mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 06:28:12 +03:00
Merge branch 'dev' into master
This commit is contained in:
AUTOMATIC1111
GitHub
commit
97e1cf69c0
4
.eslintignore
Normal file
4
.eslintignore
Normal file
@ -0,0 +1,4 @@
|
||||
extensions
|
||||
extensions-disabled
|
||||
repositories
|
||||
venv
|
||||
89
.eslintrc.js
Normal file
89
.eslintrc.js
Normal file
@ -0,0 +1,89 @@
|
||||
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",
|
||||
}
|
||||
};
|
||||
21
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
21
.github/ISSUE_TEMPLATE/bug_report.yml
vendored
@ -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:
|
||||
|
||||
49
.github/workflows/on_pull_request.yaml
vendored
49
.github/workflows/on_pull_request.yaml
vendored
@ -1,39 +1,34 @@
|
||||
# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
|
||||
name: Run Linting/Formatting on Pull Requests
|
||||
|
||||
on:
|
||||
- push
|
||||
- pull_request
|
||||
# See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
|
||||
# if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
|
||||
# pull_request:
|
||||
# branches:
|
||||
# - master
|
||||
# branches-ignore:
|
||||
# - development
|
||||
|
||||
jobs:
|
||||
lint:
|
||||
lint-python:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout Code
|
||||
uses: actions/checkout@v3
|
||||
- name: Set up Python 3.10
|
||||
uses: actions/setup-python@v4
|
||||
- uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: 3.10.6
|
||||
cache: pip
|
||||
cache-dependency-path: |
|
||||
**/requirements*txt
|
||||
- name: Install PyLint
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install pylint
|
||||
# This lets PyLint check to see if it can resolve imports
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
|
||||
python launch.py
|
||||
- name: Analysing the code with pylint
|
||||
run: |
|
||||
pylint $(git ls-files '*.py')
|
||||
python-version: 3.11
|
||||
# NB: there's no cache: pip here since we're not installing anything
|
||||
# from the requirements.txt file(s) in the repository; it's faster
|
||||
# not to have GHA download an (at the time of writing) 4 GB cache
|
||||
# of PyTorch and other dependencies.
|
||||
- name: Install Ruff
|
||||
run: pip install ruff==0.0.265
|
||||
- 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
|
||||
|
||||
6
.github/workflows/run_tests.yaml
vendored
6
.github/workflows/run_tests.yaml
vendored
@ -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()
|
||||
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@ -34,3 +34,5 @@ notification.mp3
|
||||
/test/stderr.txt
|
||||
/cache.json*
|
||||
/config_states/
|
||||
/node_modules
|
||||
/package-lock.json
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -1,16 +1,21 @@
|
||||
# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
|
||||
# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
|
||||
# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
import torch.nn.functional as F
|
||||
from contextlib import contextmanager
|
||||
|
||||
from 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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
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
|
||||
|
||||
|
||||
@ -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"]}),
|
||||
}))
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
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
|
||||
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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 = [];
|
||||
@ -27,7 +27,7 @@ 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));
|
||||
|
||||
@ -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>
|
||||
|
||||
@ -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>
|
||||
@ -6,10 +6,10 @@ let arFrameTimeout = setTimeout(function(){},0);
|
||||
function dimensionChange(e, is_width, is_height) {
|
||||
|
||||
if (is_width) {
|
||||
currentWidth = e.target.value*1.0
|
||||
currentWidth = e.target.value * 1.0;
|
||||
}
|
||||
if (is_height) {
|
||||
currentHeight = e.target.value*1.0
|
||||
currentHeight = e.target.value * 1.0;
|
||||
}
|
||||
|
||||
var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
|
||||
@ -20,7 +20,7 @@ function dimensionChange(e, is_width, is_height){
|
||||
|
||||
var targetElement = null;
|
||||
|
||||
var tabIndex = get_tab_index('mode_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
|
||||
@ -36,33 +36,33 @@ function dimensionChange(e, is_width, is_height){
|
||||
|
||||
var arPreviewRect = gradioApp().querySelector('#imageARPreview');
|
||||
if (!arPreviewRect) {
|
||||
arPreviewRect = document.createElement('div')
|
||||
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';
|
||||
@ -92,20 +92,22 @@ onUiUpdate(function(){
|
||||
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"
|
||||
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')
|
||||
e.addEventListener('input', function(e) {
|
||||
dimensionChange(e, is_width, is_height);
|
||||
});
|
||||
e.classList.add('scrollwatch');
|
||||
}
|
||||
if (is_width) {
|
||||
currentWidth = e.value*1.0
|
||||
currentWidth = e.value * 1.0;
|
||||
}
|
||||
if (is_height) {
|
||||
currentHeight = e.value*1.0
|
||||
currentHeight = e.value * 1.0;
|
||||
}
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
@ -1,48 +1,48 @@
|
||||
|
||||
contextMenuInit = function(){
|
||||
var contextMenuInit = function() {
|
||||
let eventListenerApplied = false;
|
||||
let menuSpecs = new Map();
|
||||
|
||||
const uid = function() {
|
||||
return Date.now().toString(36) + Math.random().toString(36).substring(2);
|
||||
}
|
||||
};
|
||||
|
||||
function showContextMenu(event, element, menuEntries) {
|
||||
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
|
||||
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
|
||||
|
||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
||||
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||
if (oldMenu) {
|
||||
oldMenu.remove()
|
||||
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']
|
||||
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;
|
||||
@ -62,29 +62,35 @@ contextMenuInit = function(){
|
||||
|
||||
function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
|
||||
|
||||
var currentItems = menuSpecs.get(targetElementSelector)
|
||||
var currentItems = menuSpecs.get(targetElementSelector);
|
||||
|
||||
if (!currentItems) {
|
||||
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) {
|
||||
menuSpecs.forEach(function(v) {
|
||||
let index = -1
|
||||
v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
|
||||
let index = -1;
|
||||
v.forEach(function(e, ei) {
|
||||
if (e['id'] == uid) {
|
||||
index = ei;
|
||||
}
|
||||
});
|
||||
if (index >= 0) {
|
||||
v.splice(index, 1);
|
||||
}
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
function addContextMenuEventListener() {
|
||||
@ -93,37 +99,37 @@ contextMenuInit = function(){
|
||||
}
|
||||
gradioApp().addEventListener("click", function(e) {
|
||||
if (!e.isTrusted) {
|
||||
return
|
||||
return;
|
||||
}
|
||||
|
||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
||||
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||
if (oldMenu) {
|
||||
oldMenu.remove()
|
||||
oldMenu.remove();
|
||||
}
|
||||
});
|
||||
gradioApp().addEventListener("contextmenu", function(e) {
|
||||
let oldMenu = gradioApp().querySelector('#context-menu')
|
||||
let oldMenu = gradioApp().querySelector('#context-menu');
|
||||
if (oldMenu) {
|
||||
oldMenu.remove()
|
||||
oldMenu.remove();
|
||||
}
|
||||
menuSpecs.forEach(function(v, k) {
|
||||
if (e.composedPath()[0].matches(k)) {
|
||||
showContextMenu(e,e.composedPath()[0],v)
|
||||
e.preventDefault()
|
||||
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() {
|
||||
//Start example Context Menu Items
|
||||
@ -133,34 +139,34 @@ addContextMenuEventListener = initResponse[2];
|
||||
if (!interruptbutton.offsetParent) {
|
||||
genbutton.click();
|
||||
}
|
||||
clearInterval(window.generateOnRepeatInterval)
|
||||
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');
|
||||
})
|
||||
});
|
||||
|
||||
let cancelGenerateForever = function() {
|
||||
clearInterval(window.generateOnRepeatInterval)
|
||||
}
|
||||
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()
|
||||
addContextMenuEventListener();
|
||||
});
|
||||
|
||||
5
javascript/dragdrop.js
vendored
5
javascript/dragdrop.js
vendored
@ -39,7 +39,7 @@ function dropReplaceImage( imgWrap, files ) {
|
||||
status: response.status,
|
||||
statusText: response.statusText,
|
||||
headers: response.headers
|
||||
})
|
||||
});
|
||||
}
|
||||
return response;
|
||||
};
|
||||
@ -93,5 +93,6 @@ window.addEventListener('paste', e => {
|
||||
firstFreeImageField ?
|
||||
firstFreeImageField :
|
||||
visibleImageFields[visibleImageFields.length - 1]
|
||||
, files );
|
||||
, files
|
||||
);
|
||||
});
|
||||
|
||||
@ -3,8 +3,8 @@ function keyupEditAttention(event){
|
||||
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;
|
||||
@ -69,12 +69,12 @@ 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
|
||||
closeCharacter = '>';
|
||||
delta = opts.keyedit_precision_extra;
|
||||
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
|
||||
|
||||
// do not include spaces at the end
|
||||
@ -82,7 +82,7 @@ function keyupEditAttention(event){
|
||||
selectionEnd -= 1;
|
||||
}
|
||||
if (selectionStart == selectionEnd) {
|
||||
return
|
||||
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) => {
|
||||
|
||||
@ -1,51 +1,54 @@
|
||||
|
||||
function extensions_apply(_disabled_list, _update_list, disable_all) {
|
||||
var disable = []
|
||||
var update = []
|
||||
var disable = [];
|
||||
var update = [];
|
||||
|
||||
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
|
||||
if(x.name.startsWith("enable_") && ! x.checked)
|
||||
disable.push(x.name.substring(7))
|
||||
if (x.name.startsWith("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 = []
|
||||
var disable = [];
|
||||
|
||||
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("enable_") && !x.checked) {
|
||||
disable.push(x.name.substring(7));
|
||||
}
|
||||
});
|
||||
|
||||
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
|
||||
x.innerHTML = "Loading..."
|
||||
})
|
||||
x.innerHTML = "Loading...";
|
||||
});
|
||||
|
||||
|
||||
var id = randomId()
|
||||
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..."
|
||||
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) {
|
||||
@ -64,8 +67,8 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
|
||||
if (confirmed) {
|
||||
restart_reload();
|
||||
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
|
||||
x.innerHTML = "Loading..."
|
||||
})
|
||||
x.innerHTML = "Loading...";
|
||||
});
|
||||
}
|
||||
return [confirmed, config_state_name, config_restore_type];
|
||||
}
|
||||
|
||||
@ -1,30 +1,30 @@
|
||||
function setupExtraNetworksForTab(tabname) {
|
||||
gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
|
||||
gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
|
||||
|
||||
var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
|
||||
var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
|
||||
var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
|
||||
var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
|
||||
var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
|
||||
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
|
||||
|
||||
search.classList.add('search')
|
||||
tabs.appendChild(search)
|
||||
tabs.appendChild(refresh)
|
||||
search.classList.add('search');
|
||||
tabs.appendChild(search);
|
||||
tabs.appendChild(refresh);
|
||||
|
||||
var applyFilter = function() {
|
||||
var searchTerm = search.value.toLowerCase()
|
||||
var searchTerm = search.value.toLowerCase();
|
||||
|
||||
gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
|
||||
var searchOnly = elem.querySelector('.search_only')
|
||||
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
|
||||
var searchOnly = elem.querySelector('.search_only');
|
||||
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase();
|
||||
|
||||
var visible = text.indexOf(searchTerm) != -1
|
||||
var visible = text.indexOf(searchTerm) != -1;
|
||||
|
||||
if (searchOnly && searchTerm.length < 4) {
|
||||
visible = false
|
||||
visible = false;
|
||||
}
|
||||
|
||||
elem.style.display = visible ? "" : "none"
|
||||
})
|
||||
}
|
||||
elem.style.display = visible ? "" : "none";
|
||||
});
|
||||
};
|
||||
|
||||
search.addEventListener("input", applyFilter);
|
||||
applyFilter();
|
||||
@ -36,18 +36,18 @@ function applyExtraNetworkFilter(tabname){
|
||||
setTimeout(extraNetworksApplyFilter[tabname], 1);
|
||||
}
|
||||
|
||||
var extraNetworksApplyFilter = {}
|
||||
var extraNetworksApplyFilter = {};
|
||||
var activePromptTextarea = {};
|
||||
|
||||
function setupExtraNetworks() {
|
||||
setupExtraNetworksForTab('txt2img')
|
||||
setupExtraNetworksForTab('img2img')
|
||||
setupExtraNetworksForTab('txt2img');
|
||||
setupExtraNetworksForTab('img2img');
|
||||
|
||||
function registerPrompt(tabname, id) {
|
||||
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
|
||||
|
||||
if (!activePromptTextarea[tabname]) {
|
||||
activePromptTextarea[tabname] = textarea
|
||||
activePromptTextarea[tabname] = textarea;
|
||||
}
|
||||
|
||||
textarea.addEventListener("focus", function() {
|
||||
@ -55,90 +55,105 @@ function setupExtraNetworks(){
|
||||
});
|
||||
}
|
||||
|
||||
registerPrompt('txt2img', 'txt2img_prompt')
|
||||
registerPrompt('txt2img', 'txt2img_neg_prompt')
|
||||
registerPrompt('img2img', 'img2img_prompt')
|
||||
registerPrompt('img2img', 'img2img_neg_prompt')
|
||||
registerPrompt('txt2img', 'txt2img_prompt');
|
||||
registerPrompt('txt2img', 'txt2img_neg_prompt');
|
||||
registerPrompt('img2img', 'img2img_prompt');
|
||||
registerPrompt('img2img', 'img2img_neg_prompt');
|
||||
}
|
||||
|
||||
onUiLoaded(setupExtraNetworks)
|
||||
onUiLoaded(setupExtraNetworks);
|
||||
|
||||
var re_extranet = /<([^:]+:[^:]+):[\d\.]+>/;
|
||||
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
|
||||
var re_extranet = /<([^:]+:[^:]+):[\d.]+>/;
|
||||
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
|
||||
|
||||
function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
|
||||
var m = text.match(re_extranet)
|
||||
if(! m) return false
|
||||
|
||||
var partToSearch = m[1]
|
||||
var replaced = false
|
||||
var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
|
||||
var m = text.match(re_extranet);
|
||||
var replaced = false;
|
||||
var newTextareaText;
|
||||
if (m) {
|
||||
var partToSearch = m[1];
|
||||
newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found) {
|
||||
m = found.match(re_extranet);
|
||||
if (m[1] == partToSearch) {
|
||||
replaced = true;
|
||||
return ""
|
||||
return "";
|
||||
}
|
||||
return found;
|
||||
})
|
||||
});
|
||||
} else {
|
||||
newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
|
||||
if (found == text) {
|
||||
replaced = true;
|
||||
return "";
|
||||
}
|
||||
return found;
|
||||
});
|
||||
}
|
||||
|
||||
if (replaced) {
|
||||
textarea.value = newTextareaText
|
||||
textarea.value = newTextareaText;
|
||||
return true;
|
||||
}
|
||||
|
||||
return false
|
||||
return false;
|
||||
}
|
||||
|
||||
function cardClicked(tabname, textToAdd, allowNegativePrompt) {
|
||||
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
|
||||
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
|
||||
|
||||
if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
|
||||
textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
|
||||
textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
|
||||
}
|
||||
|
||||
updateInput(textarea)
|
||||
updateInput(textarea);
|
||||
}
|
||||
|
||||
function saveCardPreview(event, tabname, filename) {
|
||||
var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea')
|
||||
var button = gradioApp().getElementById(tabname + '_save_preview')
|
||||
var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea');
|
||||
var button = gradioApp().getElementById(tabname + '_save_preview');
|
||||
|
||||
textarea.value = filename
|
||||
updateInput(textarea)
|
||||
textarea.value = filename;
|
||||
updateInput(textarea);
|
||||
|
||||
button.click()
|
||||
button.click();
|
||||
|
||||
event.stopPropagation()
|
||||
event.preventDefault()
|
||||
event.stopPropagation();
|
||||
event.preventDefault();
|
||||
}
|
||||
|
||||
function extraNetworksSearchButton(tabs_id, event) {
|
||||
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
|
||||
var button = event.target
|
||||
var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
|
||||
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea');
|
||||
var button = event.target;
|
||||
var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
|
||||
|
||||
searchTextarea.value = text
|
||||
updateInput(searchTextarea)
|
||||
searchTextarea.value = text;
|
||||
updateInput(searchTextarea);
|
||||
}
|
||||
|
||||
var globalPopup = null;
|
||||
var globalPopupInner = null;
|
||||
function popup(contents) {
|
||||
if (!globalPopup) {
|
||||
globalPopup = document.createElement('div')
|
||||
globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
|
||||
globalPopup = document.createElement('div');
|
||||
globalPopup.onclick = function() {
|
||||
globalPopup.style.display = "none";
|
||||
};
|
||||
globalPopup.classList.add('global-popup');
|
||||
|
||||
var close = document.createElement('div')
|
||||
var close = document.createElement('div');
|
||||
close.classList.add('global-popup-close');
|
||||
close.onclick = function(){ globalPopup.style.display = "none"; };
|
||||
close.onclick = function() {
|
||||
globalPopup.style.display = "none";
|
||||
};
|
||||
close.title = "Close";
|
||||
globalPopup.appendChild(close)
|
||||
globalPopup.appendChild(close);
|
||||
|
||||
globalPopupInner = document.createElement('div')
|
||||
globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
|
||||
globalPopupInner = document.createElement('div');
|
||||
globalPopupInner.onclick = function(event) {
|
||||
event.stopPropagation(); return false;
|
||||
};
|
||||
globalPopupInner.classList.add('global-popup-inner');
|
||||
globalPopup.appendChild(globalPopupInner)
|
||||
globalPopup.appendChild(globalPopupInner);
|
||||
|
||||
gradioApp().appendChild(globalPopup);
|
||||
}
|
||||
@ -150,7 +165,7 @@ function popup(contents){
|
||||
}
|
||||
|
||||
function extraNetworksShowMetadata(text) {
|
||||
var elem = document.createElement('pre')
|
||||
var elem = document.createElement('pre');
|
||||
elem.classList.add('popup-metadata');
|
||||
elem.textContent = text;
|
||||
|
||||
@ -159,7 +174,9 @@ function extraNetworksShowMetadata(text){
|
||||
|
||||
function requestGet(url, data, handler, errorHandler) {
|
||||
var xhr = new XMLHttpRequest();
|
||||
var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
|
||||
var args = Object.keys(data).map(function(k) {
|
||||
return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]);
|
||||
}).join('&');
|
||||
xhr.open("GET", url + "?" + args, true);
|
||||
|
||||
xhr.onreadystatechange = function() {
|
||||
@ -167,13 +184,13 @@ function requestGet(url, data, handler, errorHandler){
|
||||
if (xhr.status === 200) {
|
||||
try {
|
||||
var js = JSON.parse(xhr.responseText);
|
||||
handler(js)
|
||||
handler(js);
|
||||
} catch (error) {
|
||||
console.error(error);
|
||||
errorHandler()
|
||||
errorHandler();
|
||||
}
|
||||
} else {
|
||||
errorHandler()
|
||||
errorHandler();
|
||||
}
|
||||
}
|
||||
};
|
||||
@ -182,15 +199,17 @@ function requestGet(url, data, handler, errorHandler){
|
||||
}
|
||||
|
||||
function extraNetworksRequestMetadata(event, extraPage, cardName) {
|
||||
var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
|
||||
var showError = function() {
|
||||
extraNetworksShowMetadata("there was an error getting metadata");
|
||||
};
|
||||
|
||||
requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
|
||||
requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) {
|
||||
if (data && data.metadata) {
|
||||
extraNetworksShowMetadata(data.metadata)
|
||||
extraNetworksShowMetadata(data.metadata);
|
||||
} else {
|
||||
showError()
|
||||
showError();
|
||||
}
|
||||
}, showError)
|
||||
}, showError);
|
||||
|
||||
event.stopPropagation()
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
@ -3,31 +3,33 @@
|
||||
let txt2img_gallery, img2img_gallery, modal = undefined;
|
||||
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')
|
||||
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')
|
||||
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;
|
||||
}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
// 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.",
|
||||
@ -113,11 +114,9 @@ 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];
|
||||
@ -138,13 +137,32 @@ onUiUpdate(function(){
|
||||
if (tooltip) {
|
||||
span.title = tooltip;
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
gradioApp().querySelectorAll('select').forEach(function(select){
|
||||
function updateTooltipForSelect(select) {
|
||||
if (select.onchange != null) return;
|
||||
|
||||
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);
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
@ -1,18 +1,18 @@
|
||||
|
||||
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) {
|
||||
function setInactive(elem, inactive) {
|
||||
elem.classList.toggle('inactive', !!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];
|
||||
}
|
||||
|
||||
@ -5,7 +5,6 @@
|
||||
function imageMaskResize() {
|
||||
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
|
||||
if (!canvases.length) {
|
||||
canvases_fixed = false; // TODO: this is unused..?
|
||||
window.removeEventListener('resize', imageMaskResize);
|
||||
return;
|
||||
}
|
||||
|
||||
@ -5,24 +5,24 @@ function closeModal() {
|
||||
|
||||
function showModal(event) {
|
||||
const source = event.target || event.srcElement;
|
||||
const modalImage = gradioApp().getElementById("modalImage")
|
||||
const lb = gradioApp().getElementById("lightboxModal")
|
||||
modalImage.src = source.src
|
||||
const modalImage = gradioApp().getElementById("modalImage");
|
||||
const lb = gradioApp().getElementById("lightboxModal");
|
||||
modalImage.src = source.src;
|
||||
if (modalImage.style.display === 'none') {
|
||||
lb.style.setProperty('background-image', 'url(' + source.src + ')');
|
||||
}
|
||||
lb.style.display = "flex";
|
||||
lb.focus()
|
||||
lb.focus();
|
||||
|
||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
|
||||
const tabImg2Img = gradioApp().getElementById("tab_img2img")
|
||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
|
||||
const tabImg2Img = gradioApp().getElementById("tab_img2img");
|
||||
// show the save button in modal only on txt2img or img2img tabs
|
||||
if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
|
||||
gradioApp().getElementById("modal_save").style.display = "inline"
|
||||
gradioApp().getElementById("modal_save").style.display = "inline";
|
||||
} else {
|
||||
gradioApp().getElementById("modal_save").style.display = "none"
|
||||
gradioApp().getElementById("modal_save").style.display = "none";
|
||||
}
|
||||
event.stopPropagation()
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
function negmod(n, m) {
|
||||
@ -30,14 +30,15 @@ function negmod(n, m) {
|
||||
}
|
||||
|
||||
function updateOnBackgroundChange() {
|
||||
const modalImage = gradioApp().getElementById("modalImage")
|
||||
const modalImage = gradioApp().getElementById("modalImage");
|
||||
if (modalImage && modalImage.offsetParent) {
|
||||
let currentButton = selected_gallery_button();
|
||||
|
||||
if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
|
||||
modalImage.src = currentButton.children[0].src;
|
||||
if (modalImage.style.display === 'none') {
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
||||
const modal = gradioApp().getElementById("lightboxModal");
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -49,68 +50,68 @@ function modalImageSwitch(offset) {
|
||||
if (galleryButtons.length > 1) {
|
||||
var currentButton = selected_gallery_button();
|
||||
|
||||
var result = -1
|
||||
var result = -1;
|
||||
galleryButtons.forEach(function(v, i) {
|
||||
if (v == currentButton) {
|
||||
result = i
|
||||
result = i;
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
if (result != -1) {
|
||||
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
|
||||
nextButton.click()
|
||||
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
|
||||
nextButton.click();
|
||||
const modalImage = gradioApp().getElementById("modalImage");
|
||||
const modal = gradioApp().getElementById("lightboxModal");
|
||||
modalImage.src = nextButton.children[0].src;
|
||||
if (modalImage.style.display === 'none') {
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||
}
|
||||
setTimeout(function() {
|
||||
modal.focus()
|
||||
}, 10)
|
||||
modal.focus();
|
||||
}, 10);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function saveImage() {
|
||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
|
||||
const tabImg2Img = gradioApp().getElementById("tab_img2img")
|
||||
const saveTxt2Img = "save_txt2img"
|
||||
const saveImg2Img = "save_img2img"
|
||||
const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
|
||||
const tabImg2Img = gradioApp().getElementById("tab_img2img");
|
||||
const saveTxt2Img = "save_txt2img";
|
||||
const saveImg2Img = "save_img2img";
|
||||
if (tabTxt2Img.style.display != "none") {
|
||||
gradioApp().getElementById(saveTxt2Img).click()
|
||||
gradioApp().getElementById(saveTxt2Img).click();
|
||||
} else if (tabImg2Img.style.display != "none") {
|
||||
gradioApp().getElementById(saveImg2Img).click()
|
||||
gradioApp().getElementById(saveImg2Img).click();
|
||||
} else {
|
||||
console.error("missing implementation for saving modal of this type")
|
||||
console.error("missing implementation for saving modal of this type");
|
||||
}
|
||||
}
|
||||
|
||||
function modalSaveImage(event) {
|
||||
saveImage()
|
||||
event.stopPropagation()
|
||||
saveImage();
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
function modalNextImage(event) {
|
||||
modalImageSwitch(1)
|
||||
event.stopPropagation()
|
||||
modalImageSwitch(1);
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
function modalPrevImage(event) {
|
||||
modalImageSwitch(-1)
|
||||
event.stopPropagation()
|
||||
modalImageSwitch(-1);
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
function modalKeyHandler(event) {
|
||||
switch (event.key) {
|
||||
case "s":
|
||||
saveImage()
|
||||
saveImage();
|
||||
break;
|
||||
case "ArrowLeft":
|
||||
modalPrevImage(event)
|
||||
modalPrevImage(event);
|
||||
break;
|
||||
case "ArrowRight":
|
||||
modalNextImage(event)
|
||||
modalNextImage(event);
|
||||
break;
|
||||
case "Escape":
|
||||
closeModal();
|
||||
@ -119,26 +120,27 @@ function modalKeyHandler(event) {
|
||||
}
|
||||
|
||||
function setupImageForLightbox(e) {
|
||||
if (e.dataset.modded)
|
||||
if (e.dataset.modded) {
|
||||
return;
|
||||
}
|
||||
|
||||
e.dataset.modded = true;
|
||||
e.style.cursor='pointer'
|
||||
e.style.userSelect='none'
|
||||
e.style.cursor = 'pointer';
|
||||
e.style.userSelect = 'none';
|
||||
|
||||
var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
|
||||
var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
|
||||
|
||||
// For Firefox, listening on click first switched to next image then shows the lightbox.
|
||||
// If you know how to fix this without switching to mousedown event, please.
|
||||
// For other browsers the event is click to make it possiblr to drag picture.
|
||||
var event = isFirefox ? 'mousedown' : 'click'
|
||||
var event = isFirefox ? 'mousedown' : 'click';
|
||||
|
||||
e.addEventListener(event, function(evt) {
|
||||
if (!opts.js_modal_lightbox || evt.button != 0) return;
|
||||
|
||||
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
|
||||
evt.preventDefault()
|
||||
showModal(evt)
|
||||
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);
|
||||
evt.preventDefault();
|
||||
showModal(evt);
|
||||
}, true);
|
||||
|
||||
}
|
||||
@ -149,8 +151,8 @@ function modalZoomSet(modalImage, enable) {
|
||||
|
||||
function modalZoomToggle(event) {
|
||||
var modalImage = gradioApp().getElementById("modalImage");
|
||||
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
|
||||
event.stopPropagation()
|
||||
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
function modalTileImageToggle(event) {
|
||||
@ -159,93 +161,87 @@ function modalTileImageToggle(event) {
|
||||
const isTiling = modalImage.style.display === 'none';
|
||||
if (isTiling) {
|
||||
modalImage.style.display = 'block';
|
||||
modal.style.setProperty('background-image', 'none')
|
||||
modal.style.setProperty('background-image', 'none');
|
||||
} else {
|
||||
modalImage.style.display = 'none';
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`)
|
||||
modal.style.setProperty('background-image', `url(${modalImage.src})`);
|
||||
}
|
||||
|
||||
event.stopPropagation()
|
||||
}
|
||||
|
||||
function galleryImageHandler(e) {
|
||||
//if (e && e.parentElement.tagName == 'BUTTON') {
|
||||
e.onclick = showGalleryImage;
|
||||
//}
|
||||
event.stopPropagation();
|
||||
}
|
||||
|
||||
onUiUpdate(function() {
|
||||
var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
|
||||
var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img');
|
||||
if (fullImg_preview != null) {
|
||||
fullImg_preview.forEach(setupImageForLightbox);
|
||||
}
|
||||
updateOnBackgroundChange();
|
||||
})
|
||||
});
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
//const modalFragment = document.createDocumentFragment();
|
||||
const modal = document.createElement('div')
|
||||
const modal = document.createElement('div');
|
||||
modal.onclick = closeModal;
|
||||
modal.id = "lightboxModal";
|
||||
modal.tabIndex = 0
|
||||
modal.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.tabIndex = 0;
|
||||
modal.addEventListener('keydown', modalKeyHandler, true);
|
||||
|
||||
const modalControls = document.createElement('div')
|
||||
const modalControls = document.createElement('div');
|
||||
modalControls.className = 'modalControls gradio-container';
|
||||
modal.append(modalControls);
|
||||
|
||||
const modalZoom = document.createElement('span')
|
||||
const modalZoom = document.createElement('span');
|
||||
modalZoom.className = 'modalZoom cursor';
|
||||
modalZoom.innerHTML = '⤡'
|
||||
modalZoom.addEventListener('click', modalZoomToggle, true)
|
||||
modalZoom.innerHTML = '⤡';
|
||||
modalZoom.addEventListener('click', modalZoomToggle, true);
|
||||
modalZoom.title = "Toggle zoomed view";
|
||||
modalControls.appendChild(modalZoom)
|
||||
modalControls.appendChild(modalZoom);
|
||||
|
||||
const modalTileImage = document.createElement('span')
|
||||
const modalTileImage = document.createElement('span');
|
||||
modalTileImage.className = 'modalTileImage cursor';
|
||||
modalTileImage.innerHTML = '⊞'
|
||||
modalTileImage.addEventListener('click', modalTileImageToggle, true)
|
||||
modalTileImage.innerHTML = '⊞';
|
||||
modalTileImage.addEventListener('click', modalTileImageToggle, true);
|
||||
modalTileImage.title = "Preview tiling";
|
||||
modalControls.appendChild(modalTileImage)
|
||||
modalControls.appendChild(modalTileImage);
|
||||
|
||||
const modalSave = document.createElement("span")
|
||||
modalSave.className = "modalSave cursor"
|
||||
modalSave.id = "modal_save"
|
||||
modalSave.innerHTML = "🖫"
|
||||
modalSave.addEventListener("click", modalSaveImage, true)
|
||||
modalSave.title = "Save Image(s)"
|
||||
modalControls.appendChild(modalSave)
|
||||
const modalSave = document.createElement("span");
|
||||
modalSave.className = "modalSave cursor";
|
||||
modalSave.id = "modal_save";
|
||||
modalSave.innerHTML = "🖫";
|
||||
modalSave.addEventListener("click", modalSaveImage, true);
|
||||
modalSave.title = "Save Image(s)";
|
||||
modalControls.appendChild(modalSave);
|
||||
|
||||
const modalClose = document.createElement('span')
|
||||
const modalClose = document.createElement('span');
|
||||
modalClose.className = 'modalClose cursor';
|
||||
modalClose.innerHTML = '×'
|
||||
modalClose.innerHTML = '×';
|
||||
modalClose.onclick = closeModal;
|
||||
modalClose.title = "Close image viewer";
|
||||
modalControls.appendChild(modalClose)
|
||||
modalControls.appendChild(modalClose);
|
||||
|
||||
const modalImage = document.createElement('img')
|
||||
const modalImage = document.createElement('img');
|
||||
modalImage.id = 'modalImage';
|
||||
modalImage.onclick = closeModal;
|
||||
modalImage.tabIndex = 0
|
||||
modalImage.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.appendChild(modalImage)
|
||||
modalImage.tabIndex = 0;
|
||||
modalImage.addEventListener('keydown', modalKeyHandler, true);
|
||||
modal.appendChild(modalImage);
|
||||
|
||||
const modalPrev = document.createElement('a')
|
||||
const modalPrev = document.createElement('a');
|
||||
modalPrev.className = 'modalPrev';
|
||||
modalPrev.innerHTML = '❮'
|
||||
modalPrev.tabIndex = 0
|
||||
modalPrev.innerHTML = '❮';
|
||||
modalPrev.tabIndex = 0;
|
||||
modalPrev.addEventListener('click', modalPrevImage, true);
|
||||
modalPrev.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.appendChild(modalPrev)
|
||||
modalPrev.addEventListener('keydown', modalKeyHandler, true);
|
||||
modal.appendChild(modalPrev);
|
||||
|
||||
const modalNext = document.createElement('a')
|
||||
const modalNext = document.createElement('a');
|
||||
modalNext.className = 'modalNext';
|
||||
modalNext.innerHTML = '❯'
|
||||
modalNext.tabIndex = 0
|
||||
modalNext.innerHTML = '❯';
|
||||
modalNext.tabIndex = 0;
|
||||
modalNext.addEventListener('click', modalNextImage, true);
|
||||
modalNext.addEventListener('keydown', modalKeyHandler, true)
|
||||
modalNext.addEventListener('keydown', modalKeyHandler, true);
|
||||
|
||||
modal.appendChild(modalNext)
|
||||
modal.appendChild(modalNext);
|
||||
|
||||
try {
|
||||
gradioApp().appendChild(modal);
|
||||
|
||||
@ -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;
|
||||
}
|
||||
|
||||
@ -1,10 +1,9 @@
|
||||
|
||||
// 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',
|
||||
@ -17,13 +16,13 @@ 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;
|
||||
@ -31,7 +30,7 @@ function hasLocalization() {
|
||||
|
||||
function textNodesUnder(el) {
|
||||
var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
|
||||
while(n=walk.nextNode()) a.push(n);
|
||||
while ((n = walk.nextNode())) a.push(n);
|
||||
return a;
|
||||
}
|
||||
|
||||
@ -39,13 +38,13 @@ function canBeTranslated(node, text){
|
||||
if (!text) return false;
|
||||
if (!node.parentElement) return false;
|
||||
|
||||
var parentType = node.parentElement.nodeName
|
||||
var parentType = node.parentElement.nodeName;
|
||||
if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
|
||||
|
||||
if (parentType == 'OPTION' || parentType == 'SPAN') {
|
||||
var pnode = node
|
||||
var pnode = node;
|
||||
for (var level = 0; level < 4; level++) {
|
||||
pnode = pnode.parentElement
|
||||
pnode = pnode.parentElement;
|
||||
if (!pnode) break;
|
||||
|
||||
if (ignore_ids_for_localization[pnode.id] == parentType) return false;
|
||||
@ -54,58 +53,58 @@ function canBeTranslated(node, text){
|
||||
|
||||
if (re_num.test(text)) return false;
|
||||
if (re_emoji.test(text)) return false;
|
||||
return true
|
||||
return true;
|
||||
}
|
||||
|
||||
function getTranslation(text) {
|
||||
if(! text) return undefined
|
||||
if (!text) return undefined;
|
||||
|
||||
if (translated_lines[text] === undefined) {
|
||||
original_lines[text] = 1
|
||||
original_lines[text] = 1;
|
||||
}
|
||||
|
||||
tl = localization[text]
|
||||
var tl = localization[text];
|
||||
if (tl !== undefined) {
|
||||
translated_lines[tl] = 1
|
||||
translated_lines[tl] = 1;
|
||||
}
|
||||
|
||||
return tl
|
||||
return tl;
|
||||
}
|
||||
|
||||
function processTextNode(node) {
|
||||
var text = node.textContent.trim()
|
||||
var text = node.textContent.trim();
|
||||
|
||||
if(! canBeTranslated(node, text)) return
|
||||
if (!canBeTranslated(node, text)) return;
|
||||
|
||||
tl = getTranslation(text)
|
||||
var tl = getTranslation(text);
|
||||
if (tl !== undefined) {
|
||||
node.textContent = tl
|
||||
node.textContent = tl;
|
||||
}
|
||||
}
|
||||
|
||||
function processNode(node) {
|
||||
if (node.nodeType == 3) {
|
||||
processTextNode(node)
|
||||
return
|
||||
processTextNode(node);
|
||||
return;
|
||||
}
|
||||
|
||||
if (node.title) {
|
||||
tl = getTranslation(node.title)
|
||||
let tl = getTranslation(node.title);
|
||||
if (tl !== undefined) {
|
||||
node.title = tl
|
||||
node.title = tl;
|
||||
}
|
||||
}
|
||||
|
||||
if (node.placeholder) {
|
||||
tl = getTranslation(node.placeholder)
|
||||
let tl = getTranslation(node.placeholder);
|
||||
if (tl !== undefined) {
|
||||
node.placeholder = tl
|
||||
node.placeholder = tl;
|
||||
}
|
||||
}
|
||||
|
||||
textNodesUnder(node).forEach(function(node) {
|
||||
processTextNode(node)
|
||||
})
|
||||
processTextNode(node);
|
||||
});
|
||||
}
|
||||
|
||||
function dumpTranslations() {
|
||||
@ -115,7 +114,7 @@ function dumpTranslations(){
|
||||
// original_lines, so do that now.
|
||||
processNode(gradioApp());
|
||||
}
|
||||
var dumped = {}
|
||||
var dumped = {};
|
||||
if (localization.rtl) {
|
||||
dumped.rtl = true;
|
||||
}
|
||||
@ -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));
|
||||
@ -150,12 +149,12 @@ document.addEventListener("DOMContentLoaded", function () {
|
||||
onUiUpdate(function(m) {
|
||||
m.forEach(function(mutation) {
|
||||
mutation.addedNodes.forEach(function(node) {
|
||||
processNode(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});
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
@ -6,7 +6,7 @@ let notificationButton = null;
|
||||
|
||||
onUiUpdate(function() {
|
||||
if (notificationButton == null) {
|
||||
notificationButton = gradioApp().getElementById('request_notifications')
|
||||
notificationButton = gradioApp().getElementById('request_notifications');
|
||||
|
||||
if (notificationButton != null) {
|
||||
notificationButton.addEventListener('click', () => {
|
||||
|
||||
@ -17,13 +17,13 @@ function request(url, data, handler, errorHandler){
|
||||
if (xhr.status === 200) {
|
||||
try {
|
||||
var js = JSON.parse(xhr.responseText);
|
||||
handler(js)
|
||||
handler(js);
|
||||
} catch (error) {
|
||||
console.error(error);
|
||||
errorHandler()
|
||||
errorHandler();
|
||||
}
|
||||
} else {
|
||||
errorHandler()
|
||||
errorHandler();
|
||||
}
|
||||
}
|
||||
};
|
||||
@ -32,21 +32,21 @@ function request(url, data, handler, errorHandler){
|
||||
}
|
||||
|
||||
function pad2(x) {
|
||||
return x<10 ? '0'+x : 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)
|
||||
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)
|
||||
return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
|
||||
} else {
|
||||
return Math.floor(secs) + "s"
|
||||
return Math.floor(secs) + "s";
|
||||
}
|
||||
}
|
||||
|
||||
function setTitle(progress) {
|
||||
var title = 'Stable Diffusion'
|
||||
var title = 'Stable Diffusion';
|
||||
|
||||
if (opts.show_progress_in_title && progress) {
|
||||
title = '[' + progress.trim() + '] ' + title;
|
||||
@ -59,119 +59,119 @@ function setTitle(progress){
|
||||
|
||||
|
||||
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)+")"
|
||||
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 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'
|
||||
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)
|
||||
divProgress.appendChild(divInner);
|
||||
parentProgressbar.insertBefore(divProgress, progressbarContainer);
|
||||
|
||||
if (parentGallery) {
|
||||
var livePreview = document.createElement('div')
|
||||
livePreview.className='livePreview'
|
||||
parentGallery.insertBefore(livePreview, gallery)
|
||||
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()
|
||||
}
|
||||
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){
|
||||
request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
|
||||
if (res.completed) {
|
||||
removeProgressBar()
|
||||
return
|
||||
removeProgressBar();
|
||||
return;
|
||||
}
|
||||
|
||||
var rect = progressbarContainer.getBoundingClientRect()
|
||||
var rect = progressbarContainer.getBoundingClientRect();
|
||||
|
||||
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) + '%'
|
||||
progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
|
||||
}
|
||||
|
||||
if (res.eta) {
|
||||
progressText += " ETA: " + formatTime(res.eta)
|
||||
progressText += " ETA: " + formatTime(res.eta);
|
||||
}
|
||||
|
||||
|
||||
setTitle(progressText)
|
||||
setTitle(progressText);
|
||||
|
||||
if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
|
||||
progressText = res.textinfo + " " + progressText
|
||||
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) {
|
||||
removeProgressBar()
|
||||
return
|
||||
removeProgressBar();
|
||||
return;
|
||||
}
|
||||
|
||||
if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
|
||||
removeProgressBar()
|
||||
return
|
||||
removeProgressBar();
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
if (res.live_preview && gallery) {
|
||||
var rect = gallery.getBoundingClientRect()
|
||||
rect = gallery.getBoundingClientRect();
|
||||
if (rect.width) {
|
||||
livePreview.style.width = rect.width + "px"
|
||||
livePreview.style.height = rect.height + "px"
|
||||
livePreview.style.width = rect.width + "px";
|
||||
livePreview.style.height = rect.height + "px";
|
||||
}
|
||||
|
||||
var img = new Image();
|
||||
img.onload = function() {
|
||||
livePreview.appendChild(img)
|
||||
livePreview.appendChild(img);
|
||||
if (livePreview.childElementCount > 2) {
|
||||
livePreview.removeChild(livePreview.firstElementChild)
|
||||
}
|
||||
livePreview.removeChild(livePreview.firstElementChild);
|
||||
}
|
||||
};
|
||||
img.src = res.live_preview;
|
||||
}
|
||||
|
||||
|
||||
if (onProgress) {
|
||||
onProgress(res)
|
||||
onProgress(res);
|
||||
}
|
||||
|
||||
setTimeout(() => {
|
||||
fun(id_task, res.id_live_preview);
|
||||
}, opts.live_preview_refresh_period || 500)
|
||||
}, opts.live_preview_refresh_period || 500);
|
||||
}, function() {
|
||||
removeProgressBar()
|
||||
})
|
||||
}
|
||||
removeProgressBar();
|
||||
});
|
||||
};
|
||||
|
||||
fun(id_task, 0)
|
||||
fun(id_task, 0);
|
||||
}
|
||||
|
||||
@ -2,16 +2,16 @@
|
||||
|
||||
|
||||
function start_training_textual_inversion() {
|
||||
gradioApp().querySelector('#ti_error').innerHTML=''
|
||||
gradioApp().querySelector('#ti_error').innerHTML = '';
|
||||
|
||||
var id = randomId()
|
||||
var id = randomId();
|
||||
requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
|
||||
gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo
|
||||
})
|
||||
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;
|
||||
}
|
||||
|
||||
311
javascript/ui.js
311
javascript/ui.js
@ -1,7 +1,7 @@
|
||||
// various functions for interaction with ui.py not large enough to warrant putting them in separate files
|
||||
|
||||
function set_theme(theme) {
|
||||
var gradioURL = window.location.href
|
||||
var gradioURL = window.location.href;
|
||||
if (!gradioURL.includes('?__theme=')) {
|
||||
window.location.replace(gradioURL + '?__theme=' + theme);
|
||||
}
|
||||
@ -14,7 +14,7 @@ function all_gallery_buttons() {
|
||||
if (elem.parentElement.offsetParent) {
|
||||
visibleGalleryButtons.push(elem);
|
||||
}
|
||||
})
|
||||
});
|
||||
return visibleGalleryButtons;
|
||||
}
|
||||
|
||||
@ -25,7 +25,7 @@ function selected_gallery_button() {
|
||||
if (elem.parentElement.offsetParent) {
|
||||
visibleCurrentButton = elem;
|
||||
}
|
||||
})
|
||||
});
|
||||
return visibleCurrentButton;
|
||||
}
|
||||
|
||||
@ -33,10 +33,14 @@ function selected_gallery_index(){
|
||||
var buttons = all_gallery_buttons();
|
||||
var button = selected_gallery_button();
|
||||
|
||||
var result = -1
|
||||
buttons.forEach(function(v, i){ if(v==button) { result = i } })
|
||||
var result = -1;
|
||||
buttons.forEach(function(v, i) {
|
||||
if (v == button) {
|
||||
result = i;
|
||||
}
|
||||
});
|
||||
|
||||
return result
|
||||
return result;
|
||||
}
|
||||
|
||||
function extract_image_from_gallery(gallery) {
|
||||
@ -47,7 +51,7 @@ function extract_image_from_gallery(gallery){
|
||||
return [gallery[0]];
|
||||
}
|
||||
|
||||
var index = selected_gallery_index()
|
||||
var index = selected_gallery_index();
|
||||
|
||||
if (index < 0 || index >= gallery.length) {
|
||||
// Use the first image in the gallery as the default
|
||||
@ -58,11 +62,11 @@ function extract_image_from_gallery(gallery){
|
||||
}
|
||||
|
||||
function args_to_array(args) {
|
||||
var res = []
|
||||
var res = [];
|
||||
for (var i = 0; i < args.length; i++) {
|
||||
res.push(args[i])
|
||||
res.push(args[i]);
|
||||
}
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function switch_to_txt2img() {
|
||||
@ -95,13 +99,6 @@ function switch_to_inpaint_sketch(){
|
||||
return args_to_array(arguments);
|
||||
}
|
||||
|
||||
function switch_to_inpaint(){
|
||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
|
||||
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[2].click();
|
||||
|
||||
return args_to_array(arguments);
|
||||
}
|
||||
|
||||
function switch_to_extras() {
|
||||
gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
|
||||
|
||||
@ -109,38 +106,39 @@ function switch_to_extras(){
|
||||
}
|
||||
|
||||
function get_tab_index(tabId) {
|
||||
var res = 0
|
||||
var res = 0;
|
||||
|
||||
gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i) {
|
||||
if(button.className.indexOf('selected') != -1)
|
||||
res = i
|
||||
})
|
||||
if (button.className.indexOf('selected') != -1) {
|
||||
res = i;
|
||||
}
|
||||
});
|
||||
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function create_tab_index_args(tabId, args) {
|
||||
var res = []
|
||||
var res = [];
|
||||
for (var i = 0; i < args.length; i++) {
|
||||
res.push(args[i])
|
||||
res.push(args[i]);
|
||||
}
|
||||
|
||||
res[0] = get_tab_index(tabId)
|
||||
res[0] = get_tab_index(tabId);
|
||||
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function get_img2img_tab_index() {
|
||||
let res = args_to_array(arguments)
|
||||
res.splice(-2)
|
||||
res[0] = get_tab_index('mode_img2img')
|
||||
return res
|
||||
let res = args_to_array(arguments);
|
||||
res.splice(-2);
|
||||
res[0] = get_tab_index('mode_img2img');
|
||||
return res;
|
||||
}
|
||||
|
||||
function create_submit_args(args) {
|
||||
var res = []
|
||||
var res = [];
|
||||
for (var i = 0; i < args.length; i++) {
|
||||
res.push(args[i])
|
||||
res.push(args[i]);
|
||||
}
|
||||
|
||||
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
|
||||
@ -148,158 +146,157 @@ function create_submit_args(args){
|
||||
// I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
|
||||
// If gradio at some point stops sending outputs, this may break something
|
||||
if (Array.isArray(res[res.length - 3])) {
|
||||
res[res.length - 3] = null
|
||||
res[res.length - 3] = null;
|
||||
}
|
||||
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function showSubmitButtons(tabname, show) {
|
||||
gradioApp().getElementById(tabname+'_interrupt').style.display = show ? "none" : "block"
|
||||
gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block"
|
||||
gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block";
|
||||
gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block";
|
||||
}
|
||||
|
||||
function showRestoreProgressButton(tabname, show) {
|
||||
var button = gradioApp().getElementById(tabname + "_restore_progress")
|
||||
if(! button) return
|
||||
var button = gradioApp().getElementById(tabname + "_restore_progress");
|
||||
if (!button) return;
|
||||
|
||||
button.style.display = show ? "flex" : "none"
|
||||
button.style.display = show ? "flex" : "none";
|
||||
}
|
||||
|
||||
function submit() {
|
||||
rememberGallerySelection('txt2img_gallery')
|
||||
showSubmitButtons('txt2img', false)
|
||||
showSubmitButtons('txt2img', false);
|
||||
|
||||
var id = randomId()
|
||||
var id = randomId();
|
||||
localStorage.setItem("txt2img_task_id", id);
|
||||
|
||||
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
|
||||
showSubmitButtons('txt2img', true)
|
||||
localStorage.removeItem("txt2img_task_id")
|
||||
showRestoreProgressButton('txt2img', false)
|
||||
})
|
||||
showSubmitButtons('txt2img', true);
|
||||
localStorage.removeItem("txt2img_task_id");
|
||||
showRestoreProgressButton('txt2img', false);
|
||||
});
|
||||
|
||||
var res = create_submit_args(arguments)
|
||||
var res = create_submit_args(arguments);
|
||||
|
||||
res[0] = id
|
||||
res[0] = id;
|
||||
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function submit_img2img() {
|
||||
rememberGallerySelection('img2img_gallery')
|
||||
showSubmitButtons('img2img', false)
|
||||
showSubmitButtons('img2img', false);
|
||||
|
||||
var id = randomId()
|
||||
var id = randomId();
|
||||
localStorage.setItem("img2img_task_id", id);
|
||||
|
||||
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
|
||||
showSubmitButtons('img2img', true)
|
||||
localStorage.removeItem("img2img_task_id")
|
||||
showRestoreProgressButton('img2img', false)
|
||||
})
|
||||
showSubmitButtons('img2img', true);
|
||||
localStorage.removeItem("img2img_task_id");
|
||||
showRestoreProgressButton('img2img', false);
|
||||
});
|
||||
|
||||
var res = create_submit_args(arguments)
|
||||
var res = create_submit_args(arguments);
|
||||
|
||||
res[0] = id
|
||||
res[1] = get_tab_index('mode_img2img')
|
||||
res[0] = id;
|
||||
res[1] = get_tab_index('mode_img2img');
|
||||
|
||||
return res
|
||||
return res;
|
||||
}
|
||||
|
||||
function restoreProgressTxt2img() {
|
||||
showRestoreProgressButton("txt2img", false)
|
||||
var id = localStorage.getItem("txt2img_task_id")
|
||||
showRestoreProgressButton("txt2img", false);
|
||||
var id = localStorage.getItem("txt2img_task_id");
|
||||
|
||||
id = localStorage.getItem("txt2img_task_id")
|
||||
id = localStorage.getItem("txt2img_task_id");
|
||||
|
||||
if (id) {
|
||||
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
|
||||
showSubmitButtons('txt2img', true)
|
||||
}, null, 0)
|
||||
showSubmitButtons('txt2img', true);
|
||||
}, null, 0);
|
||||
}
|
||||
|
||||
return id
|
||||
return id;
|
||||
}
|
||||
|
||||
function restoreProgressImg2img() {
|
||||
showRestoreProgressButton("img2img", false)
|
||||
showRestoreProgressButton("img2img", false);
|
||||
|
||||
var id = localStorage.getItem("img2img_task_id")
|
||||
var id = localStorage.getItem("img2img_task_id");
|
||||
|
||||
if (id) {
|
||||
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
|
||||
showSubmitButtons('img2img', true)
|
||||
}, null, 0)
|
||||
showSubmitButtons('img2img', true);
|
||||
}, null, 0);
|
||||
}
|
||||
|
||||
return id
|
||||
return id;
|
||||
}
|
||||
|
||||
|
||||
onUiLoaded(function() {
|
||||
showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"))
|
||||
showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"))
|
||||
showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"));
|
||||
showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"));
|
||||
});
|
||||
|
||||
|
||||
function modelmerger() {
|
||||
var id = randomId()
|
||||
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
|
||||
var id = randomId();
|
||||
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function() {});
|
||||
|
||||
var res = create_submit_args(arguments)
|
||||
res[0] = id
|
||||
return res
|
||||
var res = create_submit_args(arguments);
|
||||
res[0] = id;
|
||||
return res;
|
||||
}
|
||||
|
||||
|
||||
function ask_for_style_name(_, prompt_text, negative_prompt_text) {
|
||||
var name_ = prompt('Style name:')
|
||||
return [name_, prompt_text, negative_prompt_text]
|
||||
var name_ = prompt('Style name:');
|
||||
return [name_, prompt_text, negative_prompt_text];
|
||||
}
|
||||
|
||||
function confirm_clear_prompt(prompt, negative_prompt) {
|
||||
if (confirm("Delete prompt?")) {
|
||||
prompt = ""
|
||||
negative_prompt = ""
|
||||
prompt = "";
|
||||
negative_prompt = "";
|
||||
}
|
||||
|
||||
return [prompt, negative_prompt]
|
||||
return [prompt, negative_prompt];
|
||||
}
|
||||
|
||||
|
||||
promptTokecountUpdateFuncs = {}
|
||||
var promptTokecountUpdateFuncs = {};
|
||||
|
||||
function recalculatePromptTokens(name) {
|
||||
if (promptTokecountUpdateFuncs[name]) {
|
||||
promptTokecountUpdateFuncs[name]()
|
||||
promptTokecountUpdateFuncs[name]();
|
||||
}
|
||||
}
|
||||
|
||||
function recalculate_prompts_txt2img() {
|
||||
recalculatePromptTokens('txt2img_prompt')
|
||||
recalculatePromptTokens('txt2img_neg_prompt')
|
||||
recalculatePromptTokens('txt2img_prompt');
|
||||
recalculatePromptTokens('txt2img_neg_prompt');
|
||||
return args_to_array(arguments);
|
||||
}
|
||||
|
||||
function recalculate_prompts_img2img() {
|
||||
recalculatePromptTokens('img2img_prompt')
|
||||
recalculatePromptTokens('img2img_neg_prompt')
|
||||
recalculatePromptTokens('img2img_prompt');
|
||||
recalculatePromptTokens('img2img_neg_prompt');
|
||||
return args_to_array(arguments);
|
||||
}
|
||||
|
||||
|
||||
var opts = {}
|
||||
var opts = {};
|
||||
onUiUpdate(function() {
|
||||
if (Object.keys(opts).length != 0) return;
|
||||
|
||||
var json_elem = gradioApp().getElementById('settings_json')
|
||||
var json_elem = gradioApp().getElementById('settings_json');
|
||||
if (json_elem == null) return;
|
||||
|
||||
var textarea = json_elem.querySelector('textarea')
|
||||
var jsdata = textarea.value
|
||||
opts = JSON.parse(jsdata)
|
||||
executeCallbacks(optionsChangedCallbacks);
|
||||
var textarea = json_elem.querySelector('textarea');
|
||||
var jsdata = textarea.value;
|
||||
opts = JSON.parse(jsdata);
|
||||
|
||||
executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/
|
||||
|
||||
Object.defineProperty(textarea, 'value', {
|
||||
set: function(newValue) {
|
||||
@ -308,7 +305,7 @@ onUiUpdate(function(){
|
||||
valueProp.set.call(textarea, newValue);
|
||||
|
||||
if (oldValue != newValue) {
|
||||
opts = JSON.parse(textarea.value)
|
||||
opts = JSON.parse(textarea.value);
|
||||
}
|
||||
|
||||
executeCallbacks(optionsChangedCallbacks);
|
||||
@ -319,77 +316,85 @@ onUiUpdate(function(){
|
||||
}
|
||||
});
|
||||
|
||||
json_elem.parentElement.style.display="none"
|
||||
json_elem.parentElement.style.display = "none";
|
||||
|
||||
function registerTextarea(id, id_counter, id_button) {
|
||||
var prompt = gradioApp().getElementById(id)
|
||||
var counter = gradioApp().getElementById(id_counter)
|
||||
var prompt = gradioApp().getElementById(id);
|
||||
var counter = gradioApp().getElementById(id_counter);
|
||||
var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
|
||||
|
||||
if (counter.parentElement == prompt.parentElement) {
|
||||
return
|
||||
return;
|
||||
}
|
||||
|
||||
prompt.parentElement.insertBefore(counter, prompt)
|
||||
prompt.parentElement.style.position = "relative"
|
||||
prompt.parentElement.insertBefore(counter, prompt);
|
||||
prompt.parentElement.style.position = "relative";
|
||||
|
||||
promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
|
||||
promptTokecountUpdateFuncs[id] = function() {
|
||||
update_token_counter(id_button);
|
||||
};
|
||||
textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
|
||||
}
|
||||
|
||||
registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
|
||||
registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button')
|
||||
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
|
||||
registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
|
||||
registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
|
||||
registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
|
||||
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
|
||||
registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
|
||||
|
||||
var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
|
||||
var settings_tabs = gradioApp().querySelector('#settings div')
|
||||
var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
|
||||
var settings_tabs = gradioApp().querySelector('#settings div');
|
||||
if (show_all_pages && settings_tabs) {
|
||||
settings_tabs.appendChild(show_all_pages)
|
||||
settings_tabs.appendChild(show_all_pages);
|
||||
show_all_pages.onclick = function() {
|
||||
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
|
||||
if(elem.id == "settings_tab_licenses")
|
||||
if (elem.id == "settings_tab_licenses") {
|
||||
return;
|
||||
}
|
||||
|
||||
elem.style.display = "block";
|
||||
})
|
||||
});
|
||||
};
|
||||
}
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
onOptionsChanged(function() {
|
||||
var elem = gradioApp().getElementById('sd_checkpoint_hash')
|
||||
var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
|
||||
var shorthash = sd_checkpoint_hash.substring(0,10)
|
||||
var elem = gradioApp().getElementById('sd_checkpoint_hash');
|
||||
var sd_checkpoint_hash = opts.sd_checkpoint_hash || "";
|
||||
var shorthash = sd_checkpoint_hash.substring(0, 10);
|
||||
|
||||
if (elem && elem.textContent != shorthash) {
|
||||
elem.textContent = shorthash
|
||||
elem.title = sd_checkpoint_hash
|
||||
elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
|
||||
elem.textContent = shorthash;
|
||||
elem.title = sd_checkpoint_hash;
|
||||
elem.href = "https://google.com/search?q=" + sd_checkpoint_hash;
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
let txt2img_textarea, img2img_textarea = undefined;
|
||||
let wait_time = 800
|
||||
let wait_time = 800;
|
||||
let token_timeouts = {};
|
||||
|
||||
function update_txt2img_tokens(...args) {
|
||||
update_token_counter("txt2img_token_button")
|
||||
if (args.length == 2)
|
||||
return args[0]
|
||||
update_token_counter("txt2img_token_button");
|
||||
if (args.length == 2) {
|
||||
return args[0];
|
||||
}
|
||||
return args;
|
||||
}
|
||||
|
||||
function update_img2img_tokens(...args) {
|
||||
update_token_counter("img2img_token_button")
|
||||
if (args.length == 2)
|
||||
return args[0]
|
||||
update_token_counter(
|
||||
"img2img_token_button"
|
||||
);
|
||||
if (args.length == 2) {
|
||||
return args[0];
|
||||
}
|
||||
return args;
|
||||
}
|
||||
|
||||
function update_token_counter(button_id) {
|
||||
if (token_timeouts[button_id])
|
||||
if (token_timeouts[button_id]) {
|
||||
clearTimeout(token_timeouts[button_id]);
|
||||
}
|
||||
token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
|
||||
}
|
||||
|
||||
@ -401,19 +406,19 @@ function restart_reload(){
|
||||
location.reload();
|
||||
}, function() {
|
||||
setTimeout(requestPing, 500);
|
||||
})
|
||||
}
|
||||
});
|
||||
};
|
||||
|
||||
setTimeout(requestPing, 2000);
|
||||
|
||||
return []
|
||||
return [];
|
||||
}
|
||||
|
||||
// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits
|
||||
// will only visible on web page and not sent to python.
|
||||
function updateInput(target) {
|
||||
let e = new Event("input", { bubbles: true })
|
||||
Object.defineProperty(e, "target", {value: target})
|
||||
let e = new Event("input", {bubbles: true});
|
||||
Object.defineProperty(e, "target", {value: target});
|
||||
target.dispatchEvent(e);
|
||||
}
|
||||
|
||||
@ -421,12 +426,12 @@ function updateInput(target){
|
||||
var desiredCheckpointName = null;
|
||||
function selectCheckpoint(name) {
|
||||
desiredCheckpointName = name;
|
||||
gradioApp().getElementById('change_checkpoint').click()
|
||||
gradioApp().getElementById('change_checkpoint').click();
|
||||
}
|
||||
|
||||
function currentImg2imgSourceResolution(_, _, scaleBy){
|
||||
var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img')
|
||||
return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]
|
||||
function currentImg2imgSourceResolution(w, h, scaleBy) {
|
||||
var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img');
|
||||
return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy];
|
||||
}
|
||||
|
||||
function updateImg2imgResizeToTextAfterChangingImage() {
|
||||
@ -434,8 +439,34 @@ function updateImg2imgResizeToTextAfterChangingImage(){
|
||||
// There may be a better solution, but this is simple and straightforward so I'm going with it.
|
||||
|
||||
setTimeout(function() {
|
||||
gradioApp().getElementById('img2img_update_resize_to').click()
|
||||
gradioApp().getElementById('img2img_update_resize_to').click();
|
||||
}, 500);
|
||||
|
||||
return []
|
||||
return [];
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
function setRandomSeed(elem_id) {
|
||||
var input = gradioApp().querySelector("#" + elem_id + " input");
|
||||
if (!input) return [];
|
||||
|
||||
input.value = "-1";
|
||||
updateInput(input);
|
||||
return [];
|
||||
}
|
||||
|
||||
function switchWidthHeight(tabname) {
|
||||
var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]");
|
||||
var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]");
|
||||
if (!width || !height) return [];
|
||||
|
||||
var tmp = width.value;
|
||||
width.value = height.value;
|
||||
height.value = tmp;
|
||||
|
||||
updateInput(width);
|
||||
updateInput(height);
|
||||
return [];
|
||||
}
|
||||
|
||||
@ -1,41 +1,62 @@
|
||||
// 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';
|
||||
var settingsHintsSetup = false;
|
||||
|
||||
elem = gradioApp().querySelector('#'+elem_id)
|
||||
elem.insertBefore(a, elem.querySelector('label'))
|
||||
onOptionsChanged(function() {
|
||||
if (settingsHintsSetup) return;
|
||||
settingsHintsSetup = true;
|
||||
|
||||
return a
|
||||
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){
|
||||
function settingsHintsShowQuicksettings() {
|
||||
requestGet("./internal/quicksettings-hint", {}, function(data) {
|
||||
var table = document.createElement('table')
|
||||
table.className = 'settings-value-table'
|
||||
var table = document.createElement('table');
|
||||
table.className = 'settings-value-table';
|
||||
|
||||
data.forEach(function(obj) {
|
||||
var tr = document.createElement('tr')
|
||||
var td = document.createElement('td')
|
||||
td.textContent = obj.name
|
||||
tr.appendChild(td)
|
||||
var 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);
|
||||
})
|
||||
});
|
||||
})
|
||||
|
||||
|
||||
}
|
||||
|
||||
110
launch.py
110
launch.py
@ -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)
|
||||
|
||||
|
||||
BIN
modules/Roboto-Regular.ttf
Normal file
BIN
modules/Roboto-Regular.ttf
Normal file
Binary file not shown.
@ -15,7 +15,8 @@ from secrets import compare_digest
|
||||
|
||||
import modules.shared as shared
|
||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
|
||||
from modules.api.models import *
|
||||
from modules.api import models
|
||||
from modules.shared import opts
|
||||
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
|
||||
from modules.textual_inversion.preprocess import preprocess
|
||||
@ -25,21 +26,24 @@ from modules.sd_models import checkpoints_list, unload_model_weights, reload_mod
|
||||
from modules.sd_models_config import find_checkpoint_config_near_filename
|
||||
from modules.realesrgan_model import get_realesrgan_models
|
||||
from modules import devices
|
||||
from typing import List
|
||||
from typing import Dict, List, Any
|
||||
import piexif
|
||||
import piexif.helper
|
||||
|
||||
|
||||
def upscaler_to_index(name: str):
|
||||
try:
|
||||
return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
|
||||
except:
|
||||
raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e
|
||||
|
||||
|
||||
def script_name_to_index(name, scripts):
|
||||
try:
|
||||
return [script.title().lower() for script in scripts].index(name.lower())
|
||||
except:
|
||||
raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
|
||||
|
||||
|
||||
def validate_sampler_name(name):
|
||||
config = sd_samplers.all_samplers_map.get(name, None)
|
||||
@ -48,20 +52,23 @@ def validate_sampler_name(name):
|
||||
|
||||
return name
|
||||
|
||||
|
||||
def setUpscalers(req: dict):
|
||||
reqDict = vars(req)
|
||||
reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
|
||||
reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
|
||||
return reqDict
|
||||
|
||||
|
||||
def decode_base64_to_image(encoding):
|
||||
if encoding.startswith("data:image/"):
|
||||
encoding = encoding.split(";")[1].split(",")[1]
|
||||
try:
|
||||
image = Image.open(BytesIO(base64.b64decode(encoding)))
|
||||
return image
|
||||
except Exception as err:
|
||||
raise HTTPException(status_code=500, detail="Invalid encoded image")
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail="Invalid encoded image") from e
|
||||
|
||||
|
||||
def encode_pil_to_base64(image):
|
||||
with io.BytesIO() as output_bytes:
|
||||
@ -92,6 +99,7 @@ def encode_pil_to_base64(image):
|
||||
|
||||
return base64.b64encode(bytes_data)
|
||||
|
||||
|
||||
def api_middleware(app: FastAPI):
|
||||
rich_available = True
|
||||
try:
|
||||
@ -99,7 +107,7 @@ def api_middleware(app: FastAPI):
|
||||
import starlette # importing just so it can be placed on silent list
|
||||
from rich.console import Console
|
||||
console = Console()
|
||||
except:
|
||||
except Exception:
|
||||
import traceback
|
||||
rich_available = False
|
||||
|
||||
@ -157,7 +165,7 @@ def api_middleware(app: FastAPI):
|
||||
class Api:
|
||||
def __init__(self, app: FastAPI, queue_lock: Lock):
|
||||
if shared.cmd_opts.api_auth:
|
||||
self.credentials = dict()
|
||||
self.credentials = {}
|
||||
for auth in shared.cmd_opts.api_auth.split(","):
|
||||
user, password = auth.split(":")
|
||||
self.credentials[user] = password
|
||||
@ -166,36 +174,37 @@ class Api:
|
||||
self.app = app
|
||||
self.queue_lock = queue_lock
|
||||
api_middleware(self.app)
|
||||
self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
|
||||
self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
|
||||
self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
|
||||
self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
|
||||
self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
|
||||
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
|
||||
self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
|
||||
self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
|
||||
self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
|
||||
self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
|
||||
self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
|
||||
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
|
||||
self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
|
||||
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
|
||||
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
|
||||
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
|
||||
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
|
||||
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
|
||||
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
|
||||
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
|
||||
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
|
||||
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
|
||||
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
|
||||
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
|
||||
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
|
||||
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
|
||||
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
|
||||
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
|
||||
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
|
||||
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
|
||||
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
|
||||
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
|
||||
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
|
||||
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
|
||||
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
|
||||
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
|
||||
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
|
||||
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
|
||||
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
|
||||
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
|
||||
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
|
||||
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
|
||||
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
|
||||
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
|
||||
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
|
||||
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
|
||||
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
|
||||
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
|
||||
|
||||
self.default_script_arg_txt2img = []
|
||||
self.default_script_arg_img2img = []
|
||||
@ -221,10 +230,18 @@ class Api:
|
||||
return script, script_idx
|
||||
|
||||
def get_scripts_list(self):
|
||||
t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
|
||||
i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
|
||||
t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
|
||||
i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
|
||||
|
||||
return ScriptsList(txt2img = t2ilist, img2img = i2ilist)
|
||||
return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
|
||||
|
||||
def get_script_info(self):
|
||||
res = []
|
||||
|
||||
for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]:
|
||||
res += [script.api_info for script in script_list if script.api_info is not None]
|
||||
|
||||
return res
|
||||
|
||||
def get_script(self, script_name, script_runner):
|
||||
if script_name is None or script_name == "":
|
||||
@ -264,11 +281,11 @@ class Api:
|
||||
if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
|
||||
for alwayson_script_name in request.alwayson_scripts.keys():
|
||||
alwayson_script = self.get_script(alwayson_script_name, script_runner)
|
||||
if alwayson_script == None:
|
||||
if alwayson_script is None:
|
||||
raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
|
||||
# Selectable script in always on script param check
|
||||
if alwayson_script.alwayson == False:
|
||||
raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
|
||||
if alwayson_script.alwayson is False:
|
||||
raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
|
||||
# always on script with no arg should always run so you don't really need to add them to the requests
|
||||
if "args" in request.alwayson_scripts[alwayson_script_name]:
|
||||
# min between arg length in scriptrunner and arg length in the request
|
||||
@ -276,7 +293,7 @@ class Api:
|
||||
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
|
||||
return script_args
|
||||
|
||||
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
|
||||
def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
|
||||
script_runner = scripts.scripts_txt2img
|
||||
if not script_runner.scripts:
|
||||
script_runner.initialize_scripts(False)
|
||||
@ -310,7 +327,7 @@ class Api:
|
||||
p.outpath_samples = opts.outdir_txt2img_samples
|
||||
|
||||
shared.state.begin()
|
||||
if selectable_scripts != None:
|
||||
if selectable_scripts is not None:
|
||||
p.script_args = script_args
|
||||
processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
|
||||
else:
|
||||
@ -320,9 +337,9 @@ class Api:
|
||||
|
||||
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
|
||||
|
||||
return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
||||
return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
|
||||
|
||||
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
|
||||
def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
|
||||
init_images = img2imgreq.init_images
|
||||
if init_images is None:
|
||||
raise HTTPException(status_code=404, detail="Init image not found")
|
||||
@ -367,7 +384,7 @@ class Api:
|
||||
p.outpath_samples = opts.outdir_img2img_samples
|
||||
|
||||
shared.state.begin()
|
||||
if selectable_scripts != None:
|
||||
if selectable_scripts is not None:
|
||||
p.script_args = script_args
|
||||
processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
|
||||
else:
|
||||
@ -381,9 +398,9 @@ class Api:
|
||||
img2imgreq.init_images = None
|
||||
img2imgreq.mask = None
|
||||
|
||||
return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
||||
return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
|
||||
|
||||
def extras_single_image_api(self, req: ExtrasSingleImageRequest):
|
||||
def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
|
||||
reqDict = setUpscalers(req)
|
||||
|
||||
reqDict['image'] = decode_base64_to_image(reqDict['image'])
|
||||
@ -391,9 +408,9 @@ class Api:
|
||||
with self.queue_lock:
|
||||
result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
|
||||
return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
||||
return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
||||
|
||||
def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
|
||||
def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
|
||||
reqDict = setUpscalers(req)
|
||||
|
||||
image_list = reqDict.pop('imageList', [])
|
||||
@ -402,15 +419,15 @@ class Api:
|
||||
with self.queue_lock:
|
||||
result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
|
||||
return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||
|
||||
def pnginfoapi(self, req: PNGInfoRequest):
|
||||
def pnginfoapi(self, req: models.PNGInfoRequest):
|
||||
if(not req.image.strip()):
|
||||
return PNGInfoResponse(info="")
|
||||
return models.PNGInfoResponse(info="")
|
||||
|
||||
image = decode_base64_to_image(req.image.strip())
|
||||
if image is None:
|
||||
return PNGInfoResponse(info="")
|
||||
return models.PNGInfoResponse(info="")
|
||||
|
||||
geninfo, items = images.read_info_from_image(image)
|
||||
if geninfo is None:
|
||||
@ -418,13 +435,13 @@ class Api:
|
||||
|
||||
items = {**{'parameters': geninfo}, **items}
|
||||
|
||||
return PNGInfoResponse(info=geninfo, items=items)
|
||||
return models.PNGInfoResponse(info=geninfo, items=items)
|
||||
|
||||
def progressapi(self, req: ProgressRequest = Depends()):
|
||||
def progressapi(self, req: models.ProgressRequest = Depends()):
|
||||
# copy from check_progress_call of ui.py
|
||||
|
||||
if shared.state.job_count == 0:
|
||||
return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
|
||||
return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
|
||||
|
||||
# avoid dividing zero
|
||||
progress = 0.01
|
||||
@ -446,9 +463,9 @@ class Api:
|
||||
if shared.state.current_image and not req.skip_current_image:
|
||||
current_image = encode_pil_to_base64(shared.state.current_image)
|
||||
|
||||
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
||||
return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
|
||||
|
||||
def interrogateapi(self, interrogatereq: InterrogateRequest):
|
||||
def interrogateapi(self, interrogatereq: models.InterrogateRequest):
|
||||
image_b64 = interrogatereq.image
|
||||
if image_b64 is None:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
@ -465,7 +482,7 @@ class Api:
|
||||
else:
|
||||
raise HTTPException(status_code=404, detail="Model not found")
|
||||
|
||||
return InterrogateResponse(caption=processed)
|
||||
return models.InterrogateResponse(caption=processed)
|
||||
|
||||
def interruptapi(self):
|
||||
shared.state.interrupt()
|
||||
@ -570,36 +587,36 @@ class Api:
|
||||
filename = create_embedding(**args) # create empty embedding
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
||||
shared.state.end()
|
||||
return CreateResponse(info=f"create embedding filename: {filename}")
|
||||
return models.CreateResponse(info=f"create embedding filename: {filename}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"create embedding error: {e}")
|
||||
return models.TrainResponse(info=f"create embedding error: {e}")
|
||||
|
||||
def create_hypernetwork(self, args: dict):
|
||||
try:
|
||||
shared.state.begin()
|
||||
filename = create_hypernetwork(**args) # create empty embedding
|
||||
shared.state.end()
|
||||
return CreateResponse(info=f"create hypernetwork filename: {filename}")
|
||||
return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"create hypernetwork error: {e}")
|
||||
return models.TrainResponse(info=f"create hypernetwork error: {e}")
|
||||
|
||||
def preprocess(self, args: dict):
|
||||
try:
|
||||
shared.state.begin()
|
||||
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info = 'preprocess complete')
|
||||
return models.PreprocessResponse(info = 'preprocess complete')
|
||||
except KeyError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
|
||||
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info=f"preprocess error: {e}")
|
||||
return models.PreprocessResponse(info=f"preprocess error: {e}")
|
||||
except FileNotFoundError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info=f'preprocess error: {e}')
|
||||
return models.PreprocessResponse(info=f'preprocess error: {e}')
|
||||
|
||||
def train_embedding(self, args: dict):
|
||||
try:
|
||||
@ -617,10 +634,10 @@ class Api:
|
||||
if not apply_optimizations:
|
||||
sd_hijack.apply_optimizations()
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
except AssertionError as msg:
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"train embedding error: {msg}")
|
||||
return models.TrainResponse(info=f"train embedding error: {msg}")
|
||||
|
||||
def train_hypernetwork(self, args: dict):
|
||||
try:
|
||||
@ -641,14 +658,15 @@ class Api:
|
||||
if not apply_optimizations:
|
||||
sd_hijack.apply_optimizations()
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
except AssertionError as msg:
|
||||
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
except AssertionError:
|
||||
shared.state.end()
|
||||
return TrainResponse(info=f"train embedding error: {error}")
|
||||
return models.TrainResponse(info=f"train embedding error: {error}")
|
||||
|
||||
def get_memory(self):
|
||||
try:
|
||||
import os, psutil
|
||||
import os
|
||||
import psutil
|
||||
process = psutil.Process(os.getpid())
|
||||
res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
|
||||
ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
|
||||
@ -678,7 +696,7 @@ class Api:
|
||||
cuda = {'error': 'unavailable'}
|
||||
except Exception as err:
|
||||
cuda = {'error': f'{err}'}
|
||||
return MemoryResponse(ram = ram, cuda = cuda)
|
||||
return models.MemoryResponse(ram=ram, cuda=cuda)
|
||||
|
||||
def launch(self, server_name, port):
|
||||
self.app.include_router(self.router)
|
||||
|
||||
@ -223,7 +223,8 @@ 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)
|
||||
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)")
|
||||
|
||||
|
||||
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")
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
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')
|
||||
|
||||
@ -1,14 +1,12 @@
|
||||
# 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:
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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]
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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']
|
||||
|
||||
@ -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':
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from modules import extra_networks, shared, extra_networks
|
||||
from modules import extra_networks, shared
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
|
||||
|
||||
@ -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":
|
||||
|
||||
@ -1,15 +1,11 @@
|
||||
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'),
|
||||
]
|
||||
|
||||
@ -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
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
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:
|
||||
|
||||
@ -1,19 +1,17 @@
|
||||
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):
|
||||
|
||||
@ -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))
|
||||
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
|
||||
|
||||
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)
|
||||
|
||||
# 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)
|
||||
|
||||
@ -1,19 +1,15 @@
|
||||
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
|
||||
|
||||
@ -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})"
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
import torch
|
||||
import platform
|
||||
from modules import paths
|
||||
from modules.sd_hijack_utils import CondFunc
|
||||
from packaging import version
|
||||
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
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 = []
|
||||
|
||||
@ -52,7 +52,7 @@ class DDPM(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,
|
||||
@ -107,7 +107,7 @@ class DDPM(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, only_model=load_only_unet)
|
||||
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
|
||||
|
||||
# If initialing from EMA-only checkpoint, create EMA model after loading.
|
||||
if self.use_ema and not load_ema:
|
||||
@ -194,7 +194,9 @@ class DDPM(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):
|
||||
ignore_keys = ignore_keys or []
|
||||
|
||||
sd = torch.load(path, map_location="cpu")
|
||||
if "state_dict" in list(sd.keys()):
|
||||
sd = sd["state_dict"]
|
||||
@ -403,7 +405,7 @@ class DDPM(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)
|
||||
@ -411,7 +413,7 @@ class DDPM(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):
|
||||
@ -473,13 +475,13 @@ class LatentDiffusion(DDPM):
|
||||
conditioning_key = None
|
||||
ckpt_path = kwargs.pop("ckpt_path", None)
|
||||
ignore_keys = kwargs.pop("ignore_keys", [])
|
||||
super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
|
||||
super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **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
|
||||
@ -891,16 +893,6 @@ class LatentDiffusion(DDPM):
|
||||
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):
|
||||
@ -1140,7 +1132,7 @@ class LatentDiffusion(DDPM):
|
||||
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]
|
||||
|
||||
@ -1171,8 +1163,10 @@ class LatentDiffusion(DDPM):
|
||||
|
||||
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()
|
||||
@ -1219,8 +1213,10 @@ class LatentDiffusion(DDPM):
|
||||
|
||||
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
|
||||
@ -1235,7 +1231,7 @@ class LatentDiffusion(DDPM):
|
||||
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,
|
||||
@ -1267,7 +1263,7 @@ class LatentDiffusion(DDPM):
|
||||
|
||||
use_ddim = False
|
||||
|
||||
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,
|
||||
@ -1295,7 +1291,7 @@ class LatentDiffusion(DDPM):
|
||||
|
||||
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:
|
||||
@ -1337,7 +1333,7 @@ class LatentDiffusion(DDPM):
|
||||
|
||||
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.
|
||||
@ -1439,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion):
|
||||
# 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]
|
||||
|
||||
@ -1 +1 @@
|
||||
from .sampler import UniPCSampler
|
||||
from .sampler import UniPCSampler # noqa: F401
|
||||
|
||||
@ -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}")
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
from tqdm.auto import trange
|
||||
import tqdm
|
||||
|
||||
|
||||
class NoiseScheduleVP:
|
||||
@ -179,13 +178,13 @@ def model_wrapper(
|
||||
model,
|
||||
noise_schedule,
|
||||
model_type="noise",
|
||||
model_kwargs={},
|
||||
model_kwargs=None,
|
||||
guidance_type="uncond",
|
||||
#condition=None,
|
||||
#unconditional_condition=None,
|
||||
guidance_scale=1.,
|
||||
classifier_fn=None,
|
||||
classifier_kwargs={},
|
||||
classifier_kwargs=None,
|
||||
):
|
||||
"""Create a wrapper function for the noise prediction model.
|
||||
|
||||
@ -276,6 +275,9 @@ def model_wrapper(
|
||||
A noise prediction model that accepts the noised data and the continuous time as the inputs.
|
||||
"""
|
||||
|
||||
model_kwargs = model_kwargs or {}
|
||||
classifier_kwargs = classifier_kwargs or {}
|
||||
|
||||
def get_model_input_time(t_continuous):
|
||||
"""
|
||||
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
|
||||
@ -342,7 +344,7 @@ def model_wrapper(
|
||||
t_in = torch.cat([t_continuous] * 2)
|
||||
if isinstance(condition, dict):
|
||||
assert isinstance(unconditional_condition, dict)
|
||||
c_in = dict()
|
||||
c_in = {}
|
||||
for k in condition:
|
||||
if isinstance(condition[k], list):
|
||||
c_in[k] = [torch.cat([
|
||||
@ -353,7 +355,7 @@ def model_wrapper(
|
||||
unconditional_condition[k],
|
||||
condition[k]])
|
||||
elif isinstance(condition, list):
|
||||
c_in = list()
|
||||
c_in = []
|
||||
assert isinstance(unconditional_condition, list)
|
||||
for i in range(len(condition)):
|
||||
c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
|
||||
@ -757,6 +759,7 @@ class UniPC:
|
||||
vec_t = timesteps[0].expand((x.shape[0]))
|
||||
model_prev_list = [self.model_fn(x, vec_t)]
|
||||
t_prev_list = [vec_t]
|
||||
with tqdm.tqdm(total=steps) as pbar:
|
||||
# Init the first `order` values by lower order multistep DPM-Solver.
|
||||
for init_order in range(1, order):
|
||||
vec_t = timesteps[init_order].expand(x.shape[0])
|
||||
@ -767,7 +770,9 @@ class UniPC:
|
||||
self.after_update(x, model_x)
|
||||
model_prev_list.append(model_x)
|
||||
t_prev_list.append(vec_t)
|
||||
for step in trange(order, steps + 1):
|
||||
pbar.update()
|
||||
|
||||
for step in range(order, steps + 1):
|
||||
vec_t = timesteps[step].expand(x.shape[0])
|
||||
if lower_order_final:
|
||||
step_order = min(order, steps + 1 - step)
|
||||
@ -791,6 +796,7 @@ class UniPC:
|
||||
if model_x is None:
|
||||
model_x = self.model_fn(x, vec_t)
|
||||
model_prev_list[-1] = model_x
|
||||
pbar.update()
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
if denoise_to_zero:
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
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'
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
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
|
||||
|
||||
@ -2,8 +2,14 @@
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import shlex
|
||||
|
||||
script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
|
||||
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
|
||||
sys.argv += shlex.split(commandline_args)
|
||||
|
||||
modules_path = os.path.dirname(os.path.realpath(__file__))
|
||||
script_path = os.path.dirname(modules_path)
|
||||
|
||||
sd_configs_path = os.path.join(script_path, "configs")
|
||||
sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
|
||||
@ -12,7 +18,7 @@ default_sd_model_file = sd_model_file
|
||||
|
||||
# Parse the --data-dir flag first so we can use it as a base for our other argument default values
|
||||
parser_pre = argparse.ArgumentParser(add_help=False)
|
||||
parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
|
||||
parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
|
||||
cmd_opts_pre = parser_pre.parse_known_args()[0]
|
||||
|
||||
data_path = cmd_opts_pre.data_dir
|
||||
@ -21,3 +27,5 @@ models_path = os.path.join(data_path, "models")
|
||||
extensions_dir = os.path.join(data_path, "extensions")
|
||||
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
|
||||
config_states_dir = os.path.join(script_path, "config_states")
|
||||
|
||||
roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')
|
||||
|
||||
@ -2,7 +2,6 @@ import json
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
import hashlib
|
||||
|
||||
import torch
|
||||
@ -11,10 +10,10 @@ from PIL import Image, ImageFilter, ImageOps
|
||||
import random
|
||||
import cv2
|
||||
from skimage import exposure
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import modules.sd_hijack
|
||||
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
|
||||
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
|
||||
from modules.sd_hijack import model_hijack
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
@ -31,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
|
||||
from einops import repeat, rearrange
|
||||
from blendmodes.blend import blendLayers, BlendType
|
||||
|
||||
|
||||
# some of those options should not be changed at all because they would break the model, so I removed them from options.
|
||||
opt_C = 4
|
||||
opt_f = 8
|
||||
@ -150,6 +150,8 @@ class StableDiffusionProcessing:
|
||||
self.override_settings_restore_afterwards = override_settings_restore_afterwards
|
||||
self.is_using_inpainting_conditioning = False
|
||||
self.disable_extra_networks = False
|
||||
self.token_merging_ratio = 0
|
||||
self.token_merging_ratio_hr = 0
|
||||
|
||||
if not seed_enable_extras:
|
||||
self.subseed = -1
|
||||
@ -165,6 +167,7 @@ class StableDiffusionProcessing:
|
||||
self.all_subseeds = None
|
||||
self.iteration = 0
|
||||
self.is_hr_pass = False
|
||||
self.sampler = None
|
||||
|
||||
|
||||
@property
|
||||
@ -274,6 +277,12 @@ class StableDiffusionProcessing:
|
||||
def close(self):
|
||||
self.sampler = None
|
||||
|
||||
def get_token_merging_ratio(self, for_hr=False):
|
||||
if for_hr:
|
||||
return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
|
||||
|
||||
return self.token_merging_ratio or opts.token_merging_ratio
|
||||
|
||||
|
||||
class Processed:
|
||||
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
|
||||
@ -303,6 +312,8 @@ class Processed:
|
||||
self.styles = p.styles
|
||||
self.job_timestamp = state.job_timestamp
|
||||
self.clip_skip = opts.CLIP_stop_at_last_layers
|
||||
self.token_merging_ratio = p.token_merging_ratio
|
||||
self.token_merging_ratio_hr = p.token_merging_ratio_hr
|
||||
|
||||
self.eta = p.eta
|
||||
self.ddim_discretize = p.ddim_discretize
|
||||
@ -310,6 +321,7 @@ class Processed:
|
||||
self.s_tmin = p.s_tmin
|
||||
self.s_tmax = p.s_tmax
|
||||
self.s_noise = p.s_noise
|
||||
self.s_min_uncond = p.s_min_uncond
|
||||
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
|
||||
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
|
||||
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
|
||||
@ -360,6 +372,9 @@ class Processed:
|
||||
def infotext(self, p: StableDiffusionProcessing, index):
|
||||
return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
|
||||
|
||||
def get_token_merging_ratio(self, for_hr=False):
|
||||
return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
|
||||
|
||||
|
||||
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
|
||||
def slerp(val, low, high):
|
||||
@ -472,6 +487,13 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
|
||||
enable_hr = getattr(p, 'enable_hr', False)
|
||||
token_merging_ratio = p.get_token_merging_ratio()
|
||||
token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
|
||||
|
||||
uses_ensd = opts.eta_noise_seed_delta != 0
|
||||
if uses_ensd:
|
||||
uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
|
||||
|
||||
generation_params = {
|
||||
"Steps": p.steps,
|
||||
@ -489,15 +511,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
"Denoising strength": getattr(p, 'denoising_strength', None),
|
||||
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
|
||||
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
||||
"ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
|
||||
"Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
|
||||
"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
|
||||
"Init image hash": getattr(p, 'init_img_hash', None),
|
||||
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
|
||||
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
|
||||
**p.extra_generation_params,
|
||||
"Version": program_version() if opts.add_version_to_infotext else None,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
||||
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
|
||||
|
||||
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
|
||||
@ -523,9 +546,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
if k == 'sd_vae':
|
||||
sd_vae.reload_vae_weights()
|
||||
|
||||
sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
|
||||
|
||||
res = process_images_inner(p)
|
||||
|
||||
finally:
|
||||
sd_models.apply_token_merging(p.sd_model, 0)
|
||||
|
||||
# restore opts to original state
|
||||
if p.override_settings_restore_afterwards:
|
||||
for k, v in stored_opts.items():
|
||||
@ -660,12 +687,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
file.write(processed.infotext(p, 0))
|
||||
|
||||
step_multiplier = 1
|
||||
if not shared.opts.dont_fix_second_order_samplers_schedule:
|
||||
try:
|
||||
step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
|
||||
except:
|
||||
pass
|
||||
sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
|
||||
step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
|
||||
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
|
||||
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
|
||||
|
||||
@ -978,8 +1001,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
x = None
|
||||
devices.torch_gc()
|
||||
|
||||
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
|
||||
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
|
||||
|
||||
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
|
||||
|
||||
self.is_hr_pass = False
|
||||
|
||||
return samples
|
||||
@ -1141,3 +1168,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
devices.torch_gc()
|
||||
|
||||
return samples
|
||||
|
||||
def get_token_merging_ratio(self, for_hr=False):
|
||||
return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio
|
||||
|
||||
@ -95,9 +95,20 @@ def progressapi(req: ProgressRequest):
|
||||
image = shared.state.current_image
|
||||
if image is not None:
|
||||
buffered = io.BytesIO()
|
||||
image.save(buffered, format="png")
|
||||
|
||||
if opts.live_previews_image_format == "png":
|
||||
# using optimize for large images takes an enormous amount of time
|
||||
if max(*image.size) <= 256:
|
||||
save_kwargs = {"optimize": True}
|
||||
else:
|
||||
save_kwargs = {"optimize": False, "compress_level": 1}
|
||||
|
||||
else:
|
||||
save_kwargs = {}
|
||||
|
||||
image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
|
||||
base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
|
||||
live_preview = f"data:image/png;base64,{base64_image}"
|
||||
live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
|
||||
id_live_preview = shared.state.id_live_preview
|
||||
else:
|
||||
live_preview = None
|
||||
|
||||
@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
||||
"""
|
||||
|
||||
def collect_steps(steps, tree):
|
||||
l = [steps]
|
||||
res = [steps]
|
||||
|
||||
class CollectSteps(lark.Visitor):
|
||||
def scheduled(self, tree):
|
||||
tree.children[-1] = float(tree.children[-1])
|
||||
if tree.children[-1] < 1:
|
||||
tree.children[-1] *= steps
|
||||
tree.children[-1] = min(steps, int(tree.children[-1]))
|
||||
l.append(tree.children[-1])
|
||||
res.append(tree.children[-1])
|
||||
|
||||
def alternate(self, tree):
|
||||
l.extend(range(1, steps+1))
|
||||
res.extend(range(1, steps+1))
|
||||
|
||||
CollectSteps().visit(tree)
|
||||
return sorted(set(l))
|
||||
return sorted(set(res))
|
||||
|
||||
def at_step(step, tree):
|
||||
class AtStep(lark.Transformer):
|
||||
@ -92,7 +95,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
||||
def get_schedule(prompt):
|
||||
try:
|
||||
tree = schedule_parser.parse(prompt)
|
||||
except lark.exceptions.LarkError as e:
|
||||
except lark.exceptions.LarkError:
|
||||
if 0:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
@ -140,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
|
||||
conds = model.get_learned_conditioning(texts)
|
||||
|
||||
cond_schedule = []
|
||||
for i, (end_at_step, text) in enumerate(prompt_schedule):
|
||||
for i, (end_at_step, _) in enumerate(prompt_schedule):
|
||||
cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
|
||||
|
||||
cache[prompt] = cond_schedule
|
||||
@ -216,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
|
||||
res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
|
||||
for i, cond_schedule in enumerate(c):
|
||||
target_index = 0
|
||||
for current, (end_at, cond) in enumerate(cond_schedule):
|
||||
if current_step <= end_at:
|
||||
for current, entry in enumerate(cond_schedule):
|
||||
if current_step <= entry.end_at_step:
|
||||
target_index = current
|
||||
break
|
||||
res[i] = cond_schedule[target_index].cond
|
||||
@ -231,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
|
||||
tensors = []
|
||||
conds_list = []
|
||||
|
||||
for batch_no, composable_prompts in enumerate(c.batch):
|
||||
for composable_prompts in c.batch:
|
||||
conds_for_batch = []
|
||||
|
||||
for cond_index, composable_prompt in enumerate(composable_prompts):
|
||||
for composable_prompt in composable_prompts:
|
||||
target_index = 0
|
||||
for current, (end_at, cond) in enumerate(composable_prompt.schedules):
|
||||
if current_step <= end_at:
|
||||
for current, entry in enumerate(composable_prompt.schedules):
|
||||
if current_step <= entry.end_at_step:
|
||||
target_index = current
|
||||
break
|
||||
|
||||
|
||||
@ -17,9 +17,9 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
self.user_path = path
|
||||
super().__init__()
|
||||
try:
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from realesrgan import RealESRGANer
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
|
||||
from realesrgan import RealESRGANer # noqa: F401
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
|
||||
self.enable = True
|
||||
self.scalers = []
|
||||
scalers = self.load_models(path)
|
||||
@ -134,6 +134,6 @@ def get_realesrgan_models(scaler):
|
||||
),
|
||||
]
|
||||
return models
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
print("Error making Real-ESRGAN models list:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
@ -95,16 +95,16 @@ def check_pt(filename, extra_handler):
|
||||
|
||||
except zipfile.BadZipfile:
|
||||
|
||||
# if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
|
||||
# if it's not a zip file, it's an old pytorch format, with five objects written to pickle
|
||||
with open(filename, "rb") as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
unpickler.extra_handler = extra_handler
|
||||
for i in range(5):
|
||||
for _ in range(5):
|
||||
unpickler.load()
|
||||
|
||||
|
||||
def load(filename, *args, **kwargs):
|
||||
return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
|
||||
return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
|
||||
|
||||
|
||||
def load_with_extra(filename, extra_handler=None, *args, **kwargs):
|
||||
|
||||
@ -53,6 +53,21 @@ class CFGDenoiserParams:
|
||||
|
||||
|
||||
class CFGDenoisedParams:
|
||||
def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
|
||||
self.x = x
|
||||
"""Latent image representation in the process of being denoised"""
|
||||
|
||||
self.sampling_step = sampling_step
|
||||
"""Current Sampling step number"""
|
||||
|
||||
self.total_sampling_steps = total_sampling_steps
|
||||
"""Total number of sampling steps planned"""
|
||||
|
||||
self.inner_model = inner_model
|
||||
"""Inner model reference used for denoising"""
|
||||
|
||||
|
||||
class AfterCFGCallbackParams:
|
||||
def __init__(self, x, sampling_step, total_sampling_steps):
|
||||
self.x = x
|
||||
"""Latent image representation in the process of being denoised"""
|
||||
@ -87,6 +102,7 @@ callback_map = dict(
|
||||
callbacks_image_saved=[],
|
||||
callbacks_cfg_denoiser=[],
|
||||
callbacks_cfg_denoised=[],
|
||||
callbacks_cfg_after_cfg=[],
|
||||
callbacks_before_component=[],
|
||||
callbacks_after_component=[],
|
||||
callbacks_image_grid=[],
|
||||
@ -186,6 +202,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams):
|
||||
report_exception(c, 'cfg_denoised_callback')
|
||||
|
||||
|
||||
def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
|
||||
for c in callback_map['callbacks_cfg_after_cfg']:
|
||||
try:
|
||||
c.callback(params)
|
||||
except Exception:
|
||||
report_exception(c, 'cfg_after_cfg_callback')
|
||||
|
||||
|
||||
def before_component_callback(component, **kwargs):
|
||||
for c in callback_map['callbacks_before_component']:
|
||||
try:
|
||||
@ -332,6 +356,14 @@ def on_cfg_denoised(callback):
|
||||
add_callback(callback_map['callbacks_cfg_denoised'], callback)
|
||||
|
||||
|
||||
def on_cfg_after_cfg(callback):
|
||||
"""register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
|
||||
The callback is called with one argument:
|
||||
- params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
|
||||
"""
|
||||
add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
|
||||
|
||||
|
||||
def on_before_component(callback):
|
||||
"""register a function to be called before a component is created.
|
||||
The callback is called with arguments:
|
||||
|
||||
@ -2,7 +2,6 @@ import os
|
||||
import sys
|
||||
import traceback
|
||||
import importlib.util
|
||||
from types import ModuleType
|
||||
|
||||
|
||||
def load_module(path):
|
||||
|
||||
@ -17,6 +17,9 @@ class PostprocessImageArgs:
|
||||
|
||||
|
||||
class Script:
|
||||
name = None
|
||||
"""script's internal name derived from title"""
|
||||
|
||||
filename = None
|
||||
args_from = None
|
||||
args_to = None
|
||||
@ -25,8 +28,8 @@ class Script:
|
||||
is_txt2img = False
|
||||
is_img2img = False
|
||||
|
||||
"""A gr.Group component that has all script's UI inside it"""
|
||||
group = None
|
||||
"""A gr.Group component that has all script's UI inside it"""
|
||||
|
||||
infotext_fields = None
|
||||
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
|
||||
@ -38,6 +41,9 @@ class Script:
|
||||
various "Send to <X>" buttons when clicked
|
||||
"""
|
||||
|
||||
api_info = None
|
||||
"""Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
|
||||
|
||||
def title(self):
|
||||
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
|
||||
|
||||
@ -231,7 +237,7 @@ def load_scripts():
|
||||
syspath = sys.path
|
||||
|
||||
def register_scripts_from_module(module):
|
||||
for key, script_class in module.__dict__.items():
|
||||
for script_class in module.__dict__.values():
|
||||
if type(script_class) != type:
|
||||
continue
|
||||
|
||||
@ -295,9 +301,9 @@ class ScriptRunner:
|
||||
|
||||
auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
|
||||
|
||||
for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
|
||||
script = script_class()
|
||||
script.filename = path
|
||||
for script_data in auto_processing_scripts + scripts_data:
|
||||
script = script_data.script_class()
|
||||
script.filename = script_data.path
|
||||
script.is_txt2img = not is_img2img
|
||||
script.is_img2img = is_img2img
|
||||
|
||||
@ -313,6 +319,8 @@ class ScriptRunner:
|
||||
self.selectable_scripts.append(script)
|
||||
|
||||
def setup_ui(self):
|
||||
import modules.api.models as api_models
|
||||
|
||||
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
|
||||
|
||||
inputs = [None]
|
||||
@ -327,9 +335,28 @@ class ScriptRunner:
|
||||
if controls is None:
|
||||
return
|
||||
|
||||
script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
|
||||
api_args = []
|
||||
|
||||
for control in controls:
|
||||
control.custom_script_source = os.path.basename(script.filename)
|
||||
|
||||
arg_info = api_models.ScriptArg(label=control.label or "")
|
||||
|
||||
for field in ("value", "minimum", "maximum", "step", "choices"):
|
||||
v = getattr(control, field, None)
|
||||
if v is not None:
|
||||
setattr(arg_info, field, v)
|
||||
|
||||
api_args.append(arg_info)
|
||||
|
||||
script.api_info = api_models.ScriptInfo(
|
||||
name=script.name,
|
||||
is_img2img=script.is_img2img,
|
||||
is_alwayson=script.alwayson,
|
||||
args=api_args,
|
||||
)
|
||||
|
||||
if script.infotext_fields is not None:
|
||||
self.infotext_fields += script.infotext_fields
|
||||
|
||||
@ -492,7 +519,7 @@ class ScriptRunner:
|
||||
module = script_loading.load_module(script.filename)
|
||||
cache[filename] = module
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
for script_class in module.__dict__.values():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
self.scripts[si] = script_class()
|
||||
self.scripts[si].filename = filename
|
||||
|
||||
@ -17,7 +17,7 @@ class ScriptPostprocessingForMainUI(scripts.Script):
|
||||
return self.postprocessing_controls.values()
|
||||
|
||||
def postprocess_image(self, p, script_pp, *args):
|
||||
args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
|
||||
args_dict = dict(zip(self.postprocessing_controls, args))
|
||||
|
||||
pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
|
||||
pp.info = {}
|
||||
|
||||
@ -66,9 +66,9 @@ class ScriptPostprocessingRunner:
|
||||
def initialize_scripts(self, scripts_data):
|
||||
self.scripts = []
|
||||
|
||||
for script_class, path, basedir, script_module in scripts_data:
|
||||
script: ScriptPostprocessing = script_class()
|
||||
script.filename = path
|
||||
for script_data in scripts_data:
|
||||
script: ScriptPostprocessing = script_data.script_class()
|
||||
script.filename = script_data.path
|
||||
|
||||
if script.name == "Simple Upscale":
|
||||
continue
|
||||
@ -124,7 +124,7 @@ class ScriptPostprocessingRunner:
|
||||
script_args = args[script.args_from:script.args_to]
|
||||
|
||||
process_args = {}
|
||||
for (name, component), value in zip(script.controls.items(), script_args):
|
||||
for (name, _component), value in zip(script.controls.items(), script_args):
|
||||
process_args[name] = value
|
||||
|
||||
script.process(pp, **process_args)
|
||||
|
||||
@ -61,7 +61,7 @@ class DisableInitialization:
|
||||
if res is None:
|
||||
res = original(url, *args, local_files_only=False, **kwargs)
|
||||
return res
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
return original(url, *args, local_files_only=False, **kwargs)
|
||||
|
||||
def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
|
||||
|
||||
@ -3,7 +3,7 @@ from torch.nn.functional import silu
|
||||
from types import MethodType
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
|
||||
from modules import devices, sd_hijack_optimizations, shared
|
||||
from modules.hypernetworks import hypernetwork
|
||||
from modules.shared import cmd_opts
|
||||
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
|
||||
@ -37,7 +37,7 @@ def apply_optimizations():
|
||||
|
||||
optimization_method = None
|
||||
|
||||
can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
|
||||
can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) # not everyone has torch 2.x to use sdp
|
||||
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
|
||||
print("Applying xformers cross attention optimization.")
|
||||
@ -118,7 +118,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
|
||||
try:
|
||||
#Delete temporary weights if appended
|
||||
del sd_model._custom_loss_weight
|
||||
except AttributeError as e:
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
#If we have an old loss function, reset the loss function to the original one
|
||||
@ -133,7 +133,7 @@ def apply_weighted_forward(sd_model):
|
||||
def undo_weighted_forward(sd_model):
|
||||
try:
|
||||
del sd_model.weighted_forward
|
||||
except AttributeError as e:
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
|
||||
@ -216,6 +216,9 @@ class StableDiffusionModelHijack:
|
||||
self.comments = []
|
||||
|
||||
def get_prompt_lengths(self, text):
|
||||
if self.clip is None:
|
||||
return "-", "-"
|
||||
|
||||
_, token_count = self.clip.process_texts([text])
|
||||
|
||||
return token_count, self.clip.get_target_prompt_token_count(token_count)
|
||||
|
||||
@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
|
||||
self.hijack.fixes = [x.fixes for x in batch_chunk]
|
||||
|
||||
for fixes in self.hijack.fixes:
|
||||
for position, embedding in fixes:
|
||||
for _position, embedding in fixes:
|
||||
used_embeddings[embedding.name] = embedding
|
||||
|
||||
z = self.process_tokens(tokens, multipliers)
|
||||
|
||||
@ -1,16 +1,10 @@
|
||||
import os
|
||||
import torch
|
||||
|
||||
from einops import repeat
|
||||
from omegaconf import ListConfig
|
||||
|
||||
import ldm.models.diffusion.ddpm
|
||||
import ldm.models.diffusion.ddim
|
||||
import ldm.models.diffusion.plms
|
||||
|
||||
from ldm.models.diffusion.ddpm import LatentDiffusion
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from ldm.models.diffusion.ddim import DDIMSampler, noise_like
|
||||
from ldm.models.diffusion.ddim import noise_like
|
||||
from ldm.models.diffusion.sampling_util import norm_thresholding
|
||||
|
||||
|
||||
@ -29,7 +23,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
|
||||
|
||||
if isinstance(c, dict):
|
||||
assert isinstance(unconditional_conditioning, dict)
|
||||
c_in = dict()
|
||||
c_in = {}
|
||||
for k in c:
|
||||
if isinstance(c[k], list):
|
||||
c_in[k] = [
|
||||
|
||||
@ -1,8 +1,5 @@
|
||||
import collections
|
||||
import os.path
|
||||
import sys
|
||||
import gc
|
||||
import time
|
||||
|
||||
|
||||
def should_hijack_ip2p(checkpoint_info):
|
||||
from modules import sd_models_config
|
||||
@ -10,4 +7,4 @@ def should_hijack_ip2p(checkpoint_info):
|
||||
ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
|
||||
cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
|
||||
|
||||
return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
|
||||
return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename
|
||||
|
||||
@ -49,7 +49,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
v_in = self.to_v(context_v)
|
||||
del context, context_k, context_v, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
dtype = q.dtype
|
||||
@ -98,7 +98,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
@ -229,7 +229,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
|
||||
with devices.without_autocast(disable=not shared.opts.upcast_attn):
|
||||
k = k * self.scale
|
||||
|
||||
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))
|
||||
r = einsum_op(q, k, v)
|
||||
r = r.to(dtype)
|
||||
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
|
||||
@ -296,7 +296,6 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
|
||||
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
|
||||
# the big matmul fits into our memory limit; do everything in 1 chunk,
|
||||
# i.e. send it down the unchunked fast-path
|
||||
query_chunk_size = q_tokens
|
||||
kv_chunk_size = k_tokens
|
||||
|
||||
with devices.without_autocast(disable=q.dtype == v.dtype):
|
||||
@ -335,7 +334,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
k_in = self.to_k(context_k)
|
||||
v_in = self.to_v(context_v)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
dtype = q.dtype
|
||||
@ -461,7 +460,7 @@ def xformers_attnblock_forward(self, x):
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
b, c, h, w = q.shape
|
||||
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
|
||||
q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
|
||||
dtype = q.dtype
|
||||
if shared.opts.upcast_attn:
|
||||
q, k = q.float(), k.float()
|
||||
@ -483,7 +482,7 @@ def sdp_attnblock_forward(self, x):
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
b, c, h, w = q.shape
|
||||
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
|
||||
q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
|
||||
dtype = q.dtype
|
||||
if shared.opts.upcast_attn:
|
||||
q, k = q.float(), k.float()
|
||||
@ -507,7 +506,7 @@ def sub_quad_attnblock_forward(self, x):
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
b, c, h, w = q.shape
|
||||
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
|
||||
q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
|
||||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
|
||||
@ -1,8 +1,6 @@
|
||||
import open_clip.tokenizer
|
||||
import torch
|
||||
|
||||
from modules import sd_hijack_clip, devices
|
||||
from modules.shared import opts
|
||||
|
||||
|
||||
class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
|
||||
|
||||
@ -15,9 +15,9 @@ import ldm.modules.midas as midas
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
|
||||
from modules.paths import models_path
|
||||
from modules.sd_hijack_inpainting import do_inpainting_hijack
|
||||
from modules.timer import Timer
|
||||
import tomesd
|
||||
|
||||
model_dir = "Stable-diffusion"
|
||||
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
|
||||
@ -87,8 +87,7 @@ class CheckpointInfo:
|
||||
|
||||
try:
|
||||
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
||||
|
||||
from transformers import logging, CLIPModel
|
||||
from transformers import logging, CLIPModel # noqa: F401
|
||||
|
||||
logging.set_verbosity_error()
|
||||
except Exception:
|
||||
@ -239,7 +238,7 @@ def read_metadata_from_safetensors(filename):
|
||||
if isinstance(v, str) and v[0:1] == '{':
|
||||
try:
|
||||
res[k] = json.loads(v)
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return res
|
||||
@ -415,6 +414,9 @@ class SdModelData:
|
||||
def get_sd_model(self):
|
||||
if self.sd_model is None:
|
||||
with self.lock:
|
||||
if self.sd_model is not None:
|
||||
return self.sd_model
|
||||
|
||||
try:
|
||||
load_model()
|
||||
except Exception as e:
|
||||
@ -467,7 +469,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
||||
try:
|
||||
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
|
||||
sd_model = instantiate_from_config(sd_config.model)
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if sd_model is None:
|
||||
@ -538,13 +540,12 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
|
||||
if sd_model is None or checkpoint_config != sd_model.used_config:
|
||||
del sd_model
|
||||
checkpoints_loaded.clear()
|
||||
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
|
||||
return model_data.sd_model
|
||||
|
||||
try:
|
||||
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
print("Failed to load checkpoint, restoring previous")
|
||||
load_model_weights(sd_model, current_checkpoint_info, None, timer)
|
||||
raise
|
||||
@ -565,7 +566,7 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
|
||||
|
||||
def unload_model_weights(sd_model=None, info=None):
|
||||
from modules import lowvram, devices, sd_hijack
|
||||
from modules import devices, sd_hijack
|
||||
timer = Timer()
|
||||
|
||||
if model_data.sd_model:
|
||||
@ -580,3 +581,29 @@ def unload_model_weights(sd_model=None, info=None):
|
||||
print(f"Unloaded weights {timer.summary()}.")
|
||||
|
||||
return sd_model
|
||||
|
||||
|
||||
def apply_token_merging(sd_model, token_merging_ratio):
|
||||
"""
|
||||
Applies speed and memory optimizations from tomesd.
|
||||
"""
|
||||
|
||||
current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
|
||||
|
||||
if current_token_merging_ratio == token_merging_ratio:
|
||||
return
|
||||
|
||||
if current_token_merging_ratio > 0:
|
||||
tomesd.remove_patch(sd_model)
|
||||
|
||||
if token_merging_ratio > 0:
|
||||
tomesd.apply_patch(
|
||||
sd_model,
|
||||
ratio=token_merging_ratio,
|
||||
use_rand=False, # can cause issues with some samplers
|
||||
merge_attn=True,
|
||||
merge_crossattn=False,
|
||||
merge_mlp=False
|
||||
)
|
||||
|
||||
sd_model.applied_token_merged_ratio = token_merging_ratio
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
import re
|
||||
import os
|
||||
|
||||
import torch
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
|
||||
|
||||
# imports for functions that previously were here and are used by other modules
|
||||
from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
|
||||
from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
|
||||
|
||||
all_samplers = [
|
||||
*sd_samplers_kdiffusion.samplers_data_k_diffusion,
|
||||
@ -14,12 +14,18 @@ samplers_for_img2img = []
|
||||
samplers_map = {}
|
||||
|
||||
|
||||
def create_sampler(name, model):
|
||||
def find_sampler_config(name):
|
||||
if name is not None:
|
||||
config = all_samplers_map.get(name, None)
|
||||
else:
|
||||
config = all_samplers[0]
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def create_sampler(name, model):
|
||||
config = find_sampler_config(name)
|
||||
|
||||
assert config is not None, f'bad sampler name: {name}'
|
||||
|
||||
sampler = config.constructor(model)
|
||||
|
||||
@ -2,7 +2,7 @@ from collections import namedtuple
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from modules import devices, processing, images, sd_vae_approx
|
||||
from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
|
||||
|
||||
from modules.shared import opts, state
|
||||
import modules.shared as shared
|
||||
@ -22,7 +22,7 @@ def setup_img2img_steps(p, steps=None):
|
||||
return steps, t_enc
|
||||
|
||||
|
||||
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
|
||||
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
|
||||
|
||||
|
||||
def single_sample_to_image(sample, approximation=None):
|
||||
@ -30,15 +30,19 @@ def single_sample_to_image(sample, approximation=None):
|
||||
approximation = approximation_indexes.get(opts.show_progress_type, 0)
|
||||
|
||||
if approximation == 2:
|
||||
x_sample = sd_vae_approx.cheap_approximation(sample)
|
||||
x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
|
||||
elif approximation == 1:
|
||||
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
|
||||
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
|
||||
elif approximation == 3:
|
||||
x_sample = sample * 1.5
|
||||
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
|
||||
else:
|
||||
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
|
||||
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
|
||||
|
||||
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
|
||||
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
|
||||
return Image.fromarray(x_sample)
|
||||
|
||||
|
||||
@ -58,6 +62,25 @@ def store_latent(decoded):
|
||||
shared.state.assign_current_image(sample_to_image(decoded))
|
||||
|
||||
|
||||
def is_sampler_using_eta_noise_seed_delta(p):
|
||||
"""returns whether sampler from config will use eta noise seed delta for image creation"""
|
||||
|
||||
sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
|
||||
|
||||
eta = p.eta
|
||||
|
||||
if eta is None and p.sampler is not None:
|
||||
eta = p.sampler.eta
|
||||
|
||||
if eta is None and sampler_config is not None:
|
||||
eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
|
||||
|
||||
if eta == 0:
|
||||
return False
|
||||
|
||||
return sampler_config.options.get("uses_ensd", False)
|
||||
|
||||
|
||||
class InterruptedException(BaseException):
|
||||
pass
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ import modules.models.diffusion.uni_pc
|
||||
|
||||
|
||||
samplers_data_compvis = [
|
||||
sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
|
||||
sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),
|
||||
sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
|
||||
sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
|
||||
]
|
||||
@ -55,7 +55,7 @@ class VanillaStableDiffusionSampler:
|
||||
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
|
||||
x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
|
||||
|
||||
res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
|
||||
res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
|
||||
|
||||
x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
|
||||
|
||||
@ -83,7 +83,7 @@ class VanillaStableDiffusionSampler:
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
||||
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
cond = tensor
|
||||
|
||||
# for DDIM, shapes must match, we can't just process cond and uncond independently;
|
||||
@ -134,7 +134,11 @@ class VanillaStableDiffusionSampler:
|
||||
self.update_step(x)
|
||||
|
||||
def initialize(self, p):
|
||||
if self.is_ddim:
|
||||
self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
|
||||
else:
|
||||
self.eta = 0.0
|
||||
|
||||
if self.eta != 0.0:
|
||||
p.extra_generation_params["Eta DDIM"] = self.eta
|
||||
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
from collections import deque
|
||||
import torch
|
||||
import inspect
|
||||
import einops
|
||||
import k_diffusion.sampling
|
||||
from modules import prompt_parser, devices, sd_samplers_common
|
||||
|
||||
@ -9,25 +8,26 @@ from modules.shared import opts, state
|
||||
import modules.shared as shared
|
||||
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
|
||||
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
|
||||
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
|
||||
|
||||
samplers_k_diffusion = [
|
||||
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
|
||||
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
|
||||
('Euler', 'sample_euler', ['k_euler'], {}),
|
||||
('LMS', 'sample_lms', ['k_lms'], {}),
|
||||
('Heun', 'sample_heun', ['k_heun'], {}),
|
||||
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
|
||||
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
|
||||
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
|
||||
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
|
||||
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
|
||||
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
|
||||
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True}),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
|
||||
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
|
||||
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
|
||||
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
|
||||
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
|
||||
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
|
||||
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
|
||||
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
|
||||
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True}),
|
||||
]
|
||||
|
||||
samplers_data_k_diffusion = [
|
||||
@ -87,7 +87,7 @@ class CFGDenoiser(torch.nn.Module):
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
|
||||
|
||||
assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
|
||||
assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
|
||||
|
||||
batch_size = len(conds_list)
|
||||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
@ -161,7 +161,7 @@ class CFGDenoiser(torch.nn.Module):
|
||||
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
|
||||
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
|
||||
|
||||
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
|
||||
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
|
||||
cfg_denoised_callback(denoised_params)
|
||||
|
||||
devices.test_for_nans(x_out, "unet")
|
||||
@ -181,6 +181,10 @@ class CFGDenoiser(torch.nn.Module):
|
||||
if self.mask is not None:
|
||||
denoised = self.init_latent * self.mask + self.nmask * denoised
|
||||
|
||||
after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
|
||||
cfg_after_cfg_callback(after_cfg_callback_params)
|
||||
denoised = after_cfg_callback_params.x
|
||||
|
||||
self.step += 1
|
||||
return denoised
|
||||
|
||||
|
||||
@ -1,8 +1,5 @@
|
||||
import torch
|
||||
import safetensors.torch
|
||||
import os
|
||||
import collections
|
||||
from collections import namedtuple
|
||||
from modules import paths, shared, devices, script_callbacks, sd_models
|
||||
import glob
|
||||
from copy import deepcopy
|
||||
@ -88,10 +85,10 @@ def refresh_vae_list():
|
||||
|
||||
|
||||
def find_vae_near_checkpoint(checkpoint_file):
|
||||
checkpoint_path = os.path.splitext(checkpoint_file)[0]
|
||||
for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
|
||||
if os.path.isfile(vae_location):
|
||||
return vae_location
|
||||
checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0]
|
||||
for vae_file in vae_dict.values():
|
||||
if os.path.basename(vae_file).startswith(checkpoint_path):
|
||||
return vae_file
|
||||
|
||||
return None
|
||||
|
||||
|
||||
88
modules/sd_vae_taesd.py
Normal file
88
modules/sd_vae_taesd.py
Normal file
@ -0,0 +1,88 @@
|
||||
"""
|
||||
Tiny AutoEncoder for Stable Diffusion
|
||||
(DNN for encoding / decoding SD's latent space)
|
||||
|
||||
https://github.com/madebyollin/taesd
|
||||
"""
|
||||
import os
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from modules import devices, paths_internal
|
||||
|
||||
sd_vae_taesd = None
|
||||
|
||||
|
||||
def conv(n_in, n_out, **kwargs):
|
||||
return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
|
||||
|
||||
|
||||
class Clamp(nn.Module):
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return torch.tanh(x / 3) * 3
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, n_in, n_out):
|
||||
super().__init__()
|
||||
self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
|
||||
self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
|
||||
self.fuse = nn.ReLU()
|
||||
|
||||
def forward(self, x):
|
||||
return self.fuse(self.conv(x) + self.skip(x))
|
||||
|
||||
|
||||
def decoder():
|
||||
return nn.Sequential(
|
||||
Clamp(), conv(4, 64), nn.ReLU(),
|
||||
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
|
||||
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
|
||||
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
|
||||
Block(64, 64), conv(64, 3),
|
||||
)
|
||||
|
||||
|
||||
class TAESD(nn.Module):
|
||||
latent_magnitude = 3
|
||||
latent_shift = 0.5
|
||||
|
||||
def __init__(self, decoder_path="taesd_decoder.pth"):
|
||||
"""Initialize pretrained TAESD on the given device from the given checkpoints."""
|
||||
super().__init__()
|
||||
self.decoder = decoder()
|
||||
self.decoder.load_state_dict(
|
||||
torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
|
||||
|
||||
@staticmethod
|
||||
def unscale_latents(x):
|
||||
"""[0, 1] -> raw latents"""
|
||||
return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
|
||||
|
||||
|
||||
def download_model(model_path):
|
||||
model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth'
|
||||
|
||||
if not os.path.exists(model_path):
|
||||
os.makedirs(os.path.dirname(model_path), exist_ok=True)
|
||||
|
||||
print(f'Downloading TAESD decoder to: {model_path}')
|
||||
torch.hub.download_url_to_file(model_url, model_path)
|
||||
|
||||
|
||||
def model():
|
||||
global sd_vae_taesd
|
||||
|
||||
if sd_vae_taesd is None:
|
||||
model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth")
|
||||
download_model(model_path)
|
||||
|
||||
if os.path.exists(model_path):
|
||||
sd_vae_taesd = TAESD(model_path)
|
||||
sd_vae_taesd.eval()
|
||||
sd_vae_taesd.to(devices.device, devices.dtype)
|
||||
else:
|
||||
raise FileNotFoundError('TAESD model not found')
|
||||
|
||||
return sd_vae_taesd.decoder
|
||||
@ -1,12 +1,10 @@
|
||||
import argparse
|
||||
import datetime
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import requests
|
||||
|
||||
from PIL import Image
|
||||
import gradio as gr
|
||||
import tqdm
|
||||
|
||||
@ -15,7 +13,7 @@ import modules.memmon
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
|
||||
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
|
||||
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
|
||||
from ldm.models.diffusion.ddpm import LatentDiffusion
|
||||
|
||||
demo = None
|
||||
@ -113,8 +111,47 @@ class State:
|
||||
id_live_preview = 0
|
||||
textinfo = None
|
||||
time_start = None
|
||||
need_restart = False
|
||||
server_start = None
|
||||
_server_command_signal = threading.Event()
|
||||
_server_command: str | None = None
|
||||
|
||||
@property
|
||||
def need_restart(self) -> bool:
|
||||
# Compatibility getter for need_restart.
|
||||
return self.server_command == "restart"
|
||||
|
||||
@need_restart.setter
|
||||
def need_restart(self, value: bool) -> None:
|
||||
# Compatibility setter for need_restart.
|
||||
if value:
|
||||
self.server_command = "restart"
|
||||
|
||||
@property
|
||||
def server_command(self):
|
||||
return self._server_command
|
||||
|
||||
@server_command.setter
|
||||
def server_command(self, value: str | None) -> None:
|
||||
"""
|
||||
Set the server command to `value` and signal that it's been set.
|
||||
"""
|
||||
self._server_command = value
|
||||
self._server_command_signal.set()
|
||||
|
||||
def wait_for_server_command(self, timeout: float | None = None) -> str | None:
|
||||
"""
|
||||
Wait for server command to get set; return and clear the value and signal.
|
||||
"""
|
||||
if self._server_command_signal.wait(timeout):
|
||||
self._server_command_signal.clear()
|
||||
req = self._server_command
|
||||
self._server_command = None
|
||||
return req
|
||||
return None
|
||||
|
||||
def request_restart(self) -> None:
|
||||
self.interrupt()
|
||||
self.server_command = "restart"
|
||||
|
||||
def skip(self):
|
||||
self.skipped = True
|
||||
@ -202,8 +239,9 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
|
||||
|
||||
face_restorers = []
|
||||
|
||||
|
||||
class OptionInfo:
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
|
||||
self.default = default
|
||||
self.label = label
|
||||
self.component = component
|
||||
@ -212,9 +250,33 @@ class OptionInfo:
|
||||
self.section = section
|
||||
self.refresh = refresh
|
||||
|
||||
self.comment_before = comment_before
|
||||
"""HTML text that will be added after label in UI"""
|
||||
|
||||
self.comment_after = comment_after
|
||||
"""HTML text that will be added before label in UI"""
|
||||
|
||||
def link(self, label, url):
|
||||
self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
|
||||
return self
|
||||
|
||||
def js(self, label, js_func):
|
||||
self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
|
||||
return self
|
||||
|
||||
def info(self, info):
|
||||
self.comment_after += f"<span class='info'>({info})</span>"
|
||||
return self
|
||||
|
||||
def needs_restart(self):
|
||||
self.comment_after += " <span class='info'>(requires restart)</span>"
|
||||
return self
|
||||
|
||||
|
||||
|
||||
|
||||
def options_section(section_identifier, options_dict):
|
||||
for k, v in options_dict.items():
|
||||
for v in options_dict.values():
|
||||
v.section = section_identifier
|
||||
|
||||
return options_dict
|
||||
@ -243,7 +305,7 @@ options_templates = {}
|
||||
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
|
||||
"samples_save": OptionInfo(True, "Always save all generated images"),
|
||||
"samples_format": OptionInfo('png', 'File format for images'),
|
||||
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
|
||||
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
|
||||
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
|
||||
|
||||
"grid_save": OptionInfo(True, "Always save all generated image grids"),
|
||||
@ -262,10 +324,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
||||
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
|
||||
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
|
||||
"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
|
||||
"export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
|
||||
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
|
||||
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
|
||||
"img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
|
||||
"img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
|
||||
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
@ -293,31 +355,30 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
|
||||
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
|
||||
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
|
||||
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
|
||||
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
"SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
"show_warnings": OptionInfo(False, "Show warnings in console."),
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
|
||||
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
|
||||
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
@ -339,20 +400,27 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
|
||||
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
|
||||
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
|
||||
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
|
||||
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
|
||||
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
|
||||
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
|
||||
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
|
||||
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('optimizations', "Optimizations"), {
|
||||
"s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
|
||||
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
|
||||
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
|
||||
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
@ -364,30 +432,35 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
|
||||
"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
|
||||
"interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
||||
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
||||
"deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
|
||||
"deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
|
||||
"deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
|
||||
"deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
|
||||
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
|
||||
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
|
||||
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
|
||||
"img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
|
||||
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
|
||||
@ -400,17 +473,16 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"js_modal_lightbox_gamepad": OptionInfo(True, "Navigate image viewer with gamepad"),
|
||||
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_restart(),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
|
||||
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('infotext', "Infotext"), {
|
||||
@ -423,27 +495,27 @@ options_templates.update(options_section(('infotext', "Infotext"), {
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
||||
"show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
"show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
||||
"show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
|
||||
"show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
|
||||
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
||||
"live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
|
||||
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_restart(),
|
||||
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
|
||||
"eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
|
||||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
|
||||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
|
||||
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
||||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
|
||||
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
|
||||
}))
|
||||
|
||||
@ -460,6 +532,7 @@ options_templates.update(options_section((None, "Hidden options"), {
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
}))
|
||||
|
||||
|
||||
options_templates.update()
|
||||
|
||||
|
||||
@ -571,7 +644,9 @@ class Options:
|
||||
func()
|
||||
|
||||
def dumpjson(self):
|
||||
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
|
||||
d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
|
||||
d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
|
||||
d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
|
||||
return json.dumps(d)
|
||||
|
||||
def add_option(self, key, info):
|
||||
@ -582,11 +657,11 @@ class Options:
|
||||
|
||||
section_ids = {}
|
||||
settings_items = self.data_labels.items()
|
||||
for k, item in settings_items:
|
||||
for _, item in settings_items:
|
||||
if item.section not in section_ids:
|
||||
section_ids[item.section] = len(section_ids)
|
||||
|
||||
self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
|
||||
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
|
||||
|
||||
def cast_value(self, key, value):
|
||||
"""casts an arbitrary to the same type as this setting's value with key
|
||||
@ -748,11 +823,14 @@ def walk_files(path, allowed_extensions=None):
|
||||
if allowed_extensions is not None:
|
||||
allowed_extensions = set(allowed_extensions)
|
||||
|
||||
for root, dirs, files in os.walk(path):
|
||||
for root, _, files in os.walk(path, followlinks=True):
|
||||
for filename in files:
|
||||
if allowed_extensions is not None:
|
||||
_, ext = os.path.splitext(filename)
|
||||
if ext not in allowed_extensions:
|
||||
continue
|
||||
|
||||
if not opts.list_hidden_files and ("/." in root or "\\." in root):
|
||||
continue
|
||||
|
||||
yield os.path.join(root, filename)
|
||||
|
||||
@ -1,18 +1,9 @@
|
||||
# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime
|
||||
from __future__ import annotations
|
||||
|
||||
import csv
|
||||
import os
|
||||
import os.path
|
||||
import typing
|
||||
import collections.abc as abc
|
||||
import tempfile
|
||||
import shutil
|
||||
|
||||
if typing.TYPE_CHECKING:
|
||||
# Only import this when code is being type-checked, it doesn't have any effect at runtime
|
||||
from .processing import StableDiffusionProcessing
|
||||
|
||||
|
||||
class PromptStyle(typing.NamedTuple):
|
||||
name: str
|
||||
@ -52,7 +43,7 @@ class StyleDatabase:
|
||||
return
|
||||
|
||||
with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
|
||||
reader = csv.DictReader(file)
|
||||
reader = csv.DictReader(file, skipinitialspace=True)
|
||||
for row in reader:
|
||||
# Support loading old CSV format with "name, text"-columns
|
||||
prompt = row["prompt"] if "prompt" in row else row["text"]
|
||||
|
||||
@ -202,13 +202,14 @@ def efficient_dot_product_attention(
|
||||
value=value,
|
||||
)
|
||||
|
||||
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
|
||||
# and pass slices to be mutated, instead of torch.cat()ing the returned slices
|
||||
res = torch.cat([
|
||||
compute_query_chunk_attn(
|
||||
res = torch.zeros_like(query)
|
||||
for i in range(math.ceil(q_tokens / query_chunk_size)):
|
||||
attn_scores = compute_query_chunk_attn(
|
||||
query=get_query_chunk(i * query_chunk_size),
|
||||
key=key,
|
||||
value=value,
|
||||
) for i in range(math.ceil(q_tokens / query_chunk_size))
|
||||
], dim=1)
|
||||
)
|
||||
|
||||
res[:, i * query_chunk_size:i * query_chunk_size + attn_scores.shape[1], :] = attn_scores
|
||||
|
||||
return res
|
||||
|
||||
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Loading…
Reference in New Issue
Block a user