Merge branch 'dev' into master

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AUTOMATIC1111 2023-07-13 15:21:39 +03:00 committed by GitHub
commit b7c5b30f14
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49 changed files with 704 additions and 389 deletions

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@ -18,7 +18,7 @@ jobs:
# 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
run: pip install ruff==0.0.272
- name: Run Ruff
run: ruff .
lint-js:

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@ -42,7 +42,7 @@ jobs:
--no-half
--disable-opt-split-attention
--use-cpu all
--add-stop-route
--api-server-stop
2>&1 | tee output.txt &
- name: Run tests
run: |
@ -50,7 +50,7 @@ jobs:
python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
- name: Kill test server
if: always()
run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
- name: Show coverage
run: |
python -m coverage combine .coverage*

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@ -135,8 +135,11 @@ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-w
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.

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@ -12,7 +12,7 @@ import safetensors.torch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config, ismap
from modules import shared, sd_hijack
from modules import shared, sd_hijack, devices
cached_ldsr_model: torch.nn.Module = None
@ -112,8 +112,7 @@ class LDSR:
gc.collect()
if torch.cuda.is_available:
torch.cuda.empty_cache()
devices.torch_gc()
im_og = image
width_og, height_og = im_og.size
@ -150,8 +149,7 @@ class LDSR:
del model
gc.collect()
if torch.cuda.is_available:
torch.cuda.empty_cache()
devices.torch_gc()
return a

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@ -1,7 +1,6 @@
import os
from basicsr.utils.download_util import load_file_from_url
from modules.modelloader import load_file_from_url
from modules.upscaler import Upscaler, UpscalerData
from ldsr_model_arch import LDSR
from modules import shared, script_callbacks, errors
@ -43,20 +42,17 @@ class UpscalerLDSR(Upscaler):
if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
model = local_safetensors_path
else:
model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
try:
return LDSR(model, yaml)
except Exception:
errors.report("Error importing LDSR", exc_info=True)
return None
return LDSR(model, yaml)
def do_upscale(self, img, path):
ldsr = self.load_model(path)
if ldsr is None:
print("NO LDSR!")
try:
ldsr = self.load_model(path)
except Exception:
errors.report(f"Failed loading LDSR model {path}", exc_info=True)
return img
ddim_steps = shared.opts.ldsr_steps
return ldsr.super_resolution(img, ddim_steps, self.scale)

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@ -443,7 +443,7 @@ def list_available_loras():
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
for filename in sorted(candidates, key=str.lower):
for filename in candidates:
if os.path.isdir(filename):
continue

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@ -1,4 +1,3 @@
import os.path
import sys
import PIL.Image
@ -6,12 +5,11 @@ import numpy as np
import torch
from tqdm import tqdm
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import devices, modelloader, script_callbacks, errors
from scunet_model_arch import SCUNet as net
from scunet_model_arch import SCUNet
from modules.modelloader import load_file_from_url
from modules.shared import opts
@ -28,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
scalers = []
add_model2 = True
for file in model_paths:
if "http" in file:
if file.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(file)
@ -87,11 +85,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
def do_upscale(self, img: PIL.Image.Image, selected_file):
torch.cuda.empty_cache()
devices.torch_gc()
model = self.load_model(selected_file)
if model is None:
print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
try:
model = self.load_model(selected_file)
except Exception as e:
print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
return img
device = devices.get_device_for('scunet')
@ -111,7 +110,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
del torch_img, torch_output
torch.cuda.empty_cache()
devices.torch_gc()
output = np_output.transpose((1, 2, 0)) # CHW to HWC
output = output[:, :, ::-1] # BGR to RGB
@ -119,15 +118,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
def load_model(self, path: str):
device = devices.get_device_for('scunet')
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
if path.startswith("http"):
# TODO: this doesn't use `path` at all?
filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
else:
filename = path
if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
return None
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
model = SCUNet(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 _, v in model.named_parameters():

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@ -1,34 +1,35 @@
import os
import sys
import platform
import numpy as np
import torch
from PIL import Image
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 opts, state
from swinir_model_arch import SwinIR as net
from swinir_model_arch_v2 import Swin2SR as net2
from swinir_model_arch import SwinIR
from swinir_model_arch_v2 import Swin2SR
from modules.upscaler import Upscaler, UpscalerData
SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
device_swinir = devices.get_device_for('swinir')
class UpscalerSwinIR(Upscaler):
def __init__(self, dirname):
self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
self.name = "SwinIR"
self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
"-L_x4_GAN.pth "
self.model_url = SWINIR_MODEL_URL
self.model_name = "SwinIR 4x"
self.user_path = dirname
super().__init__()
scalers = []
model_files = self.find_models(ext_filter=[".pt", ".pth"])
for model in model_files:
if "http" in model:
if model.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(model)
@ -37,42 +38,54 @@ class UpscalerSwinIR(Upscaler):
self.scalers = scalers
def do_upscale(self, img, model_file):
model = self.load_model(model_file)
if model is None:
return img
model = model.to(device_swinir, dtype=devices.dtype)
use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
current_config = (model_file, opts.SWIN_tile)
if use_compile and self._cached_model_config == current_config:
model = self._cached_model
else:
self._cached_model = None
try:
model = self.load_model(model_file)
except Exception as e:
print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
return img
model = model.to(device_swinir, dtype=devices.dtype)
if use_compile:
model = torch.compile(model)
self._cached_model = model
self._cached_model_config = current_config
img = upscale(img, model)
try:
torch.cuda.empty_cache()
except Exception:
pass
devices.torch_gc()
return img
def load_model(self, path, scale=4):
if "http" in path:
dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
if path.startswith("http"):
filename = modelloader.load_file_from_url(
url=path,
model_dir=self.model_download_path,
file_name=f"{self.model_name.replace(' ', '_')}.pth",
)
else:
filename = path
if filename is None or not os.path.exists(filename):
return None
if filename.endswith(".v2.pth"):
model = net2(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="1conv",
model = Swin2SR(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="1conv",
)
params = None
else:
model = net(
model = SwinIR(
upscale=scale,
in_chans=3,
img_size=64,
@ -172,6 +185,8 @@ def on_ui_settings():
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
script_callbacks.on_ui_settings(on_ui_settings)

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@ -200,7 +200,8 @@ onUiLoaded(async() => {
canvas_hotkey_move: "KeyF",
canvas_hotkey_overlap: "KeyO",
canvas_disabled_functions: [],
canvas_show_tooltip: true
canvas_show_tooltip: true,
canvas_blur_prompt: false
};
const functionMap = {
@ -608,6 +609,19 @@ onUiLoaded(async() => {
// Handle keydown events
function handleKeyDown(event) {
// Disable key locks to make pasting from the buffer work correctly
if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") {
return;
}
// before activating shortcut, ensure user is not actively typing in an input field
if (!hotkeysConfig.canvas_blur_prompt) {
if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') {
return;
}
}
const hotkeyActions = {
[hotkeysConfig.canvas_hotkey_reset]: resetZoom,
[hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
@ -686,6 +700,20 @@ onUiLoaded(async() => {
// Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
function handleMoveKeyDown(e) {
// Disable key locks to make pasting from the buffer work correctly
if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
return;
}
// before activating shortcut, ensure user is not actively typing in an input field
if (!hotkeysConfig.canvas_blur_prompt) {
if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') {
return;
}
}
if (e.code === hotkeysConfig.canvas_hotkey_move) {
if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
e.preventDefault();

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@ -9,5 +9,6 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
"canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
"canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
"canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
"canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
"canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
}))

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@ -100,11 +100,12 @@ function keyupEditAttention(event) {
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);
var endParenPos = text.substring(selectionEnd).indexOf(')');
text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1);
selectionStart--;
selectionEnd--;
} else {
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
}
target.focus();

41
javascript/edit-order.js Normal file
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@ -0,0 +1,41 @@
/* alt+left/right moves text in prompt */
function keyupEditOrder(event) {
if (!opts.keyedit_move) return;
let target = event.originalTarget || event.composedPath()[0];
if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
if (!event.altKey) return;
let isLeft = event.key == "ArrowLeft";
let isRight = event.key == "ArrowRight";
if (!isLeft && !isRight) return;
event.preventDefault();
let selectionStart = target.selectionStart;
let selectionEnd = target.selectionEnd;
let text = target.value;
let items = text.split(",");
let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length;
let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length;
let range = indexEnd - indexStart + 1;
if (isLeft && indexStart > 0) {
items.splice(indexStart - 1, 0, ...items.splice(indexStart, range));
target.value = items.join();
target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1);
target.selectionEnd = items.slice(0, indexEnd).join().length;
} else if (isRight && indexEnd < items.length - 1) {
items.splice(indexStart + 1, 0, ...items.splice(indexStart, range));
target.value = items.join();
target.selectionStart = items.slice(0, indexStart + 1).join().length + 1;
target.selectionEnd = items.slice(0, indexEnd + 2).join().length;
}
event.preventDefault();
updateInput(target);
}
addEventListener('keydown', (event) => {
keyupEditOrder(event);
});

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@ -72,3 +72,21 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
}
return [confirmed, config_state_name, config_restore_type];
}
function toggle_all_extensions(event) {
gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) {
checkbox_el.checked = event.target.checked;
});
}
function toggle_extension() {
let all_extensions_toggled = true;
for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) {
if (!checkbox_el.checked) {
all_extensions_toggled = false;
break;
}
}
gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled;
}

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@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@ -22,7 +22,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases
from modules.sd_vae import vae_dict
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
@ -30,13 +30,7 @@ from modules import devices
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 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
from contextlib import closing
def script_name_to_index(name, scripts):
@ -84,6 +78,8 @@ def encode_pil_to_base64(image):
image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
if image.mode == "RGBA":
image = image.convert("RGB")
parameters = image.info.get('parameters', None)
exif_bytes = piexif.dump({
"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
@ -209,6 +205,11 @@ class Api:
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])
if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
self.default_script_arg_txt2img = []
self.default_script_arg_img2img = []
@ -324,19 +325,19 @@ class Api:
args.pop('save_images', None)
with self.queue_lock:
p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
p.scripts = script_runner
p.outpath_grids = opts.outdir_txt2img_grids
p.outpath_samples = opts.outdir_txt2img_samples
with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
p.scripts = script_runner
p.outpath_grids = opts.outdir_txt2img_grids
p.outpath_samples = opts.outdir_txt2img_samples
shared.state.begin()
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:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
shared.state.end()
shared.state.begin(job="scripts_txt2img")
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:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
shared.state.end()
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
@ -380,20 +381,20 @@ class Api:
args.pop('save_images', None)
with self.queue_lock:
p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
p.init_images = [decode_base64_to_image(x) for x in init_images]
p.scripts = script_runner
p.outpath_grids = opts.outdir_img2img_grids
p.outpath_samples = opts.outdir_img2img_samples
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
p.init_images = [decode_base64_to_image(x) for x in init_images]
p.scripts = script_runner
p.outpath_grids = opts.outdir_img2img_grids
p.outpath_samples = opts.outdir_img2img_samples
shared.state.begin()
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:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
shared.state.end()
shared.state.begin(job="scripts_img2img")
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:
p.script_args = tuple(script_args) # Need to pass args as tuple here
processed = process_images(p)
shared.state.end()
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
@ -517,6 +518,10 @@ class Api:
return options
def set_config(self, req: Dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found")
for k, v in req.items():
shared.opts.set(k, v)
@ -598,44 +603,42 @@ class Api:
def create_embedding(self, args: dict):
try:
shared.state.begin()
shared.state.begin(job="create_embedding")
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 models.CreateResponse(info=f"create embedding filename: {filename}")
except AssertionError as e:
shared.state.end()
return models.TrainResponse(info=f"create embedding error: {e}")
finally:
shared.state.end()
def create_hypernetwork(self, args: dict):
try:
shared.state.begin()
shared.state.begin(job="create_hypernetwork")
filename = create_hypernetwork(**args) # create empty embedding
shared.state.end()
return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
except AssertionError as e:
shared.state.end()
return models.TrainResponse(info=f"create hypernetwork error: {e}")
finally:
shared.state.end()
def preprocess(self, args: dict):
try:
shared.state.begin()
shared.state.begin(job="preprocess")
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
shared.state.end()
return models.PreprocessResponse(info = 'preprocess complete')
return models.PreprocessResponse(info='preprocess complete')
except KeyError as e:
shared.state.end()
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
except AssertionError as e:
shared.state.end()
except Exception as e:
return models.PreprocessResponse(info=f"preprocess error: {e}")
except FileNotFoundError as e:
finally:
shared.state.end()
return models.PreprocessResponse(info=f'preprocess error: {e}')
def train_embedding(self, args: dict):
try:
shared.state.begin()
shared.state.begin(job="train_embedding")
apply_optimizations = shared.opts.training_xattention_optimizations
error = None
filename = ''
@ -648,15 +651,15 @@ class Api:
finally:
if not apply_optimizations:
sd_hijack.apply_optimizations()
shared.state.end()
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
except AssertionError as msg:
shared.state.end()
except Exception as msg:
return models.TrainResponse(info=f"train embedding error: {msg}")
finally:
shared.state.end()
def train_hypernetwork(self, args: dict):
try:
shared.state.begin()
shared.state.begin(job="train_hypernetwork")
shared.loaded_hypernetworks = []
apply_optimizations = shared.opts.training_xattention_optimizations
error = None
@ -674,9 +677,10 @@ class Api:
sd_hijack.apply_optimizations()
shared.state.end()
return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
except AssertionError:
except Exception as exc:
return models.TrainResponse(info=f"train embedding error: {exc}")
finally:
shared.state.end()
return models.TrainResponse(info=f"train embedding error: {error}")
def get_memory(self):
try:
@ -716,3 +720,16 @@ class Api:
def launch(self, server_name, port):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive)
def kill_webui(self):
restart.stop_program()
def restart_webui(self):
if restart.is_restartable():
restart.restart_program()
return Response(status_code=501)
def stop_webui(request):
shared.state.server_command = "stop"
return Response("Stopping.")

View File

@ -274,10 +274,6 @@ class PromptStyleItem(BaseModel):
prompt: Optional[str] = Field(title="Prompt")
negative_prompt: Optional[str] = Field(title="Negative Prompt")
class ArtistItem(BaseModel):
name: str = Field(title="Name")
score: float = Field(title="Score")
category: str = Field(title="Category")
class EmbeddingItem(BaseModel):
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")

View File

@ -1,3 +1,4 @@
from functools import wraps
import html
import threading
import time
@ -18,6 +19,7 @@ def wrap_queued_call(func):
def wrap_gradio_gpu_call(func, extra_outputs=None):
@wraps(func)
def f(*args, **kwargs):
# if the first argument is a string that says "task(...)", it is treated as a job id
@ -28,7 +30,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
id_task = None
with queue_lock:
shared.state.begin()
shared.state.begin(job=id_task)
progress.start_task(id_task)
try:
@ -45,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
@wraps(func)
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:

View File

@ -107,4 +107,5 @@ parser.add_argument("--no-hashing", action='store_true', help="disable sha256 ha
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')
parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')

View File

@ -15,7 +15,6 @@ model_dir = "Codeformer"
model_path = os.path.join(models_path, model_dir)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
have_codeformer = False
codeformer = None
@ -100,7 +99,7 @@ def setup_model(dirname):
output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
devices.torch_gc()
except Exception:
errors.report('Failed inference for CodeFormer', exc_info=True)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
@ -123,9 +122,6 @@ def setup_model(dirname):
return restored_img
global have_codeformer
have_codeformer = True
global codeformer
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)

View File

@ -15,13 +15,6 @@ def has_mps() -> bool:
else:
return mac_specific.has_mps
def extract_device_id(args, name):
for x in range(len(args)):
if name in args[x]:
return args[x + 1]
return None
def get_cuda_device_string():
from modules import shared
@ -56,11 +49,15 @@ def get_device_for(task):
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(get_cuda_device_string()):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
if has_mps():
mac_specific.torch_mps_gc()
def enable_tf32():
if torch.cuda.is_available():

View File

@ -1,15 +1,13 @@
import os
import sys
import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
import modules.esrgan_model_arch as arch
from modules import modelloader, images, devices
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
from modules.upscaler import Upscaler, UpscalerData
def mod2normal(state_dict):
@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler):
scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
if "http" in file:
if file.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(file)
@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler):
self.scalers.append(scaler_data)
def do_upscale(self, img, selected_model):
model = self.load_model(selected_model)
if model is None:
try:
model = self.load_model(selected_model)
except Exception as e:
print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
return img
model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
def load_model(self, path: str):
if "http" in path:
filename = load_file_from_url(
if path.startswith("http"):
# TODO: this doesn't use `path` at all?
filename = modelloader.load_file_from_url(
url=self.model_url,
model_dir=self.model_download_path,
file_name=f"{self.model_name}.pth",
progress=True,
)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print(f"Unable to load {self.model_path} from {filename}")
return None
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

View File

@ -103,6 +103,9 @@ def activate(p, extra_network_data):
except Exception as e:
errors.display(e, f"activating extra network {extra_network_name}")
if p.scripts is not None:
p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data)
def deactivate(p, extra_network_data):
"""call deactivate for extra networks in extra_network_data in specified order, then call

View File

@ -73,8 +73,7 @@ def to_half(tensor, enable):
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
shared.state.begin()
shared.state.job = 'model-merge'
shared.state.begin(job="model-merge")
def fail(message):
shared.state.textinfo = message

View File

@ -174,31 +174,6 @@ def send_image_and_dimensions(x):
return img, w, h
def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
"""Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
If the infotext has no hash, then a hypernet with the same name will be selected instead.
"""
hypernet_name = hypernet_name.lower()
if hypernet_hash is not None:
# Try to match the hash in the name
for hypernet_key in shared.hypernetworks.keys():
result = re_hypernet_hash.search(hypernet_key)
if result is not None and result[1] == hypernet_hash:
return hypernet_key
else:
# Fall back to a hypernet with the same name
for hypernet_key in shared.hypernetworks.keys():
if hypernet_key.lower().startswith(hypernet_name):
return hypernet_key
return None
def restore_old_hires_fix_params(res):
"""for infotexts that specify old First pass size parameter, convert it into
width, height, and hr scale"""
@ -332,10 +307,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
return res
settings_map = {}
infotext_to_setting_name_mapping = [
('Clip skip', 'CLIP_stop_at_last_layers', ),
('Conditional mask weight', 'inpainting_mask_weight'),

View File

@ -25,7 +25,7 @@ def gfpgann():
return None
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
if len(models) == 1 and "http" in models[0]:
if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)

View File

@ -3,6 +3,7 @@ import glob
import html
import os
import inspect
from contextlib import closing
import modules.textual_inversion.dataset
import torch
@ -353,17 +354,6 @@ def load_hypernetworks(names, multipliers=None):
shared.loaded_hypernetworks.append(hypernetwork)
def find_closest_hypernetwork_name(search: str):
if not search:
return None
search = search.lower()
applicable = [name for name in shared.hypernetworks if search in name.lower()]
if not applicable:
return None
applicable = sorted(applicable, key=lambda name: len(name))
return applicable[0]
def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
@ -446,18 +436,6 @@ def statistics(data):
return total_information, recent_information
def report_statistics(loss_info:dict):
keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
for key in keys:
try:
print("Loss statistics for file " + key)
info, recent = statistics(list(loss_info[key]))
print(info)
print(recent)
except Exception as e:
print(e)
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):
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@ -734,8 +712,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
preview_text = p.prompt
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images) > 0 else None
with closing(p):
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images) > 0 else None
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
@ -770,7 +749,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
pbar.leave = False
pbar.close()
hypernetwork.eval()
#report_statistics(loss_dict)
sd_hijack_checkpoint.remove()

View File

@ -1,3 +1,5 @@
from __future__ import annotations
import datetime
import pytz
@ -10,7 +12,7 @@ import re
import numpy as np
import piexif
import piexif.helper
from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
import string
import json
import hashlib
@ -139,6 +141,11 @@ class GridAnnotation:
def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB')
color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB')
color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB')
def wrap(drawing, text, font, line_length):
lines = ['']
for word in text.split():
@ -168,9 +175,6 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
fnt = get_font(fontsize)
color_active = (0, 0, 0)
color_inactive = (153, 153, 153)
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
cols = im.width // width
@ -179,7 +183,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
calc_img = Image.new("RGB", (1, 1), "white")
calc_img = Image.new("RGB", (1, 1), color_background)
calc_d = ImageDraw.Draw(calc_img)
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
@ -200,7 +204,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background)
for row in range(rows):
for col in range(cols):
@ -302,12 +306,14 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
if ratio < src_ratio:
fill_height = height // 2 - src_h // 2
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
if fill_height > 0:
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
elif ratio > src_ratio:
fill_width = width // 2 - src_w // 2
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
if fill_width > 0:
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
return res
@ -372,8 +378,8 @@ class FilenameGenerator:
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
'user': lambda self: self.p.user,
'vae_filename': lambda self: self.get_vae_filename(),
}
default_time_format = '%Y%m%d%H%M%S'
@ -497,13 +503,23 @@ def get_next_sequence_number(path, basename):
return result + 1
def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None):
def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'):
"""
Saves image to filename, including geninfo as text information for generation info.
For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key.
For JPG images, there's no dictionary and geninfo just replaces the EXIF description.
"""
if extension is None:
extension = os.path.splitext(filename)[1]
image_format = Image.registered_extensions()[extension]
if extension.lower() == '.png':
existing_pnginfo = existing_pnginfo or {}
if opts.enable_pnginfo:
existing_pnginfo[pnginfo_section_name] = geninfo
if opts.enable_pnginfo:
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
@ -622,7 +638,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
"""
temp_file_path = f"{filename_without_extension}.tmp"
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
os.replace(temp_file_path, filename_without_extension + extension)
@ -639,12 +655,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
ratio = image.width / image.height
resize_to = None
if oversize and ratio > 1:
image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)
elif oversize:
image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)
if resize_to is not None:
try:
# Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16
image = image.resize(resize_to, LANCZOS)
except Exception:
image = image.resize(resize_to)
try:
_atomically_save_image(image, fullfn_without_extension, ".jpg")
except Exception as e:
@ -662,8 +684,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
return fullfn, txt_fullfn
def read_info_from_image(image):
items = image.info or {}
IGNORED_INFO_KEYS = {
'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
'icc_profile', 'chromaticity', 'photoshop',
}
def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
items = (image.info or {}).copy()
geninfo = items.pop('parameters', None)
@ -679,9 +708,7 @@ def read_info_from_image(image):
items['exif comment'] = exif_comment
geninfo = exif_comment
for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
'icc_profile', 'chromaticity']:
for field in IGNORED_INFO_KEYS:
items.pop(field, None)
if items.get("Software", None) == "NovelAI":

View File

@ -1,23 +1,26 @@
import os
from contextlib import closing
from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
import gradio as gr
from modules import sd_samplers
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules import sd_samplers, images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
from modules.images import save_image
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.scripts
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0):
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
processing.fix_seed(p)
images = shared.listfiles(input_dir)
images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
is_inpaint_batch = False
if inpaint_mask_dir:
@ -36,6 +39,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
state.job_count = len(images) * p.n_iter
# extract "default" params to use in case getting png info fails
prompt = p.prompt
negative_prompt = p.negative_prompt
seed = p.seed
cfg_scale = p.cfg_scale
sampler_name = p.sampler_name
steps = p.steps
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@ -79,25 +90,45 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
mask_image = Image.open(mask_image_path)
p.image_mask = mask_image
if use_png_info:
try:
info_img = img
if png_info_dir:
info_img_path = os.path.join(png_info_dir, os.path.basename(image))
info_img = Image.open(info_img_path)
geninfo, _ = imgutil.read_info_from_image(info_img)
parsed_parameters = parse_generation_parameters(geninfo)
parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
except Exception:
parsed_parameters = {}
p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
p.seed = int(parsed_parameters.get("Seed", seed))
p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
p.steps = int(parsed_parameters.get("Steps", steps))
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
proc = process_images(p)
for n, processed_image in enumerate(proc.images):
filename = image_path.name
filename = image_path.stem
infotext = proc.infotext(p, n)
relpath = os.path.dirname(os.path.relpath(image, input_dir))
if n > 0:
left, right = os.path.splitext(filename)
filename = f"{left}-{n}{right}"
filename += f"-{n}"
if not save_normally:
os.makedirs(output_dir, exist_ok=True)
os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
if processed_image.mode == 'RGBA':
processed_image = processed_image.convert("RGB")
processed_image.save(os.path.join(output_dir, filename))
save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@ -180,24 +211,25 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
p.scripts = modules.scripts.scripts_img2img
p.script_args = args
p.user = request.username
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
if mask:
p.extra_generation_params["Mask blur"] = mask_blur
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by)
process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args)
if processed is None:
processed = process_images(p)
p.close()
processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args)
if processed is None:
processed = process_images(p)
shared.total_tqdm.clear()

View File

@ -184,8 +184,7 @@ class InterrogateModels:
def interrogate(self, pil_image):
res = ""
shared.state.begin()
shared.state.job = 'interrogate'
shared.state.begin(job="interrogate")
try:
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()

View File

@ -142,15 +142,15 @@ def git_clone(url, dir, name, commithash=None):
if commithash is None:
return
current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
if current_hash == commithash:
return
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
if commithash is not None:
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")

View File

@ -1,22 +1,45 @@
import logging
import torch
import platform
from modules.sd_hijack_utils import CondFunc
from packaging import version
log = logging.getLogger(__name__)
# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
# check `getattr` and try it for compatibility
# before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+,
# use check `getattr` and try it for compatibility.
# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty,
# since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279
def check_for_mps() -> bool:
if not getattr(torch, 'has_mps', False):
return False
try:
torch.zeros(1).to(torch.device("mps"))
return True
except Exception:
return False
if version.parse(torch.__version__) <= version.parse("2.0.1"):
if not getattr(torch, 'has_mps', False):
return False
try:
torch.zeros(1).to(torch.device("mps"))
return True
except Exception:
return False
else:
return torch.backends.mps.is_available() and torch.backends.mps.is_built()
has_mps = check_for_mps()
def torch_mps_gc() -> None:
try:
from modules.shared import state
if state.current_latent is not None:
log.debug("`current_latent` is set, skipping MPS garbage collection")
return
from torch.mps import empty_cache
empty_cache()
except Exception:
log.warning("MPS garbage collection failed", exc_info=True)
# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
def cumsum_fix(input, cumsum_func, *args, **kwargs):
if input.device.type == 'mps':

View File

@ -1,3 +1,5 @@
from __future__ import annotations
import os
import shutil
import importlib
@ -8,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale
from modules.paths import script_path, models_path
def load_file_from_url(
url: str,
*,
model_dir: str,
progress: bool = True,
file_name: str | None = None,
) -> str:
"""Download a file from `url` into `model_dir`, using the file present if possible.
Returns the path to the downloaded file.
"""
os.makedirs(model_dir, exist_ok=True)
if not file_name:
parts = urlparse(url)
file_name = os.path.basename(parts.path)
cached_file = os.path.abspath(os.path.join(model_dir, file_name))
if not os.path.exists(cached_file):
print(f'Downloading: "{url}" to {cached_file}\n')
from torch.hub import download_url_to_file
download_url_to_file(url, cached_file, progress=progress)
return cached_file
def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
"""
A one-and done loader to try finding the desired models in specified directories.
@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
if model_url is not None and len(output) == 0:
if download_name is not None:
from basicsr.utils.download_util import load_file_from_url
dl = load_file_from_url(model_url, places[0], True, download_name)
output.append(dl)
output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
else:
output.append(model_url)
@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
def friendly_name(file: str):
if "http" in file:
if file.startswith("http"):
file = urlparse(file).path
file = os.path.basename(file)

View File

@ -38,17 +38,3 @@ for d, must_exist, what, options in path_dirs:
else:
sys.path.append(d)
paths[what] = d
class Prioritize:
def __init__(self, name):
self.name = name
self.path = None
def __enter__(self):
self.path = sys.path.copy()
sys.path = [paths[self.name]] + sys.path
def __exit__(self, exc_type, exc_val, exc_tb):
sys.path = self.path
self.path = None

View File

@ -9,8 +9,7 @@ from modules.shared import opts
def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
devices.torch_gc()
shared.state.begin()
shared.state.job = 'extras'
shared.state.begin(job="extras")
image_data = []
image_names = []
@ -54,7 +53,9 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
for image, name in zip(image_data, image_names):
shared.state.textinfo = name
existing_pnginfo = image.info or {}
parameters, existing_pnginfo = images.read_info_from_image(image)
if parameters:
existing_pnginfo["parameters"] = parameters
pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))

View File

@ -184,6 +184,8 @@ class StableDiffusionProcessing:
self.uc = None
self.c = None
self.user = None
@property
def sd_model(self):
return shared.sd_model
@ -549,7 +551,7 @@ def program_version():
return res
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
index = position_in_batch + iteration * p.batch_size
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
@ -573,7 +575,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"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,
@ -585,13 +587,15 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"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,
"User": p.user if opts.add_user_name_to_info else None,
}
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])
prompt_text = p.prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
def process_images(p: StableDiffusionProcessing) -> Processed:
@ -602,7 +606,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
if sd_models.checkpoint_alisases.get(p.override_settings.get('sd_model_checkpoint')) is None:
if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
p.override_settings.pop('sd_model_checkpoint', None)
sd_models.reload_model_weights()
@ -663,8 +667,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
@ -824,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
grid = images.image_grid(output_images, p.batch_size)
if opts.return_grid:
text = infotext()
text = infotext(use_main_prompt=True)
infotexts.insert(0, text)
if opts.enable_pnginfo:
grid.info["parameters"] = text
@ -832,7 +836,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
index_of_first_image = 1
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
if not p.disable_extra_networks and p.extra_network_data:
extra_networks.deactivate(p, p.extra_network_data)
@ -1074,6 +1078,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
if self.scripts is not None:
self.scripts.before_hr(self)
samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, 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())

View File

@ -2,7 +2,6 @@ import os
import numpy as np
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from modules.upscaler import Upscaler, UpscalerData
@ -43,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler):
if not self.enable:
return img
info = self.load_model(path)
if not os.path.exists(info.local_data_path):
print(f"Unable to load RealESRGAN model: {info.name}")
try:
info = self.load_model(path)
except Exception:
errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
return img
upsampler = RealESRGANer(
@ -63,20 +63,17 @@ class UpscalerRealESRGAN(Upscaler):
return image
def load_model(self, path):
try:
info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
if info is None:
print(f"Unable to find model info: {path}")
return None
if info.local_data_path.startswith("http"):
info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
return info
except Exception:
errors.report("Error making Real-ESRGAN models list", exc_info=True)
return None
for scaler in self.scalers:
if scaler.data_path == path:
if scaler.local_data_path.startswith("http"):
scaler.local_data_path = modelloader.load_file_from_url(
scaler.data_path,
model_dir=self.model_download_path,
)
if not os.path.exists(scaler.local_data_path):
raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
return scaler
raise ValueError(f"Unable to find model info: {path}")
def load_models(self, _):
return get_realesrgan_models(self)

View File

@ -1,6 +1,7 @@
import os
import re
import sys
import inspect
from collections import namedtuple
import gradio as gr
@ -116,6 +117,21 @@ class Script:
pass
def after_extra_networks_activate(self, p, *args, **kwargs):
"""
Calledafter extra networks activation, before conds calculation
allow modification of the network after extra networks activation been applied
won't be call if p.disable_extra_networks
**kwargs will have those items:
- batch_number - index of current batch, from 0 to number of batches-1
- prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
- seeds - list of seeds for current batch
- subseeds - list of subseeds for current batch
- extra_network_data - list of ExtraNetworkParams for current stage
"""
pass
def process_batch(self, p, *args, **kwargs):
"""
Same as process(), but called for every batch.
@ -186,6 +202,11 @@ class Script:
return f'script_{tabname}{title}_{item_id}'
def before_hr(self, p, *args):
"""
This function is called before hires fix start.
"""
pass
current_basedir = paths.script_path
@ -249,7 +270,7 @@ def load_scripts():
def register_scripts_from_module(module):
for script_class in module.__dict__.values():
if type(script_class) != type:
if not inspect.isclass(script_class):
continue
if issubclass(script_class, Script):
@ -483,6 +504,14 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True)
def after_extra_networks_activate(self, p, **kwargs):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.after_extra_networks_activate(p, *script_args, **kwargs)
except Exception:
errors.report(f"Error running after_extra_networks_activate: {script.filename}", exc_info=True)
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
try:
@ -548,6 +577,15 @@ class ScriptRunner:
self.scripts[si].args_to = args_to
def before_hr(self, p):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.before_hr(p, *script_args)
except Exception:
errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None
scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None

View File

@ -23,7 +23,8 @@ model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
checkpoints_list = {}
checkpoint_alisases = {}
checkpoint_aliases = {}
checkpoint_alisases = checkpoint_aliases # for compatibility with old name
checkpoints_loaded = collections.OrderedDict()
@ -66,7 +67,7 @@ class CheckpointInfo:
def register(self):
checkpoints_list[self.title] = self
for id in self.ids:
checkpoint_alisases[id] = self
checkpoint_aliases[id] = self
def calculate_shorthash(self):
self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
@ -112,7 +113,7 @@ def checkpoint_tiles():
def list_models():
checkpoints_list.clear()
checkpoint_alisases.clear()
checkpoint_aliases.clear()
cmd_ckpt = shared.cmd_opts.ckpt
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
@ -136,7 +137,7 @@ def list_models():
def get_closet_checkpoint_match(search_string):
checkpoint_info = checkpoint_alisases.get(search_string, None)
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
return checkpoint_info
@ -166,7 +167,7 @@ def select_checkpoint():
"""Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
@ -247,7 +248,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
if not shared.opts.disable_mmap_load_safetensors:
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
else:
pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@ -585,7 +591,6 @@ def unload_model_weights(sd_model=None, info=None):
sd_model = None
gc.collect()
devices.torch_gc()
torch.cuda.empty_cache()
print(f"Unloaded weights {timer.summary()}.")

View File

@ -1,9 +1,11 @@
import datetime
import json
import os
import re
import sys
import threading
import time
import logging
import gradio as gr
import torch
@ -18,6 +20,8 @@ from modules.paths_internal import models_path, script_path, data_path, sd_confi
from ldm.models.diffusion.ddpm import LatentDiffusion
from typing import Optional
log = logging.getLogger(__name__)
demo = None
parser = cmd_args.parser
@ -144,12 +148,15 @@ class State:
def request_restart(self) -> None:
self.interrupt()
self.server_command = "restart"
log.info("Received restart request")
def skip(self):
self.skipped = True
log.info("Received skip request")
def interrupt(self):
self.interrupted = True
log.info("Received interrupt request")
def nextjob(self):
if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
@ -173,7 +180,7 @@ class State:
return obj
def begin(self):
def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.job_count = -1
self.processing_has_refined_job_count = False
@ -187,10 +194,13 @@ class State:
self.interrupted = False
self.textinfo = None
self.time_start = time.time()
self.job = job
devices.torch_gc()
log.info("Starting job %s", job)
def end(self):
duration = time.time() - self.time_start
log.info("Ending job %s (%.2f seconds)", self.job, duration)
self.job = ""
self.job_count = 0
@ -311,6 +321,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
"grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"font": OptionInfo("", "Font for image grids that have text"),
"grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
@ -376,6 +390,7 @@ options_templates.update(options_section(('system', "System"), {
"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 \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
}))
options_templates.update(options_section(('training', "Training"), {
@ -470,7 +485,6 @@ options_templates.update(options_section(('ui', "User interface"), {
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
@ -481,6 +495,7 @@ options_templates.update(options_section(('ui', "User interface"), {
"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"),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"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(),
@ -493,6 +508,7 @@ options_templates.update(options_section(('ui', "User interface"), {
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
@ -817,8 +833,12 @@ mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
mem_mon.start()
def natural_sort_key(s, regex=re.compile('([0-9]+)')):
return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
def listfiles(dirname):
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
@ -843,8 +863,11 @@ def walk_files(path, allowed_extensions=None):
if allowed_extensions is not None:
allowed_extensions = set(allowed_extensions)
for root, _, files in os.walk(path, followlinks=True):
for filename in files:
items = list(os.walk(path, followlinks=True))
items = sorted(items, key=lambda x: natural_sort_key(x[0]))
for root, _, files in items:
for filename in sorted(files, key=natural_sort_key):
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
if ext not in allowed_extensions:

View File

@ -2,11 +2,51 @@ import datetime
import json
import os
saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
saved_params_shared = {
"batch_size",
"clip_grad_mode",
"clip_grad_value",
"create_image_every",
"data_root",
"gradient_step",
"initial_step",
"latent_sampling_method",
"learn_rate",
"log_directory",
"model_hash",
"model_name",
"num_of_dataset_images",
"steps",
"template_file",
"training_height",
"training_width",
}
saved_params_ti = {
"embedding_name",
"num_vectors_per_token",
"save_embedding_every",
"save_image_with_stored_embedding",
}
saved_params_hypernet = {
"activation_func",
"add_layer_norm",
"hypernetwork_name",
"layer_structure",
"save_hypernetwork_every",
"use_dropout",
"weight_init",
}
saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
saved_params_previews = {
"preview_cfg_scale",
"preview_height",
"preview_negative_prompt",
"preview_prompt",
"preview_sampler_index",
"preview_seed",
"preview_steps",
"preview_width",
}
def save_settings_to_file(log_directory, all_params):

View File

@ -7,7 +7,7 @@ from modules import paths, shared, images, deepbooru
from modules.textual_inversion import autocrop
def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
try:
if process_caption:
shared.interrogator.load()

View File

@ -1,5 +1,6 @@
import os
from collections import namedtuple
from contextlib import closing
import torch
import tqdm
@ -584,8 +585,9 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
preview_text = p.prompt
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images) > 0 else None
with closing(p):
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images) > 0 else None
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)

View File

@ -1,13 +1,15 @@
from contextlib import closing
import modules.scripts
from modules import sd_samplers, processing
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.shared import opts, cmd_opts
import modules.shared as shared
from modules.ui import plaintext_to_html
import gradio as gr
def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args):
def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
@ -48,15 +50,16 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
p.user = request.username
if cmd_opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
processed = modules.scripts.scripts_txt2img.run(p, *args)
with closing(p):
processed = modules.scripts.scripts_txt2img.run(p, *args)
if processed is None:
processed = processing.process_images(p)
p.close()
if processed is None:
processed = processing.process_images(p)
shared.total_tqdm.clear()

View File

@ -155,7 +155,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
img = Image.open(image)
filename = os.path.basename(image)
left, _ = os.path.splitext(filename)
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a'))
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
return [gr.update(), None]
@ -733,6 +733,10 @@ def create_ui():
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
@ -773,7 +777,7 @@ def create_ui():
selected_scale_tab = gr.State(value=0)
with gr.Tabs():
with gr.Tab(label="Resize to") as tab_scale_to:
with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@ -782,7 +786,7 @@ def create_ui():
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
with gr.Tab(label="Resize by") as tab_scale_by:
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
with FormRow():
@ -934,6 +938,9 @@ def create_ui():
img2img_batch_output_dir,
img2img_batch_inpaint_mask_dir,
override_settings,
img2img_batch_use_png_info,
img2img_batch_png_info_props,
img2img_batch_png_info_dir,
] + custom_inputs,
outputs=[
img2img_gallery,

View File

@ -138,7 +138,10 @@ def extension_table():
<table id="extensions">
<thead>
<tr>
<th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
<th>
<input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
<abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr>
</th>
<th>URL</th>
<th>Branch</th>
<th>Version</th>
@ -170,7 +173,7 @@ def extension_table():
code += f"""
<tr>
<td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
<td><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
<td>{remote}</td>
<td>{ext.branch}</td>
<td>{version_link}</td>
@ -421,9 +424,19 @@ sort_ordering = [
(False, lambda x: x.get('name', 'z')),
(True, lambda x: x.get('name', 'z')),
(False, lambda x: 'z'),
(True, lambda x: x.get('commit_time', '')),
(True, lambda x: x.get('created_at', '')),
(True, lambda x: x.get('stars', 0)),
]
def get_date(info: dict, key):
try:
return datetime.strptime(info.get(key), "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d")
except (ValueError, TypeError):
return ''
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
@ -448,7 +461,10 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
name = ext.get("name", "noname")
stars = int(ext.get("stars", 0))
added = ext.get('added', 'unknown')
update_time = get_date(ext, 'commit_time')
create_time = get_date(ext, 'created_at')
url = ext.get("url", None)
description = ext.get("description", "")
extension_tags = ext.get("tags", [])
@ -475,7 +491,8 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
code += f"""
<tr>
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
<td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
<td>{html.escape(description)}<p class="info">
<span class="date_added">Update: {html.escape(update_time)} Added: {html.escape(added)} Created: {html.escape(create_time)}</span><span class="star_count">stars: <b>{stars}</b></a></p></td>
<td>{install_code}</td>
</tr>
@ -559,7 +576,7 @@ def create_ui():
with gr.Row():
hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
with gr.Row():
search_extensions_text = gr.Text(label="Search").style(container=False)
@ -568,9 +585,9 @@ def create_ui():
available_extensions_table = gr.HTML()
refresh_available_extensions_button.click(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
inputs=[available_extensions_index, hide_tags, sort_column],
outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
)
install_extension_button.click(

View File

@ -30,8 +30,8 @@ def fetch_file(filename: str = ""):
raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
ext = os.path.splitext(filename)[1].lower()
if ext not in (".png", ".jpg", ".jpeg", ".webp"):
raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@ -90,8 +90,8 @@ class ExtraNetworksPage:
subdirs = {}
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
for root, dirs, _ in os.walk(parentdir, followlinks=True):
for dirname in dirs:
for root, dirs, _ in sorted(os.walk(parentdir, followlinks=True), key=lambda x: shared.natural_sort_key(x[0])):
for dirname in sorted(dirs, key=shared.natural_sort_key):
x = os.path.join(root, dirname)
if not os.path.isdir(x):

View File

@ -260,13 +260,20 @@ class UiSettings:
component = self.component_dict[k]
info = opts.data_labels[k]
change_handler = component.release if hasattr(component, 'release') else component.change
change_handler(
fn=lambda value, k=k: self.run_settings_single(value, key=k),
inputs=[component],
outputs=[component, self.text_settings],
show_progress=info.refresh is not None,
)
if isinstance(component, gr.Textbox):
methods = [component.submit, component.blur]
elif hasattr(component, 'release'):
methods = [component.release]
else:
methods = [component.change]
for method in methods:
method(
fn=lambda value, k=k: self.run_settings_single(value, key=k),
inputs=[component],
outputs=[component, self.text_settings],
show_progress=info.refresh is not None,
)
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
button_set_checkpoint.click(

View File

@ -704,11 +704,24 @@ table.popup-table .link{
margin: 0;
}
#available_extensions .date_added{
opacity: 0.85;
#available_extensions .info{
margin: 0.5em 0;
display: flex;
margin-top: auto;
opacity: 0.80;
font-size: 90%;
}
#available_extensions .date_added{
margin-right: auto;
display: inline-block;
}
#available_extensions .star_count{
margin-left: auto;
display: inline-block;
}
/* replace original footer with ours */
footer {

View File

@ -11,13 +11,24 @@ import json
from threading import Thread
from typing import Iterable
from fastapi import FastAPI, Response
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from packaging import version
import logging
# We can't use cmd_opts for this because it will not have been initialized at this point.
log_level = os.environ.get("SD_WEBUI_LOG_LEVEL")
if log_level:
log_level = getattr(logging, log_level.upper(), None) or logging.INFO
logging.basicConfig(
level=log_level,
format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
)
logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh...
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
from modules import paths, timer, import_hook, errors, devices # noqa: F401
@ -32,7 +43,7 @@ warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvisi
startup_timer.record("import torch")
import gradio
import gradio # noqa: F401
startup_timer.record("import gradio")
import ldm.modules.encoders.modules # noqa: F401
@ -359,12 +370,11 @@ def api_only():
modules.script_callbacks.app_started_callback(None, app)
print(f"Startup time: {startup_timer.summary()}.")
api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
def stop_route(request):
shared.state.server_command = "stop"
return Response("Stopping.")
api.launch(
server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
port=cmd_opts.port if cmd_opts.port else 7861,
root_path = f"/{cmd_opts.subpath}"
)
def webui():
@ -403,9 +413,8 @@ def webui():
"docs_url": "/docs",
"redoc_url": "/redoc",
},
root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else "",
)
if cmd_opts.add_stop_route:
app.add_route("/_stop", stop_route, methods=["POST"])
# after initial launch, disable --autolaunch for subsequent restarts
cmd_opts.autolaunch = False
@ -436,11 +445,6 @@ def webui():
timer.startup_record = startup_timer.dump()
print(f"Startup time: {startup_timer.summary()}.")
if cmd_opts.subpath:
redirector = FastAPI()
redirector.get("/")
gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}")
try:
while True:
server_command = shared.state.wait_for_server_command(timeout=5)

View File

@ -4,26 +4,28 @@
# change the variables in webui-user.sh instead #
#################################################
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
# If run from macOS, load defaults from webui-macos-env.sh
if [[ "$OSTYPE" == "darwin"* ]]; then
if [[ -f webui-macos-env.sh ]]
if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]]
then
source ./webui-macos-env.sh
source "$SCRIPT_DIR"/webui-macos-env.sh
fi
fi
# Read variables from webui-user.sh
# shellcheck source=/dev/null
if [[ -f webui-user.sh ]]
if [[ -f "$SCRIPT_DIR"/webui-user.sh ]]
then
source ./webui-user.sh
source "$SCRIPT_DIR"/webui-user.sh
fi
# Set defaults
# Install directory without trailing slash
if [[ -z "${install_dir}" ]]
then
install_dir="$(pwd)"
install_dir="$SCRIPT_DIR"
fi
# Name of the subdirectory (defaults to stable-diffusion-webui)
@ -131,6 +133,10 @@ case "$gpu_info" in
;;
*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
;;
*"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
export TORCH_COMMAND="pip install --pre torch==2.1.0.dev-20230614+rocm5.5 torchvision==0.16.0.dev-20230614+rocm5.5 --index-url https://download.pytorch.org/whl/nightly/rocm5.5"
# Navi 3 needs at least 5.5 which is only on the nightly chain
;;
*"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
printf "\n%s\n" "${delimiter}"
printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"