Merge branch 'master' into fix-vram

This commit is contained in:
Jairo Correa 2022-09-30 18:58:51 -03:00
commit ad1fbbae93
42 changed files with 2484 additions and 1448 deletions

7
.gitignore vendored
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@ -1,10 +1,13 @@
__pycache__
/ESRGAN
*.ckpt
*.pth
/ESRGAN/*
/SwinIR/*
/repositories
/venv
/tmp
/model.ckpt
/models/**/*.ckpt
/models/**/*
/GFPGANv1.3.pth
/gfpgan/weights/*.pth
/ui-config.json

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@ -3,50 +3,64 @@ A browser interface based on Gradio library for Stable Diffusion.
![](txt2img_Screenshot.png)
Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Prompt matrix
- Prompt
- Stable Diffusion upscale
- Attention
- Loopback
- X/Y plot
- Attention, specify parts of text that the model should pay more attention to
- a man in a ((txuedo)) - will pay more attentinoto tuxedo
- a man in a (txuedo:1.21) - alternative syntax
- Loopback, run img2img procvessing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network with a lot of third party models
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR, neural network upscaler
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Interrupt processing at any time
- 4GB video card support
- Correct seeds for batches
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Prompt length validation
- Generation parameters added as text to PNG
- Tab to view an existing picture's generation parameters
- get length of prompt in tokensas you type
- get a warning after geenration if some text was truncated
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- Settings page
- Running custom code from UI
- Running arbitrary python code from UI (must run with commandline flag to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Random artist button
- Tiling support: UI checkbox to create images that can be tiled like textures
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Negative prompt
- Styles
- Variations
- Seed resizing
- CLIP interrogator
- Prompt Editing
- Batch Processing
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative
- Highres Fix
- LDSR Upscaling
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge two checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@ -83,6 +97,9 @@ bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusio
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).

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@ -1 +0,0 @@

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@ -359,7 +359,6 @@ Antanas Sutkus,0.7369492,black-white
Leonora Carrington,0.73726475,scribbles
Hieronymus Bosch,0.7369955,scribbles
A. J. Casson,0.73666203,scribbles
A.J.Casson,0.73666203,scribbles
Chaim Soutine,0.73662066,scribbles
Artur Bordalo,0.7364549,weird
Thomas Allom,0.68792284,fineart
@ -1907,7 +1906,6 @@ Alex Schomburg,0.46614102,digipa-low-impact
Bastien L. Deharme,0.583349,special
František Jakub Prokyš,0.58782333,fineart
Jesper Ejsing,0.58782053,fineart
Jesper Ejsing,0.58782053,fineart
Odd Nerdrum,0.53551745,digipa-high-impact
Tom Lovell,0.5877577,fineart
Ayami Kojima,0.5877416,fineart

1 artist score category
359 Leonora Carrington 0.73726475 scribbles
360 Hieronymus Bosch 0.7369955 scribbles
361 A. J. Casson 0.73666203 scribbles
A.J.Casson 0.73666203 scribbles
362 Chaim Soutine 0.73662066 scribbles
363 Artur Bordalo 0.7364549 weird
364 Thomas Allom 0.68792284 fineart
1906 Bastien L. Deharme 0.583349 special
1907 František Jakub Prokyš 0.58782333 fineart
1908 Jesper Ejsing 0.58782053 fineart
Jesper Ejsing 0.58782053 fineart
1909 Odd Nerdrum 0.53551745 digipa-high-impact
1910 Tom Lovell 0.5877577 fineart
1911 Ayami Kojima 0.5877416 fineart

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@ -15,6 +15,7 @@ titles = {
"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
"\u{1f3a8}": "Add a random artist to the prompt.",
"\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
"\uD83D\uDCC2": "Open images output directory",
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
@ -57,8 +58,8 @@ 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], [prompt], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
"Loopback": "Process an image, use it as an input, repeat.",

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@ -186,10 +186,12 @@ onUiUpdate(function(){
if (!txt2img_textarea) {
txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
}
if (!img2img_textarea) {
img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
}
})
@ -197,6 +199,14 @@ let txt2img_textarea, img2img_textarea = undefined;
let wait_time = 800
let token_timeout;
function submit_prompt(event, generate_button_id) {
if (event.altKey && event.keyCode === 13) {
event.preventDefault();
gradioApp().getElementById(generate_button_id).click();
return;
}
}
function update_token_counter(button_id) {
if (token_timeout)
clearTimeout(token_timeout);

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@ -1,5 +1,4 @@
# this scripts installs necessary requirements and launches main program in webui.py
import subprocess
import os
import sys
@ -19,10 +18,9 @@ gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/Tencen
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "9e3002b7cd64df7870e08527b7664eb2f2f5f3f5")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
ldsr_commit_hash = os.environ.get('LDSR_COMMIT_HASH', "abf33e7002d59d9085081bce93ec798dcabd49af")
args = shlex.split(commandline_args)
@ -120,8 +118,6 @@ git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming
git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
# Using my repo until my changes are merged, as this makes interfacing with our version of SD-web a lot easier
git_clone("https://github.com/Hafiidz/latent-diffusion", repo_dir('latent-diffusion'), "LDSR", ldsr_commit_hash)
if not is_installed("lpips"):
run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
@ -130,6 +126,9 @@ run_pip(f"install -r {requirements_file}", "requirements for Web UI")
sys.argv += args
if "--exit" in args:
print("Exiting because of --exit argument")
exit(0)
def start_webui():
print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")

78
modules/bsrgan_model.py Normal file
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@ -0,0 +1,78 @@
import os.path
import sys
import traceback
import PIL.Image
import numpy as np
import torch
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import shared, modelloader
from modules.bsrgan_model_arch import RRDBNet
from modules.paths import models_path
class UpscalerBSRGAN(modules.upscaler.Upscaler):
def __init__(self, dirname):
self.name = "BSRGAN"
self.model_path = os.path.join(models_path, self.name)
self.model_name = "BSRGAN 4x"
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
self.user_path = dirname
super().__init__()
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
scalers = []
if len(model_paths) == 0:
scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
if "http" in file:
name = self.model_name
else:
name = modelloader.friendly_name(file)
try:
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
scalers.append(scaler_data)
except Exception:
print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
self.scalers = scalers
def do_upscale(self, img: PIL.Image, selected_file):
torch.cuda.empty_cache()
model = self.load_model(selected_file)
if model is None:
return img
model.to(shared.device)
torch.cuda.empty_cache()
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(shared.device)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
output = 255. * np.moveaxis(output, 0, 2)
output = output.astype(np.uint8)
output = output[:, :, ::-1]
torch.cuda.empty_cache()
return PIL.Image.fromarray(output, 'RGB')
def load_model(self, path: str):
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)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
return None
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
return model

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@ -0,0 +1,102 @@
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5 * 0.2 + x
class RRDB(nn.Module):
'''Residual in Residual Dense Block'''
def __init__(self, nf, gc=32):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
return out * 0.2 + x
class RRDBNet(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
super(RRDBNet, self).__init__()
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.sf = sf
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
self.RRDB_trunk = make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
if self.sf==4:
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.conv_first(x)
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
if self.sf==4:
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return out

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@ -5,31 +5,31 @@ import traceback
import cv2
import torch
from modules import shared, devices
from modules.paths import script_path
import modules.shared
import modules.face_restoration
from importlib import reload
import modules.shared
from modules import shared, devices, modelloader
from modules.paths import script_path, models_path
# codeformer people made a choice to include modified basicsr librry to their projectwhich makes
# it utterly impossiblr to use it alongside with other libraries that also use basicsr, like GFPGAN.
# codeformer people made a choice to include modified basicsr library to their project which makes
# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
# I am making a choice to include some files from codeformer to work around this issue.
pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
model_dir = "Codeformer"
model_path = os.path.join(models_path, model_dir)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
have_codeformer = False
codeformer = None
def setup_codeformer():
def setup_model(dirname):
global model_path
if not os.path.exists(model_path):
os.makedirs(model_path)
path = modules.paths.paths.get("CodeFormer", None)
if path is None:
return
# both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own
#stored_sys_path = sys.path
#sys.path = [path] + sys.path
try:
from torchvision.transforms.functional import normalize
from modules.codeformer.codeformer_arch import CodeFormer
@ -44,18 +44,23 @@ def setup_codeformer():
def name(self):
return "CodeFormer"
def __init__(self):
def __init__(self, dirname):
self.net = None
self.face_helper = None
self.cmd_dir = dirname
def create_models(self):
if self.net is not None and self.face_helper is not None:
self.net.to(devices.device_codeformer)
return self.net, self.face_helper
model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
if len(model_paths) != 0:
ckpt_path = model_paths[0]
else:
print("Unable to load codeformer model.")
return None, None
net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
@ -74,6 +79,9 @@ def setup_codeformer():
original_resolution = np_image.shape[0:2]
self.create_models()
if self.net is None or self.face_helper is None:
return np_image
self.face_helper.clean_all()
self.face_helper.read_image(np_image)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
@ -116,7 +124,7 @@ def setup_codeformer():
have_codeformer = True
global codeformer
codeformer = FaceRestorerCodeFormer()
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)
except Exception:

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@ -1,26 +1,22 @@
import os
import sys
import traceback
import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
import modules.esrgam_model_arch as arch
from modules import shared
from modules.shared import opts
from modules import shared, modelloader, images
from modules.devices import has_mps
import modules.images
from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
def load_model(filename):
def fix_model_layers(crt_model, pretrained_net):
# this code is adapted from https://github.com/xinntao/ESRGAN
pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
if 'conv_first.weight' in pretrained_net:
crt_model.load_state_dict(pretrained_net)
return crt_model
return pretrained_net
if 'model.0.weight' not in pretrained_net:
is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
@ -72,9 +68,59 @@ def load_model(filename):
crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
crt_model.load_state_dict(crt_net)
crt_model.eval()
return crt_model
return crt_net
class UpscalerESRGAN(Upscaler):
def __init__(self, dirname):
self.name = "ESRGAN"
self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
self.model_name = "ESRGAN 4x"
self.scalers = []
self.user_path = dirname
self.model_path = os.path.join(models_path, self.name)
super().__init__()
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
scalers = []
if len(model_paths) == 0:
scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
if "http" in file:
name = self.model_name
else:
name = modelloader.friendly_name(file)
scaler_data = UpscalerData(name, file, self, 4)
self.scalers.append(scaler_data)
def do_upscale(self, img, selected_model):
model = self.load_model(selected_model)
if model is None:
return img
model.to(shared.device)
img = esrgan_upscale(model, img)
return img
def load_model(self, path: str):
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
file_name="%s.pth" % self.model_name,
progress=True)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print("Unable to load %s from %s" % (self.model_path, filename))
return None
pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
crt_model.load_state_dict(pretrained_net)
crt_model.eval()
return crt_model
def upscale_without_tiling(model, img):
img = np.array(img)
@ -95,7 +141,7 @@ def esrgan_upscale(model, img):
if opts.ESRGAN_tile == 0:
return upscale_without_tiling(model, img)
grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
newtiles = []
scale_factor = 1
@ -110,32 +156,6 @@ def esrgan_upscale(model, img):
newrow.append([x * scale_factor, w * scale_factor, output])
newtiles.append([y * scale_factor, h * scale_factor, newrow])
newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
output = modules.images.combine_grid(newgrid)
newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
output = images.combine_grid(newgrid)
return output
class UpscalerESRGAN(modules.images.Upscaler):
def __init__(self, filename, title):
self.name = title
self.model = load_model(filename)
def do_upscale(self, img):
model = self.model.to(shared.device)
img = esrgan_upscale(model, img)
return img
def load_models(dirname):
for file in os.listdir(dirname):
path = os.path.join(dirname, file)
model_name, extension = os.path.splitext(file)
if extension != '.pt' and extension != '.pth':
continue
try:
modules.shared.sd_upscalers.append(UpscalerESRGAN(path, model_name))
except Exception:
print(f"Error loading ESRGAN model: {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)

View File

@ -40,6 +40,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
outputs = []
for image, image_name in zip(imageArr, imageNameArr):
if image is None:
return outputs, "Please select an input image.", ''
existing_pnginfo = image.info or {}
image = image.convert("RGB")
@ -74,7 +76,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
c = cached_images.get(key)
if c is None:
upscaler = shared.sd_upscalers[scaler_index]
c = upscaler.upscale(image, image.width * resize, image.height * resize)
c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
cached_images[key] = c
return c
@ -143,7 +145,7 @@ def run_pnginfo(image):
return '', geninfo, info
def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half):
def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
@ -191,8 +193,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
if save_as_half:
theta_0[key] = theta_0[key].half()
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
filename = filename if custom_name == '' else (custom_name + '.ckpt')
output_modelname = os.path.join(ckpt_dir, filename)
print(f"Saving to {output_modelname}...")
torch.save(primary_model, output_modelname)

View File

@ -1,39 +1,25 @@
import os
import sys
import traceback
from glob import glob
from modules import shared, devices
from modules.shared import cmd_opts
from modules.paths import script_path
import facexlib
import gfpgan
import modules.face_restoration
from modules import shared, devices, modelloader
from modules.paths import models_path
def gfpgan_model_path():
from modules.shared import cmd_opts
filemask = 'GFPGAN*.pth'
if cmd_opts.gfpgan_model is not None:
return cmd_opts.gfpgan_model
places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
filename = None
for place in places:
filename = next(iter(glob(os.path.join(place, filemask))), None)
if filename is not None:
break
return filename
model_dir = "GFPGAN"
user_path = None
model_path = os.path.join(models_path, model_dir)
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
have_gfpgan = False
loaded_gfpgan_model = None
def gfpgan():
def gfpgann():
global loaded_gfpgan_model
global model_path
if loaded_gfpgan_model is not None:
loaded_gfpgan_model.gfpgan.to(shared.device)
return loaded_gfpgan_model
@ -41,7 +27,16 @@ def gfpgan():
if gfpgan_constructor is None:
return None
model = gfpgan_constructor(model_path=gfpgan_model_path() or 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth', upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
if len(models) == 1 and "http" in models[0]:
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)
model_file = latest_file
else:
print("Unable to load gfpgan model!")
return None
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
model.gfpgan.to(shared.device)
loaded_gfpgan_model = model
@ -50,8 +45,9 @@ def gfpgan():
def gfpgan_fix_faces(np_image):
global loaded_gfpgan_model
model = gfpgan()
model = gfpgann()
if model is None:
return np_image
np_image_bgr = np_image[:, :, ::-1]
cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
np_image = gfpgan_output_bgr[:, :, ::-1]
@ -64,21 +60,39 @@ def gfpgan_fix_faces(np_image):
return np_image
have_gfpgan = False
gfpgan_constructor = None
def setup_gfpgan():
def setup_model(dirname):
global model_path
if not os.path.exists(model_path):
os.makedirs(model_path)
try:
gfpgan_model_path()
if os.path.exists(cmd_opts.gfpgan_dir):
sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
from gfpgan import GFPGANer
from facexlib import detection, parsing
global user_path
global have_gfpgan
have_gfpgan = True
global gfpgan_constructor
load_file_from_url_orig = gfpgan.utils.load_file_from_url
facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
def my_load_file_from_url(**kwargs):
return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
def facex_load_file_from_url(**kwargs):
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
def facex_load_file_from_url2(**kwargs):
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
gfpgan.utils.load_file_from_url = my_load_file_from_url
facexlib.detection.load_file_from_url = facex_load_file_from_url
facexlib.parsing.load_file_from_url = facex_load_file_from_url2
user_path = dirname
have_gfpgan = True
gfpgan_constructor = GFPGANer
class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):

View File

@ -11,7 +11,6 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
from fonts.ttf import Roboto
import string
import modules.shared
from modules import sd_samplers, shared
from modules.shared import opts, cmd_opts
@ -52,8 +51,8 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
cols = math.ceil((w - overlap) / non_overlap_width)
rows = math.ceil((h - overlap) / non_overlap_height)
dx = (w - tile_w) / (cols-1) if cols > 1 else 0
dy = (h - tile_h) / (rows-1) if rows > 1 else 0
dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
grid = Grid([], tile_w, tile_h, w, h, overlap)
for row in range(rows):
@ -67,7 +66,7 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
for col in range(cols):
x = int(col * dx)
if x+tile_w >= w:
if x + tile_w >= w:
x = w - tile_w
tile = image.crop((x, y, x + tile_w, y + tile_h))
@ -132,7 +131,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
if not line.is_active:
drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)
drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
draw_y += line.size[1] + line_spacing
@ -171,7 +170,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
ver_texts]
pad_top = max(hor_text_heights) + line_spacing * 2
@ -213,8 +213,19 @@ def resize_image(resize_mode, im, width, height):
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
return im.resize((w, h), resample=LANCZOS)
upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
return upscaler.upscale(im, w, h)
scale = max(w / im.width, h / im.height)
if scale > 1.0:
upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
upscaler = upscalers[0]
im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
if im.width != w or im.height != h:
im = im.resize((w, h), resample=LANCZOS)
return im
if resize_mode == 0:
res = resize(im, width, height)
@ -256,7 +267,7 @@ def resize_image(resize_mode, im, width, height):
invalid_filename_chars = '<>:"/\\|?*\n'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s'+string.punctuation+']+')
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
max_filename_part_length = 128
@ -278,6 +289,16 @@ def apply_filename_pattern(x, p, seed, prompt):
if prompt is not None:
x = x.replace("[prompt]", sanitize_filename_part(prompt))
if "[prompt_no_styles]" in x:
prompt_no_style = prompt
for style in shared.prompt_styles.get_style_prompts(p.styles):
if len(style) > 0:
style_parts = [y for y in style.split("{prompt}")]
for part in style_parts:
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
x = x.replace("[prompt_spaces]", sanitize_filename_part(prompt, replace_spaces=False))
if "[prompt_words]" in x:
words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
@ -290,7 +311,7 @@ def apply_filename_pattern(x, p, seed, prompt):
x = x.replace("[cfg]", str(p.cfg_scale))
x = x.replace("[width]", str(p.width))
x = x.replace("[height]", str(p.height))
x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]), replace_spaces=False))
x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
@ -303,6 +324,7 @@ def apply_filename_pattern(x, p, seed, prompt):
return x
def get_next_sequence_number(path, basename):
"""
Determines and returns the next sequence number to use when saving an image in the specified directory.
@ -316,7 +338,7 @@ def get_next_sequence_number(path, basename):
prefix_length = len(basename)
for p in os.listdir(path):
if p.startswith(basename):
l = os.path.splitext(p[prefix_length:])[0].split('-') #splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
try:
result = max(int(l[0]), result)
except ValueError:
@ -324,6 +346,7 @@ def get_next_sequence_number(path, basename):
return result + 1
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
if short_filename or prompt is None or seed is None:
file_decoration = ""
@ -361,7 +384,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
fullfn = "a.png"
fullfn_without_extension = "a"
for i in range(500):
fn = f"{basecount+i:05}" if basename == '' else f"{basename}-{basecount+i:04}"
fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
if not os.path.exists(fullfn):
@ -403,31 +426,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
file.write(info + "\n")
class Upscaler:
name = "Lanczos"
def do_upscale(self, img):
return img
def upscale(self, img, w, h):
for i in range(3):
if img.width >= w and img.height >= h:
break
img = self.do_upscale(img)
if img.width != w or img.height != h:
img = img.resize((int(w), int(h)), resample=LANCZOS)
return img
class UpscalerNone(Upscaler):
name = "None"
def upscale(self, img, w, h):
return img
modules.shared.sd_upscalers.append(UpscalerNone())
modules.shared.sd_upscalers.append(Upscaler())

View File

@ -1,67 +1,56 @@
import os
import sys
import traceback
from collections import namedtuple
from basicsr.utils.download_util import load_file_from_url
import modules.images
from modules.upscaler import Upscaler, UpscalerData
from modules.ldsr_model_arch import LDSR
from modules import shared
from modules.paths import script_path
LDSRModelInfo = namedtuple("LDSRModelInfo", ["name", "location", "model", "netscale"])
ldsr_models = []
have_ldsr = False
LDSR_obj = None
from modules.paths import models_path
class UpscalerLDSR(modules.images.Upscaler):
def __init__(self, steps):
self.steps = steps
class UpscalerLDSR(Upscaler):
def __init__(self, user_path):
self.name = "LDSR"
self.model_path = os.path.join(models_path, self.name)
self.user_path = user_path
self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
super().__init__()
scaler_data = UpscalerData("LDSR", None, self)
self.scalers = [scaler_data]
def do_upscale(self, img):
return upscale_with_ldsr(img)
def load_model(self, path: str):
# Remove incorrect project.yaml file if too big
yaml_path = os.path.join(self.model_path, "project.yaml")
old_model_path = os.path.join(self.model_path, "model.pth")
new_model_path = os.path.join(self.model_path, "model.ckpt")
if os.path.exists(yaml_path):
statinfo = os.stat(yaml_path)
if statinfo.st_size >= 10485760:
print("Removing invalid LDSR YAML file.")
os.remove(yaml_path)
if os.path.exists(old_model_path):
print("Renaming model from model.pth to model.ckpt")
os.rename(old_model_path, new_model_path)
model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
file_name="model.ckpt", progress=True)
yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
file_name="project.yaml", progress=True)
try:
return LDSR(model, yaml)
def add_lsdr():
modules.shared.sd_upscalers.append(UpscalerLDSR(100))
except Exception:
print("Error importing LDSR:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return None
def setup_ldsr():
path = modules.paths.paths.get("LDSR", None)
if path is None:
return
global have_ldsr
global LDSR_obj
try:
from LDSR import LDSR
model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
repo_path = 'latent-diffusion/experiments/pretrained_models/'
model_path = load_file_from_url(url=model_url, model_dir=os.path.join("repositories", repo_path),
progress=True, file_name="model.chkpt")
yaml_path = load_file_from_url(url=yaml_url, model_dir=os.path.join("repositories", repo_path),
progress=True, file_name="project.yaml")
have_ldsr = True
LDSR_obj = LDSR(model_path, yaml_path)
except Exception:
print("Error importing LDSR:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
have_ldsr = False
def upscale_with_ldsr(image):
setup_ldsr()
if not have_ldsr or LDSR_obj is None:
return image
ddim_steps = shared.opts.ldsr_steps
pre_scale = shared.opts.ldsr_pre_down
post_scale = shared.opts.ldsr_post_down
image = LDSR_obj.super_resolution(image, ddim_steps, pre_scale, post_scale)
return image
def do_upscale(self, img, path):
ldsr = self.load_model(path)
if ldsr is None:
print("NO LDSR!")
return img
ddim_steps = shared.opts.ldsr_steps
return ldsr.super_resolution(img, ddim_steps, self.scale)

222
modules/ldsr_model_arch.py Normal file
View File

@ -0,0 +1,222 @@
import gc
import time
import warnings
import numpy as np
import torch
import torchvision
from PIL import Image
from einops import rearrange, repeat
from omegaconf import OmegaConf
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config, ismap
warnings.filterwarnings("ignore", category=UserWarning)
# Create LDSR Class
class LDSR:
def load_model_from_config(self, half_attention):
print(f"Loading model from {self.modelPath}")
pl_sd = torch.load(self.modelPath, map_location="cpu")
sd = pl_sd["state_dict"]
config = OmegaConf.load(self.yamlPath)
model = instantiate_from_config(config.model)
model.load_state_dict(sd, strict=False)
model.cuda()
if half_attention:
model = model.half()
model.eval()
return {"model": model}
def __init__(self, model_path, yaml_path):
self.modelPath = model_path
self.yamlPath = yaml_path
@staticmethod
def run(model, selected_path, custom_steps, eta):
example = get_cond(selected_path)
n_runs = 1
guider = None
ckwargs = None
ddim_use_x0_pred = False
temperature = 1.
eta = eta
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
x_t = None
logs = None
for n 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])
logs = make_convolutional_sample(example, model,
custom_steps=custom_steps,
eta=eta, quantize_x0=False,
custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
model = self.load_model_from_config(half_attention)
# Run settings
diffusion_steps = int(steps)
eta = 1.0
down_sample_method = 'Lanczos'
gc.collect()
torch.cuda.empty_cache()
im_og = image
width_og, height_og = im_og.size
# If we can adjust the max upscale size, then the 4 below should be our variable
down_sample_rate = target_scale / 4
wd = width_og * down_sample_rate
hd = height_og * down_sample_rate
width_downsampled_pre = int(wd)
height_downsampled_pre = int(hd)
if down_sample_rate != 1:
print(
f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
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)")
logs = self.run(model["model"], im_og, diffusion_steps, eta)
sample = logs["sample"]
sample = sample.detach().cpu()
sample = torch.clamp(sample, -1., 1.)
sample = (sample + 1.) / 2. * 255
sample = sample.numpy().astype(np.uint8)
sample = np.transpose(sample, (0, 2, 3, 1))
a = Image.fromarray(sample[0])
del model
gc.collect()
torch.cuda.empty_cache()
return a
def get_cond(selected_path):
example = dict()
up_f = 4
c = selected_path.convert('RGB')
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(torch.device("cuda"))
example["LR_image"] = c
example["image"] = c_up
return example
@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
corrector_kwargs=None, x_t=None
):
ddim = DDIMSampler(model)
bs = shape[0]
shape = shape[1:]
print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_t=x_t)
return samples, intermediates
@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()
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
if custom_shape is not None:
z = torch.randn(custom_shape)
print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key == 'class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
quantize_x0=quantize_x0, mask=None, x0=z0,
temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_t=x_T)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
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:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log

140
modules/modelloader.py Normal file
View File

@ -0,0 +1,140 @@
import glob
import os
import shutil
import importlib
from urllib.parse import urlparse
from basicsr.utils.download_util import load_file_from_url
from modules import shared
from modules.upscaler import Upscaler
from modules.paths import script_path, models_path
def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
"""
A one-and done loader to try finding the desired models in specified directories.
@param download_name: Specify to download from model_url immediately.
@param model_url: If no other models are found, this will be downloaded on upscale.
@param model_path: The location to store/find models in.
@param command_path: A command-line argument to search for models in first.
@param ext_filter: An optional list of filename extensions to filter by
@return: A list of paths containing the desired model(s)
"""
output = []
if ext_filter is None:
ext_filter = []
try:
places = []
if command_path is not None and command_path != model_path:
pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
if os.path.exists(pretrained_path):
print(f"Appending path: {pretrained_path}")
places.append(pretrained_path)
elif os.path.exists(command_path):
places.append(command_path)
places.append(model_path)
for place in places:
if os.path.exists(place):
for file in glob.iglob(place + '**/**', recursive=True):
full_path = os.path.join(place, file)
if os.path.isdir(full_path):
continue
if len(ext_filter) != 0:
model_name, extension = os.path.splitext(file)
if extension not in ext_filter:
continue
if file not in output:
output.append(full_path)
if model_url is not None and len(output) == 0:
if download_name is not None:
dl = load_file_from_url(model_url, model_path, True, download_name)
output.append(dl)
else:
output.append(model_url)
except Exception:
pass
return output
def friendly_name(file: str):
if "http" in file:
file = urlparse(file).path
file = os.path.basename(file)
model_name, extension = os.path.splitext(file)
return model_name
def cleanup_models():
# This code could probably be more efficient if we used a tuple list or something to store the src/destinations
# and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
# somehow auto-register and just do these things...
root_path = script_path
src_path = models_path
dest_path = os.path.join(models_path, "Stable-diffusion")
move_files(src_path, dest_path, ".ckpt")
src_path = os.path.join(root_path, "ESRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path)
src_path = os.path.join(root_path, "gfpgan")
dest_path = os.path.join(models_path, "GFPGAN")
move_files(src_path, dest_path)
src_path = os.path.join(root_path, "SwinIR")
dest_path = os.path.join(models_path, "SwinIR")
move_files(src_path, dest_path)
src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
dest_path = os.path.join(models_path, "LDSR")
move_files(src_path, dest_path)
def move_files(src_path: str, dest_path: str, ext_filter: str = None):
try:
if not os.path.exists(dest_path):
os.makedirs(dest_path)
if os.path.exists(src_path):
for file in os.listdir(src_path):
fullpath = os.path.join(src_path, file)
if os.path.isfile(fullpath):
if ext_filter is not None:
if ext_filter not in file:
continue
print(f"Moving {file} from {src_path} to {dest_path}.")
try:
shutil.move(fullpath, dest_path)
except:
pass
if len(os.listdir(src_path)) == 0:
print(f"Removing empty folder: {src_path}")
shutil.rmtree(src_path, True)
except:
pass
def load_upscalers():
datas = []
for cls in Upscaler.__subclasses__():
name = cls.__name__
module_name = cls.__module__
module = importlib.import_module(module_name)
class_ = getattr(module, name)
cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
opt_string = None
try:
opt_string = shared.opts.__getattr__(cmd_name)
except:
pass
scaler = class_(opt_string)
for child in scaler.scalers:
datas.append(child)
shared.sd_upscalers = datas

View File

@ -3,9 +3,10 @@ import os
import sys
script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
models_path = os.path.join(script_path, "models")
sys.path.insert(0, script_path)
# search for directory of stable diffsuion in following palces
# search for directory of stable diffusion in following places
sd_path = None
possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)]
for possible_sd_path in possible_sd_paths:
@ -15,21 +16,24 @@ for possible_sd_path in possible_sd_paths:
assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion'),
(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers'),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer'),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP'),
(os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR'),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion'),
(sd_path, 'ldm', 'Stable Diffusion', []),
(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
]
paths = {}
for d, must_exist, what in path_dirs:
for d, must_exist, what, options in path_dirs:
must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
if not os.path.exists(must_exist_path):
print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
else:
d = os.path.abspath(d)
sys.path.append(d)
if "atstart" in options:
sys.path.insert(0, d)
else:
sys.path.append(d)
paths[what] = d

View File

@ -508,8 +508,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
image = upscaler.upscale(image, self.width, self.height)
image = images.resize_image(0, image, self.width, self.height)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
batch_images.append(image)

View File

@ -126,5 +126,93 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
return res
re_attention = re.compile(r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""", re.X)
#get_learned_conditioning_prompt_schedules(["fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"], 100)
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
Example:
'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
produces:
[
['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]
]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith('\\'):
res.append([text[1:], 1.0])
elif text == '(':
round_brackets.append(len(res))
elif text == '[':
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ')' and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == ']' and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
return res

View File

@ -1,119 +1,135 @@
import os
import sys
import traceback
from collections import namedtuple
import numpy as np
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
import modules.images
from modules.upscaler import Upscaler, UpscalerData
from modules.paths import models_path
from modules.shared import cmd_opts, opts
RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
realesrgan_models = []
have_realesrgan = False
class UpscalerRealESRGAN(Upscaler):
def __init__(self, path):
self.name = "RealESRGAN"
self.model_path = os.path.join(models_path, self.name)
self.user_path = path
super().__init__()
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
self.enable = True
self.scalers = []
scalers = self.load_models(path)
for scaler in scalers:
if scaler.name in opts.realesrgan_enabled_models:
self.scalers.append(scaler)
except Exception:
print("Error importing Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
self.enable = False
self.scalers = []
def do_upscale(self, img, path):
if not self.enable:
return img
info = self.load_model(path)
if not os.path.exists(info.data_path):
print("Unable to load RealESRGAN model: %s" % info.name)
return img
upsampler = RealESRGANer(
scale=info.scale,
model_path=info.data_path,
model=info.model(),
half=not cmd_opts.no_half,
tile=opts.ESRGAN_tile,
tile_pad=opts.ESRGAN_tile_overlap,
)
upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
image = Image.fromarray(upsampled)
return image
def load_model(self, path):
try:
info = None
for scaler in self.scalers:
if scaler.data_path == path:
info = scaler
if info is None:
print(f"Unable to find model info: {path}")
return None
model_file = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
info.data_path = model_file
return info
except Exception as e:
print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return None
def load_models(self, _):
return get_realesrgan_models(self)
def get_realesrgan_models():
def get_realesrgan_models(scaler):
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
models = [
RealesrganModelInfo(
name="Real-ESRGAN General x4x3",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
netscale=4,
UpscalerData(
name="R-ESRGAN General 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
),
RealesrganModelInfo(
name="Real-ESRGAN General WDN x4x3",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
netscale=4,
UpscalerData(
name="R-ESRGAN General WDN 4xV3",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
),
RealesrganModelInfo(
name="Real-ESRGAN AnimeVideo",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
netscale=4,
UpscalerData(
name="R-ESRGAN AnimeVideo",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
scale=4,
upscaler=scaler,
model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
),
RealesrganModelInfo(
name="Real-ESRGAN 4x plus",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
netscale=4,
UpscalerData(
name="R-ESRGAN 4x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
scale=4,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
),
RealesrganModelInfo(
name="Real-ESRGAN 4x plus anime 6B",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
netscale=4,
UpscalerData(
name="R-ESRGAN 4x+ Anime6B",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
scale=4,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
),
RealesrganModelInfo(
name="Real-ESRGAN 2x plus",
location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
netscale=2,
UpscalerData(
name="R-ESRGAN 2x+",
path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
scale=2,
upscaler=scaler,
model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
),
]
return models
except Exception as e:
print("Error makeing Real-ESRGAN midels list:", file=sys.stderr)
print("Error making Real-ESRGAN models list:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
class UpscalerRealESRGAN(modules.images.Upscaler):
def __init__(self, upscaling, model_index):
self.upscaling = upscaling
self.model_index = model_index
self.name = realesrgan_models[model_index].name
def do_upscale(self, img):
return upscale_with_realesrgan(img, self.upscaling, self.model_index)
def setup_realesrgan():
global realesrgan_models
global have_realesrgan
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
realesrgan_models = get_realesrgan_models()
have_realesrgan = True
for i, model in enumerate(realesrgan_models):
if model.name in opts.realesrgan_enabled_models:
modules.shared.sd_upscalers.append(UpscalerRealESRGAN(model.netscale, i))
except Exception:
print("Error importing Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
have_realesrgan = False
def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
if not have_realesrgan:
return image
info = realesrgan_models[RealESRGAN_model_index]
model = info.model()
upsampler = RealESRGANer(
scale=info.netscale,
model_path=info.location,
model=model,
half=not cmd_opts.no_half,
tile=opts.ESRGAN_tile,
tile_pad=opts.ESRGAN_tile_overlap,
)
upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
image = Image.fromarray(upsampled)
return image

View File

@ -55,7 +55,7 @@ def load_scripts(basedir):
if not os.path.exists(basedir):
return
for filename in os.listdir(basedir):
for filename in sorted(os.listdir(basedir)):
path = os.path.join(basedir, filename)
if not os.path.isfile(path):

View File

@ -6,6 +6,7 @@ import torch
import numpy as np
from torch import einsum
from modules import prompt_parser
from modules.shared import opts, device, cmd_opts
from ldm.util import default
@ -204,6 +205,7 @@ class StableDiffusionModelHijack:
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
# diffuser concepts
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
@ -223,19 +225,25 @@ class StableDiffusionModelHijack:
for fn in os.listdir(dirname):
try:
process_file(os.path.join(dirname, fn), fn)
fullfn = os.path.join(dirname, fn)
if os.stat(fullfn).st_size == 0:
continue
process_file(fullfn, fn)
except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
def hijack(self, m):
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
self.clip = m.cond_stage_model
ldm.modules.diffusionmodules.model.nonlinearity = silu
@ -255,6 +263,14 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
def undo_hijack(self, m):
if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
m.cond_stage_model = m.cond_stage_model.wrapped
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
@ -269,6 +285,7 @@ class StableDiffusionModelHijack:
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
return remade_batch_tokens[0], token_count, max_length
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
def __init__(self, wrapped, hijack):
super().__init__()
@ -294,7 +311,92 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
if mult != 1.0:
self.token_mults[ident] = mult
def process_text(self, text):
def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
maxlen = self.wrapped.max_length
if opts.enable_emphasis:
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
fixes = []
remade_tokens = []
multipliers = []
for tokens, (text, weight) in zip(tokenized, parsed):
i = 0
while i < len(tokens):
token = tokens[i]
possible_matches = self.hijack.ids_lookup.get(token, None)
if possible_matches is None:
remade_tokens.append(token)
multipliers.append(weight)
else:
found = False
for ids, word in possible_matches:
if tokens[i:i + len(ids)] == ids:
emb_len = int(self.hijack.word_embeddings[word].shape[0])
fixes.append((len(remade_tokens), word))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
i += len(ids) - 1
found = True
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
break
if not found:
remade_tokens.append(token)
multipliers.append(weight)
i += 1
if len(remade_tokens) > maxlen - 2:
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
ovf = remade_tokens[maxlen - 2:]
overflowing_words = [vocab.get(int(x), "") for x in ovf]
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
token_count = len(remade_tokens)
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
return remade_tokens, fixes, multipliers, token_count
def process_text(self, texts):
used_custom_terms = []
remade_batch_tokens = []
hijack_comments = []
hijack_fixes = []
token_count = 0
cache = {}
batch_multipliers = []
for line in texts:
if line in cache:
remade_tokens, fixes, multipliers = cache[line]
else:
remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
cache[line] = (remade_tokens, fixes, multipliers)
remade_batch_tokens.append(remade_tokens)
hijack_fixes.append(fixes)
batch_multipliers.append(multipliers)
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def process_text_old(self, text):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
maxlen = self.wrapped.max_length
@ -370,12 +472,18 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
if opts.use_old_emphasis_implementation:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
else:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
self.hijack.comments = hijack_comments
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
tokens = torch.asarray(remade_batch_tokens).to(device)
outputs = self.wrapped.transformer(input_ids=tokens)

View File

@ -8,7 +8,14 @@ from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
from modules import shared
from modules import shared, modelloader
from modules.paths import models_path
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
model_name = "sd-v1-4.ckpt"
model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
user_dir = None
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
checkpoints_list = {}
@ -23,21 +30,30 @@ except Exception:
pass
def setup_model(dirname):
global user_dir
user_dir = dirname
if not os.path.exists(model_path):
os.makedirs(model_path)
checkpoints_list.clear()
list_models()
def checkpoint_tiles():
print(sorted([x.title for x in checkpoints_list.values()]))
return sorted([x.title for x in checkpoints_list.values()])
def list_models():
checkpoints_list.clear()
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
def modeltitle(path, h):
def modeltitle(path, shorthash):
abspath = os.path.abspath(path)
if abspath.startswith(model_dir):
name = abspath.replace(model_dir, '')
if user_dir is not None and abspath.startswith(user_dir):
name = abspath.replace(user_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
name = os.path.basename(path)
@ -46,21 +62,27 @@ def list_models():
shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
return f'{name} [{h}]', shortname
return f'{name} [{shorthash}]', shortname
cmd_ckpt = shared.cmd_opts.ckpt
if os.path.exists(cmd_ckpt):
h = model_hash(cmd_ckpt)
title, model_name = modeltitle(cmd_ckpt, h)
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
title, short_model_name = modeltitle(cmd_ckpt, h)
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
shared.opts.sd_model_checkpoint = title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
for filename in model_list:
h = model_hash(filename)
title, short_model_name = modeltitle(filename, h)
checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
if os.path.exists(model_dir):
for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
h = model_hash(filename)
title, model_name = modeltitle(filename, h)
checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
def get_closet_checkpoint_match(searchString):
applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
if len(applicable) > 0:
return applicable[0]
return None
def model_hash(filename):
@ -138,7 +160,7 @@ def load_model():
def reload_model_weights(sd_model, info=None):
from modules import lowvram, devices
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
if sd_model.sd_model_checkpint == checkpoint_info.filename:
@ -149,8 +171,12 @@ def reload_model_weights(sd_model, info=None):
else:
sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(sd_model)
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
sd_hijack.model_hijack.hijack(sd_model)
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
sd_model.to(devices.device)

View File

@ -4,7 +4,6 @@ import torch
import tqdm
from PIL import Image
import inspect
import k_diffusion.sampling
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
@ -23,6 +22,8 @@ samplers_k_diffusion = [
('Heun', 'sample_heun', ['k_heun']),
('DPM2', 'sample_dpm_2', ['k_dpm_2']),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
]
samplers_data_k_diffusion = [
@ -36,7 +37,7 @@ samplers = [
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
sampler_extra_params = {
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
@ -309,8 +310,13 @@ class KDiffusionSampler:
x = x * sigmas[0]
extra_params_kwargs = self.initialize(p)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
if 'sigma_min' in inspect.signature(self.func).parameters:
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
if 'n' in inspect.signature(self.func).parameters:
extra_params_kwargs['n'] = steps
else:
extra_params_kwargs['sigmas'] = sigmas
samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
return samples

View File

@ -1,26 +1,27 @@
import sys
import argparse
import datetime
import json
import os
import sys
import gradio as gr
import tqdm
import datetime
import modules.artists
from modules.paths import script_path, sd_path
from modules.devices import get_optimal_device
import modules.styles
import modules.interrogate
import modules.memmon
import modules.sd_models
import modules.styles
from modules.devices import get_optimal_device
from modules.paths import script_path, sd_path
sd_model_file = os.path.join(script_path, 'model.ckpt')
default_sd_model_file = sd_model_file
model_path = os.path.join(script_path, 'models')
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@ -34,8 +35,13 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
parser.add_argument("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))
parser.add_argument("--swinir-models-path", type=str, help="path to directory with SwinIR models", default=os.path.join(script_path, 'SwinIR'))
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
@ -53,7 +59,6 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
cmd_opts = parser.parse_args()
device = get_optimal_device()
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
@ -61,6 +66,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
config_filename = cmd_opts.ui_settings_file
class State:
interrupted = False
job = ""
@ -95,13 +101,13 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
interrogator = modules.interrogate.InterrogateModels("interrogate")
face_restorers = []
modules.sd_models.list_models()
# This was moved to webui.py with the other model "setup" calls.
# modules.sd_models.list_models()
def realesrgan_models_names():
import modules.realesrgan_model
return [x.name for x in modules.realesrgan_model.get_realesrgan_models()]
return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
class OptionInfo:
@ -167,13 +173,10 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
options_templates.update(options_section(('upscaling', "Upscaling"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"realesrgan_enabled_models": OptionInfo(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
"SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
"SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
"ldsr_pre_down": OptionInfo(1, "LDSR Pre-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
"ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
}))
@ -190,12 +193,13 @@ options_templates.update(options_section(('system', "System"), {
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),

View File

@ -53,6 +53,12 @@ class StyleDatabase:
negative_prompt = row.get("negative_prompt", "")
self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt)
def get_style_prompts(self, styles):
return [self.styles.get(x, self.no_style).prompt for x in styles]
def get_negative_style_prompts(self, styles):
return [self.styles.get(x, self.no_style).negative_prompt for x in styles]
def apply_styles_to_prompt(self, prompt, styles):
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles])

View File

@ -1,123 +0,0 @@
import sys
import traceback
import cv2
import os
import contextlib
import numpy as np
from PIL import Image
import torch
import modules.images
from modules.shared import cmd_opts, opts, device
from modules.swinir_arch import SwinIR as net
precision_scope = (
torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
)
def load_model(filename, scale=4):
model = net(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
pretrained_model = torch.load(filename)
model.load_state_dict(pretrained_model["params_ema"], strict=True)
if not cmd_opts.no_half:
model = model.half()
return model
def load_models(dirname):
for file in os.listdir(dirname):
path = os.path.join(dirname, file)
model_name, extension = os.path.splitext(file)
if extension != ".pt" and extension != ".pth":
continue
try:
modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name))
except Exception:
print(f"Error loading SwinIR model: {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
def upscale(
img,
model,
tile=opts.SWIN_tile,
tile_overlap=opts.SWIN_tile_overlap,
window_size=8,
scale=4,
):
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(device)
with torch.no_grad(), precision_scope("cuda"):
_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
output = inference(img, model, tile, tile_overlap, window_size, scale)
output = output[..., : h_old * scale, : w_old * scale]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(output, "RGB")
def inference(img, model, tile, tile_overlap, window_size, scale):
# test the image tile by tile
b, c, h, w = img.size()
tile = min(tile, h, w)
assert tile % window_size == 0, "tile size should be a multiple of window_size"
sf = scale
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device)
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
].add_(out_patch_mask)
output = E.div_(W)
return output
class UpscalerSwin(modules.images.Upscaler):
def __init__(self, filename, title):
self.name = title
self.model = load_model(filename)
def do_upscale(self, img):
model = self.model.to(device)
img = upscale(img, model)
return img

139
modules/swinir_model.py Normal file
View File

@ -0,0 +1,139 @@
import contextlib
import os
import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from modules import modelloader
from modules.paths import models_path
from modules.shared import cmd_opts, opts, device
from modules.swinir_model_arch import SwinIR as net
from modules.upscaler import Upscaler, UpscalerData
precision_scope = (
torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
)
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_name = "SwinIR 4x"
self.model_path = os.path.join(models_path, self.name)
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:
name = self.model_name
else:
name = modelloader.friendly_name(model)
model_data = UpscalerData(name, model, self)
scalers.append(model_data)
self.scalers = scalers
def do_upscale(self, img, model_file):
model = self.load_model(model_file)
if model is None:
return img
model = model.to(device)
img = upscale(img, model)
try:
torch.cuda.empty_cache()
except:
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)
else:
filename = path
if filename is None or not os.path.exists(filename):
return None
model = net(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
pretrained_model = torch.load(filename)
model.load_state_dict(pretrained_model["params_ema"], strict=True)
if not cmd_opts.no_half:
model = model.half()
return model
def upscale(
img,
model,
tile=opts.SWIN_tile,
tile_overlap=opts.SWIN_tile_overlap,
window_size=8,
scale=4,
):
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(device)
with torch.no_grad(), precision_scope("cuda"):
_, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
output = inference(img, model, tile, tile_overlap, window_size, scale)
output = output[..., : h_old * scale, : w_old * scale]
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
return Image.fromarray(output, "RGB")
def inference(img, model, tile, tile_overlap, window_size, scale):
# test the image tile by tile
b, c, h, w = img.size()
tile = min(tile, h, w)
assert tile % window_size == 0, "tile size should be a multiple of window_size"
sf = scale
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device)
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
output = E.div_(W)
return output

File diff suppressed because it is too large Load Diff

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@ -9,10 +9,13 @@ import random
import sys
import time
import traceback
import platform
import subprocess as sp
import numpy as np
import torch
from PIL import Image, PngImagePlugin
import piexif
import gradio as gr
import gradio.utils
@ -61,7 +64,7 @@ random_symbol = '\U0001f3b2\ufe0f' # 🎲️
reuse_symbol = '\u267b\ufe0f' # ♻️
art_symbol = '\U0001f3a8' # 🎨
paste_symbol = '\u2199\ufe0f' # ↙
folder_symbol = '\uD83D\uDCC2'
def plaintext_to_html(text):
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
@ -111,18 +114,26 @@ def save_files(js_data, images, index):
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
filename_base = str(int(time.time() * 1000))
extension = opts.samples_format.lower()
for i, filedata in enumerate(images):
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"
filepath = os.path.join(opts.outdir_save, filename)
if filedata.startswith("data:image/png;base64,"):
filedata = filedata[len("data:image/png;base64,"):]
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text('parameters', infotexts[i])
image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
image.save(filepath, quality=opts.jpeg_quality, pnginfo=pnginfo)
if opts.enable_pnginfo and extension == 'png':
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text('parameters', infotexts[i])
image.save(filepath, pnginfo=pnginfo)
else:
image.save(filepath, quality=opts.jpeg_quality)
if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
piexif.insert(piexif.dump({"Exif": {
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
}}), filepath)
filenames.append(filename)
@ -369,7 +380,7 @@ def create_toprow(is_img2img):
with gr.Column(scale=1):
with gr.Row():
interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
submit = gr.Button('Generate', elem_id="generate", variant='primary')
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
interrupt.click(
fn=lambda: shared.state.interrupt(),
@ -461,6 +472,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
send_to_img2img = gr.Button('Send to img2img')
send_to_inpaint = gr.Button('Send to inpaint')
send_to_extras = gr.Button('Send to extras')
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
with gr.Group():
html_info = gr.HTML()
@ -586,7 +599,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
with gr.Row():
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
@ -637,6 +650,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
img2img_send_to_img2img = gr.Button('Send to img2img')
img2img_send_to_inpaint = gr.Button('Send to inpaint')
img2img_send_to_extras = gr.Button('Send to extras')
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
with gr.Group():
html_info = gr.HTML()
@ -809,6 +824,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
html_info = gr.HTML()
extras_send_to_img2img = gr.Button('Send to img2img')
extras_send_to_inpaint = gr.Button('Send to inpaint')
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
submit.click(
fn=run_extras,
@ -874,6 +891,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
with gr.Row():
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
custom_name = gr.Textbox(label="Custom Name (Optional)")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
save_as_half = gr.Checkbox(value=False, label="Safe as float16")
@ -907,6 +925,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
components = []
component_dict = {}
def open_folder(f):
if not shared.cmd_opts.hide_ui_dir_config:
path = os.path.normpath(f)
if platform.system() == "Windows":
os.startfile(path)
elif platform.system() == "Darwin":
sp.Popen(["open", path])
else:
sp.Popen(["xdg-open", path])
def run_settings(*args):
changed = 0
@ -1013,15 +1041,26 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
inputs=components,
outputs=[result, text_settings],
)
def modelmerger(*args):
try:
results = run_modelmerger(*args)
except Exception as e:
print("Error loading/saving model file:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
modules.sd_models.list_models() #To remove the potentially missing models from the list
return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
return results
modelmerger_merge.click(
fn=run_modelmerger,
fn=modelmerger,
inputs=[
primary_model_name,
secondary_model_name,
interp_method,
interp_amount,
save_as_half,
custom_name,
],
outputs=[
submit_result,
@ -1068,6 +1107,24 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
outputs=[extras_image],
)
open_txt2img_folder.click(
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
inputs=[],
outputs=[],
)
open_img2img_folder.click(
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
inputs=[],
outputs=[],
)
open_extras_folder.click(
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
inputs=[],
outputs=[],
)
img2img_send_to_extras.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery_extras",

121
modules/upscaler.py Normal file
View File

@ -0,0 +1,121 @@
import os
from abc import abstractmethod
import PIL
import numpy as np
import torch
from PIL import Image
import modules.shared
from modules import modelloader, shared
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
from modules.paths import models_path
class Upscaler:
name = None
model_path = None
model_name = None
model_url = None
enable = True
filter = None
model = None
user_path = None
scalers: []
tile = True
def __init__(self, create_dirs=False):
self.mod_pad_h = None
self.tile_size = modules.shared.opts.ESRGAN_tile
self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
self.device = modules.shared.device
self.img = None
self.output = None
self.scale = 1
self.half = not modules.shared.cmd_opts.no_half
self.pre_pad = 0
self.mod_scale = None
if self.name is not None and create_dirs:
self.model_path = os.path.join(models_path, self.name)
if not os.path.exists(self.model_path):
os.makedirs(self.model_path)
try:
import cv2
self.can_tile = True
except:
pass
@abstractmethod
def do_upscale(self, img: PIL.Image, selected_model: str):
return img
def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
self.scale = scale
dest_w = img.width * scale
dest_h = img.height * scale
for i in range(3):
if img.width >= dest_w and img.height >= dest_h:
break
img = self.do_upscale(img, selected_model)
if img.width != dest_w or img.height != dest_h:
img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
return img
@abstractmethod
def load_model(self, path: str):
pass
def find_models(self, ext_filter=None) -> list:
return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path)
def update_status(self, prompt):
print(f"\nextras: {prompt}", file=shared.progress_print_out)
class UpscalerData:
name = None
data_path = None
scale: int = 4
scaler: Upscaler = None
model: None
def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
self.name = name
self.data_path = path
self.scaler = upscaler
self.scale = scale
self.model = model
class UpscalerNone(Upscaler):
name = "None"
scalers = []
def load_model(self, path):
pass
def do_upscale(self, img, selected_model=None):
return img
def __init__(self, dirname=None):
super().__init__(False)
self.scalers = [UpscalerData("None", None, self)]
class UpscalerLanczos(Upscaler):
scalers = []
def do_upscale(self, img, selected_model=None):
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS)
def load_model(self, _):
pass
def __init__(self, dirname=None):
super().__init__(False)
self.name = "Lanczos"
self.scalers = [UpscalerData("Lanczos", None, self)]

View File

@ -4,7 +4,7 @@ fairscale==0.4.4
fonts
font-roboto
gfpgan
gradio
gradio==3.4b3
invisible-watermark
numpy
omegaconf

View File

@ -11,46 +11,8 @@ from modules import images, processing, devices
from modules.processing import Processed, process_images
from modules.shared import opts, cmd_opts, state
# https://github.com/parlance-zz/g-diffuser-bot
def expand(x, dir, amount, power=0.75):
is_left = dir == 3
is_right = dir == 1
is_up = dir == 0
is_down = dir == 2
if is_left or is_right:
noise = np.zeros((x.shape[0], amount, 3), dtype=float)
indexes = np.random.random((x.shape[0], amount)) ** power * (1 - np.arange(amount) / amount)
if is_right:
indexes = 1 - indexes
indexes = (indexes * (x.shape[1] - 1)).astype(int)
for row in range(x.shape[0]):
if is_left:
noise[row] = x[row][indexes[row]]
else:
noise[row] = np.flip(x[row][indexes[row]], axis=0)
x = np.concatenate([noise, x] if is_left else [x, noise], axis=1)
return x
if is_up or is_down:
noise = np.zeros((amount, x.shape[1], 3), dtype=float)
indexes = np.random.random((x.shape[1], amount)) ** power * (1 - np.arange(amount) / amount)
if is_down:
indexes = 1 - indexes
indexes = (indexes * x.shape[0] - 1).astype(int)
for row in range(x.shape[1]):
if is_up:
noise[:, row] = x[:, row][indexes[row]]
else:
noise[:, row] = np.flip(x[:, row][indexes[row]], axis=0)
x = np.concatenate([noise, x] if is_up else [x, noise], axis=0)
return x
# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):

View File

@ -34,7 +34,7 @@ class Script(scripts.Script):
seed = p.seed
init_img = p.init_images[0]
img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
devices.torch_gc()

View File

@ -45,11 +45,8 @@ def apply_sampler(p, x, xs):
def apply_checkpoint(p, x, xs):
applicable = [info for info in modules.sd_models.checkpoints_list.values() if x in info.title]
assert len(applicable) > 0, f'Checkpoint {x} for found'
info = applicable[0]
info = modules.sd_models.get_closet_checkpoint_match(x)
assert info is not None, f'Checkpoint for {x} not found'
modules.sd_models.reload_model_weights(shared.sd_model, info)
@ -159,6 +156,9 @@ class Script(scripts.Script):
p.batch_size = 1
def process_axis(opt, vals):
if opt.label == 'Nothing':
return [0]
valslist = [x.strip() for x in vals.split(",")]
if opt.type == int:

View File

@ -1,5 +1,11 @@
.output-html p {margin: 0 0.5em;}
.row > *,
.row > .gr-form > * {
min-width: min(120px, 100%);
flex: 1 1 0%;
}
.performance {
font-size: 0.85em;
color: #444;
@ -17,7 +23,7 @@
text-align: right;
}
#generate{
#txt2img_generate, #img2img_generate {
min-height: 4.5em;
}
@ -43,13 +49,17 @@
margin-right: auto;
}
#random_seed, #random_subseed, #reuse_seed, #reuse_subseed{
#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
min-width: auto;
flex-grow: 0;
padding-left: 0.25em;
padding-right: 0.25em;
}
#hidden_element{
display: none;
}
#seed_row, #subseed_row{
gap: 0.5rem;
}

View File

@ -21,6 +21,9 @@ export COMMANDLINE_ARGS=""
# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
#venv_dir="venv"
# script to launch to start the app
#export LAUNCH_SCRIPT="launch.py"
# install command for torch
#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"

View File

@ -1,37 +1,38 @@
import os
import threading
from modules import devices
from modules.paths import script_path
import signal
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.ui
import threading
import modules.paths
import modules.codeformer_model as codeformer
import modules.esrgan_model as esrgan
import modules.bsrgan_model as bsrgan
import modules.extras
import modules.face_restoration
import modules.gfpgan_model as gfpgan
import modules.img2img
import modules.ldsr_model as ldsr
import modules.lowvram
import modules.realesrgan_model as realesrgan
import modules.scripts
import modules.sd_hijack
import modules.codeformer_model
import modules.gfpgan_model
import modules.face_restoration
import modules.realesrgan_model as realesrgan
import modules.esrgan_model as esrgan
import modules.ldsr_model as ldsr
import modules.extras
import modules.lowvram
import modules.txt2img
import modules.img2img
import modules.swinir as swinir
import modules.sd_models
import modules.shared as shared
import modules.swinir_model as swinir
import modules.txt2img
import modules.ui
from modules import modelloader
from modules.paths import script_path
from modules.shared import cmd_opts
modules.codeformer_model.setup_codeformer()
modules.gfpgan_model.setup_gfpgan()
modelloader.cleanup_models()
modules.sd_models.setup_model(cmd_opts.ckpt_dir)
codeformer.setup_model(cmd_opts.codeformer_models_path)
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
esrgan.load_models(cmd_opts.esrgan_models_path)
swinir.load_models(cmd_opts.swinir_models_path)
realesrgan.setup_realesrgan()
ldsr.add_lsdr()
modelloader.load_upscalers()
queue_lock = threading.Lock()
@ -47,6 +48,8 @@ def wrap_queued_call(func):
def wrap_gradio_gpu_call(func):
def f(*args, **kwargs):
devices.torch_gc()
shared.state.sampling_step = 0
shared.state.job_count = -1
shared.state.job_no = 0
@ -62,6 +65,8 @@ def wrap_gradio_gpu_call(func):
shared.state.job = ""
shared.state.job_count = 0
devices.torch_gc()
return res
return modules.ui.wrap_gradio_call(f)

View File

@ -41,6 +41,11 @@ then
venv_dir="venv"
fi
if [[ -z "${LAUNCH_SCRIPT}" ]]
then
LAUNCH_SCRIPT="launch.py"
fi
# Disable sentry logging
export ERROR_REPORTING=FALSE
@ -133,4 +138,4 @@ fi
printf "\n%s\n" "${delimiter}"
printf "Launching launch.py..."
printf "\n%s\n" "${delimiter}"
"${python_cmd}" launch.py
"${python_cmd}" "${LAUNCH_SCRIPT}"