||||||
|
}
|
||||||
|
});
|
||||||
|
@ -2,16 +2,16 @@
|
|||||||
|
|
||||||
let lastHeadImg = null;
|
let lastHeadImg = null;
|
||||||
|
|
||||||
notificationButton = null
|
let notificationButton = null;
|
||||||
|
|
||||||
onUiUpdate(function(){
|
onAfterUiUpdate(function() {
|
||||||
if(notificationButton == null){
|
if (notificationButton == null) {
|
||||||
notificationButton = gradioApp().getElementById('request_notifications')
|
notificationButton = gradioApp().getElementById('request_notifications');
|
||||||
|
|
||||||
if(notificationButton != null){
|
if (notificationButton != null) {
|
||||||
notificationButton.addEventListener('click', function (evt) {
|
notificationButton.addEventListener('click', () => {
|
||||||
Notification.requestPermission();
|
void Notification.requestPermission();
|
||||||
},true);
|
}, true);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -42,7 +42,7 @@ onUiUpdate(function(){
|
|||||||
}
|
}
|
||||||
);
|
);
|
||||||
|
|
||||||
notification.onclick = function(_){
|
notification.onclick = function(_) {
|
||||||
parent.focus();
|
parent.focus();
|
||||||
this.close();
|
this.close();
|
||||||
};
|
};
|
||||||
|
153
javascript/profilerVisualization.js
Normal file
153
javascript/profilerVisualization.js
Normal file
@ -0,0 +1,153 @@
|
|||||||
|
|
||||||
|
function createRow(table, cellName, items) {
|
||||||
|
var tr = document.createElement('tr');
|
||||||
|
var res = [];
|
||||||
|
|
||||||
|
items.forEach(function(x, i) {
|
||||||
|
if (x === undefined) {
|
||||||
|
res.push(null);
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
var td = document.createElement(cellName);
|
||||||
|
td.textContent = x;
|
||||||
|
tr.appendChild(td);
|
||||||
|
res.push(td);
|
||||||
|
|
||||||
|
var colspan = 1;
|
||||||
|
for (var n = i + 1; n < items.length; n++) {
|
||||||
|
if (items[n] !== undefined) {
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
|
||||||
|
colspan += 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (colspan > 1) {
|
||||||
|
td.colSpan = colspan;
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
table.appendChild(tr);
|
||||||
|
|
||||||
|
return res;
|
||||||
|
}
|
||||||
|
|
||||||
|
function showProfile(path, cutoff = 0.05) {
|
||||||
|
requestGet(path, {}, function(data) {
|
||||||
|
var table = document.createElement('table');
|
||||||
|
table.className = 'popup-table';
|
||||||
|
|
||||||
|
data.records['total'] = data.total;
|
||||||
|
var keys = Object.keys(data.records).sort(function(a, b) {
|
||||||
|
return data.records[b] - data.records[a];
|
||||||
|
});
|
||||||
|
var items = keys.map(function(x) {
|
||||||
|
return {key: x, parts: x.split('/'), time: data.records[x]};
|
||||||
|
});
|
||||||
|
var maxLength = items.reduce(function(a, b) {
|
||||||
|
return Math.max(a, b.parts.length);
|
||||||
|
}, 0);
|
||||||
|
|
||||||
|
var cols = createRow(table, 'th', ['record', 'seconds']);
|
||||||
|
cols[0].colSpan = maxLength;
|
||||||
|
|
||||||
|
function arraysEqual(a, b) {
|
||||||
|
return !(a < b || b < a);
|
||||||
|
}
|
||||||
|
|
||||||
|
var addLevel = function(level, parent, hide) {
|
||||||
|
var matching = items.filter(function(x) {
|
||||||
|
return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent);
|
||||||
|
});
|
||||||
|
var sorted = matching.sort(function(a, b) {
|
||||||
|
return b.time - a.time;
|
||||||
|
});
|
||||||
|
var othersTime = 0;
|
||||||
|
var othersList = [];
|
||||||
|
var othersRows = [];
|
||||||
|
var childrenRows = [];
|
||||||
|
sorted.forEach(function(x) {
|
||||||
|
var visible = x.time >= cutoff && !hide;
|
||||||
|
|
||||||
|
var cells = [];
|
||||||
|
for (var i = 0; i < maxLength; i++) {
|
||||||
|
cells.push(x.parts[i]);
|
||||||
|
}
|
||||||
|
cells.push(x.time.toFixed(3));
|
||||||
|
var cols = createRow(table, 'td', cells);
|
||||||
|
for (i = 0; i < level; i++) {
|
||||||
|
cols[i].className = 'muted';
|
||||||
|
}
|
||||||
|
|
||||||
|
var tr = cols[0].parentNode;
|
||||||
|
if (!visible) {
|
||||||
|
tr.classList.add("hidden");
|
||||||
|
}
|
||||||
|
|
||||||
|
if (x.time >= cutoff) {
|
||||||
|
childrenRows.push(tr);
|
||||||
|
} else {
|
||||||
|
othersTime += x.time;
|
||||||
|
othersList.push(x.parts[level]);
|
||||||
|
othersRows.push(tr);
|
||||||
|
}
|
||||||
|
|
||||||
|
var children = addLevel(level + 1, parent.concat([x.parts[level]]), true);
|
||||||
|
if (children.length > 0) {
|
||||||
|
var cell = cols[level];
|
||||||
|
var onclick = function() {
|
||||||
|
cell.classList.remove("link");
|
||||||
|
cell.removeEventListener("click", onclick);
|
||||||
|
children.forEach(function(x) {
|
||||||
|
x.classList.remove("hidden");
|
||||||
|
});
|
||||||
|
};
|
||||||
|
cell.classList.add("link");
|
||||||
|
cell.addEventListener("click", onclick);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
|
if (othersTime > 0) {
|
||||||
|
var cells = [];
|
||||||
|
for (var i = 0; i < maxLength; i++) {
|
||||||
|
cells.push(parent[i]);
|
||||||
|
}
|
||||||
|
cells.push(othersTime.toFixed(3));
|
||||||
|
cells[level] = 'others';
|
||||||
|
var cols = createRow(table, 'td', cells);
|
||||||
|
for (i = 0; i < level; i++) {
|
||||||
|
cols[i].className = 'muted';
|
||||||
|
}
|
||||||
|
|
||||||
|
var cell = cols[level];
|
||||||
|
var tr = cell.parentNode;
|
||||||
|
var onclick = function() {
|
||||||
|
tr.classList.add("hidden");
|
||||||
|
cell.classList.remove("link");
|
||||||
|
cell.removeEventListener("click", onclick);
|
||||||
|
othersRows.forEach(function(x) {
|
||||||
|
x.classList.remove("hidden");
|
||||||
|
});
|
||||||
|
};
|
||||||
|
|
||||||
|
cell.title = othersList.join(", ");
|
||||||
|
cell.classList.add("link");
|
||||||
|
cell.addEventListener("click", onclick);
|
||||||
|
|
||||||
|
if (hide) {
|
||||||
|
tr.classList.add("hidden");
|
||||||
|
}
|
||||||
|
|
||||||
|
childrenRows.push(tr);
|
||||||
|
}
|
||||||
|
|
||||||
|
return childrenRows;
|
||||||
|
};
|
||||||
|
|
||||||
|
addLevel(0, []);
|
||||||
|
|
||||||
|
popup(table);
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
@ -1,30 +1,29 @@
|
|||||||
// code related to showing and updating progressbar shown as the image is being made
|
// code related to showing and updating progressbar shown as the image is being made
|
||||||
|
|
||||||
function rememberGallerySelection(id_gallery){
|
function rememberGallerySelection() {
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
function getGallerySelectedIndex(id_gallery){
|
function getGallerySelectedIndex() {
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
function request(url, data, handler, errorHandler){
|
function request(url, data, handler, errorHandler) {
|
||||||
var xhr = new XMLHttpRequest();
|
var xhr = new XMLHttpRequest();
|
||||||
var url = url;
|
|
||||||
xhr.open("POST", url, true);
|
xhr.open("POST", url, true);
|
||||||
xhr.setRequestHeader("Content-Type", "application/json");
|
xhr.setRequestHeader("Content-Type", "application/json");
|
||||||
xhr.onreadystatechange = function () {
|
xhr.onreadystatechange = function() {
|
||||||
if (xhr.readyState === 4) {
|
if (xhr.readyState === 4) {
|
||||||
if (xhr.status === 200) {
|
if (xhr.status === 200) {
|
||||||
try {
|
try {
|
||||||
var js = JSON.parse(xhr.responseText);
|
var js = JSON.parse(xhr.responseText);
|
||||||
handler(js)
|
handler(js);
|
||||||
} catch (error) {
|
} catch (error) {
|
||||||
console.error(error);
|
console.error(error);
|
||||||
errorHandler()
|
errorHandler();
|
||||||
}
|
}
|
||||||
} else{
|
} else {
|
||||||
errorHandler()
|
errorHandler();
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
@ -32,147 +31,147 @@ function request(url, data, handler, errorHandler){
|
|||||||
xhr.send(js);
|
xhr.send(js);
|
||||||
}
|
}
|
||||||
|
|
||||||
function pad2(x){
|
function pad2(x) {
|
||||||
return x<10 ? '0'+x : x
|
return x < 10 ? '0' + x : x;
|
||||||
}
|
}
|
||||||
|
|
||||||
function formatTime(secs){
|
function formatTime(secs) {
|
||||||
if(secs > 3600){
|
if (secs > 3600) {
|
||||||
return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60)
|
return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60);
|
||||||
} else if(secs > 60){
|
} else if (secs > 60) {
|
||||||
return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60)
|
return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
|
||||||
} else{
|
} else {
|
||||||
return Math.floor(secs) + "s"
|
return Math.floor(secs) + "s";
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
function setTitle(progress){
|
function setTitle(progress) {
|
||||||
var title = 'Stable Diffusion'
|
var title = 'Stable Diffusion';
|
||||||
|
|
||||||
if(opts.show_progress_in_title && progress){
|
if (opts.show_progress_in_title && progress) {
|
||||||
title = '[' + progress.trim() + '] ' + title;
|
title = '[' + progress.trim() + '] ' + title;
|
||||||
}
|
}
|
||||||
|
|
||||||
if(document.title != title){
|
if (document.title != title) {
|
||||||
document.title = title;
|
document.title = title;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
function randomId(){
|
function randomId() {
|
||||||
return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")"
|
return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")";
|
||||||
}
|
}
|
||||||
|
|
||||||
// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
|
// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
|
||||||
// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
|
// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
|
||||||
// calls onProgress every time there is a progress update
|
// calls onProgress every time there is a progress update
|
||||||
function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){
|
function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) {
|
||||||
var dateStart = new Date()
|
var dateStart = new Date();
|
||||||
var wasEverActive = false
|
var wasEverActive = false;
|
||||||
var parentProgressbar = progressbarContainer.parentNode
|
var parentProgressbar = progressbarContainer.parentNode;
|
||||||
var parentGallery = gallery ? gallery.parentNode : null
|
var parentGallery = gallery ? gallery.parentNode : null;
|
||||||
|
|
||||||
var divProgress = document.createElement('div')
|
var divProgress = document.createElement('div');
|
||||||
divProgress.className='progressDiv'
|
divProgress.className = 'progressDiv';
|
||||||
divProgress.style.display = opts.show_progressbar ? "block" : "none"
|
divProgress.style.display = opts.show_progressbar ? "block" : "none";
|
||||||
var divInner = document.createElement('div')
|
var divInner = document.createElement('div');
|
||||||
divInner.className='progress'
|
divInner.className = 'progress';
|
||||||
|
|
||||||
divProgress.appendChild(divInner)
|
divProgress.appendChild(divInner);
|
||||||
parentProgressbar.insertBefore(divProgress, progressbarContainer)
|
parentProgressbar.insertBefore(divProgress, progressbarContainer);
|
||||||
|
|
||||||
if(parentGallery){
|
if (parentGallery) {
|
||||||
var livePreview = document.createElement('div')
|
var livePreview = document.createElement('div');
|
||||||
livePreview.className='livePreview'
|
livePreview.className = 'livePreview';
|
||||||
parentGallery.insertBefore(livePreview, gallery)
|
parentGallery.insertBefore(livePreview, gallery);
|
||||||
}
|
}
|
||||||
|
|
||||||
var removeProgressBar = function(){
|
var removeProgressBar = function() {
|
||||||
setTitle("")
|
setTitle("");
|
||||||
parentProgressbar.removeChild(divProgress)
|
parentProgressbar.removeChild(divProgress);
|
||||||
if(parentGallery) parentGallery.removeChild(livePreview)
|
if (parentGallery) parentGallery.removeChild(livePreview);
|
||||||
atEnd()
|
atEnd();
|
||||||
}
|
};
|
||||||
|
|
||||||
var fun = function(id_task, id_live_preview){
|
var fun = function(id_task, id_live_preview) {
|
||||||
request("./internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){
|
request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
|
||||||
if(res.completed){
|
if (res.completed) {
|
||||||
removeProgressBar()
|
removeProgressBar();
|
||||||
return
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
var rect = progressbarContainer.getBoundingClientRect()
|
var rect = progressbarContainer.getBoundingClientRect();
|
||||||
|
|
||||||
if(rect.width){
|
if (rect.width) {
|
||||||
divProgress.style.width = rect.width + "px";
|
divProgress.style.width = rect.width + "px";
|
||||||
}
|
}
|
||||||
|
|
||||||
progressText = ""
|
let progressText = "";
|
||||||
|
|
||||||
divInner.style.width = ((res.progress || 0) * 100.0) + '%'
|
divInner.style.width = ((res.progress || 0) * 100.0) + '%';
|
||||||
divInner.style.background = res.progress ? "" : "transparent"
|
divInner.style.background = res.progress ? "" : "transparent";
|
||||||
|
|
||||||
if(res.progress > 0){
|
if (res.progress > 0) {
|
||||||
progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'
|
progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
|
||||||
}
|
}
|
||||||
|
|
||||||
if(res.eta){
|
if (res.eta) {
|
||||||
progressText += " ETA: " + formatTime(res.eta)
|
progressText += " ETA: " + formatTime(res.eta);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
setTitle(progressText)
|
setTitle(progressText);
|
||||||
|
|
||||||
if(res.textinfo && res.textinfo.indexOf("\n") == -1){
|
if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
|
||||||
progressText = res.textinfo + " " + progressText
|
progressText = res.textinfo + " " + progressText;
|
||||||
}
|
}
|
||||||
|
|
||||||
divInner.textContent = progressText
|
divInner.textContent = progressText;
|
||||||
|
|
||||||
var elapsedFromStart = (new Date() - dateStart) / 1000
|
var elapsedFromStart = (new Date() - dateStart) / 1000;
|
||||||
|
|
||||||
if(res.active) wasEverActive = true;
|
if (res.active) wasEverActive = true;
|
||||||
|
|
||||||
if(! res.active && wasEverActive){
|
if (!res.active && wasEverActive) {
|
||||||
removeProgressBar()
|
removeProgressBar();
|
||||||
return
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
if(elapsedFromStart > 5 && !res.queued && !res.active){
|
if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
|
||||||
removeProgressBar()
|
removeProgressBar();
|
||||||
return
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
if(res.live_preview && gallery){
|
if (res.live_preview && gallery) {
|
||||||
var rect = gallery.getBoundingClientRect()
|
rect = gallery.getBoundingClientRect();
|
||||||
if(rect.width){
|
if (rect.width) {
|
||||||
livePreview.style.width = rect.width + "px"
|
livePreview.style.width = rect.width + "px";
|
||||||
livePreview.style.height = rect.height + "px"
|
livePreview.style.height = rect.height + "px";
|
||||||
}
|
}
|
||||||
|
|
||||||
var img = new Image();
|
var img = new Image();
|
||||||
img.onload = function() {
|
img.onload = function() {
|
||||||
livePreview.appendChild(img)
|
livePreview.appendChild(img);
|
||||||
if(livePreview.childElementCount > 2){
|
if (livePreview.childElementCount > 2) {
|
||||||
livePreview.removeChild(livePreview.firstElementChild)
|
livePreview.removeChild(livePreview.firstElementChild);
|
||||||
}
|
}
|
||||||
}
|
};
|
||||||
img.src = res.live_preview;
|
img.src = res.live_preview;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
if(onProgress){
|
if (onProgress) {
|
||||||
onProgress(res)
|
onProgress(res);
|
||||||
}
|
}
|
||||||
|
|
||||||
setTimeout(() => {
|
setTimeout(() => {
|
||||||
fun(id_task, res.id_live_preview);
|
fun(id_task, res.id_live_preview);
|
||||||
}, opts.live_preview_refresh_period || 500)
|
}, opts.live_preview_refresh_period || 500);
|
||||||
}, function(){
|
}, function() {
|
||||||
removeProgressBar()
|
removeProgressBar();
|
||||||
})
|
});
|
||||||
}
|
};
|
||||||
|
|
||||||
fun(id_task, 0)
|
fun(id_task, 0);
|
||||||
}
|
}
|
||||||
|
@ -1,17 +1,17 @@
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
function start_training_textual_inversion(){
|
function start_training_textual_inversion() {
|
||||||
gradioApp().querySelector('#ti_error').innerHTML=''
|
gradioApp().querySelector('#ti_error').innerHTML = '';
|
||||||
|
|
||||||
var id = randomId()
|
var id = randomId();
|
||||||
requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function(){}, function(progress){
|
requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
|
||||||
gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo
|
gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
|
||||||
})
|
});
|
||||||
|
|
||||||
var res = args_to_array(arguments)
|
var res = Array.from(arguments);
|
||||||
|
|
||||||
res[0] = id
|
res[0] = id;
|
||||||
|
|
||||||
return res
|
return res;
|
||||||
}
|
}
|
||||||
|
83
javascript/token-counters.js
Normal file
83
javascript/token-counters.js
Normal file
@ -0,0 +1,83 @@
|
|||||||
|
let promptTokenCountDebounceTime = 800;
|
||||||
|
let promptTokenCountTimeouts = {};
|
||||||
|
var promptTokenCountUpdateFunctions = {};
|
||||||
|
|
||||||
|
function update_txt2img_tokens(...args) {
|
||||||
|
// Called from Gradio
|
||||||
|
update_token_counter("txt2img_token_button");
|
||||||
|
if (args.length == 2) {
|
||||||
|
return args[0];
|
||||||
|
}
|
||||||
|
return args;
|
||||||
|
}
|
||||||
|
|
||||||
|
function update_img2img_tokens(...args) {
|
||||||
|
// Called from Gradio
|
||||||
|
update_token_counter("img2img_token_button");
|
||||||
|
if (args.length == 2) {
|
||||||
|
return args[0];
|
||||||
|
}
|
||||||
|
return args;
|
||||||
|
}
|
||||||
|
|
||||||
|
function update_token_counter(button_id) {
|
||||||
|
if (opts.disable_token_counters) {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
if (promptTokenCountTimeouts[button_id]) {
|
||||||
|
clearTimeout(promptTokenCountTimeouts[button_id]);
|
||||||
|
}
|
||||||
|
promptTokenCountTimeouts[button_id] = setTimeout(
|
||||||
|
() => gradioApp().getElementById(button_id)?.click(),
|
||||||
|
promptTokenCountDebounceTime,
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
function recalculatePromptTokens(name) {
|
||||||
|
promptTokenCountUpdateFunctions[name]?.();
|
||||||
|
}
|
||||||
|
|
||||||
|
function recalculate_prompts_txt2img() {
|
||||||
|
// Called from Gradio
|
||||||
|
recalculatePromptTokens('txt2img_prompt');
|
||||||
|
recalculatePromptTokens('txt2img_neg_prompt');
|
||||||
|
return Array.from(arguments);
|
||||||
|
}
|
||||||
|
|
||||||
|
function recalculate_prompts_img2img() {
|
||||||
|
// Called from Gradio
|
||||||
|
recalculatePromptTokens('img2img_prompt');
|
||||||
|
recalculatePromptTokens('img2img_neg_prompt');
|
||||||
|
return Array.from(arguments);
|
||||||
|
}
|
||||||
|
|
||||||
|
function setupTokenCounting(id, id_counter, id_button) {
|
||||||
|
var prompt = gradioApp().getElementById(id);
|
||||||
|
var counter = gradioApp().getElementById(id_counter);
|
||||||
|
var textarea = gradioApp().querySelector(`#${id} > label > textarea`);
|
||||||
|
|
||||||
|
if (opts.disable_token_counters) {
|
||||||
|
counter.style.display = "none";
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (counter.parentElement == prompt.parentElement) {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
prompt.parentElement.insertBefore(counter, prompt);
|
||||||
|
prompt.parentElement.style.position = "relative";
|
||||||
|
|
||||||
|
promptTokenCountUpdateFunctions[id] = function() {
|
||||||
|
update_token_counter(id_button);
|
||||||
|
};
|
||||||
|
textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
|
||||||
|
}
|
||||||
|
|
||||||
|
function setupTokenCounters() {
|
||||||
|
setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
|
||||||
|
setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
|
||||||
|
setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
|
||||||
|
setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
|
||||||
|
}
|
444
javascript/ui.js
444
javascript/ui.js
@ -1,9 +1,9 @@
|
|||||||
// various functions for interaction with ui.py not large enough to warrant putting them in separate files
|
// various functions for interaction with ui.py not large enough to warrant putting them in separate files
|
||||||
|
|
||||||
function set_theme(theme){
|
function set_theme(theme) {
|
||||||
gradioURL = window.location.href
|
var gradioURL = window.location.href;
|
||||||
if (!gradioURL.includes('?__theme=')) {
|
if (!gradioURL.includes('?__theme=')) {
|
||||||
window.location.replace(gradioURL + '?__theme=' + theme);
|
window.location.replace(gradioURL + '?__theme=' + theme);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -14,7 +14,7 @@ function all_gallery_buttons() {
|
|||||||
if (elem.parentElement.offsetParent) {
|
if (elem.parentElement.offsetParent) {
|
||||||
visibleGalleryButtons.push(elem);
|
visibleGalleryButtons.push(elem);
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
return visibleGalleryButtons;
|
return visibleGalleryButtons;
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -25,31 +25,35 @@ function selected_gallery_button() {
|
|||||||
if (elem.parentElement.offsetParent) {
|
if (elem.parentElement.offsetParent) {
|
||||||
visibleCurrentButton = elem;
|
visibleCurrentButton = elem;
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
return visibleCurrentButton;
|
return visibleCurrentButton;
|
||||||
}
|
}
|
||||||
|
|
||||||
function selected_gallery_index(){
|
function selected_gallery_index() {
|
||||||
var buttons = all_gallery_buttons();
|
var buttons = all_gallery_buttons();
|
||||||
var button = selected_gallery_button();
|
var button = selected_gallery_button();
|
||||||
|
|
||||||
var result = -1
|
var result = -1;
|
||||||
buttons.forEach(function(v, i){ if(v==button) { result = i } })
|
buttons.forEach(function(v, i) {
|
||||||
|
if (v == button) {
|
||||||
|
result = i;
|
||||||
|
}
|
||||||
|
});
|
||||||
|
|
||||||
return result
|
return result;
|
||||||
}
|
}
|
||||||
|
|
||||||
function extract_image_from_gallery(gallery){
|
function extract_image_from_gallery(gallery) {
|
||||||
if (gallery.length == 0){
|
if (gallery.length == 0) {
|
||||||
return [null];
|
return [null];
|
||||||
}
|
}
|
||||||
if (gallery.length == 1){
|
if (gallery.length == 1) {
|
||||||
return [gallery[0]];
|
return [gallery[0]];
|
||||||
}
|
}
|
||||||
|
|
||||||
index = selected_gallery_index()
|
var index = selected_gallery_index();
|
||||||
|
|
||||||
if (index < 0 || index >= gallery.length){
|
if (index < 0 || index >= gallery.length) {
|
||||||
// Use the first image in the gallery as the default
|
// Use the first image in the gallery as the default
|
||||||
index = 0;
|
index = 0;
|
||||||
}
|
}
|
||||||
@ -57,199 +61,205 @@ function extract_image_from_gallery(gallery){
|
|||||||
return [gallery[index]];
|
return [gallery[index]];
|
||||||
}
|
}
|
||||||
|
|
||||||
function args_to_array(args){
|
window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around
|
||||||
res = []
|
|
||||||
for(var i=0;i<args.length;i++){
|
|
||||||
res.push(args[i])
|
|
||||||
}
|
|
||||||
return res
|
|
||||||
}
|
|
||||||
|
|
||||||
function switch_to_txt2img(){
|
function switch_to_txt2img() {
|
||||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
|
gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
|
||||||
|
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function switch_to_img2img_tab(no){
|
function switch_to_img2img_tab(no) {
|
||||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
|
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
|
||||||
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click();
|
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click();
|
||||||
}
|
}
|
||||||
function switch_to_img2img(){
|
function switch_to_img2img() {
|
||||||
switch_to_img2img_tab(0);
|
switch_to_img2img_tab(0);
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function switch_to_sketch(){
|
function switch_to_sketch() {
|
||||||
switch_to_img2img_tab(1);
|
switch_to_img2img_tab(1);
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function switch_to_inpaint(){
|
function switch_to_inpaint() {
|
||||||
switch_to_img2img_tab(2);
|
switch_to_img2img_tab(2);
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function switch_to_inpaint_sketch(){
|
function switch_to_inpaint_sketch() {
|
||||||
switch_to_img2img_tab(3);
|
switch_to_img2img_tab(3);
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function switch_to_inpaint(){
|
function switch_to_extras() {
|
||||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
|
|
||||||
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[2].click();
|
|
||||||
|
|
||||||
return args_to_array(arguments);
|
|
||||||
}
|
|
||||||
|
|
||||||
function switch_to_extras(){
|
|
||||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
|
gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
|
||||||
|
|
||||||
return args_to_array(arguments);
|
return Array.from(arguments);
|
||||||
}
|
}
|
||||||
|
|
||||||
function get_tab_index(tabId){
|
function get_tab_index(tabId) {
|
||||||
var res = 0
|
let buttons = gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button');
|
||||||
|
for (let i = 0; i < buttons.length; i++) {
|
||||||
gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
|
if (buttons[i].classList.contains('selected')) {
|
||||||
if(button.className.indexOf('selected') != -1)
|
return i;
|
||||||
res = i
|
}
|
||||||
})
|
|
||||||
|
|
||||||
return res
|
|
||||||
}
|
|
||||||
|
|
||||||
function create_tab_index_args(tabId, args){
|
|
||||||
var res = []
|
|
||||||
for(var i=0; i<args.length; i++){
|
|
||||||
res.push(args[i])
|
|
||||||
}
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
res[0] = get_tab_index(tabId)
|
function create_tab_index_args(tabId, args) {
|
||||||
|
var res = Array.from(args);
|
||||||
return res
|
res[0] = get_tab_index(tabId);
|
||||||
|
return res;
|
||||||
}
|
}
|
||||||
|
|
||||||
function get_img2img_tab_index() {
|
function get_img2img_tab_index() {
|
||||||
let res = args_to_array(arguments)
|
let res = Array.from(arguments);
|
||||||
res.splice(-2)
|
res.splice(-2);
|
||||||
res[0] = get_tab_index('mode_img2img')
|
res[0] = get_tab_index('mode_img2img');
|
||||||
return res
|
return res;
|
||||||
}
|
}
|
||||||
|
|
||||||
function create_submit_args(args){
|
function create_submit_args(args) {
|
||||||
res = []
|
var res = Array.from(args);
|
||||||
for(var i=0;i<args.length;i++){
|
|
||||||
res.push(args[i])
|
|
||||||
}
|
|
||||||
|
|
||||||
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
|
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
|
||||||
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
|
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
|
||||||
// I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
|
// I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
|
||||||
// If gradio at some point stops sending outputs, this may break something
|
// If gradio at some point stops sending outputs, this may break something
|
||||||
if(Array.isArray(res[res.length - 3])){
|
if (Array.isArray(res[res.length - 3])) {
|
||||||
res[res.length - 3] = null
|
res[res.length - 3] = null;
|
||||||
}
|
}
|
||||||
|
|
||||||
return res
|
return res;
|
||||||
}
|
}
|
||||||
|
|
||||||
function showSubmitButtons(tabname, show){
|
function showSubmitButtons(tabname, show) {
|
||||||
gradioApp().getElementById(tabname+'_interrupt').style.display = show ? "none" : "block"
|
gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block";
|
||||||
gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block"
|
gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block";
|
||||||
}
|
}
|
||||||
|
|
||||||
function submit(){
|
function showRestoreProgressButton(tabname, show) {
|
||||||
rememberGallerySelection('txt2img_gallery')
|
var button = gradioApp().getElementById(tabname + "_restore_progress");
|
||||||
showSubmitButtons('txt2img', false)
|
if (!button) return;
|
||||||
|
|
||||||
var id = randomId()
|
button.style.display = show ? "flex" : "none";
|
||||||
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
|
|
||||||
showSubmitButtons('txt2img', true)
|
|
||||||
|
|
||||||
})
|
|
||||||
|
|
||||||
var res = create_submit_args(arguments)
|
|
||||||
|
|
||||||
res[0] = id
|
|
||||||
|
|
||||||
return res
|
|
||||||
}
|
}
|
||||||
|
|
||||||
function submit_img2img(){
|
function submit() {
|
||||||
rememberGallerySelection('img2img_gallery')
|
showSubmitButtons('txt2img', false);
|
||||||
showSubmitButtons('img2img', false)
|
|
||||||
|
|
||||||
var id = randomId()
|
var id = randomId();
|
||||||
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
|
localStorage.setItem("txt2img_task_id", id);
|
||||||
showSubmitButtons('img2img', true)
|
|
||||||
})
|
|
||||||
|
|
||||||
var res = create_submit_args(arguments)
|
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
|
||||||
|
showSubmitButtons('txt2img', true);
|
||||||
|
localStorage.removeItem("txt2img_task_id");
|
||||||
|
showRestoreProgressButton('txt2img', false);
|
||||||
|
});
|
||||||
|
|
||||||
res[0] = id
|
var res = create_submit_args(arguments);
|
||||||
res[1] = get_tab_index('mode_img2img')
|
|
||||||
|
|
||||||
return res
|
res[0] = id;
|
||||||
|
|
||||||
|
return res;
|
||||||
}
|
}
|
||||||
|
|
||||||
function modelmerger(){
|
function submit_img2img() {
|
||||||
var id = randomId()
|
showSubmitButtons('img2img', false);
|
||||||
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
|
|
||||||
|
|
||||||
var res = create_submit_args(arguments)
|
var id = randomId();
|
||||||
res[0] = id
|
localStorage.setItem("img2img_task_id", id);
|
||||||
return res
|
|
||||||
|
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
|
||||||
|
showSubmitButtons('img2img', true);
|
||||||
|
localStorage.removeItem("img2img_task_id");
|
||||||
|
showRestoreProgressButton('img2img', false);
|
||||||
|
});
|
||||||
|
|
||||||
|
var res = create_submit_args(arguments);
|
||||||
|
|
||||||
|
res[0] = id;
|
||||||
|
res[1] = get_tab_index('mode_img2img');
|
||||||
|
|
||||||
|
return res;
|
||||||
|
}
|
||||||
|
|
||||||
|
function restoreProgressTxt2img() {
|
||||||
|
showRestoreProgressButton("txt2img", false);
|
||||||
|
var id = localStorage.getItem("txt2img_task_id");
|
||||||
|
|
||||||
|
id = localStorage.getItem("txt2img_task_id");
|
||||||
|
|
||||||
|
if (id) {
|
||||||
|
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
|
||||||
|
showSubmitButtons('txt2img', true);
|
||||||
|
}, null, 0);
|
||||||
|
}
|
||||||
|
|
||||||
|
return id;
|
||||||
|
}
|
||||||
|
|
||||||
|
function restoreProgressImg2img() {
|
||||||
|
showRestoreProgressButton("img2img", false);
|
||||||
|
|
||||||
|
var id = localStorage.getItem("img2img_task_id");
|
||||||
|
|
||||||
|
if (id) {
|
||||||
|
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
|
||||||
|
showSubmitButtons('img2img', true);
|
||||||
|
}, null, 0);
|
||||||
|
}
|
||||||
|
|
||||||
|
return id;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
onUiLoaded(function() {
|
||||||
|
showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"));
|
||||||
|
showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"));
|
||||||
|
});
|
||||||
|
|
||||||
|
|
||||||
|
function modelmerger() {
|
||||||
|
var id = randomId();
|
||||||
|
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function() {});
|
||||||
|
|
||||||
|
var res = create_submit_args(arguments);
|
||||||
|
res[0] = id;
|
||||||
|
return res;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
function ask_for_style_name(_, prompt_text, negative_prompt_text) {
|
function ask_for_style_name(_, prompt_text, negative_prompt_text) {
|
||||||
name_ = prompt('Style name:')
|
var name_ = prompt('Style name:');
|
||||||
return [name_, prompt_text, negative_prompt_text]
|
return [name_, prompt_text, negative_prompt_text];
|
||||||
}
|
}
|
||||||
|
|
||||||
function confirm_clear_prompt(prompt, negative_prompt) {
|
function confirm_clear_prompt(prompt, negative_prompt) {
|
||||||
if(confirm("Delete prompt?")) {
|
if (confirm("Delete prompt?")) {
|
||||||
prompt = ""
|
prompt = "";
|
||||||
negative_prompt = ""
|
negative_prompt = "";
|
||||||
}
|
}
|
||||||
|
|
||||||
return [prompt, negative_prompt]
|
return [prompt, negative_prompt];
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
promptTokecountUpdateFuncs = {}
|
var opts = {};
|
||||||
|
onAfterUiUpdate(function() {
|
||||||
|
if (Object.keys(opts).length != 0) return;
|
||||||
|
|
||||||
function recalculatePromptTokens(name){
|
var json_elem = gradioApp().getElementById('settings_json');
|
||||||
if(promptTokecountUpdateFuncs[name]){
|
if (json_elem == null) return;
|
||||||
promptTokecountUpdateFuncs[name]()
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function recalculate_prompts_txt2img(){
|
var textarea = json_elem.querySelector('textarea');
|
||||||
recalculatePromptTokens('txt2img_prompt')
|
var jsdata = textarea.value;
|
||||||
recalculatePromptTokens('txt2img_neg_prompt')
|
opts = JSON.parse(jsdata);
|
||||||
return args_to_array(arguments);
|
|
||||||
}
|
|
||||||
|
|
||||||
function recalculate_prompts_img2img(){
|
executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/
|
||||||
recalculatePromptTokens('img2img_prompt')
|
|
||||||
recalculatePromptTokens('img2img_neg_prompt')
|
|
||||||
return args_to_array(arguments);
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
opts = {}
|
|
||||||
onUiUpdate(function(){
|
|
||||||
if(Object.keys(opts).length != 0) return;
|
|
||||||
|
|
||||||
json_elem = gradioApp().getElementById('settings_json')
|
|
||||||
if(json_elem == null) return;
|
|
||||||
|
|
||||||
var textarea = json_elem.querySelector('textarea')
|
|
||||||
var jsdata = textarea.value
|
|
||||||
opts = JSON.parse(jsdata)
|
|
||||||
executeCallbacks(optionsChangedCallbacks);
|
|
||||||
|
|
||||||
Object.defineProperty(textarea, 'value', {
|
Object.defineProperty(textarea, 'value', {
|
||||||
set: function(newValue) {
|
set: function(newValue) {
|
||||||
@ -258,7 +268,7 @@ onUiUpdate(function(){
|
|||||||
valueProp.set.call(textarea, newValue);
|
valueProp.set.call(textarea, newValue);
|
||||||
|
|
||||||
if (oldValue != newValue) {
|
if (oldValue != newValue) {
|
||||||
opts = JSON.parse(textarea.value)
|
opts = JSON.parse(textarea.value);
|
||||||
}
|
}
|
||||||
|
|
||||||
executeCallbacks(optionsChangedCallbacks);
|
executeCallbacks(optionsChangedCallbacks);
|
||||||
@ -269,95 +279,109 @@ onUiUpdate(function(){
|
|||||||
}
|
}
|
||||||
});
|
});
|
||||||
|
|
||||||
json_elem.parentElement.style.display="none"
|
json_elem.parentElement.style.display = "none";
|
||||||
|
|
||||||
function registerTextarea(id, id_counter, id_button){
|
setupTokenCounters();
|
||||||
var prompt = gradioApp().getElementById(id)
|
|
||||||
var counter = gradioApp().getElementById(id_counter)
|
|
||||||
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
|
|
||||||
|
|
||||||
if(counter.parentElement == prompt.parentElement){
|
var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
|
||||||
return
|
var settings_tabs = gradioApp().querySelector('#settings div');
|
||||||
}
|
if (show_all_pages && settings_tabs) {
|
||||||
|
settings_tabs.appendChild(show_all_pages);
|
||||||
|
show_all_pages.onclick = function() {
|
||||||
|
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
|
||||||
|
if (elem.id == "settings_tab_licenses") {
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
prompt.parentElement.insertBefore(counter, prompt)
|
|
||||||
prompt.parentElement.style.position = "relative"
|
|
||||||
|
|
||||||
promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
|
|
||||||
textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
|
|
||||||
}
|
|
||||||
|
|
||||||
registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
|
|
||||||
registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button')
|
|
||||||
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
|
|
||||||
registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
|
|
||||||
|
|
||||||
show_all_pages = gradioApp().getElementById('settings_show_all_pages')
|
|
||||||
settings_tabs = gradioApp().querySelector('#settings div')
|
|
||||||
if(show_all_pages && settings_tabs){
|
|
||||||
settings_tabs.appendChild(show_all_pages)
|
|
||||||
show_all_pages.onclick = function(){
|
|
||||||
gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
|
|
||||||
elem.style.display = "block";
|
elem.style.display = "block";
|
||||||
})
|
});
|
||||||
}
|
};
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
|
|
||||||
onOptionsChanged(function(){
|
onOptionsChanged(function() {
|
||||||
elem = gradioApp().getElementById('sd_checkpoint_hash')
|
var elem = gradioApp().getElementById('sd_checkpoint_hash');
|
||||||
sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
|
var sd_checkpoint_hash = opts.sd_checkpoint_hash || "";
|
||||||
shorthash = sd_checkpoint_hash.substr(0,10)
|
var shorthash = sd_checkpoint_hash.substring(0, 10);
|
||||||
|
|
||||||
if(elem && elem.textContent != shorthash){
|
if (elem && elem.textContent != shorthash) {
|
||||||
elem.textContent = shorthash
|
elem.textContent = shorthash;
|
||||||
elem.title = sd_checkpoint_hash
|
elem.title = sd_checkpoint_hash;
|
||||||
elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
|
elem.href = "https://google.com/search?q=" + sd_checkpoint_hash;
|
||||||
}
|
}
|
||||||
})
|
});
|
||||||
|
|
||||||
let txt2img_textarea, img2img_textarea = undefined;
|
let txt2img_textarea, img2img_textarea = undefined;
|
||||||
let wait_time = 800
|
|
||||||
let token_timeouts = {};
|
|
||||||
|
|
||||||
function update_txt2img_tokens(...args) {
|
function restart_reload() {
|
||||||
update_token_counter("txt2img_token_button")
|
document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
|
||||||
if (args.length == 2)
|
|
||||||
return args[0]
|
|
||||||
return args;
|
|
||||||
}
|
|
||||||
|
|
||||||
function update_img2img_tokens(...args) {
|
var requestPing = function() {
|
||||||
update_token_counter("img2img_token_button")
|
requestGet("./internal/ping", {}, function(data) {
|
||||||
if (args.length == 2)
|
location.reload();
|
||||||
return args[0]
|
}, function() {
|
||||||
return args;
|
setTimeout(requestPing, 500);
|
||||||
}
|
});
|
||||||
|
};
|
||||||
|
|
||||||
function update_token_counter(button_id) {
|
setTimeout(requestPing, 2000);
|
||||||
if (token_timeouts[button_id])
|
|
||||||
clearTimeout(token_timeouts[button_id]);
|
|
||||||
token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
|
|
||||||
}
|
|
||||||
|
|
||||||
function restart_reload(){
|
return [];
|
||||||
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
|
|
||||||
setTimeout(function(){location.reload()},2000)
|
|
||||||
|
|
||||||
return []
|
|
||||||
}
|
}
|
||||||
|
|
||||||
// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits
|
// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits
|
||||||
// will only visible on web page and not sent to python.
|
// will only visible on web page and not sent to python.
|
||||||
function updateInput(target){
|
function updateInput(target) {
|
||||||
let e = new Event("input", { bubbles: true })
|
let e = new Event("input", {bubbles: true});
|
||||||
Object.defineProperty(e, "target", {value: target})
|
Object.defineProperty(e, "target", {value: target});
|
||||||
target.dispatchEvent(e);
|
target.dispatchEvent(e);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
var desiredCheckpointName = null;
|
var desiredCheckpointName = null;
|
||||||
function selectCheckpoint(name){
|
function selectCheckpoint(name) {
|
||||||
desiredCheckpointName = name;
|
desiredCheckpointName = name;
|
||||||
gradioApp().getElementById('change_checkpoint').click()
|
gradioApp().getElementById('change_checkpoint').click();
|
||||||
|
}
|
||||||
|
|
||||||
|
function currentImg2imgSourceResolution(w, h, scaleBy) {
|
||||||
|
var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img');
|
||||||
|
return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy];
|
||||||
|
}
|
||||||
|
|
||||||
|
function updateImg2imgResizeToTextAfterChangingImage() {
|
||||||
|
// At the time this is called from gradio, the image has no yet been replaced.
|
||||||
|
// There may be a better solution, but this is simple and straightforward so I'm going with it.
|
||||||
|
|
||||||
|
setTimeout(function() {
|
||||||
|
gradioApp().getElementById('img2img_update_resize_to').click();
|
||||||
|
}, 500);
|
||||||
|
|
||||||
|
return [];
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
function setRandomSeed(elem_id) {
|
||||||
|
var input = gradioApp().querySelector("#" + elem_id + " input");
|
||||||
|
if (!input) return [];
|
||||||
|
|
||||||
|
input.value = "-1";
|
||||||
|
updateInput(input);
|
||||||
|
return [];
|
||||||
|
}
|
||||||
|
|
||||||
|
function switchWidthHeight(tabname) {
|
||||||
|
var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]");
|
||||||
|
var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]");
|
||||||
|
if (!width || !height) return [];
|
||||||
|
|
||||||
|
var tmp = width.value;
|
||||||
|
width.value = height.value;
|
||||||
|
height.value = tmp;
|
||||||
|
|
||||||
|
updateInput(width);
|
||||||
|
updateInput(height);
|
||||||
|
return [];
|
||||||
}
|
}
|
||||||
|
62
javascript/ui_settings_hints.js
Normal file
62
javascript/ui_settings_hints.js
Normal file
@ -0,0 +1,62 @@
|
|||||||
|
// various hints and extra info for the settings tab
|
||||||
|
|
||||||
|
var settingsHintsSetup = false;
|
||||||
|
|
||||||
|
onOptionsChanged(function() {
|
||||||
|
if (settingsHintsSetup) return;
|
||||||
|
settingsHintsSetup = true;
|
||||||
|
|
||||||
|
gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) {
|
||||||
|
var name = div.id.substr(8);
|
||||||
|
var commentBefore = opts._comments_before[name];
|
||||||
|
var commentAfter = opts._comments_after[name];
|
||||||
|
|
||||||
|
if (!commentBefore && !commentAfter) return;
|
||||||
|
|
||||||
|
var span = null;
|
||||||
|
if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span');
|
||||||
|
else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild;
|
||||||
|
else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild;
|
||||||
|
else span = div.querySelector('label span').firstChild;
|
||||||
|
|
||||||
|
if (!span) return;
|
||||||
|
|
||||||
|
if (commentBefore) {
|
||||||
|
var comment = document.createElement('DIV');
|
||||||
|
comment.className = 'settings-comment';
|
||||||
|
comment.innerHTML = commentBefore;
|
||||||
|
span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
|
||||||
|
span.parentElement.insertBefore(comment, span);
|
||||||
|
span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
|
||||||
|
}
|
||||||
|
if (commentAfter) {
|
||||||
|
comment = document.createElement('DIV');
|
||||||
|
comment.className = 'settings-comment';
|
||||||
|
comment.innerHTML = commentAfter;
|
||||||
|
span.parentElement.insertBefore(comment, span.nextSibling);
|
||||||
|
span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
function settingsHintsShowQuicksettings() {
|
||||||
|
requestGet("./internal/quicksettings-hint", {}, function(data) {
|
||||||
|
var table = document.createElement('table');
|
||||||
|
table.className = 'popup-table';
|
||||||
|
|
||||||
|
data.forEach(function(obj) {
|
||||||
|
var tr = document.createElement('tr');
|
||||||
|
var td = document.createElement('td');
|
||||||
|
td.textContent = obj.name;
|
||||||
|
tr.appendChild(td);
|
||||||
|
|
||||||
|
td = document.createElement('td');
|
||||||
|
td.textContent = obj.label;
|
||||||
|
tr.appendChild(td);
|
||||||
|
|
||||||
|
table.appendChild(tr);
|
||||||
|
});
|
||||||
|
|
||||||
|
popup(table);
|
||||||
|
});
|
||||||
|
}
|
371
launch.py
371
launch.py
@ -1,356 +1,39 @@
|
|||||||
# this scripts installs necessary requirements and launches main program in webui.py
|
from modules import launch_utils
|
||||||
import subprocess
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import importlib.util
|
|
||||||
import shlex
|
|
||||||
import platform
|
|
||||||
import json
|
|
||||||
|
|
||||||
from modules import cmd_args
|
|
||||||
from modules.paths_internal import script_path, extensions_dir
|
|
||||||
|
|
||||||
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
|
args = launch_utils.args
|
||||||
sys.argv += shlex.split(commandline_args)
|
python = launch_utils.python
|
||||||
|
git = launch_utils.git
|
||||||
|
index_url = launch_utils.index_url
|
||||||
|
dir_repos = launch_utils.dir_repos
|
||||||
|
|
||||||
args, _ = cmd_args.parser.parse_known_args()
|
commit_hash = launch_utils.commit_hash
|
||||||
|
git_tag = launch_utils.git_tag
|
||||||
|
|
||||||
python = sys.executable
|
run = launch_utils.run
|
||||||
git = os.environ.get('GIT', "git")
|
is_installed = launch_utils.is_installed
|
||||||
index_url = os.environ.get('INDEX_URL', "")
|
repo_dir = launch_utils.repo_dir
|
||||||
stored_commit_hash = None
|
|
||||||
skip_install = False
|
|
||||||
dir_repos = "repositories"
|
|
||||||
|
|
||||||
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
|
run_pip = launch_utils.run_pip
|
||||||
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
|
check_run_python = launch_utils.check_run_python
|
||||||
|
git_clone = launch_utils.git_clone
|
||||||
|
git_pull_recursive = launch_utils.git_pull_recursive
|
||||||
|
list_extensions = launch_utils.list_extensions
|
||||||
|
run_extension_installer = launch_utils.run_extension_installer
|
||||||
|
prepare_environment = launch_utils.prepare_environment
|
||||||
|
configure_for_tests = launch_utils.configure_for_tests
|
||||||
|
start = launch_utils.start
|
||||||
|
|
||||||
|
|
||||||
def check_python_version():
|
def main():
|
||||||
is_windows = platform.system() == "Windows"
|
if not args.skip_prepare_environment:
|
||||||
major = sys.version_info.major
|
prepare_environment()
|
||||||
minor = sys.version_info.minor
|
|
||||||
micro = sys.version_info.micro
|
|
||||||
|
|
||||||
if is_windows:
|
if args.test_server:
|
||||||
supported_minors = [10]
|
configure_for_tests()
|
||||||
else:
|
|
||||||
supported_minors = [7, 8, 9, 10, 11]
|
|
||||||
|
|
||||||
if not (major == 3 and minor in supported_minors):
|
start()
|
||||||
import modules.errors
|
|
||||||
|
|
||||||
modules.errors.print_error_explanation(f"""
|
|
||||||
INCOMPATIBLE PYTHON VERSION
|
|
||||||
|
|
||||||
This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
|
|
||||||
If you encounter an error with "RuntimeError: Couldn't install torch." message,
|
|
||||||
or any other error regarding unsuccessful package (library) installation,
|
|
||||||
please downgrade (or upgrade) to the latest version of 3.10 Python
|
|
||||||
and delete current Python and "venv" folder in WebUI's directory.
|
|
||||||
|
|
||||||
You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
|
|
||||||
|
|
||||||
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
|
|
||||||
|
|
||||||
Use --skip-python-version-check to suppress this warning.
|
|
||||||
""")
|
|
||||||
|
|
||||||
|
|
||||||
def commit_hash():
|
|
||||||
global stored_commit_hash
|
|
||||||
|
|
||||||
if stored_commit_hash is not None:
|
|
||||||
return stored_commit_hash
|
|
||||||
|
|
||||||
try:
|
|
||||||
stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
|
|
||||||
except Exception:
|
|
||||||
stored_commit_hash = "<none>"
|
|
||||||
|
|
||||||
return stored_commit_hash
|
|
||||||
|
|
||||||
|
|
||||||
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
|
|
||||||
if desc is not None:
|
|
||||||
print(desc)
|
|
||||||
|
|
||||||
if live:
|
|
||||||
result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
|
|
||||||
if result.returncode != 0:
|
|
||||||
raise RuntimeError(f"""{errdesc or 'Error running command'}.
|
|
||||||
Command: {command}
|
|
||||||
Error code: {result.returncode}""")
|
|
||||||
|
|
||||||
return ""
|
|
||||||
|
|
||||||
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
|
|
||||||
|
|
||||||
if result.returncode != 0:
|
|
||||||
|
|
||||||
message = f"""{errdesc or 'Error running command'}.
|
|
||||||
Command: {command}
|
|
||||||
Error code: {result.returncode}
|
|
||||||
stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
|
|
||||||
stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
|
|
||||||
"""
|
|
||||||
raise RuntimeError(message)
|
|
||||||
|
|
||||||
return result.stdout.decode(encoding="utf8", errors="ignore")
|
|
||||||
|
|
||||||
|
|
||||||
def check_run(command):
|
|
||||||
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
|
|
||||||
return result.returncode == 0
|
|
||||||
|
|
||||||
|
|
||||||
def is_installed(package):
|
|
||||||
try:
|
|
||||||
spec = importlib.util.find_spec(package)
|
|
||||||
except ModuleNotFoundError:
|
|
||||||
return False
|
|
||||||
|
|
||||||
return spec is not None
|
|
||||||
|
|
||||||
|
|
||||||
def repo_dir(name):
|
|
||||||
return os.path.join(script_path, dir_repos, name)
|
|
||||||
|
|
||||||
|
|
||||||
def run_python(code, desc=None, errdesc=None):
|
|
||||||
return run(f'"{python}" -c "{code}"', desc, errdesc)
|
|
||||||
|
|
||||||
|
|
||||||
def run_pip(args, desc=None):
|
|
||||||
if skip_install:
|
|
||||||
return
|
|
||||||
|
|
||||||
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
|
|
||||||
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
|
|
||||||
|
|
||||||
|
|
||||||
def check_run_python(code):
|
|
||||||
return check_run(f'"{python}" -c "{code}"')
|
|
||||||
|
|
||||||
|
|
||||||
def git_clone(url, dir, name, commithash=None):
|
|
||||||
# TODO clone into temporary dir and move if successful
|
|
||||||
|
|
||||||
if os.path.exists(dir):
|
|
||||||
if commithash is None:
|
|
||||||
return
|
|
||||||
|
|
||||||
current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
|
|
||||||
if current_hash == commithash:
|
|
||||||
return
|
|
||||||
|
|
||||||
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
|
|
||||||
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
|
|
||||||
return
|
|
||||||
|
|
||||||
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
|
|
||||||
|
|
||||||
if commithash is not None:
|
|
||||||
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
|
|
||||||
|
|
||||||
|
|
||||||
def git_pull_recursive(dir):
|
|
||||||
for subdir, _, _ in os.walk(dir):
|
|
||||||
if os.path.exists(os.path.join(subdir, '.git')):
|
|
||||||
try:
|
|
||||||
output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
|
|
||||||
print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
|
|
||||||
except subprocess.CalledProcessError as e:
|
|
||||||
print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
|
|
||||||
|
|
||||||
|
|
||||||
def version_check(commit):
|
|
||||||
try:
|
|
||||||
import requests
|
|
||||||
commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
|
|
||||||
if commit != "<none>" and commits['commit']['sha'] != commit:
|
|
||||||
print("--------------------------------------------------------")
|
|
||||||
print("| You are not up to date with the most recent release. |")
|
|
||||||
print("| Consider running `git pull` to update. |")
|
|
||||||
print("--------------------------------------------------------")
|
|
||||||
elif commits['commit']['sha'] == commit:
|
|
||||||
print("You are up to date with the most recent release.")
|
|
||||||
else:
|
|
||||||
print("Not a git clone, can't perform version check.")
|
|
||||||
except Exception as e:
|
|
||||||
print("version check failed", e)
|
|
||||||
|
|
||||||
|
|
||||||
def run_extension_installer(extension_dir):
|
|
||||||
path_installer = os.path.join(extension_dir, "install.py")
|
|
||||||
if not os.path.isfile(path_installer):
|
|
||||||
return
|
|
||||||
|
|
||||||
try:
|
|
||||||
env = os.environ.copy()
|
|
||||||
env['PYTHONPATH'] = os.path.abspath(".")
|
|
||||||
|
|
||||||
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
|
|
||||||
except Exception as e:
|
|
||||||
print(e, file=sys.stderr)
|
|
||||||
|
|
||||||
|
|
||||||
def list_extensions(settings_file):
|
|
||||||
settings = {}
|
|
||||||
|
|
||||||
try:
|
|
||||||
if os.path.isfile(settings_file):
|
|
||||||
with open(settings_file, "r", encoding="utf8") as file:
|
|
||||||
settings = json.load(file)
|
|
||||||
except Exception as e:
|
|
||||||
print(e, file=sys.stderr)
|
|
||||||
|
|
||||||
disabled_extensions = set(settings.get('disabled_extensions', []))
|
|
||||||
disable_all_extensions = settings.get('disable_all_extensions', 'none')
|
|
||||||
|
|
||||||
if disable_all_extensions != 'none':
|
|
||||||
return []
|
|
||||||
|
|
||||||
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
|
|
||||||
|
|
||||||
|
|
||||||
def run_extensions_installers(settings_file):
|
|
||||||
if not os.path.isdir(extensions_dir):
|
|
||||||
return
|
|
||||||
|
|
||||||
for dirname_extension in list_extensions(settings_file):
|
|
||||||
run_extension_installer(os.path.join(extensions_dir, dirname_extension))
|
|
||||||
|
|
||||||
|
|
||||||
def prepare_environment():
|
|
||||||
global skip_install
|
|
||||||
|
|
||||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
|
|
||||||
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
|
|
||||||
|
|
||||||
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
|
|
||||||
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
|
|
||||||
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
|
|
||||||
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
|
|
||||||
|
|
||||||
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
|
|
||||||
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
|
|
||||||
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
|
|
||||||
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
|
|
||||||
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
|
|
||||||
|
|
||||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
|
|
||||||
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
|
|
||||||
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
|
|
||||||
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
|
|
||||||
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
|
||||||
|
|
||||||
if not args.skip_python_version_check:
|
|
||||||
check_python_version()
|
|
||||||
|
|
||||||
commit = commit_hash()
|
|
||||||
|
|
||||||
print(f"Python {sys.version}")
|
|
||||||
print(f"Commit hash: {commit}")
|
|
||||||
|
|
||||||
if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
|
|
||||||
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
|
|
||||||
|
|
||||||
if not args.skip_torch_cuda_test:
|
|
||||||
run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
|
|
||||||
|
|
||||||
if not is_installed("gfpgan"):
|
|
||||||
run_pip(f"install {gfpgan_package}", "gfpgan")
|
|
||||||
|
|
||||||
if not is_installed("clip"):
|
|
||||||
run_pip(f"install {clip_package}", "clip")
|
|
||||||
|
|
||||||
if not is_installed("open_clip"):
|
|
||||||
run_pip(f"install {openclip_package}", "open_clip")
|
|
||||||
|
|
||||||
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
|
|
||||||
if platform.system() == "Windows":
|
|
||||||
if platform.python_version().startswith("3.10"):
|
|
||||||
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
|
|
||||||
else:
|
|
||||||
print("Installation of xformers is not supported in this version of Python.")
|
|
||||||
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
|
|
||||||
if not is_installed("xformers"):
|
|
||||||
exit(0)
|
|
||||||
elif platform.system() == "Linux":
|
|
||||||
run_pip(f"install {xformers_package}", "xformers")
|
|
||||||
|
|
||||||
if not is_installed("pyngrok") and args.ngrok:
|
|
||||||
run_pip("install pyngrok", "ngrok")
|
|
||||||
|
|
||||||
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
|
|
||||||
|
|
||||||
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
|
|
||||||
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
|
|
||||||
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
|
|
||||||
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
|
|
||||||
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
|
|
||||||
|
|
||||||
if not is_installed("lpips"):
|
|
||||||
run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
|
|
||||||
|
|
||||||
if not os.path.isfile(requirements_file):
|
|
||||||
requirements_file = os.path.join(script_path, requirements_file)
|
|
||||||
run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
|
|
||||||
|
|
||||||
run_extensions_installers(settings_file=args.ui_settings_file)
|
|
||||||
|
|
||||||
if args.update_check:
|
|
||||||
version_check(commit)
|
|
||||||
|
|
||||||
if args.update_all_extensions:
|
|
||||||
git_pull_recursive(extensions_dir)
|
|
||||||
|
|
||||||
if "--exit" in sys.argv:
|
|
||||||
print("Exiting because of --exit argument")
|
|
||||||
exit(0)
|
|
||||||
|
|
||||||
if args.tests and not args.no_tests:
|
|
||||||
exitcode = tests(args.tests)
|
|
||||||
exit(exitcode)
|
|
||||||
|
|
||||||
|
|
||||||
def tests(test_dir):
|
|
||||||
if "--api" not in sys.argv:
|
|
||||||
sys.argv.append("--api")
|
|
||||||
if "--ckpt" not in sys.argv:
|
|
||||||
sys.argv.append("--ckpt")
|
|
||||||
sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
|
|
||||||
if "--skip-torch-cuda-test" not in sys.argv:
|
|
||||||
sys.argv.append("--skip-torch-cuda-test")
|
|
||||||
if "--disable-nan-check" not in sys.argv:
|
|
||||||
sys.argv.append("--disable-nan-check")
|
|
||||||
if "--no-tests" not in sys.argv:
|
|
||||||
sys.argv.append("--no-tests")
|
|
||||||
|
|
||||||
print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
|
|
||||||
|
|
||||||
os.environ['COMMANDLINE_ARGS'] = ""
|
|
||||||
with open(os.path.join(script_path, 'test/stdout.txt'), "w", encoding="utf8") as stdout, open(os.path.join(script_path, 'test/stderr.txt'), "w", encoding="utf8") as stderr:
|
|
||||||
proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
|
|
||||||
|
|
||||||
import test.server_poll
|
|
||||||
exitcode = test.server_poll.run_tests(proc, test_dir)
|
|
||||||
|
|
||||||
print(f"Stopping Web UI process with id {proc.pid}")
|
|
||||||
proc.kill()
|
|
||||||
return exitcode
|
|
||||||
|
|
||||||
|
|
||||||
def start():
|
|
||||||
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
|
|
||||||
import webui
|
|
||||||
if '--nowebui' in sys.argv:
|
|
||||||
webui.api_only()
|
|
||||||
else:
|
|
||||||
webui.webui()
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
prepare_environment()
|
main()
|
||||||
start()
|
|
||||||
|
BIN
modules/Roboto-Regular.ttf
Normal file
BIN
modules/Roboto-Regular.ttf
Normal file
Binary file not shown.
@ -1,12 +1,12 @@
|
|||||||
import base64
|
import base64
|
||||||
import io
|
import io
|
||||||
|
import os
|
||||||
import time
|
import time
|
||||||
import datetime
|
import datetime
|
||||||
import uvicorn
|
import uvicorn
|
||||||
import gradio as gr
|
import gradio as gr
|
||||||
from threading import Lock
|
from threading import Lock
|
||||||
from io import BytesIO
|
from io import BytesIO
|
||||||
from gradio.processing_utils import decode_base64_to_file
|
|
||||||
from fastapi import APIRouter, Depends, FastAPI, Request, Response
|
from fastapi import APIRouter, Depends, FastAPI, Request, Response
|
||||||
from fastapi.security import HTTPBasic, HTTPBasicCredentials
|
from fastapi.security import HTTPBasic, HTTPBasicCredentials
|
||||||
from fastapi.exceptions import HTTPException
|
from fastapi.exceptions import HTTPException
|
||||||
@ -15,32 +15,31 @@ from fastapi.encoders import jsonable_encoder
|
|||||||
from secrets import compare_digest
|
from secrets import compare_digest
|
||||||
|
|
||||||
import modules.shared as shared
|
import modules.shared as shared
|
||||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
|
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
|
||||||
from modules.api.models import *
|
from modules.api import models
|
||||||
|
from modules.shared import opts
|
||||||
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
||||||
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
|
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
|
||||||
from modules.textual_inversion.preprocess import preprocess
|
from modules.textual_inversion.preprocess import preprocess
|
||||||
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
|
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
|
||||||
from PIL import PngImagePlugin,Image
|
from PIL import PngImagePlugin,Image
|
||||||
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
|
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases
|
||||||
|
from modules.sd_vae import vae_dict
|
||||||
from modules.sd_models_config import find_checkpoint_config_near_filename
|
from modules.sd_models_config import find_checkpoint_config_near_filename
|
||||||
from modules.realesrgan_model import get_realesrgan_models
|
from modules.realesrgan_model import get_realesrgan_models
|
||||||
from modules import devices
|
from modules import devices
|
||||||
from typing import List
|
from typing import Dict, List, Any
|
||||||
import piexif
|
import piexif
|
||||||
import piexif.helper
|
import piexif.helper
|
||||||
|
from contextlib import closing
|
||||||
|
|
||||||
def upscaler_to_index(name: str):
|
|
||||||
try:
|
|
||||||
return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
|
|
||||||
except:
|
|
||||||
raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
|
|
||||||
|
|
||||||
def script_name_to_index(name, scripts):
|
def script_name_to_index(name, scripts):
|
||||||
try:
|
try:
|
||||||
return [script.title().lower() for script in scripts].index(name.lower())
|
return [script.title().lower() for script in scripts].index(name.lower())
|
||||||
except:
|
except Exception as e:
|
||||||
raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
|
raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
|
||||||
|
|
||||||
|
|
||||||
def validate_sampler_name(name):
|
def validate_sampler_name(name):
|
||||||
config = sd_samplers.all_samplers_map.get(name, None)
|
config = sd_samplers.all_samplers_map.get(name, None)
|
||||||
@ -49,20 +48,23 @@ def validate_sampler_name(name):
|
|||||||
|
|
||||||
return name
|
return name
|
||||||
|
|
||||||
|
|
||||||
def setUpscalers(req: dict):
|
def setUpscalers(req: dict):
|
||||||
reqDict = vars(req)
|
reqDict = vars(req)
|
||||||
reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
|
reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
|
||||||
reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
|
reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
|
||||||
return reqDict
|
return reqDict
|
||||||
|
|
||||||
|
|
||||||
def decode_base64_to_image(encoding):
|
def decode_base64_to_image(encoding):
|
||||||
if encoding.startswith("data:image/"):
|
if encoding.startswith("data:image/"):
|
||||||
encoding = encoding.split(";")[1].split(",")[1]
|
encoding = encoding.split(";")[1].split(",")[1]
|
||||||
try:
|
try:
|
||||||
image = Image.open(BytesIO(base64.b64decode(encoding)))
|
image = Image.open(BytesIO(base64.b64decode(encoding)))
|
||||||
return image
|
return image
|
||||||
except Exception as err:
|
except Exception as e:
|
||||||
raise HTTPException(status_code=500, detail="Invalid encoded image")
|
raise HTTPException(status_code=500, detail="Invalid encoded image") from e
|
||||||
|
|
||||||
|
|
||||||
def encode_pil_to_base64(image):
|
def encode_pil_to_base64(image):
|
||||||
with io.BytesIO() as output_bytes:
|
with io.BytesIO() as output_bytes:
|
||||||
@ -77,6 +79,8 @@ def encode_pil_to_base64(image):
|
|||||||
image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
|
image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
|
||||||
|
|
||||||
elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
|
elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
|
||||||
|
if image.mode == "RGBA":
|
||||||
|
image = image.convert("RGB")
|
||||||
parameters = image.info.get('parameters', None)
|
parameters = image.info.get('parameters', None)
|
||||||
exif_bytes = piexif.dump({
|
exif_bytes = piexif.dump({
|
||||||
"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
|
"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
|
||||||
@ -93,16 +97,18 @@ def encode_pil_to_base64(image):
|
|||||||
|
|
||||||
return base64.b64encode(bytes_data)
|
return base64.b64encode(bytes_data)
|
||||||
|
|
||||||
|
|
||||||
def api_middleware(app: FastAPI):
|
def api_middleware(app: FastAPI):
|
||||||
rich_available = True
|
rich_available = False
|
||||||
try:
|
try:
|
||||||
import anyio # importing just so it can be placed on silent list
|
if os.environ.get('WEBUI_RICH_EXCEPTIONS', None) is not None:
|
||||||
import starlette # importing just so it can be placed on silent list
|
import anyio # importing just so it can be placed on silent list
|
||||||
from rich.console import Console
|
import starlette # importing just so it can be placed on silent list
|
||||||
console = Console()
|
from rich.console import Console
|
||||||
except:
|
console = Console()
|
||||||
import traceback
|
rich_available = True
|
||||||
rich_available = False
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
@app.middleware("http")
|
@app.middleware("http")
|
||||||
async def log_and_time(req: Request, call_next):
|
async def log_and_time(req: Request, call_next):
|
||||||
@ -113,14 +119,14 @@ def api_middleware(app: FastAPI):
|
|||||||
endpoint = req.scope.get('path', 'err')
|
endpoint = req.scope.get('path', 'err')
|
||||||
if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
|
if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
|
||||||
print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
|
print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
|
||||||
t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
|
t=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
|
||||||
code = res.status_code,
|
code=res.status_code,
|
||||||
ver = req.scope.get('http_version', '0.0'),
|
ver=req.scope.get('http_version', '0.0'),
|
||||||
cli = req.scope.get('client', ('0:0.0.0', 0))[0],
|
cli=req.scope.get('client', ('0:0.0.0', 0))[0],
|
||||||
prot = req.scope.get('scheme', 'err'),
|
prot=req.scope.get('scheme', 'err'),
|
||||||
method = req.scope.get('method', 'err'),
|
method=req.scope.get('method', 'err'),
|
||||||
endpoint = endpoint,
|
endpoint=endpoint,
|
||||||
duration = duration,
|
duration=duration,
|
||||||
))
|
))
|
||||||
return res
|
return res
|
||||||
|
|
||||||
@ -131,12 +137,13 @@ def api_middleware(app: FastAPI):
|
|||||||
"body": vars(e).get('body', ''),
|
"body": vars(e).get('body', ''),
|
||||||
"errors": str(e),
|
"errors": str(e),
|
||||||
}
|
}
|
||||||
print(f"API error: {request.method}: {request.url} {err}")
|
if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
|
||||||
if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
|
message = f"API error: {request.method}: {request.url} {err}"
|
||||||
if rich_available:
|
if rich_available:
|
||||||
|
print(message)
|
||||||
console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
|
console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
|
||||||
else:
|
else:
|
||||||
traceback.print_exc()
|
errors.report(message, exc_info=True)
|
||||||
return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
|
return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
|
||||||
|
|
||||||
@app.middleware("http")
|
@app.middleware("http")
|
||||||
@ -158,7 +165,7 @@ def api_middleware(app: FastAPI):
|
|||||||
class Api:
|
class Api:
|
||||||
def __init__(self, app: FastAPI, queue_lock: Lock):
|
def __init__(self, app: FastAPI, queue_lock: Lock):
|
||||||
if shared.cmd_opts.api_auth:
|
if shared.cmd_opts.api_auth:
|
||||||
self.credentials = dict()
|
self.credentials = {}
|
||||||
for auth in shared.cmd_opts.api_auth.split(","):
|
for auth in shared.cmd_opts.api_auth.split(","):
|
||||||
user, password = auth.split(":")
|
user, password = auth.split(":")
|
||||||
self.credentials[user] = password
|
self.credentials[user] = password
|
||||||
@ -167,36 +174,44 @@ class Api:
|
|||||||
self.app = app
|
self.app = app
|
||||||
self.queue_lock = queue_lock
|
self.queue_lock = queue_lock
|
||||||
api_middleware(self.app)
|
api_middleware(self.app)
|
||||||
self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
|
self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
|
||||||
self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
|
self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
|
||||||
self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
|
self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
|
||||||
self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
|
self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
|
||||||
self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
|
self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
|
||||||
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
|
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
|
||||||
self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
|
self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
|
self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
|
self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
|
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
|
||||||
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
|
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
|
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
|
||||||
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
|
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
|
||||||
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
|
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
|
||||||
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
|
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
|
||||||
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
|
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
|
||||||
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
|
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
|
||||||
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
|
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
|
||||||
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
|
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
|
||||||
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
|
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
|
||||||
|
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
|
||||||
|
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
|
||||||
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
|
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
|
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
|
||||||
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
|
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
|
||||||
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
|
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
|
||||||
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
|
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
|
||||||
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
|
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
|
||||||
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
|
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
|
||||||
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
|
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
|
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
|
||||||
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
|
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
|
||||||
|
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
|
||||||
|
|
||||||
|
if shared.cmd_opts.api_server_stop:
|
||||||
|
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
|
||||||
|
self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
|
||||||
|
self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
|
||||||
|
|
||||||
self.default_script_arg_txt2img = []
|
self.default_script_arg_txt2img = []
|
||||||
self.default_script_arg_img2img = []
|
self.default_script_arg_img2img = []
|
||||||
@ -220,17 +235,25 @@ class Api:
|
|||||||
script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
|
script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
|
||||||
script = script_runner.selectable_scripts[script_idx]
|
script = script_runner.selectable_scripts[script_idx]
|
||||||
return script, script_idx
|
return script, script_idx
|
||||||
|
|
||||||
def get_scripts_list(self):
|
|
||||||
t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
|
|
||||||
i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
|
|
||||||
|
|
||||||
return ScriptsList(txt2img = t2ilist, img2img = i2ilist)
|
def get_scripts_list(self):
|
||||||
|
t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
|
||||||
|
i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
|
||||||
|
|
||||||
|
return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
|
||||||
|
|
||||||
|
def get_script_info(self):
|
||||||
|
res = []
|
||||||
|
|
||||||
|
for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]:
|
||||||
|
res += [script.api_info for script in script_list if script.api_info is not None]
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
def get_script(self, script_name, script_runner):
|
def get_script(self, script_name, script_runner):
|
||||||
if script_name is None or script_name == "":
|
if script_name is None or script_name == "":
|
||||||
return None, None
|
return None, None
|
||||||
|
|
||||||
script_idx = script_name_to_index(script_name, script_runner.scripts)
|
script_idx = script_name_to_index(script_name, script_runner.scripts)
|
||||||
return script_runner.scripts[script_idx]
|
return script_runner.scripts[script_idx]
|
||||||
|
|
||||||
@ -262,20 +285,22 @@ class Api:
|
|||||||
script_args[0] = selectable_idx + 1
|
script_args[0] = selectable_idx + 1
|
||||||
|
|
||||||
# Now check for always on scripts
|
# Now check for always on scripts
|
||||||
if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
|
if request.alwayson_scripts:
|
||||||
for alwayson_script_name in request.alwayson_scripts.keys():
|
for alwayson_script_name in request.alwayson_scripts.keys():
|
||||||
alwayson_script = self.get_script(alwayson_script_name, script_runner)
|
alwayson_script = self.get_script(alwayson_script_name, script_runner)
|
||||||
if alwayson_script == None:
|
if alwayson_script is None:
|
||||||
raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
|
raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
|
||||||
# Selectable script in always on script param check
|
# Selectable script in always on script param check
|
||||||
if alwayson_script.alwayson == False:
|
if alwayson_script.alwayson is False:
|
||||||
raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
|
raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
|
||||||
# always on script with no arg should always run so you don't really need to add them to the requests
|
# always on script with no arg should always run so you don't really need to add them to the requests
|
||||||
if "args" in request.alwayson_scripts[alwayson_script_name]:
|
if "args" in request.alwayson_scripts[alwayson_script_name]:
|
||||||
script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
|
# min between arg length in scriptrunner and arg length in the request
|
||||||
|
for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
|
||||||
|
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
|
||||||
return script_args
|
return script_args
|
||||||
|
|
||||||
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
|
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
|
||||||
script_runner = scripts.scripts_txt2img
|
script_runner = scripts.scripts_txt2img
|
||||||
if not script_runner.scripts:
|
if not script_runner.scripts:
|
||||||
script_runner.initialize_scripts(False)
|
script_runner.initialize_scripts(False)
|
||||||
@ -303,25 +328,27 @@ class Api:
|
|||||||
args.pop('save_images', None)
|
args.pop('save_images', None)
|
||||||
|
|
||||||
with self.queue_lock:
|
with self.queue_lock:
|
||||||
p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
|
with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
|
||||||
p.scripts = script_runner
|
p.scripts = script_runner
|
||||||
p.outpath_grids = opts.outdir_txt2img_grids
|
p.outpath_grids = opts.outdir_txt2img_grids
|
||||||
p.outpath_samples = opts.outdir_txt2img_samples
|
p.outpath_samples = opts.outdir_txt2img_samples
|
||||||
|
|
||||||
shared.state.begin()
|
try:
|
||||||
if selectable_scripts != None:
|
shared.state.begin(job="scripts_txt2img")
|
||||||
p.script_args = script_args
|
if selectable_scripts is not None:
|
||||||
processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
|
p.script_args = script_args
|
||||||
else:
|
processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
|
||||||
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
else:
|
||||||
processed = process_images(p)
|
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
||||||
shared.state.end()
|
processed = process_images(p)
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
||||||
|
|
||||||
return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
||||||
|
|
||||||
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
|
def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
|
||||||
init_images = img2imgreq.init_images
|
init_images = img2imgreq.init_images
|
||||||
if init_images is None:
|
if init_images is None:
|
||||||
raise HTTPException(status_code=404, detail="Init image not found")
|
raise HTTPException(status_code=404, detail="Init image not found")
|
||||||
@ -359,20 +386,22 @@ class Api:
|
|||||||
args.pop('save_images', None)
|
args.pop('save_images', None)
|
||||||
|
|
||||||
with self.queue_lock:
|
with self.queue_lock:
|
||||||
p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
|
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
|
||||||
p.init_images = [decode_base64_to_image(x) for x in init_images]
|
p.init_images = [decode_base64_to_image(x) for x in init_images]
|
||||||
p.scripts = script_runner
|
p.scripts = script_runner
|
||||||
p.outpath_grids = opts.outdir_img2img_grids
|
p.outpath_grids = opts.outdir_img2img_grids
|
||||||
p.outpath_samples = opts.outdir_img2img_samples
|
p.outpath_samples = opts.outdir_img2img_samples
|
||||||
|
|
||||||
shared.state.begin()
|
try:
|
||||||
if selectable_scripts != None:
|
shared.state.begin(job="scripts_img2img")
|
||||||
p.script_args = script_args
|
if selectable_scripts is not None:
|
||||||
processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
|
p.script_args = script_args
|
||||||
else:
|
processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
|
||||||
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
else:
|
||||||
processed = process_images(p)
|
p.script_args = tuple(script_args) # Need to pass args as tuple here
|
||||||
shared.state.end()
|
processed = process_images(p)
|
||||||
|
finally:
|
||||||
|
shared.state.end()
|
||||||
|
|
||||||
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
||||||
|
|
||||||
@ -380,9 +409,9 @@ class Api:
|
|||||||
img2imgreq.init_images = None
|
img2imgreq.init_images = None
|
||||||
img2imgreq.mask = None
|
img2imgreq.mask = None
|
||||||
|
|
||||||
return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
||||||
|
|
||||||
def extras_single_image_api(self, req: ExtrasSingleImageRequest):
|
def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
|
||||||
reqDict = setUpscalers(req)
|
reqDict = setUpscalers(req)
|
||||||
|
|
||||||
reqDict['image'] = decode_base64_to_image(reqDict['image'])
|
reqDict['image'] = decode_base64_to_image(reqDict['image'])
|
||||||
@ -390,31 +419,26 @@ class Api:
|
|||||||
with self.queue_lock:
|
with self.queue_lock:
|
||||||
result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||||
|
|
||||||
return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
||||||
|
|
||||||
def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
|
def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
|
||||||
reqDict = setUpscalers(req)
|
reqDict = setUpscalers(req)
|
||||||
|
|
||||||
def prepareFiles(file):
|
image_list = reqDict.pop('imageList', [])
|
||||||
file = decode_base64_to_file(file.data, file_path=file.name)
|
image_folder = [decode_base64_to_image(x.data) for x in image_list]
|
||||||
file.orig_name = file.name
|
|
||||||
return file
|
|
||||||
|
|
||||||
reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
|
|
||||||
reqDict.pop('imageList')
|
|
||||||
|
|
||||||
with self.queue_lock:
|
with self.queue_lock:
|
||||||
result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||||
|
|
||||||
return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||||
|
|
||||||
def pnginfoapi(self, req: PNGInfoRequest):
|
def pnginfoapi(self, req: models.PNGInfoRequest):
|
||||||
if(not req.image.strip()):
|
if(not req.image.strip()):
|
||||||
return PNGInfoResponse(info="")
|
return models.PNGInfoResponse(info="")
|
||||||
|
|
||||||
image = decode_base64_to_image(req.image.strip())
|
image = decode_base64_to_image(req.image.strip())
|
||||||
if image is None:
|
if image is None:
|
||||||
return PNGInfoResponse(info="")
|
return models.PNGInfoResponse(info="")
|
||||||
|
|
||||||
geninfo, items = images.read_info_from_image(image)
|
geninfo, items = images.read_info_from_image(image)
|
||||||
if geninfo is None:
|
if geninfo is None:
|
||||||
@ -422,13 +446,13 @@ class Api:
|
|||||||
|
|
||||||
items = {**{'parameters': geninfo}, **items}
|
items = {**{'parameters': geninfo}, **items}
|
||||||
|
|
||||||
return PNGInfoResponse(info=geninfo, items=items)
|
return models.PNGInfoResponse(info=geninfo, items=items)
|
||||||
|
|
||||||
def progressapi(self, req: ProgressRequest = Depends()):
|
def progressapi(self, req: models.ProgressRequest = Depends()):
|
||||||
# copy from check_progress_call of ui.py
|
# copy from check_progress_call of ui.py
|
||||||
|
|
||||||
if shared.state.job_count == 0:
|
if shared.state.job_count == 0:
|
||||||
return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
|
return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
|
||||||
|
|
||||||
# avoid dividing zero
|
# avoid dividing zero
|
||||||
progress = 0.01
|
progress = 0.01
|
||||||
@ -450,9 +474,9 @@ class Api:
|
|||||||
if shared.state.current_image and not req.skip_current_image:
|
if shared.state.current_image and not req.skip_current_image:
|
||||||
current_image = encode_pil_to_base64(shared.state.current_image)
|
current_image = encode_pil_to_base64(shared.state.current_image)
|
||||||
|
|
||||||
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
||||||
|
|
||||||
def interrogateapi(self, interrogatereq: InterrogateRequest):
|
def interrogateapi(self, interrogatereq: models.InterrogateRequest):
|
||||||
image_b64 = interrogatereq.image
|
image_b64 = interrogatereq.image
|
||||||
if image_b64 is None:
|
if image_b64 is None:
|
||||||
raise HTTPException(status_code=404, detail="Image not found")
|
raise HTTPException(status_code=404, detail="Image not found")
|
||||||
@ -469,7 +493,7 @@ class Api:
|
|||||||
else:
|
else:
|
||||||
raise HTTPException(status_code=404, detail="Model not found")
|
raise HTTPException(status_code=404, detail="Model not found")
|
||||||
|
|
||||||
return InterrogateResponse(caption=processed)
|
return models.InterrogateResponse(caption=processed)
|
||||||
|
|
||||||
def interruptapi(self):
|
def interruptapi(self):
|
||||||
shared.state.interrupt()
|
shared.state.interrupt()
|
||||||
@ -501,6 +525,10 @@ class Api:
|
|||||||
return options
|
return options
|
||||||
|
|
||||||
def set_config(self, req: Dict[str, Any]):
|
def set_config(self, req: Dict[str, Any]):
|
||||||
|
checkpoint_name = req.get("sd_model_checkpoint", None)
|
||||||
|
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
|
||||||
|
raise RuntimeError(f"model {checkpoint_name!r} not found")
|
||||||
|
|
||||||
for k, v in req.items():
|
for k, v in req.items():
|
||||||
shared.opts.set(k, v)
|
shared.opts.set(k, v)
|
||||||
|
|
||||||
@ -525,9 +553,20 @@ class Api:
|
|||||||
for upscaler in shared.sd_upscalers
|
for upscaler in shared.sd_upscalers
|
||||||
]
|
]
|
||||||
|
|
||||||
|
def get_latent_upscale_modes(self):
|
||||||
|
return [
|
||||||
|
{
|
||||||
|
"name": upscale_mode,
|
||||||
|
}
|
||||||
|
for upscale_mode in [*(shared.latent_upscale_modes or {})]
|
||||||
|
]
|
||||||
|
|
||||||
def get_sd_models(self):
|
def get_sd_models(self):
|
||||||
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
|
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
|
||||||
|
|
||||||
|
def get_sd_vaes(self):
|
||||||
|
return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
|
||||||
|
|
||||||
def get_hypernetworks(self):
|
def get_hypernetworks(self):
|
||||||
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
|
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
|
||||||
|
|
||||||
@ -566,48 +605,47 @@ class Api:
|
|||||||
}
|
}
|
||||||
|
|
||||||
def refresh_checkpoints(self):
|
def refresh_checkpoints(self):
|
||||||
shared.refresh_checkpoints()
|
with self.queue_lock:
|
||||||
|
shared.refresh_checkpoints()
|
||||||
|
|
||||||
def create_embedding(self, args: dict):
|
def create_embedding(self, args: dict):
|
||||||
try:
|
try:
|
||||||
shared.state.begin()
|
shared.state.begin(job="create_embedding")
|
||||||
filename = create_embedding(**args) # create empty embedding
|
filename = create_embedding(**args) # create empty embedding
|
||||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
||||||
shared.state.end()
|
return models.CreateResponse(info=f"create embedding filename: {filename}")
|
||||||
return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
|
|
||||||
except AssertionError as e:
|
except AssertionError as e:
|
||||||
|
return models.TrainResponse(info=f"create embedding error: {e}")
|
||||||
|
finally:
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return TrainResponse(info = "create embedding error: {error}".format(error = e))
|
|
||||||
|
|
||||||
def create_hypernetwork(self, args: dict):
|
def create_hypernetwork(self, args: dict):
|
||||||
try:
|
try:
|
||||||
shared.state.begin()
|
shared.state.begin(job="create_hypernetwork")
|
||||||
filename = create_hypernetwork(**args) # create empty embedding
|
filename = create_hypernetwork(**args) # create empty embedding
|
||||||
shared.state.end()
|
return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
|
||||||
return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
|
|
||||||
except AssertionError as e:
|
except AssertionError as e:
|
||||||
|
return models.TrainResponse(info=f"create hypernetwork error: {e}")
|
||||||
|
finally:
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
|
|
||||||
|
|
||||||
def preprocess(self, args: dict):
|
def preprocess(self, args: dict):
|
||||||
try:
|
try:
|
||||||
shared.state.begin()
|
shared.state.begin(job="preprocess")
|
||||||
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return PreprocessResponse(info = 'preprocess complete')
|
return models.PreprocessResponse(info='preprocess complete')
|
||||||
except KeyError as e:
|
except KeyError as e:
|
||||||
|
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
|
||||||
|
except Exception as e:
|
||||||
|
return models.PreprocessResponse(info=f"preprocess error: {e}")
|
||||||
|
finally:
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
|
|
||||||
except AssertionError as e:
|
|
||||||
shared.state.end()
|
|
||||||
return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
|
|
||||||
except FileNotFoundError as e:
|
|
||||||
shared.state.end()
|
|
||||||
return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
|
|
||||||
|
|
||||||
def train_embedding(self, args: dict):
|
def train_embedding(self, args: dict):
|
||||||
try:
|
try:
|
||||||
shared.state.begin()
|
shared.state.begin(job="train_embedding")
|
||||||
apply_optimizations = shared.opts.training_xattention_optimizations
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
||||||
error = None
|
error = None
|
||||||
filename = ''
|
filename = ''
|
||||||
@ -620,15 +658,15 @@ class Api:
|
|||||||
finally:
|
finally:
|
||||||
if not apply_optimizations:
|
if not apply_optimizations:
|
||||||
sd_hijack.apply_optimizations()
|
sd_hijack.apply_optimizations()
|
||||||
shared.state.end()
|
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||||
return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
|
except Exception as msg:
|
||||||
except AssertionError as msg:
|
return models.TrainResponse(info=f"train embedding error: {msg}")
|
||||||
|
finally:
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
|
|
||||||
|
|
||||||
def train_hypernetwork(self, args: dict):
|
def train_hypernetwork(self, args: dict):
|
||||||
try:
|
try:
|
||||||
shared.state.begin()
|
shared.state.begin(job="train_hypernetwork")
|
||||||
shared.loaded_hypernetworks = []
|
shared.loaded_hypernetworks = []
|
||||||
apply_optimizations = shared.opts.training_xattention_optimizations
|
apply_optimizations = shared.opts.training_xattention_optimizations
|
||||||
error = None
|
error = None
|
||||||
@ -645,14 +683,16 @@ class Api:
|
|||||||
if not apply_optimizations:
|
if not apply_optimizations:
|
||||||
sd_hijack.apply_optimizations()
|
sd_hijack.apply_optimizations()
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
|
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||||
except AssertionError as msg:
|
except Exception as exc:
|
||||||
|
return models.TrainResponse(info=f"train embedding error: {exc}")
|
||||||
|
finally:
|
||||||
shared.state.end()
|
shared.state.end()
|
||||||
return TrainResponse(info="train embedding error: {error}".format(error=error))
|
|
||||||
|
|
||||||
def get_memory(self):
|
def get_memory(self):
|
||||||
try:
|
try:
|
||||||
import os, psutil
|
import os
|
||||||
|
import psutil
|
||||||
process = psutil.Process(os.getpid())
|
process = psutil.Process(os.getpid())
|
||||||
res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
|
res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
|
||||||
ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
|
ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
|
||||||
@ -679,11 +719,24 @@ class Api:
|
|||||||
'events': warnings,
|
'events': warnings,
|
||||||
}
|
}
|
||||||
else:
|
else:
|
||||||
cuda = { 'error': 'unavailable' }
|
cuda = {'error': 'unavailable'}
|
||||||
except Exception as err:
|
except Exception as err:
|
||||||
cuda = { 'error': f'{err}' }
|
cuda = {'error': f'{err}'}
|
||||||
return MemoryResponse(ram = ram, cuda = cuda)
|
return models.MemoryResponse(ram=ram, cuda=cuda)
|
||||||
|
|
||||||
def launch(self, server_name, port):
|
def launch(self, server_name, port, root_path):
|
||||||
self.app.include_router(self.router)
|
self.app.include_router(self.router)
|
||||||
uvicorn.run(self.app, host=server_name, port=port)
|
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
|
||||||
|
|
||||||
|
def kill_webui(self):
|
||||||
|
restart.stop_program()
|
||||||
|
|
||||||
|
def restart_webui(self):
|
||||||
|
if restart.is_restartable():
|
||||||
|
restart.restart_program()
|
||||||
|
return Response(status_code=501)
|
||||||
|
|
||||||
|
def stop_webui(request):
|
||||||
|
shared.state.server_command = "stop"
|
||||||
|
return Response("Stopping.")
|
||||||
|
|
||||||
|
@ -1,4 +1,5 @@
|
|||||||
import inspect
|
import inspect
|
||||||
|
|
||||||
from pydantic import BaseModel, Field, create_model
|
from pydantic import BaseModel, Field, create_model
|
||||||
from typing import Any, Optional
|
from typing import Any, Optional
|
||||||
from typing_extensions import Literal
|
from typing_extensions import Literal
|
||||||
@ -207,11 +208,10 @@ class PreprocessResponse(BaseModel):
|
|||||||
fields = {}
|
fields = {}
|
||||||
for key, metadata in opts.data_labels.items():
|
for key, metadata in opts.data_labels.items():
|
||||||
value = opts.data.get(key)
|
value = opts.data.get(key)
|
||||||
optType = opts.typemap.get(type(metadata.default), type(value))
|
optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any
|
||||||
|
|
||||||
if (metadata is not None):
|
if metadata is not None:
|
||||||
fields.update({key: (Optional[optType], Field(
|
fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
|
||||||
default=metadata.default ,description=metadata.label))})
|
|
||||||
else:
|
else:
|
||||||
fields.update({key: (Optional[optType], Field())})
|
fields.update({key: (Optional[optType], Field())})
|
||||||
|
|
||||||
@ -223,8 +223,9 @@ for key in _options:
|
|||||||
if(_options[key].dest != 'help'):
|
if(_options[key].dest != 'help'):
|
||||||
flag = _options[key]
|
flag = _options[key]
|
||||||
_type = str
|
_type = str
|
||||||
if _options[key].default is not None: _type = type(_options[key].default)
|
if _options[key].default is not None:
|
||||||
flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
|
_type = type(_options[key].default)
|
||||||
|
flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
|
||||||
|
|
||||||
FlagsModel = create_model("Flags", **flags)
|
FlagsModel = create_model("Flags", **flags)
|
||||||
|
|
||||||
@ -240,6 +241,9 @@ class UpscalerItem(BaseModel):
|
|||||||
model_url: Optional[str] = Field(title="URL")
|
model_url: Optional[str] = Field(title="URL")
|
||||||
scale: Optional[float] = Field(title="Scale")
|
scale: Optional[float] = Field(title="Scale")
|
||||||
|
|
||||||
|
class LatentUpscalerModeItem(BaseModel):
|
||||||
|
name: str = Field(title="Name")
|
||||||
|
|
||||||
class SDModelItem(BaseModel):
|
class SDModelItem(BaseModel):
|
||||||
title: str = Field(title="Title")
|
title: str = Field(title="Title")
|
||||||
model_name: str = Field(title="Model Name")
|
model_name: str = Field(title="Model Name")
|
||||||
@ -248,6 +252,10 @@ class SDModelItem(BaseModel):
|
|||||||
filename: str = Field(title="Filename")
|
filename: str = Field(title="Filename")
|
||||||
config: Optional[str] = Field(title="Config file")
|
config: Optional[str] = Field(title="Config file")
|
||||||
|
|
||||||
|
class SDVaeItem(BaseModel):
|
||||||
|
model_name: str = Field(title="Model Name")
|
||||||
|
filename: str = Field(title="Filename")
|
||||||
|
|
||||||
class HypernetworkItem(BaseModel):
|
class HypernetworkItem(BaseModel):
|
||||||
name: str = Field(title="Name")
|
name: str = Field(title="Name")
|
||||||
path: Optional[str] = Field(title="Path")
|
path: Optional[str] = Field(title="Path")
|
||||||
@ -266,10 +274,6 @@ class PromptStyleItem(BaseModel):
|
|||||||
prompt: Optional[str] = Field(title="Prompt")
|
prompt: Optional[str] = Field(title="Prompt")
|
||||||
negative_prompt: Optional[str] = Field(title="Negative Prompt")
|
negative_prompt: Optional[str] = Field(title="Negative Prompt")
|
||||||
|
|
||||||
class ArtistItem(BaseModel):
|
|
||||||
name: str = Field(title="Name")
|
|
||||||
score: float = Field(title="Score")
|
|
||||||
category: str = Field(title="Category")
|
|
||||||
|
|
||||||
class EmbeddingItem(BaseModel):
|
class EmbeddingItem(BaseModel):
|
||||||
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
|
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
|
||||||
@ -286,6 +290,23 @@ class MemoryResponse(BaseModel):
|
|||||||
ram: dict = Field(title="RAM", description="System memory stats")
|
ram: dict = Field(title="RAM", description="System memory stats")
|
||||||
cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
|
cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
|
||||||
|
|
||||||
|
|
||||||
class ScriptsList(BaseModel):
|
class ScriptsList(BaseModel):
|
||||||
txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
|
txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
|
||||||
img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
|
img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
|
||||||
|
|
||||||
|
|
||||||
|
class ScriptArg(BaseModel):
|
||||||
|
label: str = Field(default=None, title="Label", description="Name of the argument in UI")
|
||||||
|
value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
|
||||||
|
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
|
||||||
|
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
|
||||||
|
step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
|
||||||
|
choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
|
||||||
|
|
||||||
|
|
||||||
|
class ScriptInfo(BaseModel):
|
||||||
|
name: str = Field(default=None, title="Name", description="Script name")
|
||||||
|
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
|
||||||
|
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
|
||||||
|
args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
|
||||||
|
120
modules/cache.py
Normal file
120
modules/cache.py
Normal file
@ -0,0 +1,120 @@
|
|||||||
|
import json
|
||||||
|
import os.path
|
||||||
|
import threading
|
||||||
|
import time
|
||||||
|
|
||||||
|
from modules.paths import data_path, script_path
|
||||||
|
|
||||||
|
cache_filename = os.path.join(data_path, "cache.json")
|
||||||
|
cache_data = None
|
||||||
|
cache_lock = threading.Lock()
|
||||||
|
|
||||||
|
dump_cache_after = None
|
||||||
|
dump_cache_thread = None
|
||||||
|
|
||||||
|
|
||||||
|
def dump_cache():
|
||||||
|
"""
|
||||||
|
Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written.
|
||||||
|
"""
|
||||||
|
|
||||||
|
global dump_cache_after
|
||||||
|
global dump_cache_thread
|
||||||
|
|
||||||
|
def thread_func():
|
||||||
|
global dump_cache_after
|
||||||
|
global dump_cache_thread
|
||||||
|
|
||||||
|
while dump_cache_after is not None and time.time() < dump_cache_after:
|
||||||
|
time.sleep(1)
|
||||||
|
|
||||||
|
with cache_lock:
|
||||||
|
with open(cache_filename, "w", encoding="utf8") as file:
|
||||||
|
json.dump(cache_data, file, indent=4)
|
||||||
|
|
||||||
|
dump_cache_after = None
|
||||||
|
dump_cache_thread = None
|
||||||
|
|
||||||
|
with cache_lock:
|
||||||
|
dump_cache_after = time.time() + 5
|
||||||
|
if dump_cache_thread is None:
|
||||||
|
dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func)
|
||||||
|
dump_cache_thread.start()
|
||||||
|
|
||||||
|
|
||||||
|
def cache(subsection):
|
||||||
|
"""
|
||||||
|
Retrieves or initializes a cache for a specific subsection.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
subsection (str): The subsection identifier for the cache.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The cache data for the specified subsection.
|
||||||
|
"""
|
||||||
|
|
||||||
|
global cache_data
|
||||||
|
|
||||||
|
if cache_data is None:
|
||||||
|
with cache_lock:
|
||||||
|
if cache_data is None:
|
||||||
|
if not os.path.isfile(cache_filename):
|
||||||
|
cache_data = {}
|
||||||
|
else:
|
||||||
|
try:
|
||||||
|
with open(cache_filename, "r", encoding="utf8") as file:
|
||||||
|
cache_data = json.load(file)
|
||||||
|
except Exception:
|
||||||
|
os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
|
||||||
|
print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
|
||||||
|
cache_data = {}
|
||||||
|
|
||||||
|
s = cache_data.get(subsection, {})
|
||||||
|
cache_data[subsection] = s
|
||||||
|
|
||||||
|
return s
|
||||||
|
|
||||||
|
|
||||||
|
def cached_data_for_file(subsection, title, filename, func):
|
||||||
|
"""
|
||||||
|
Retrieves or generates data for a specific file, using a caching mechanism.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
subsection (str): The subsection of the cache to use.
|
||||||
|
title (str): The title of the data entry in the subsection of the cache.
|
||||||
|
filename (str): The path to the file to be checked for modifications.
|
||||||
|
func (callable): A function that generates the data if it is not available in the cache.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict or None: The cached or generated data, or None if data generation fails.
|
||||||
|
|
||||||
|
The `cached_data_for_file` function implements a caching mechanism for data stored in files.
|
||||||
|
It checks if the data associated with the given `title` is present in the cache and compares the
|
||||||
|
modification time of the file with the cached modification time. If the file has been modified,
|
||||||
|
the cache is considered invalid and the data is regenerated using the provided `func`.
|
||||||
|
Otherwise, the cached data is returned.
|
||||||
|
|
||||||
|
If the data generation fails, None is returned to indicate the failure. Otherwise, the generated
|
||||||
|
or cached data is returned as a dictionary.
|
||||||
|
"""
|
||||||
|
|
||||||
|
existing_cache = cache(subsection)
|
||||||
|
ondisk_mtime = os.path.getmtime(filename)
|
||||||
|
|
||||||
|
entry = existing_cache.get(title)
|
||||||
|
if entry:
|
||||||
|
cached_mtime = entry.get("mtime", 0)
|
||||||
|
if ondisk_mtime > cached_mtime:
|
||||||
|
entry = None
|
||||||
|
|
||||||
|
if not entry or 'value' not in entry:
|
||||||
|
value = func()
|
||||||
|
if value is None:
|
||||||
|
return None
|
||||||
|
|
||||||
|
entry = {'mtime': ondisk_mtime, 'value': value}
|
||||||
|
existing_cache[title] = entry
|
||||||
|
|
||||||
|
dump_cache()
|
||||||
|
|
||||||
|
return entry['value']
|
@ -1,10 +1,9 @@
|
|||||||
|
from functools import wraps
|
||||||
import html
|
import html
|
||||||
import sys
|
|
||||||
import threading
|
import threading
|
||||||
import traceback
|
|
||||||
import time
|
import time
|
||||||
|
|
||||||
from modules import shared, progress
|
from modules import shared, progress, errors
|
||||||
|
|
||||||
queue_lock = threading.Lock()
|
queue_lock = threading.Lock()
|
||||||
|
|
||||||
@ -20,21 +19,23 @@ def wrap_queued_call(func):
|
|||||||
|
|
||||||
|
|
||||||
def wrap_gradio_gpu_call(func, extra_outputs=None):
|
def wrap_gradio_gpu_call(func, extra_outputs=None):
|
||||||
|
@wraps(func)
|
||||||
def f(*args, **kwargs):
|
def f(*args, **kwargs):
|
||||||
|
|
||||||
# if the first argument is a string that says "task(...)", it is treated as a job id
|
# if the first argument is a string that says "task(...)", it is treated as a job id
|
||||||
if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
|
if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
|
||||||
id_task = args[0]
|
id_task = args[0]
|
||||||
progress.add_task_to_queue(id_task)
|
progress.add_task_to_queue(id_task)
|
||||||
else:
|
else:
|
||||||
id_task = None
|
id_task = None
|
||||||
|
|
||||||
with queue_lock:
|
with queue_lock:
|
||||||
shared.state.begin()
|
shared.state.begin(job=id_task)
|
||||||
progress.start_task(id_task)
|
progress.start_task(id_task)
|
||||||
|
|
||||||
try:
|
try:
|
||||||
res = func(*args, **kwargs)
|
res = func(*args, **kwargs)
|
||||||
|
progress.record_results(id_task, res)
|
||||||
finally:
|
finally:
|
||||||
progress.finish_task(id_task)
|
progress.finish_task(id_task)
|
||||||
|
|
||||||
@ -46,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
|
|||||||
|
|
||||||
|
|
||||||
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
||||||
|
@wraps(func)
|
||||||
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
|
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
|
||||||
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
|
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
|
||||||
if run_memmon:
|
if run_memmon:
|
||||||
@ -55,16 +57,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
|||||||
try:
|
try:
|
||||||
res = list(func(*args, **kwargs))
|
res = list(func(*args, **kwargs))
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
# When printing out our debug argument list, do not print out more than a MB of text
|
# When printing out our debug argument list,
|
||||||
max_debug_str_len = 131072 # (1024*1024)/8
|
# do not print out more than a 100 KB of text
|
||||||
|
max_debug_str_len = 131072
|
||||||
print("Error completing request", file=sys.stderr)
|
message = "Error completing request"
|
||||||
argStr = f"Arguments: {str(args)} {str(kwargs)}"
|
arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
|
||||||
print(argStr[:max_debug_str_len], file=sys.stderr)
|
if len(arg_str) > max_debug_str_len:
|
||||||
if len(argStr) > max_debug_str_len:
|
arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
|
||||||
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
|
errors.report(f"{message}\n{arg_str}", exc_info=True)
|
||||||
|
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
|
||||||
shared.state.job = ""
|
shared.state.job = ""
|
||||||
shared.state.job_count = 0
|
shared.state.job_count = 0
|
||||||
@ -72,7 +72,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
|||||||
if extra_outputs_array is None:
|
if extra_outputs_array is None:
|
||||||
extra_outputs_array = [None, '']
|
extra_outputs_array = [None, '']
|
||||||
|
|
||||||
res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
|
error_message = f'{type(e).__name__}: {e}'
|
||||||
|
res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
|
||||||
|
|
||||||
shared.state.skipped = False
|
shared.state.skipped = False
|
||||||
shared.state.interrupted = False
|
shared.state.interrupted = False
|
||||||
@ -84,9 +85,9 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
|||||||
elapsed = time.perf_counter() - t
|
elapsed = time.perf_counter() - t
|
||||||
elapsed_m = int(elapsed // 60)
|
elapsed_m = int(elapsed // 60)
|
||||||
elapsed_s = elapsed % 60
|
elapsed_s = elapsed % 60
|
||||||
elapsed_text = f"{elapsed_s:.2f}s"
|
elapsed_text = f"{elapsed_s:.1f} sec."
|
||||||
if elapsed_m > 0:
|
if elapsed_m > 0:
|
||||||
elapsed_text = f"{elapsed_m}m "+elapsed_text
|
elapsed_text = f"{elapsed_m} min. "+elapsed_text
|
||||||
|
|
||||||
if run_memmon:
|
if run_memmon:
|
||||||
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
|
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
|
||||||
@ -94,16 +95,23 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
|||||||
reserved_peak = mem_stats['reserved_peak']
|
reserved_peak = mem_stats['reserved_peak']
|
||||||
sys_peak = mem_stats['system_peak']
|
sys_peak = mem_stats['system_peak']
|
||||||
sys_total = mem_stats['total']
|
sys_total = mem_stats['total']
|
||||||
sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
|
sys_pct = sys_peak/max(sys_total, 1) * 100
|
||||||
|
|
||||||
vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
|
toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)"
|
||||||
|
toltip_r = "Reserved: total amout of video memory allocated by the Torch library "
|
||||||
|
toltip_sys = "System: peak amout of video memory allocated by all running programs, out of total capacity"
|
||||||
|
|
||||||
|
text_a = f"<abbr title='{toltip_a}'>A</abbr>: <span class='measurement'>{active_peak/1024:.2f} GB</span>"
|
||||||
|
text_r = f"<abbr title='{toltip_r}'>R</abbr>: <span class='measurement'>{reserved_peak/1024:.2f} GB</span>"
|
||||||
|
text_sys = f"<abbr title='{toltip_sys}'>Sys</abbr>: <span class='measurement'>{sys_peak/1024:.1f}/{sys_total/1024:g} GB</span> ({sys_pct:.1f}%)"
|
||||||
|
|
||||||
|
vram_html = f"<p class='vram'>{text_a}, <wbr>{text_r}, <wbr>{text_sys}</p>"
|
||||||
else:
|
else:
|
||||||
vram_html = ''
|
vram_html = ''
|
||||||
|
|
||||||
# last item is always HTML
|
# last item is always HTML
|
||||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}</div>"
|
||||||
|
|
||||||
return tuple(res)
|
return tuple(res)
|
||||||
|
|
||||||
return f
|
return f
|
||||||
|
|
||||||
|
@ -1,6 +1,7 @@
|
|||||||
import argparse
|
import argparse
|
||||||
|
import json
|
||||||
import os
|
import os
|
||||||
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
|
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
|
||||||
|
|
||||||
parser = argparse.ArgumentParser()
|
parser = argparse.ArgumentParser()
|
||||||
|
|
||||||
@ -10,10 +11,11 @@ parser.add_argument("--skip-python-version-check", action='store_true', help="la
|
|||||||
parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
|
parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
|
||||||
parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
|
parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
|
||||||
parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
|
parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
|
||||||
parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
|
parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
|
||||||
parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
|
parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
|
||||||
parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
|
parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
|
||||||
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
|
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
|
||||||
|
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
|
||||||
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
|
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
|
||||||
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
|
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
|
||||||
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
|
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
|
||||||
@ -39,7 +41,8 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
|
|||||||
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
|
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
|
||||||
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
|
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
|
||||||
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
|
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
|
||||||
parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
|
parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
|
||||||
|
parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
|
||||||
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
|
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
|
||||||
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
|
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
|
||||||
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
|
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
|
||||||
@ -51,16 +54,16 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
|
|||||||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||||
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
|
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
|
||||||
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
||||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
|
||||||
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
|
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
|
||||||
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
|
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
|
||||||
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
|
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
|
||||||
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
|
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
|
||||||
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
|
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization")
|
||||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
|
||||||
parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
|
parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
|
||||||
parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
|
parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
|
||||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
|
||||||
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
|
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
|
||||||
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
|
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
|
||||||
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
||||||
@ -75,6 +78,7 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l
|
|||||||
parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
|
parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
|
||||||
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
|
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
|
||||||
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
|
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
|
||||||
|
parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
|
||||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||||
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
|
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
|
||||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||||
@ -95,9 +99,14 @@ parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(
|
|||||||
parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
|
parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
|
||||||
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
|
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
|
||||||
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
|
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
|
||||||
|
parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
|
||||||
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
|
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
|
||||||
parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
|
parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
|
||||||
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
|
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
|
||||||
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
|
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
|
||||||
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
|
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
|
||||||
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
|
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
|
||||||
|
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
|
||||||
|
parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
|
||||||
|
parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
|
||||||
|
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
|
||||||
|
@ -1,14 +1,12 @@
|
|||||||
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
|
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
|
||||||
|
|
||||||
import math
|
import math
|
||||||
import numpy as np
|
|
||||||
import torch
|
import torch
|
||||||
from torch import nn, Tensor
|
from torch import nn, Tensor
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
from typing import Optional, List
|
from typing import Optional
|
||||||
|
|
||||||
from modules.codeformer.vqgan_arch import *
|
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
|
||||||
from basicsr.utils import get_root_logger
|
|
||||||
from basicsr.utils.registry import ARCH_REGISTRY
|
from basicsr.utils.registry import ARCH_REGISTRY
|
||||||
|
|
||||||
def calc_mean_std(feat, eps=1e-5):
|
def calc_mean_std(feat, eps=1e-5):
|
||||||
@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module):
|
|||||||
tgt_mask: Optional[Tensor] = None,
|
tgt_mask: Optional[Tensor] = None,
|
||||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||||
query_pos: Optional[Tensor] = None):
|
query_pos: Optional[Tensor] = None):
|
||||||
|
|
||||||
# self attention
|
# self attention
|
||||||
tgt2 = self.norm1(tgt)
|
tgt2 = self.norm1(tgt)
|
||||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||||
@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module):
|
|||||||
|
|
||||||
@ARCH_REGISTRY.register()
|
@ARCH_REGISTRY.register()
|
||||||
class CodeFormer(VQAutoEncoder):
|
class CodeFormer(VQAutoEncoder):
|
||||||
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
|
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
|
||||||
codebook_size=1024, latent_size=256,
|
codebook_size=1024, latent_size=256,
|
||||||
connect_list=['32', '64', '128', '256'],
|
connect_list=('32', '64', '128', '256'),
|
||||||
fix_modules=['quantize','generator']):
|
fix_modules=('quantize', 'generator')):
|
||||||
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
|
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
|
||||||
|
|
||||||
if fix_modules is not None:
|
if fix_modules is not None:
|
||||||
@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
|
|||||||
self.feat_emb = nn.Linear(256, self.dim_embd)
|
self.feat_emb = nn.Linear(256, self.dim_embd)
|
||||||
|
|
||||||
# transformer
|
# transformer
|
||||||
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
|
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
|
||||||
for _ in range(self.n_layers)])
|
for _ in range(self.n_layers)])
|
||||||
|
|
||||||
# logits_predict head
|
# logits_predict head
|
||||||
self.idx_pred_layer = nn.Sequential(
|
self.idx_pred_layer = nn.Sequential(
|
||||||
nn.LayerNorm(dim_embd),
|
nn.LayerNorm(dim_embd),
|
||||||
nn.Linear(dim_embd, codebook_size, bias=False))
|
nn.Linear(dim_embd, codebook_size, bias=False))
|
||||||
|
|
||||||
self.channels = {
|
self.channels = {
|
||||||
'16': 512,
|
'16': 512,
|
||||||
'32': 256,
|
'32': 256,
|
||||||
@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
|
|||||||
enc_feat_dict = {}
|
enc_feat_dict = {}
|
||||||
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
||||||
for i, block in enumerate(self.encoder.blocks):
|
for i, block in enumerate(self.encoder.blocks):
|
||||||
x = block(x)
|
x = block(x)
|
||||||
if i in out_list:
|
if i in out_list:
|
||||||
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
||||||
|
|
||||||
@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder):
|
|||||||
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
||||||
|
|
||||||
for i, block in enumerate(self.generator.blocks):
|
for i, block in enumerate(self.generator.blocks):
|
||||||
x = block(x)
|
x = block(x)
|
||||||
if i in fuse_list: # fuse after i-th block
|
if i in fuse_list: # fuse after i-th block
|
||||||
f_size = str(x.shape[-1])
|
f_size = str(x.shape[-1])
|
||||||
if w>0:
|
if w>0:
|
||||||
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
|
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
|
||||||
out = x
|
out = x
|
||||||
# logits doesn't need softmax before cross_entropy loss
|
# logits doesn't need softmax before cross_entropy loss
|
||||||
return out, logits, lq_feat
|
return out, logits, lq_feat
|
||||||
|
@ -2,20 +2,18 @@
|
|||||||
|
|
||||||
'''
|
'''
|
||||||
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
||||||
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
https://ghproxy.com/https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
||||||
|
|
||||||
'''
|
'''
|
||||||
import numpy as np
|
|
||||||
import torch
|
import torch
|
||||||
import torch.nn as nn
|
import torch.nn as nn
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
import copy
|
|
||||||
from basicsr.utils import get_root_logger
|
from basicsr.utils import get_root_logger
|
||||||
from basicsr.utils.registry import ARCH_REGISTRY
|
from basicsr.utils.registry import ARCH_REGISTRY
|
||||||
|
|
||||||
def normalize(in_channels):
|
def normalize(in_channels):
|
||||||
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
|
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
|
||||||
|
|
||||||
|
|
||||||
@torch.jit.script
|
@torch.jit.script
|
||||||
def swish(x):
|
def swish(x):
|
||||||
@ -212,15 +210,15 @@ class AttnBlock(nn.Module):
|
|||||||
# compute attention
|
# compute attention
|
||||||
b, c, h, w = q.shape
|
b, c, h, w = q.shape
|
||||||
q = q.reshape(b, c, h*w)
|
q = q.reshape(b, c, h*w)
|
||||||
q = q.permute(0, 2, 1)
|
q = q.permute(0, 2, 1)
|
||||||
k = k.reshape(b, c, h*w)
|
k = k.reshape(b, c, h*w)
|
||||||
w_ = torch.bmm(q, k)
|
w_ = torch.bmm(q, k)
|
||||||
w_ = w_ * (int(c)**(-0.5))
|
w_ = w_ * (int(c)**(-0.5))
|
||||||
w_ = F.softmax(w_, dim=2)
|
w_ = F.softmax(w_, dim=2)
|
||||||
|
|
||||||
# attend to values
|
# attend to values
|
||||||
v = v.reshape(b, c, h*w)
|
v = v.reshape(b, c, h*w)
|
||||||
w_ = w_.permute(0, 2, 1)
|
w_ = w_.permute(0, 2, 1)
|
||||||
h_ = torch.bmm(v, w_)
|
h_ = torch.bmm(v, w_)
|
||||||
h_ = h_.reshape(b, c, h, w)
|
h_ = h_.reshape(b, c, h, w)
|
||||||
|
|
||||||
@ -272,18 +270,18 @@ class Encoder(nn.Module):
|
|||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
for block in self.blocks:
|
for block in self.blocks:
|
||||||
x = block(x)
|
x = block(x)
|
||||||
|
|
||||||
return x
|
return x
|
||||||
|
|
||||||
|
|
||||||
class Generator(nn.Module):
|
class Generator(nn.Module):
|
||||||
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
|
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.nf = nf
|
self.nf = nf
|
||||||
self.ch_mult = ch_mult
|
self.ch_mult = ch_mult
|
||||||
self.num_resolutions = len(self.ch_mult)
|
self.num_resolutions = len(self.ch_mult)
|
||||||
self.num_res_blocks = res_blocks
|
self.num_res_blocks = res_blocks
|
||||||
self.resolution = img_size
|
self.resolution = img_size
|
||||||
self.attn_resolutions = attn_resolutions
|
self.attn_resolutions = attn_resolutions
|
||||||
self.in_channels = emb_dim
|
self.in_channels = emb_dim
|
||||||
self.out_channels = 3
|
self.out_channels = 3
|
||||||
@ -317,29 +315,29 @@ class Generator(nn.Module):
|
|||||||
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
|
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
|
||||||
|
|
||||||
self.blocks = nn.ModuleList(blocks)
|
self.blocks = nn.ModuleList(blocks)
|
||||||
|
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
for block in self.blocks:
|
for block in self.blocks:
|
||||||
x = block(x)
|
x = block(x)
|
||||||
|
|
||||||
return x
|
return x
|
||||||
|
|
||||||
|
|
||||||
@ARCH_REGISTRY.register()
|
@ARCH_REGISTRY.register()
|
||||||
class VQAutoEncoder(nn.Module):
|
class VQAutoEncoder(nn.Module):
|
||||||
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
|
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
|
||||||
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
|
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
logger = get_root_logger()
|
logger = get_root_logger()
|
||||||
self.in_channels = 3
|
self.in_channels = 3
|
||||||
self.nf = nf
|
self.nf = nf
|
||||||
self.n_blocks = res_blocks
|
self.n_blocks = res_blocks
|
||||||
self.codebook_size = codebook_size
|
self.codebook_size = codebook_size
|
||||||
self.embed_dim = emb_dim
|
self.embed_dim = emb_dim
|
||||||
self.ch_mult = ch_mult
|
self.ch_mult = ch_mult
|
||||||
self.resolution = img_size
|
self.resolution = img_size
|
||||||
self.attn_resolutions = attn_resolutions
|
self.attn_resolutions = attn_resolutions or [16]
|
||||||
self.quantizer_type = quantizer
|
self.quantizer_type = quantizer
|
||||||
self.encoder = Encoder(
|
self.encoder = Encoder(
|
||||||
self.in_channels,
|
self.in_channels,
|
||||||
@ -365,11 +363,11 @@ class VQAutoEncoder(nn.Module):
|
|||||||
self.kl_weight
|
self.kl_weight
|
||||||
)
|
)
|
||||||
self.generator = Generator(
|
self.generator = Generator(
|
||||||
self.nf,
|
self.nf,
|
||||||
self.embed_dim,
|
self.embed_dim,
|
||||||
self.ch_mult,
|
self.ch_mult,
|
||||||
self.n_blocks,
|
self.n_blocks,
|
||||||
self.resolution,
|
self.resolution,
|
||||||
self.attn_resolutions
|
self.attn_resolutions
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -434,4 +432,4 @@ class VQGANDiscriminator(nn.Module):
|
|||||||
raise ValueError('Wrong params!')
|
raise ValueError('Wrong params!')
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
return self.main(x)
|
return self.main(x)
|
||||||
|
@ -1,13 +1,11 @@
|
|||||||
import os
|
import os
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
import torch
|
import torch
|
||||||
|
|
||||||
import modules.face_restoration
|
import modules.face_restoration
|
||||||
import modules.shared
|
import modules.shared
|
||||||
from modules import shared, devices, modelloader
|
from modules import shared, devices, modelloader, errors
|
||||||
from modules.paths import models_path
|
from modules.paths import models_path
|
||||||
|
|
||||||
# codeformer people made a choice to include modified basicsr library to their project which makes
|
# codeformer people made a choice to include modified basicsr library to their project which makes
|
||||||
@ -15,16 +13,13 @@ from modules.paths import models_path
|
|||||||
# I am making a choice to include some files from codeformer to work around this issue.
|
# I am making a choice to include some files from codeformer to work around this issue.
|
||||||
model_dir = "Codeformer"
|
model_dir = "Codeformer"
|
||||||
model_path = os.path.join(models_path, model_dir)
|
model_path = os.path.join(models_path, model_dir)
|
||||||
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
|
model_url = 'https://ghproxy.com/https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
|
||||||
|
|
||||||
have_codeformer = False
|
|
||||||
codeformer = None
|
codeformer = None
|
||||||
|
|
||||||
|
|
||||||
def setup_model(dirname):
|
def setup_model(dirname):
|
||||||
global model_path
|
os.makedirs(model_path, exist_ok=True)
|
||||||
if not os.path.exists(model_path):
|
|
||||||
os.makedirs(model_path)
|
|
||||||
|
|
||||||
path = modules.paths.paths.get("CodeFormer", None)
|
path = modules.paths.paths.get("CodeFormer", None)
|
||||||
if path is None:
|
if path is None:
|
||||||
@ -33,11 +28,9 @@ def setup_model(dirname):
|
|||||||
try:
|
try:
|
||||||
from torchvision.transforms.functional import normalize
|
from torchvision.transforms.functional import normalize
|
||||||
from modules.codeformer.codeformer_arch import CodeFormer
|
from modules.codeformer.codeformer_arch import CodeFormer
|
||||||
from basicsr.utils.download_util import load_file_from_url
|
from basicsr.utils import img2tensor, tensor2img
|
||||||
from basicsr.utils import imwrite, img2tensor, tensor2img
|
|
||||||
from facelib.utils.face_restoration_helper import FaceRestoreHelper
|
from facelib.utils.face_restoration_helper import FaceRestoreHelper
|
||||||
from facelib.detection.retinaface import retinaface
|
from facelib.detection.retinaface import retinaface
|
||||||
from modules.shared import cmd_opts
|
|
||||||
|
|
||||||
net_class = CodeFormer
|
net_class = CodeFormer
|
||||||
|
|
||||||
@ -96,7 +89,7 @@ def setup_model(dirname):
|
|||||||
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
|
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
|
||||||
self.face_helper.align_warp_face()
|
self.face_helper.align_warp_face()
|
||||||
|
|
||||||
for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
|
for cropped_face in self.face_helper.cropped_faces:
|
||||||
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
|
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
|
||||||
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
|
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
|
||||||
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
|
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
|
||||||
@ -106,9 +99,9 @@ def setup_model(dirname):
|
|||||||
output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
|
output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
|
||||||
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
|
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
|
||||||
del output
|
del output
|
||||||
torch.cuda.empty_cache()
|
devices.torch_gc()
|
||||||
except Exception as error:
|
except Exception:
|
||||||
print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
|
errors.report('Failed inference for CodeFormer', exc_info=True)
|
||||||
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
|
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
|
||||||
|
|
||||||
restored_face = restored_face.astype('uint8')
|
restored_face = restored_face.astype('uint8')
|
||||||
@ -129,15 +122,11 @@ def setup_model(dirname):
|
|||||||
|
|
||||||
return restored_img
|
return restored_img
|
||||||
|
|
||||||
global have_codeformer
|
|
||||||
have_codeformer = True
|
|
||||||
|
|
||||||
global codeformer
|
global codeformer
|
||||||
codeformer = FaceRestorerCodeFormer(dirname)
|
codeformer = FaceRestorerCodeFormer(dirname)
|
||||||
shared.face_restorers.append(codeformer)
|
shared.face_restorers.append(codeformer)
|
||||||
|
|
||||||
except Exception:
|
except Exception:
|
||||||
print("Error setting up CodeFormer:", file=sys.stderr)
|
errors.report("Error setting up CodeFormer", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
|
||||||
# sys.path = stored_sys_path
|
# sys.path = stored_sys_path
|
||||||
|
197
modules/config_states.py
Normal file
197
modules/config_states.py
Normal file
@ -0,0 +1,197 @@
|
|||||||
|
"""
|
||||||
|
Supports saving and restoring webui and extensions from a known working set of commits
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import json
|
||||||
|
import time
|
||||||
|
import tqdm
|
||||||
|
|
||||||
|
from datetime import datetime
|
||||||
|
from collections import OrderedDict
|
||||||
|
import git
|
||||||
|
|
||||||
|
from modules import shared, extensions, errors
|
||||||
|
from modules.paths_internal import script_path, config_states_dir
|
||||||
|
|
||||||
|
|
||||||
|
all_config_states = OrderedDict()
|
||||||
|
|
||||||
|
|
||||||
|
def list_config_states():
|
||||||
|
global all_config_states
|
||||||
|
|
||||||
|
all_config_states.clear()
|
||||||
|
os.makedirs(config_states_dir, exist_ok=True)
|
||||||
|
|
||||||
|
config_states = []
|
||||||
|
for filename in os.listdir(config_states_dir):
|
||||||
|
if filename.endswith(".json"):
|
||||||
|
path = os.path.join(config_states_dir, filename)
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
j = json.load(f)
|
||||||
|
j["filepath"] = path
|
||||||
|
config_states.append(j)
|
||||||
|
|
||||||
|
config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
|
||||||
|
|
||||||
|
for cs in config_states:
|
||||||
|
timestamp = time.asctime(time.gmtime(cs["created_at"]))
|
||||||
|
name = cs.get("name", "Config")
|
||||||
|
full_name = f"{name}: {timestamp}"
|
||||||
|
all_config_states[full_name] = cs
|
||||||
|
|
||||||
|
return all_config_states
|
||||||
|
|
||||||
|
|
||||||
|
def get_webui_config():
|
||||||
|
webui_repo = None
|
||||||
|
|
||||||
|
try:
|
||||||
|
if os.path.exists(os.path.join(script_path, ".git")):
|
||||||
|
webui_repo = git.Repo(script_path)
|
||||||
|
except Exception:
|
||||||
|
errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
|
||||||
|
|
||||||
|
webui_remote = None
|
||||||
|
webui_commit_hash = None
|
||||||
|
webui_commit_date = None
|
||||||
|
webui_branch = None
|
||||||
|
if webui_repo and not webui_repo.bare:
|
||||||
|
try:
|
||||||
|
webui_remote = next(webui_repo.remote().urls, None)
|
||||||
|
head = webui_repo.head.commit
|
||||||
|
webui_commit_date = webui_repo.head.commit.committed_date
|
||||||
|
webui_commit_hash = head.hexsha
|
||||||
|
webui_branch = webui_repo.active_branch.name
|
||||||
|
|
||||||
|
except Exception:
|
||||||
|
webui_remote = None
|
||||||
|
|
||||||
|
return {
|
||||||
|
"remote": webui_remote,
|
||||||
|
"commit_hash": webui_commit_hash,
|
||||||
|
"commit_date": webui_commit_date,
|
||||||
|
"branch": webui_branch,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def get_extension_config():
|
||||||
|
ext_config = {}
|
||||||
|
|
||||||
|
for ext in extensions.extensions:
|
||||||
|
ext.read_info_from_repo()
|
||||||
|
|
||||||
|
entry = {
|
||||||
|
"name": ext.name,
|
||||||
|
"path": ext.path,
|
||||||
|
"enabled": ext.enabled,
|
||||||
|
"is_builtin": ext.is_builtin,
|
||||||
|
"remote": ext.remote,
|
||||||
|
"commit_hash": ext.commit_hash,
|
||||||
|
"commit_date": ext.commit_date,
|
||||||
|
"branch": ext.branch,
|
||||||
|
"have_info_from_repo": ext.have_info_from_repo
|
||||||
|
}
|
||||||
|
|
||||||
|
ext_config[ext.name] = entry
|
||||||
|
|
||||||
|
return ext_config
|
||||||
|
|
||||||
|
|
||||||
|
def get_config():
|
||||||
|
creation_time = datetime.now().timestamp()
|
||||||
|
webui_config = get_webui_config()
|
||||||
|
ext_config = get_extension_config()
|
||||||
|
|
||||||
|
return {
|
||||||
|
"created_at": creation_time,
|
||||||
|
"webui": webui_config,
|
||||||
|
"extensions": ext_config
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def restore_webui_config(config):
|
||||||
|
print("* Restoring webui state...")
|
||||||
|
|
||||||
|
if "webui" not in config:
|
||||||
|
print("Error: No webui data saved to config")
|
||||||
|
return
|
||||||
|
|
||||||
|
webui_config = config["webui"]
|
||||||
|
|
||||||
|
if "commit_hash" not in webui_config:
|
||||||
|
print("Error: No commit saved to webui config")
|
||||||
|
return
|
||||||
|
|
||||||
|
webui_commit_hash = webui_config.get("commit_hash", None)
|
||||||
|
webui_repo = None
|
||||||
|
|
||||||
|
try:
|
||||||
|
if os.path.exists(os.path.join(script_path, ".git")):
|
||||||
|
webui_repo = git.Repo(script_path)
|
||||||
|
except Exception:
|
||||||
|
errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
|
||||||
|
return
|
||||||
|
|
||||||
|
try:
|
||||||
|
webui_repo.git.fetch(all=True)
|
||||||
|
webui_repo.git.reset(webui_commit_hash, hard=True)
|
||||||
|
print(f"* Restored webui to commit {webui_commit_hash}.")
|
||||||
|
except Exception:
|
||||||
|
errors.report(f"Error restoring webui to commit{webui_commit_hash}")
|
||||||
|
|
||||||
|
|
||||||
|
def restore_extension_config(config):
|
||||||
|
print("* Restoring extension state...")
|
||||||
|
|
||||||
|
if "extensions" not in config:
|
||||||
|
print("Error: No extension data saved to config")
|
||||||
|
return
|
||||||
|
|
||||||
|
ext_config = config["extensions"]
|
||||||
|
|
||||||
|
results = []
|
||||||
|
disabled = []
|
||||||
|
|
||||||
|
for ext in tqdm.tqdm(extensions.extensions):
|
||||||
|
if ext.is_builtin:
|
||||||
|
continue
|
||||||
|
|
||||||
|
ext.read_info_from_repo()
|
||||||
|
current_commit = ext.commit_hash
|
||||||
|
|
||||||
|
if ext.name not in ext_config:
|
||||||
|
ext.disabled = True
|
||||||
|
disabled.append(ext.name)
|
||||||
|
results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled"))
|
||||||
|
continue
|
||||||
|
|
||||||
|
entry = ext_config[ext.name]
|
||||||
|
|
||||||
|
if "commit_hash" in entry and entry["commit_hash"]:
|
||||||
|
try:
|
||||||
|
ext.fetch_and_reset_hard(entry["commit_hash"])
|
||||||
|
ext.read_info_from_repo()
|
||||||
|
if current_commit != entry["commit_hash"]:
|
||||||
|
results.append((ext, current_commit[:8], True, entry["commit_hash"][:8]))
|
||||||
|
except Exception as ex:
|
||||||
|
results.append((ext, current_commit[:8], False, ex))
|
||||||
|
else:
|
||||||
|
results.append((ext, current_commit[:8], False, "No commit hash found in config"))
|
||||||
|
|
||||||
|
if not entry.get("enabled", False):
|
||||||
|
ext.disabled = True
|
||||||
|
disabled.append(ext.name)
|
||||||
|
else:
|
||||||
|
ext.disabled = False
|
||||||
|
|
||||||
|
shared.opts.disabled_extensions = disabled
|
||||||
|
shared.opts.save(shared.config_filename)
|
||||||
|
|
||||||
|
print("* Finished restoring extensions. Results:")
|
||||||
|
for ext, prev_commit, success, result in results:
|
||||||
|
if success:
|
||||||
|
print(f" + {ext.name}: {prev_commit} -> {result}")
|
||||||
|
else:
|
||||||
|
print(f" ! {ext.name}: FAILURE ({result})")
|
@ -2,7 +2,6 @@ import os
|
|||||||
import re
|
import re
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
from PIL import Image
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
from modules import modelloader, paths, deepbooru_model, devices, images, shared
|
from modules import modelloader, paths, deepbooru_model, devices, images, shared
|
||||||
@ -20,7 +19,7 @@ class DeepDanbooru:
|
|||||||
|
|
||||||
files = modelloader.load_models(
|
files = modelloader.load_models(
|
||||||
model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
|
model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
|
||||||
model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
|
model_url='https://ghproxy.com/https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
|
||||||
ext_filter=[".pt"],
|
ext_filter=[".pt"],
|
||||||
download_name='model-resnet_custom_v3.pt',
|
download_name='model-resnet_custom_v3.pt',
|
||||||
)
|
)
|
||||||
@ -79,7 +78,7 @@ class DeepDanbooru:
|
|||||||
|
|
||||||
res = []
|
res = []
|
||||||
|
|
||||||
filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
|
filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
|
||||||
|
|
||||||
for tag in [x for x in tags if x not in filtertags]:
|
for tag in [x for x in tags if x not in filtertags]:
|
||||||
probability = probability_dict[tag]
|
probability = probability_dict[tag]
|
||||||
|
@ -4,7 +4,7 @@ import torch.nn.functional as F
|
|||||||
|
|
||||||
from modules import devices
|
from modules import devices
|
||||||
|
|
||||||
# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
|
# see https://ghproxy.com/https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
|
||||||
|
|
||||||
|
|
||||||
class DeepDanbooruModel(nn.Module):
|
class DeepDanbooruModel(nn.Module):
|
||||||
|
@ -1,5 +1,7 @@
|
|||||||
import sys
|
import sys
|
||||||
import contextlib
|
import contextlib
|
||||||
|
from functools import lru_cache
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
from modules import errors
|
from modules import errors
|
||||||
|
|
||||||
@ -13,13 +15,6 @@ def has_mps() -> bool:
|
|||||||
else:
|
else:
|
||||||
return mac_specific.has_mps
|
return mac_specific.has_mps
|
||||||
|
|
||||||
def extract_device_id(args, name):
|
|
||||||
for x in range(len(args)):
|
|
||||||
if name in args[x]:
|
|
||||||
return args[x + 1]
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def get_cuda_device_string():
|
def get_cuda_device_string():
|
||||||
from modules import shared
|
from modules import shared
|
||||||
@ -54,18 +49,22 @@ def get_device_for(task):
|
|||||||
|
|
||||||
|
|
||||||
def torch_gc():
|
def torch_gc():
|
||||||
|
|
||||||
if torch.cuda.is_available():
|
if torch.cuda.is_available():
|
||||||
with torch.cuda.device(get_cuda_device_string()):
|
with torch.cuda.device(get_cuda_device_string()):
|
||||||
torch.cuda.empty_cache()
|
torch.cuda.empty_cache()
|
||||||
torch.cuda.ipc_collect()
|
torch.cuda.ipc_collect()
|
||||||
|
|
||||||
|
if has_mps():
|
||||||
|
mac_specific.torch_mps_gc()
|
||||||
|
|
||||||
|
|
||||||
def enable_tf32():
|
def enable_tf32():
|
||||||
if torch.cuda.is_available():
|
if torch.cuda.is_available():
|
||||||
|
|
||||||
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
|
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
|
||||||
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
|
# see https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
|
||||||
if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
|
if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
|
||||||
torch.backends.cudnn.benchmark = True
|
torch.backends.cudnn.benchmark = True
|
||||||
|
|
||||||
torch.backends.cuda.matmul.allow_tf32 = True
|
torch.backends.cuda.matmul.allow_tf32 = True
|
||||||
@ -92,14 +91,18 @@ def cond_cast_float(input):
|
|||||||
|
|
||||||
|
|
||||||
def randn(seed, shape):
|
def randn(seed, shape):
|
||||||
|
from modules.shared import opts
|
||||||
|
|
||||||
torch.manual_seed(seed)
|
torch.manual_seed(seed)
|
||||||
if device.type == 'mps':
|
if opts.randn_source == "CPU" or device.type == 'mps':
|
||||||
return torch.randn(shape, device=cpu).to(device)
|
return torch.randn(shape, device=cpu).to(device)
|
||||||
return torch.randn(shape, device=device)
|
return torch.randn(shape, device=device)
|
||||||
|
|
||||||
|
|
||||||
def randn_without_seed(shape):
|
def randn_without_seed(shape):
|
||||||
if device.type == 'mps':
|
from modules.shared import opts
|
||||||
|
|
||||||
|
if opts.randn_source == "CPU" or device.type == 'mps':
|
||||||
return torch.randn(shape, device=cpu).to(device)
|
return torch.randn(shape, device=cpu).to(device)
|
||||||
return torch.randn(shape, device=device)
|
return torch.randn(shape, device=device)
|
||||||
|
|
||||||
@ -150,3 +153,19 @@ def test_for_nans(x, where):
|
|||||||
message += " Use --disable-nan-check commandline argument to disable this check."
|
message += " Use --disable-nan-check commandline argument to disable this check."
|
||||||
|
|
||||||
raise NansException(message)
|
raise NansException(message)
|
||||||
|
|
||||||
|
|
||||||
|
@lru_cache
|
||||||
|
def first_time_calculation():
|
||||||
|
"""
|
||||||
|
just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
|
||||||
|
spends about 2.7 seconds doing that, at least wih NVidia.
|
||||||
|
"""
|
||||||
|
|
||||||
|
x = torch.zeros((1, 1)).to(device, dtype)
|
||||||
|
linear = torch.nn.Linear(1, 1).to(device, dtype)
|
||||||
|
linear(x)
|
||||||
|
|
||||||
|
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
|
||||||
|
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
|
||||||
|
conv2d(x)
|
||||||
|
@ -1,8 +1,42 @@
|
|||||||
import sys
|
import sys
|
||||||
|
import textwrap
|
||||||
import traceback
|
import traceback
|
||||||
|
|
||||||
|
|
||||||
|
exception_records = []
|
||||||
|
|
||||||
|
|
||||||
|
def record_exception():
|
||||||
|
_, e, tb = sys.exc_info()
|
||||||
|
if e is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
if exception_records and exception_records[-1] == e:
|
||||||
|
return
|
||||||
|
|
||||||
|
exception_records.append((e, tb))
|
||||||
|
|
||||||
|
if len(exception_records) > 5:
|
||||||
|
exception_records.pop(0)
|
||||||
|
|
||||||
|
|
||||||
|
def report(message: str, *, exc_info: bool = False) -> None:
|
||||||
|
"""
|
||||||
|
Print an error message to stderr, with optional traceback.
|
||||||
|
"""
|
||||||
|
|
||||||
|
record_exception()
|
||||||
|
|
||||||
|
for line in message.splitlines():
|
||||||
|
print("***", line, file=sys.stderr)
|
||||||
|
if exc_info:
|
||||||
|
print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
|
||||||
|
print("---", file=sys.stderr)
|
||||||
|
|
||||||
|
|
||||||
def print_error_explanation(message):
|
def print_error_explanation(message):
|
||||||
|
record_exception()
|
||||||
|
|
||||||
lines = message.strip().split("\n")
|
lines = message.strip().split("\n")
|
||||||
max_len = max([len(x) for x in lines])
|
max_len = max([len(x) for x in lines])
|
||||||
|
|
||||||
@ -12,15 +46,21 @@ def print_error_explanation(message):
|
|||||||
print('=' * max_len, file=sys.stderr)
|
print('=' * max_len, file=sys.stderr)
|
||||||
|
|
||||||
|
|
||||||
def display(e: Exception, task):
|
def display(e: Exception, task, *, full_traceback=False):
|
||||||
|
record_exception()
|
||||||
|
|
||||||
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
|
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
te = traceback.TracebackException.from_exception(e)
|
||||||
|
if full_traceback:
|
||||||
|
# include frames leading up to the try-catch block
|
||||||
|
te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
|
||||||
|
print(*te.format(), sep="", file=sys.stderr)
|
||||||
|
|
||||||
message = str(e)
|
message = str(e)
|
||||||
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
|
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
|
||||||
print_error_explanation("""
|
print_error_explanation("""
|
||||||
The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its config file.
|
The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its config file.
|
||||||
See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
|
See https://ghproxy.com/https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
|
||||||
""")
|
""")
|
||||||
|
|
||||||
|
|
||||||
@ -28,6 +68,8 @@ already_displayed = {}
|
|||||||
|
|
||||||
|
|
||||||
def display_once(e: Exception, task):
|
def display_once(e: Exception, task):
|
||||||
|
record_exception()
|
||||||
|
|
||||||
if task in already_displayed:
|
if task in already_displayed:
|
||||||
return
|
return
|
||||||
|
|
||||||
|
@ -1,24 +1,20 @@
|
|||||||
import os
|
import sys
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
from basicsr.utils.download_util import load_file_from_url
|
|
||||||
|
|
||||||
import modules.esrgan_model_arch as arch
|
import modules.esrgan_model_arch as arch
|
||||||
from modules import shared, modelloader, images, devices
|
from modules import modelloader, images, devices
|
||||||
from modules.upscaler import Upscaler, UpscalerData
|
|
||||||
from modules.shared import opts
|
from modules.shared import opts
|
||||||
|
from modules.upscaler import Upscaler, UpscalerData
|
||||||
|
|
||||||
|
|
||||||
def mod2normal(state_dict):
|
def mod2normal(state_dict):
|
||||||
# this code is copied from https://github.com/victorca25/iNNfer
|
# this code is copied from https://ghproxy.com/https://github.com/victorca25/iNNfer
|
||||||
if 'conv_first.weight' in state_dict:
|
if 'conv_first.weight' in state_dict:
|
||||||
crt_net = {}
|
crt_net = {}
|
||||||
items = []
|
items = list(state_dict)
|
||||||
for k, v in state_dict.items():
|
|
||||||
items.append(k)
|
|
||||||
|
|
||||||
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
||||||
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
||||||
@ -48,13 +44,11 @@ def mod2normal(state_dict):
|
|||||||
|
|
||||||
|
|
||||||
def resrgan2normal(state_dict, nb=23):
|
def resrgan2normal(state_dict, nb=23):
|
||||||
# this code is copied from https://github.com/victorca25/iNNfer
|
# this code is copied from https://ghproxy.com/https://github.com/victorca25/iNNfer
|
||||||
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
|
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
|
||||||
re8x = 0
|
re8x = 0
|
||||||
crt_net = {}
|
crt_net = {}
|
||||||
items = []
|
items = list(state_dict)
|
||||||
for k, v in state_dict.items():
|
|
||||||
items.append(k)
|
|
||||||
|
|
||||||
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
crt_net['model.0.weight'] = state_dict['conv_first.weight']
|
||||||
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
crt_net['model.0.bias'] = state_dict['conv_first.bias']
|
||||||
@ -78,7 +72,7 @@ def resrgan2normal(state_dict, nb=23):
|
|||||||
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
|
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
|
||||||
|
|
||||||
if 'conv_up3.weight' in state_dict:
|
if 'conv_up3.weight' in state_dict:
|
||||||
# modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
|
# modification supporting: https://ghproxy.com/https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
|
||||||
re8x = 3
|
re8x = 3
|
||||||
crt_net['model.9.weight'] = state_dict['conv_up3.weight']
|
crt_net['model.9.weight'] = state_dict['conv_up3.weight']
|
||||||
crt_net['model.9.bias'] = state_dict['conv_up3.bias']
|
crt_net['model.9.bias'] = state_dict['conv_up3.bias']
|
||||||
@ -93,7 +87,7 @@ def resrgan2normal(state_dict, nb=23):
|
|||||||
|
|
||||||
|
|
||||||
def infer_params(state_dict):
|
def infer_params(state_dict):
|
||||||
# this code is copied from https://github.com/victorca25/iNNfer
|
# this code is copied from https://ghproxy.com/https://github.com/victorca25/iNNfer
|
||||||
scale2x = 0
|
scale2x = 0
|
||||||
scalemin = 6
|
scalemin = 6
|
||||||
n_uplayer = 0
|
n_uplayer = 0
|
||||||
@ -127,7 +121,7 @@ def infer_params(state_dict):
|
|||||||
class UpscalerESRGAN(Upscaler):
|
class UpscalerESRGAN(Upscaler):
|
||||||
def __init__(self, dirname):
|
def __init__(self, dirname):
|
||||||
self.name = "ESRGAN"
|
self.name = "ESRGAN"
|
||||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
|
self.model_url = "https://ghproxy.com/https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
|
||||||
self.model_name = "ESRGAN_4x"
|
self.model_name = "ESRGAN_4x"
|
||||||
self.scalers = []
|
self.scalers = []
|
||||||
self.user_path = dirname
|
self.user_path = dirname
|
||||||
@ -138,7 +132,7 @@ class UpscalerESRGAN(Upscaler):
|
|||||||
scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
|
scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
|
||||||
scalers.append(scaler_data)
|
scalers.append(scaler_data)
|
||||||
for file in model_paths:
|
for file in model_paths:
|
||||||
if "http" in file:
|
if file.startswith("http"):
|
||||||
name = self.model_name
|
name = self.model_name
|
||||||
else:
|
else:
|
||||||
name = modelloader.friendly_name(file)
|
name = modelloader.friendly_name(file)
|
||||||
@ -147,23 +141,25 @@ class UpscalerESRGAN(Upscaler):
|
|||||||
self.scalers.append(scaler_data)
|
self.scalers.append(scaler_data)
|
||||||
|
|
||||||
def do_upscale(self, img, selected_model):
|
def do_upscale(self, img, selected_model):
|
||||||
model = self.load_model(selected_model)
|
try:
|
||||||
if model is None:
|
model = self.load_model(selected_model)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
|
||||||
return img
|
return img
|
||||||
model.to(devices.device_esrgan)
|
model.to(devices.device_esrgan)
|
||||||
img = esrgan_upscale(model, img)
|
img = esrgan_upscale(model, img)
|
||||||
return img
|
return img
|
||||||
|
|
||||||
def load_model(self, path: str):
|
def load_model(self, path: str):
|
||||||
if "http" in path:
|
if path.startswith("http"):
|
||||||
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
|
# TODO: this doesn't use `path` at all?
|
||||||
file_name="%s.pth" % self.model_name,
|
filename = modelloader.load_file_from_url(
|
||||||
progress=True)
|
url=self.model_url,
|
||||||
|
model_dir=self.model_download_path,
|
||||||
|
file_name=f"{self.model_name}.pth",
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
filename = path
|
filename = path
|
||||||
if not os.path.exists(filename) or filename is None:
|
|
||||||
print("Unable to load %s from %s" % (self.model_path, filename))
|
|
||||||
return None
|
|
||||||
|
|
||||||
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
|
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
|
||||||
|
|
||||||
|
@ -1,8 +1,7 @@
|
|||||||
# this file is adapted from https://github.com/victorca25/iNNfer
|
# this file is adapted from https://ghproxy.com/https://github.com/victorca25/iNNfer
|
||||||
|
|
||||||
from collections import OrderedDict
|
from collections import OrderedDict
|
||||||
import math
|
import math
|
||||||
import functools
|
|
||||||
import torch
|
import torch
|
||||||
import torch.nn as nn
|
import torch.nn as nn
|
||||||
import torch.nn.functional as F
|
import torch.nn.functional as F
|
||||||
@ -38,7 +37,7 @@ class RRDBNet(nn.Module):
|
|||||||
elif upsample_mode == 'pixelshuffle':
|
elif upsample_mode == 'pixelshuffle':
|
||||||
upsample_block = pixelshuffle_block
|
upsample_block = pixelshuffle_block
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
|
raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
|
||||||
if upscale == 3:
|
if upscale == 3:
|
||||||
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
|
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
|
||||||
else:
|
else:
|
||||||
@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module):
|
|||||||
Modified options that can be used:
|
Modified options that can be used:
|
||||||
- "Partial Convolution based Padding" arXiv:1811.11718
|
- "Partial Convolution based Padding" arXiv:1811.11718
|
||||||
- "Spectral normalization" arXiv:1802.05957
|
- "Spectral normalization" arXiv:1802.05957
|
||||||
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
|
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
|
||||||
{Rakotonirina} and A. {Rasoanaivo}
|
{Rakotonirina} and A. {Rasoanaivo}
|
||||||
"""
|
"""
|
||||||
|
|
||||||
@ -171,7 +170,7 @@ class GaussianNoise(nn.Module):
|
|||||||
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
|
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
|
||||||
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
|
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
|
||||||
x = x + sampled_noise
|
x = x + sampled_noise
|
||||||
return x
|
return x
|
||||||
|
|
||||||
def conv1x1(in_planes, out_planes, stride=1):
|
def conv1x1(in_planes, out_planes, stride=1):
|
||||||
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
||||||
@ -183,7 +182,7 @@ def conv1x1(in_planes, out_planes, stride=1):
|
|||||||
|
|
||||||
class SRVGGNetCompact(nn.Module):
|
class SRVGGNetCompact(nn.Module):
|
||||||
"""A compact VGG-style network structure for super-resolution.
|
"""A compact VGG-style network structure for super-resolution.
|
||||||
This class is copied from https://github.com/xinntao/Real-ESRGAN
|
This class is copied from https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN
|
||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
|
def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
|
||||||
@ -261,10 +260,10 @@ class Upsample(nn.Module):
|
|||||||
|
|
||||||
def extra_repr(self):
|
def extra_repr(self):
|
||||||
if self.scale_factor is not None:
|
if self.scale_factor is not None:
|
||||||
info = 'scale_factor=' + str(self.scale_factor)
|
info = f'scale_factor={self.scale_factor}'
|
||||||
else:
|
else:
|
||||||
info = 'size=' + str(self.size)
|
info = f'size={self.size}'
|
||||||
info += ', mode=' + self.mode
|
info += f', mode={self.mode}'
|
||||||
return info
|
return info
|
||||||
|
|
||||||
|
|
||||||
@ -350,7 +349,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
|
|||||||
elif act_type == 'sigmoid': # [0, 1] range output
|
elif act_type == 'sigmoid': # [0, 1] range output
|
||||||
layer = nn.Sigmoid()
|
layer = nn.Sigmoid()
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
|
raise NotImplementedError(f'activation layer [{act_type}] is not found')
|
||||||
return layer
|
return layer
|
||||||
|
|
||||||
|
|
||||||
@ -372,7 +371,7 @@ def norm(norm_type, nc):
|
|||||||
elif norm_type == 'none':
|
elif norm_type == 'none':
|
||||||
def norm_layer(x): return Identity()
|
def norm_layer(x): return Identity()
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
|
raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
|
||||||
return layer
|
return layer
|
||||||
|
|
||||||
|
|
||||||
@ -388,7 +387,7 @@ def pad(pad_type, padding):
|
|||||||
elif pad_type == 'zero':
|
elif pad_type == 'zero':
|
||||||
layer = nn.ZeroPad2d(padding)
|
layer = nn.ZeroPad2d(padding)
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
|
raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
|
||||||
return layer
|
return layer
|
||||||
|
|
||||||
|
|
||||||
@ -432,15 +431,17 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
|
|||||||
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
|
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
|
||||||
spectral_norm=False):
|
spectral_norm=False):
|
||||||
""" Conv layer with padding, normalization, activation """
|
""" Conv layer with padding, normalization, activation """
|
||||||
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
|
assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
|
||||||
padding = get_valid_padding(kernel_size, dilation)
|
padding = get_valid_padding(kernel_size, dilation)
|
||||||
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
|
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
|
||||||
padding = padding if pad_type == 'zero' else 0
|
padding = padding if pad_type == 'zero' else 0
|
||||||
|
|
||||||
if convtype=='PartialConv2D':
|
if convtype=='PartialConv2D':
|
||||||
|
from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
|
||||||
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||||
dilation=dilation, bias=bias, groups=groups)
|
dilation=dilation, bias=bias, groups=groups)
|
||||||
elif convtype=='DeformConv2D':
|
elif convtype=='DeformConv2D':
|
||||||
|
from torchvision.ops import DeformConv2d # not tested
|
||||||
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
|
||||||
dilation=dilation, bias=bias, groups=groups)
|
dilation=dilation, bias=bias, groups=groups)
|
||||||
elif convtype=='Conv3D':
|
elif convtype=='Conv3D':
|
||||||
|
@ -1,17 +1,13 @@
|
|||||||
import os
|
import os
|
||||||
import sys
|
import threading
|
||||||
import traceback
|
|
||||||
|
|
||||||
import time
|
from modules import shared, errors, cache
|
||||||
import git
|
from modules.gitpython_hack import Repo
|
||||||
|
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
|
||||||
from modules import shared
|
|
||||||
from modules.paths_internal import extensions_dir, extensions_builtin_dir
|
|
||||||
|
|
||||||
extensions = []
|
extensions = []
|
||||||
|
|
||||||
if not os.path.exists(extensions_dir):
|
os.makedirs(extensions_dir, exist_ok=True)
|
||||||
os.makedirs(extensions_dir)
|
|
||||||
|
|
||||||
|
|
||||||
def active():
|
def active():
|
||||||
@ -24,6 +20,9 @@ def active():
|
|||||||
|
|
||||||
|
|
||||||
class Extension:
|
class Extension:
|
||||||
|
lock = threading.Lock()
|
||||||
|
cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
|
||||||
|
|
||||||
def __init__(self, name, path, enabled=True, is_builtin=False):
|
def __init__(self, name, path, enabled=True, is_builtin=False):
|
||||||
self.name = name
|
self.name = name
|
||||||
self.path = path
|
self.path = path
|
||||||
@ -31,37 +30,65 @@ class Extension:
|
|||||||
self.status = ''
|
self.status = ''
|
||||||
self.can_update = False
|
self.can_update = False
|
||||||
self.is_builtin = is_builtin
|
self.is_builtin = is_builtin
|
||||||
|
self.commit_hash = ''
|
||||||
|
self.commit_date = None
|
||||||
self.version = ''
|
self.version = ''
|
||||||
|
self.branch = None
|
||||||
self.remote = None
|
self.remote = None
|
||||||
self.have_info_from_repo = False
|
self.have_info_from_repo = False
|
||||||
|
|
||||||
|
def to_dict(self):
|
||||||
|
return {x: getattr(self, x) for x in self.cached_fields}
|
||||||
|
|
||||||
|
def from_dict(self, d):
|
||||||
|
for field in self.cached_fields:
|
||||||
|
setattr(self, field, d[field])
|
||||||
|
|
||||||
def read_info_from_repo(self):
|
def read_info_from_repo(self):
|
||||||
if self.have_info_from_repo:
|
if self.is_builtin or self.have_info_from_repo:
|
||||||
return
|
return
|
||||||
|
|
||||||
self.have_info_from_repo = True
|
def read_from_repo():
|
||||||
|
with self.lock:
|
||||||
|
if self.have_info_from_repo:
|
||||||
|
return
|
||||||
|
|
||||||
|
self.do_read_info_from_repo()
|
||||||
|
|
||||||
|
return self.to_dict()
|
||||||
|
try:
|
||||||
|
d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
|
||||||
|
self.from_dict(d)
|
||||||
|
except FileNotFoundError:
|
||||||
|
pass
|
||||||
|
self.status = 'unknown' if self.status == '' else self.status
|
||||||
|
|
||||||
|
def do_read_info_from_repo(self):
|
||||||
repo = None
|
repo = None
|
||||||
try:
|
try:
|
||||||
if os.path.exists(os.path.join(self.path, ".git")):
|
if os.path.exists(os.path.join(self.path, ".git")):
|
||||||
repo = git.Repo(self.path)
|
repo = Repo(self.path)
|
||||||
except Exception:
|
except Exception:
|
||||||
print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
|
errors.report(f"Error reading github repository info from {self.path}", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
|
||||||
if repo is None or repo.bare:
|
if repo is None or repo.bare:
|
||||||
self.remote = None
|
self.remote = None
|
||||||
else:
|
else:
|
||||||
try:
|
try:
|
||||||
self.status = 'unknown'
|
|
||||||
self.remote = next(repo.remote().urls, None)
|
self.remote = next(repo.remote().urls, None)
|
||||||
head = repo.head.commit
|
commit = repo.head.commit
|
||||||
ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
|
self.commit_date = commit.committed_date
|
||||||
self.version = f'{head.hexsha[:8]} ({ts})'
|
if repo.active_branch:
|
||||||
|
self.branch = repo.active_branch.name
|
||||||
|
self.commit_hash = commit.hexsha
|
||||||
|
self.version = self.commit_hash[:8]
|
||||||
|
|
||||||
except Exception:
|
except Exception:
|
||||||
|
errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
|
||||||
self.remote = None
|
self.remote = None
|
||||||
|
|
||||||
|
self.have_info_from_repo = True
|
||||||
|
|
||||||
def list_files(self, subdir, extension):
|
def list_files(self, subdir, extension):
|
||||||
from modules import scripts
|
from modules import scripts
|
||||||
|
|
||||||
@ -78,22 +105,34 @@ class Extension:
|
|||||||
return res
|
return res
|
||||||
|
|
||||||
def check_updates(self):
|
def check_updates(self):
|
||||||
repo = git.Repo(self.path)
|
repo = Repo(self.path)
|
||||||
for fetch in repo.remote().fetch(dry_run=True):
|
for fetch in repo.remote().fetch(dry_run=True):
|
||||||
if fetch.flags != fetch.HEAD_UPTODATE:
|
if fetch.flags != fetch.HEAD_UPTODATE:
|
||||||
self.can_update = True
|
self.can_update = True
|
||||||
self.status = "behind"
|
self.status = "new commits"
|
||||||
return
|
return
|
||||||
|
|
||||||
|
try:
|
||||||
|
origin = repo.rev_parse('origin')
|
||||||
|
if repo.head.commit != origin:
|
||||||
|
self.can_update = True
|
||||||
|
self.status = "behind HEAD"
|
||||||
|
return
|
||||||
|
except Exception:
|
||||||
|
self.can_update = False
|
||||||
|
self.status = "unknown (remote error)"
|
||||||
|
return
|
||||||
|
|
||||||
self.can_update = False
|
self.can_update = False
|
||||||
self.status = "latest"
|
self.status = "latest"
|
||||||
|
|
||||||
def fetch_and_reset_hard(self):
|
def fetch_and_reset_hard(self, commit='origin'):
|
||||||
repo = git.Repo(self.path)
|
repo = Repo(self.path)
|
||||||
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
|
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
|
||||||
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
|
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
|
||||||
repo.git.fetch(all=True)
|
repo.git.fetch(all=True)
|
||||||
repo.git.reset('origin', hard=True)
|
repo.git.reset(commit, hard=True)
|
||||||
|
self.have_info_from_repo = False
|
||||||
|
|
||||||
|
|
||||||
def list_extensions():
|
def list_extensions():
|
||||||
|
@ -4,19 +4,42 @@ from collections import defaultdict
|
|||||||
from modules import errors
|
from modules import errors
|
||||||
|
|
||||||
extra_network_registry = {}
|
extra_network_registry = {}
|
||||||
|
extra_network_aliases = {}
|
||||||
|
|
||||||
|
|
||||||
def initialize():
|
def initialize():
|
||||||
extra_network_registry.clear()
|
extra_network_registry.clear()
|
||||||
|
extra_network_aliases.clear()
|
||||||
|
|
||||||
|
|
||||||
def register_extra_network(extra_network):
|
def register_extra_network(extra_network):
|
||||||
extra_network_registry[extra_network.name] = extra_network
|
extra_network_registry[extra_network.name] = extra_network
|
||||||
|
|
||||||
|
|
||||||
|
def register_extra_network_alias(extra_network, alias):
|
||||||
|
extra_network_aliases[alias] = extra_network
|
||||||
|
|
||||||
|
|
||||||
|
def register_default_extra_networks():
|
||||||
|
from modules.extra_networks_hypernet import ExtraNetworkHypernet
|
||||||
|
register_extra_network(ExtraNetworkHypernet())
|
||||||
|
|
||||||
|
|
||||||
class ExtraNetworkParams:
|
class ExtraNetworkParams:
|
||||||
def __init__(self, items=None):
|
def __init__(self, items=None):
|
||||||
self.items = items or []
|
self.items = items or []
|
||||||
|
self.positional = []
|
||||||
|
self.named = {}
|
||||||
|
|
||||||
|
for item in self.items:
|
||||||
|
parts = item.split('=', 2) if isinstance(item, str) else [item]
|
||||||
|
if len(parts) == 2:
|
||||||
|
self.named[parts[0]] = parts[1]
|
||||||
|
else:
|
||||||
|
self.positional.append(item)
|
||||||
|
|
||||||
|
def __eq__(self, other):
|
||||||
|
return self.items == other.items
|
||||||
|
|
||||||
|
|
||||||
class ExtraNetwork:
|
class ExtraNetwork:
|
||||||
@ -65,20 +88,26 @@ def activate(p, extra_network_data):
|
|||||||
"""call activate for extra networks in extra_network_data in specified order, then call
|
"""call activate for extra networks in extra_network_data in specified order, then call
|
||||||
activate for all remaining registered networks with an empty argument list"""
|
activate for all remaining registered networks with an empty argument list"""
|
||||||
|
|
||||||
|
activated = []
|
||||||
|
|
||||||
for extra_network_name, extra_network_args in extra_network_data.items():
|
for extra_network_name, extra_network_args in extra_network_data.items():
|
||||||
extra_network = extra_network_registry.get(extra_network_name, None)
|
extra_network = extra_network_registry.get(extra_network_name, None)
|
||||||
|
|
||||||
|
if extra_network is None:
|
||||||
|
extra_network = extra_network_aliases.get(extra_network_name, None)
|
||||||
|
|
||||||
if extra_network is None:
|
if extra_network is None:
|
||||||
print(f"Skipping unknown extra network: {extra_network_name}")
|
print(f"Skipping unknown extra network: {extra_network_name}")
|
||||||
continue
|
continue
|
||||||
|
|
||||||
try:
|
try:
|
||||||
extra_network.activate(p, extra_network_args)
|
extra_network.activate(p, extra_network_args)
|
||||||
|
activated.append(extra_network)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
|
errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
|
||||||
|
|
||||||
for extra_network_name, extra_network in extra_network_registry.items():
|
for extra_network_name, extra_network in extra_network_registry.items():
|
||||||
args = extra_network_data.get(extra_network_name, None)
|
if extra_network in activated:
|
||||||
if args is not None:
|
|
||||||
continue
|
continue
|
||||||
|
|
||||||
try:
|
try:
|
||||||
@ -86,12 +115,15 @@ def activate(p, extra_network_data):
|
|||||||
except Exception as e:
|
except Exception as e:
|
||||||
errors.display(e, f"activating extra network {extra_network_name}")
|
errors.display(e, f"activating extra network {extra_network_name}")
|
||||||
|
|
||||||
|
if p.scripts is not None:
|
||||||
|
p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data)
|
||||||
|
|
||||||
|
|
||||||
def deactivate(p, extra_network_data):
|
def deactivate(p, extra_network_data):
|
||||||
"""call deactivate for extra networks in extra_network_data in specified order, then call
|
"""call deactivate for extra networks in extra_network_data in specified order, then call
|
||||||
deactivate for all remaining registered networks"""
|
deactivate for all remaining registered networks"""
|
||||||
|
|
||||||
for extra_network_name, extra_network_args in extra_network_data.items():
|
for extra_network_name in extra_network_data:
|
||||||
extra_network = extra_network_registry.get(extra_network_name, None)
|
extra_network = extra_network_registry.get(extra_network_name, None)
|
||||||
if extra_network is None:
|
if extra_network is None:
|
||||||
continue
|
continue
|
||||||
|
@ -1,4 +1,4 @@
|
|||||||
from modules import extra_networks, shared, extra_networks
|
from modules import extra_networks, shared
|
||||||
from modules.hypernetworks import hypernetwork
|
from modules.hypernetworks import hypernetwork
|
||||||
|
|
||||||
|
|
||||||
@ -9,14 +9,15 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
|
|||||||
def activate(self, p, params_list):
|
def activate(self, p, params_list):
|
||||||
additional = shared.opts.sd_hypernetwork
|
additional = shared.opts.sd_hypernetwork
|
||||||
|
|
||||||
if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
|
if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional):
|
||||||
p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
|
hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
|
||||||
|
p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
|
||||||
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
||||||
|
|
||||||
names = []
|
names = []
|
||||||
multipliers = []
|
multipliers = []
|
||||||
for params in params_list:
|
for params in params_list:
|
||||||
assert len(params.items) > 0
|
assert params.items
|
||||||
|
|
||||||
names.append(params.items[0])
|
names.append(params.items[0])
|
||||||
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
|
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
|
||||||
|
@ -1,6 +1,7 @@
|
|||||||
import os
|
import os
|
||||||
import re
|
import re
|
||||||
import shutil
|
import shutil
|
||||||
|
import json
|
||||||
|
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
@ -71,9 +72,8 @@ def to_half(tensor, enable):
|
|||||||
return tensor
|
return tensor
|
||||||
|
|
||||||
|
|
||||||
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
|
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
|
||||||
shared.state.begin()
|
shared.state.begin(job="model-merge")
|
||||||
shared.state.job = 'model-merge'
|
|
||||||
|
|
||||||
def fail(message):
|
def fail(message):
|
||||||
shared.state.textinfo = message
|
shared.state.textinfo = message
|
||||||
@ -135,14 +135,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
|||||||
result_is_instruct_pix2pix_model = False
|
result_is_instruct_pix2pix_model = False
|
||||||
|
|
||||||
if theta_func2:
|
if theta_func2:
|
||||||
shared.state.textinfo = f"Loading B"
|
shared.state.textinfo = "Loading B"
|
||||||
print(f"Loading {secondary_model_info.filename}...")
|
print(f"Loading {secondary_model_info.filename}...")
|
||||||
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
|
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
|
||||||
else:
|
else:
|
||||||
theta_1 = None
|
theta_1 = None
|
||||||
|
|
||||||
if theta_func1:
|
if theta_func1:
|
||||||
shared.state.textinfo = f"Loading C"
|
shared.state.textinfo = "Loading C"
|
||||||
print(f"Loading {tertiary_model_info.filename}...")
|
print(f"Loading {tertiary_model_info.filename}...")
|
||||||
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
|
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
|
||||||
|
|
||||||
@ -198,7 +198,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
|||||||
result_is_inpainting_model = True
|
result_is_inpainting_model = True
|
||||||
else:
|
else:
|
||||||
theta_0[key] = theta_func2(a, b, multiplier)
|
theta_0[key] = theta_func2(a, b, multiplier)
|
||||||
|
|
||||||
theta_0[key] = to_half(theta_0[key], save_as_half)
|
theta_0[key] = to_half(theta_0[key], save_as_half)
|
||||||
|
|
||||||
shared.state.sampling_step += 1
|
shared.state.sampling_step += 1
|
||||||
@ -241,13 +241,58 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
|||||||
shared.state.textinfo = "Saving"
|
shared.state.textinfo = "Saving"
|
||||||
print(f"Saving to {output_modelname}...")
|
print(f"Saving to {output_modelname}...")
|
||||||
|
|
||||||
|
metadata = None
|
||||||
|
|
||||||
|
if save_metadata:
|
||||||
|
metadata = {"format": "pt"}
|
||||||
|
|
||||||
|
merge_recipe = {
|
||||||
|
"type": "webui", # indicate this model was merged with webui's built-in merger
|
||||||
|
"primary_model_hash": primary_model_info.sha256,
|
||||||
|
"secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
|
||||||
|
"tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
|
||||||
|
"interp_method": interp_method,
|
||||||
|
"multiplier": multiplier,
|
||||||
|
"save_as_half": save_as_half,
|
||||||
|
"custom_name": custom_name,
|
||||||
|
"config_source": config_source,
|
||||||
|
"bake_in_vae": bake_in_vae,
|
||||||
|
"discard_weights": discard_weights,
|
||||||
|
"is_inpainting": result_is_inpainting_model,
|
||||||
|
"is_instruct_pix2pix": result_is_instruct_pix2pix_model
|
||||||
|
}
|
||||||
|
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
|
||||||
|
|
||||||
|
sd_merge_models = {}
|
||||||
|
|
||||||
|
def add_model_metadata(checkpoint_info):
|
||||||
|
checkpoint_info.calculate_shorthash()
|
||||||
|
sd_merge_models[checkpoint_info.sha256] = {
|
||||||
|
"name": checkpoint_info.name,
|
||||||
|
"legacy_hash": checkpoint_info.hash,
|
||||||
|
"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
|
||||||
|
}
|
||||||
|
|
||||||
|
sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
|
||||||
|
|
||||||
|
add_model_metadata(primary_model_info)
|
||||||
|
if secondary_model_info:
|
||||||
|
add_model_metadata(secondary_model_info)
|
||||||
|
if tertiary_model_info:
|
||||||
|
add_model_metadata(tertiary_model_info)
|
||||||
|
|
||||||
|
metadata["sd_merge_models"] = json.dumps(sd_merge_models)
|
||||||
|
|
||||||
_, extension = os.path.splitext(output_modelname)
|
_, extension = os.path.splitext(output_modelname)
|
||||||
if extension.lower() == ".safetensors":
|
if extension.lower() == ".safetensors":
|
||||||
safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
|
safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
|
||||||
else:
|
else:
|
||||||
torch.save(theta_0, output_modelname)
|
torch.save(theta_0, output_modelname)
|
||||||
|
|
||||||
sd_models.list_models()
|
sd_models.list_models()
|
||||||
|
created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
|
||||||
|
if created_model:
|
||||||
|
created_model.calculate_shorthash()
|
||||||
|
|
||||||
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
|
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
|
||||||
|
|
||||||
|
@ -1,15 +1,12 @@
|
|||||||
import base64
|
import base64
|
||||||
import html
|
|
||||||
import io
|
import io
|
||||||
import math
|
import json
|
||||||
import os
|
import os
|
||||||
import re
|
import re
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
import gradio as gr
|
import gradio as gr
|
||||||
from modules.paths import data_path
|
from modules.paths import data_path
|
||||||
from modules import shared, ui_tempdir, script_callbacks
|
from modules import shared, ui_tempdir, script_callbacks
|
||||||
import tempfile
|
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
|
|
||||||
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
|
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
|
||||||
@ -23,14 +20,14 @@ registered_param_bindings = []
|
|||||||
|
|
||||||
|
|
||||||
class ParamBinding:
|
class ParamBinding:
|
||||||
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
|
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
|
||||||
self.paste_button = paste_button
|
self.paste_button = paste_button
|
||||||
self.tabname = tabname
|
self.tabname = tabname
|
||||||
self.source_text_component = source_text_component
|
self.source_text_component = source_text_component
|
||||||
self.source_image_component = source_image_component
|
self.source_image_component = source_image_component
|
||||||
self.source_tabname = source_tabname
|
self.source_tabname = source_tabname
|
||||||
self.override_settings_component = override_settings_component
|
self.override_settings_component = override_settings_component
|
||||||
self.paste_field_names = paste_field_names
|
self.paste_field_names = paste_field_names or []
|
||||||
|
|
||||||
|
|
||||||
def reset():
|
def reset():
|
||||||
@ -38,20 +35,27 @@ def reset():
|
|||||||
|
|
||||||
|
|
||||||
def quote(text):
|
def quote(text):
|
||||||
if ',' not in str(text):
|
if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
|
||||||
return text
|
return text
|
||||||
|
|
||||||
text = str(text)
|
return json.dumps(text, ensure_ascii=False)
|
||||||
text = text.replace('\\', '\\\\')
|
|
||||||
text = text.replace('"', '\\"')
|
|
||||||
return f'"{text}"'
|
def unquote(text):
|
||||||
|
if len(text) == 0 or text[0] != '"' or text[-1] != '"':
|
||||||
|
return text
|
||||||
|
|
||||||
|
try:
|
||||||
|
return json.loads(text)
|
||||||
|
except Exception:
|
||||||
|
return text
|
||||||
|
|
||||||
|
|
||||||
def image_from_url_text(filedata):
|
def image_from_url_text(filedata):
|
||||||
if filedata is None:
|
if filedata is None:
|
||||||
return None
|
return None
|
||||||
|
|
||||||
if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
|
if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
|
||||||
filedata = filedata[0]
|
filedata = filedata[0]
|
||||||
|
|
||||||
if type(filedata) == dict and filedata.get("is_file", False):
|
if type(filedata) == dict and filedata.get("is_file", False):
|
||||||
@ -59,6 +63,7 @@ def image_from_url_text(filedata):
|
|||||||
is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
|
is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
|
||||||
assert is_in_right_dir, 'trying to open image file outside of allowed directories'
|
assert is_in_right_dir, 'trying to open image file outside of allowed directories'
|
||||||
|
|
||||||
|
filename = filename.rsplit('?', 1)[0]
|
||||||
return Image.open(filename)
|
return Image.open(filename)
|
||||||
|
|
||||||
if type(filedata) == list:
|
if type(filedata) == list:
|
||||||
@ -129,6 +134,7 @@ def connect_paste_params_buttons():
|
|||||||
_js=jsfunc,
|
_js=jsfunc,
|
||||||
inputs=[binding.source_image_component],
|
inputs=[binding.source_image_component],
|
||||||
outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
|
outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
|
||||||
|
show_progress=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
if binding.source_text_component is not None and fields is not None:
|
if binding.source_text_component is not None and fields is not None:
|
||||||
@ -140,6 +146,7 @@ def connect_paste_params_buttons():
|
|||||||
fn=lambda *x: x,
|
fn=lambda *x: x,
|
||||||
inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
|
inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
|
||||||
outputs=[field for field, name in fields if name in paste_field_names],
|
outputs=[field for field, name in fields if name in paste_field_names],
|
||||||
|
show_progress=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
binding.paste_button.click(
|
binding.paste_button.click(
|
||||||
@ -147,6 +154,7 @@ def connect_paste_params_buttons():
|
|||||||
_js=f"switch_to_{binding.tabname}",
|
_js=f"switch_to_{binding.tabname}",
|
||||||
inputs=None,
|
inputs=None,
|
||||||
outputs=None,
|
outputs=None,
|
||||||
|
show_progress=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@ -166,31 +174,6 @@ def send_image_and_dimensions(x):
|
|||||||
return img, w, h
|
return img, w, h
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
|
|
||||||
"""Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
|
|
||||||
|
|
||||||
Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
|
|
||||||
parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
|
|
||||||
|
|
||||||
If the infotext has no hash, then a hypernet with the same name will be selected instead.
|
|
||||||
"""
|
|
||||||
hypernet_name = hypernet_name.lower()
|
|
||||||
if hypernet_hash is not None:
|
|
||||||
# Try to match the hash in the name
|
|
||||||
for hypernet_key in shared.hypernetworks.keys():
|
|
||||||
result = re_hypernet_hash.search(hypernet_key)
|
|
||||||
if result is not None and result[1] == hypernet_hash:
|
|
||||||
return hypernet_key
|
|
||||||
else:
|
|
||||||
# Fall back to a hypernet with the same name
|
|
||||||
for hypernet_key in shared.hypernetworks.keys():
|
|
||||||
if hypernet_key.lower().startswith(hypernet_name):
|
|
||||||
return hypernet_key
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def restore_old_hires_fix_params(res):
|
def restore_old_hires_fix_params(res):
|
||||||
"""for infotexts that specify old First pass size parameter, convert it into
|
"""for infotexts that specify old First pass size parameter, convert it into
|
||||||
width, height, and hr scale"""
|
width, height, and hr scale"""
|
||||||
@ -247,28 +230,40 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
|
|||||||
lines.append(lastline)
|
lines.append(lastline)
|
||||||
lastline = ''
|
lastline = ''
|
||||||
|
|
||||||
for i, line in enumerate(lines):
|
for line in lines:
|
||||||
line = line.strip()
|
line = line.strip()
|
||||||
if line.startswith("Negative prompt:"):
|
if line.startswith("Negative prompt:"):
|
||||||
done_with_prompt = True
|
done_with_prompt = True
|
||||||
line = line[16:].strip()
|
line = line[16:].strip()
|
||||||
|
|
||||||
if done_with_prompt:
|
if done_with_prompt:
|
||||||
negative_prompt += ("" if negative_prompt == "" else "\n") + line
|
negative_prompt += ("" if negative_prompt == "" else "\n") + line
|
||||||
else:
|
else:
|
||||||
prompt += ("" if prompt == "" else "\n") + line
|
prompt += ("" if prompt == "" else "\n") + line
|
||||||
|
|
||||||
|
if shared.opts.infotext_styles != "Ignore":
|
||||||
|
found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
|
||||||
|
|
||||||
|
if shared.opts.infotext_styles == "Apply":
|
||||||
|
res["Styles array"] = found_styles
|
||||||
|
elif shared.opts.infotext_styles == "Apply if any" and found_styles:
|
||||||
|
res["Styles array"] = found_styles
|
||||||
|
|
||||||
res["Prompt"] = prompt
|
res["Prompt"] = prompt
|
||||||
res["Negative prompt"] = negative_prompt
|
res["Negative prompt"] = negative_prompt
|
||||||
|
|
||||||
for k, v in re_param.findall(lastline):
|
for k, v in re_param.findall(lastline):
|
||||||
v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
|
try:
|
||||||
m = re_imagesize.match(v)
|
if v[0] == '"' and v[-1] == '"':
|
||||||
if m is not None:
|
v = unquote(v)
|
||||||
res[k+"-1"] = m.group(1)
|
|
||||||
res[k+"-2"] = m.group(2)
|
m = re_imagesize.match(v)
|
||||||
else:
|
if m is not None:
|
||||||
res[k] = v
|
res[f"{k}-1"] = m.group(1)
|
||||||
|
res[f"{k}-2"] = m.group(2)
|
||||||
|
else:
|
||||||
|
res[k] = v
|
||||||
|
except Exception:
|
||||||
|
print(f"Error parsing \"{k}: {v}\"")
|
||||||
|
|
||||||
# Missing CLIP skip means it was set to 1 (the default)
|
# Missing CLIP skip means it was set to 1 (the default)
|
||||||
if "Clip skip" not in res:
|
if "Clip skip" not in res:
|
||||||
@ -282,20 +277,45 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
|
|||||||
res["Hires resize-1"] = 0
|
res["Hires resize-1"] = 0
|
||||||
res["Hires resize-2"] = 0
|
res["Hires resize-2"] = 0
|
||||||
|
|
||||||
|
if "Hires sampler" not in res:
|
||||||
|
res["Hires sampler"] = "Use same sampler"
|
||||||
|
|
||||||
|
if "Hires prompt" not in res:
|
||||||
|
res["Hires prompt"] = ""
|
||||||
|
|
||||||
|
if "Hires negative prompt" not in res:
|
||||||
|
res["Hires negative prompt"] = ""
|
||||||
|
|
||||||
restore_old_hires_fix_params(res)
|
restore_old_hires_fix_params(res)
|
||||||
|
|
||||||
|
# Missing RNG means the default was set, which is GPU RNG
|
||||||
|
if "RNG" not in res:
|
||||||
|
res["RNG"] = "GPU"
|
||||||
|
|
||||||
|
if "Schedule type" not in res:
|
||||||
|
res["Schedule type"] = "Automatic"
|
||||||
|
|
||||||
|
if "Schedule max sigma" not in res:
|
||||||
|
res["Schedule max sigma"] = 0
|
||||||
|
|
||||||
|
if "Schedule min sigma" not in res:
|
||||||
|
res["Schedule min sigma"] = 0
|
||||||
|
|
||||||
|
if "Schedule rho" not in res:
|
||||||
|
res["Schedule rho"] = 0
|
||||||
|
|
||||||
return res
|
return res
|
||||||
|
|
||||||
|
|
||||||
settings_map = {}
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
infotext_to_setting_name_mapping = [
|
infotext_to_setting_name_mapping = [
|
||||||
('Clip skip', 'CLIP_stop_at_last_layers', ),
|
('Clip skip', 'CLIP_stop_at_last_layers', ),
|
||||||
('Conditional mask weight', 'inpainting_mask_weight'),
|
('Conditional mask weight', 'inpainting_mask_weight'),
|
||||||
('Model hash', 'sd_model_checkpoint'),
|
('Model hash', 'sd_model_checkpoint'),
|
||||||
('ENSD', 'eta_noise_seed_delta'),
|
('ENSD', 'eta_noise_seed_delta'),
|
||||||
|
('Schedule type', 'k_sched_type'),
|
||||||
|
('Schedule max sigma', 'sigma_max'),
|
||||||
|
('Schedule min sigma', 'sigma_min'),
|
||||||
|
('Schedule rho', 'rho'),
|
||||||
('Noise multiplier', 'initial_noise_multiplier'),
|
('Noise multiplier', 'initial_noise_multiplier'),
|
||||||
('Eta', 'eta_ancestral'),
|
('Eta', 'eta_ancestral'),
|
||||||
('Eta DDIM', 'eta_ddim'),
|
('Eta DDIM', 'eta_ddim'),
|
||||||
@ -304,6 +324,11 @@ infotext_to_setting_name_mapping = [
|
|||||||
('UniPC skip type', 'uni_pc_skip_type'),
|
('UniPC skip type', 'uni_pc_skip_type'),
|
||||||
('UniPC order', 'uni_pc_order'),
|
('UniPC order', 'uni_pc_order'),
|
||||||
('UniPC lower order final', 'uni_pc_lower_order_final'),
|
('UniPC lower order final', 'uni_pc_lower_order_final'),
|
||||||
|
('Token merging ratio', 'token_merging_ratio'),
|
||||||
|
('Token merging ratio hr', 'token_merging_ratio_hr'),
|
||||||
|
('RNG', 'randn_source'),
|
||||||
|
('NGMS', 's_min_uncond'),
|
||||||
|
('Pad conds', 'pad_cond_uncond'),
|
||||||
]
|
]
|
||||||
|
|
||||||
|
|
||||||
@ -395,7 +420,7 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
|
|||||||
|
|
||||||
vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
|
vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
|
||||||
|
|
||||||
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
|
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
|
||||||
|
|
||||||
paste_fields = paste_fields + [(override_settings_component, paste_settings)]
|
paste_fields = paste_fields + [(override_settings_component, paste_settings)]
|
||||||
|
|
||||||
@ -403,12 +428,12 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
|
|||||||
fn=paste_func,
|
fn=paste_func,
|
||||||
inputs=[input_comp],
|
inputs=[input_comp],
|
||||||
outputs=[x[0] for x in paste_fields],
|
outputs=[x[0] for x in paste_fields],
|
||||||
|
show_progress=False,
|
||||||
)
|
)
|
||||||
button.click(
|
button.click(
|
||||||
fn=None,
|
fn=None,
|
||||||
_js=f"recalculate_prompts_{tabname}",
|
_js=f"recalculate_prompts_{tabname}",
|
||||||
inputs=[],
|
inputs=[],
|
||||||
outputs=[],
|
outputs=[],
|
||||||
|
show_progress=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@ -1,17 +1,15 @@
|
|||||||
import os
|
import os
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
import facexlib
|
import facexlib
|
||||||
import gfpgan
|
import gfpgan
|
||||||
|
|
||||||
import modules.face_restoration
|
import modules.face_restoration
|
||||||
from modules import paths, shared, devices, modelloader
|
from modules import paths, shared, devices, modelloader, errors
|
||||||
|
|
||||||
model_dir = "GFPGAN"
|
model_dir = "GFPGAN"
|
||||||
user_path = None
|
user_path = None
|
||||||
model_path = os.path.join(paths.models_path, model_dir)
|
model_path = os.path.join(paths.models_path, model_dir)
|
||||||
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
|
model_url = "https://ghproxy.com/https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
|
||||||
have_gfpgan = False
|
have_gfpgan = False
|
||||||
loaded_gfpgan_model = None
|
loaded_gfpgan_model = None
|
||||||
|
|
||||||
@ -27,7 +25,7 @@ def gfpgann():
|
|||||||
return None
|
return None
|
||||||
|
|
||||||
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
|
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
|
||||||
if len(models) == 1 and "http" in models[0]:
|
if len(models) == 1 and models[0].startswith("http"):
|
||||||
model_file = models[0]
|
model_file = models[0]
|
||||||
elif len(models) != 0:
|
elif len(models) != 0:
|
||||||
latest_file = max(models, key=os.path.getctime)
|
latest_file = max(models, key=os.path.getctime)
|
||||||
@ -72,13 +70,10 @@ gfpgan_constructor = None
|
|||||||
|
|
||||||
|
|
||||||
def setup_model(dirname):
|
def setup_model(dirname):
|
||||||
global model_path
|
|
||||||
if not os.path.exists(model_path):
|
|
||||||
os.makedirs(model_path)
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
|
os.makedirs(model_path, exist_ok=True)
|
||||||
from gfpgan import GFPGANer
|
from gfpgan import GFPGANer
|
||||||
from facexlib import detection, parsing
|
from facexlib import detection, parsing # noqa: F401
|
||||||
global user_path
|
global user_path
|
||||||
global have_gfpgan
|
global have_gfpgan
|
||||||
global gfpgan_constructor
|
global gfpgan_constructor
|
||||||
@ -112,5 +107,4 @@ def setup_model(dirname):
|
|||||||
|
|
||||||
shared.face_restorers.append(FaceRestorerGFPGAN())
|
shared.face_restorers.append(FaceRestorerGFPGAN())
|
||||||
except Exception:
|
except Exception:
|
||||||
print("Error setting up GFPGAN:", file=sys.stderr)
|
errors.report("Error setting up GFPGAN", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
42
modules/gitpython_hack.py
Normal file
42
modules/gitpython_hack.py
Normal file
@ -0,0 +1,42 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import io
|
||||||
|
import subprocess
|
||||||
|
|
||||||
|
import git
|
||||||
|
|
||||||
|
|
||||||
|
class Git(git.Git):
|
||||||
|
"""
|
||||||
|
Git subclassed to never use persistent processes.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs):
|
||||||
|
raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})")
|
||||||
|
|
||||||
|
def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]:
|
||||||
|
ret = subprocess.check_output(
|
||||||
|
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"],
|
||||||
|
input=self._prepare_ref(ref),
|
||||||
|
cwd=self._working_dir,
|
||||||
|
timeout=2,
|
||||||
|
)
|
||||||
|
return self._parse_object_header(ret)
|
||||||
|
|
||||||
|
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
|
||||||
|
# Not really streaming, per se; this buffers the entire object in memory.
|
||||||
|
# Shouldn't be a problem for our use case, since we're only using this for
|
||||||
|
# object headers (commit objects).
|
||||||
|
ret = subprocess.check_output(
|
||||||
|
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"],
|
||||||
|
input=self._prepare_ref(ref),
|
||||||
|
cwd=self._working_dir,
|
||||||
|
timeout=30,
|
||||||
|
)
|
||||||
|
bio = io.BytesIO(ret)
|
||||||
|
hexsha, typename, size = self._parse_object_header(bio.readline())
|
||||||
|
return (hexsha, typename, size, self.CatFileContentStream(size, bio))
|
||||||
|
|
||||||
|
|
||||||
|
class Repo(git.Repo):
|
||||||
|
GitCommandWrapperType = Git
|
@ -1,38 +1,11 @@
|
|||||||
import hashlib
|
import hashlib
|
||||||
import json
|
|
||||||
import os.path
|
import os.path
|
||||||
|
|
||||||
import filelock
|
|
||||||
|
|
||||||
from modules import shared
|
from modules import shared
|
||||||
from modules.paths import data_path
|
import modules.cache
|
||||||
|
|
||||||
|
dump_cache = modules.cache.dump_cache
|
||||||
cache_filename = os.path.join(data_path, "cache.json")
|
cache = modules.cache.cache
|
||||||
cache_data = None
|
|
||||||
|
|
||||||
|
|
||||||
def dump_cache():
|
|
||||||
with filelock.FileLock(cache_filename+".lock"):
|
|
||||||
with open(cache_filename, "w", encoding="utf8") as file:
|
|
||||||
json.dump(cache_data, file, indent=4)
|
|
||||||
|
|
||||||
|
|
||||||
def cache(subsection):
|
|
||||||
global cache_data
|
|
||||||
|
|
||||||
if cache_data is None:
|
|
||||||
with filelock.FileLock(cache_filename+".lock"):
|
|
||||||
if not os.path.isfile(cache_filename):
|
|
||||||
cache_data = {}
|
|
||||||
else:
|
|
||||||
with open(cache_filename, "r", encoding="utf8") as file:
|
|
||||||
cache_data = json.load(file)
|
|
||||||
|
|
||||||
s = cache_data.get(subsection, {})
|
|
||||||
cache_data[subsection] = s
|
|
||||||
|
|
||||||
return s
|
|
||||||
|
|
||||||
|
|
||||||
def calculate_sha256(filename):
|
def calculate_sha256(filename):
|
||||||
@ -46,8 +19,8 @@ def calculate_sha256(filename):
|
|||||||
return hash_sha256.hexdigest()
|
return hash_sha256.hexdigest()
|
||||||
|
|
||||||
|
|
||||||
def sha256_from_cache(filename, title):
|
def sha256_from_cache(filename, title, use_addnet_hash=False):
|
||||||
hashes = cache("hashes")
|
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
|
||||||
ondisk_mtime = os.path.getmtime(filename)
|
ondisk_mtime = os.path.getmtime(filename)
|
||||||
|
|
||||||
if title not in hashes:
|
if title not in hashes:
|
||||||
@ -62,10 +35,10 @@ def sha256_from_cache(filename, title):
|
|||||||
return cached_sha256
|
return cached_sha256
|
||||||
|
|
||||||
|
|
||||||
def sha256(filename, title):
|
def sha256(filename, title, use_addnet_hash=False):
|
||||||
hashes = cache("hashes")
|
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
|
||||||
|
|
||||||
sha256_value = sha256_from_cache(filename, title)
|
sha256_value = sha256_from_cache(filename, title, use_addnet_hash)
|
||||||
if sha256_value is not None:
|
if sha256_value is not None:
|
||||||
return sha256_value
|
return sha256_value
|
||||||
|
|
||||||
@ -73,7 +46,11 @@ def sha256(filename, title):
|
|||||||
return None
|
return None
|
||||||
|
|
||||||
print(f"Calculating sha256 for {filename}: ", end='')
|
print(f"Calculating sha256 for {filename}: ", end='')
|
||||||
sha256_value = calculate_sha256(filename)
|
if use_addnet_hash:
|
||||||
|
with open(filename, "rb") as file:
|
||||||
|
sha256_value = addnet_hash_safetensors(file)
|
||||||
|
else:
|
||||||
|
sha256_value = calculate_sha256(filename)
|
||||||
print(f"{sha256_value}")
|
print(f"{sha256_value}")
|
||||||
|
|
||||||
hashes[title] = {
|
hashes[title] = {
|
||||||
@ -86,6 +63,19 @@ def sha256(filename, title):
|
|||||||
return sha256_value
|
return sha256_value
|
||||||
|
|
||||||
|
|
||||||
|
def addnet_hash_safetensors(b):
|
||||||
|
"""kohya-ss hash for safetensors from https://ghproxy.com/https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
|
||||||
|
hash_sha256 = hashlib.sha256()
|
||||||
|
blksize = 1024 * 1024
|
||||||
|
|
||||||
|
b.seek(0)
|
||||||
|
header = b.read(8)
|
||||||
|
n = int.from_bytes(header, "little")
|
||||||
|
|
||||||
|
offset = n + 8
|
||||||
|
b.seek(offset)
|
||||||
|
for chunk in iter(lambda: b.read(blksize), b""):
|
||||||
|
hash_sha256.update(chunk)
|
||||||
|
|
||||||
|
return hash_sha256.hexdigest()
|
||||||
|
|
||||||
|
@ -1,24 +1,22 @@
|
|||||||
import csv
|
|
||||||
import datetime
|
import datetime
|
||||||
import glob
|
import glob
|
||||||
import html
|
import html
|
||||||
import os
|
import os
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
import inspect
|
import inspect
|
||||||
|
from contextlib import closing
|
||||||
|
|
||||||
import modules.textual_inversion.dataset
|
import modules.textual_inversion.dataset
|
||||||
import torch
|
import torch
|
||||||
import tqdm
|
import tqdm
|
||||||
from einops import rearrange, repeat
|
from einops import rearrange, repeat
|
||||||
from ldm.util import default
|
from ldm.util import default
|
||||||
from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
|
from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
|
||||||
from modules.textual_inversion import textual_inversion, logging
|
from modules.textual_inversion import textual_inversion, logging
|
||||||
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
||||||
from torch import einsum
|
from torch import einsum
|
||||||
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
|
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
|
||||||
|
|
||||||
from collections import defaultdict, deque
|
from collections import deque
|
||||||
from statistics import stdev, mean
|
from statistics import stdev, mean
|
||||||
|
|
||||||
|
|
||||||
@ -178,34 +176,34 @@ class Hypernetwork:
|
|||||||
|
|
||||||
def weights(self):
|
def weights(self):
|
||||||
res = []
|
res = []
|
||||||
for k, layers in self.layers.items():
|
for layers in self.layers.values():
|
||||||
for layer in layers:
|
for layer in layers:
|
||||||
res += layer.parameters()
|
res += layer.parameters()
|
||||||
return res
|
return res
|
||||||
|
|
||||||
def train(self, mode=True):
|
def train(self, mode=True):
|
||||||
for k, layers in self.layers.items():
|
for layers in self.layers.values():
|
||||||
for layer in layers:
|
for layer in layers:
|
||||||
layer.train(mode=mode)
|
layer.train(mode=mode)
|
||||||
for param in layer.parameters():
|
for param in layer.parameters():
|
||||||
param.requires_grad = mode
|
param.requires_grad = mode
|
||||||
|
|
||||||
def to(self, device):
|
def to(self, device):
|
||||||
for k, layers in self.layers.items():
|
for layers in self.layers.values():
|
||||||
for layer in layers:
|
for layer in layers:
|
||||||
layer.to(device)
|
layer.to(device)
|
||||||
|
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def set_multiplier(self, multiplier):
|
def set_multiplier(self, multiplier):
|
||||||
for k, layers in self.layers.items():
|
for layers in self.layers.values():
|
||||||
for layer in layers:
|
for layer in layers:
|
||||||
layer.multiplier = multiplier
|
layer.multiplier = multiplier
|
||||||
|
|
||||||
return self
|
return self
|
||||||
|
|
||||||
def eval(self):
|
def eval(self):
|
||||||
for k, layers in self.layers.items():
|
for layers in self.layers.values():
|
||||||
for layer in layers:
|
for layer in layers:
|
||||||
layer.eval()
|
layer.eval()
|
||||||
for param in layer.parameters():
|
for param in layer.parameters():
|
||||||
@ -326,17 +324,14 @@ def load_hypernetwork(name):
|
|||||||
if path is None:
|
if path is None:
|
||||||
return None
|
return None
|
||||||
|
|
||||||
hypernetwork = Hypernetwork()
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
|
hypernetwork = Hypernetwork()
|
||||||
hypernetwork.load(path)
|
hypernetwork.load(path)
|
||||||
|
return hypernetwork
|
||||||
except Exception:
|
except Exception:
|
||||||
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
errors.report(f"Error loading hypernetwork {path}", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
return None
|
return None
|
||||||
|
|
||||||
return hypernetwork
|
|
||||||
|
|
||||||
|
|
||||||
def load_hypernetworks(names, multipliers=None):
|
def load_hypernetworks(names, multipliers=None):
|
||||||
already_loaded = {}
|
already_loaded = {}
|
||||||
@ -359,17 +354,6 @@ def load_hypernetworks(names, multipliers=None):
|
|||||||
shared.loaded_hypernetworks.append(hypernetwork)
|
shared.loaded_hypernetworks.append(hypernetwork)
|
||||||
|
|
||||||
|
|
||||||
def find_closest_hypernetwork_name(search: str):
|
|
||||||
if not search:
|
|
||||||
return None
|
|
||||||
search = search.lower()
|
|
||||||
applicable = [name for name in shared.hypernetworks if search in name.lower()]
|
|
||||||
if not applicable:
|
|
||||||
return None
|
|
||||||
applicable = sorted(applicable, key=lambda name: len(name))
|
|
||||||
return applicable[0]
|
|
||||||
|
|
||||||
|
|
||||||
def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
|
def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
|
||||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
|
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
|
||||||
|
|
||||||
@ -394,7 +378,7 @@ def apply_hypernetworks(hypernetworks, context, layer=None):
|
|||||||
return context_k, context_v
|
return context_k, context_v
|
||||||
|
|
||||||
|
|
||||||
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs):
|
||||||
h = self.heads
|
h = self.heads
|
||||||
|
|
||||||
q = self.to_q(x)
|
q = self.to_q(x)
|
||||||
@ -404,7 +388,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
|||||||
k = self.to_k(context_k)
|
k = self.to_k(context_k)
|
||||||
v = self.to_v(context_v)
|
v = self.to_v(context_v)
|
||||||
|
|
||||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
|
||||||
|
|
||||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||||
|
|
||||||
@ -452,18 +436,6 @@ def statistics(data):
|
|||||||
return total_information, recent_information
|
return total_information, recent_information
|
||||||
|
|
||||||
|
|
||||||
def report_statistics(loss_info:dict):
|
|
||||||
keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
|
|
||||||
for key in keys:
|
|
||||||
try:
|
|
||||||
print("Loss statistics for file " + key)
|
|
||||||
info, recent = statistics(list(loss_info[key]))
|
|
||||||
print(info)
|
|
||||||
print(recent)
|
|
||||||
except Exception as e:
|
|
||||||
print(e)
|
|
||||||
|
|
||||||
|
|
||||||
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
|
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
|
||||||
# Remove illegal characters from name.
|
# Remove illegal characters from name.
|
||||||
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
|
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
|
||||||
@ -541,7 +513,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
return hypernetwork, filename
|
return hypernetwork, filename
|
||||||
|
|
||||||
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
|
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
|
||||||
|
|
||||||
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
|
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
|
||||||
if clip_grad:
|
if clip_grad:
|
||||||
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
|
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
|
||||||
@ -594,7 +566,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
print(e)
|
print(e)
|
||||||
|
|
||||||
scaler = torch.cuda.amp.GradScaler()
|
scaler = torch.cuda.amp.GradScaler()
|
||||||
|
|
||||||
batch_size = ds.batch_size
|
batch_size = ds.batch_size
|
||||||
gradient_step = ds.gradient_step
|
gradient_step = ds.gradient_step
|
||||||
# n steps = batch_size * gradient_step * n image processed
|
# n steps = batch_size * gradient_step * n image processed
|
||||||
@ -620,7 +592,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
try:
|
try:
|
||||||
sd_hijack_checkpoint.add()
|
sd_hijack_checkpoint.add()
|
||||||
|
|
||||||
for i in range((steps-initial_step) * gradient_step):
|
for _ in range((steps-initial_step) * gradient_step):
|
||||||
if scheduler.finished:
|
if scheduler.finished:
|
||||||
break
|
break
|
||||||
if shared.state.interrupted:
|
if shared.state.interrupted:
|
||||||
@ -637,7 +609,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
|
|
||||||
if clip_grad:
|
if clip_grad:
|
||||||
clip_grad_sched.step(hypernetwork.step)
|
clip_grad_sched.step(hypernetwork.step)
|
||||||
|
|
||||||
with devices.autocast():
|
with devices.autocast():
|
||||||
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
|
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
|
||||||
if use_weight:
|
if use_weight:
|
||||||
@ -658,14 +630,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
|
|
||||||
_loss_step += loss.item()
|
_loss_step += loss.item()
|
||||||
scaler.scale(loss).backward()
|
scaler.scale(loss).backward()
|
||||||
|
|
||||||
# go back until we reach gradient accumulation steps
|
# go back until we reach gradient accumulation steps
|
||||||
if (j + 1) % gradient_step != 0:
|
if (j + 1) % gradient_step != 0:
|
||||||
continue
|
continue
|
||||||
loss_logging.append(_loss_step)
|
loss_logging.append(_loss_step)
|
||||||
if clip_grad:
|
if clip_grad:
|
||||||
clip_grad(weights, clip_grad_sched.learn_rate)
|
clip_grad(weights, clip_grad_sched.learn_rate)
|
||||||
|
|
||||||
scaler.step(optimizer)
|
scaler.step(optimizer)
|
||||||
scaler.update()
|
scaler.update()
|
||||||
hypernetwork.step += 1
|
hypernetwork.step += 1
|
||||||
@ -675,7 +647,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
_loss_step = 0
|
_loss_step = 0
|
||||||
|
|
||||||
steps_done = hypernetwork.step + 1
|
steps_done = hypernetwork.step + 1
|
||||||
|
|
||||||
epoch_num = hypernetwork.step // steps_per_epoch
|
epoch_num = hypernetwork.step // steps_per_epoch
|
||||||
epoch_step = hypernetwork.step % steps_per_epoch
|
epoch_step = hypernetwork.step % steps_per_epoch
|
||||||
|
|
||||||
@ -740,8 +712,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||||||
|
|
||||||
preview_text = p.prompt
|
preview_text = p.prompt
|
||||||
|
|
||||||
processed = processing.process_images(p)
|
with closing(p):
|
||||||
image = processed.images[0] if len(processed.images) > 0 else None
|
processed = processing.process_images(p)
|
||||||
|
image = processed.images[0] if len(processed.images) > 0 else None
|
||||||
|
|
||||||
if unload:
|
if unload:
|
||||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||||
@ -771,12 +744,11 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||||||
</p>
|
</p>
|
||||||
"""
|
"""
|
||||||
except Exception:
|
except Exception:
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
errors.report("Exception in training hypernetwork", exc_info=True)
|
||||||
finally:
|
finally:
|
||||||
pbar.leave = False
|
pbar.leave = False
|
||||||
pbar.close()
|
pbar.close()
|
||||||
hypernetwork.eval()
|
hypernetwork.eval()
|
||||||
#report_statistics(loss_dict)
|
|
||||||
sd_hijack_checkpoint.remove()
|
sd_hijack_checkpoint.remove()
|
||||||
|
|
||||||
|
|
||||||
|
@ -1,19 +1,17 @@
|
|||||||
import html
|
import html
|
||||||
import os
|
|
||||||
import re
|
|
||||||
|
|
||||||
import gradio as gr
|
import gradio as gr
|
||||||
import modules.hypernetworks.hypernetwork
|
import modules.hypernetworks.hypernetwork
|
||||||
from modules import devices, sd_hijack, shared
|
from modules import devices, sd_hijack, shared
|
||||||
|
|
||||||
not_available = ["hardswish", "multiheadattention"]
|
not_available = ["hardswish", "multiheadattention"]
|
||||||
keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
|
keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
|
||||||
|
|
||||||
|
|
||||||
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
|
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
|
||||||
filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
|
filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
|
||||||
|
|
||||||
return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
|
return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
|
||||||
|
|
||||||
|
|
||||||
def train_hypernetwork(*args):
|
def train_hypernetwork(*args):
|
||||||
|
@ -1,6 +1,6 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
import datetime
|
import datetime
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
import pytz
|
import pytz
|
||||||
import io
|
import io
|
||||||
@ -12,18 +12,27 @@ import re
|
|||||||
import numpy as np
|
import numpy as np
|
||||||
import piexif
|
import piexif
|
||||||
import piexif.helper
|
import piexif.helper
|
||||||
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
|
from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
|
||||||
from fonts.ttf import Roboto
|
|
||||||
import string
|
import string
|
||||||
import json
|
import json
|
||||||
import hashlib
|
import hashlib
|
||||||
|
|
||||||
from modules import sd_samplers, shared, script_callbacks, errors
|
from modules import sd_samplers, shared, script_callbacks, errors
|
||||||
from modules.shared import opts, cmd_opts
|
from modules.paths_internal import roboto_ttf_file
|
||||||
|
from modules.shared import opts
|
||||||
|
|
||||||
|
import modules.sd_vae as sd_vae
|
||||||
|
|
||||||
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||||||
|
|
||||||
|
|
||||||
|
def get_font(fontsize: int):
|
||||||
|
try:
|
||||||
|
return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
|
||||||
|
except Exception:
|
||||||
|
return ImageFont.truetype(roboto_ttf_file, fontsize)
|
||||||
|
|
||||||
|
|
||||||
def image_grid(imgs, batch_size=1, rows=None):
|
def image_grid(imgs, batch_size=1, rows=None):
|
||||||
if rows is None:
|
if rows is None:
|
||||||
if opts.n_rows > 0:
|
if opts.n_rows > 0:
|
||||||
@ -132,6 +141,11 @@ class GridAnnotation:
|
|||||||
|
|
||||||
|
|
||||||
def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
||||||
|
|
||||||
|
color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB')
|
||||||
|
color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB')
|
||||||
|
color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB')
|
||||||
|
|
||||||
def wrap(drawing, text, font, line_length):
|
def wrap(drawing, text, font, line_length):
|
||||||
lines = ['']
|
lines = ['']
|
||||||
for word in text.split():
|
for word in text.split():
|
||||||
@ -142,14 +156,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
|||||||
lines.append(word)
|
lines.append(word)
|
||||||
return lines
|
return lines
|
||||||
|
|
||||||
def get_font(fontsize):
|
|
||||||
try:
|
|
||||||
return ImageFont.truetype(opts.font or Roboto, fontsize)
|
|
||||||
except Exception:
|
|
||||||
return ImageFont.truetype(Roboto, fontsize)
|
|
||||||
|
|
||||||
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
|
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
|
||||||
for i, line in enumerate(lines):
|
for line in lines:
|
||||||
fnt = initial_fnt
|
fnt = initial_fnt
|
||||||
fontsize = initial_fontsize
|
fontsize = initial_fontsize
|
||||||
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
|
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
|
||||||
@ -167,9 +175,6 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
|||||||
|
|
||||||
fnt = get_font(fontsize)
|
fnt = get_font(fontsize)
|
||||||
|
|
||||||
color_active = (0, 0, 0)
|
|
||||||
color_inactive = (153, 153, 153)
|
|
||||||
|
|
||||||
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
|
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
|
||||||
|
|
||||||
cols = im.width // width
|
cols = im.width // width
|
||||||
@ -178,7 +183,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
|||||||
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
|
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
|
||||||
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
|
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
|
||||||
|
|
||||||
calc_img = Image.new("RGB", (1, 1), "white")
|
calc_img = Image.new("RGB", (1, 1), color_background)
|
||||||
calc_d = ImageDraw.Draw(calc_img)
|
calc_d = ImageDraw.Draw(calc_img)
|
||||||
|
|
||||||
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
|
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
|
||||||
@ -199,7 +204,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
|
|||||||
|
|
||||||
pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
|
pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
|
||||||
|
|
||||||
result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
|
result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background)
|
||||||
|
|
||||||
for row in range(rows):
|
for row in range(rows):
|
||||||
for col in range(cols):
|
for col in range(cols):
|
||||||
@ -301,12 +306,14 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
|
|||||||
|
|
||||||
if ratio < src_ratio:
|
if ratio < src_ratio:
|
||||||
fill_height = height // 2 - src_h // 2
|
fill_height = height // 2 - src_h // 2
|
||||||
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
|
if fill_height > 0:
|
||||||
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
|
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
|
||||||
|
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
|
||||||
elif ratio > src_ratio:
|
elif ratio > src_ratio:
|
||||||
fill_width = width // 2 - src_w // 2
|
fill_width = width // 2 - src_w // 2
|
||||||
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
|
if fill_width > 0:
|
||||||
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
|
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
|
||||||
|
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
|
||||||
|
|
||||||
return res
|
return res
|
||||||
|
|
||||||
@ -318,6 +325,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
|
|||||||
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
|
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
|
||||||
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
|
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
|
||||||
max_filename_part_length = 128
|
max_filename_part_length = 128
|
||||||
|
NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
|
||||||
|
|
||||||
|
|
||||||
def sanitize_filename_part(text, replace_spaces=True):
|
def sanitize_filename_part(text, replace_spaces=True):
|
||||||
@ -334,8 +342,20 @@ def sanitize_filename_part(text, replace_spaces=True):
|
|||||||
|
|
||||||
|
|
||||||
class FilenameGenerator:
|
class FilenameGenerator:
|
||||||
|
def get_vae_filename(self): #get the name of the VAE file.
|
||||||
|
if sd_vae.loaded_vae_file is None:
|
||||||
|
return "NoneType"
|
||||||
|
file_name = os.path.basename(sd_vae.loaded_vae_file)
|
||||||
|
split_file_name = file_name.split('.')
|
||||||
|
if len(split_file_name) > 1 and split_file_name[0] == '':
|
||||||
|
return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
|
||||||
|
else:
|
||||||
|
return split_file_name[0]
|
||||||
|
|
||||||
replacements = {
|
replacements = {
|
||||||
'seed': lambda self: self.seed if self.seed is not None else '',
|
'seed': lambda self: self.seed if self.seed is not None else '',
|
||||||
|
'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
|
||||||
|
'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
|
||||||
'steps': lambda self: self.p and self.p.steps,
|
'steps': lambda self: self.p and self.p.steps,
|
||||||
'cfg': lambda self: self.p and self.p.cfg_scale,
|
'cfg': lambda self: self.p and self.p.cfg_scale,
|
||||||
'width': lambda self: self.image.width,
|
'width': lambda self: self.image.width,
|
||||||
@ -343,7 +363,7 @@ class FilenameGenerator:
|
|||||||
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
|
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
|
||||||
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
|
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
|
||||||
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
|
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
|
||||||
'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
|
'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
|
||||||
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
|
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
|
||||||
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
|
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
|
||||||
'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
|
'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
|
||||||
@ -352,14 +372,40 @@ class FilenameGenerator:
|
|||||||
'prompt_no_styles': lambda self: self.prompt_no_style(),
|
'prompt_no_styles': lambda self: self.prompt_no_style(),
|
||||||
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
|
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
|
||||||
'prompt_words': lambda self: self.prompt_words(),
|
'prompt_words': lambda self: self.prompt_words(),
|
||||||
|
'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
|
||||||
|
'batch_size': lambda self: self.p.batch_size,
|
||||||
|
'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
|
||||||
|
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
|
||||||
|
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
|
||||||
|
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
|
||||||
|
'user': lambda self: self.p.user,
|
||||||
|
'vae_filename': lambda self: self.get_vae_filename(),
|
||||||
|
'none': lambda self: '', # Overrides the default so you can get just the sequence number
|
||||||
}
|
}
|
||||||
default_time_format = '%Y%m%d%H%M%S'
|
default_time_format = '%Y%m%d%H%M%S'
|
||||||
|
|
||||||
def __init__(self, p, seed, prompt, image):
|
def __init__(self, p, seed, prompt, image, zip=False):
|
||||||
self.p = p
|
self.p = p
|
||||||
self.seed = seed
|
self.seed = seed
|
||||||
self.prompt = prompt
|
self.prompt = prompt
|
||||||
self.image = image
|
self.image = image
|
||||||
|
self.zip = zip
|
||||||
|
|
||||||
|
def hasprompt(self, *args):
|
||||||
|
lower = self.prompt.lower()
|
||||||
|
if self.p is None or self.prompt is None:
|
||||||
|
return None
|
||||||
|
outres = ""
|
||||||
|
for arg in args:
|
||||||
|
if arg != "":
|
||||||
|
division = arg.split("|")
|
||||||
|
expected = division[0].lower()
|
||||||
|
default = division[1] if len(division) > 1 else ""
|
||||||
|
if lower.find(expected) >= 0:
|
||||||
|
outres = f'{outres}{expected}'
|
||||||
|
else:
|
||||||
|
outres = outres if default == "" else f'{outres}{default}'
|
||||||
|
return sanitize_filename_part(outres)
|
||||||
|
|
||||||
def prompt_no_style(self):
|
def prompt_no_style(self):
|
||||||
if self.p is None or self.prompt is None:
|
if self.p is None or self.prompt is None:
|
||||||
@ -367,7 +413,7 @@ class FilenameGenerator:
|
|||||||
|
|
||||||
prompt_no_style = self.prompt
|
prompt_no_style = self.prompt
|
||||||
for style in shared.prompt_styles.get_style_prompts(self.p.styles):
|
for style in shared.prompt_styles.get_style_prompts(self.p.styles):
|
||||||
if len(style) > 0:
|
if style:
|
||||||
for part in style.split("{prompt}"):
|
for part in style.split("{prompt}"):
|
||||||
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
|
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
|
||||||
|
|
||||||
@ -376,7 +422,7 @@ class FilenameGenerator:
|
|||||||
return sanitize_filename_part(prompt_no_style, replace_spaces=False)
|
return sanitize_filename_part(prompt_no_style, replace_spaces=False)
|
||||||
|
|
||||||
def prompt_words(self):
|
def prompt_words(self):
|
||||||
words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
|
words = [x for x in re_nonletters.split(self.prompt or "") if x]
|
||||||
if len(words) == 0:
|
if len(words) == 0:
|
||||||
words = ["empty"]
|
words = ["empty"]
|
||||||
return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
|
return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
|
||||||
@ -384,16 +430,16 @@ class FilenameGenerator:
|
|||||||
def datetime(self, *args):
|
def datetime(self, *args):
|
||||||
time_datetime = datetime.datetime.now()
|
time_datetime = datetime.datetime.now()
|
||||||
|
|
||||||
time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
|
time_format = args[0] if (args and args[0] != "") else self.default_time_format
|
||||||
try:
|
try:
|
||||||
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
|
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
|
||||||
except pytz.exceptions.UnknownTimeZoneError as _:
|
except pytz.exceptions.UnknownTimeZoneError:
|
||||||
time_zone = None
|
time_zone = None
|
||||||
|
|
||||||
time_zone_time = time_datetime.astimezone(time_zone)
|
time_zone_time = time_datetime.astimezone(time_zone)
|
||||||
try:
|
try:
|
||||||
formatted_time = time_zone_time.strftime(time_format)
|
formatted_time = time_zone_time.strftime(time_format)
|
||||||
except (ValueError, TypeError) as _:
|
except (ValueError, TypeError):
|
||||||
formatted_time = time_zone_time.strftime(self.default_time_format)
|
formatted_time = time_zone_time.strftime(self.default_time_format)
|
||||||
|
|
||||||
return sanitize_filename_part(formatted_time, replace_spaces=False)
|
return sanitize_filename_part(formatted_time, replace_spaces=False)
|
||||||
@ -403,9 +449,9 @@ class FilenameGenerator:
|
|||||||
|
|
||||||
for m in re_pattern.finditer(x):
|
for m in re_pattern.finditer(x):
|
||||||
text, pattern = m.groups()
|
text, pattern = m.groups()
|
||||||
res += text
|
|
||||||
|
|
||||||
if pattern is None:
|
if pattern is None:
|
||||||
|
res += text
|
||||||
continue
|
continue
|
||||||
|
|
||||||
pattern_args = []
|
pattern_args = []
|
||||||
@ -423,14 +469,15 @@ class FilenameGenerator:
|
|||||||
replacement = fun(self, *pattern_args)
|
replacement = fun(self, *pattern_args)
|
||||||
except Exception:
|
except Exception:
|
||||||
replacement = None
|
replacement = None
|
||||||
print(f"Error adding [{pattern}] to filename", file=sys.stderr)
|
errors.report(f"Error adding [{pattern}] to filename", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
|
||||||
if replacement is not None:
|
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
|
||||||
res += str(replacement)
|
continue
|
||||||
|
elif replacement is not None:
|
||||||
|
res += text + str(replacement)
|
||||||
continue
|
continue
|
||||||
|
|
||||||
res += f'[{pattern}]'
|
res += f'{text}[{pattern}]'
|
||||||
|
|
||||||
return res
|
return res
|
||||||
|
|
||||||
@ -443,20 +490,66 @@ def get_next_sequence_number(path, basename):
|
|||||||
"""
|
"""
|
||||||
result = -1
|
result = -1
|
||||||
if basename != '':
|
if basename != '':
|
||||||
basename = basename + "-"
|
basename = f"{basename}-"
|
||||||
|
|
||||||
prefix_length = len(basename)
|
prefix_length = len(basename)
|
||||||
for p in os.listdir(path):
|
for p in os.listdir(path):
|
||||||
if p.startswith(basename):
|
if p.startswith(basename):
|
||||||
l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
|
parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
|
||||||
try:
|
try:
|
||||||
result = max(int(l[0]), result)
|
result = max(int(parts[0]), result)
|
||||||
except ValueError:
|
except ValueError:
|
||||||
pass
|
pass
|
||||||
|
|
||||||
return result + 1
|
return result + 1
|
||||||
|
|
||||||
|
|
||||||
|
def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'):
|
||||||
|
"""
|
||||||
|
Saves image to filename, including geninfo as text information for generation info.
|
||||||
|
For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key.
|
||||||
|
For JPG images, there's no dictionary and geninfo just replaces the EXIF description.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if extension is None:
|
||||||
|
extension = os.path.splitext(filename)[1]
|
||||||
|
|
||||||
|
image_format = Image.registered_extensions()[extension]
|
||||||
|
|
||||||
|
if extension.lower() == '.png':
|
||||||
|
existing_pnginfo = existing_pnginfo or {}
|
||||||
|
if opts.enable_pnginfo:
|
||||||
|
existing_pnginfo[pnginfo_section_name] = geninfo
|
||||||
|
|
||||||
|
if opts.enable_pnginfo:
|
||||||
|
pnginfo_data = PngImagePlugin.PngInfo()
|
||||||
|
for k, v in (existing_pnginfo or {}).items():
|
||||||
|
pnginfo_data.add_text(k, str(v))
|
||||||
|
else:
|
||||||
|
pnginfo_data = None
|
||||||
|
|
||||||
|
image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
|
||||||
|
|
||||||
|
elif extension.lower() in (".jpg", ".jpeg", ".webp"):
|
||||||
|
if image.mode == 'RGBA':
|
||||||
|
image = image.convert("RGB")
|
||||||
|
elif image.mode == 'I;16':
|
||||||
|
image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
|
||||||
|
|
||||||
|
image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
|
||||||
|
|
||||||
|
if opts.enable_pnginfo and geninfo is not None:
|
||||||
|
exif_bytes = piexif.dump({
|
||||||
|
"Exif": {
|
||||||
|
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
|
||||||
|
},
|
||||||
|
})
|
||||||
|
|
||||||
|
piexif.insert(exif_bytes, filename)
|
||||||
|
else:
|
||||||
|
image.save(filename, format=image_format, quality=opts.jpeg_quality)
|
||||||
|
|
||||||
|
|
||||||
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
|
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
|
||||||
"""Save an image.
|
"""Save an image.
|
||||||
|
|
||||||
@ -509,12 +602,12 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||||||
else:
|
else:
|
||||||
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
|
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
|
||||||
|
|
||||||
|
file_decoration = namegen.apply(file_decoration) + suffix
|
||||||
|
|
||||||
add_number = opts.save_images_add_number or file_decoration == ''
|
add_number = opts.save_images_add_number or file_decoration == ''
|
||||||
|
|
||||||
if file_decoration != "" and add_number:
|
if file_decoration != "" and add_number:
|
||||||
file_decoration = "-" + file_decoration
|
file_decoration = f"-{file_decoration}"
|
||||||
|
|
||||||
file_decoration = namegen.apply(file_decoration) + suffix
|
|
||||||
|
|
||||||
if add_number:
|
if add_number:
|
||||||
basecount = get_next_sequence_number(path, basename)
|
basecount = get_next_sequence_number(path, basename)
|
||||||
@ -541,38 +634,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||||||
info = params.pnginfo.get(pnginfo_section_name, None)
|
info = params.pnginfo.get(pnginfo_section_name, None)
|
||||||
|
|
||||||
def _atomically_save_image(image_to_save, filename_without_extension, extension):
|
def _atomically_save_image(image_to_save, filename_without_extension, extension):
|
||||||
# save image with .tmp extension to avoid race condition when another process detects new image in the directory
|
"""
|
||||||
temp_file_path = filename_without_extension + ".tmp"
|
save image with .tmp extension to avoid race condition when another process detects new image in the directory
|
||||||
image_format = Image.registered_extensions()[extension]
|
"""
|
||||||
|
temp_file_path = f"{filename_without_extension}.tmp"
|
||||||
|
|
||||||
if extension.lower() == '.png':
|
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
|
||||||
pnginfo_data = PngImagePlugin.PngInfo()
|
|
||||||
if opts.enable_pnginfo:
|
|
||||||
for k, v in params.pnginfo.items():
|
|
||||||
pnginfo_data.add_text(k, str(v))
|
|
||||||
|
|
||||||
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
|
|
||||||
|
|
||||||
elif extension.lower() in (".jpg", ".jpeg", ".webp"):
|
|
||||||
if image_to_save.mode == 'RGBA':
|
|
||||||
image_to_save = image_to_save.convert("RGB")
|
|
||||||
elif image_to_save.mode == 'I;16':
|
|
||||||
image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
|
|
||||||
|
|
||||||
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
|
|
||||||
|
|
||||||
if opts.enable_pnginfo and info is not None:
|
|
||||||
exif_bytes = piexif.dump({
|
|
||||||
"Exif": {
|
|
||||||
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
|
|
||||||
},
|
|
||||||
})
|
|
||||||
|
|
||||||
piexif.insert(exif_bytes, temp_file_path)
|
|
||||||
else:
|
|
||||||
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
|
|
||||||
|
|
||||||
# atomically rename the file with correct extension
|
|
||||||
os.replace(temp_file_path, filename_without_extension + extension)
|
os.replace(temp_file_path, filename_without_extension + extension)
|
||||||
|
|
||||||
fullfn_without_extension, extension = os.path.splitext(params.filename)
|
fullfn_without_extension, extension = os.path.splitext(params.filename)
|
||||||
@ -588,12 +656,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||||||
oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
|
oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
|
||||||
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
|
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
|
||||||
ratio = image.width / image.height
|
ratio = image.width / image.height
|
||||||
|
resize_to = None
|
||||||
if oversize and ratio > 1:
|
if oversize and ratio > 1:
|
||||||
image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
|
resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)
|
||||||
elif oversize:
|
elif oversize:
|
||||||
image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
|
resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)
|
||||||
|
|
||||||
|
if resize_to is not None:
|
||||||
|
try:
|
||||||
|
# Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16
|
||||||
|
image = image.resize(resize_to, LANCZOS)
|
||||||
|
except Exception:
|
||||||
|
image = image.resize(resize_to)
|
||||||
try:
|
try:
|
||||||
_atomically_save_image(image, fullfn_without_extension, ".jpg")
|
_atomically_save_image(image, fullfn_without_extension, ".jpg")
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
@ -602,7 +676,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||||||
if opts.save_txt and info is not None:
|
if opts.save_txt and info is not None:
|
||||||
txt_fullfn = f"{fullfn_without_extension}.txt"
|
txt_fullfn = f"{fullfn_without_extension}.txt"
|
||||||
with open(txt_fullfn, "w", encoding="utf8") as file:
|
with open(txt_fullfn, "w", encoding="utf8") as file:
|
||||||
file.write(info + "\n")
|
file.write(f"{info}\n")
|
||||||
else:
|
else:
|
||||||
txt_fullfn = None
|
txt_fullfn = None
|
||||||
|
|
||||||
@ -611,8 +685,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||||||
return fullfn, txt_fullfn
|
return fullfn, txt_fullfn
|
||||||
|
|
||||||
|
|
||||||
def read_info_from_image(image):
|
IGNORED_INFO_KEYS = {
|
||||||
items = image.info or {}
|
'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
|
||||||
|
'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
|
||||||
|
'icc_profile', 'chromaticity', 'photoshop',
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
|
||||||
|
items = (image.info or {}).copy()
|
||||||
|
|
||||||
geninfo = items.pop('parameters', None)
|
geninfo = items.pop('parameters', None)
|
||||||
|
|
||||||
@ -628,9 +709,8 @@ def read_info_from_image(image):
|
|||||||
items['exif comment'] = exif_comment
|
items['exif comment'] = exif_comment
|
||||||
geninfo = exif_comment
|
geninfo = exif_comment
|
||||||
|
|
||||||
for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
|
for field in IGNORED_INFO_KEYS:
|
||||||
'loop', 'background', 'timestamp', 'duration']:
|
items.pop(field, None)
|
||||||
items.pop(field, None)
|
|
||||||
|
|
||||||
if items.get("Software", None) == "NovelAI":
|
if items.get("Software", None) == "NovelAI":
|
||||||
try:
|
try:
|
||||||
@ -641,8 +721,7 @@ def read_info_from_image(image):
|
|||||||
Negative prompt: {json_info["uc"]}
|
Negative prompt: {json_info["uc"]}
|
||||||
Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
|
Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
|
||||||
except Exception:
|
except Exception:
|
||||||
print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
|
errors.report("Error parsing NovelAI image generation parameters", exc_info=True)
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
|
|
||||||
return geninfo, items
|
return geninfo, items
|
||||||
|
|
||||||
|
@ -1,33 +1,34 @@
|
|||||||
import math
|
|
||||||
import os
|
import os
|
||||||
import sys
|
from contextlib import closing
|
||||||
import traceback
|
from pathlib import Path
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
|
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
|
||||||
|
import gradio as gr
|
||||||
|
|
||||||
from modules import devices, sd_samplers
|
from modules import sd_samplers, images as imgutil
|
||||||
from modules.generation_parameters_copypaste import create_override_settings_dict
|
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
|
||||||
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
|
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
|
||||||
from modules.shared import opts, state
|
from modules.shared import opts, state
|
||||||
|
from modules.images import save_image
|
||||||
import modules.shared as shared
|
import modules.shared as shared
|
||||||
import modules.processing as processing
|
import modules.processing as processing
|
||||||
from modules.ui import plaintext_to_html
|
from modules.ui import plaintext_to_html
|
||||||
import modules.images as images
|
|
||||||
import modules.scripts
|
import modules.scripts
|
||||||
|
|
||||||
|
|
||||||
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
|
||||||
processing.fix_seed(p)
|
processing.fix_seed(p)
|
||||||
|
|
||||||
images = shared.listfiles(input_dir)
|
images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
|
||||||
|
|
||||||
is_inpaint_batch = False
|
is_inpaint_batch = False
|
||||||
if inpaint_mask_dir:
|
if inpaint_mask_dir:
|
||||||
inpaint_masks = shared.listfiles(inpaint_mask_dir)
|
inpaint_masks = shared.listfiles(inpaint_mask_dir)
|
||||||
is_inpaint_batch = len(inpaint_masks) > 0
|
is_inpaint_batch = bool(inpaint_masks)
|
||||||
if is_inpaint_batch:
|
|
||||||
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
|
if is_inpaint_batch:
|
||||||
|
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
|
||||||
|
|
||||||
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
|
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
|
||||||
|
|
||||||
@ -38,6 +39,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
|||||||
|
|
||||||
state.job_count = len(images) * p.n_iter
|
state.job_count = len(images) * p.n_iter
|
||||||
|
|
||||||
|
# extract "default" params to use in case getting png info fails
|
||||||
|
prompt = p.prompt
|
||||||
|
negative_prompt = p.negative_prompt
|
||||||
|
seed = p.seed
|
||||||
|
cfg_scale = p.cfg_scale
|
||||||
|
sampler_name = p.sampler_name
|
||||||
|
steps = p.steps
|
||||||
|
|
||||||
for i, image in enumerate(images):
|
for i, image in enumerate(images):
|
||||||
state.job = f"{i+1} out of {len(images)}"
|
state.job = f"{i+1} out of {len(images)}"
|
||||||
if state.skipped:
|
if state.skipped:
|
||||||
@ -46,39 +55,80 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
|||||||
if state.interrupted:
|
if state.interrupted:
|
||||||
break
|
break
|
||||||
|
|
||||||
img = Image.open(image)
|
try:
|
||||||
|
img = Image.open(image)
|
||||||
|
except UnidentifiedImageError as e:
|
||||||
|
print(e)
|
||||||
|
continue
|
||||||
# Use the EXIF orientation of photos taken by smartphones.
|
# Use the EXIF orientation of photos taken by smartphones.
|
||||||
img = ImageOps.exif_transpose(img)
|
img = ImageOps.exif_transpose(img)
|
||||||
|
|
||||||
|
if to_scale:
|
||||||
|
p.width = int(img.width * scale_by)
|
||||||
|
p.height = int(img.height * scale_by)
|
||||||
|
|
||||||
p.init_images = [img] * p.batch_size
|
p.init_images = [img] * p.batch_size
|
||||||
|
|
||||||
|
image_path = Path(image)
|
||||||
if is_inpaint_batch:
|
if is_inpaint_batch:
|
||||||
# try to find corresponding mask for an image using simple filename matching
|
# try to find corresponding mask for an image using simple filename matching
|
||||||
mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
|
if len(inpaint_masks) == 1:
|
||||||
# if not found use first one ("same mask for all images" use-case)
|
|
||||||
if not mask_image_path in inpaint_masks:
|
|
||||||
mask_image_path = inpaint_masks[0]
|
mask_image_path = inpaint_masks[0]
|
||||||
|
else:
|
||||||
|
# try to find corresponding mask for an image using simple filename matching
|
||||||
|
mask_image_dir = Path(inpaint_mask_dir)
|
||||||
|
masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
|
||||||
|
|
||||||
|
if len(masks_found) == 0:
|
||||||
|
print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
|
||||||
|
continue
|
||||||
|
|
||||||
|
# it should contain only 1 matching mask
|
||||||
|
# otherwise user has many masks with the same name but different extensions
|
||||||
|
mask_image_path = masks_found[0]
|
||||||
|
|
||||||
mask_image = Image.open(mask_image_path)
|
mask_image = Image.open(mask_image_path)
|
||||||
p.image_mask = mask_image
|
p.image_mask = mask_image
|
||||||
|
|
||||||
|
if use_png_info:
|
||||||
|
try:
|
||||||
|
info_img = img
|
||||||
|
if png_info_dir:
|
||||||
|
info_img_path = os.path.join(png_info_dir, os.path.basename(image))
|
||||||
|
info_img = Image.open(info_img_path)
|
||||||
|
geninfo, _ = imgutil.read_info_from_image(info_img)
|
||||||
|
parsed_parameters = parse_generation_parameters(geninfo)
|
||||||
|
parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
|
||||||
|
except Exception:
|
||||||
|
parsed_parameters = {}
|
||||||
|
|
||||||
|
p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
|
||||||
|
p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
|
||||||
|
p.seed = int(parsed_parameters.get("Seed", seed))
|
||||||
|
p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
|
||||||
|
p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
|
||||||
|
p.steps = int(parsed_parameters.get("Steps", steps))
|
||||||
|
|
||||||
proc = modules.scripts.scripts_img2img.run(p, *args)
|
proc = modules.scripts.scripts_img2img.run(p, *args)
|
||||||
if proc is None:
|
if proc is None:
|
||||||
proc = process_images(p)
|
proc = process_images(p)
|
||||||
|
|
||||||
for n, processed_image in enumerate(proc.images):
|
for n, processed_image in enumerate(proc.images):
|
||||||
filename = os.path.basename(image)
|
filename = image_path.stem
|
||||||
|
infotext = proc.infotext(p, n)
|
||||||
|
relpath = os.path.dirname(os.path.relpath(image, input_dir))
|
||||||
|
|
||||||
if n > 0:
|
if n > 0:
|
||||||
left, right = os.path.splitext(filename)
|
filename += f"-{n}"
|
||||||
filename = f"{left}-{n}{right}"
|
|
||||||
|
|
||||||
if not save_normally:
|
if not save_normally:
|
||||||
os.makedirs(output_dir, exist_ok=True)
|
os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
|
||||||
if processed_image.mode == 'RGBA':
|
if processed_image.mode == 'RGBA':
|
||||||
processed_image = processed_image.convert("RGB")
|
processed_image = processed_image.convert("RGB")
|
||||||
processed_image.save(os.path.join(output_dir, filename))
|
save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
|
||||||
|
|
||||||
|
|
||||||
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
|
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
|
||||||
override_settings = create_override_settings_dict(override_settings_texts)
|
override_settings = create_override_settings_dict(override_settings_texts)
|
||||||
|
|
||||||
is_batch = mode == 5
|
is_batch = mode == 5
|
||||||
@ -92,7 +142,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||||||
elif mode == 2: # inpaint
|
elif mode == 2: # inpaint
|
||||||
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
|
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
|
||||||
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
|
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
|
||||||
mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
|
mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
|
||||||
|
mask = ImageChops.lighter(alpha_mask, mask).convert('L')
|
||||||
image = image.convert("RGB")
|
image = image.convert("RGB")
|
||||||
elif mode == 3: # inpaint sketch
|
elif mode == 3: # inpaint sketch
|
||||||
image = inpaint_color_sketch
|
image = inpaint_color_sketch
|
||||||
@ -114,6 +165,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||||||
if image is not None:
|
if image is not None:
|
||||||
image = ImageOps.exif_transpose(image)
|
image = ImageOps.exif_transpose(image)
|
||||||
|
|
||||||
|
if selected_scale_tab == 1 and not is_batch:
|
||||||
|
assert image, "Can't scale by because no image is selected"
|
||||||
|
|
||||||
|
width = int(image.width * scale_by)
|
||||||
|
height = int(image.height * scale_by)
|
||||||
|
|
||||||
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||||
|
|
||||||
p = StableDiffusionProcessingImg2Img(
|
p = StableDiffusionProcessingImg2Img(
|
||||||
@ -151,27 +208,28 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||||||
override_settings=override_settings,
|
override_settings=override_settings,
|
||||||
)
|
)
|
||||||
|
|
||||||
p.scripts = modules.scripts.scripts_txt2img
|
p.scripts = modules.scripts.scripts_img2img
|
||||||
p.script_args = args
|
p.script_args = args
|
||||||
|
|
||||||
|
p.user = request.username
|
||||||
|
|
||||||
if shared.cmd_opts.enable_console_prompts:
|
if shared.cmd_opts.enable_console_prompts:
|
||||||
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
|
||||||
|
|
||||||
if mask:
|
if mask:
|
||||||
p.extra_generation_params["Mask blur"] = mask_blur
|
p.extra_generation_params["Mask blur"] = mask_blur
|
||||||
|
|
||||||
if is_batch:
|
with closing(p):
|
||||||
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
if is_batch:
|
||||||
|
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
|
||||||
|
|
||||||
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
|
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
|
||||||
|
|
||||||
processed = Processed(p, [], p.seed, "")
|
processed = Processed(p, [], p.seed, "")
|
||||||
else:
|
else:
|
||||||
processed = modules.scripts.scripts_img2img.run(p, *args)
|
processed = modules.scripts.scripts_img2img.run(p, *args)
|
||||||
if processed is None:
|
if processed is None:
|
||||||
processed = process_images(p)
|
processed = process_images(p)
|
||||||
|
|
||||||
p.close()
|
|
||||||
|
|
||||||
shared.total_tqdm.clear()
|
shared.total_tqdm.clear()
|
||||||
|
|
||||||
@ -182,4 +240,4 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
|||||||
if opts.do_not_show_images:
|
if opts.do_not_show_images:
|
||||||
processed.images = []
|
processed.images = []
|
||||||
|
|
||||||
return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
|
return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
|
||||||
|
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Reference in New Issue
Block a user