diff --git a/.eslintignore b/.eslintignore new file mode 100644 index 00000000..1cfd9487 --- /dev/null +++ b/.eslintignore @@ -0,0 +1,4 @@ +extensions +extensions-disabled +repositories +venv \ No newline at end of file diff --git a/.eslintrc.js b/.eslintrc.js new file mode 100644 index 00000000..f33aca09 --- /dev/null +++ b/.eslintrc.js @@ -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", + } +}; diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 00000000..4104da63 --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,2 @@ +# Apply ESlint +9c54b78d9dde5601e916f308d9a9d6953ec39430 \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 7d435297..d80b24e2 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -43,10 +43,19 @@ body: - type: input id: commit attributes: - label: 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.) + label: Version or Commit where the problem happens + 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: 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 id: platforms attributes: @@ -59,6 +68,35 @@ body: - iOS - Android - 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 id: browsers attributes: diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 69056331..c9fcda2e 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -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. - -**Additional notes and description of your changes** - -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. \ No newline at end of file +- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) +- [ ] I have performed a self-review of my own code +- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style) +- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests) diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml index a168be5b..8ebf5918 100644 --- a/.github/workflows/on_pull_request.yaml +++ b/.github/workflows/on_pull_request.yaml @@ -1,39 +1,34 @@ -# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file name: Run Linting/Formatting on Pull Requests on: - push - 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: - lint: + lint-python: runs-on: ubuntu-latest steps: - name: Checkout Code uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v4 + - uses: actions/setup-python@v4 with: - python-version: 3.10.6 - cache: pip - cache-dependency-path: | - **/requirements*txt - - name: Install PyLint - run: | - python -m pip install --upgrade pip - pip install pylint - # This lets PyLint check to see if it can resolve imports - - name: Install dependencies - run: | - export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit" - python launch.py - - name: Analysing the code with pylint - run: | - pylint $(git ls-files '*.py') + python-version: 3.11 + # NB: there's no cache: pip here since we're not installing anything + # from the requirements.txt file(s) in the repository; it's faster + # not to have GHA download an (at the time of writing) 4 GB cache + # of PyTorch and other dependencies. + - name: Install Ruff + run: pip install ruff==0.0.272 + - name: Run Ruff + run: ruff . + lint-js: + runs-on: ubuntu-latest + steps: + - name: Checkout Code + 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 diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index 9a0b8d22..178c026a 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -17,13 +17,54 @@ jobs: cache: pip cache-dependency-path: | **/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 + --no-half + --disable-opt-split-attention + --use-cpu all + --api-server-stop + 2>&1 | tee output.txt & - name: Run tests - run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test - - name: Upload main app stdout-stderr + run: | + 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 if: always() with: - name: stdout-stderr - path: | - test/stdout.txt - test/stderr.txt + name: output + path: output.txt + - name: Upload coverage HTML + uses: actions/upload-artifact@v3 + if: always() + with: + name: htmlcov + path: htmlcov diff --git a/.gitignore b/.gitignore index 7328401f..09734267 100644 --- a/.gitignore +++ b/.gitignore @@ -34,3 +34,6 @@ notification.mp3 /test/stderr.txt /cache.json* /config_states/ +/node_modules +/package-lock.json +/.coverage* diff --git a/CHANGELOG.md b/CHANGELOG.md index 8cf444ca..6a31f35b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,56 +1,193 @@ +## 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 `` 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 + * 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 + * 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 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' + * 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) + * 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: - * gradio bumped to 3.29.0 - * torch bumped to 2.0.1 - * --subpath option for gradio for use with reverse proxy - * linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable) - * possible frontend optimization: do not apply localizations if there are none - * Add extra `None` option for VAE in XYZ plot + * 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 + * 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 + * tooltip localization support + * add API method to get LoRA models with prompt ### Bug Fixes: - * re-add /docs endpoint + * 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 + * 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 @@ -58,20 +195,20 @@ ## 1.1.1 ### Bug Fixes: - * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle + * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle ## 1.1.0 ### Features: - * switch to torch 2.0.0 (except for AMD GPUs) + * 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 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 - * setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs + * 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 - * (optimization) option to remove negative conditioning at low sigma values #9177 + * 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 @@ -80,22 +217,22 @@ * button to restore the progress from session lost / tab reload ### Minor: - * gradio bumped to 3.28.1 - * in extra tab, change extras "scale to" to sliders + * 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 insall into current directory rather than /home/username - * sort textual inversion embeddings by name (case insensitive) + * 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 - * extra networks UI in now fixed height and scrollable - * add disable_tls_verify arg for use with self-signed certs + * 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 + * add reload callback + * add `is_hr_pass` field for processing ### Bug Fixes: * fix broken batch image processing on 'Extras/Batch Process' tab @@ -111,10 +248,10 @@ * 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 - * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format - * do not fail all Loras if some have failed to load when making a picture + * 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 diff --git a/README.md b/README.md index 67a1a83a..73d94960 100644 --- a/README.md +++ b/README.md @@ -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 - a man in a `((tuxedo))` - will pay more attention to tuxedo - 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 - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion @@ -99,6 +99,12 @@ Alternatively, use online services (like Google Colab): - [List of Online Services](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://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://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) + ### Automatic Installation on Windows 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). @@ -158,5 +164,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC +- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You) diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index bc11cc6e..7f450086 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -88,7 +88,7 @@ class LDSR: x_t = None logs = None - for n in range(n_runs): + for _ in range(n_runs): 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 = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) @@ -110,7 +110,6 @@ class LDSR: diffusion_steps = int(steps) eta = 1.0 - down_sample_method = 'Lanczos' gc.collect() if torch.cuda.is_available: @@ -131,11 +130,11 @@ class LDSR: im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) else: 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_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')) - + logs = self.run(model["model"], im_padded, diffusion_steps, eta) sample = logs["sample"] @@ -158,7 +157,7 @@ class LDSR: def get_cond(selected_path): - example = dict() + example = {} up_f = 4 c = selected_path.convert('RGB') c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) @@ -196,7 +195,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s @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, 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, return_first_stage_outputs=True, @@ -244,7 +243,7 @@ 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) log["sample_noquant"] = x_sample_noquant log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) - except: + except Exception: pass log["sample"] = x_sample diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py index da19cff1..bd78dece 100644 --- a/extensions-builtin/LDSR/scripts/ldsr_model.py +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -1,13 +1,11 @@ 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 ldsr_model_arch import LDSR -from modules import shared, script_callbacks -import sd_hijack_autoencoder, sd_hijack_ddpm_v1 +from modules import shared, script_callbacks, errors +import sd_hijack_autoencoder # noqa: F401 +import sd_hijack_ddpm_v1 # noqa: F401 class UpscalerLDSR(Upscaler): @@ -44,22 +42,17 @@ class UpscalerLDSR(Upscaler): if local_safetensors_path is not None and os.path.exists(local_safetensors_path): model = local_safetensors_path else: - model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True) + model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") - yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True) + yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") - try: - return LDSR(model, yaml) - - except Exception: - print("Error importing LDSR:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - return None + return LDSR(model, yaml) def do_upscale(self, img, path): - ldsr = self.load_model(path) - if ldsr is None: - print("NO LDSR!") + try: + ldsr = self.load_model(path) + except Exception: + errors.report(f"Failed loading LDSR model {path}", exc_info=True) return img ddim_steps = shared.opts.ldsr_steps return ldsr.super_resolution(img, ddim_steps, self.scale) diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py index 8e03c7f8..c29d274d 100644 --- a/extensions-builtin/LDSR/sd_hijack_autoencoder.py +++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py @@ -1,16 +1,21 @@ # 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 # 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 pytorch_lightning as pl import torch.nn.functional as F 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.util import instantiate_from_config import ldm.models.autoencoder +from packaging import version class VQModel(pl.LightningModule): def __init__(self, @@ -19,7 +24,7 @@ class VQModel(pl.LightningModule): n_embed, embed_dim, ckpt_path=None, - ignore_keys=[], + ignore_keys=None, image_key="image", colorize_nlabels=None, monitor=None, @@ -57,7 +62,7 @@ class VQModel(pl.LightningModule): print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") 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.lr_g_factor = lr_g_factor @@ -76,18 +81,19 @@ class VQModel(pl.LightningModule): if context is not None: 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"] keys = list(sd.keys()) for k in keys: - for ik in ignore_keys: + for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) 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}") + if unexpected: print(f"Unexpected Keys: {unexpected}") def on_train_batch_end(self, *args, **kwargs): @@ -165,7 +171,7 @@ class VQModel(pl.LightningModule): def validation_step(self, batch, batch_idx): log_dict = self._validation_step(batch, batch_idx) 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 def _validation_step(self, batch, batch_idx, suffix=""): @@ -232,7 +238,7 @@ class VQModel(pl.LightningModule): return self.decoder.conv_out.weight def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): - log = dict() + log = {} x = self.get_input(batch, self.image_key) x = x.to(self.device) if only_inputs: @@ -249,7 +255,8 @@ class VQModel(pl.LightningModule): if plot_ema: with self.ema_scope(): 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 return log @@ -264,7 +271,7 @@ class VQModel(pl.LightningModule): class VQModelInterface(VQModel): 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 def encode(self, x): @@ -282,5 +289,5 @@ class VQModelInterface(VQModel): dec = self.decoder(quant) return dec -setattr(ldm.models.autoencoder, "VQModel", VQModel) -setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface) +ldm.models.autoencoder.VQModel = VQModel +ldm.models.autoencoder.VQModelInterface = VQModelInterface diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py index 5c0488e5..04adc5eb 100644 --- a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule): beta_schedule="linear", loss_type="l2", ckpt_path=None, - ignore_keys=[], + ignore_keys=None, load_only_unet=False, monitor="val/loss", use_ema=True, @@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule): if monitor is not None: self.monitor = monitor 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, 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: 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") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: - for ik in ignore_keys: + for ik in ignore_keys or []: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) 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}") - if len(unexpected) > 0: + if unexpected: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): @@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule): @torch.no_grad() 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) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) @@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule): log["inputs"] = x # get diffusion row - diffusion_row = list() + diffusion_row = [] x_start = x[:n_row] for t in range(self.num_timesteps): @@ -444,13 +444,13 @@ class LatentDiffusionV1(DDPMV1): conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) 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.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 - except: + except Exception: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor @@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1): self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False - self.bbox_tokenizer = None + self.bbox_tokenizer = None self.restarted_from_ckpt = False 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 ) # 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], force_not_quantize=predict_cids or force_not_quantize) 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())) 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): if isinstance(cond, dict): @@ -900,7 +890,7 @@ class LatentDiffusionV1(DDPMV1): if hasattr(self, "split_input_params"): 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) stride = self.split_input_params["stride"] # eg. (64, 64) @@ -1126,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1): if cond is not None: if isinstance(cond, dict): 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: 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: intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) + if callback: + callback(i) + if img_callback: + img_callback(img, i) return img, intermediates @torch.no_grad() @@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1): if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) - if callback: callback(i) - if img_callback: img_callback(img, i) + if callback: + callback(i) + if img_callback: + img_callback(img, i) if return_intermediates: return img, intermediates @@ -1221,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1): if cond is not None: if isinstance(cond, dict): 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: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, @@ -1253,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1): use_ddim = ddim_steps is not None - log = dict() + log = {} z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, @@ -1280,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1): if plot_diffusion_rows: # get diffusion row - diffusion_row = list() + diffusion_row = [] z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: @@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1): if inpaint: # 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) # zeros will be filled in 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 def __init__(self, cond_stage_key, *args, **kwargs): 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): - 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' dset = self.trainer.datamodule.datasets[key] @@ -1443,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1): logs['bbox_image'] = cond_img return logs -setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1) -setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1) -setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1) -setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1) +ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1 +ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1 +ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1 +ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1 diff --git a/extensions-builtin/LDSR/vqvae_quantize.py b/extensions-builtin/LDSR/vqvae_quantize.py new file mode 100644 index 00000000..dd14b8fd --- /dev/null +++ b/extensions-builtin/LDSR/vqvae_quantize.py @@ -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 diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index ccb249ac..66ee9c85 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -9,19 +9,37 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): def activate(self, p, params_list): additional = shared.opts.sd_lora - if additional != "None" 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 lora.available_loras and not any(x for x in params_list if x.items[0] == additional): p.all_prompts = [x + f"" for x in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) names = [] multipliers = [] for params in params_list: - assert len(params.items) > 0 + assert params.items names.append(params.items[0]) multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0) lora.load_loras(names, multipliers) + if shared.opts.lora_add_hashes_to_infotext: + lora_hashes = [] + for item in lora.loaded_loras: + shorthash = item.lora_on_disk.shorthash + if not shorthash: + continue + + alias = item.mentioned_name + if not alias: + continue + + alias = alias.replace(":", "").replace(",", "") + + lora_hashes.append(f"{alias}: {shorthash}") + + if lora_hashes: + p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes) + def deactivate(self, p): pass diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index b5d0c98f..cd46e6c7 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -1,10 +1,9 @@ -import glob import os import re import torch from typing import Union -from modules import shared, devices, sd_models, errors, scripts +from modules import shared, devices, sd_models, errors, scripts, sd_hijack, hashes metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} @@ -77,9 +76,9 @@ class LoraOnDisk: self.name = name self.filename = filename self.metadata = {} + self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" - _, ext = os.path.splitext(filename) - if ext.lower() == ".safetensors": + if self.is_safetensors: try: self.metadata = sd_models.read_metadata_from_safetensors(filename) except Exception as e: @@ -95,14 +94,43 @@ class LoraOnDisk: 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 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 + '' + ) + + def set_hash(self, v): + self.hash = v + self.shorthash = self.hash[0:12] + + if self.shorthash: + available_lora_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): + if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in forbidden_lora_aliases: + return self.name + else: + return self.alias + class LoraModule: - def __init__(self, name): + def __init__(self, name, lora_on_disk: LoraOnDisk): self.name = name + self.lora_on_disk = lora_on_disk self.multiplier = 1.0 self.modules = {} self.mtime = None + self.mentioned_name = None + """the text that was used to add lora to prompt - can be either name or an alias""" + class LoraUpDownModule: def __init__(self): @@ -127,11 +155,11 @@ def assign_lora_names_to_compvis_modules(sd_model): sd_model.lora_layer_mapping = lora_layer_mapping -def load_lora(name, filename): - lora = LoraModule(name) - lora.mtime = os.path.getmtime(filename) +def load_lora(name, lora_on_disk): + lora = LoraModule(name, lora_on_disk) + lora.mtime = os.path.getmtime(lora_on_disk.filename) - sd = sd_models.read_state_dict(filename) + sd = sd_models.read_state_dict(lora_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, 'lora_layer_mapping'): @@ -177,7 +205,7 @@ def load_lora(name, filename): 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__}' + raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}") with torch.no_grad(): module.weight.copy_(weight) @@ -189,10 +217,10 @@ def load_lora(name, filename): 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' + raise AssertionError(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}") + if keys_failed_to_match: + print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}") return lora @@ -207,30 +235,41 @@ def load_loras(names, multipliers=None): loaded_loras.clear() loras_on_disk = [available_lora_aliases.get(name, None) for name in names] - if any([x is None for x in loras_on_disk]): + if any(x is None for x in loras_on_disk): list_available_loras() loras_on_disk = [available_lora_aliases.get(name, None) for name in names] + failed_to_load_loras = [] + 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: try: - lora = load_lora(name, lora_on_disk.filename) + lora = load_lora(name, lora_on_disk) except Exception as e: errors.display(e, f"loading Lora {lora_on_disk.filename}") continue + lora.mentioned_name = name + + lora_on_disk.read_hash() + if lora is None: + failed_to_load_loras.append(name) print(f"Couldn't find Lora with name {name}") continue lora.multiplier = multipliers[i] if multipliers else 1.0 loaded_loras.append(lora) + if failed_to_load_loras: + sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras)) + def lora_calc_updown(lora, module, target): with torch.no_grad(): @@ -314,7 +353,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu print(f'failed to calculate lora weights for layer {lora_layer_name}') - setattr(self, "lora_current_names", wanted_names) + self.lora_current_names = wanted_names def lora_forward(module, input, original_forward): @@ -348,8 +387,8 @@ def lora_forward(module, input, original_forward): def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): - setattr(self, "lora_current_names", ()) - setattr(self, "lora_weights_backup", None) + self.lora_current_names = () + self.lora_weights_backup = None def lora_Linear_forward(self, input): @@ -398,17 +437,22 @@ def list_available_loras(): available_loras.clear() available_lora_aliases.clear() forbidden_lora_aliases.clear() - forbidden_lora_aliases.update({"none": 1}) + available_lora_hash_lookup.clear() + forbidden_lora_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"])) - for filename in sorted(candidates, key=str.lower): + for filename in candidates: if os.path.isdir(filename): continue name = os.path.splitext(os.path.basename(filename))[0] - entry = LoraOnDisk(name, filename) + try: + entry = LoraOnDisk(name, filename) + except OSError: # should catch FileNotFoundError and PermissionError etc. + errors.report(f"Failed to load LoRA {name} from {filename}", exc_info=True) + continue available_loras[name] = entry @@ -428,7 +472,7 @@ def infotext_pasted(infotext, params): added = [] - for k, v in params.items(): + for k in params: if not k.startswith("AddNet Model "): continue @@ -452,8 +496,10 @@ def infotext_pasted(infotext, params): if added: params["Prompt"] += "\n" + "".join(added) + available_loras = {} available_lora_aliases = {} +available_lora_hash_lookup = {} forbidden_lora_aliases = {} loaded_loras = [] diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 060bda05..e650f469 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -1,3 +1,5 @@ +import re + import torch import gradio as gr from fastapi import FastAPI @@ -53,8 +55,9 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { - "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras), - "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), + "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras), + "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"), })) @@ -77,6 +80,37 @@ def api_loras(_: gr.Blocks, app: FastAPI): async def get_loras(): return [create_lora_json(obj) for obj in lora.available_loras.values()] + @app.post("/sdapi/v1/refresh-loras") + async def refresh_loras(): + return lora.list_available_loras() + script_callbacks.on_app_started(api_loras) +re_lora = re.compile(""), "local_preview": f"{path}.{shared.opts.samples_format}", "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None, + "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, + } def allowed_directories_for_previews(self): diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index c7fd5739..ffef26b2 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -1,19 +1,16 @@ -import os.path import sys -import traceback import PIL.Image import numpy as np import torch from tqdm import tqdm -from basicsr.utils.download_util import load_file_from_url - import modules.upscaler -from modules import devices, modelloader -from scunet_model_arch import SCUNet as net +from modules import devices, modelloader, script_callbacks, errors +from scunet_model_arch import SCUNet + +from modules.modelloader import load_file_from_url from modules.shared import opts -from modules import images class UpscalerScuNET(modules.upscaler.Upscaler): @@ -29,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers = [] add_model2 = True for file in model_paths: - if "http" in file: + if file.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(file) @@ -39,8 +36,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) scalers.append(scaler_data) except Exception: - print(f"Error loading ScuNET model: {file}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + errors.report(f"Error loading ScuNET model: {file}", exc_info=True) if add_model2: scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) scalers.append(scaler_data2) @@ -91,9 +87,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler): torch.cuda.empty_cache() - model = self.load_model(selected_file) - if model is None: - print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) + 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 device = devices.get_device_for('scunet') @@ -121,20 +118,27 @@ class UpscalerScuNET(modules.upscaler.Upscaler): def load_model(self, path: str): device = devices.get_device_for('scunet') - if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, - progress=True) + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") else: filename = path - if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: - 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 = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() - for k, v in model.named_parameters(): + for _, v in model.named_parameters(): v.requires_grad = False model = model.to(device) 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) diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py index 43ca8d36..b51a8806 100644 --- a/extensions-builtin/ScuNET/scunet_model_arch.py +++ b/extensions-builtin/ScuNET/scunet_model_arch.py @@ -61,7 +61,9 @@ class WMSA(nn.Module): Returns: 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) h_windows = x.size(1) w_windows = x.size(2) @@ -85,8 +87,9 @@ class WMSA(nn.Module): 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) - if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), - dims=(1, 2)) + if self.type != 'W': + output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2)) + return output def relative_embedding(self): @@ -262,4 +265,4 @@ class SCUNet(nn.Module): nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) \ No newline at end of file + nn.init.constant_(m.weight, 1.0) diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index e8783bca..c6bc53a8 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,18 +1,17 @@ -import contextlib -import os +import sys import numpy as np import torch from PIL import Image -from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader, devices, script_callbacks, shared -from modules.shared import cmd_opts, opts, state -from swinir_model_arch import SwinIR as net -from swinir_model_arch_v2 import Swin2SR as net2 +from modules.shared import opts, state +from swinir_model_arch import SwinIR +from swinir_model_arch_v2 import Swin2SR from modules.upscaler import Upscaler, UpscalerData +SWINIR_MODEL_URL = "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') @@ -20,16 +19,14 @@ device_swinir = devices.get_device_for('swinir') class UpscalerSwinIR(Upscaler): def __init__(self, dirname): self.name = "SwinIR" - self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ - "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ - "-L_x4_GAN.pth " + self.model_url = SWINIR_MODEL_URL self.model_name = "SwinIR 4x" self.user_path = dirname super().__init__() scalers = [] model_files = self.find_models(ext_filter=[".pt", ".pth"]) for model in model_files: - if "http" in model: + if model.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(model) @@ -38,42 +35,45 @@ class UpscalerSwinIR(Upscaler): self.scalers = scalers def do_upscale(self, img, model_file): - model = self.load_model(model_file) - if model is 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) img = upscale(img, model) try: torch.cuda.empty_cache() - except: + except Exception: pass return img def load_model(self, path, scale=4): - if "http" in path: - dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") - filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True) + if path.startswith("http"): + filename = modelloader.load_file_from_url( + url=path, + model_dir=self.model_download_path, + file_name=f"{self.model_name.replace(' ', '_')}.pth", + ) else: filename = path - if filename is None or not os.path.exists(filename): - return None if filename.endswith(".v2.pth"): - model = net2( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6], - embed_dim=180, - num_heads=[6, 6, 6, 6, 6, 6], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="1conv", + model = Swin2SR( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6], + embed_dim=180, + num_heads=[6, 6, 6, 6, 6, 6], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="1conv", ) params = None else: - model = net( + model = SwinIR( upscale=scale, in_chans=3, img_size=64, @@ -151,7 +151,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale): for w_idx in w_idx_list: if state.interrupted or state.skipped: break - + 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) diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py index 863f42db..93b93274 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch.py +++ b/extensions-builtin/SwinIR/swinir_model_arch.py @@ -644,7 +644,7 @@ class SwinIR(nn.Module): """ 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, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, @@ -805,7 +805,7 @@ class SwinIR(nn.Module): def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) - + self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range @@ -844,7 +844,7 @@ class SwinIR(nn.Module): H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): + for layer in self.layers: flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py index 0e28ae6e..dad22cca 100644 --- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py +++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py @@ -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., - pretrained_window_size=[0, 0]): + pretrained_window_size=(0, 0)): super().__init__() self.dim = dim @@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module): attn_mask = None self.register_buffer("attn_mask", attn_mask) - + def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size @@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module): 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)) - return attn_mask + return attn_mask def forward(self, x, 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 else: attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - + # merge windows 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 @@ -369,7 +369,7 @@ class PatchMerging(nn.Module): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 - return flops + return flops class BasicLayer(nn.Module): """ 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.norm2.bias, 0) nn.init.constant_(blk.norm2.weight, 0) - + class PatchEmbed(nn.Module): r""" Image to Patch Embedding 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]) if self.norm is not None: flops += Ho * Wo * self.embed_dim - return flops + return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). @@ -531,7 +531,7 @@ class RSTB(nn.Module): num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, @@ -622,7 +622,7 @@ class Upsample(nn.Sequential): else: raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') super(Upsample, self).__init__(*m) - + class Upsample_hf(nn.Sequential): """Upsample module. @@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential): m.append(nn.PixelShuffle(3)) else: 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): @@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential): H, W = self.input_resolution flops = H * W * self.num_feat * 3 * 9 return flops - - + + class Swin2SR(nn.Module): r""" Swin2SR @@ -698,8 +698,8 @@ class Swin2SR(nn.Module): """ 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], - window_size=7, mlp_ratio=4., qkv_bias=True, + embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), + window_size=7, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, 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], window_size=window_size, mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, 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 norm_layer=norm_layer, @@ -776,7 +776,7 @@ class Swin2SR(nn.Module): ) self.layers.append(layer) - + if self.upsampler == 'pixelshuffle_hf': self.layers_hf = nn.ModuleList() for i_layer in range(self.num_layers): @@ -787,7 +787,7 @@ class Swin2SR(nn.Module): num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, + qkv_bias=qkv_bias, 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 norm_layer=norm_layer, @@ -799,7 +799,7 @@ class Swin2SR(nn.Module): ) self.layers_hf.append(layer) - + self.norm = norm_layer(self.num_features) # 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_after_aux = nn.Sequential( nn.Conv2d(3, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) + nn.LeakyReLU(inplace=True)) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - + elif self.upsampler == 'pixelshuffle_hf': self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) @@ -846,7 +846,7 @@ class Swin2SR(nn.Module): nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - + elif self.upsampler == 'pixelshuffledirect': # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, @@ -905,7 +905,7 @@ class Swin2SR(nn.Module): x = self.patch_unembed(x, x_size) return x - + def forward_features_hf(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) @@ -919,7 +919,7 @@ class Swin2SR(nn.Module): x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) - return x + return x def forward(self, x): H, W = x.shape[2:] @@ -951,7 +951,7 @@ class Swin2SR(nn.Module): x = self.conv_after_body(self.forward_features(x)) + x x_before = self.conv_before_upsample(x) x_out = self.conv_last(self.upsample(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_before_upsample_hf(x_hf) @@ -977,15 +977,15 @@ class Swin2SR(nn.Module): x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) - + x = x / self.img_range + self.mean if self.upsampler == "pixelshuffle_aux": return x[:, :, :H*self.upscale, :W*self.upscale], aux - + elif self.upsampler == "pixelshuffle_hf": 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] - + else: return x[:, :, :H*self.upscale, :W*self.upscale] @@ -994,7 +994,7 @@ class Swin2SR(nn.Module): H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): + for layer in self.layers: flops += layer.flops() flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() @@ -1014,4 +1014,4 @@ if __name__ == '__main__': x = torch.randn((1, 3, height, width)) x = model(x) - print(x.shape) \ No newline at end of file + print(x.shape) diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js new file mode 100644 index 00000000..30199dcd --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -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}${info.keySuffix}` : + configValue.charAt(configValue.length - 1); + return { + key, + action: info.action, + disabled: configValue === "disable" + }; + }); + + for (const hotkey of hotkeys) { + if (hotkey.disabled) { + continue; + } + + const p = document.createElement("p"); + p.innerHTML = `${hotkey.key} - ${hotkey.action}`; + tooltipContent.appendChild(p); + } + + // Add information and content elements to the tooltip element + tooltip.appendChild(info); + tooltip.appendChild(tooltipContent); + + // Add a hint element to the target element + toolTipElemnt.appendChild(tooltip); + } + + //Show tool tip if setting enable + if (hotkeysConfig.canvas_show_tooltip) { + createTooltip(); + } + + // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui. + function fixCanvas() { + const activeTab = getActiveTab(elements).textContent.trim(); + + if (activeTab !== "img2img") { + const img = targetElement.querySelector(`${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; +}); diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py new file mode 100644 index 00000000..380176ce --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -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"]}), +})) diff --git a/extensions-builtin/canvas-zoom-and-pan/style.css b/extensions-builtin/canvas-zoom-and-pan/style.css new file mode 100644 index 00000000..6bcc9570 --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/style.css @@ -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;} +} + diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py new file mode 100644 index 00000000..a05e10d8 --- /dev/null +++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py @@ -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") +})) diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js index 5c7a836a..114cf94c 100644 --- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -4,39 +4,39 @@ // 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(textArea, counterElt) { - var counts = {}; - (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => { - counts[bracket] = (counts[bracket] || 0) + 1; - }); - var errors = []; + var counts = {}; + (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => { + counts[bracket] = (counts[bracket] || 0) + 1; + }); + 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 ${kind}.` - ); + function checkPair(open, close, kind) { + if (counts[open] !== counts[close]) { + errors.push( + `${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); + checkPair('(', ')', 'round brackets'); + checkPair('[', ']', 'square brackets'); + checkPair('{', '}', 'curly brackets'); + counterElt.title = errors.join('\n'); + counterElt.classList.toggle('error', errors.length !== 0); } function setupBracketChecking(id_prompt, id_counter) { - var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); - var counter = gradioApp().getElementById(id_counter) + var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); + var counter = gradioApp().getElementById(id_counter); - if (textarea && counter) { - textarea.addEventListener("input", () => checkBrackets(textarea, 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'); +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'); }); diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index 1d546217..68a84c3a 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -1,15 +1,14 @@ -
+
+ {background_image} {metadata_button} -
- +
{name} {description}
- diff --git a/html/footer.html b/html/footer.html index bad87ff6..69b2372c 100644 --- a/html/footer.html +++ b/html/footer.html @@ -1,10 +1,12 @@
- API + API  •  Github  •  Gradio  •  + Startup profile +  •  Reload UI

diff --git a/html/licenses.html b/html/licenses.html index bc995aa0..ef6f2c0a 100644 --- a/html/licenses.html +++ b/html/licenses.html @@ -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, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + +

TAESD

+Tiny AutoEncoder for Stable Diffusion option for live previews +
+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.
\ No newline at end of file diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 5160081d..2cf2d571 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -1,111 +1,113 @@ - -let currentWidth = null; -let currentHeight = null; -let arFrameTimeout = setTimeout(function(){},0); - -function dimensionChange(e, is_width, is_height){ - - if(is_width){ - currentWidth = e.target.value*1.0 - } - if(is_height){ - currentHeight = e.target.value*1.0 - } - - var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; - - if(!inImg2img){ - return; - } - - var targetElement = null; - - var tabIndex = get_tab_index('mode_img2img') - if(tabIndex == 0){ // img2img - targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img'); - } else if(tabIndex == 1){ //Sketch - targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); - } else if(tabIndex == 2){ // Inpaint - targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); - } else if(tabIndex == 3){ // Inpaint sketch - targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); - } - - - if(targetElement){ - - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(!arPreviewRect){ - arPreviewRect = document.createElement('div') - arPreviewRect.id = "imageARPreview"; - gradioApp().appendChild(arPreviewRect) - } - - - - var viewportOffset = targetElement.getBoundingClientRect(); - - var viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight ) - - var scaledx = targetElement.naturalWidth*viewportscale - var scaledy = targetElement.naturalHeight*viewportscale - - var cleintRectTop = (viewportOffset.top+window.scrollY) - var cleintRectLeft = (viewportOffset.left+window.scrollX) - var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2) - var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2) - - var arscale = Math.min( scaledx/currentWidth, scaledy/currentHeight ) - var arscaledx = currentWidth*arscale - var arscaledy = currentHeight*arscale - - var arRectTop = cleintRectCentreY-(arscaledy/2) - var arRectLeft = cleintRectCentreX-(arscaledx/2) - var arRectWidth = arscaledx - var arRectHeight = arscaledy - - arPreviewRect.style.top = arRectTop+'px'; - arPreviewRect.style.left = arRectLeft+'px'; - arPreviewRect.style.width = arRectWidth+'px'; - arPreviewRect.style.height = arRectHeight+'px'; - - clearTimeout(arFrameTimeout); - arFrameTimeout = setTimeout(function(){ - arPreviewRect.style.display = 'none'; - },2000); - - arPreviewRect.style.display = 'block'; - - } - -} - - -onUiUpdate(function(){ - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(arPreviewRect){ - arPreviewRect.style.display = 'none'; - } - var tabImg2img = gradioApp().querySelector("#tab_img2img"); - if (tabImg2img) { - var inImg2img = tabImg2img.style.display == "block"; - if(inImg2img){ - let inputs = gradioApp().querySelectorAll('input'); - inputs.forEach(function(e){ - var is_width = e.parentElement.id == "img2img_width" - var is_height = e.parentElement.id == "img2img_height" - - if((is_width || is_height) && !e.classList.contains('scrollwatch')){ - e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} ) - e.classList.add('scrollwatch') - } - if(is_width){ - currentWidth = e.value*1.0 - } - if(is_height){ - currentHeight = e.value*1.0 - } - }) - } - } -}); + +let currentWidth = null; +let currentHeight = null; +let arFrameTimeout = setTimeout(function() {}, 0); + +function dimensionChange(e, is_width, is_height) { + + if (is_width) { + currentWidth = e.target.value * 1.0; + } + if (is_height) { + currentHeight = e.target.value * 1.0; + } + + var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; + + if (!inImg2img) { + return; + } + + var targetElement = null; + + var tabIndex = get_tab_index('mode_img2img'); + if (tabIndex == 0) { // img2img + targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img'); + } else if (tabIndex == 1) { //Sketch + targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); + } else if (tabIndex == 2) { // Inpaint + targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); + } else if (tabIndex == 3) { // Inpaint sketch + targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); + } + + + if (targetElement) { + + var arPreviewRect = gradioApp().querySelector('#imageARPreview'); + if (!arPreviewRect) { + arPreviewRect = document.createElement('div'); + arPreviewRect.id = "imageARPreview"; + gradioApp().appendChild(arPreviewRect); + } + + + + var viewportOffset = targetElement.getBoundingClientRect(); + + var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight); + + var scaledx = targetElement.naturalWidth * viewportscale; + var scaledy = targetElement.naturalHeight * viewportscale; + + var cleintRectTop = (viewportOffset.top + window.scrollY); + var cleintRectLeft = (viewportOffset.left + window.scrollX); + var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2); + var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2); + + var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight); + var arscaledx = currentWidth * arscale; + var arscaledy = currentHeight * arscale; + + var arRectTop = cleintRectCentreY - (arscaledy / 2); + var arRectLeft = cleintRectCentreX - (arscaledx / 2); + var arRectWidth = arscaledx; + var arRectHeight = arscaledy; + + arPreviewRect.style.top = arRectTop + 'px'; + arPreviewRect.style.left = arRectLeft + 'px'; + arPreviewRect.style.width = arRectWidth + 'px'; + arPreviewRect.style.height = arRectHeight + 'px'; + + clearTimeout(arFrameTimeout); + 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'; + } + var tabImg2img = gradioApp().querySelector("#tab_img2img"); + if (tabImg2img) { + var inImg2img = tabImg2img.style.display == "block"; + if (inImg2img) { + let inputs = gradioApp().querySelectorAll('input'); + inputs.forEach(function(e) { + var is_width = e.parentElement.id == "img2img_width"; + var is_height = e.parentElement.id == "img2img_height"; + + if ((is_width || is_height) && !e.classList.contains('scrollwatch')) { + e.addEventListener('input', function(e) { + dimensionChange(e, is_width, is_height); + }); + e.classList.add('scrollwatch'); + } + if (is_width) { + currentWidth = e.value * 1.0; + } + if (is_height) { + currentHeight = e.value * 1.0; + } + }); + } + } +}); diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index b2bdf053..ccae242f 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -1,166 +1,176 @@ - -contextMenuInit = function(){ - let eventListenerApplied=false; - let menuSpecs = new Map(); - - const uid = function(){ - return Date.now().toString(36) + Math.random().toString(36).substring(2); - } - - function showContextMenu(event,element,menuEntries){ - let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; - let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; - - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - - let baseStyle = window.getComputedStyle(uiCurrentTab) - - const contextMenu = document.createElement('nav') - contextMenu.id = "context-menu" - contextMenu.style.background = baseStyle.background - contextMenu.style.color = baseStyle.color - contextMenu.style.fontFamily = baseStyle.fontFamily - contextMenu.style.top = posy+'px' - contextMenu.style.left = posx+'px' - - - - const contextMenuList = document.createElement('ul') - contextMenuList.className = 'context-menu-items'; - contextMenu.append(contextMenuList); - - menuEntries.forEach(function(entry){ - let contextMenuEntry = document.createElement('a') - contextMenuEntry.innerHTML = entry['name'] - contextMenuEntry.addEventListener("click", function() { - entry['func'](); - }) - contextMenuList.append(contextMenuEntry); - - }) - - gradioApp().appendChild(contextMenu) - - let menuWidth = contextMenu.offsetWidth + 4; - let menuHeight = contextMenu.offsetHeight + 4; - - let windowWidth = window.innerWidth; - let windowHeight = window.innerHeight; - - if ( (windowWidth - posx) < menuWidth ) { - contextMenu.style.left = windowWidth - menuWidth + "px"; - } - - if ( (windowHeight - posy) < menuHeight ) { - contextMenu.style.top = windowHeight - menuHeight + "px"; - } - - } - - function appendContextMenuOption(targetElementSelector,entryName,entryFunction){ - - var currentItems = menuSpecs.get(targetElementSelector) - - if(!currentItems){ - currentItems = [] - menuSpecs.set(targetElementSelector,currentItems); - } - let newItem = {'id':targetElementSelector+'_'+uid(), - 'name':entryName, - 'func':entryFunction, - 'isNew':true} - - currentItems.push(newItem) - return newItem['id'] - } - - function removeContextMenuOption(uid){ - menuSpecs.forEach(function(v) { - let index = -1 - v.forEach(function(e,ei){if(e['id']==uid){index=ei}}) - if(index>=0){ - v.splice(index, 1); - } - }) - } - - function addContextMenuEventListener(){ - if(eventListenerApplied){ - return; - } - gradioApp().addEventListener("click", function(e) { - if(! e.isTrusted){ - return - } - - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - }); - gradioApp().addEventListener("contextmenu", function(e) { - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - menuSpecs.forEach(function(v,k) { - if(e.composedPath()[0].matches(k)){ - showContextMenu(e,e.composedPath()[0],v) - e.preventDefault() - } - }) - }); - eventListenerApplied=true - - } - - return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener] -} - -initResponse = contextMenuInit(); -appendContextMenuOption = initResponse[0]; -removeContextMenuOption = initResponse[1]; -addContextMenuEventListener = initResponse[2]; - -(function(){ - //Start example Context Menu Items - let generateOnRepeat = function(genbuttonid,interruptbuttonid){ - let genbutton = gradioApp().querySelector(genbuttonid); - let interruptbutton = gradioApp().querySelector(interruptbuttonid); - if(!interruptbutton.offsetParent){ - genbutton.click(); - } - clearInterval(window.generateOnRepeatInterval) - window.generateOnRepeatInterval = setInterval(function(){ - if(!interruptbutton.offsetParent){ - genbutton.click(); - } - }, - 500) - } - - appendContextMenuOption('#txt2img_generate','Generate forever',function(){ - generateOnRepeat('#txt2img_generate','#txt2img_interrupt'); - }) - appendContextMenuOption('#img2img_generate','Generate forever',function(){ - generateOnRepeat('#img2img_generate','#img2img_interrupt'); - }) - - let cancelGenerateForever = function(){ - clearInterval(window.generateOnRepeatInterval) - } - - appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever) - 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 - -onUiUpdate(function(){ - addContextMenuEventListener() -}); + +var contextMenuInit = function() { + let eventListenerApplied = false; + let menuSpecs = new Map(); + + const uid = function() { + return Date.now().toString(36) + Math.random().toString(36).substring(2); + }; + + function showContextMenu(event, element, menuEntries) { + let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; + let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + + let baseStyle = window.getComputedStyle(uiCurrentTab); + + const contextMenu = document.createElement('nav'); + contextMenu.id = "context-menu"; + contextMenu.style.background = baseStyle.background; + contextMenu.style.color = baseStyle.color; + contextMenu.style.fontFamily = baseStyle.fontFamily; + contextMenu.style.top = posy + 'px'; + contextMenu.style.left = posx + 'px'; + + + + const contextMenuList = document.createElement('ul'); + contextMenuList.className = 'context-menu-items'; + contextMenu.append(contextMenuList); + + menuEntries.forEach(function(entry) { + let contextMenuEntry = document.createElement('a'); + contextMenuEntry.innerHTML = entry['name']; + contextMenuEntry.addEventListener("click", function() { + entry['func'](); + }); + contextMenuList.append(contextMenuEntry); + + }); + + gradioApp().appendChild(contextMenu); + + let menuWidth = contextMenu.offsetWidth + 4; + let menuHeight = contextMenu.offsetHeight + 4; + + let windowWidth = window.innerWidth; + let windowHeight = window.innerHeight; + + if ((windowWidth - posx) < menuWidth) { + contextMenu.style.left = windowWidth - menuWidth + "px"; + } + + if ((windowHeight - posy) < menuHeight) { + contextMenu.style.top = windowHeight - menuHeight + "px"; + } + + } + + function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { + + var currentItems = menuSpecs.get(targetElementSelector); + + if (!currentItems) { + currentItems = []; + menuSpecs.set(targetElementSelector, currentItems); + } + let newItem = { + id: targetElementSelector + '_' + uid(), + name: entryName, + func: entryFunction, + isNew: true + }; + + currentItems.push(newItem); + return newItem['id']; + } + + function removeContextMenuOption(uid) { + menuSpecs.forEach(function(v) { + let index = -1; + v.forEach(function(e, ei) { + if (e['id'] == uid) { + index = ei; + } + }); + if (index >= 0) { + v.splice(index, 1); + } + }); + } + + function addContextMenuEventListener() { + if (eventListenerApplied) { + return; + } + gradioApp().addEventListener("click", function(e) { + if (!e.isTrusted) { + return; + } + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + }); + gradioApp().addEventListener("contextmenu", function(e) { + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + menuSpecs.forEach(function(v, k) { + if (e.composedPath()[0].matches(k)) { + showContextMenu(e, e.composedPath()[0], v); + e.preventDefault(); + } + }); + }); + eventListenerApplied = true; + + } + + return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; +}; + +var initResponse = contextMenuInit(); +var appendContextMenuOption = initResponse[0]; +var removeContextMenuOption = initResponse[1]; +var addContextMenuEventListener = initResponse[2]; + +(function() { + //Start example Context Menu Items + let generateOnRepeat = function(genbuttonid, interruptbuttonid) { + let genbutton = gradioApp().querySelector(genbuttonid); + let interruptbutton = gradioApp().querySelector(interruptbuttonid); + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + clearInterval(window.generateOnRepeatInterval); + window.generateOnRepeatInterval = setInterval(function() { + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + }, + 500); + }; + + let generateOnRepeat_txt2img = function() { + generateOnRepeat('#txt2img_generate', '#txt2img_interrupt'); + }; + + let generateOnRepeat_img2img = function() { + generateOnRepeat('#img2img_generate', '#img2img_interrupt'); + }; + + appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img); + appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img); + + let cancelGenerateForever = function() { + clearInterval(window.generateOnRepeatInterval); + }; + + appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever); + 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 + +onAfterUiUpdate(addContextMenuEventListener); diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js index fe008924..5803daea 100644 --- a/javascript/dragdrop.js +++ b/javascript/dragdrop.js @@ -1,11 +1,11 @@ // 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); } -function dropReplaceImage( imgWrap, files ) { - if ( ! isValidImageList( files ) ) { +function dropReplaceImage(imgWrap, files) { + if (!isValidImageList(files)) { return; } @@ -14,46 +14,61 @@ function dropReplaceImage( imgWrap, files ) { imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); const callback = () => { const fileInput = imgWrap.querySelector('input[type="file"]'); - if ( fileInput ) { - if ( files.length === 0 ) { + if (fileInput) { + if (files.length === 0) { files = new DataTransfer(); files.items.add(tmpFile); fileInput.files = files.files; } else { 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 const oldFetch = window.fetch; - window.fetch = async (input, options) => { + window.fetch = async(input, options) => { const response = await oldFetch(input, options); - if ( 'api/predict/' === input ) { + if ('api/predict/' === input) { const content = await response.text(); window.fetch = oldFetch; - window.requestAnimationFrame( () => callback() ); + window.requestAnimationFrame(() => callback()); return new Response(content, { status: response.status, statusText: response.statusText, headers: response.headers - }) + }); } return response; - }; + }; } 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 => { const target = e.composedPath()[0]; - const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) { - return; - } + if (!eventHasFiles(e)) return; + + var targetImage = target.closest('[data-testid="image"]'); + if (!dragDropTargetIsPrompt(target) && !targetImage) return; + e.stopPropagation(); e.preventDefault(); e.dataTransfer.dropEffect = 'copy'; @@ -61,37 +76,55 @@ window.document.addEventListener('dragover', e => { window.document.addEventListener('drop', e => { 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; } - 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 => { const files = e.clipboardData.files; - if ( ! isValidImageList( files ) ) { + if (!isValidImageList(files)) { return; } const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] - .filter(el => uiElementIsVisible(el)); - if ( ! visibleImageFields.length ) { + .filter(el => uiElementIsVisible(el)) + .sort((a, b) => uiElementInSight(b) - uiElementInSight(a)); + + + if (!visibleImageFields.length) { return; } - + const firstFreeImageField = visibleImageFields .filter(el => el.querySelector('input[type=file]'))?.[0]; dropReplaceImage( firstFreeImageField ? - firstFreeImageField : - visibleImageFields[visibleImageFields.length - 1] - , files ); + firstFreeImageField : + visibleImageFields[visibleImageFields.length - 1] + , files + ); }); diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index d2c2f190..8906c892 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,120 +1,121 @@ -function keyupEditAttention(event){ - let target = event.originalTarget || event.composedPath()[0]; - if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return; - if (! (event.metaKey || event.ctrlKey)) return; - - let isPlus = event.key == "ArrowUp" - let isMinus = event.key == "ArrowDown" - if (!isPlus && !isMinus) return; - - let selectionStart = target.selectionStart; - let selectionEnd = target.selectionEnd; - let text = target.value; - - function selectCurrentParenthesisBlock(OPEN, CLOSE){ - if (selectionStart !== selectionEnd) return false; - - // Find opening parenthesis around current cursor - const before = text.substring(0, selectionStart); - let beforeParen = before.lastIndexOf(OPEN); - if (beforeParen == -1) return false; - let beforeParenClose = before.lastIndexOf(CLOSE); - while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { - beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); - beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); - } - - // Find closing parenthesis around current cursor - const after = text.substring(selectionStart); - let afterParen = after.indexOf(CLOSE); - if (afterParen == -1) return false; - let afterParenOpen = after.indexOf(OPEN); - while (afterParenOpen !== -1 && afterParen > afterParenOpen) { - afterParen = after.indexOf(CLOSE, afterParen + 1); - afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); - } - if (beforeParen === -1 || afterParen === -1) return false; - - // Set the selection to the text between the parenthesis - const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); - const lastColon = parenContent.lastIndexOf(":"); - selectionStart = beforeParen + 1; - selectionEnd = selectionStart + lastColon; - target.setSelectionRange(selectionStart, selectionEnd); - return true; - } - - function selectCurrentWord(){ - if (selectionStart !== selectionEnd) return false; - const delimiters = opts.keyedit_delimiters + " \r\n\t"; - - // seek backward until to find beggining - while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { - selectionStart--; - } - - // seek forward to find end - while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { - selectionEnd++; - } - - target.setSelectionRange(selectionStart, selectionEnd); - return true; - } - - // If the user hasn't selected anything, let's select their current parenthesis block or word - if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { - selectCurrentWord(); - } - - event.preventDefault(); - - var closeCharacter = ')' - var delta = opts.keyedit_precision_attention - - if (selectionStart > 0 && text[selectionStart - 1] == '<'){ - closeCharacter = '>' - delta = opts.keyedit_precision_extra - } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { - - // do not include spaces at the end - while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){ - selectionEnd -= 1; - } - if(selectionStart == selectionEnd){ - return - } - - text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); - - selectionStart += 1; - selectionEnd += 1; - } - - var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; - 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) { - text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5); - selectionStart--; - selectionEnd--; - } else { - text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1); - } - - target.focus(); - target.value = text; - target.selectionStart = selectionStart; - target.selectionEnd = selectionEnd; - - updateInput(target) -} - -addEventListener('keydown', (event) => { - keyupEditAttention(event); -}); +function keyupEditAttention(event) { + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!(event.metaKey || event.ctrlKey)) return; + + let isPlus = event.key == "ArrowUp"; + let isMinus = event.key == "ArrowDown"; + if (!isPlus && !isMinus) return; + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + + function selectCurrentParenthesisBlock(OPEN, CLOSE) { + if (selectionStart !== selectionEnd) return false; + + // Find opening parenthesis around current cursor + const before = text.substring(0, selectionStart); + let beforeParen = before.lastIndexOf(OPEN); + if (beforeParen == -1) return false; + let beforeParenClose = before.lastIndexOf(CLOSE); + while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { + beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); + beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); + } + + // Find closing parenthesis around current cursor + const after = text.substring(selectionStart); + let afterParen = after.indexOf(CLOSE); + if (afterParen == -1) return false; + let afterParenOpen = after.indexOf(OPEN); + while (afterParenOpen !== -1 && afterParen > afterParenOpen) { + afterParen = after.indexOf(CLOSE, afterParen + 1); + afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); + } + if (beforeParen === -1 || afterParen === -1) return false; + + // Set the selection to the text between the parenthesis + const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + function selectCurrentWord() { + if (selectionStart !== selectionEnd) return false; + const delimiters = opts.keyedit_delimiters + " \r\n\t"; + + // seek backward until to find beggining + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { + selectCurrentWord(); + } + + event.preventDefault(); + + var closeCharacter = ')'; + var delta = opts.keyedit_precision_attention; + + if (selectionStart > 0 && text[selectionStart - 1] == '<') { + closeCharacter = '>'; + delta = opts.keyedit_precision_extra; + } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { + + // do not include spaces at the end + while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { + selectionEnd -= 1; + } + if (selectionStart == selectionEnd) { + return; + } + + text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); + + selectionStart += 1; + selectionEnd += 1; + } + + var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; + 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); +}); diff --git a/javascript/edit-order.js b/javascript/edit-order.js new file mode 100644 index 00000000..ad983d33 --- /dev/null +++ b/javascript/edit-order.js @@ -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; + event.preventDefault() + + let isLeft = event.key == "ArrowLeft"; + let isRight = event.key == "ArrowRight"; + if (!isLeft && !isRight) return; + + 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); +}); diff --git a/javascript/extensions.js b/javascript/extensions.js index 2a2d2f8e..1f7254c5 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,71 +1,92 @@ - -function extensions_apply(_disabled_list, _update_list, disable_all){ - var disable = [] - var update = [] - - gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){ - if(x.name.startsWith("enable_") && ! x.checked) - disable.push(x.name.substring(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] -} - -function extensions_check(){ - var disable = [] - - 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..." - }) - - - var id = randomId() - requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){ - - }) - - return [id, JSON.stringify(disable)] -} - -function install_extension_from_index(button, url){ - button.disabled = "disabled" - button.value = "Installing..." - - var textarea = gradioApp().querySelector('#extension_to_install textarea') - 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 extensions_apply(_disabled_list, _update_list, disable_all) { + var disable = []; + var update = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(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]; +} + +function extensions_check() { + var disable = []; + + 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..."; + }); + + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() { + + }); + + return [id, JSON.stringify(disable)]; +} + +function install_extension_from_index(button, url) { + button.disabled = "disabled"; + button.value = "Installing..."; + + var textarea = gradioApp().querySelector('#extension_to_install textarea'); + 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; +} diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index c85bc79a..b87bca3e 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -1,196 +1,265 @@ -function setupExtraNetworksForTab(tabname){ - gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks') - - var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div') - var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea') - var refresh = gradioApp().getElementById(tabname+'_extra_refresh') - - search.classList.add('search') - tabs.appendChild(search) - tabs.appendChild(refresh) - - var applyFilter = function(){ - var searchTerm = search.value.toLowerCase() - - gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){ - var searchOnly = elem.querySelector('.search_only') - var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase() - - var visible = text.indexOf(searchTerm) != -1 - - if(searchOnly && searchTerm.length < 4){ - visible = false - } - - elem.style.display = visible ? "" : "none" - }) - } - - search.addEventListener("input", applyFilter); - applyFilter(); - - extraNetworksApplyFilter[tabname] = applyFilter; -} - -function applyExtraNetworkFilter(tabname){ - setTimeout(extraNetworksApplyFilter[tabname], 1); -} - -var extraNetworksApplyFilter = {} -var activePromptTextarea = {}; - -function setupExtraNetworks(){ - setupExtraNetworksForTab('txt2img') - setupExtraNetworksForTab('img2img') - - function registerPrompt(tabname, id){ - var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); - - if (! activePromptTextarea[tabname]){ - activePromptTextarea[tabname] = textarea - } - - textarea.addEventListener("focus", function(){ - activePromptTextarea[tabname] = textarea; - }); - } - - registerPrompt('txt2img', 'txt2img_prompt') - registerPrompt('txt2img', 'txt2img_neg_prompt') - registerPrompt('img2img', 'img2img_prompt') - registerPrompt('img2img', 'img2img_neg_prompt') -} - -onUiLoaded(setupExtraNetworks) - -var re_extranet = /<([^:]+:[^:]+):[\d\.]+>/; -var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g; - -function tryToRemoveExtraNetworkFromPrompt(textarea, text){ - var m = text.match(re_extranet) - if(! m) return false - - var partToSearch = m[1] - var replaced = false - var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){ - m = found.match(re_extranet); - if(m[1] == partToSearch){ - replaced = true; - return "" - } - return found; - }) - - if(replaced){ - textarea.value = newTextareaText - return true; - } - - return false -} - -function cardClicked(tabname, textToAdd, allowNegativePrompt){ - 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 - } - - updateInput(textarea) -} - -function saveCardPreview(event, tabname, filename){ - var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea') - var button = gradioApp().getElementById(tabname + '_save_preview') - - textarea.value = filename - updateInput(textarea) - - button.click() - - 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 popup(contents){ - if(! globalPopup){ - globalPopup = document.createElement('div') - globalPopup.onclick = function(){ globalPopup.style.display = "none"; }; - globalPopup.classList.add('global-popup'); - - var close = document.createElement('div') - close.classList.add('global-popup-close'); - close.onclick = function(){ globalPopup.style.display = "none"; }; - 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().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() -} +function setupExtraNetworksForTab(tabname) { + gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks'); + + var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div'); + var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea'); + var sort = gradioApp().getElementById(tabname + '_extra_sort'); + var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder'); + var refresh = gradioApp().getElementById(tabname + '_extra_refresh'); + + search.classList.add('search'); + sort.classList.add('sort'); + sortOrder.classList.add('sortorder'); + sort.dataset.sortkey = 'sortDefault'; + tabs.appendChild(search); + tabs.appendChild(sort); + tabs.appendChild(sortOrder); + tabs.appendChild(refresh); + + var applyFilter = function() { + var searchTerm = search.value.toLowerCase(); + + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { + var searchOnly = elem.querySelector('.search_only'); + var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase(); + + var visible = text.indexOf(searchTerm) != -1; + + if (searchOnly && searchTerm.length < 4) { + visible = false; + } + + elem.style.display = visible ? "" : "none"; + }); + }; + + var applySort = function() { + 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" : "") : ""; + if (!sortKey || sortKeyStore == sort.dataset.sortkey) { + return; + } + + sort.dataset.sortkey = sortKeyStore; + + var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card'); + cards.forEach(function(card) { + card.originalParentElement = card.parentElement; + }); + var sortedCards = Array.from(cards); + sortedCards.sort(function(cardA, cardB) { + var a = cardA.dataset[sortKey]; + var b = cardB.dataset[sortKey]; + if (!isNaN(a) && !isNaN(b)) { + return parseInt(a) - parseInt(b); + } + + return (a < b ? -1 : (a > b ? 1 : 0)); + }); + if (reverse) { + sortedCards.reverse(); + } + cards.forEach(function(card) { + card.remove(); + }); + sortedCards.forEach(function(card) { + card.originalParentElement.appendChild(card); + }); + }; + + search.addEventListener("input", applyFilter); + applyFilter(); + ["change", "blur", "click"].forEach(function(evt) { + sort.querySelector("input").addEventListener(evt, applySort); + }); + sortOrder.addEventListener("click", function() { + sortOrder.classList.toggle("sortReverse"); + applySort(); + }); + + extraNetworksApplyFilter[tabname] = applyFilter; +} + +function applyExtraNetworkFilter(tabname) { + setTimeout(extraNetworksApplyFilter[tabname], 1); +} + +var extraNetworksApplyFilter = {}; +var activePromptTextarea = {}; + +function setupExtraNetworks() { + setupExtraNetworksForTab('txt2img'); + setupExtraNetworksForTab('img2img'); + + function registerPrompt(tabname, id) { + var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); + + if (!activePromptTextarea[tabname]) { + activePromptTextarea[tabname] = textarea; + } + + textarea.addEventListener("focus", function() { + activePromptTextarea[tabname] = textarea; + }); + } + + registerPrompt('txt2img', 'txt2img_prompt'); + registerPrompt('txt2img', 'txt2img_neg_prompt'); + registerPrompt('img2img', 'img2img_prompt'); + registerPrompt('img2img', 'img2img_neg_prompt'); +} + +onUiLoaded(setupExtraNetworks); + +var re_extranet = /<([^:]+:[^:]+):[\d.]+>/; +var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g; + +function tryToRemoveExtraNetworkFromPrompt(textarea, text) { + var m = text.match(re_extranet); + var replaced = false; + var newTextareaText; + if (m) { + var partToSearch = m[1]; + newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found) { + m = found.match(re_extranet); + if (m[1] == partToSearch) { + replaced = true; + return ""; + } + return found; + }); + } else { + newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) { + if (found == text) { + replaced = true; + return ""; + } + return found; + }); + } + + if (replaced) { + textarea.value = newTextareaText; + return true; + } + + return false; +} + +function cardClicked(tabname, textToAdd, allowNegativePrompt) { + 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; + } + + updateInput(textarea); +} + +function saveCardPreview(event, tabname, filename) { + var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea'); + var button = gradioApp().getElementById(tabname + '_save_preview'); + + textarea.value = filename; + updateInput(textarea); + + button.click(); + + 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 popup(contents) { + if (!globalPopup) { + globalPopup = document.createElement('div'); + globalPopup.onclick = function() { + globalPopup.style.display = "none"; + }; + globalPopup.classList.add('global-popup'); + + var close = document.createElement('div'); + close.classList.add('global-popup-close'); + close.onclick = function() { + globalPopup.style.display = "none"; + }; + 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().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(); +} diff --git a/javascript/generationParams.js b/javascript/generationParams.js index ef64ee2e..7c0fd221 100644 --- a/javascript/generationParams.js +++ b/javascript/generationParams.js @@ -1,33 +1,35 @@ // 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; -onUiUpdate(function(){ - if (!txt2img_gallery) { - txt2img_gallery = attachGalleryListeners("txt2img") - } - if (!img2img_gallery) { - img2img_gallery = attachGalleryListeners("img2img") - } - if (!modal) { - modal = gradioApp().getElementById('lightboxModal') - modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] }); - } +onAfterUiUpdate(function() { + if (!txt2img_gallery) { + txt2img_gallery = attachGalleryListeners("txt2img"); + } + if (!img2img_gallery) { + img2img_gallery = attachGalleryListeners("img2img"); + } + if (!modal) { + modal = gradioApp().getElementById('lightboxModal'); + modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']}); + } }); let modalObserver = new MutationObserver(function(mutations) { - mutations.forEach(function(mutationRecord) { - let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText - if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) - gradioApp().getElementById(selectedTab+"_generation_info_button")?.click() - }); + mutations.forEach(function(mutationRecord) { + let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText; + if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) { + gradioApp().getElementById(selectedTab + "_generation_info_button")?.click(); + } + }); }); function attachGalleryListeners(tab_name) { - var gallery = gradioApp().querySelector('#'+tab_name+'_gallery') - gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click()); - gallery?.addEventListener('keydown', (e) => { - if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow - gradioApp().getElementById(tab_name+"_generation_info_button").click() - }); - return gallery; + var gallery = gradioApp().querySelector('#' + tab_name + '_gallery'); + gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click()); + gallery?.addEventListener('keydown', (e) => { + if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow + gradioApp().getElementById(tab_name + "_generation_info_button").click(); + } + }); + return gallery; } diff --git a/javascript/hints.js b/javascript/hints.js index 3746df99..dc75ce31 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -1,20 +1,21 @@ // 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 method": "Which algorithm to use to produce the image", - "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", - "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", - "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", + "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", + "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", + "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", - "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)", + "\u{1F4D0}": "Auto detect size from img2img", + "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", "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", - "\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.", "\u{1f4c2}": "Open images output directory", "\u{1f4be}": "Save style", @@ -40,7 +41,7 @@ titles = { "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.", - + "Skip": "Stop processing current image and continue processing.", "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.", @@ -66,8 +67,8 @@ titles = { "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], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime], [datetime