Unverified Commit eeb1de43 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub

Merge branch 'master' into gradient-clipping

parents d85c2cb2 b7deea47
name: Run basic features tests on CPU with empty SD model
on:
- push
- pull_request
jobs:
test:
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Set up Python 3.10
uses: actions/setup-python@v4
with:
python-version: 3.10.6
- uses: actions/cache@v3
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: ${{ runner.os }}-pip-
- name: Run tests
run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
- name: Upload main app stdout-stderr
uses: actions/upload-artifact@v3
if: always()
with:
name: stdout-stderr
path: |
test/stdout.txt
test/stderr.txt
__pycache__
*.ckpt
*.safetensors
*.pth
/ESRGAN/*
/SwinIR/*
......
......@@ -70,7 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
......@@ -82,28 +82,9 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- Use VAEs
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
## Where are Aesthetic Gradients?!?!
Aesthetic Gradients are now an extension. You can install it using git:
```commandline
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
```
After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
## Where is History/Image browser?!?!
Image browser is now an extension. You can install it using git:
```commandline
git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser extensions/images-browser
```
After running this command, make sure that you have `images-browser` dir in webui's `extensions` directory and restart
the UI. The interface for Image browser should appear exactly the same as it was.
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
## 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.
......@@ -146,6 +127,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
......@@ -154,6 +137,7 @@ The documentation was moved from this README over to the project's [wiki](https:
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
......
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: modules.xlmr.BertSeriesModelWithTransformation
params:
name: "XLMR-Large"
\ No newline at end of file
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
import os
import gc
import time
import warnings
......@@ -8,27 +9,49 @@ import torchvision
from PIL import Image
from einops import rearrange, repeat
from omegaconf import OmegaConf
import safetensors.torch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config, ismap
from modules import shared, sd_hijack
warnings.filterwarnings("ignore", category=UserWarning)
cached_ldsr_model: torch.nn.Module = None
# 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()
global cached_ldsr_model
if shared.opts.ldsr_cached and cached_ldsr_model is not None:
print("Loading model from cache")
model: torch.nn.Module = cached_ldsr_model
else:
print(f"Loading model from {self.modelPath}")
_, extension = os.path.splitext(self.modelPath)
if extension.lower() == ".safetensors":
pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
else:
pl_sd = torch.load(self.modelPath, map_location="cpu")
sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
config = OmegaConf.load(self.yamlPath)
config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
model: torch.nn.Module = instantiate_from_config(config.model)
model.load_state_dict(sd, strict=False)
model = model.to(shared.device)
if half_attention:
model = model.half()
if shared.cmd_opts.opt_channelslast:
model = model.to(memory_format=torch.channels_last)
sd_hijack.model_hijack.hijack(model) # apply optimization
model.eval()
if shared.opts.ldsr_cached:
cached_ldsr_model = model
return {"model": model}
def __init__(self, model_path, yaml_path):
......@@ -93,7 +116,8 @@ class LDSR:
down_sample_method = 'Lanczos'
gc.collect()
torch.cuda.empty_cache()
if torch.cuda.is_available:
torch.cuda.empty_cache()
im_og = image
width_og, height_og = im_og.size
......@@ -130,7 +154,9 @@ class LDSR:
del model
gc.collect()
torch.cuda.empty_cache()
if torch.cuda.is_available:
torch.cuda.empty_cache()
return a
......@@ -145,7 +171,7 @@ def get_cond(selected_path):
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(torch.device("cuda"))
c = c.to(shared.device)
example["LR_image"] = c
example["image"] = c_up
......
import os
from modules import paths
def preload(parser):
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))
......@@ -5,8 +5,9 @@ import traceback
from basicsr.utils.download_util import load_file_from_url
from modules.upscaler import Upscaler, UpscalerData
from modules.ldsr_model_arch import LDSR
from modules import shared
from ldsr_model_arch import LDSR
from modules import shared, script_callbacks
import sd_hijack_autoencoder, sd_hijack_ddpm_v1
class UpscalerLDSR(Upscaler):
......@@ -24,6 +25,7 @@ class UpscalerLDSR(Upscaler):
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")
safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
if os.path.exists(yaml_path):
statinfo = os.stat(yaml_path)
if statinfo.st_size >= 10485760:
......@@ -32,8 +34,11 @@ class UpscalerLDSR(Upscaler):
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)
if os.path.exists(safetensors_model_path):
model = safetensors_model_path
else:
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)
......@@ -52,3 +57,13 @@ class UpscalerLDSR(Upscaler):
return img
ddim_steps = shared.opts.ldsr_steps
return ldsr.super_resolution(img, ddim_steps, self.scale)
def on_ui_settings():
import gradio as gr
shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
script_callbacks.on_ui_settings(on_ui_settings)
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import os
from modules import paths
def preload(parser):
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
......@@ -9,7 +9,7 @@ from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import devices, modelloader
from modules.scunet_model_arch import SCUNet as net
from scunet_model_arch import SCUNet as net
class UpscalerScuNET(modules.upscaler.Upscaler):
......@@ -49,12 +49,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
if model is None:
return img
device = devices.device_scunet
device = devices.get_device_for('scunet')
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), device)
img = img.unsqueeze(0).to(device)
with torch.no_grad():
output = model(img)
......@@ -66,7 +66,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
return PIL.Image.fromarray(output, 'RGB')
def load_model(self, path: str):
device = devices.device_scunet
device = devices.get_device_for('scunet')
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
progress=True)
......
import os
from modules import paths
def preload(parser):
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
......@@ -7,15 +7,14 @@ from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from tqdm import tqdm
from modules import modelloader, devices
from modules import modelloader, devices, script_callbacks, shared
from modules.shared import cmd_opts, opts
from modules.swinir_model_arch import SwinIR as net
from modules.swinir_model_arch_v2 import Swin2SR as net2
from swinir_model_arch import SwinIR as net
from swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
precision_scope = (
torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
)
device_swinir = devices.get_device_for('swinir')
class UpscalerSwinIR(Upscaler):
......@@ -42,7 +41,7 @@ class UpscalerSwinIR(Upscaler):
model = self.load_model(model_file)
if model is None:
return img
model = model.to(devices.device_swinir)
model = model.to(device_swinir, dtype=devices.dtype)
img = upscale(img, model)
try:
torch.cuda.empty_cache()
......@@ -94,25 +93,27 @@ class UpscalerSwinIR(Upscaler):
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, 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,
tile=None,
tile_overlap=None,
window_size=8,
scale=4,
):
tile = tile or opts.SWIN_tile
tile_overlap = tile_overlap or opts.SWIN_tile_overlap
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
with torch.no_grad(), precision_scope("cuda"):
img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
with torch.no_grad(), devices.autocast():
_, _, 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
......@@ -139,8 +140,8 @@ def inference(img, model, tile, tile_overlap, window_size, 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=devices.device_swinir).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
for h_idx in h_idx_list:
......@@ -159,3 +160,13 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
output = E.div_(W)
return output
def on_ui_settings():
import gradio as gr
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
script_callbacks.on_ui_settings(on_ui_settings)
// Stable Diffusion WebUI - Bracket checker
// Version 1.0
// By Hingashi no Florin/Bwin4L
// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
function checkBrackets(evt) {
textArea = evt.target;
tabName = evt.target.parentElement.parentElement.id.split("_")[0];
counterElt = document.querySelector('gradio-app').shadowRoot.querySelector('#' + tabName + '_token_counter');
promptName = evt.target.parentElement.parentElement.id.includes('neg') ? ' negative' : '';
errorStringParen = '(' + tabName + promptName + ' prompt) - Different number of opening and closing parentheses detected.\n';
errorStringSquare = '[' + tabName + promptName + ' prompt] - Different number of opening and closing square brackets detected.\n';
errorStringCurly = '{' + tabName + promptName + ' prompt} - Different number of opening and closing curly brackets detected.\n';
openBracketRegExp = /\(/g;
closeBracketRegExp = /\)/g;
openSquareBracketRegExp = /\[/g;
closeSquareBracketRegExp = /\]/g;
openCurlyBracketRegExp = /\{/g;
closeCurlyBracketRegExp = /\}/g;
totalOpenBracketMatches = 0;
totalCloseBracketMatches = 0;
totalOpenSquareBracketMatches = 0;
totalCloseSquareBracketMatches = 0;
totalOpenCurlyBracketMatches = 0;
totalCloseCurlyBracketMatches = 0;
openBracketMatches = textArea.value.match(openBracketRegExp);
if(openBracketMatches) {
totalOpenBracketMatches = openBracketMatches.length;
}
closeBracketMatches = textArea.value.match(closeBracketRegExp);
if(closeBracketMatches) {
totalCloseBracketMatches = closeBracketMatches.length;
}
openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
if(openSquareBracketMatches) {
totalOpenSquareBracketMatches = openSquareBracketMatches.length;
}
closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
if(closeSquareBracketMatches) {
totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
}
openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
if(openCurlyBracketMatches) {
totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
}
closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
if(closeCurlyBracketMatches) {
totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
}
if(totalOpenBracketMatches != totalCloseBracketMatches) {
if(!counterElt.title.includes(errorStringParen)) {
counterElt.title += errorStringParen;
}
} else {
counterElt.title = counterElt.title.replace(errorStringParen, '');
}
if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
if(!counterElt.title.includes(errorStringSquare)) {
counterElt.title += errorStringSquare;
}
} else {
counterElt.title = counterElt.title.replace(errorStringSquare, '');
}
if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
if(!counterElt.title.includes(errorStringCurly)) {
counterElt.title += errorStringCurly;
}
} else {
counterElt.title = counterElt.title.replace(errorStringCurly, '');
}
if(counterElt.title != '') {
counterElt.style = 'color: #FF5555;';
} else {
counterElt.style = '';
}
}
var shadowRootLoaded = setInterval(function() {
var shadowTextArea = document.querySelector('gradio-app').shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
if(shadowTextArea.length < 1) {
return false;
}
clearInterval(shadowRootLoaded);
document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_prompt').onkeyup = checkBrackets;
document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_neg_prompt').onkeyup = checkBrackets;
document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_prompt').onkeyup = checkBrackets;
document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_neg_prompt').onkeyup = checkBrackets;
}, 1000);
import random
from modules import script_callbacks, shared
import gradio as gr
art_symbol = '\U0001f3a8' # 🎨
global_prompt = None
related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
def roll_artist(prompt):
allowed_cats = set([x for x in shared.artist_db.categories() if len(shared.opts.random_artist_categories)==0 or x in shared.opts.random_artist_categories])
artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
return prompt + ", " + artist.name if prompt != '' else artist.name
def add_roll_button(prompt):
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
roll.click(
fn=roll_artist,
_js="update_txt2img_tokens",
inputs=[
prompt,
],
outputs=[
prompt,
]
)
def after_component(component, **kwargs):
global global_prompt
elem_id = kwargs.get('elem_id', None)
if elem_id not in related_ids:
return
if elem_id == "txt2img_prompt":
global_prompt = component
elif elem_id == "txt2img_clear_prompt":
add_roll_button(global_prompt)
elif elem_id == "img2img_prompt":
global_prompt = component
elif elem_id == "img2img_clear_prompt":
add_roll_button(global_prompt)
script_callbacks.on_after_component(after_component)
<div>
<a href="/docs">API</a>
 • 
<a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
 • 
<a href="https://gradio.app">Gradio</a>
 • 
<a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
</div>
This diff is collapsed.
......@@ -9,7 +9,7 @@ contextMenuInit = function(){
function showContextMenu(event,element,menuEntries){
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){
......@@ -61,15 +61,15 @@ contextMenuInit = function(){
}
function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
currentItems = menuSpecs.get(targetEmementSelector)
function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
currentItems = menuSpecs.get(targetElementSelector)
if(!currentItems){
currentItems = []
menuSpecs.set(targetEmementSelector,currentItems);
menuSpecs.set(targetElementSelector,currentItems);
}
let newItem = {'id':targetEmementSelector+'_'+uid(),
let newItem = {'id':targetElementSelector+'_'+uid(),
'name':entryName,
'func':entryFunction,
'isNew':true}
......@@ -97,7 +97,7 @@ contextMenuInit = function(){
if(source.id && source.id.indexOf('check_progress')>-1){
return
}
let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){
oldMenu.remove()
......@@ -117,7 +117,7 @@ contextMenuInit = function(){
})
});
eventListenerApplied=true
}
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
......@@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
generateOnRepeat('#img2img_generate','#img2img_interrupt');
})
let cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval)
let cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval)
}
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
......@@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#roll','Roll three',
function(){
function(){
let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
setTimeout(function(){rollbutton.click()},100)
setTimeout(function(){rollbutton.click()},200)
......
......@@ -9,11 +9,19 @@ function dropReplaceImage( imgWrap, files ) {
return;
}
const tmpFile = files[0];
imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
const callback = () => {
const fileInput = imgWrap.querySelector('input[type="file"]');
if ( fileInput ) {
fileInput.files = files;
if ( files.length === 0 ) {
files = new DataTransfer();
files.items.add(tmpFile);
fileInput.files = files.files;
} else {
fileInput.files = files;
}
fileInput.dispatchEvent(new Event('change'));
}
};
......
// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
let txt2img_gallery, img2img_gallery, modal = undefined;
onUiUpdate(function(){
if (!txt2img_gallery) {
txt2img_gallery = attachGalleryListeners("txt2img")
}
if (!img2img_gallery) {
img2img_gallery = attachGalleryListeners("img2img")
}
if (!modal) {
modal = gradioApp().getElementById('lightboxModal')
modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
}
});
let modalObserver = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
gradioApp().getElementById(selectedTab+"_generation_info_button").click()
});
});
function attachGalleryListeners(tab_name) {
gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
gallery?.addEventListener('keydown', (e) => {
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
gradioApp().getElementById(tab_name+"_generation_info_button").click()
});
return gallery;
}
......@@ -6,6 +6,7 @@ titles = {
"GFPGAN": "Restore low quality faces using GFPGAN neural network",
"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
"Batch count": "How many batches of images to create",
"Batch size": "How many image to create in a single batch",
......@@ -17,6 +18,7 @@ titles = {
"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
"\u{1f4c2}": "Open images output directory",
"\u{1f4be}": "Save style",
"\U0001F5D1": "Clear prompt",
"\u{1f4cb}": "Apply selected styles to current prompt",
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
......@@ -62,8 +64,8 @@ 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_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [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], [datetime<Format>], [datetime<Format><Time Zone>], [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], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [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], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [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.",
......@@ -94,6 +96,11 @@ titles = {
"Add difference": "Result = A + (B - C) * M",
"Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
"Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
"Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",
"Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality."
}
......
......@@ -15,7 +15,7 @@ onUiUpdate(function(){
}
}
const galleryPreviews = gradioApp().querySelectorAll('img.h-full.w-full.overflow-hidden');
const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden');
if (galleryPreviews == null) return;
......
......@@ -3,7 +3,7 @@ global_progressbars = {}
galleries = {}
galleryObservers = {}
// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
timeoutIds = {}
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
......@@ -20,21 +20,21 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
var skip = id_skip ? gradioApp().getElementById(id_skip) : null
var interrupt = gradioApp().getElementById(id_interrupt)
if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
if(progressbar.innerText){
let newtitle = 'Stable Diffusion - ' + progressbar.innerText
let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
if(document.title != newtitle){
document.title = newtitle;
document.title = newtitle;
}
}else{
let newtitle = 'Stable Diffusion'
if(document.title != newtitle){
document.title = newtitle;
document.title = newtitle;
}
}
}
if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
global_progressbars[id_progressbar] = progressbar
......@@ -63,7 +63,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
skip.style.display = "none"
}
interrupt.style.display = "none"
//disconnect observer once generation finished, so user can close selected image if they want
if (galleryObservers[id_gallery]) {
galleryObservers[id_gallery].disconnect();
......@@ -92,14 +92,26 @@ function check_gallery(id_gallery){
if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
// automatically re-open previously selected index (if exists)
activeElement = gradioApp().activeElement;
let scrollX = window.scrollX;
let scrollY = window.scrollY;
galleryButtons[prevSelectedIndex].click();
showGalleryImage();
// When the gallery button is clicked, it gains focus and scrolls itself into view
// We need to scroll back to the previous position
setTimeout(function (){
window.scrollTo(scrollX, scrollY);
}, 50);
if(activeElement){
// i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
// if somenoe has a better solution please by all means
setTimeout(function() { activeElement.focus() }, 1);
// if someone has a better solution please by all means
setTimeout(function (){
activeElement.focus({
preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
})
}, 1);
}
}
})
......
// various functions for interation with ui.py not large enough to warrant putting them in separate files
// various functions for interaction with ui.py not large enough to warrant putting them in separate files
function set_theme(theme){
gradioURL = window.location.href
......@@ -8,8 +8,8 @@ function set_theme(theme){
}
function selected_gallery_index(){
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item')
var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2')
var result = -1
buttons.forEach(function(v, i){ if(v==button) { result = i } })
......@@ -19,7 +19,7 @@ function selected_gallery_index(){
function extract_image_from_gallery(gallery){
if(gallery.length == 1){
return gallery[0]
return [gallery[0]]
}
index = selected_gallery_index()
......@@ -28,7 +28,7 @@ function extract_image_from_gallery(gallery){
return [null]
}
return gallery[index];
return [gallery[index]];
}
function args_to_array(args){
......@@ -100,7 +100,7 @@ function create_submit_args(args){
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
// I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
// I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
// If gradio at some point stops sending outputs, this may break something
if(Array.isArray(res[res.length - 3])){
res[res.length - 3] = null
......@@ -131,6 +131,15 @@ function ask_for_style_name(_, prompt_text, negative_prompt_text) {
return [name_, prompt_text, negative_prompt_text]
}
function confirm_clear_prompt(prompt, negative_prompt) {
if(confirm("Delete prompt?")) {
prompt = ""
negative_prompt = ""
}
return [prompt, negative_prompt]
}
opts = {}
......@@ -179,6 +188,17 @@ onUiUpdate(function(){
img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
}
show_all_pages = gradioApp().getElementById('settings_show_all_pages')
settings_tabs = gradioApp().querySelector('#settings div')
if(show_all_pages && settings_tabs){
settings_tabs.appendChild(show_all_pages)
show_all_pages.onclick = function(){
gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
elem.style.display = "block";
})
}
}
})
let txt2img_textarea, img2img_textarea = undefined;
......
......@@ -5,6 +5,8 @@ import sys
import importlib.util
import shlex
import platform
import argparse
import json
dir_repos = "repositories"
dir_extensions = "extensions"
......@@ -17,6 +19,19 @@ def extract_arg(args, name):
return [x for x in args if x != name], name in args
def extract_opt(args, name):
opt = None
is_present = False
if name in args:
is_present = True
idx = args.index(name)
del args[idx]
if idx < len(args) and args[idx][0] != "-":
opt = args[idx]
del args[idx]
return args, is_present, opt
def run(command, desc=None, errdesc=None, custom_env=None):
if desc is not None:
print(desc)
......@@ -105,56 +120,78 @@ def version_check(commit):
print("version check failed", e)
def run_extensions_installers():
if not os.path.isdir(dir_extensions):
def run_extension_installer(extension_dir):
path_installer = os.path.join(extension_dir, "install.py")
if not os.path.isfile(path_installer):
return
for dirname_extension in os.listdir(dir_extensions):
path_installer = os.path.join(dir_extensions, dirname_extension, "install.py")
if not os.path.isfile(path_installer):
continue
try:
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
def list_extensions(settings_file):
settings = {}
try:
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except Exception as e:
print(e, file=sys.stderr)
disabled_extensions = set(settings.get('disabled_extensions', []))
return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
try:
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
def run_extensions_installers(settings_file):
if not os.path.isdir(dir_extensions):
return
for dirname_extension in list_extensions(settings_file):
run_extension_installer(os.path.join(dir_extensions, dirname_extension))
def prepare_enviroment():
def prepare_environment():
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
sys.argv += shlex.split(commandline_args)
test_argv = [x for x in sys.argv if x != '--tests']
parser = argparse.ArgumentParser()
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
args, _ = parser.parse_known_args(sys.argv)
sys.argv, _ = extract_arg(sys.argv, '-f')
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
sys.argv, update_check = extract_arg(sys.argv, '--update-check')
sys.argv, run_tests = extract_arg(sys.argv, '--tests')
sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
xformers = '--xformers' in sys.argv
deepdanbooru = '--deepdanbooru' in sys.argv
ngrok = '--ngrok' in sys.argv
try:
......@@ -177,6 +214,9 @@ def prepare_enviroment():
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
if (not is_installed("xformers") or reinstall_xformers) and xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
......@@ -189,15 +229,12 @@ def prepare_enviroment():
elif platform.system() == "Linux":
run_pip("install xformers", "xformers")
if not is_installed("deepdanbooru") and deepdanbooru:
run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
if not is_installed("pyngrok") and ngrok:
run_pip("install pyngrok", "ngrok")
os.makedirs(dir_repos, exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
......@@ -208,7 +245,7 @@ def prepare_enviroment():
run_pip(f"install -r {requirements_file}", "requirements for Web UI")
run_extensions_installers()
run_extensions_installers(settings_file=args.ui_settings_file)
if update_check:
version_check(commit)
......@@ -218,24 +255,30 @@ def prepare_enviroment():
exit(0)
if run_tests:
tests(test_argv)
exit(0)
exitcode = tests(test_dir)
exit(exitcode)
def tests(argv):
if "--api" not in argv:
argv.append("--api")
def tests(test_dir):
if "--api" not in sys.argv:
sys.argv.append("--api")
if "--ckpt" not in sys.argv:
sys.argv.append("--ckpt")
sys.argv.append("./test/test_files/empty.pt")
if "--skip-torch-cuda-test" not in sys.argv:
sys.argv.append("--skip-torch-cuda-test")
print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
proc = subprocess.Popen([sys.executable, *argv], stdout=stdout, stderr=stderr)
proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
import test.server_poll
test.server_poll.run_tests()
exitcode = test.server_poll.run_tests(proc, test_dir)
print(f"Stopping Web UI process with id {proc.pid}")
proc.kill()
return exitcode
def start():
......@@ -248,5 +291,5 @@ def start():
if __name__ == "__main__":
prepare_enviroment()
prepare_environment()
start()
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......@@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
......@@ -170,14 +170,24 @@ class ProgressResponse(BaseModel):
class InterrogateRequest(BaseModel):
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
model: str = Field(default="clip", title="Model", description="The interrogate model used.")
class InterrogateResponse(BaseModel):
caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
class TrainResponse(BaseModel):
info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
class CreateResponse(BaseModel):
info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
class PreprocessResponse(BaseModel):
info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
fields = {}
for key, value in opts.data.items():
metadata = opts.data_labels.get(key)
optType = opts.typemap.get(type(value), type(value))
for key, metadata in opts.data_labels.items():
value = opts.data.get(key)
optType = opts.typemap.get(type(metadata.default), type(value))
if (metadata is not None):
fields.update({key: (Optional[optType], Field(
......@@ -239,3 +249,13 @@ class ArtistItem(BaseModel):
score: float = Field(title="Score")
category: str = Field(title="Category")
class EmbeddingItem(BaseModel):
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
class EmbeddingsResponse(BaseModel):
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
import html
import sys
import threading
import traceback
import time
from modules import shared
queue_lock = threading.Lock()
def wrap_queued_call(func):
def f(*args, **kwargs):
with queue_lock:
res = func(*args, **kwargs)
return res
return f
def wrap_gradio_gpu_call(func, extra_outputs=None):
def f(*args, **kwargs):
shared.state.begin()
with queue_lock:
res = func(*args, **kwargs)
shared.state.end()
return res
return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:
shared.mem_mon.monitor()
t = time.perf_counter()
try:
res = list(func(*args, **kwargs))
except Exception as e:
# When printing out our debug argument list, do not print out more than a MB of text
max_debug_str_len = 131072 # (1024*1024)/8
print("Error completing request", file=sys.stderr)
argStr = f"Arguments: {str(args)} {str(kwargs)}"
print(argStr[:max_debug_str_len], file=sys.stderr)
if len(argStr) > max_debug_str_len:
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
shared.state.job = ""
shared.state.job_count = 0
if extra_outputs_array is None:
extra_outputs_array = [None, '']
res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
shared.state.skipped = False
shared.state.interrupted = False
shared.state.job_count = 0
if not add_stats:
return tuple(res)
elapsed = time.perf_counter() - t
elapsed_m = int(elapsed // 60)
elapsed_s = elapsed % 60
elapsed_text = f"{elapsed_s:.2f}s"
if elapsed_m > 0:
elapsed_text = f"{elapsed_m}m "+elapsed_text
if run_memmon:
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
active_peak = mem_stats['active_peak']
reserved_peak = mem_stats['reserved_peak']
sys_peak = mem_stats['system_peak']
sys_total = mem_stats['total']
sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
else:
vram_html = ''
# last item is always HTML
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
return tuple(res)
return f
......@@ -382,7 +382,7 @@ class VQAutoEncoder(nn.Module):
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
logger.info(f'vqgan is loaded from: {model_path} [params]')
else:
raise ValueError(f'Wrong params!')
raise ValueError('Wrong params!')
def forward(self, x):
......@@ -431,7 +431,7 @@ class VQGANDiscriminator(nn.Module):
elif 'params' in chkpt:
self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
else:
raise ValueError(f'Wrong params!')
raise ValueError('Wrong params!')
def forward(self, x):
return self.main(x)
\ No newline at end of file
......@@ -36,6 +36,7 @@ def setup_model(dirname):
from basicsr.utils.download_util import load_file_from_url
from basicsr.utils import imwrite, img2tensor, tensor2img
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from facelib.detection.retinaface import retinaface
from modules.shared import cmd_opts
net_class = CodeFormer
......@@ -65,6 +66,8 @@ def setup_model(dirname):
net.load_state_dict(checkpoint)
net.eval()
if hasattr(retinaface, 'device'):
retinaface.device = devices.device_codeformer
face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
self.net = net
......
import os.path
from concurrent.futures import ProcessPoolExecutor
import multiprocessing
import time
import os
import re
import torch
from PIL import Image
import numpy as np
from modules import modelloader, paths, deepbooru_model, devices, images, shared
re_special = re.compile(r'([\\()])')
def get_deepbooru_tags(pil_image):
"""
This method is for running only one image at a time for simple use. Used to the img2img interrogate.
"""
from modules import shared # prevents circular reference
try:
create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
return get_tags_from_process(pil_image)
finally:
release_process()
OPT_INCLUDE_RANKS = "include_ranks"
def create_deepbooru_opts():
from modules import shared
return {
"use_spaces": shared.opts.deepbooru_use_spaces,
"use_escape": shared.opts.deepbooru_escape,
"alpha_sort": shared.opts.deepbooru_sort_alpha,
OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
}
def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
model, tags = get_deepbooru_tags_model()
while True: # while process is running, keep monitoring queue for new image
pil_image = queue.get()
if pil_image == "QUIT":
break
else:
deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
def create_deepbooru_process(threshold, deepbooru_opts):
"""
Creates deepbooru process. A queue is created to send images into the process. This enables multiple images
to be processed in a row without reloading the model or creating a new process. To return the data, a shared
dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned
to the dictionary and the method adding the image to the queue should wait for this value to be updated with
the tags.
"""
from modules import shared # prevents circular reference
context = multiprocessing.get_context("spawn")
shared.deepbooru_process_manager = context.Manager()
shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
shared.deepbooru_process.start()
def get_tags_from_process(image):
from modules import shared
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process_queue.put(image)
while shared.deepbooru_process_return["value"] == -1:
time.sleep(0.2)
caption = shared.deepbooru_process_return["value"]
shared.deepbooru_process_return["value"] = -1
return caption
def release_process():
"""
Stops the deepbooru process to return used memory
"""
from modules import shared # prevents circular reference
shared.deepbooru_process_queue.put("QUIT")
shared.deepbooru_process.join()
shared.deepbooru_process_queue = None
shared.deepbooru_process = None
shared.deepbooru_process_return = None
shared.deepbooru_process_manager = None
def get_deepbooru_tags_model():
import deepdanbooru as dd
import tensorflow as tf
import numpy as np
this_folder = os.path.dirname(__file__)
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
if not os.path.exists(os.path.join(model_path, 'project.json')):
# there is no point importing these every time
import zipfile
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(
r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
model_path)
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
zip_ref.extractall(model_path)
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
tags = dd.project.load_tags_from_project(model_path)
model = dd.project.load_model_from_project(
model_path, compile_model=False
)
return model, tags
def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
import deepdanbooru as dd
import tensorflow as tf
import numpy as np
alpha_sort = deepbooru_opts['alpha_sort']
use_spaces = deepbooru_opts['use_spaces']
use_escape = deepbooru_opts['use_escape']
include_ranks = deepbooru_opts['include_ranks']
width = model.input_shape[2]
height = model.input_shape[1]
image = np.array(pil_image)
image = tf.image.resize(
image,
size=(height, width),
method=tf.image.ResizeMethod.AREA,
preserve_aspect_ratio=True,
)
image = image.numpy() # EagerTensor to np.array
image = dd.image.transform_and_pad_image(image, width, height)
image = image / 255.0
image_shape = image.shape
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
y = model.predict(image)[0]
result_dict = {}
for i, tag in enumerate(tags):
result_dict[tag] = y[i]
unsorted_tags_in_theshold = []
result_tags_print = []
for tag in tags:
if result_dict[tag] >= threshold:
class DeepDanbooru:
def __init__(self):
self.model = None
def load(self):
if self.model is not None:
return
files = modelloader.load_models(
model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
ext_filter=[".pt"],
download_name='model-resnet_custom_v3.pt',
)
self.model = deepbooru_model.DeepDanbooruModel()
self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
self.model.eval()
self.model.to(devices.cpu, devices.dtype)
def start(self):
self.load()
self.model.to(devices.device)
def stop(self):
if not shared.opts.interrogate_keep_models_in_memory:
self.model.to(devices.cpu)
devices.torch_gc()
def tag(self, pil_image):
self.start()
res = self.tag_multi(pil_image)
self.stop()
return res
def tag_multi(self, pil_image, force_disable_ranks=False):
threshold = shared.opts.interrogate_deepbooru_score_threshold
use_spaces = shared.opts.deepbooru_use_spaces
use_escape = shared.opts.deepbooru_escape
alpha_sort = shared.opts.deepbooru_sort_alpha
include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
with torch.no_grad(), devices.autocast():
x = torch.from_numpy(a).to(devices.device)
y = self.model(x)[0].detach().cpu().numpy()
probability_dict = {}
for tag, probability in zip(self.model.tags, y):
if probability < threshold:
continue
if tag.startswith("rating:"):
continue
unsorted_tags_in_theshold.append((result_dict[tag], tag))
result_tags_print.append(f'{result_dict[tag]} {tag}')
# sort tags
result_tags_out = []
sort_ndx = 0
if alpha_sort:
sort_ndx = 1
# sort by reverse by likelihood and normal for alpha, and format tag text as requested
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
for weight, tag in unsorted_tags_in_theshold:
tag_outformat = tag
if use_spaces:
tag_outformat = tag_outformat.replace('_', ' ')
if use_escape:
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
if include_ranks:
tag_outformat = f"({tag_outformat}:{weight:.3f})"
result_tags_out.append(tag_outformat)
print('\n'.join(sorted(result_tags_print, reverse=True)))
return ', '.join(result_tags_out)
probability_dict[tag] = probability
if alpha_sort:
tags = sorted(probability_dict)
else:
tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
res = []
filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
for tag in [x for x in tags if x not in filtertags]:
probability = probability_dict[tag]
tag_outformat = tag
if use_spaces:
tag_outformat = tag_outformat.replace('_', ' ')
if use_escape:
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
if include_ranks:
tag_outformat = f"({tag_outformat}:{probability:.3f})"
res.append(tag_outformat)
return ", ".join(res)
model = DeepDanbooru()
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......@@ -2,9 +2,30 @@ import sys
import traceback
def print_error_explanation(message):
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])
print('=' * max_len, file=sys.stderr)
for line in lines:
print(line, file=sys.stderr)
print('=' * max_len, file=sys.stderr)
def display(e: Exception, task):
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
message = str(e)
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
print_error_explanation("""
The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file.
See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
""")
def run(code, task):
try:
code()
except Exception as e:
print(f"{task}: {type(e).__name__}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
display(task, e)
......@@ -199,7 +199,7 @@ def upscale_without_tiling(model, img):
img = img[:, :, ::-1]
img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
img = torch.from_numpy(img).float()
img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
......
......@@ -6,9 +6,9 @@ import git
from modules import paths, shared
extensions = []
extensions_dir = os.path.join(paths.script_path, "extensions")
extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
def active():
......@@ -16,12 +16,13 @@ def active():
class Extension:
def __init__(self, name, path, enabled=True):
def __init__(self, name, path, enabled=True, is_builtin=False):
self.name = name
self.path = path
self.enabled = enabled
self.status = ''
self.can_update = False
self.is_builtin = is_builtin
repo = None
try:
......@@ -66,9 +67,12 @@ class Extension:
self.can_update = False
self.status = "latest"
def pull(self):
def fetch_and_reset_hard(self):
repo = git.Repo(self.path)
repo.remotes.origin.pull()
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch('--all')
repo.git.reset('--hard', 'origin')
def list_extensions():
......@@ -77,10 +81,19 @@ def list_extensions():
if not os.path.isdir(extensions_dir):
return
for dirname in sorted(os.listdir(extensions_dir)):
path = os.path.join(extensions_dir, dirname)
if not os.path.isdir(path):
continue
paths = []
for dirname in [extensions_dir, extensions_builtin_dir]:
if not os.path.isdir(dirname):
return
for extension_dirname in sorted(os.listdir(dirname)):
path = os.path.join(dirname, extension_dirname)
if not os.path.isdir(path):
continue
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
for dirname, path, is_builtin in paths:
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
extensions.append(extension)
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......@@ -36,7 +36,9 @@ def gfpgann():
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)
if hasattr(facexlib.detection.retinaface, 'device'):
facexlib.detection.retinaface.device = devices.device_gfpgan
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
loaded_gfpgan_model = model
return model
......
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import sys
# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
if "--xformers" not in "".join(sys.argv):
sys.modules["xformers"] = None
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......@@ -71,10 +71,13 @@ class MemUsageMonitor(threading.Thread):
def read(self):
if not self.disabled:
free, total = torch.cuda.mem_get_info()
self.data["free"] = free
self.data["total"] = total
torch_stats = torch.cuda.memory_stats(self.device)
self.data["active"] = torch_stats["active.all.current"]
self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
self.data["reserved"] = torch_stats["reserved_bytes.all.current"]
self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
self.data["system_peak"] = total - self.data["min_free"]
......
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