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Stable Diffusion Webui
Commit 97e1cf69 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
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Merge branch 'dev' into master

parents 484948f5 bb431df5
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.eslintignore 0 → 100644
View file @ 97e1cf69
extensions
extensions-disabled
repositories
venv
\ No newline at end of file
.eslintrc.js 0 → 100644
View file @ 97e1cf69
module.exports = {
env: {
browser: true,
es2021: true,
},
extends: "eslint:recommended",
parserOptions: {
ecmaVersion: "latest",
},
rules: {
"arrow-spacing": "error",
"block-spacing": "error",
"brace-style": "error",
"comma-dangle": ["error", "only-multiline"],
"comma-spacing": "error",
"comma-style": ["error", "last"],
"curly": ["error", "multi-line", "consistent"],
"eol-last": "error",
"func-call-spacing": "error",
"function-call-argument-newline": ["error", "consistent"],
"function-paren-newline": ["error", "consistent"],
"indent": ["error", 4],
"key-spacing": "error",
"keyword-spacing": "error",
"linebreak-style": ["error", "unix"],
"no-extra-semi": "error",
"no-mixed-spaces-and-tabs": "error",
"no-trailing-spaces": "error",
"no-whitespace-before-property": "error",
"object-curly-newline": ["error", {consistent: true, multiline: true}],
"quote-props": ["error", "consistent-as-needed"],
"semi": ["error", "always"],
"semi-spacing": "error",
"semi-style": ["error", "last"],
"space-before-blocks": "error",
"space-before-function-paren": ["error", "never"],
"space-in-parens": ["error", "never"],
"space-infix-ops": "error",
"space-unary-ops": "error",
"switch-colon-spacing": "error",
"template-curly-spacing": ["error", "never"],
"unicode-bom": "error",
"no-multi-spaces": "error",
"object-curly-spacing": ["error", "never"],
"operator-linebreak": ["error", "after"],
"no-unused-vars": "off",
"no-redeclare": "off",
},
globals: {
// this file
module: "writable",
//script.js
gradioApp: "writable",
onUiLoaded: "writable",
onUiUpdate: "writable",
onOptionsChanged: "writable",
uiCurrentTab: "writable",
uiElementIsVisible: "writable",
executeCallbacks: "writable",
//ui.js
opts: "writable",
all_gallery_buttons: "writable",
selected_gallery_button: "writable",
selected_gallery_index: "writable",
args_to_array: "writable",
switch_to_txt2img: "writable",
switch_to_img2img_tab: "writable",
switch_to_img2img: "writable",
switch_to_sketch: "writable",
switch_to_inpaint: "writable",
switch_to_inpaint_sketch: "writable",
switch_to_extras: "writable",
get_tab_index: "writable",
create_submit_args: "writable",
restart_reload: "writable",
updateInput: "writable",
//extraNetworks.js
requestGet: "writable",
popup: "writable",
// from python
localization: "writable",
// progrssbar.js
randomId: "writable",
requestProgress: "writable",
// imageviewer.js
modalPrevImage: "writable",
modalNextImage: "writable",
}
};
.github/ISSUE_TEMPLATE/bug_report.yml
View file @ 97e1cf69
......@@ -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
View file @ 97e1cf69
# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
name: Run Linting/Formatting on Pull Requests
on:
- push
- pull_request
# See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
# if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
# pull_request:
# branches:
# - master
# branches-ignore:
# - development
jobs:
lint:
lint-python:
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Set up Python 3.10
uses: actions/setup-python@v4
- uses: actions/setup-python@v4
with:
python-version: 3.10.6
cache: pip
cache-dependency-path: |
**/requirements*txt
- name: Install PyLint
run: |
python -m pip install --upgrade pip
pip install pylint
# This lets PyLint check to see if it can resolve imports
- name: Install dependencies
run: |
export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
python launch.py
- name: Analysing the code with pylint
run: |
pylint $(git ls-files '*.py')
python-version: 3.11
# NB: there's no cache: pip here since we're not installing anything
# from the requirements.txt file(s) in the repository; it's faster
# not to have GHA download an (at the time of writing) 4 GB cache
# of PyTorch and other dependencies.
- name: Install Ruff
run: pip install ruff==0.0.265
- name: Run Ruff
run: ruff .
lint-js:
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Install Node.js
uses: actions/setup-node@v3
with:
node-version: 18
- run: npm i --ci
- run: npm run lint
.github/workflows/run_tests.yaml
View file @ 97e1cf69
......@@ -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 @ 97e1cf69
......@@ -34,3 +34,5 @@ notification.mp3
/test/stderr.txt
/cache.json*
/config_states/
/node_modules
/package-lock.json
\ No newline at end of file
README.md
View file @ 97e1cf69
......@@ -99,6 +99,12 @@ Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Installation on Windows 10/11 with NVidia-GPUs using release package
1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
2. Run `update.bat`.
3. Run `run.bat`.
> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
......@@ -158,5 +164,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
extensions-builtin/LDSR/ldsr_model_arch.py
View file @ 97e1cf69
......@@ -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:
......@@ -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 @ 97e1cf69
......@@ -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):
......
extensions-builtin/LDSR/sd_hijack_autoencoder.py
View file @ 97e1cf69
# 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 @ 97e1cf69
......@@ -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
......@@ -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):
......@@ -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/lora.py
View file @ 97e1cf69
import glob
import os
import re
import torch
......@@ -177,7 +176,7 @@ def load_lora(name, filename):
else:
print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
continue
assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
with torch.no_grad():
module.weight.copy_(weight)
......@@ -189,7 +188,7 @@ def load_lora(name, filename):
elif lora_key == "lora_down.weight":
lora_module.down = module
else:
assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
if len(keys_failed_to_match) > 0:
print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
......@@ -207,7 +206,7 @@ def load_loras(names, multipliers=None):
loaded_loras.clear()
loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
if any([x is None for x in loras_on_disk]):
if any(x is None for x in loras_on_disk):
list_available_loras()
loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
......@@ -314,7 +313,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
print(f'failed to calculate lora weights for layer {lora_layer_name}')
setattr(self, "lora_current_names", wanted_names)
self.lora_current_names = wanted_names
def lora_forward(module, input, original_forward):
......@@ -348,8 +347,8 @@ def lora_forward(module, input, original_forward):
def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
setattr(self, "lora_current_names", ())
setattr(self, "lora_weights_backup", None)
self.lora_current_names = ()
self.lora_weights_backup = None
def lora_Linear_forward(self, input):
......@@ -428,7 +427,7 @@ def infotext_pasted(infotext, params):
added = []
for k, v in params.items():
for k in params:
if not k.startswith("AddNet Model "):
continue
......
extensions-builtin/Lora/scripts/lora_script.py
View file @ 97e1cf69
......@@ -53,7 +53,7 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
"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"]}),
}))
......
extensions-builtin/ScuNET/scripts/scunet_model.py
View file @ 97e1cf69
......@@ -10,10 +10,9 @@ 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
from modules import images
class UpscalerScuNET(modules.upscaler.Upscaler):
......@@ -133,8 +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 @ 97e1cf69
......@@ -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):
......
extensions-builtin/SwinIR/scripts/swinir_model.py
View file @ 97e1cf69
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
......
extensions-builtin/SwinIR/swinir_model_arch.py
View file @ 97e1cf69
......@@ -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,
......@@ -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 @ 97e1cf69
......@@ -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
......@@ -698,7 +698,7 @@ 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],
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,
......@@ -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()
......
extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
View file @ 97e1cf69
......@@ -5,7 +5,7 @@
function checkBrackets(textArea, counterElt) {
var counts = {};
(textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
(textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
counts[bracket] = (counts[bracket] || 0) + 1;
});
var errors = [];
......@@ -27,14 +27,14 @@ function checkBrackets(textArea, counterElt) {
function setupBracketChecking(id_prompt, id_counter) {
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
var counter = gradioApp().getElementById(id_counter)
var counter = gradioApp().getElementById(id_counter);
if (textarea && counter) {
textarea.addEventListener("input", () => checkBrackets(textarea, counter));
}
}
onUiLoaded(function () {
onUiLoaded(function() {
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
setupBracketChecking('img2img_prompt', 'img2img_token_counter');
......
html/extra-networks-card.html
View file @ 97e1cf69
......@@ -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{serach_only}'>{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 @ 97e1cf69
......@@ -662,3 +662,29 @@ 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 @ 97e1cf69
let currentWidth = null;
let currentHeight = null;
let arFrameTimeout = setTimeout(function(){},0);
let arFrameTimeout = setTimeout(function() {}, 0);
function dimensionChange(e, is_width, is_height){
function dimensionChange(e, is_width, is_height) {
if(is_width){
currentWidth = e.target.value*1.0
if (is_width) {
currentWidth = e.target.value * 1.0;
}
if(is_height){
currentHeight = e.target.value*1.0
if (is_height) {
currentHeight = e.target.value * 1.0;
}
var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
if(!inImg2img){
if (!inImg2img) {
return;
}
var targetElement = null;
var tabIndex = get_tab_index('mode_img2img')
if(tabIndex == 0){ // img2img
var tabIndex = get_tab_index('mode_img2img');
if (tabIndex == 0) { // img2img
targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
} else if(tabIndex == 1){ //Sketch
} else if (tabIndex == 1) { //Sketch
targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
} else if(tabIndex == 2){ // Inpaint
} else if (tabIndex == 2) { // Inpaint
targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
} else if(tabIndex == 3){ // Inpaint sketch
} else if (tabIndex == 3) { // Inpaint sketch
targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
}
if(targetElement){
if (targetElement) {
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
if(!arPreviewRect){
arPreviewRect = document.createElement('div')
if (!arPreviewRect) {
arPreviewRect = document.createElement('div');
arPreviewRect.id = "imageARPreview";
gradioApp().appendChild(arPreviewRect)
gradioApp().appendChild(arPreviewRect);
}
var viewportOffset = targetElement.getBoundingClientRect();
var viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
var scaledx = targetElement.naturalWidth*viewportscale
var scaledy = targetElement.naturalHeight*viewportscale
var scaledx = targetElement.naturalWidth * viewportscale;
var scaledy = targetElement.naturalHeight * viewportscale;
var cleintRectTop = (viewportOffset.top+window.scrollY)
var cleintRectLeft = (viewportOffset.left+window.scrollX)
var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
var cleintRectTop = (viewportOffset.top + window.scrollY);
var cleintRectLeft = (viewportOffset.left + window.scrollX);
var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
var arscale = Math.min( scaledx/currentWidth, scaledy/currentHeight )
var arscaledx = currentWidth*arscale
var arscaledy = currentHeight*arscale
var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
var arscaledx = currentWidth * arscale;
var arscaledy = currentHeight * arscale;
var arRectTop = cleintRectCentreY-(arscaledy/2)
var arRectLeft = cleintRectCentreX-(arscaledx/2)
var arRectWidth = arscaledx
var arRectHeight = arscaledy
var arRectTop = cleintRectCentreY - (arscaledy / 2);
var arRectLeft = cleintRectCentreX - (arscaledx / 2);
var arRectWidth = arscaledx;
var arRectHeight = arscaledy;
arPreviewRect.style.top = arRectTop+'px';
arPreviewRect.style.left = arRectLeft+'px';
arPreviewRect.style.width = arRectWidth+'px';
arPreviewRect.style.height = arRectHeight+'px';
arPreviewRect.style.top = arRectTop + 'px';
arPreviewRect.style.left = arRectLeft + 'px';
arPreviewRect.style.width = arRectWidth + 'px';
arPreviewRect.style.height = arRectHeight + 'px';
clearTimeout(arFrameTimeout);
arFrameTimeout = setTimeout(function(){
arFrameTimeout = setTimeout(function() {
arPreviewRect.style.display = 'none';
},2000);
}, 2000);
arPreviewRect.style.display = 'block';
......@@ -81,31 +81,33 @@ function dimensionChange(e, is_width, is_height){
}
onUiUpdate(function(){
onUiUpdate(function() {
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
if(arPreviewRect){
if (arPreviewRect) {
arPreviewRect.style.display = 'none';
}
var tabImg2img = gradioApp().querySelector("#tab_img2img");
if (tabImg2img) {
var inImg2img = tabImg2img.style.display == "block";
if(inImg2img){
if (inImg2img) {
let inputs = gradioApp().querySelectorAll('input');
inputs.forEach(function(e){
var is_width = e.parentElement.id == "img2img_width"
var is_height = e.parentElement.id == "img2img_height"
if((is_width || is_height) && !e.classList.contains('scrollwatch')){
e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
e.classList.add('scrollwatch')
inputs.forEach(function(e) {
var is_width = e.parentElement.id == "img2img_width";
var is_height = e.parentElement.id == "img2img_height";
if ((is_width || is_height) && !e.classList.contains('scrollwatch')) {
e.addEventListener('input', function(e) {
dimensionChange(e, is_width, is_height);
});
e.classList.add('scrollwatch');
}
if(is_width){
currentWidth = e.value*1.0
if (is_width) {
currentWidth = e.value * 1.0;
}
if(is_height){
currentHeight = e.value*1.0
if (is_height) {
currentHeight = e.value * 1.0;
}
})
});
}
}
});
javascript/contextMenus.js
View file @ 97e1cf69
contextMenuInit = function(){
let eventListenerApplied=false;
var contextMenuInit = function() {
let eventListenerApplied = false;
let menuSpecs = new Map();
const uid = function(){
const uid = function() {
return Date.now().toString(36) + Math.random().toString(36).substring(2);
}
};
function showContextMenu(event,element,menuEntries){
function showContextMenu(event, element, menuEntries) {
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){
oldMenu.remove()
let oldMenu = gradioApp().querySelector('#context-menu');
if (oldMenu) {
oldMenu.remove();
}
let baseStyle = window.getComputedStyle(uiCurrentTab)
let baseStyle = window.getComputedStyle(uiCurrentTab);
const contextMenu = document.createElement('nav')
contextMenu.id = "context-menu"
contextMenu.style.background = baseStyle.background
contextMenu.style.color = baseStyle.color
contextMenu.style.fontFamily = baseStyle.fontFamily
contextMenu.style.top = posy+'px'
contextMenu.style.left = posx+'px'
const contextMenu = document.createElement('nav');
contextMenu.id = "context-menu";
contextMenu.style.background = baseStyle.background;
contextMenu.style.color = baseStyle.color;
contextMenu.style.fontFamily = baseStyle.fontFamily;
contextMenu.style.top = posy + 'px';
contextMenu.style.left = posx + 'px';
const contextMenuList = document.createElement('ul')
const contextMenuList = document.createElement('ul');
contextMenuList.className = 'context-menu-items';
contextMenu.append(contextMenuList);
menuEntries.forEach(function(entry){
let contextMenuEntry = document.createElement('a')
contextMenuEntry.innerHTML = entry['name']
menuEntries.forEach(function(entry) {
let contextMenuEntry = document.createElement('a');
contextMenuEntry.innerHTML = entry['name'];
contextMenuEntry.addEventListener("click", function() {
entry['func']();
})
});
contextMenuList.append(contextMenuEntry);
})
});
gradioApp().appendChild(contextMenu)
gradioApp().appendChild(contextMenu);
let menuWidth = contextMenu.offsetWidth + 4;
let menuHeight = contextMenu.offsetHeight + 4;
......@@ -50,117 +50,123 @@ contextMenuInit = function(){
let windowWidth = window.innerWidth;
let windowHeight = window.innerHeight;
if ( (windowWidth - posx) < menuWidth ) {
if ((windowWidth - posx) < menuWidth) {
contextMenu.style.left = windowWidth - menuWidth + "px";
}
if ( (windowHeight - posy) < menuHeight ) {
if ((windowHeight - posy) < menuHeight) {
contextMenu.style.top = windowHeight - menuHeight + "px";
}
}
function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
var currentItems = menuSpecs.get(targetElementSelector)
var currentItems = menuSpecs.get(targetElementSelector);
if(!currentItems){
currentItems = []
menuSpecs.set(targetElementSelector,currentItems);
if (!currentItems) {
currentItems = [];
menuSpecs.set(targetElementSelector, currentItems);
}
let newItem = {'id':targetElementSelector+'_'+uid(),
'name':entryName,
'func':entryFunction,
'isNew':true}
let newItem = {
id: targetElementSelector + '_' + uid(),
name: entryName,
func: entryFunction,
isNew: true
};
currentItems.push(newItem)
return newItem['id']
currentItems.push(newItem);
return newItem['id'];
}
function removeContextMenuOption(uid){
function removeContextMenuOption(uid) {
menuSpecs.forEach(function(v) {
let index = -1
v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
if(index>=0){
let index = -1;
v.forEach(function(e, ei) {
if (e['id'] == uid) {
index = ei;
}
});
if (index >= 0) {
v.splice(index, 1);
}
})
});
}
function addContextMenuEventListener(){
if(eventListenerApplied){
function addContextMenuEventListener() {
if (eventListenerApplied) {
return;
}
gradioApp().addEventListener("click", function(e) {
if(! e.isTrusted){
return
if (!e.isTrusted) {
return;
}
let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){
oldMenu.remove()
let oldMenu = gradioApp().querySelector('#context-menu');
if (oldMenu) {
oldMenu.remove();
}
});
gradioApp().addEventListener("contextmenu", function(e) {
let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){
oldMenu.remove()
let oldMenu = gradioApp().querySelector('#context-menu');
if (oldMenu) {
oldMenu.remove();
}
menuSpecs.forEach(function(v,k) {
if(e.composedPath()[0].matches(k)){
showContextMenu(e,e.composedPath()[0],v)
e.preventDefault()
menuSpecs.forEach(function(v, k) {
if (e.composedPath()[0].matches(k)) {
showContextMenu(e, e.composedPath()[0], v);
e.preventDefault();
}
})
});
eventListenerApplied=true
});
eventListenerApplied = true;
}
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
}
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];
};
initResponse = contextMenuInit();
appendContextMenuOption = initResponse[0];
removeContextMenuOption = initResponse[1];
addContextMenuEventListener = initResponse[2];
var initResponse = contextMenuInit();
var appendContextMenuOption = initResponse[0];
var removeContextMenuOption = initResponse[1];
var addContextMenuEventListener = initResponse[2];
(function(){
(function() {
//Start example Context Menu Items
let generateOnRepeat = function(genbuttonid,interruptbuttonid){
let generateOnRepeat = function(genbuttonid, interruptbuttonid) {
let genbutton = gradioApp().querySelector(genbuttonid);
let interruptbutton = gradioApp().querySelector(interruptbuttonid);
if(!interruptbutton.offsetParent){
if (!interruptbutton.offsetParent) {
genbutton.click();
}
clearInterval(window.generateOnRepeatInterval)
window.generateOnRepeatInterval = setInterval(function(){
if(!interruptbutton.offsetParent){
clearInterval(window.generateOnRepeatInterval);
window.generateOnRepeatInterval = setInterval(function() {
if (!interruptbutton.offsetParent) {
genbutton.click();
}
},
500)
}
500);
};
appendContextMenuOption('#txt2img_generate','Generate forever',function(){
generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
})
appendContextMenuOption('#img2img_generate','Generate forever',function(){
generateOnRepeat('#img2img_generate','#img2img_interrupt');
})
appendContextMenuOption('#txt2img_generate', 'Generate forever', function() {
generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
});
appendContextMenuOption('#img2img_generate', 'Generate forever', function() {
generateOnRepeat('#img2img_generate', '#img2img_interrupt');
});
let cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval)
}
let cancelGenerateForever = function() {
clearInterval(window.generateOnRepeatInterval);
};
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever);
appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever);
})();
//End example Context Menu Items
onUiUpdate(function(){
addContextMenuEventListener()
onUiUpdate(function() {
addContextMenuEventListener();
});
javascript/dragdrop.js
View file @ 97e1cf69
// allows drag-dropping files into gradio image elements, and also pasting images from clipboard
function isValidImageList( files ) {
function isValidImageList(files) {
return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
}
function dropReplaceImage( imgWrap, files ) {
if ( ! isValidImageList( files ) ) {
function dropReplaceImage(imgWrap, files) {
if (!isValidImageList(files)) {
return;
}
......@@ -14,8 +14,8 @@ function dropReplaceImage( imgWrap, files ) {
imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
const callback = () => {
const fileInput = imgWrap.querySelector('input[type="file"]');
if ( fileInput ) {
if ( files.length === 0 ) {
if (fileInput) {
if (files.length === 0) {
files = new DataTransfer();
files.items.add(tmpFile);
fileInput.files = files.files;
......@@ -26,32 +26,32 @@ function dropReplaceImage( imgWrap, files ) {
}
};
if ( imgWrap.closest('#pnginfo_image') ) {
if (imgWrap.closest('#pnginfo_image')) {
// special treatment for PNG Info tab, wait for fetch request to finish
const oldFetch = window.fetch;
window.fetch = async (input, options) => {
window.fetch = async(input, options) => {
const response = await oldFetch(input, options);
if ( 'api/predict/' === input ) {
if ('api/predict/' === input) {
const content = await response.text();
window.fetch = oldFetch;
window.requestAnimationFrame( () => callback() );
window.requestAnimationFrame(() => callback());
return new Response(content, {
status: response.status,
statusText: response.statusText,
headers: response.headers
})
});
}
return response;
};
} else {
window.requestAnimationFrame( () => callback() );
window.requestAnimationFrame(() => callback());
}
}
window.document.addEventListener('dragover', e => {
const target = e.composedPath()[0];
const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
if (!imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
return;
}
e.stopPropagation();
......@@ -65,24 +65,24 @@ window.document.addEventListener('drop', e => {
return;
}
const imgWrap = target.closest('[data-testid="image"]');
if ( !imgWrap ) {
if (!imgWrap) {
return;
}
e.stopPropagation();
e.preventDefault();
const files = e.dataTransfer.files;
dropReplaceImage( imgWrap, files );
dropReplaceImage(imgWrap, files);
});
window.addEventListener('paste', e => {
const files = e.clipboardData.files;
if ( ! isValidImageList( files ) ) {
if (!isValidImageList(files)) {
return;
}
const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
.filter(el => uiElementIsVisible(el));
if ( ! visibleImageFields.length ) {
if (!visibleImageFields.length) {
return;
}
......@@ -93,5 +93,6 @@ window.addEventListener('paste', e => {
firstFreeImageField ?
firstFreeImageField :
visibleImageFields[visibleImageFields.length - 1]
, files );
, files
);
});
javascript/edit-attention.js
View file @ 97e1cf69
function keyupEditAttention(event){
function keyupEditAttention(event) {
let target = event.originalTarget || event.composedPath()[0];
if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
if (! (event.metaKey || event.ctrlKey)) return;
if (!target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
if (!(event.metaKey || event.ctrlKey)) return;
let isPlus = event.key == "ArrowUp"
let isMinus = event.key == "ArrowDown"
let isPlus = event.key == "ArrowUp";
let isMinus = event.key == "ArrowDown";
if (!isPlus && !isMinus) return;
let selectionStart = target.selectionStart;
let selectionEnd = target.selectionEnd;
let text = target.value;
function selectCurrentParenthesisBlock(OPEN, CLOSE){
function selectCurrentParenthesisBlock(OPEN, CLOSE) {
if (selectionStart !== selectionEnd) return false;
// Find opening parenthesis around current cursor
......@@ -44,7 +44,7 @@ function keyupEditAttention(event){
return true;
}
function selectCurrentWord(){
function selectCurrentWord() {
if (selectionStart !== selectionEnd) return false;
const delimiters = opts.keyedit_delimiters + " \r\n\t";
......@@ -69,20 +69,20 @@ function keyupEditAttention(event){
event.preventDefault();
var closeCharacter = ')'
var delta = opts.keyedit_precision_attention
var closeCharacter = ')';
var delta = opts.keyedit_precision_attention;
if (selectionStart > 0 && text[selectionStart - 1] == '<'){
closeCharacter = '>'
delta = opts.keyedit_precision_extra
if (selectionStart > 0 && text[selectionStart - 1] == '<') {
closeCharacter = '>';
delta = opts.keyedit_precision_extra;
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
// do not include spaces at the end
while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
selectionEnd -= 1;
}
if(selectionStart == selectionEnd){
return
if (selectionStart == selectionEnd) {
return;
}
text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
......@@ -97,7 +97,7 @@ function keyupEditAttention(event){
weight += isPlus ? delta : -delta;
weight = parseFloat(weight.toPrecision(12));
if(String(weight).length == 1) weight += ".0"
if (String(weight).length == 1) weight += ".0";
if (closeCharacter == ')' && weight == 1) {
text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
......@@ -112,7 +112,7 @@ function keyupEditAttention(event){
target.selectionStart = selectionStart;
target.selectionEnd = selectionEnd;
updateInput(target)
updateInput(target);
}
addEventListener('keydown', (event) => {
......
javascript/extensions.js
View file @ 97e1cf69
function extensions_apply(_disabled_list, _update_list, disable_all){
var disable = []
var update = []
function extensions_apply(_disabled_list, _update_list, disable_all) {
var disable = [];
var update = [];
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
if(x.name.startsWith("enable_") && ! x.checked)
disable.push(x.name.substring(7))
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
if (x.name.startsWith("enable_") && !x.checked) {
disable.push(x.name.substring(7));
}
if(x.name.startsWith("update_") && x.checked)
update.push(x.name.substring(7))
})
if (x.name.startsWith("update_") && x.checked) {
update.push(x.name.substring(7));
}
});
restart_reload()
restart_reload();
return [JSON.stringify(disable), JSON.stringify(update), disable_all]
return [JSON.stringify(disable), JSON.stringify(update), disable_all];
}
function extensions_check(){
var disable = []
function extensions_check() {
var disable = [];
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
if(x.name.startsWith("enable_") && ! x.checked)
disable.push(x.name.substring(7))
})
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
if (x.name.startsWith("enable_") && !x.checked) {
disable.push(x.name.substring(7));
}
});
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
x.innerHTML = "Loading..."
})
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
x.innerHTML = "Loading...";
});
var id = randomId()
requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
var id = randomId();
requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
})
});
return [id, JSON.stringify(disable)]
return [id, JSON.stringify(disable)];
}
function install_extension_from_index(button, url){
button.disabled = "disabled"
button.value = "Installing..."
function install_extension_from_index(button, url) {
button.disabled = "disabled";
button.value = "Installing...";
var textarea = gradioApp().querySelector('#extension_to_install textarea')
textarea.value = url
updateInput(textarea)
var textarea = gradioApp().querySelector('#extension_to_install textarea');
textarea.value = url;
updateInput(textarea);
gradioApp().querySelector('#install_extension_button').click()
gradioApp().querySelector('#install_extension_button').click();
}
function config_state_confirm_restore(_, config_state_name, config_restore_type) {
......@@ -63,9 +66,9 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
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..."
})
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
x.innerHTML = "Loading...";
});
}
return [confirmed, config_state_name, config_restore_type];
}
javascript/extraNetworks.js
View file @ 97e1cf69
This diff is collapsed.
javascript/generationParams.js
View file @ 97e1cf69
// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
let txt2img_gallery, img2img_gallery, modal = undefined;
onUiUpdate(function(){
onUiUpdate(function() {
if (!txt2img_gallery) {
txt2img_gallery = attachGalleryListeners("txt2img")
txt2img_gallery = attachGalleryListeners("txt2img");
}
if (!img2img_gallery) {
img2img_gallery = attachGalleryListeners("img2img")
img2img_gallery = attachGalleryListeners("img2img");
}
if (!modal) {
modal = gradioApp().getElementById('lightboxModal')
modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
modal = gradioApp().getElementById('lightboxModal');
modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']});
}
});
let modalObserver = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText;
if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) {
gradioApp().getElementById(selectedTab + "_generation_info_button")?.click();
}
});
});
function attachGalleryListeners(tab_name) {
var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
var gallery = gradioApp().querySelector('#' + tab_name + '_gallery');
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click());
gallery?.addEventListener('keydown', (e) => {
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
gradioApp().getElementById(tab_name+"_generation_info_button").click()
if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow
gradioApp().getElementById(tab_name + "_generation_info_button").click();
}
});
return gallery;
}
javascript/hints.js
View file @ 97e1cf69
// mouseover tooltips for various UI elements
titles = {
var titles = {
"Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
"Sampling method": "Which algorithm to use to produce the image",
"GFPGAN": "Restore low quality faces using GFPGAN neural network",
......@@ -9,6 +9,7 @@ titles = {
"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
"\u{1F4D0}": "Auto detect size from img2img",
"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
"CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
......@@ -66,8 +67,8 @@ titles = {
"Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
"Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; 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.",
......@@ -113,20 +114,18 @@ titles = {
"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.",
"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."
}
};
onUiUpdate(function(){
gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
function updateTooltipForSpan(span) {
if (span.title) return; // already has a title
let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
if(!tooltip){
if (!tooltip) {
tooltip = localization[titles[span.value]] || titles[span.value];
}
if(!tooltip){
if (!tooltip) {
for (const c of span.classList) {
if (c in titles) {
tooltip = localization[titles[c]] || titles[c];
......@@ -135,16 +134,35 @@ onUiUpdate(function(){
}
}
if(tooltip){
if (tooltip) {
span.title = tooltip;
}
})
}
gradioApp().querySelectorAll('select').forEach(function(select){
function updateTooltipForSelect(select) {
if (select.onchange != null) return;
select.onchange = function(){
select.onchange = function() {
select.title = localization[titles[select.value]] || titles[select.value] || "";
};
}
var 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 @ 97e1cf69
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) {
function setInactive(elem, inactive) {
elem.classList.toggle('inactive', !!inactive);
}
var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
var 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" : ""
gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : "";
setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0);
setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0);
setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0);
return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y];
}
javascript/imageMaskFix.js
View file @ 97e1cf69
......@@ -4,17 +4,16 @@
*/
function imageMaskResize() {
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
if ( ! canvases.length ) {
canvases_fixed = false; // TODO: this is unused..?
window.removeEventListener( 'resize', imageMaskResize );
if (!canvases.length) {
window.removeEventListener('resize', imageMaskResize);
return;
}
const wrapper = canvases[0].closest('.touch-none');
const previewImage = wrapper.previousElementSibling;
if ( ! previewImage.complete ) {
previewImage.addEventListener( 'load', imageMaskResize);
if (!previewImage.complete) {
previewImage.addEventListener('load', imageMaskResize);
return;
}
......@@ -24,15 +23,15 @@ function imageMaskResize() {
const nh = previewImage.naturalHeight;
const portrait = nh > nw;
const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw);
const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh);
wrapper.style.width = `${wW}px`;
wrapper.style.height = `${wH}px`;
wrapper.style.left = `0px`;
wrapper.style.top = `0px`;
canvases.forEach( c => {
canvases.forEach(c => {
c.style.width = c.style.height = '';
c.style.maxWidth = '100%';
c.style.maxHeight = '100%';
......@@ -41,4 +40,4 @@ function imageMaskResize() {
}
onUiUpdate(imageMaskResize);
window.addEventListener( 'resize', imageMaskResize);
window.addEventListener('resize', imageMaskResize);
javascript/imageParams.js
View file @ 97e1cf69
window.onload = (function(){
window.onload = (function() {
window.addEventListener('drop', e => {
const target = e.composedPath()[0];
if (target.placeholder.indexOf("Prompt") == -1) return;
......@@ -10,7 +10,7 @@ window.onload = (function(){
const imgParent = gradioApp().getElementById(prompt_target);
const files = e.dataTransfer.files;
const fileInput = imgParent.querySelector('input[type="file"]');
if ( fileInput ) {
if (fileInput) {
fileInput.files = files;
fileInput.dispatchEvent(new Event('change'));
}
......
javascript/imageviewer.js
View file @ 97e1cf69
This diff is collapsed.
javascript/imageviewerGamepad.js
View file @ 97e1cf69
window.addEventListener('gamepadconnected', (e) => {
const index = e.gamepad.index;
let isWaiting = false;
setInterval(async () => {
setInterval(async() => {
if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
const gamepad = navigator.getGamepads()[index];
const xValue = gamepad.axes[0];
......@@ -14,7 +14,7 @@ window.addEventListener('gamepadconnected', (e) => {
}
if (isWaiting) {
await sleepUntil(() => {
const xValue = navigator.getGamepads()[index].axes[0]
const xValue = navigator.getGamepads()[index].axes[0];
if (xValue < 0.3 && xValue > -0.3) {
return true;
}
......
javascript/localization.js
View file @ 97e1cf69
// localization = {} -- the dict with translations is created by the backend
ignore_ids_for_localization={
var ignore_ids_for_localization = {
setting_sd_hypernetwork: 'OPTION',
setting_sd_model_checkpoint: 'OPTION',
setting_realesrgan_enabled_models: 'OPTION',
modelmerger_primary_model_name: 'OPTION',
modelmerger_secondary_model_name: 'OPTION',
modelmerger_tertiary_model_name: 'OPTION',
......@@ -17,111 +16,111 @@ ignore_ids_for_localization={
setting_realesrgan_enabled_models: 'SPAN',
extras_upscaler_1: 'SPAN',
extras_upscaler_2: 'SPAN',
}
};
re_num = /^[\.\d]+$/
re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
var re_num = /^[.\d]+$/;
var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
original_lines = {}
translated_lines = {}
var original_lines = {};
var 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);
function textNodesUnder(el) {
var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
while ((n = walk.nextNode())) a.push(n);
return a;
}
function canBeTranslated(node, text){
if(! text) return false;
if(! node.parentElement) return false;
function canBeTranslated(node, text) {
if (!text) return false;
if (!node.parentElement) return false;
var parentType = node.parentElement.nodeName
if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
var parentType = node.parentElement.nodeName;
if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
if (parentType=='OPTION' || parentType=='SPAN'){
var pnode = node
for(var level=0; level<4; level++){
pnode = pnode.parentElement
if(! pnode) break;
if (parentType == 'OPTION' || parentType == 'SPAN') {
var pnode = node;
for (var level = 0; level < 4; level++) {
pnode = pnode.parentElement;
if (!pnode) break;
if(ignore_ids_for_localization[pnode.id] == parentType) return false;
if (ignore_ids_for_localization[pnode.id] == parentType) return false;
}
}
if(re_num.test(text)) return false;
if(re_emoji.test(text)) return false;
return true
if (re_num.test(text)) return false;
if (re_emoji.test(text)) return false;
return true;
}
function getTranslation(text){
if(! text) return undefined
function getTranslation(text) {
if (!text) return undefined;
if(translated_lines[text] === undefined){
original_lines[text] = 1
if (translated_lines[text] === undefined) {
original_lines[text] = 1;
}
tl = localization[text]
if(tl !== undefined){
translated_lines[tl] = 1
var tl = localization[text];
if (tl !== undefined) {
translated_lines[tl] = 1;
}
return tl
return tl;
}
function processTextNode(node){
var text = node.textContent.trim()
function processTextNode(node) {
var text = node.textContent.trim();
if(! canBeTranslated(node, text)) return
if (!canBeTranslated(node, text)) return;
tl = getTranslation(text)
if(tl !== undefined){
node.textContent = tl
var tl = getTranslation(text);
if (tl !== undefined) {
node.textContent = tl;
}
}
function processNode(node){
if(node.nodeType == 3){
processTextNode(node)
return
function processNode(node) {
if (node.nodeType == 3) {
processTextNode(node);
return;
}
if(node.title){
tl = getTranslation(node.title)
if(tl !== undefined){
node.title = tl
if (node.title) {
let tl = getTranslation(node.title);
if (tl !== undefined) {
node.title = tl;
}
}
if(node.placeholder){
tl = getTranslation(node.placeholder)
if(tl !== undefined){
node.placeholder = tl
if (node.placeholder) {
let tl = getTranslation(node.placeholder);
if (tl !== undefined) {
node.placeholder = tl;
}
}
textNodesUnder(node).forEach(function(node){
processTextNode(node)
})
textNodesUnder(node).forEach(function(node) {
processTextNode(node);
});
}
function dumpTranslations(){
if(!hasLocalization()) {
function dumpTranslations() {
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 = {}
var dumped = {};
if (localization.rtl) {
dumped.rtl = true;
}
for (const text in original_lines) {
if(dumped[text] !== undefined) continue;
if (dumped[text] !== undefined) continue;
dumped[text] = localization[text] || text;
}
......@@ -129,7 +128,7 @@ function dumpTranslations(){
}
function download_localization() {
var text = JSON.stringify(dumpTranslations(), null, 4)
var text = JSON.stringify(dumpTranslations(), null, 4);
var element = document.createElement('a');
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
......@@ -142,20 +141,20 @@ function download_localization() {
document.body.removeChild(element);
}
document.addEventListener("DOMContentLoaded", function () {
document.addEventListener("DOMContentLoaded", function() {
if (!hasLocalization()) {
return;
}
onUiUpdate(function (m) {
m.forEach(function (mutation) {
mutation.addedNodes.forEach(function (node) {
processNode(node)
})
onUiUpdate(function(m) {
m.forEach(function(mutation) {
mutation.addedNodes.forEach(function(node) {
processNode(node);
});
});
});
})
processNode(gradioApp())
processNode(gradioApp());
if (localization.rtl) { // if the language is from right to left,
(new MutationObserver((mutations, observer) => { // wait for the style to load
......@@ -170,8 +169,8 @@ document.addEventListener("DOMContentLoaded", function () {
}
}
}
})
});
})).observe(gradioApp(), { childList: true });
});
})).observe(gradioApp(), {childList: true});
}
})
});
javascript/notification.js
View file @ 97e1cf69
......@@ -4,14 +4,14 @@ let lastHeadImg = null;
let notificationButton = null;
onUiUpdate(function(){
if(notificationButton == null){
notificationButton = gradioApp().getElementById('request_notifications')
onUiUpdate(function() {
if (notificationButton == null) {
notificationButton = gradioApp().getElementById('request_notifications');
if(notificationButton != null){
if (notificationButton != null) {
notificationButton.addEventListener('click', () => {
void Notification.requestPermission();
},true);
}, true);
}
}
......@@ -42,7 +42,7 @@ onUiUpdate(function(){
}
);
notification.onclick = function(_){
notification.onclick = function(_) {
parent.focus();
this.close();
};
......
javascript/progressbar.js
View file @ 97e1cf69
// code related to showing and updating progressbar shown as the image is being made
function rememberGallerySelection(){
function rememberGallerySelection() {
}
function getGallerySelectedIndex(){
function getGallerySelectedIndex() {
}
function request(url, data, handler, errorHandler){
function request(url, data, handler, errorHandler) {
var xhr = new XMLHttpRequest();
xhr.open("POST", url, true);
xhr.setRequestHeader("Content-Type", "application/json");
xhr.onreadystatechange = function () {
xhr.onreadystatechange = function() {
if (xhr.readyState === 4) {
if (xhr.status === 200) {
try {
var js = JSON.parse(xhr.responseText);
handler(js)
handler(js);
} catch (error) {
console.error(error);
errorHandler()
errorHandler();
}
} else{
errorHandler()
} else {
errorHandler();
}
}
};
......@@ -31,147 +31,147 @@ function request(url, data, handler, errorHandler){
xhr.send(js);
}
function pad2(x){
return x<10 ? '0'+x : x
function pad2(x) {
return x < 10 ? '0' + x : x;
}
function formatTime(secs){
if(secs > 3600){
return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60)
} else if(secs > 60){
return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60)
} else{
return Math.floor(secs) + "s"
function formatTime(secs) {
if (secs > 3600) {
return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60);
} else if (secs > 60) {
return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
} else {
return Math.floor(secs) + "s";
}
}
function setTitle(progress){
var title = 'Stable Diffusion'
function setTitle(progress) {
var title = 'Stable Diffusion';
if(opts.show_progress_in_title && progress){
if (opts.show_progress_in_title && progress) {
title = '[' + progress.trim() + '] ' + title;
}
if(document.title != title){
if (document.title != title) {
document.title = title;
}
}
function randomId(){
return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")"
function randomId() {
return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")";
}
// 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, inactivityTimeout=40){
var dateStart = new Date()
var wasEverActive = false
var parentProgressbar = progressbarContainer.parentNode
var parentGallery = gallery ? gallery.parentNode : null
var divProgress = document.createElement('div')
divProgress.className='progressDiv'
divProgress.style.display = opts.show_progressbar ? "block" : "none"
var divInner = document.createElement('div')
divInner.className='progress'
divProgress.appendChild(divInner)
parentProgressbar.insertBefore(divProgress, progressbarContainer)
if(parentGallery){
var livePreview = document.createElement('div')
livePreview.className='livePreview'
parentGallery.insertBefore(livePreview, gallery)
}
var removeProgressBar = function(){
setTitle("")
parentProgressbar.removeChild(divProgress)
if(parentGallery) parentGallery.removeChild(livePreview)
atEnd()
}
function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) {
var dateStart = new Date();
var wasEverActive = false;
var parentProgressbar = progressbarContainer.parentNode;
var parentGallery = gallery ? gallery.parentNode : null;
var divProgress = document.createElement('div');
divProgress.className = 'progressDiv';
divProgress.style.display = opts.show_progressbar ? "block" : "none";
var divInner = document.createElement('div');
divInner.className = 'progress';
divProgress.appendChild(divInner);
parentProgressbar.insertBefore(divProgress, progressbarContainer);
if (parentGallery) {
var livePreview = document.createElement('div');
livePreview.className = 'livePreview';
parentGallery.insertBefore(livePreview, gallery);
}
var removeProgressBar = function() {
setTitle("");
parentProgressbar.removeChild(divProgress);
if (parentGallery) parentGallery.removeChild(livePreview);
atEnd();
};
var fun = function(id_task, id_live_preview){
request("./internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){
if(res.completed){
removeProgressBar()
return
var fun = function(id_task, id_live_preview) {
request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
if (res.completed) {
removeProgressBar();
return;
}
var rect = progressbarContainer.getBoundingClientRect()
var rect = progressbarContainer.getBoundingClientRect();
if(rect.width){
if (rect.width) {
divProgress.style.width = rect.width + "px";
}
let progressText = ""
let progressText = "";
divInner.style.width = ((res.progress || 0) * 100.0) + '%'
divInner.style.background = res.progress ? "" : "transparent"
divInner.style.width = ((res.progress || 0) * 100.0) + '%';
divInner.style.background = res.progress ? "" : "transparent";
if(res.progress > 0){
progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'
if (res.progress > 0) {
progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
}
if(res.eta){
progressText += " ETA: " + formatTime(res.eta)
if (res.eta) {
progressText += " ETA: " + formatTime(res.eta);
}
setTitle(progressText)
setTitle(progressText);
if(res.textinfo && res.textinfo.indexOf("\n") == -1){
progressText = res.textinfo + " " + progressText
if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
progressText = res.textinfo + " " + progressText;
}
divInner.textContent = progressText
divInner.textContent = progressText;
var elapsedFromStart = (new Date() - dateStart) / 1000
var elapsedFromStart = (new Date() - dateStart) / 1000;
if(res.active) wasEverActive = true;
if (res.active) wasEverActive = true;
if(! res.active && wasEverActive){
removeProgressBar()
return
if (!res.active && wasEverActive) {
removeProgressBar();
return;
}
if(elapsedFromStart > inactivityTimeout && !res.queued && !res.active){
removeProgressBar()
return
if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
removeProgressBar();
return;
}
if(res.live_preview && gallery){
var rect = gallery.getBoundingClientRect()
if(rect.width){
livePreview.style.width = rect.width + "px"
livePreview.style.height = rect.height + "px"
if (res.live_preview && gallery) {
rect = gallery.getBoundingClientRect();
if (rect.width) {
livePreview.style.width = rect.width + "px";
livePreview.style.height = rect.height + "px";
}
var img = new Image();
img.onload = function() {
livePreview.appendChild(img)
if(livePreview.childElementCount > 2){
livePreview.removeChild(livePreview.firstElementChild)
}
livePreview.appendChild(img);
if (livePreview.childElementCount > 2) {
livePreview.removeChild(livePreview.firstElementChild);
}
};
img.src = res.live_preview;
}
if(onProgress){
onProgress(res)
if (onProgress) {
onProgress(res);
}
setTimeout(() => {
fun(id_task, res.id_live_preview);
}, opts.live_preview_refresh_period || 500)
}, function(){
removeProgressBar()
})
}
}, opts.live_preview_refresh_period || 500);
}, function() {
removeProgressBar();
});
};
fun(id_task, 0)
fun(id_task, 0);
}
javascript/textualInversion.js
View file @ 97e1cf69
function start_training_textual_inversion(){
gradioApp().querySelector('#ti_error').innerHTML=''
function start_training_textual_inversion() {
gradioApp().querySelector('#ti_error').innerHTML = '';
var id = randomId()
requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function(){}, function(progress){
gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo
})
var id = randomId();
requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
});
var res = args_to_array(arguments)
var res = args_to_array(arguments);
res[0] = id
res[0] = id;
return res
return res;
}
javascript/ui.js
View file @ 97e1cf69
This diff is collapsed.
javascript/ui_settings_hints.js
View file @ 97e1cf69
// various hints and extra info for the settings tab
onUiLoaded(function(){
createLink = function(elem_id, text, href){
var a = document.createElement('A')
a.textContent = text
a.target = '_blank';
elem = gradioApp().querySelector('#'+elem_id)
elem.insertBefore(a, elem.querySelector('label'))
return a
var settingsHintsSetup = false;
onOptionsChanged(function() {
if (settingsHintsSetup) return;
settingsHintsSetup = true;
gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) {
var name = div.id.substr(8);
var commentBefore = opts._comments_before[name];
var commentAfter = opts._comments_after[name];
if (!commentBefore && !commentAfter) return;
var span = null;
if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span');
else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild;
else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild;
else span = div.querySelector('label span').firstChild;
if (!span) return;
if (commentBefore) {
var comment = document.createElement('DIV');
comment.className = 'settings-comment';
comment.innerHTML = commentBefore;
span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
span.parentElement.insertBefore(comment, span);
span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
}
if (commentAfter) {
comment = document.createElement('DIV');
comment.className = 'settings-comment';
comment.innerHTML = commentAfter;
span.parentElement.insertBefore(comment, span.nextSibling);
span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling);
}
});
});
createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
requestGet("./internal/quicksettings-hint", {}, function(data){
var table = document.createElement('table')
table.className = 'settings-value-table'
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)
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)
td = document.createElement('td');
td.textContent = obj.label;
tr.appendChild(td);
table.appendChild(tr)
})
table.appendChild(tr);
});
popup(table);
})
});
})
}
launch.py
View file @ 97e1cf69
......@@ -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
stored_git_tag = None
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'
......@@ -57,65 +55,52 @@ Use --skip-python-version-check to suppress this warning.
""")
@lru_cache()
def commit_hash():
global stored_commit_hash
if stored_commit_hash is not None:
return stored_commit_hash
try:
stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
stored_commit_hash = "<none>"
return stored_commit_hash
return "<none>"
@lru_cache()
def git_tag():
global stored_git_tag
if stored_git_tag is not None:
return stored_git_tag
try:
stored_git_tag = run(f"{git} describe --tags").strip()
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
stored_git_tag = "<none>"
return stored_git_tag
return "<none>"
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):
......@@ -131,11 +116,7 @@ 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(command, desc=None, live=False):
def run_pip(command, desc=None, live=default_command_live):
if args.skip_install:
return
......@@ -143,8 +124,9 @@ def run_pip(command, desc=None, live=False):
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):
......@@ -237,13 +219,14 @@ def run_extensions_installers(settings_file):
def prepare_environment():
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu118")
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==0.0.17')
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")
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")
......@@ -270,8 +253,11 @@ def prepare_environment():
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")
......@@ -294,8 +280,8 @@ def prepare_environment():
elif platform.system() == "Linux":
run_pip(f"install {xformers_package}", "xformers")
if not is_installed("pyngrok") and args.ngrok:
run_pip("install pyngrok", "ngrok")
if not is_installed("ngrok") and args.ngrok:
run_pip("install ngrok", "ngrok")
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
......
modules/api/api.py
View file @ 97e1cf69
This diff is collapsed.
modules/api/models.py
View file @ 97e1cf69
......@@ -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/cmd_args.py
View file @ 97e1cf69
import argparse
import json
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()
......@@ -39,7 +40,8 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
......@@ -103,3 +105,4 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
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 @ 97e1cf69
# 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):
......@@ -163,8 +161,8 @@ class Fuse_sft_block(nn.Module):
class CodeFormer(VQAutoEncoder):
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:
......
modules/codeformer/vqgan_arch.py
View file @ 97e1cf69
......@@ -5,11 +5,9 @@ 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
......@@ -328,7 +326,7 @@ class Generator(nn.Module):
@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()
......@@ -339,7 +337,7 @@ class VQAutoEncoder(nn.Module):
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,
......
modules/codeformer_model.py
View file @ 97e1cf69
......@@ -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
View file @ 97e1cf69
......@@ -14,7 +14,7 @@ from collections import OrderedDict
import git
from modules import shared, extensions
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir
from modules.paths_internal import script_path, config_states_dir
all_config_states = OrderedDict()
......@@ -35,7 +35,7 @@ def list_config_states():
j["filepath"] = path
config_states.append(j)
config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
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"]))
......@@ -83,6 +83,8 @@ def get_extension_config():
ext_config = {}
for ext in extensions.extensions:
ext.read_info_from_repo()
entry = {
"name": ext.name,
"path": ext.path,
......
modules/deepbooru.py
View file @ 97e1cf69
......@@ -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 @ 97e1cf69
......@@ -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
......
modules/esrgan_model.py
View file @ 97e1cf69
......@@ -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']
......
modules/esrgan_model_arch.py
View file @ 97e1cf69
......@@ -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
......@@ -438,9 +437,11 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
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 @ 97e1cf69
import os
import sys
import threading
import traceback
import time
from datetime import datetime
import git
from modules import shared
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
......@@ -25,6 +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
......@@ -43,8 +44,13 @@ class Extension:
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")):
......@@ -59,18 +65,18 @@ class Extension:
try:
self.status = 'unknown'
self.remote = next(repo.remote().urls, None)
head = repo.head.commit
self.commit_date = repo.head.commit.committed_date
ts = time.asctime(time.gmtime(self.commit_date))
if repo.active_branch:
self.branch = repo.active_branch.name
self.commit_hash = head.hexsha
self.version = f'{self.commit_hash[:8]} ({ts})'
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
......
modules/extra_networks.py
View file @ 97e1cf69
......@@ -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 @ 97e1cf69
from modules import extra_networks, shared, extra_networks
from modules import extra_networks, shared
from modules.hypernetworks import hypernetwork
......
modules/extras.py
View file @ 97e1cf69
......@@ -136,14 +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')
......@@ -242,9 +242,11 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
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,
......@@ -262,15 +264,17 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
}
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
sd_merge_models = {}
def add_model_metadata(checkpoint_info):
checkpoint_info.calculate_shorthash()
metadata["sd_merge_models"][checkpoint_info.sha256] = {
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)
}
metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
add_model_metadata(primary_model_info)
if secondary_model_info:
......@@ -278,7 +282,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
if tertiary_model_info:
add_model_metadata(tertiary_model_info)
metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
metadata["sd_merge_models"] = json.dumps(sd_merge_models)
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
......
modules/generation_parameters_copypaste.py
View file @ 97e1cf69
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():
......@@ -251,7 +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
......@@ -312,6 +308,8 @@ 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'),
]
......
modules/gfpgan_model.py
View file @ 97e1cf69
......@@ -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/hypernetworks/hypernetwork.py
View file @ 97e1cf69
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
......@@ -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:
......
modules/hypernetworks/ui.py
View file @ 97e1cf69
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 @ 97e1cf69
......@@ -13,17 +13,24 @@ import numpy as np
import piexif
import piexif.helper
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
from fonts.ttf import Roboto
import string
import json
import hashlib
from modules import sd_samplers, shared, script_callbacks, errors
from modules.shared import opts, cmd_opts
from modules.paths_internal import roboto_ttf_file
from modules.shared import opts
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
def get_font(fontsize: int):
try:
return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
except Exception:
return ImageFont.truetype(roboto_ttf_file, fontsize)
def image_grid(imgs, batch_size=1, rows=None):
if rows is None:
if opts.n_rows > 0:
......@@ -142,14 +149,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
lines.append(word)
return lines
def get_font(fontsize):
try:
return ImageFont.truetype(opts.font or Roboto, fontsize)
except Exception:
return ImageFont.truetype(Roboto, fontsize)
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
for i, line in enumerate(lines):
for line in lines:
fnt = initial_fnt
fontsize = initial_fontsize
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
......@@ -409,13 +410,13 @@ class FilenameGenerator:
time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
try:
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
except pytz.exceptions.UnknownTimeZoneError as _:
except pytz.exceptions.UnknownTimeZoneError:
time_zone = None
time_zone_time = time_datetime.astimezone(time_zone)
try:
formatted_time = time_zone_time.strftime(time_format)
except (ValueError, TypeError) as _:
except (ValueError, TypeError):
formatted_time = time_zone_time.strftime(self.default_time_format)
return sanitize_filename_part(formatted_time, replace_spaces=False)
......@@ -472,15 +473,52 @@ def get_next_sequence_number(path, basename):
prefix_length = len(basename)
for p in os.listdir(path):
if p.startswith(basename):
l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
try:
result = max(int(l[0]), result)
result = max(int(parts[0]), result)
except ValueError:
pass
return result + 1
def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None):
if extension is None:
extension = os.path.splitext(filename)[1]
image_format = Image.registered_extensions()[extension]
existing_pnginfo = existing_pnginfo or {}
if opts.enable_pnginfo:
existing_pnginfo['parameters'] = geninfo
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
pnginfo_data.add_text(k, str(v))
image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
elif extension.lower() in (".jpg", ".jpeg", ".webp"):
if image.mode == 'RGBA':
image = image.convert("RGB")
elif image.mode == 'I;16':
image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
if opts.enable_pnginfo and geninfo is not None:
exif_bytes = piexif.dump({
"Exif": {
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
},
})
piexif.insert(exif_bytes, filename)
else:
image.save(filename, format=image_format, quality=opts.jpeg_quality)
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
"""Save an image.
......@@ -565,38 +603,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
info = params.pnginfo.get(pnginfo_section_name, None)
def _atomically_save_image(image_to_save, filename_without_extension, extension):
# save image with .tmp extension to avoid race condition when another process detects new image in the directory
"""
save image with .tmp extension to avoid race condition when another process detects new image in the directory
"""
temp_file_path = f"{filename_without_extension}.tmp"
image_format = Image.registered_extensions()[extension]
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
if opts.enable_pnginfo:
for k, v in params.pnginfo.items():
pnginfo_data.add_text(k, str(v))
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
elif extension.lower() in (".jpg", ".jpeg", ".webp"):
if image_to_save.mode == 'RGBA':
image_to_save = image_to_save.convert("RGB")
elif image_to_save.mode == 'I;16':
image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
if opts.enable_pnginfo and info is not None:
exif_bytes = piexif.dump({
"Exif": {
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
},
})
piexif.insert(exif_bytes, temp_file_path)
else:
image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
# atomically rename the file with correct extension
os.replace(temp_file_path, filename_without_extension + extension)
fullfn_without_extension, extension = os.path.splitext(params.filename)
......
modules/img2img.py
View file @ 97e1cf69
import math
import os
import sys
import traceback
import numpy as np
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
......@@ -59,7 +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
......
modules/interrogate.py
View file @ 97e1cf69
......@@ -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
......@@ -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)
......@@ -208,8 +207,8 @@ 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})"
......
modules/mac_specific.py
View file @ 97e1cf69
import torch
import platform
from modules import paths
from modules.sd_hijack_utils import CondFunc
from packaging import version
......
modules/modelloader.py
View file @ 97e1cf69
import glob
import os
import shutil
import importlib
......@@ -40,7 +39,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
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]):
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)
......@@ -108,12 +107,12 @@ 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
......@@ -127,7 +126,7 @@ def load_upscalers():
full_model = f"modules.{model_name}_model"
try:
importlib.import_module(full_model)
except:
except Exception:
pass
datas = []
......
modules/models/diffusion/ddpm_edit.py
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modules/models/diffusion/uni_pc/__init__.py
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from .sampler import UniPCSampler
from .sampler import UniPCSampler # noqa: F401
modules/models/diffusion/uni_pc/sampler.py
View file @ 97e1cf69
......@@ -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
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modules/ngrok.py
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from pyngrok import ngrok, conf, exception
import ngrok
def connect(token, port, region):
# Connect to ngrok for ingress
def connect(token, port, options):
account = None
if token is None:
token = 'None'
......@@ -10,28 +11,19 @@ def connect(token, port, region):
token, username, password = token.split(':', 2)
account = f"{username}:{password}"
config = conf.PyngrokConfig(
auth_token=token, region=region
)
# For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
if not options.get('authtoken_from_env'):
options['authtoken'] = token
if account:
options['basic_auth'] = account
if not options.get('session_metadata'):
options['session_metadata'] = 'stable-diffusion-webui'
# Guard for existing tunnels
existing = ngrok.get_tunnels(pyngrok_config=config)
if existing:
for established in existing:
# Extra configuration in the case that the user is also using ngrok for other tunnels
if established.config['addr'][-4:] == str(port):
public_url = existing[0].public_url
print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
'You can use this link after the launch is complete.')
return
try:
if account is None:
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
else:
public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
except exception.PyngrokNgrokError:
print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url()
except Exception as e:
print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n'
f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
else:
print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
......
modules/paths.py
View file @ 97e1cf69
import os
import sys
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir # noqa: F401
import modules.safe
import modules.safe # noqa: F401
# data_path = cmd_opts_pre.data
......
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modules/sd_models_config.py
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import re
import os
import torch
......
modules/sd_samplers.py
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