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Stable Diffusion Webui
Commit 04b4508a authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
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Merge branch 'dev' into improve-frontend-responsiveness

parents 7201d940 b397f63e
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.github/ISSUE_TEMPLATE/bug_report.yml
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......@@ -47,6 +47,15 @@ body:
description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
validations:
required: true
- type: dropdown
id: py-version
attributes:
label: What Python version are you running on ?
multiple: false
options:
- Python 3.10.x
- Python 3.11.x (above, no supported yet)
- Python 3.9.x (below, no recommended)
- type: dropdown
id: platforms
attributes:
......@@ -59,6 +68,18 @@ body:
- iOS
- Android
- Other/Cloud
- type: dropdown
id: device
attributes:
label: What device are you running WebUI on?
multiple: true
options:
- Nvidia GPUs (RTX 20 above)
- Nvidia GPUs (GTX 16 below)
- AMD GPUs (RX 6000 above)
- AMD GPUs (RX 5000 below)
- CPU
- Other GPUs
- type: dropdown
id: browsers
attributes:
......
.github/workflows/on_pull_request.yaml
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......@@ -18,22 +18,29 @@ jobs:
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Set up Python 3.10
uses: actions/setup-python@v4
- uses: actions/setup-python@v4
with:
python-version: 3.10.6
cache: pip
cache-dependency-path: |
**/requirements*txt
- name: Install PyLint
run: |
python -m pip install --upgrade pip
pip install pylint
# This lets PyLint check to see if it can resolve imports
- name: Install dependencies
run: |
export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
python launch.py
- name: Analysing the code with pylint
run: |
pylint $(git ls-files '*.py')
python-version: 3.11
# NB: there's no cache: pip here since we're not installing anything
# from the requirements.txt file(s) in the repository; it's faster
# not to have GHA download an (at the time of writing) 4 GB cache
# of PyTorch and other dependencies.
- name: Install Ruff
run: pip install ruff==0.0.265
- name: Run Ruff
run: ruff .
# The rest are currently disabled pending fixing of e.g. installing the torch dependency.
# - name: Install PyLint
# run: |
# python -m pip install --upgrade pip
# pip install pylint
# # This lets PyLint check to see if it can resolve imports
# - name: Install dependencies
# run: |
# export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
# python launch.py
# - name: Analysing the code with pylint
# run: |
# pylint $(git ls-files '*.py')
.github/workflows/run_tests.yaml
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......@@ -17,8 +17,14 @@ jobs:
cache: pip
cache-dependency-path: |
**/requirements*txt
launch.py
- name: Run tests
run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
env:
PIP_DISABLE_PIP_VERSION_CHECK: "1"
PIP_PROGRESS_BAR: "off"
TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
WEBUI_LAUNCH_LIVE_OUTPUT: "1"
- name: Upload main app stdout-stderr
uses: actions/upload-artifact@v3
if: always()
......
.gitignore
View file @ 04b4508a
......@@ -32,4 +32,5 @@ notification.mp3
/extensions
/test/stdout.txt
/test/stderr.txt
/cache.json
/cache.json*
/config_states/
CHANGELOG.md 0 → 100644
View file @ 04b4508a
## 1.2.1
### Features:
* add an option to always refer to lora by filenames
### Bug Fixes:
* never refer to lora by an alias if multiple loras have same alias or the alias is called none
* fix upscalers disappearing after the user reloads UI
* allow bf16 in safe unpickler (resolves problems with loading some loras)
* allow web UI to be ran fully offline
* fix localizations not working
* fix error for loras: 'LatentDiffusion' object has no attribute 'lora_layer_mapping'
## 1.2.0
### Features:
* do not wait for stable diffusion model to load at startup
* add filename patterns: [denoising]
* directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
* Lora: for the `<...>` text in prompt, use name of Lora that is in the metdata of the file, if present, instead of filename (both can be used to activate lora)
* Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
* Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
* Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
* add version to infotext, footer and console output when starting
* add links to wiki for filename pattern settings
* add extended info for quicksettings setting and use multiselect input instead of a text field
### Minor:
* gradio bumped to 3.29.0
* torch bumped to 2.0.1
* --subpath option for gradio for use with reverse proxy
* linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
* possible frontend optimization: do not apply localizations if there are none
* Add extra `None` option for VAE in XYZ plot
* print error to console when batch processing in img2img fails
* create HTML for extra network pages only on demand
* allow directories starting with . to still list their models for lora, checkpoints, etc
* put infotext options into their own category in settings tab
* do not show licenses page when user selects Show all pages in settings
### Extensions:
* Tooltip localization support
* Add api method to get LoRA models with prompt
### Bug Fixes:
* re-add /docs endpoint
* fix gamepad navigation
* make the lightbox fullscreen image function properly
* fix squished thumbnails in extras tab
* keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
* fix webui showing the same image if you configure the generation to always save results into same file
* fix bug with upscalers not working properly
* Fix MPS on PyTorch 2.0.1, Intel Macs
* make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
* prevent Reload UI button/link from reloading the page when it's not yet ready
* fix prompts from file script failing to read contents from a drag/drop file
## 1.1.1
### Bug Fixes:
* fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle
## 1.1.0
### Features:
* switch to torch 2.0.0 (except for AMD GPUs)
* visual improvements to custom code scripts
* add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]
* add support for saving init images in img2img, and record their hashes in infotext for reproducability
* automatically select current word when adjusting weight with ctrl+up/down
* add dropdowns for X/Y/Z plot
* setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
* support Gradio's theme API
* use TCMalloc on Linux by default; possible fix for memory leaks
* (optimization) option to remove negative conditioning at low sigma values #9177
* embed model merge metadata in .safetensors file
* extension settings backup/restore feature #9169
* add "resize by" and "resize to" tabs to img2img
* add option "keep original size" to textual inversion images preprocess
* image viewer scrolling via analog stick
* button to restore the progress from session lost / tab reload
### Minor:
* gradio bumped to 3.28.1
* in extra tab, change extras "scale to" to sliders
* add labels to tool buttons to make it possible to hide them
* add tiled inference support for ScuNET
* add branch support for extension installation
* change linux installation script to insall into current directory rather than /home/username
* sort textual inversion embeddings by name (case insensitive)
* allow styles.csv to be symlinked or mounted in docker
* remove the "do not add watermark to images" option
* make selected tab configurable with UI config
* extra networks UI in now fixed height and scrollable
* add disable_tls_verify arg for use with self-signed certs
### Extensions:
* Add reload callback
* add is_hr_pass field for processing
### Bug Fixes:
* fix broken batch image processing on 'Extras/Batch Process' tab
* add "None" option to extra networks dropdowns
* fix FileExistsError for CLIP Interrogator
* fix /sdapi/v1/txt2img endpoint not working on Linux #9319
* fix disappearing live previews and progressbar during slow tasks
* fix fullscreen image view not working properly in some cases
* prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
* fix prompt schedule for second order samplers
* fix image mask/composite for weird resolutions #9628
* use correct images for previews when using AND (see #9491)
* one broken image in img2img batch won't stop all processing
* fix image orientation bug in train/preprocess
* fix Ngrok recreating tunnels every reload
* fix --realesrgan-models-path and --ldsr-models-path not working
* fix --skip-install not working
* outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
* do not fail all Loras if some have failed to load when making a picture
## 1.0.0
* everything
README.md
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......@@ -99,8 +99,14 @@ Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Installation on Windows 10/11 with NVidia-GPUs using release package
1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
2. Run `update.bat`.
3. Run `run.bat`.
> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
......@@ -115,11 +121,12 @@ sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
......@@ -157,5 +164,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
\ No newline at end of file
- (You)
environment-wsl2.yaml
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......@@ -4,8 +4,8 @@ channels:
- defaults
dependencies:
- python=3.10
- pip=22.2.2
- cudatoolkit=11.3
- pytorch=1.12.1
- torchvision=0.13.1
- numpy=1.23.1
\ No newline at end of file
- pip=23.0
- cudatoolkit=11.8
- pytorch=2.0
- torchvision=0.15
- numpy=1.23
extensions-builtin/LDSR/ldsr_model_arch.py
View file @ 04b4508a
......@@ -88,7 +88,7 @@ class LDSR:
x_t = None
logs = None
for n in range(n_runs):
for _ in range(n_runs):
if custom_shape is not None:
x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
......@@ -110,7 +110,6 @@ class LDSR:
diffusion_steps = int(steps)
eta = 1.0
down_sample_method = 'Lanczos'
gc.collect()
if torch.cuda.is_available:
......@@ -131,11 +130,11 @@ class LDSR:
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
else:
print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
logs = self.run(model["model"], im_padded, diffusion_steps, eta)
sample = logs["sample"]
......@@ -158,7 +157,7 @@ class LDSR:
def get_cond(selected_path):
example = dict()
example = {}
up_f = 4
c = selected_path.convert('RGB')
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
......@@ -196,7 +195,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s
@torch.no_grad()
def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
log = dict()
log = {}
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
......@@ -244,7 +243,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except:
except Exception:
pass
log["sample"] = x_sample
......
extensions-builtin/LDSR/scripts/ldsr_model.py
View file @ 04b4508a
......@@ -7,7 +7,8 @@ from basicsr.utils.download_util import load_file_from_url
from modules.upscaler import Upscaler, UpscalerData
from ldsr_model_arch import LDSR
from modules import shared, script_callbacks
import sd_hijack_autoencoder, sd_hijack_ddpm_v1
import sd_hijack_autoencoder # noqa: F401
import sd_hijack_ddpm_v1 # noqa: F401
class UpscalerLDSR(Upscaler):
......@@ -25,22 +26,28 @@ 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")
local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
if os.path.exists(yaml_path):
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)
if os.path.exists(safetensors_model_path):
model = safetensors_model_path
if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
model = local_safetensors_path
else:
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)
model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
try:
return LDSR(model, yaml)
......
extensions-builtin/LDSR/sd_hijack_autoencoder.py
View file @ 04b4508a
# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
import numpy as np
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from torch.optim.lr_scheduler import LambdaLR
from ldm.modules.ema import LitEma
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.util import instantiate_from_config
import ldm.models.autoencoder
from packaging import version
class VQModel(pl.LightningModule):
def __init__(self,
......@@ -19,7 +24,7 @@ class VQModel(pl.LightningModule):
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
ignore_keys=None,
image_key="image",
colorize_nlabels=None,
monitor=None,
......@@ -57,7 +62,7 @@ class VQModel(pl.LightningModule):
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
self.scheduler_config = scheduler_config
self.lr_g_factor = lr_g_factor
......@@ -76,11 +81,11 @@ class VQModel(pl.LightningModule):
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list()):
def init_from_ckpt(self, path, ignore_keys=None):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
for ik in ignore_keys or []:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
......@@ -165,7 +170,7 @@ class VQModel(pl.LightningModule):
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
self._validation_step(batch, batch_idx, suffix="_ema")
return log_dict
def _validation_step(self, batch, batch_idx, suffix=""):
......@@ -232,7 +237,7 @@ class VQModel(pl.LightningModule):
return self.decoder.conv_out.weight
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
log = dict()
log = {}
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if only_inputs:
......@@ -249,7 +254,8 @@ class VQModel(pl.LightningModule):
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
if x.shape[1] > 3:
xrec_ema = self.to_rgb(xrec_ema)
log["reconstructions_ema"] = xrec_ema
return log
......@@ -264,7 +270,7 @@ class VQModel(pl.LightningModule):
class VQModelInterface(VQModel):
def __init__(self, embed_dim, *args, **kwargs):
super().__init__(embed_dim=embed_dim, *args, **kwargs)
super().__init__(*args, embed_dim=embed_dim, **kwargs)
self.embed_dim = embed_dim
def encode(self, x):
......@@ -282,5 +288,5 @@ class VQModelInterface(VQModel):
dec = self.decoder(quant)
return dec
setattr(ldm.models.autoencoder, "VQModel", VQModel)
setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
ldm.models.autoencoder.VQModel = VQModel
ldm.models.autoencoder.VQModelInterface = VQModelInterface
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
View file @ 04b4508a
......@@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
ignore_keys=None,
load_only_unet=False,
monitor="val/loss",
use_ema=True,
......@@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
......@@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule):
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
for ik in ignore_keys or []:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
......@@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
log = {}
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
......@@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
log["inputs"] = x
# get diffusion row
diffusion_row = list()
diffusion_row = []
x_start = x[:n_row]
for t in range(self.num_timesteps):
......@@ -444,13 +444,13 @@ class LatentDiffusionV1(DDPMV1):
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
except Exception:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
......@@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1):
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
......@@ -792,7 +792,7 @@ class LatentDiffusionV1(DDPMV1):
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
......@@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
......@@ -900,7 +890,7 @@ class LatentDiffusionV1(DDPMV1):
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
......@@ -1126,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
[x[:batch_size] for x in cond[key]] for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
......@@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
return img, intermediates
@torch.no_grad()
......@@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if callback:
callback(i)
if img_callback:
img_callback(img, i)
if return_intermediates:
return img, intermediates
......@@ -1221,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
[x[:batch_size] for x in cond[key]] for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
......@@ -1253,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):
use_ddim = ddim_steps is not None
log = dict()
log = {}
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
......@@ -1280,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
diffusion_row = []
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
......@@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
h, w = z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
......@@ -1424,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
logs = super().log_images(*args, batch=batch, N=N, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
......@@ -1443,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
logs['bbox_image'] = cond_img
return logs
setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
extensions-builtin/Lora/extra_networks_lora.py
View file @ 04b4508a
from modules import extra_networks, shared
import lora
class ExtraNetworkLora(extra_networks.ExtraNetwork):
def __init__(self):
super().__init__('lora')
......@@ -8,7 +9,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
def activate(self, p, params_list):
additional = shared.opts.sd_lora
if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
......
extensions-builtin/Lora/lora.py
View file @ 04b4508a
This diff is collapsed.
extensions-builtin/Lora/scripts/lora_script.py
View file @ 04b4508a
import torch
import gradio as gr
from fastapi import FastAPI
import lora
import extra_networks_lora
import ui_extra_networks_lora
from modules import script_callbacks, ui_extra_networks, extra_networks, shared
def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
......@@ -49,8 +49,34 @@ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
script_callbacks.on_before_ui(before_ui)
script_callbacks.on_infotext_pasted(lora.infotext_pasted)
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
}))
shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
"lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
}))
def create_lora_json(obj: lora.LoraOnDisk):
return {
"name": obj.name,
"alias": obj.alias,
"path": obj.filename,
"metadata": obj.metadata,
}
def api_loras(_: gr.Blocks, app: FastAPI):
@app.get("/sdapi/v1/loras")
async def get_loras():
return [create_lora_json(obj) for obj in lora.available_loras.values()]
script_callbacks.on_app_started(api_loras)
extensions-builtin/Lora/ui_extra_networks_lora.py
View file @ 04b4508a
......@@ -15,13 +15,19 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
def list_items(self):
for name, lora_on_disk in lora.available_loras.items():
path, ext = os.path.splitext(lora_on_disk.filename)
if shared.opts.lora_preferred_name == "Filename" or lora_on_disk.alias.lower() in lora.forbidden_lora_aliases:
alias = name
else:
alias = lora_on_disk.alias
yield {
"name": name,
"filename": path,
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_term": self.search_terms_from_path(lora_on_disk.filename),
"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"prompt": json.dumps(f"<lora:{alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
}
......
extensions-builtin/ScuNET/scripts/scunet_model.py
View file @ 04b4508a
......@@ -5,11 +5,14 @@ import traceback
import PIL.Image
import numpy as np
import torch
from tqdm import tqdm
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import devices, modelloader
from modules import devices, modelloader, script_callbacks
from scunet_model_arch import SCUNet as net
from modules.shared import opts
class UpscalerScuNET(modules.upscaler.Upscaler):
......@@ -42,28 +45,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
scalers.append(scaler_data2)
self.scalers = scalers
def do_upscale(self, img: PIL.Image, selected_file):
@staticmethod
@torch.no_grad()
def tiled_inference(img, model):
# test the image tile by tile
h, w = img.shape[2:]
tile = opts.SCUNET_tile
tile_overlap = opts.SCUNET_tile_overlap
if tile == 0:
return model(img)
device = devices.get_device_for('scunet')
assert tile % 8 == 0, "tile size should be a multiple of window_size"
sf = 1
stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
W = torch.zeros_like(E, dtype=devices.dtype, device=device)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch)
W[
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
].add_(out_patch_mask)
pbar.update(1)
output = E.div_(W)
return output
def do_upscale(self, img: PIL.Image.Image, selected_file):
torch.cuda.empty_cache()
model = self.load_model(selected_file)
if model is None:
print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
return img
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 = img.unsqueeze(0).to(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]
tile = opts.SCUNET_tile
h, w = img.height, img.width
np_img = np.array(img)
np_img = np_img[:, :, ::-1] # RGB to BGR
np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
if tile > h or tile > w:
_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
_img[:, :, :h, :w] = torch_img # pad image
torch_img = _img
torch_output = self.tiled_inference(torch_img, model).squeeze(0)
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
del torch_img, torch_output
torch.cuda.empty_cache()
return PIL.Image.fromarray(output, 'RGB')
output = np_output.transpose((1, 2, 0)) # CHW to HWC
output = output[:, :, ::-1] # BGR to RGB
return PIL.Image.fromarray((output * 255).astype(np.uint8))
def load_model(self, path: str):
device = devices.get_device_for('scunet')
......@@ -79,9 +132,19 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for k, v in model.named_parameters():
for _, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
return model
def on_ui_settings():
import gradio as gr
from modules import shared
shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
script_callbacks.on_ui_settings(on_ui_settings)
extensions-builtin/ScuNET/scunet_model_arch.py
View file @ 04b4508a
......@@ -61,7 +61,9 @@ class WMSA(nn.Module):
Returns:
output: tensor shape [b h w c]
"""
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
if self.type != 'W':
x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
h_windows = x.size(1)
w_windows = x.size(2)
......@@ -85,8 +87,9 @@ class WMSA(nn.Module):
output = self.linear(output)
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
dims=(1, 2))
if self.type != 'W':
output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
return output
def relative_embedding(self):
......@@ -262,4 +265,4 @@ class SCUNet(nn.Module):
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
nn.init.constant_(m.weight, 1.0)
extensions-builtin/SwinIR/scripts/swinir_model.py
View file @ 04b4508a
import contextlib
import os
import numpy as np
......@@ -8,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
from tqdm import tqdm
from modules import modelloader, devices, script_callbacks, shared
from modules.shared import cmd_opts, opts, state
from modules.shared import opts, state
from swinir_model_arch import SwinIR as net
from swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
......@@ -45,7 +44,7 @@ class UpscalerSwinIR(Upscaler):
img = upscale(img, model)
try:
torch.cuda.empty_cache()
except:
except Exception:
pass
return img
......@@ -151,7 +150,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
for w_idx in w_idx_list:
if state.interrupted or state.skipped:
break
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
......
extensions-builtin/SwinIR/swinir_model_arch.py
View file @ 04b4508a
......@@ -644,7 +644,7 @@ class SwinIR(nn.Module):
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
......@@ -805,7 +805,7 @@ class SwinIR(nn.Module):
def forward(self, x):
H, W = x.shape[2:]
x = self.check_image_size(x)
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
......@@ -844,7 +844,7 @@ class SwinIR(nn.Module):
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
for layer in self.layers:
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
......
extensions-builtin/SwinIR/swinir_model_arch_v2.py
View file @ 04b4508a
......@@ -74,7 +74,7 @@ class WindowAttention(nn.Module):
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
pretrained_window_size=[0, 0]):
pretrained_window_size=(0, 0)):
super().__init__()
self.dim = dim
......@@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module):
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def calculate_mask(self, x_size):
# calculate attention mask for SW-MSA
H, W = x_size
......@@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module):
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
return attn_mask
def forward(self, x, x_size):
H, W = x_size
......@@ -288,7 +288,7 @@ class SwinTransformerBlock(nn.Module):
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
else:
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
......@@ -369,7 +369,7 @@ class PatchMerging(nn.Module):
H, W = self.input_resolution
flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
flops += H * W * self.dim // 2
return flops
return flops
class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage.
......@@ -447,7 +447,7 @@ class BasicLayer(nn.Module):
nn.init.constant_(blk.norm1.weight, 0)
nn.init.constant_(blk.norm2.bias, 0)
nn.init.constant_(blk.norm2.weight, 0)
class PatchEmbed(nn.Module):
r""" Image to Patch Embedding
Args:
......@@ -492,7 +492,7 @@ class PatchEmbed(nn.Module):
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
if self.norm is not None:
flops += Ho * Wo * self.embed_dim
return flops
return flops
class RSTB(nn.Module):
"""Residual Swin Transformer Block (RSTB).
......@@ -531,7 +531,7 @@ class RSTB(nn.Module):
num_heads=num_heads,
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qkv_bias=qkv_bias,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path,
norm_layer=norm_layer,
......@@ -622,7 +622,7 @@ class Upsample(nn.Sequential):
else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample, self).__init__(*m)
class Upsample_hf(nn.Sequential):
"""Upsample module.
......@@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential):
m.append(nn.PixelShuffle(3))
else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample_hf, self).__init__(*m)
super(Upsample_hf, self).__init__(*m)
class UpsampleOneStep(nn.Sequential):
......@@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential):
H, W = self.input_resolution
flops = H * W * self.num_feat * 3 * 9
return flops
class Swin2SR(nn.Module):
r""" Swin2SR
......@@ -698,8 +698,8 @@ class Swin2SR(nn.Module):
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
window_size=7, mlp_ratio=4., qkv_bias=True,
embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
window_size=7, mlp_ratio=4., qkv_bias=True,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
......@@ -764,7 +764,7 @@ class Swin2SR(nn.Module):
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
norm_layer=norm_layer,
......@@ -776,7 +776,7 @@ class Swin2SR(nn.Module):
)
self.layers.append(layer)
if self.upsampler == 'pixelshuffle_hf':
self.layers_hf = nn.ModuleList()
for i_layer in range(self.num_layers):
......@@ -787,7 +787,7 @@ class Swin2SR(nn.Module):
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
norm_layer=norm_layer,
......@@ -799,7 +799,7 @@ class Swin2SR(nn.Module):
)
self.layers_hf.append(layer)
self.norm = norm_layer(self.num_features)
# build the last conv layer in deep feature extraction
......@@ -829,10 +829,10 @@ class Swin2SR(nn.Module):
self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
self.conv_after_aux = nn.Sequential(
nn.Conv2d(3, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
nn.LeakyReLU(inplace=True))
self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
elif self.upsampler == 'pixelshuffle_hf':
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
......@@ -846,7 +846,7 @@ class Swin2SR(nn.Module):
nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR (to save parameters)
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
......@@ -905,7 +905,7 @@ class Swin2SR(nn.Module):
x = self.patch_unembed(x, x_size)
return x
def forward_features_hf(self, x):
x_size = (x.shape[2], x.shape[3])
x = self.patch_embed(x)
......@@ -919,7 +919,7 @@ class Swin2SR(nn.Module):
x = self.norm(x) # B L C
x = self.patch_unembed(x, x_size)
return x
return x
def forward(self, x):
H, W = x.shape[2:]
......@@ -951,7 +951,7 @@ class Swin2SR(nn.Module):
x = self.conv_after_body(self.forward_features(x)) + x
x_before = self.conv_before_upsample(x)
x_out = self.conv_last(self.upsample(x_before))
x_hf = self.conv_first_hf(x_before)
x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
x_hf = self.conv_before_upsample_hf(x_hf)
......@@ -977,15 +977,15 @@ class Swin2SR(nn.Module):
x_first = self.conv_first(x)
res = self.conv_after_body(self.forward_features(x_first)) + x_first
x = x + self.conv_last(res)
x = x / self.img_range + self.mean
if self.upsampler == "pixelshuffle_aux":
return x[:, :, :H*self.upscale, :W*self.upscale], aux
elif self.upsampler == "pixelshuffle_hf":
x_out = x_out / self.img_range + self.mean
return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
else:
return x[:, :, :H*self.upscale, :W*self.upscale]
......@@ -994,7 +994,7 @@ class Swin2SR(nn.Module):
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
for layer in self.layers:
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
......@@ -1014,4 +1014,4 @@ if __name__ == '__main__':
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
\ No newline at end of file
print(x.shape)
extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
View file @ 04b4508a
// Stable Diffusion WebUI - Bracket checker
// Version 1.0
// By Hingashi no Florin/Bwin4L
// By Hingashi no Florin/Bwin4L & @akx
// 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, counterElt) {
errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n';
errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
errorStringCurly = '{...} - 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;
function checkBrackets(textArea, counterElt) {
var counts = {};
(textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
counts[bracket] = (counts[bracket] || 0) + 1;
});
var errors = [];
function checkPair(open, close, kind) {
if (counts[open] !== counts[close]) {
errors.push(
`${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
);
}
} else {
counterElt.title = counterElt.title.replace(errorStringCurly, '');
}
if(counterElt.title != '') {
counterElt.classList.add('error');
} else {
counterElt.classList.remove('error');
}
checkPair('(', ')', 'round brackets');
checkPair('[', ']', 'square brackets');
checkPair('{', '}', 'curly brackets');
counterElt.title = errors.join('\n');
counterElt.classList.toggle('error', errors.length !== 0);
}
function setupBracketChecking(id_prompt, id_counter){
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
var counter = gradioApp().getElementById(id_counter)
function setupBracketChecking(id_prompt, id_counter) {
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
var counter = gradioApp().getElementById(id_counter)
textarea.addEventListener("input", function(evt){
checkBrackets(evt, textarea, counter)
});
if (textarea && counter) {
textarea.addEventListener("input", () => checkBrackets(textarea, counter));
}
}
onUiLoaded(function(){
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
setupBracketChecking('img2img_prompt', 'img2img_token_counter')
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
})
\ No newline at end of file
onUiLoaded(function () {
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
setupBracketChecking('img2img_prompt', 'img2img_token_counter');
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
});
html/extra-networks-card.html
View file @ 04b4508a
......@@ -6,7 +6,7 @@
<ul>
<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
</ul>
<span style="display:none" class='search_term'>{search_term}</span>
<span style="display:none" class='search_term{search_only}'>{search_term}</span>
</div>
<span class='name'>{name}</span>
<span class='description'>{description}</span>
......
html/licenses.html
View file @ 04b4508a
......@@ -661,4 +661,30 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
</pre>
<h2><a href="https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2>
<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small>
<pre>
MIT License
Copyright (c) 2023 Ollin Boer Bohan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
</pre>
\ No newline at end of file
javascript/aspectRatioOverlay.js
View file @ 04b4508a
......@@ -45,29 +45,24 @@ function dimensionChange(e, is_width, is_height){
var viewportOffset = targetElement.getBoundingClientRect();
viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
var viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
scaledx = targetElement.naturalWidth*viewportscale
scaledy = targetElement.naturalHeight*viewportscale
var scaledx = targetElement.naturalWidth*viewportscale
var scaledy = targetElement.naturalHeight*viewportscale
cleintRectTop = (viewportOffset.top+window.scrollY)
cleintRectLeft = (viewportOffset.left+window.scrollX)
cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
var cleintRectTop = (viewportOffset.top+window.scrollY)
var cleintRectLeft = (viewportOffset.left+window.scrollX)
var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
viewRectTop = cleintRectCentreY-(scaledy/2)
viewRectLeft = cleintRectCentreX-(scaledx/2)
arRectWidth = scaledx
arRectHeight = scaledy
var arscale = Math.min( scaledx/currentWidth, scaledy/currentHeight )
var arscaledx = currentWidth*arscale
var arscaledy = currentHeight*arscale
arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight )
arscaledx = currentWidth*arscale
arscaledy = currentHeight*arscale
arRectTop = cleintRectCentreY-(arscaledy/2)
arRectLeft = cleintRectCentreX-(arscaledx/2)
arRectWidth = arscaledx
arRectHeight = arscaledy
var arRectTop = cleintRectCentreY-(arscaledy/2)
var arRectLeft = cleintRectCentreX-(arscaledx/2)
var arRectWidth = arscaledx
var arRectHeight = arscaledy
arPreviewRect.style.top = arRectTop+'px';
arPreviewRect.style.left = arRectLeft+'px';
......
javascript/contextMenus.js
View file @ 04b4508a
......@@ -4,7 +4,7 @@ contextMenuInit = function(){
let menuSpecs = new Map();
const uid = function(){
return Date.now().toString(36) + Math.random().toString(36).substr(2);
return Date.now().toString(36) + Math.random().toString(36).substring(2);
}
function showContextMenu(event,element,menuEntries){
......@@ -16,8 +16,7 @@ contextMenuInit = function(){
oldMenu.remove()
}
let tabButton = uiCurrentTab
let baseStyle = window.getComputedStyle(tabButton)
let baseStyle = window.getComputedStyle(uiCurrentTab)
const contextMenu = document.createElement('nav')
contextMenu.id = "context-menu"
......@@ -36,7 +35,7 @@ contextMenuInit = function(){
menuEntries.forEach(function(entry){
let contextMenuEntry = document.createElement('a')
contextMenuEntry.innerHTML = entry['name']
contextMenuEntry.addEventListener("click", function(e) {
contextMenuEntry.addEventListener("click", function() {
entry['func']();
})
contextMenuList.append(contextMenuEntry);
......@@ -63,7 +62,7 @@ contextMenuInit = function(){
function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
currentItems = menuSpecs.get(targetElementSelector)
var currentItems = menuSpecs.get(targetElementSelector)
if(!currentItems){
currentItems = []
......@@ -79,7 +78,7 @@ contextMenuInit = function(){
}
function removeContextMenuOption(uid){
menuSpecs.forEach(function(v,k) {
menuSpecs.forEach(function(v) {
let index = -1
v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
if(index>=0){
......@@ -93,8 +92,7 @@ contextMenuInit = function(){
return;
}
gradioApp().addEventListener("click", function(e) {
let source = e.composedPath()[0]
if(source.id && source.id.indexOf('check_progress')>-1){
if(! e.isTrusted){
return
}
......@@ -112,7 +110,6 @@ contextMenuInit = function(){
if(e.composedPath()[0].matches(k)){
showContextMenu(e,e.composedPath()[0],v)
e.preventDefault()
return
}
})
});
......@@ -161,14 +158,6 @@ addContextMenuEventListener = initResponse[2];
appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#roll','Roll three',
function(){
let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
setTimeout(function(){rollbutton.click()},100)
setTimeout(function(){rollbutton.click()},200)
setTimeout(function(){rollbutton.click()},300)
}
)
})();
//End example Context Menu Items
......
javascript/edit-attention.js
View file @ 04b4508a
......@@ -17,7 +17,7 @@ function keyupEditAttention(event){
// Find opening parenthesis around current cursor
const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN);
if (beforeParen == -1) return false;
if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
......@@ -27,7 +27,7 @@ function keyupEditAttention(event){
// Find closing parenthesis around current cursor
const after = text.substring(selectionStart);
let afterParen = after.indexOf(CLOSE);
if (afterParen == -1) return false;
if (afterParen == -1) return false;
let afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1);
......@@ -43,16 +43,34 @@ function keyupEditAttention(event){
target.setSelectionRange(selectionStart, selectionEnd);
return true;
}
function selectCurrentWord(){
if (selectionStart !== selectionEnd) return false;
const delimiters = opts.keyedit_delimiters + " \r\n\t";
// seek backward until to find beggining
while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
selectionStart--;
}
// seek forward to find end
while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
selectionEnd++;
}
// If the user hasn't selected anything, let's select their current parenthesis block
if(! selectCurrentParenthesisBlock('<', '>')){
selectCurrentParenthesisBlock('(', ')')
target.setSelectionRange(selectionStart, selectionEnd);
return true;
}
// If the user hasn't selected anything, let's select their current parenthesis block or word
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
selectCurrentWord();
}
event.preventDefault();
closeCharacter = ')'
delta = opts.keyedit_precision_attention
var closeCharacter = ')'
var delta = opts.keyedit_precision_attention
if (selectionStart > 0 && text[selectionStart - 1] == '<'){
closeCharacter = '>'
......@@ -73,15 +91,21 @@ function keyupEditAttention(event){
selectionEnd += 1;
}
end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
if (isNaN(weight)) return;
weight += isPlus ? delta : -delta;
weight = parseFloat(weight.toPrecision(12));
if(String(weight).length == 1) weight += ".0"
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
if (closeCharacter == ')' && weight == 1) {
text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
selectionStart--;
selectionEnd--;
} else {
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
}
target.focus();
target.value = text;
......@@ -93,4 +117,4 @@ function keyupEditAttention(event){
addEventListener('keydown', (event) => {
keyupEditAttention(event);
});
\ No newline at end of file
});
javascript/extensions.js
View file @ 04b4508a
function extensions_apply(_, _, disable_all){
function extensions_apply(_disabled_list, _update_list, disable_all){
var disable = []
var update = []
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
if(x.name.startsWith("enable_") && ! x.checked)
disable.push(x.name.substr(7))
disable.push(x.name.substring(7))
if(x.name.startsWith("update_") && x.checked)
update.push(x.name.substr(7))
update.push(x.name.substring(7))
})
restart_reload()
......@@ -16,12 +16,12 @@ function extensions_apply(_, _, disable_all){
return [JSON.stringify(disable), JSON.stringify(update), disable_all]
}
function extensions_check(_, _){
function extensions_check(){
var disable = []
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
if(x.name.startsWith("enable_") && ! x.checked)
disable.push(x.name.substr(7))
disable.push(x.name.substring(7))
})
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
......@@ -41,9 +41,31 @@ function install_extension_from_index(button, url){
button.disabled = "disabled"
button.value = "Installing..."
textarea = gradioApp().querySelector('#extension_to_install textarea')
var textarea = gradioApp().querySelector('#extension_to_install textarea')
textarea.value = url
updateInput(textarea)
gradioApp().querySelector('#install_extension_button').click()
}
function config_state_confirm_restore(_, config_state_name, config_restore_type) {
if (config_state_name == "Current") {
return [false, config_state_name, config_restore_type];
}
let restored = "";
if (config_restore_type == "extensions") {
restored = "all saved extension versions";
} else if (config_restore_type == "webui") {
restored = "the webui version";
} else {
restored = "the webui version and all saved extension versions";
}
let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
if (confirmed) {
restart_reload();
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
x.innerHTML = "Loading..."
})
}
return [confirmed, config_state_name, config_restore_type];
}
javascript/extraNetworks.js
View file @ 04b4508a
function setupExtraNetworksForTab(tabname){
gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
......@@ -10,16 +9,34 @@ function setupExtraNetworksForTab(tabname){
tabs.appendChild(search)
tabs.appendChild(refresh)
search.addEventListener("input", function(evt){
searchTerm = search.value.toLowerCase()
var applyFilter = function(){
var searchTerm = search.value.toLowerCase()
gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
var searchOnly = elem.querySelector('.search_only')
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
var visible = text.indexOf(searchTerm) != -1
if(searchOnly && searchTerm.length < 4){
visible = false
}
elem.style.display = visible ? "" : "none"
})
});
}
search.addEventListener("input", applyFilter);
applyFilter();
extraNetworksApplyFilter[tabname] = applyFilter;
}
function applyExtraNetworkFilter(tabname){
setTimeout(extraNetworksApplyFilter[tabname], 1);
}
var extraNetworksApplyFilter = {}
var activePromptTextarea = {};
function setupExtraNetworks(){
......@@ -51,18 +68,27 @@ var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
function tryToRemoveExtraNetworkFromPrompt(textarea, text){
var m = text.match(re_extranet)
if(! m) return false
var partToSearch = m[1]
var replaced = false
var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
m = found.match(re_extranet);
if(m[1] == partToSearch){
replaced = true;
return ""
}
return found;
})
var newTextareaText
if(m) {
var partToSearch = m[1]
newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
m = found.match(re_extranet);
if(m[1] == partToSearch){
replaced = true;
return ""
}
return found;
})
} else {
newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found){
if(found == text) {
replaced = true;
return ""
}
return found;
})
}
if(replaced){
textarea.value = newTextareaText
......@@ -96,9 +122,9 @@ function saveCardPreview(event, tabname, filename){
}
function extraNetworksSearchButton(tabs_id, event){
searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
button = event.target
text = button.classList.contains("search-all") ? "" : button.textContent.trim()
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
var button = event.target
var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
searchTextarea.value = text
updateInput(searchTextarea)
......@@ -133,7 +159,7 @@ function popup(contents){
}
function extraNetworksShowMetadata(text){
elem = document.createElement('pre')
var elem = document.createElement('pre')
elem.classList.add('popup-metadata');
elem.textContent = text;
......@@ -165,7 +191,7 @@ function requestGet(url, data, handler, errorHandler){
}
function extraNetworksRequestMetadata(event, extraPage, cardName){
showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
if(data && data.metadata){
......
javascript/generationParams.js
View file @ 04b4508a
......@@ -16,14 +16,14 @@ onUiUpdate(function(){
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()
let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
});
});
function attachGalleryListeners(tab_name) {
gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
gallery?.addEventListener('keydown', (e) => {
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
......
javascript/hints.js
View file @ 04b4508a
......@@ -22,6 +22,7 @@ titles = {
"\u{1f4cb}": "Apply selected styles to current prompt",
"\u{1f4d2}": "Paste available values into the field",
"\u{1f3b4}": "Show/hide extra networks",
"\u{1f300}": "Restore progress",
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
......@@ -65,8 +66,8 @@ 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_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
"Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.",
"Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.",
"Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
"Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
......@@ -85,7 +86,6 @@ titles = {
"vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
"Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
......@@ -111,37 +111,57 @@ titles = {
"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
"Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
"Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
"Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
}
function updateTooltipForSpan(span){
if (span.title) return; // already has a title
onUiUpdate(function(){
gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
tooltip = titles[span.textContent];
let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
if(!tooltip){
tooltip = titles[span.value];
}
if(!tooltip){
tooltip = localization[titles[span.value]] || titles[span.value];
}
if(!tooltip){
for (const c of span.classList) {
if (c in titles) {
tooltip = titles[c];
break;
}
if(!tooltip){
for (const c of span.classList) {
if (c in titles) {
tooltip = localization[titles[c]] || titles[c];
break;
}
}
}
if(tooltip){
span.title = tooltip;
}
})
if(tooltip){
span.title = tooltip;
}
}
gradioApp().querySelectorAll('select').forEach(function(select){
if (select.onchange != null) return;
function updateTooltipForSelect(select){
if (select.onchange != null) return;
select.onchange = function(){
select.title = localization[titles[select.value]] || titles[select.value] || "";
}
}
select.onchange = function(){
select.title = titles[select.value] || "";
}
})
observedTooltipElements = {"SPAN": 1, "BUTTON": 1, "SELECT": 1, "P": 1}
onUiUpdate(function(m){
m.forEach(function(record){
record.addedNodes.forEach(function(node){
if(observedTooltipElements[node.tagName]){
updateTooltipForSpan(node)
}
if(node.tagName == "SELECT"){
updateTooltipForSelect(node)
}
if(node.querySelectorAll){
node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan)
node.querySelectorAll('select').forEach(updateTooltipForSelect)
}
})
})
})
javascript/hires_fix.js
View file @ 04b4508a
function setInactive(elem, inactive){
if(inactive){
elem.classList.add('inactive')
} else{
elem.classList.remove('inactive')
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
function setInactive(elem, inactive){
elem.classList.toggle('inactive', !!inactive)
}
}
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
......
javascript/imageMaskFix.js
View file @ 04b4508a
......@@ -2,11 +2,10 @@
* temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
* @see https://github.com/gradio-app/gradio/issues/1721
*/
window.addEventListener( 'resize', () => imageMaskResize());
function imageMaskResize() {
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
if ( ! canvases.length ) {
canvases_fixed = false;
canvases_fixed = false; // TODO: this is unused..?
window.removeEventListener( 'resize', imageMaskResize );
return;
}
......@@ -15,7 +14,7 @@ function imageMaskResize() {
const previewImage = wrapper.previousElementSibling;
if ( ! previewImage.complete ) {
previewImage.addEventListener( 'load', () => imageMaskResize());
previewImage.addEventListener( 'load', imageMaskResize);
return;
}
......@@ -24,7 +23,6 @@ function imageMaskResize() {
const nw = previewImage.naturalWidth;
const nh = previewImage.naturalHeight;
const portrait = nh > nw;
const factor = portrait;
const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
......@@ -40,6 +38,7 @@ function imageMaskResize() {
c.style.maxHeight = '100%';
c.style.objectFit = 'contain';
});
}
onUiUpdate(() => imageMaskResize());
}
onUiUpdate(imageMaskResize);
window.addEventListener( 'resize', imageMaskResize);
javascript/imageParams.js
View file @ 04b4508a
window.onload = (function(){
window.addEventListener('drop', e => {
const target = e.composedPath()[0];
const idx = selected_gallery_index();
if (target.placeholder.indexOf("Prompt") == -1) return;
let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
......
javascript/imageviewer.js
View file @ 04b4508a
......@@ -57,7 +57,7 @@ function modalImageSwitch(offset) {
})
if (result != -1) {
nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
nextButton.click()
const modalImage = gradioApp().getElementById("modalImage");
const modal = gradioApp().getElementById("lightboxModal");
......@@ -144,15 +144,11 @@ function setupImageForLightbox(e) {
}
function modalZoomSet(modalImage, enable) {
if (enable) {
modalImage.classList.add('modalImageFullscreen');
} else {
modalImage.classList.remove('modalImageFullscreen');
}
if(modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
}
function modalZoomToggle(event) {
modalImage = gradioApp().getElementById("modalImage");
var modalImage = gradioApp().getElementById("modalImage");
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
event.stopPropagation()
}
......@@ -179,7 +175,7 @@ function galleryImageHandler(e) {
}
onUiUpdate(function() {
fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
if (fullImg_preview != null) {
fullImg_preview.forEach(setupImageForLightbox);
}
......@@ -251,8 +247,11 @@ document.addEventListener("DOMContentLoaded", function() {
modal.appendChild(modalNext)
gradioApp().appendChild(modal)
try {
gradioApp().appendChild(modal);
} catch (e) {
gradioApp().body.appendChild(modal);
}
document.body.appendChild(modal);
......
javascript/imageviewerGamepad.js 0 → 100644
View file @ 04b4508a
window.addEventListener('gamepadconnected', (e) => {
const index = e.gamepad.index;
let isWaiting = false;
setInterval(async () => {
if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
const gamepad = navigator.getGamepads()[index];
const xValue = gamepad.axes[0];
if (xValue <= -0.3) {
modalPrevImage(e);
isWaiting = true;
} else if (xValue >= 0.3) {
modalNextImage(e);
isWaiting = true;
}
if (isWaiting) {
await sleepUntil(() => {
const xValue = navigator.getGamepads()[index].axes[0]
if (xValue < 0.3 && xValue > -0.3) {
return true;
}
}, opts.js_modal_lightbox_gamepad_repeat);
isWaiting = false;
}
}, 10);
});
/*
Primarily for vr controller type pointer devices.
I use the wheel event because there's currently no way to do it properly with web xr.
*/
let isScrolling = false;
window.addEventListener('wheel', (e) => {
if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
isScrolling = true;
if (e.deltaX <= -0.6) {
modalPrevImage(e);
} else if (e.deltaX >= 0.6) {
modalNextImage(e);
}
setTimeout(() => {
isScrolling = false;
}, opts.js_modal_lightbox_gamepad_repeat);
});
function sleepUntil(f, timeout) {
return new Promise((resolve) => {
const timeStart = new Date();
const wait = setInterval(function() {
if (f() || new Date() - timeStart > timeout) {
clearInterval(wait);
resolve();
}
}, 20);
});
}
javascript/localization.js
View file @ 04b4508a
......@@ -25,6 +25,10 @@ re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
original_lines = {}
translated_lines = {}
function hasLocalization() {
return window.localization && Object.keys(window.localization).length > 0;
}
function textNodesUnder(el){
var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
while(n=walk.nextNode()) a.push(n);
......@@ -35,11 +39,11 @@ function canBeTranslated(node, text){
if(! text) return false;
if(! node.parentElement) return false;
parentType = node.parentElement.nodeName
var parentType = node.parentElement.nodeName
if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
if (parentType=='OPTION' || parentType=='SPAN'){
pnode = node
var pnode = node
for(var level=0; level<4; level++){
pnode = pnode.parentElement
if(! pnode) break;
......@@ -69,7 +73,7 @@ function getTranslation(text){
}
function processTextNode(node){
text = node.textContent.trim()
var text = node.textContent.trim()
if(! canBeTranslated(node, text)) return
......@@ -105,30 +109,52 @@ function processNode(node){
}
function dumpTranslations(){
dumped = {}
if(!hasLocalization()) {
// If we don't have any localization,
// we will not have traversed the app to find
// original_lines, so do that now.
processNode(gradioApp());
}
var dumped = {}
if (localization.rtl) {
dumped.rtl = true
dumped.rtl = true;
}
Object.keys(original_lines).forEach(function(text){
if(dumped[text] !== undefined) return
for (const text in original_lines) {
if(dumped[text] !== undefined) continue;
dumped[text] = localization[text] || text;
}
dumped[text] = localization[text] || text
})
return dumped;
}
function download_localization() {
var text = JSON.stringify(dumpTranslations(), null, 4)
return dumped
var element = document.createElement('a');
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
element.setAttribute('download', "localization.json");
element.style.display = 'none';
document.body.appendChild(element);
element.click();
document.body.removeChild(element);
}
onUiUpdate(function(m){
m.forEach(function(mutation){
mutation.addedNodes.forEach(function(node){
processNode(node)
})
});
})
document.addEventListener("DOMContentLoaded", function () {
if (!hasLocalization()) {
return;
}
onUiUpdate(function (m) {
m.forEach(function (mutation) {
mutation.addedNodes.forEach(function (node) {
processNode(node)
})
});
})
document.addEventListener("DOMContentLoaded", function() {
processNode(gradioApp())
if (localization.rtl) { // if the language is from right to left,
......@@ -149,17 +175,3 @@ document.addEventListener("DOMContentLoaded", function() {
})).observe(gradioApp(), { childList: true });
}
})
function download_localization() {
text = JSON.stringify(dumpTranslations(), null, 4)
var element = document.createElement('a');
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
element.setAttribute('download', "localization.json");
element.style.display = 'none';
document.body.appendChild(element);
element.click();
document.body.removeChild(element);
}
javascript/notification.js
View file @ 04b4508a
......@@ -2,15 +2,15 @@
let lastHeadImg = null;
notificationButton = null
let notificationButton = null;
onUiUpdate(function(){
if(notificationButton == null){
notificationButton = gradioApp().getElementById('request_notifications')
if(notificationButton != null){
notificationButton.addEventListener('click', function (evt) {
Notification.requestPermission();
notificationButton.addEventListener('click', () => {
void Notification.requestPermission();
},true);
}
}
......
javascript/progressbar.js
View file @ 04b4508a
// code related to showing and updating progressbar shown as the image is being made
function rememberGallerySelection(id_gallery){
function rememberGallerySelection(){
}
function getGallerySelectedIndex(id_gallery){
function getGallerySelectedIndex(){
}
function request(url, data, handler, errorHandler){
var xhr = new XMLHttpRequest();
var url = url;
xhr.open("POST", url, true);
xhr.setRequestHeader("Content-Type", "application/json");
xhr.onreadystatechange = function () {
......@@ -66,7 +65,7 @@ function randomId(){
// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
// calls onProgress every time there is a progress update
function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){
function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout=40){
var dateStart = new Date()
var wasEverActive = false
var parentProgressbar = progressbarContainer.parentNode
......@@ -107,7 +106,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
divProgress.style.width = rect.width + "px";
}
progressText = ""
let progressText = ""
divInner.style.width = ((res.progress || 0) * 100.0) + '%'
divInner.style.background = res.progress ? "" : "transparent"
......@@ -138,7 +137,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
return
}
if(elapsedFromStart > 5 && !res.queued && !res.active){
if(elapsedFromStart > inactivityTimeout && !res.queued && !res.active){
removeProgressBar()
return
}
......
javascript/ui.js
View file @ 04b4508a
// 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
var gradioURL = window.location.href
if (!gradioURL.includes('?__theme=')) {
window.location.replace(gradioURL + '?__theme=' + theme);
}
......@@ -47,7 +47,7 @@ function extract_image_from_gallery(gallery){
return [gallery[0]];
}
index = selected_gallery_index()
var index = selected_gallery_index()
if (index < 0 || index >= gallery.length){
// Use the first image in the gallery as the default
......@@ -58,7 +58,7 @@ function extract_image_from_gallery(gallery){
}
function args_to_array(args){
res = []
var res = []
for(var i=0;i<args.length;i++){
res.push(args[i])
}
......@@ -138,7 +138,7 @@ function get_img2img_tab_index() {
}
function create_submit_args(args){
res = []
var res = []
for(var i=0;i<args.length;i++){
res.push(args[i])
}
......@@ -159,14 +159,24 @@ function showSubmitButtons(tabname, show){
gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block"
}
function showRestoreProgressButton(tabname, show){
var button = gradioApp().getElementById(tabname + "_restore_progress")
if(! button) return
button.style.display = show ? "flex" : "none"
}
function submit(){
rememberGallerySelection('txt2img_gallery')
showSubmitButtons('txt2img', false)
var id = randomId()
localStorage.setItem("txt2img_task_id", id);
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
showSubmitButtons('txt2img', true)
localStorage.removeItem("txt2img_task_id")
showRestoreProgressButton('txt2img', false)
})
var res = create_submit_args(arguments)
......@@ -181,8 +191,12 @@ function submit_img2img(){
showSubmitButtons('img2img', false)
var id = randomId()
localStorage.setItem("img2img_task_id", id);
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
showSubmitButtons('img2img', true)
localStorage.removeItem("img2img_task_id")
showRestoreProgressButton('img2img', false)
})
var res = create_submit_args(arguments)
......@@ -193,6 +207,42 @@ function submit_img2img(){
return res
}
function restoreProgressTxt2img(){
showRestoreProgressButton("txt2img", false)
var id = localStorage.getItem("txt2img_task_id")
id = localStorage.getItem("txt2img_task_id")
if(id) {
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
showSubmitButtons('txt2img', true)
}, null, 0)
}
return id
}
function restoreProgressImg2img(){
showRestoreProgressButton("img2img", false)
var id = localStorage.getItem("img2img_task_id")
if(id) {
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
showSubmitButtons('img2img', true)
}, null, 0)
}
return id
}
onUiLoaded(function () {
showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"))
showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"))
});
function modelmerger(){
var id = randomId()
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
......@@ -204,7 +254,7 @@ function modelmerger(){
function ask_for_style_name(_, prompt_text, negative_prompt_text) {
name_ = prompt('Style name:')
var name_ = prompt('Style name:')
return [name_, prompt_text, negative_prompt_text]
}
......@@ -239,11 +289,11 @@ function recalculate_prompts_img2img(){
}
opts = {}
var opts = {}
onUiUpdate(function(){
if(Object.keys(opts).length != 0) return;
json_elem = gradioApp().getElementById('settings_json')
var json_elem = gradioApp().getElementById('settings_json')
if(json_elem == null) return;
var textarea = json_elem.querySelector('textarea')
......@@ -292,12 +342,15 @@ onUiUpdate(function(){
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
show_all_pages = gradioApp().getElementById('settings_show_all_pages')
settings_tabs = gradioApp().querySelector('#settings div')
var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
var settings_tabs = gradioApp().querySelector('#settings div')
if(show_all_pages && settings_tabs){
settings_tabs.appendChild(show_all_pages)
show_all_pages.onclick = function(){
gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
if(elem.id == "settings_tab_licenses")
return;
elem.style.display = "block";
})
}
......@@ -305,9 +358,9 @@ onUiUpdate(function(){
})
onOptionsChanged(function(){
elem = gradioApp().getElementById('sd_checkpoint_hash')
sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
shorthash = sd_checkpoint_hash.substr(0,10)
var elem = gradioApp().getElementById('sd_checkpoint_hash')
var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
var shorthash = sd_checkpoint_hash.substring(0,10)
if(elem && elem.textContent != shorthash){
elem.textContent = shorthash
......@@ -342,7 +395,16 @@ function update_token_counter(button_id) {
function restart_reload(){
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
setTimeout(function(){location.reload()},2000)
var requestPing = function(){
requestGet("./internal/ping", {}, function(data){
location.reload();
}, function(){
setTimeout(requestPing, 500);
})
}
setTimeout(requestPing, 2000);
return []
}
......@@ -362,6 +424,23 @@ function selectCheckpoint(name){
gradioApp().getElementById('change_checkpoint').click()
}
function currentImg2imgSourceResolution(_, _, scaleBy){
var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img')
return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]
}
function updateImg2imgResizeToTextAfterChangingImage(){
// At the time this is called from gradio, the image has no yet been replaced.
// There may be a better solution, but this is simple and straightforward so I'm going with it.
setTimeout(function() {
gradioApp().getElementById('img2img_update_resize_to').click()
}, 500);
return []
}
function setRandomSeed(target_interface) {
let seed = gradioApp().querySelector(`#${target_interface}_seed input`);
if (!seed) {
......@@ -409,3 +488,4 @@ function switchWidthHeightImg2Img() {
height.dispatchEvent(new Event("input"));
return [];
}
javascript/ui_settings_hints.js 0 → 100644
View file @ 04b4508a
// various hints and extra info for the settings tab
settingsHintsSetup = false
onOptionsChanged(function(){
if(settingsHintsSetup) return
settingsHintsSetup = true
gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div){
var name = div.id.substr(8)
var commentBefore = opts._comments_before[name]
var commentAfter = opts._comments_after[name]
if(! commentBefore && !commentAfter) return
var span = null
if(div.classList.contains('gradio-checkbox')) span = div.querySelector('label span')
else if(div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild
else if(div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild
else span = div.querySelector('label span').firstChild
if(!span) return
if(commentBefore){
var comment = document.createElement('DIV')
comment.className = 'settings-comment'
comment.innerHTML = commentBefore
span.parentElement.insertBefore(document.createTextNode('\xa0'), span)
span.parentElement.insertBefore(comment, span)
span.parentElement.insertBefore(document.createTextNode('\xa0'), span)
}
if(commentAfter){
var comment = document.createElement('DIV')
comment.className = 'settings-comment'
comment.innerHTML = commentAfter
span.parentElement.insertBefore(comment, span.nextSibling)
span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling)
}
})
})
function settingsHintsShowQuicksettings(){
requestGet("./internal/quicksettings-hint", {}, function(data){
var table = document.createElement('table')
table.className = 'settings-value-table'
data.forEach(function(obj){
var tr = document.createElement('tr')
var td = document.createElement('td')
td.textContent = obj.name
tr.appendChild(td)
var td = document.createElement('td')
td.textContent = obj.label
tr.appendChild(td)
table.appendChild(tr)
})
popup(table);
})
}
launch.py
View file @ 04b4508a
......@@ -3,25 +3,23 @@ import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import json
from functools import lru_cache
from modules import cmd_args
from modules.paths_internal import script_path, extensions_dir
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)
args, _ = cmd_args.parser.parse_known_args()
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
stored_commit_hash = None
skip_install = False
dir_repos = "repositories"
# Whether to default to printing command output
default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
......@@ -49,7 +47,7 @@ or any other error regarding unsuccessful package (library) installation,
please downgrade (or upgrade) to the latest version of 3.10 Python
and delete current Python and "venv" folder in WebUI's directory.
You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
......@@ -57,51 +55,52 @@ Use --skip-python-version-check to suppress this warning.
""")
@lru_cache()
def commit_hash():
global stored_commit_hash
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
if stored_commit_hash is not None:
return stored_commit_hash
@lru_cache()
def git_tag():
try:
stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
stored_commit_hash = "<none>"
return "<none>"
return stored_commit_hash
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
if desc is not None:
print(desc)
if live:
result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
if result.returncode != 0:
raise RuntimeError(f"""{errdesc or 'Error running command'}.
Command: {command}
Error code: {result.returncode}""")
run_kwargs = {
"args": command,
"shell": True,
"env": os.environ if custom_env is None else custom_env,
"encoding": 'utf8',
"errors": 'ignore',
}
return ""
if not live:
run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
result = subprocess.run(**run_kwargs)
if result.returncode != 0:
error_bits = [
f"{errdesc or 'Error running command'}.",
f"Command: {command}",
f"Error code: {result.returncode}",
]
if result.stdout:
error_bits.append(f"stdout: {result.stdout}")
if result.stderr:
error_bits.append(f"stderr: {result.stderr}")
raise RuntimeError("\n".join(error_bits))
message = f"""{errdesc or 'Error running command'}.
Command: {command}
Error code: {result.returncode}
stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
"""
raise RuntimeError(message)
return result.stdout.decode(encoding="utf8", errors="ignore")
def check_run(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
return result.returncode == 0
return (result.stdout or "")
def is_installed(package):
......@@ -117,20 +116,17 @@ def repo_dir(name):
return os.path.join(script_path, dir_repos, name)
def run_python(code, desc=None, errdesc=None):
return run(f'"{python}" -c "{code}"', desc, errdesc)
def run_pip(args, desc=None):
if skip_install:
def run_pip(command, desc=None, live=default_command_live):
if args.skip_install:
return
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
def check_run_python(code):
return check_run(f'"{python}" -c "{code}"')
def check_run_python(code: str) -> bool:
result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
return result.returncode == 0
def git_clone(url, dir, name, commithash=None):
......@@ -223,15 +219,14 @@ def run_extensions_installers(settings_file):
def prepare_environment():
global skip_install
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
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")
......@@ -249,15 +244,20 @@ def prepare_environment():
check_python_version()
commit = commit_hash()
tag = git_tag()
print(f"Python {sys.version}")
print(f"Version: {tag}")
print(f"Commit hash: {commit}")
if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
if not args.skip_torch_cuda_test:
run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
raise RuntimeError(
'Torch is not able to use GPU; '
'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
)
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")
......@@ -271,7 +271,7 @@ def prepare_environment():
if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
......@@ -296,7 +296,7 @@ def prepare_environment():
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
run_pip(f"install -r \"{requirements_file}\"", "requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
......@@ -305,7 +305,7 @@ def prepare_environment():
if args.update_all_extensions:
git_pull_recursive(extensions_dir)
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
......
modules/api/api.py
View file @ 04b4508a
This diff is collapsed.
modules/api/models.py
View file @ 04b4508a
......@@ -223,8 +223,9 @@ for key in _options:
if(_options[key].dest != 'help'):
flag = _options[key]
_type = str
if _options[key].default is not None: _type = type(_options[key].default)
flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
if _options[key].default is not None:
_type = type(_options[key].default)
flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
FlagsModel = create_model("Flags", **flags)
......@@ -286,6 +287,23 @@ class MemoryResponse(BaseModel):
ram: dict = Field(title="RAM", description="System memory stats")
cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
class ScriptsList(BaseModel):
txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
\ No newline at end of file
txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
class ScriptArg(BaseModel):
label: str = Field(default=None, title="Label", description="Name of the argument in UI")
value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
class ScriptInfo(BaseModel):
name: str = Field(default=None, title="Name", description="Script name")
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
modules/call_queue.py
View file @ 04b4508a
......@@ -35,6 +35,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
try:
res = func(*args, **kwargs)
progress.record_results(id_task, res)
finally:
progress.finish_task(id_task)
......@@ -59,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
max_debug_str_len = 131072 # (1024*1024)/8
print("Error completing request", file=sys.stderr)
argStr = f"Arguments: {str(args)} {str(kwargs)}"
argStr = f"Arguments: {args} {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)
......@@ -72,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
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>"]
error_message = f'{type(e).__name__}: {e}'
res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
shared.state.skipped = False
shared.state.interrupted = False
......
modules/cmd_args.py
View file @ 04b4508a
import argparse
import os
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
parser = argparse.ArgumentParser()
......@@ -95,9 +95,12 @@ parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(
parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
modules/codeformer/codeformer_arch.py
View file @ 04b4508a
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
import math
import numpy as np
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from typing import Optional, List
from typing import Optional
from modules.codeformer.vqgan_arch import *
from basicsr.utils import get_root_logger
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
from basicsr.utils.registry import ARCH_REGISTRY
def calc_mean_std(feat, eps=1e-5):
......@@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module):
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# self attention
tgt2 = self.norm1(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
......@@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module):
@ARCH_REGISTRY.register()
class CodeFormer(VQAutoEncoder):
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
codebook_size=1024, latent_size=256,
connect_list=['32', '64', '128', '256'],
fix_modules=['quantize','generator']):
connect_list=('32', '64', '128', '256'),
fix_modules=('quantize', 'generator')):
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
if fix_modules is not None:
......@@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
self.feat_emb = nn.Linear(256, self.dim_embd)
# transformer
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
for _ in range(self.n_layers)])
# logits_predict head
self.idx_pred_layer = nn.Sequential(
nn.LayerNorm(dim_embd),
nn.Linear(dim_embd, codebook_size, bias=False))
self.channels = {
'16': 512,
'32': 256,
......@@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
enc_feat_dict = {}
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.encoder.blocks):
x = block(x)
x = block(x)
if i in out_list:
enc_feat_dict[str(x.shape[-1])] = x.clone()
......@@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder):
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
for i, block in enumerate(self.generator.blocks):
x = block(x)
x = block(x)
if i in fuse_list: # fuse after i-th block
f_size = str(x.shape[-1])
if w>0:
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
out = x
# logits doesn't need softmax before cross_entropy loss
return out, logits, lq_feat
\ No newline at end of file
return out, logits, lq_feat
modules/codeformer/vqgan_arch.py
View file @ 04b4508a
......@@ -5,17 +5,15 @@ VQGAN code, adapted from the original created by the Unleashing Transformers aut
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
'''
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from basicsr.utils import get_root_logger
from basicsr.utils.registry import ARCH_REGISTRY
def normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
@torch.jit.script
def swish(x):
......@@ -212,15 +210,15 @@ class AttnBlock(nn.Module):
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h*w)
q = q.permute(0, 2, 1)
q = q.permute(0, 2, 1)
k = k.reshape(b, c, h*w)
w_ = torch.bmm(q, k)
w_ = torch.bmm(q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = F.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h*w)
w_ = w_.permute(0, 2, 1)
w_ = w_.permute(0, 2, 1)
h_ = torch.bmm(v, w_)
h_ = h_.reshape(b, c, h, w)
......@@ -272,18 +270,18 @@ class Encoder(nn.Module):
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
class Generator(nn.Module):
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
super().__init__()
self.nf = nf
self.ch_mult = ch_mult
self.nf = nf
self.ch_mult = ch_mult
self.num_resolutions = len(self.ch_mult)
self.num_res_blocks = res_blocks
self.resolution = img_size
self.resolution = img_size
self.attn_resolutions = attn_resolutions
self.in_channels = emb_dim
self.out_channels = 3
......@@ -317,29 +315,29 @@ class Generator(nn.Module):
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
self.blocks = nn.ModuleList(blocks)
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
@ARCH_REGISTRY.register()
class VQAutoEncoder(nn.Module):
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
super().__init__()
logger = get_root_logger()
self.in_channels = 3
self.nf = nf
self.n_blocks = res_blocks
self.in_channels = 3
self.nf = nf
self.n_blocks = res_blocks
self.codebook_size = codebook_size
self.embed_dim = emb_dim
self.ch_mult = ch_mult
self.resolution = img_size
self.attn_resolutions = attn_resolutions
self.attn_resolutions = attn_resolutions or [16]
self.quantizer_type = quantizer
self.encoder = Encoder(
self.in_channels,
......@@ -365,11 +363,11 @@ class VQAutoEncoder(nn.Module):
self.kl_weight
)
self.generator = Generator(
self.nf,
self.nf,
self.embed_dim,
self.ch_mult,
self.n_blocks,
self.resolution,
self.ch_mult,
self.n_blocks,
self.resolution,
self.attn_resolutions
)
......@@ -434,4 +432,4 @@ class VQGANDiscriminator(nn.Module):
raise ValueError('Wrong params!')
def forward(self, x):
return self.main(x)
\ No newline at end of file
return self.main(x)
modules/codeformer_model.py
View file @ 04b4508a
......@@ -33,11 +33,9 @@ def setup_model(dirname):
try:
from torchvision.transforms.functional import normalize
from modules.codeformer.codeformer_arch import CodeFormer
from basicsr.utils.download_util import load_file_from_url
from basicsr.utils import imwrite, img2tensor, tensor2img
from basicsr.utils import 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
......@@ -96,7 +94,7 @@ def setup_model(dirname):
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
for cropped_face in self.face_helper.cropped_faces:
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
......
modules/config_states.py 0 → 100644
View file @ 04b4508a
"""
Supports saving and restoring webui and extensions from a known working set of commits
"""
import os
import sys
import traceback
import json
import time
import tqdm
from datetime import datetime
from collections import OrderedDict
import git
from modules import shared, extensions
from modules.paths_internal import script_path, config_states_dir
all_config_states = OrderedDict()
def list_config_states():
global all_config_states
all_config_states.clear()
os.makedirs(config_states_dir, exist_ok=True)
config_states = []
for filename in os.listdir(config_states_dir):
if filename.endswith(".json"):
path = os.path.join(config_states_dir, filename)
with open(path, "r", encoding="utf-8") as f:
j = json.load(f)
j["filepath"] = path
config_states.append(j)
config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
for cs in config_states:
timestamp = time.asctime(time.gmtime(cs["created_at"]))
name = cs.get("name", "Config")
full_name = f"{name}: {timestamp}"
all_config_states[full_name] = cs
return all_config_states
def get_webui_config():
webui_repo = None
try:
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
webui_remote = None
webui_commit_hash = None
webui_commit_date = None
webui_branch = None
if webui_repo and not webui_repo.bare:
try:
webui_remote = next(webui_repo.remote().urls, None)
head = webui_repo.head.commit
webui_commit_date = webui_repo.head.commit.committed_date
webui_commit_hash = head.hexsha
webui_branch = webui_repo.active_branch.name
except Exception:
webui_remote = None
return {
"remote": webui_remote,
"commit_hash": webui_commit_hash,
"commit_date": webui_commit_date,
"branch": webui_branch,
}
def get_extension_config():
ext_config = {}
for ext in extensions.extensions:
ext.read_info_from_repo()
entry = {
"name": ext.name,
"path": ext.path,
"enabled": ext.enabled,
"is_builtin": ext.is_builtin,
"remote": ext.remote,
"commit_hash": ext.commit_hash,
"commit_date": ext.commit_date,
"branch": ext.branch,
"have_info_from_repo": ext.have_info_from_repo
}
ext_config[ext.name] = entry
return ext_config
def get_config():
creation_time = datetime.now().timestamp()
webui_config = get_webui_config()
ext_config = get_extension_config()
return {
"created_at": creation_time,
"webui": webui_config,
"extensions": ext_config
}
def restore_webui_config(config):
print("* Restoring webui state...")
if "webui" not in config:
print("Error: No webui data saved to config")
return
webui_config = config["webui"]
if "commit_hash" not in webui_config:
print("Error: No commit saved to webui config")
return
webui_commit_hash = webui_config.get("commit_hash", None)
webui_repo = None
try:
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return
try:
webui_repo.git.fetch(all=True)
webui_repo.git.reset(webui_commit_hash, hard=True)
print(f"* Restored webui to commit {webui_commit_hash}.")
except Exception:
print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
def restore_extension_config(config):
print("* Restoring extension state...")
if "extensions" not in config:
print("Error: No extension data saved to config")
return
ext_config = config["extensions"]
results = []
disabled = []
for ext in tqdm.tqdm(extensions.extensions):
if ext.is_builtin:
continue
ext.read_info_from_repo()
current_commit = ext.commit_hash
if ext.name not in ext_config:
ext.disabled = True
disabled.append(ext.name)
results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled"))
continue
entry = ext_config[ext.name]
if "commit_hash" in entry and entry["commit_hash"]:
try:
ext.fetch_and_reset_hard(entry["commit_hash"])
ext.read_info_from_repo()
if current_commit != entry["commit_hash"]:
results.append((ext, current_commit[:8], True, entry["commit_hash"][:8]))
except Exception as ex:
results.append((ext, current_commit[:8], False, ex))
else:
results.append((ext, current_commit[:8], False, "No commit hash found in config"))
if not entry.get("enabled", False):
ext.disabled = True
disabled.append(ext.name)
else:
ext.disabled = False
shared.opts.disabled_extensions = disabled
shared.opts.save(shared.config_filename)
print("* Finished restoring extensions. Results:")
for ext, prev_commit, success, result in results:
if success:
print(f" + {ext.name}: {prev_commit} -> {result}")
else:
print(f" ! {ext.name}: FAILURE ({result})")
modules/deepbooru.py
View file @ 04b4508a
......@@ -2,7 +2,6 @@ import os
import re
import torch
from PIL import Image
import numpy as np
from modules import modelloader, paths, deepbooru_model, devices, images, shared
......@@ -79,7 +78,7 @@ class DeepDanbooru:
res = []
filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
for tag in [x for x in tags if x not in filtertags]:
probability = probability_dict[tag]
......
modules/devices.py
View file @ 04b4508a
......@@ -65,7 +65,7 @@ def enable_tf32():
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
......@@ -92,14 +92,18 @@ def cond_cast_float(input):
def randn(seed, shape):
from modules.shared import opts
torch.manual_seed(seed)
if device.type == 'mps':
if opts.randn_source == "CPU" or device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
if device.type == 'mps':
from modules.shared import opts
if opts.randn_source == "CPU" or device.type == 'mps':
return torch.randn(shape, device=cpu).to(device)
return torch.randn(shape, device=device)
......
modules/esrgan_model.py
View file @ 04b4508a
......@@ -6,7 +6,7 @@ from PIL import Image
from basicsr.utils.download_util import load_file_from_url
import modules.esrgan_model_arch as arch
from modules import shared, modelloader, images, devices
from modules import modelloader, images, devices
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
......@@ -16,9 +16,7 @@ def mod2normal(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
if 'conv_first.weight' in state_dict:
crt_net = {}
items = []
for k, v in state_dict.items():
items.append(k)
items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
......@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
re8x = 0
crt_net = {}
items = []
for k, v in state_dict.items():
items.append(k)
items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
......@@ -156,13 +152,16 @@ class UpscalerESRGAN(Upscaler):
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)
filename = load_file_from_url(
url=self.model_url,
model_dir=self.model_path,
file_name=f"{self.model_name}.pth",
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))
print(f"Unable to load {self.model_path} from {filename}")
return None
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
......
modules/esrgan_model_arch.py
View file @ 04b4508a
......@@ -2,7 +2,6 @@
from collections import OrderedDict
import math
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
......@@ -38,7 +37,7 @@ class RRDBNet(nn.Module):
elif upsample_mode == 'pixelshuffle':
upsample_block = pixelshuffle_block
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
if upscale == 3:
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
else:
......@@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module):
Modified options that can be used:
- "Partial Convolution based Padding" arXiv:1811.11718
- "Spectral normalization" arXiv:1802.05957
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
- "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
{Rakotonirina} and A. {Rasoanaivo}
"""
......@@ -171,7 +170,7 @@ class GaussianNoise(nn.Module):
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
x = x + sampled_noise
return x
return x
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
......@@ -261,10 +260,10 @@ class Upsample(nn.Module):
def extra_repr(self):
if self.scale_factor is not None:
info = 'scale_factor=' + str(self.scale_factor)
info = f'scale_factor={self.scale_factor}'
else:
info = 'size=' + str(self.size)
info += ', mode=' + self.mode
info = f'size={self.size}'
info += f', mode={self.mode}'
return info
......@@ -350,7 +349,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
elif act_type == 'sigmoid': # [0, 1] range output
layer = nn.Sigmoid()
else:
raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
raise NotImplementedError(f'activation layer [{act_type}] is not found')
return layer
......@@ -372,7 +371,7 @@ def norm(norm_type, nc):
elif norm_type == 'none':
def norm_layer(x): return Identity()
else:
raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
return layer
......@@ -388,7 +387,7 @@ def pad(pad_type, padding):
elif pad_type == 'zero':
layer = nn.ZeroPad2d(padding)
else:
raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
return layer
......@@ -432,15 +431,17 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
spectral_norm=False):
""" Conv layer with padding, normalization, activation """
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
padding = get_valid_padding(kernel_size, dilation)
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
padding = padding if pad_type == 'zero' else 0
if convtype=='PartialConv2D':
from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='DeformConv2D':
from torchvision.ops import DeformConv2d # not tested
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='Conv3D':
......
modules/extensions.py
View file @ 04b4508a
import os
import sys
import threading
import traceback
import time
import git
from modules import shared
from modules.paths_internal import extensions_dir, extensions_builtin_dir
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
......@@ -24,6 +24,8 @@ def active():
class Extension:
lock = threading.Lock()
def __init__(self, name, path, enabled=True, is_builtin=False):
self.name = name
self.path = path
......@@ -31,16 +33,24 @@ class Extension:
self.status = ''
self.can_update = False
self.is_builtin = is_builtin
self.commit_hash = ''
self.commit_date = None
self.version = ''
self.branch = None
self.remote = None
self.have_info_from_repo = False
def read_info_from_repo(self):
if self.have_info_from_repo:
if self.is_builtin or self.have_info_from_repo:
return
self.have_info_from_repo = True
with self.lock:
if self.have_info_from_repo:
return
self.do_read_info_from_repo()
def do_read_info_from_repo(self):
repo = None
try:
if os.path.exists(os.path.join(self.path, ".git")):
......@@ -55,13 +65,18 @@ class Extension:
try:
self.status = 'unknown'
self.remote = next(repo.remote().urls, None)
head = repo.head.commit
ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
self.version = f'{head.hexsha[:8]} ({ts})'
except Exception:
self.commit_date = repo.head.commit.committed_date
if repo.active_branch:
self.branch = repo.active_branch.name
self.commit_hash = repo.head.commit.hexsha
self.version = repo.git.describe("--always", "--tags") # compared to `self.commit_hash[:8]` this takes about 30% more time total but since we run it in parallel we don't care
except Exception as ex:
print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
self.remote = None
self.have_info_from_repo = True
def list_files(self, subdir, extension):
from modules import scripts
......@@ -82,18 +97,30 @@ class Extension:
for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
self.status = "behind"
self.status = "new commits"
return
try:
origin = repo.rev_parse('origin')
if repo.head.commit != origin:
self.can_update = True
self.status = "behind HEAD"
return
except Exception:
self.can_update = False
self.status = "unknown (remote error)"
return
self.can_update = False
self.status = "latest"
def fetch_and_reset_hard(self):
def fetch_and_reset_hard(self, commit='origin'):
repo = git.Repo(self.path)
# 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=True)
repo.git.reset('origin', hard=True)
repo.git.reset(commit, hard=True)
self.have_info_from_repo = False
def list_extensions():
......
modules/extra_networks.py
View file @ 04b4508a
......@@ -91,7 +91,7 @@ def deactivate(p, extra_network_data):
"""call deactivate for extra networks in extra_network_data in specified order, then call
deactivate for all remaining registered networks"""
for extra_network_name, extra_network_args in extra_network_data.items():
for extra_network_name in extra_network_data:
extra_network = extra_network_registry.get(extra_network_name, None)
if extra_network is None:
continue
......
modules/extra_networks_hypernet.py
View file @ 04b4508a
from modules import extra_networks, shared, extra_networks
from modules import extra_networks, shared
from modules.hypernetworks import hypernetwork
......@@ -9,8 +9,9 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork
if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
names = []
......
modules/extras.py
View file @ 04b4508a
import os
import re
import shutil
import json
import torch
......@@ -71,7 +72,7 @@ def to_half(tensor, enable):
return tensor
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
shared.state.begin()
shared.state.job = 'model-merge'
......@@ -135,14 +136,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
result_is_instruct_pix2pix_model = False
if theta_func2:
shared.state.textinfo = f"Loading B"
shared.state.textinfo = "Loading B"
print(f"Loading {secondary_model_info.filename}...")
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
else:
theta_1 = None
if theta_func1:
shared.state.textinfo = f"Loading C"
shared.state.textinfo = "Loading C"
print(f"Loading {tertiary_model_info.filename}...")
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
......@@ -198,7 +199,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
result_is_inpainting_model = True
else:
theta_0[key] = theta_func2(a, b, multiplier)
theta_0[key] = to_half(theta_0[key], save_as_half)
shared.state.sampling_step += 1
......@@ -241,13 +242,58 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
metadata = None
if save_metadata:
metadata = {"format": "pt"}
merge_recipe = {
"type": "webui", # indicate this model was merged with webui's built-in merger
"primary_model_hash": primary_model_info.sha256,
"secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
"tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
"interp_method": interp_method,
"multiplier": multiplier,
"save_as_half": save_as_half,
"custom_name": custom_name,
"config_source": config_source,
"bake_in_vae": bake_in_vae,
"discard_weights": discard_weights,
"is_inpainting": result_is_inpainting_model,
"is_instruct_pix2pix": result_is_instruct_pix2pix_model
}
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
sd_merge_models = {}
def add_model_metadata(checkpoint_info):
checkpoint_info.calculate_shorthash()
sd_merge_models[checkpoint_info.sha256] = {
"name": checkpoint_info.name,
"legacy_hash": checkpoint_info.hash,
"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
}
sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
add_model_metadata(primary_model_info)
if secondary_model_info:
add_model_metadata(secondary_model_info)
if tertiary_model_info:
add_model_metadata(tertiary_model_info)
metadata["sd_merge_models"] = json.dumps(sd_merge_models)
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
else:
torch.save(theta_0, output_modelname)
sd_models.list_models()
created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
if created_model:
created_model.calculate_shorthash()
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
......
modules/generation_parameters_copypaste.py
View file @ 04b4508a
import base64
import html
import io
import math
import os
import re
from pathlib import Path
import gradio as gr
from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks
import tempfile
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
......@@ -23,14 +19,14 @@ registered_param_bindings = []
class ParamBinding:
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
self.paste_button = paste_button
self.tabname = tabname
self.source_text_component = source_text_component
self.source_image_component = source_image_component
self.source_tabname = source_tabname
self.override_settings_component = override_settings_component
self.paste_field_names = paste_field_names
self.paste_field_names = paste_field_names or []
def reset():
......@@ -59,6 +55,7 @@ def image_from_url_text(filedata):
is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
assert is_in_right_dir, 'trying to open image file outside of allowed directories'
filename = filename.rsplit('?', 1)[0]
return Image.open(filename)
if type(filedata) == list:
......@@ -129,6 +126,7 @@ def connect_paste_params_buttons():
_js=jsfunc,
inputs=[binding.source_image_component],
outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
show_progress=False,
)
if binding.source_text_component is not None and fields is not None:
......@@ -140,6 +138,7 @@ def connect_paste_params_buttons():
fn=lambda *x: x,
inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
outputs=[field for field, name in fields if name in paste_field_names],
show_progress=False,
)
binding.paste_button.click(
......@@ -147,6 +146,7 @@ def connect_paste_params_buttons():
_js=f"switch_to_{binding.tabname}",
inputs=None,
outputs=None,
show_progress=False,
)
......@@ -247,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
lines.append(lastline)
lastline = ''
for i, line in enumerate(lines):
for line in lines:
line = line.strip()
if line.startswith("Negative prompt:"):
done_with_prompt = True
......@@ -265,8 +265,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
m = re_imagesize.match(v)
if m is not None:
res[k+"-1"] = m.group(1)
res[k+"-2"] = m.group(2)
res[f"{k}-1"] = m.group(1)
res[f"{k}-2"] = m.group(2)
else:
res[k] = v
......@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
restore_old_hires_fix_params(res)
# Missing RNG means the default was set, which is GPU RNG
if "RNG" not in res:
res["RNG"] = "GPU"
return res
......@@ -304,6 +308,10 @@ infotext_to_setting_name_mapping = [
('UniPC skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'),
('Token merging ratio', 'token_merging_ratio'),
('Token merging ratio hr', 'token_merging_ratio_hr'),
('RNG', 'randn_source'),
('NGMS', 's_min_uncond'),
]
......@@ -403,12 +411,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
fn=paste_func,
inputs=[input_comp],
outputs=[x[0] for x in paste_fields],
show_progress=False,
)
button.click(
fn=None,
_js=f"recalculate_prompts_{tabname}",
inputs=[],
outputs=[],
show_progress=False,
)
modules/gfpgan_model.py
View file @ 04b4508a
......@@ -78,7 +78,7 @@ def setup_model(dirname):
try:
from gfpgan import GFPGANer
from facexlib import detection, parsing
from facexlib import detection, parsing # noqa: F401
global user_path
global have_gfpgan
global gfpgan_constructor
......
modules/hashes.py
View file @ 04b4508a
......@@ -13,7 +13,7 @@ cache_data = None
def dump_cache():
with filelock.FileLock(cache_filename+".lock"):
with filelock.FileLock(f"{cache_filename}.lock"):
with open(cache_filename, "w", encoding="utf8") as file:
json.dump(cache_data, file, indent=4)
......@@ -22,7 +22,7 @@ def cache(subsection):
global cache_data
if cache_data is None:
with filelock.FileLock(cache_filename+".lock"):
with filelock.FileLock(f"{cache_filename}.lock"):
if not os.path.isfile(cache_filename):
cache_data = {}
else:
......
modules/hypernetworks/hypernetwork.py
View file @ 04b4508a
import csv
import datetime
import glob
import html
......@@ -18,7 +17,7 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
from collections import defaultdict, deque
from collections import deque
from statistics import stdev, mean
......@@ -178,34 +177,34 @@ class Hypernetwork:
def weights(self):
res = []
for k, layers in self.layers.items():
for layers in self.layers.values():
for layer in layers:
res += layer.parameters()
return res
def train(self, mode=True):
for k, layers in self.layers.items():
for layers in self.layers.values():
for layer in layers:
layer.train(mode=mode)
for param in layer.parameters():
param.requires_grad = mode
def to(self, device):
for k, layers in self.layers.items():
for layers in self.layers.values():
for layer in layers:
layer.to(device)
return self
def set_multiplier(self, multiplier):
for k, layers in self.layers.items():
for layers in self.layers.values():
for layer in layers:
layer.multiplier = multiplier
return self
def eval(self):
for k, layers in self.layers.items():
for layers in self.layers.values():
for layer in layers:
layer.eval()
for param in layer.parameters():
......@@ -404,7 +403,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
k = self.to_k(context_k)
v = self.to_v(context_v)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
......@@ -541,7 +540,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
return hypernetwork, filename
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
if clip_grad:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
......@@ -594,7 +593,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
print(e)
scaler = torch.cuda.amp.GradScaler()
batch_size = ds.batch_size
gradient_step = ds.gradient_step
# n steps = batch_size * gradient_step * n image processed
......@@ -620,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
try:
sd_hijack_checkpoint.add()
for i in range((steps-initial_step) * gradient_step):
for _ in range((steps-initial_step) * gradient_step):
if scheduler.finished:
break
if shared.state.interrupted:
......@@ -637,7 +636,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
if clip_grad:
clip_grad_sched.step(hypernetwork.step)
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if use_weight:
......@@ -658,14 +657,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
_loss_step += loss.item()
scaler.scale(loss).backward()
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
loss_logging.append(_loss_step)
if clip_grad:
clip_grad(weights, clip_grad_sched.learn_rate)
scaler.step(optimizer)
scaler.update()
hypernetwork.step += 1
......@@ -675,7 +674,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
_loss_step = 0
steps_done = hypernetwork.step + 1
epoch_num = hypernetwork.step // steps_per_epoch
epoch_step = hypernetwork.step % steps_per_epoch
......
modules/hypernetworks/ui.py
View file @ 04b4508a
import html
import os
import re
import gradio as gr
import modules.hypernetworks.hypernetwork
from modules import devices, sd_hijack, shared
not_available = ["hardswish", "multiheadattention"]
keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
def train_hypernetwork(*args):
......
modules/images.py
View file @ 04b4508a
This diff is collapsed.
modules/img2img.py
View file @ 04b4508a
import math
import os
import sys
import traceback
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
from modules import devices, sd_samplers
from modules import sd_samplers
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.images as images
import modules.scripts
......@@ -46,7 +42,11 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
if state.interrupted:
break
img = Image.open(image)
try:
img = Image.open(image)
except UnidentifiedImageError as e:
print(e)
continue
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
p.init_images = [img] * p.batch_size
......@@ -55,7 +55,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
# try to find corresponding mask for an image using simple filename matching
mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
# if not found use first one ("same mask for all images" use-case)
if not mask_image_path in inpaint_masks:
if mask_image_path not in inpaint_masks:
mask_image_path = inpaint_masks[0]
mask_image = Image.open(mask_image_path)
p.image_mask = mask_image
......@@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
processed_image.save(os.path.join(output_dir, filename))
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
......@@ -114,6 +114,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
if image is not None:
image = ImageOps.exif_transpose(image)
if selected_scale_tab == 1:
assert image, "Can't scale by because no image is selected"
width = int(image.width * scale_by)
height = int(image.height * scale_by)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
......@@ -151,7 +157,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
p.scripts = modules.scripts.scripts_img2img
p.script_args = args
if shared.cmd_opts.enable_console_prompts:
......
modules/interrogate.py
View file @ 04b4508a
......@@ -11,7 +11,6 @@ import torch.hub
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import modules.shared as shared
from modules import devices, paths, shared, lowvram, modelloader, errors
blip_image_eval_size = 384
......@@ -28,11 +27,11 @@ def category_types():
def download_default_clip_interrogate_categories(content_dir):
print("Downloading CLIP categories...")
tmpdir = content_dir + "_tmp"
tmpdir = f"{content_dir}_tmp"
category_types = ["artists", "flavors", "mediums", "movements"]
try:
os.makedirs(tmpdir)
os.makedirs(tmpdir, exist_ok=True)
for category_type in category_types:
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
os.rename(tmpdir, content_dir)
......@@ -41,7 +40,7 @@ def download_default_clip_interrogate_categories(content_dir):
errors.display(e, "downloading default CLIP interrogate categories")
finally:
if os.path.exists(tmpdir):
os.remove(tmpdir)
os.removedirs(tmpdir)
class InterrogateModels:
......@@ -160,7 +159,7 @@ class InterrogateModels:
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
top_count = min(top_count, len(text_array))
text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
......@@ -208,13 +207,13 @@ class InterrogateModels:
image_features /= image_features.norm(dim=-1, keepdim=True)
for name, topn, items in self.categories():
matches = self.rank(image_features, items, top_count=topn)
for cat in self.categories():
matches = self.rank(image_features, cat.items, top_count=cat.topn)
for match, score in matches:
if shared.opts.interrogate_return_ranks:
res += f", ({match}:{score/100:.3f})"
else:
res += ", " + match
res += f", {match}"
except Exception:
print("Error interrogating", file=sys.stderr)
......
modules/localization.py
View file @ 04b4508a
......@@ -23,7 +23,7 @@ def list_localizations(dirname):
localizations[fn] = file.path
def localization_js(current_localization_name):
def localization_js(current_localization_name: str) -> str:
fn = localizations.get(current_localization_name, None)
data = {}
if fn is not None:
......@@ -34,4 +34,4 @@ def localization_js(current_localization_name):
print(f"Error loading localization from {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return f"var localization = {json.dumps(data)}\n"
return f"window.localization = {json.dumps(data)}"
modules/mac_specific.py
View file @ 04b4508a
import torch
import platform
from modules import paths
from modules.sd_hijack_utils import CondFunc
from packaging import version
......@@ -43,7 +42,7 @@ if has_mps:
# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
......@@ -54,6 +53,11 @@ if has_mps:
CondFunc('torch.cumsum', cumsum_fix_func, None)
CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
if version.parse(torch.__version__) == version.parse("2.0"):
# MPS workaround for https://github.com/pytorch/pytorch/issues/96113
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
# MPS workaround for https://github.com/pytorch/pytorch/issues/92311
if platform.processor() == 'i386':
for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
modules/masking.py
View file @ 04b4508a
......@@ -4,7 +4,7 @@ from PIL import Image, ImageFilter, ImageOps
def get_crop_region(mask, pad=0):
"""finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
h, w = mask.shape
crop_left = 0
......
modules/modelloader.py
View file @ 04b4508a
import glob
import os
import shutil
import importlib
......@@ -22,9 +21,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
"""
output = []
if ext_filter is None:
ext_filter = []
try:
places = []
......@@ -39,22 +35,14 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
places.append(model_path)
for place in places:
if os.path.exists(place):
for file in glob.iglob(place + '**/**', recursive=True):
full_path = file
if os.path.isdir(full_path):
continue
if os.path.islink(full_path) and not os.path.exists(full_path):
print(f"Skipping broken symlink: {full_path}")
continue
if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
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)
for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
if os.path.islink(full_path) and not os.path.exists(full_path):
print(f"Skipping broken symlink: {full_path}")
continue
if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
continue
if full_path not in output:
output.append(full_path)
if model_url is not None and len(output) == 0:
if download_name is not None:
......@@ -119,32 +107,15 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
print(f"Moving {file} from {src_path} to {dest_path}.")
try:
shutil.move(fullpath, dest_path)
except:
except Exception:
pass
if len(os.listdir(src_path)) == 0:
print(f"Removing empty folder: {src_path}")
shutil.rmtree(src_path, True)
except:
except Exception:
pass
builtin_upscaler_classes = []
forbidden_upscaler_classes = set()
def list_builtin_upscalers():
load_upscalers()
builtin_upscaler_classes.clear()
builtin_upscaler_classes.extend(Upscaler.__subclasses__())
def forbid_loaded_nonbuiltin_upscalers():
for cls in Upscaler.__subclasses__():
if cls not in builtin_upscaler_classes:
forbidden_upscaler_classes.add(cls)
def load_upscalers():
# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
# so we'll try to import any _model.py files before looking in __subclasses__
......@@ -155,15 +126,22 @@ def load_upscalers():
full_model = f"modules.{model_name}_model"
try:
importlib.import_module(full_model)
except:
except Exception:
pass
datas = []
commandline_options = vars(shared.cmd_opts)
for cls in Upscaler.__subclasses__():
if cls in forbidden_upscaler_classes:
continue
# some of upscaler classes will not go away after reloading their modules, and we'll end
# up with two copies of those classes. The newest copy will always be the last in the list,
# so we go from end to beginning and ignore duplicates
used_classes = {}
for cls in reversed(Upscaler.__subclasses__()):
classname = str(cls)
if classname not in used_classes:
used_classes[classname] = cls
for cls in reversed(used_classes.values()):
name = cls.__name__
cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
scaler = cls(commandline_options.get(cmd_name, None))
......
modules/models/diffusion/ddpm_edit.py
View file @ 04b4508a
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modules/models/diffusion/uni_pc/__init__.py
View file @ 04b4508a
from .sampler import UniPCSampler
from .sampler import UniPCSampler # noqa: F401
modules/models/diffusion/uni_pc/sampler.py
View file @ 04b4508a
......@@ -54,7 +54,8 @@ class UniPCSampler(object):
if conditioning is not None:
if isinstance(conditioning, dict):
ctmp = conditioning[list(conditioning.keys())[0]]
while isinstance(ctmp, list): ctmp = ctmp[0]
while isinstance(ctmp, list):
ctmp = ctmp[0]
cbs = ctmp.shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
......
modules/models/diffusion/uni_pc/uni_pc.py
View file @ 04b4508a
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modules/ngrok.py
View file @ 04b4508a
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modules/paths.py
View file @ 04b4508a
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modules/paths_internal.py
View file @ 04b4508a
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modules/postprocessing.py
View file @ 04b4508a
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modules/processing.py
View file @ 04b4508a
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modules/progress.py
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......@@ -2,7 +2,6 @@ import os
import sys
import traceback
import importlib.util
from types import ModuleType
def load_module(path):
......
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