mirror of
https://github.com/easydiffusion/easydiffusion.git
synced 2025-01-24 23:29:56 +01:00
cmdr2
GitHub
commit
ab0218050c
@ -1,101 +0,0 @@
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# this script runs inside the legacy "stable-diffusion" folder
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from sdkit.models import download_model, get_model_info_from_db
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from sdkit.utils import hash_file_quick
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import os
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import shutil
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from glob import glob
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import traceback
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models_base_dir = os.path.abspath(os.path.join("..", "models"))
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models_to_check = {
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"stable-diffusion": [
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{"file_name": "sd-v1-4.ckpt", "model_id": "1.4"},
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],
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"gfpgan": [
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{"file_name": "GFPGANv1.4.pth", "model_id": "1.4"},
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],
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"realesrgan": [
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{"file_name": "RealESRGAN_x4plus.pth", "model_id": "x4plus"},
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{"file_name": "RealESRGAN_x4plus_anime_6B.pth", "model_id": "x4plus_anime_6"},
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],
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"vae": [
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{"file_name": "vae-ft-mse-840000-ema-pruned.ckpt", "model_id": "vae-ft-mse-840000-ema-pruned"},
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],
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}
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MODEL_EXTENSIONS = { # copied from easydiffusion/model_manager.py
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"stable-diffusion": [".ckpt", ".safetensors"],
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"vae": [".vae.pt", ".ckpt", ".safetensors"],
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"hypernetwork": [".pt", ".safetensors"],
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"gfpgan": [".pth"],
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"realesrgan": [".pth"],
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"lora": [".ckpt", ".safetensors"],
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}
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def download_if_necessary(model_type: str, file_name: str, model_id: str):
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model_path = os.path.join(models_base_dir, model_type, file_name)
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expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
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other_models_exist = any_model_exists(model_type)
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known_model_exists = os.path.exists(model_path)
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known_model_is_corrupt = known_model_exists and hash_file_quick(model_path) != expected_hash
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if known_model_is_corrupt or (not other_models_exist and not known_model_exists):
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print("> download", model_type, model_id)
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download_model(model_type, model_id, download_base_dir=models_base_dir)
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def init():
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migrate_legacy_model_location()
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for model_type, models in models_to_check.items():
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for model in models:
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try:
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download_if_necessary(model_type, model["file_name"], model["model_id"])
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except:
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traceback.print_exc()
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fail(model_type)
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print(model_type, "model(s) found.")
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### utilities
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def any_model_exists(model_type: str) -> bool:
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extensions = MODEL_EXTENSIONS.get(model_type, [])
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for ext in extensions:
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if any(glob(f"{models_base_dir}/{model_type}/**/*{ext}", recursive=True)):
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return True
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return False
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def migrate_legacy_model_location():
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'Move the models inside the legacy "stable-diffusion" folder, to their respective folders'
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for model_type, models in models_to_check.items():
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for model in models:
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file_name = model["file_name"]
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if os.path.exists(file_name):
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dest_dir = os.path.join(models_base_dir, model_type)
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os.makedirs(dest_dir, exist_ok=True)
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shutil.move(file_name, os.path.join(dest_dir, file_name))
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def fail(model_name):
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print(
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f"""Error downloading the {model_name} model. Sorry about that, please try to:
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1. Run this installer again.
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2. If that doesn't fix it, please try to download the file manually. The address to download from, and the destination to save to are printed above this message.
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3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB
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4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues
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Thanks!"""
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)
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exit(1)
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### start
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init()
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@ -18,7 +18,7 @@ os_name = platform.system()
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modules_to_check = {
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"torch": ("1.11.0", "1.13.1", "2.0.0"),
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"torchvision": ("0.12.0", "0.14.1", "0.15.1"),
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"sdkit": "1.0.98",
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"sdkit": "1.0.101",
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"stable-diffusion-sdkit": "2.1.4",
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"rich": "12.6.0",
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"uvicorn": "0.19.0",
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@ -79,13 +79,6 @@ call WHERE uvicorn > .tmp
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@echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
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)
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@rem Download the required models
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call python ..\scripts\check_models.py
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if "%ERRORLEVEL%" NEQ "0" (
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pause
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exit /b
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)
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@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
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@if "%ERRORLEVEL%" NEQ "0" (
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@echo sd_weights_downloaded >> ..\scripts\install_status.txt
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@ -51,12 +51,6 @@ if ! command -v uvicorn &> /dev/null; then
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fail "UI packages not found!"
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fi
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# Download the required models
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if ! python ../scripts/check_models.py; then
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read -p "Press any key to continue"
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exit 1
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fi
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if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
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echo sd_weights_downloaded >> ../scripts/install_status.txt
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echo sd_install_complete >> ../scripts/install_status.txt
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@ -90,8 +90,8 @@ def init():
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os.makedirs(USER_SERVER_PLUGINS_DIR, exist_ok=True)
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# https://pytorch.org/docs/stable/storage.html
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warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
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warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
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load_server_plugins()
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update_render_threads()
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@ -221,12 +221,41 @@ def open_browser():
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webbrowser.open(f"http://localhost:{port}")
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Console().print(Panel(
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"\n" +
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"[white]Easy Diffusion is ready to serve requests.\n\n" +
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"A new browser tab should have been opened by now.\n" +
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f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
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title="Easy Diffusion is ready", style="bold yellow on blue"))
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Console().print(
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Panel(
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"\n"
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+ "[white]Easy Diffusion is ready to serve requests.\n\n"
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+ "A new browser tab should have been opened by now.\n"
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+ f"If not, please open your web browser and navigate to [bold yellow underline]http://localhost:{port}/\n",
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title="Easy Diffusion is ready",
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style="bold yellow on blue",
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)
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)
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def fail_and_die(fail_type: str, data: str):
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suggestions = [
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"Run this installer again.",
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"If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB",
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"If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues",
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]
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if fail_type == "model_download":
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fail_label = f"Error downloading the {data} model"
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suggestions.insert(
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1,
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"If that doesn't fix it, please try to download the file manually. The address to download from, and the destination to save to are printed above this message.",
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)
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else:
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fail_label = "Error while installing Easy Diffusion"
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msg = [f"{fail_label}. Sorry about that, please try to:"]
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for i, suggestion in enumerate(suggestions):
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msg.append(f"{i+1}. {suggestion}")
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msg.append("Thanks!")
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print("\n".join(msg))
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exit(1)
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def get_image_modifiers():
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@ -1,10 +1,14 @@
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import os
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import shutil
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from glob import glob
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import traceback
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from easydiffusion import app
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from easydiffusion.types import TaskData
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from easydiffusion.utils import log
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from sdkit import Context
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from sdkit.models import load_model, scan_model, unload_model
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from sdkit.models import load_model, scan_model, unload_model, download_model, get_model_info_from_db
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from sdkit.utils import hash_file_quick
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KNOWN_MODEL_TYPES = [
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"stable-diffusion",
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@ -13,6 +17,7 @@ KNOWN_MODEL_TYPES = [
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"gfpgan",
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"realesrgan",
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"lora",
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"codeformer",
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]
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MODEL_EXTENSIONS = {
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"stable-diffusion": [".ckpt", ".safetensors"],
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@ -21,14 +26,22 @@ MODEL_EXTENSIONS = {
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"gfpgan": [".pth"],
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"realesrgan": [".pth"],
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"lora": [".ckpt", ".safetensors"],
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"codeformer": [".pth"],
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}
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DEFAULT_MODELS = {
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"stable-diffusion": [ # needed to support the legacy installations
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"custom-model", # only one custom model file was supported initially, creatively named 'custom-model'
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"sd-v1-4", # Default fallback.
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"stable-diffusion": [
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{"file_name": "sd-v1-4.ckpt", "model_id": "1.4"},
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],
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"gfpgan": [
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{"file_name": "GFPGANv1.4.pth", "model_id": "1.4"},
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],
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"realesrgan": [
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{"file_name": "RealESRGAN_x4plus.pth", "model_id": "x4plus"},
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{"file_name": "RealESRGAN_x4plus_anime_6B.pth", "model_id": "x4plus_anime_6"},
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],
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"vae": [
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{"file_name": "vae-ft-mse-840000-ema-pruned.ckpt", "model_id": "vae-ft-mse-840000-ema-pruned"},
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],
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"gfpgan": ["GFPGANv1.3"],
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"realesrgan": ["RealESRGAN_x4plus"],
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}
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MODELS_TO_LOAD_ON_START = ["stable-diffusion", "vae", "hypernetwork", "lora"]
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@ -37,6 +50,8 @@ known_models = {}
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def init():
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make_model_folders()
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migrate_legacy_model_location() # if necessary
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download_default_models_if_necessary()
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getModels() # run this once, to cache the picklescan results
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@ -75,7 +90,7 @@ def resolve_model_to_use(model_name: str = None, model_type: str = None):
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default_models = DEFAULT_MODELS.get(model_type, [])
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config = app.getConfig()
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model_dirs = [os.path.join(app.MODELS_DIR, model_type), app.SD_DIR]
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model_dir = os.path.join(app.MODELS_DIR, model_type)
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if not model_name: # When None try user configured model.
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# config = getConfig()
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if "model" in config and model_type in config["model"]:
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@ -83,45 +98,41 @@ def resolve_model_to_use(model_name: str = None, model_type: str = None):
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if model_name:
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# Check models directory
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models_dir_path = os.path.join(app.MODELS_DIR, model_type, model_name)
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model_path = os.path.join(model_dir, model_name)
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if os.path.exists(model_path):
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return model_path
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for model_extension in model_extensions:
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if os.path.exists(models_dir_path + model_extension):
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return models_dir_path + model_extension
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if os.path.exists(model_path + model_extension):
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return model_path + model_extension
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if os.path.exists(model_name + model_extension):
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return os.path.abspath(model_name + model_extension)
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# Default locations
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if model_name in default_models:
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default_model_path = os.path.join(app.SD_DIR, model_name)
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for model_extension in model_extensions:
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if os.path.exists(default_model_path + model_extension):
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return default_model_path + model_extension
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# Can't find requested model, check the default paths.
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for default_model in default_models:
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for model_dir in model_dirs:
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default_model_path = os.path.join(model_dir, default_model)
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for model_extension in model_extensions:
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if os.path.exists(default_model_path + model_extension):
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if model_name is not None:
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log.warn(
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f"Could not find the configured custom model {model_name}{model_extension}. Using the default one: {default_model_path}{model_extension}"
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)
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return default_model_path + model_extension
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if model_type == "stable-diffusion":
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for default_model in default_models:
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default_model_path = os.path.join(model_dir, default_model["file_name"])
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if os.path.exists(default_model_path):
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if model_name is not None:
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log.warn(
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f"Could not find the configured custom model {model_name}. Using the default one: {default_model_path}"
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)
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return default_model_path
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return None
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def reload_models_if_necessary(context: Context, task_data: TaskData):
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use_upscale_lower = task_data.use_upscale.lower() if task_data.use_upscale else ""
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face_fix_lower = task_data.use_face_correction.lower() if task_data.use_face_correction else ""
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upscale_lower = task_data.use_upscale.lower() if task_data.use_upscale else ""
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model_paths_in_req = {
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"stable-diffusion": task_data.use_stable_diffusion_model,
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"vae": task_data.use_vae_model,
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"hypernetwork": task_data.use_hypernetwork_model,
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"gfpgan": task_data.use_face_correction,
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"realesrgan": task_data.use_upscale if "realesrgan" in use_upscale_lower else None,
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"latent_upscaler": True if task_data.use_upscale == "latent_upscaler" else None,
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"codeformer": task_data.use_face_correction if "codeformer" in face_fix_lower else None,
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"gfpgan": task_data.use_face_correction if "gfpgan" in face_fix_lower else None,
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"realesrgan": task_data.use_upscale if "realesrgan" in upscale_lower else None,
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"latent_upscaler": True if "latent_upscaler" in upscale_lower else None,
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"nsfw_checker": True if task_data.block_nsfw else None,
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"lora": task_data.use_lora_model,
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}
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@ -131,6 +142,11 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
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if context.model_paths.get(model_type) != path
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}
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if task_data.codeformer_upscale_faces and "realesrgan" not in models_to_reload.keys():
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models_to_reload["realesrgan"] = resolve_model_to_use(
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DEFAULT_MODELS["realesrgan"][0]["file_name"], "realesrgan"
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)
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if set_vram_optimizations(context) or set_clip_skip(context, task_data): # reload SD
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models_to_reload["stable-diffusion"] = model_paths_in_req["stable-diffusion"]
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@ -157,7 +173,13 @@ def resolve_model_paths(task_data: TaskData):
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task_data.use_lora_model = resolve_model_to_use(task_data.use_lora_model, model_type="lora")
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if task_data.use_face_correction:
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task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, "gfpgan")
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if "gfpgan" in task_data.use_face_correction.lower():
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model_type = "gfpgan"
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elif "codeformer" in task_data.use_face_correction.lower():
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model_type = "codeformer"
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download_if_necessary("codeformer", "codeformer.pth", "codeformer-0.1.0")
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task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, model_type)
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if task_data.use_upscale and "realesrgan" in task_data.use_upscale.lower():
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task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, "realesrgan")
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@ -167,7 +189,31 @@ def fail_if_models_did_not_load(context: Context):
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if model_type in context.model_load_errors:
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e = context.model_load_errors[model_type]
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raise Exception(f"Could not load the {model_type} model! Reason: " + e)
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# concat 'e', don't use in format string (injection attack)
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def download_default_models_if_necessary():
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for model_type, models in DEFAULT_MODELS.items():
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for model in models:
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try:
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download_if_necessary(model_type, model["file_name"], model["model_id"])
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except:
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traceback.print_exc()
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app.fail_and_die(fail_type="model_download", data=model_type)
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print(model_type, "model(s) found.")
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def download_if_necessary(model_type: str, file_name: str, model_id: str):
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model_path = os.path.join(app.MODELS_DIR, model_type, file_name)
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expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
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other_models_exist = any_model_exists(model_type)
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known_model_exists = os.path.exists(model_path)
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known_model_is_corrupt = known_model_exists and hash_file_quick(model_path) != expected_hash
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if known_model_is_corrupt or (not other_models_exist and not known_model_exists):
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print("> download", model_type, model_id)
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download_model(model_type, model_id, download_base_dir=app.MODELS_DIR)
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def set_vram_optimizations(context: Context):
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@ -181,6 +227,26 @@ def set_vram_optimizations(context: Context):
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return False
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|
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def migrate_legacy_model_location():
|
||||
'Move the models inside the legacy "stable-diffusion" folder, to their respective folders'
|
||||
|
||||
for model_type, models in DEFAULT_MODELS.items():
|
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for model in models:
|
||||
file_name = model["file_name"]
|
||||
legacy_path = os.path.join(app.SD_DIR, file_name)
|
||||
if os.path.exists(legacy_path):
|
||||
shutil.move(legacy_path, os.path.join(app.MODELS_DIR, model_type, file_name))
|
||||
|
||||
|
||||
def any_model_exists(model_type: str) -> bool:
|
||||
extensions = MODEL_EXTENSIONS.get(model_type, [])
|
||||
for ext in extensions:
|
||||
if any(glob(f"{app.MODELS_DIR}/{model_type}/**/*{ext}", recursive=True)):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def set_clip_skip(context: Context, task_data: TaskData):
|
||||
clip_skip = task_data.clip_skip
|
||||
|
||||
@ -238,17 +304,12 @@ def is_malicious_model(file_path):
|
||||
|
||||
def getModels():
|
||||
models = {
|
||||
"active": {
|
||||
"stable-diffusion": "sd-v1-4",
|
||||
"vae": "",
|
||||
"hypernetwork": "",
|
||||
"lora": "",
|
||||
},
|
||||
"options": {
|
||||
"stable-diffusion": ["sd-v1-4"],
|
||||
"vae": [],
|
||||
"hypernetwork": [],
|
||||
"lora": [],
|
||||
"codeformer": ["codeformer"],
|
||||
},
|
||||
}
|
||||
|
||||
@ -309,9 +370,4 @@ def getModels():
|
||||
if models_scanned > 0:
|
||||
log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")
|
||||
|
||||
# legacy
|
||||
custom_weight_path = os.path.join(app.SD_DIR, "custom-model.ckpt")
|
||||
if os.path.exists(custom_weight_path):
|
||||
models["options"]["stable-diffusion"].append("custom-model")
|
||||
|
||||
return models
|
||||
|
||||
@ -34,6 +34,7 @@ def init(device):
|
||||
context.temp_images = {}
|
||||
context.partial_x_samples = None
|
||||
context.model_load_errors = {}
|
||||
context.enable_codeformer = True
|
||||
|
||||
from easydiffusion import app
|
||||
|
||||
@ -160,7 +161,11 @@ def filter_images(req: GenerateImageRequest, task_data: TaskData, images: list,
|
||||
filter_params = {}
|
||||
if task_data.block_nsfw:
|
||||
filters_to_apply.append("nsfw_checker")
|
||||
if task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
|
||||
if task_data.use_face_correction and "codeformer" in task_data.use_face_correction.lower():
|
||||
filters_to_apply.append("codeformer")
|
||||
|
||||
filter_params["upscale_faces"] = task_data.codeformer_upscale_faces
|
||||
elif task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
|
||||
filters_to_apply.append("gfpgan")
|
||||
if task_data.use_upscale:
|
||||
if "realesrgan" in task_data.use_upscale.lower():
|
||||
|
||||
@ -23,7 +23,7 @@ class GenerateImageRequest(BaseModel):
|
||||
sampler_name: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
||||
hypernetwork_strength: float = 0
|
||||
lora_alpha: float = 0
|
||||
tiling: str = "none" # "none", "x", "y", "xy"
|
||||
tiling: str = "none" # "none", "x", "y", "xy"
|
||||
|
||||
|
||||
class TaskData(BaseModel):
|
||||
@ -51,6 +51,7 @@ class TaskData(BaseModel):
|
||||
stream_image_progress: bool = False
|
||||
stream_image_progress_interval: int = 5
|
||||
clip_skip: bool = False
|
||||
codeformer_upscale_faces: bool = False
|
||||
|
||||
|
||||
class MergeRequest(BaseModel):
|
||||
|
||||
@ -263,7 +263,12 @@
|
||||
<div><ul>
|
||||
<li><b class="settings-subheader">Render Settings</b></li>
|
||||
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slower images)</small></label></li>
|
||||
<li class="pl-5"><input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div></li>
|
||||
<li class="pl-5">
|
||||
<input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div>
|
||||
<div id="codeformer_settings" class="displayNone sub-settings">
|
||||
<input id="codeformer_upscale_faces" name="codeformer_upscale_faces" type="checkbox"><label for="codeformer_upscale_faces">Upscale Faces <small>(improves the resolution of faces)</small></label>
|
||||
</div>
|
||||
</li>
|
||||
<li class="pl-5">
|
||||
<input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Scale up by</label>
|
||||
<select id="upscale_amount" name="upscale_amount">
|
||||
@ -276,7 +281,7 @@
|
||||
<option value="RealESRGAN_x4plus_anime_6B">RealESRGAN_x4plus_anime_6B</option>
|
||||
<option value="latent_upscaler">Latent Upscaler 2x</option>
|
||||
</select>
|
||||
<div id="latent_upscaler_settings" class="displayNone">
|
||||
<div id="latent_upscaler_settings" class="displayNone sub-settings">
|
||||
<label for="latent_upscaler_steps_slider">Upscaling Steps:</label></td><td> <input id="latent_upscaler_steps_slider" name="latent_upscaler_steps_slider" class="editor-slider" value="10" type="range" min="1" max="50"> <input id="latent_upscaler_steps" name="latent_upscaler_steps" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)">
|
||||
</div>
|
||||
</li>
|
||||
|
||||
@ -1303,7 +1303,7 @@ body.wait-pause {
|
||||
display:none !important;
|
||||
}
|
||||
|
||||
#latent_upscaler_settings {
|
||||
.sub-settings {
|
||||
padding-top: 3pt;
|
||||
padding-bottom: 3pt;
|
||||
padding-left: 5pt;
|
||||
@ -1322,7 +1322,7 @@ body.wait-pause {
|
||||
box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
|
||||
z-index: 9999;
|
||||
animation: slideInRight 0.5s ease forwards;
|
||||
transition: bottom 0.5s ease; // Add a transition to smoothly reposition the toasts
|
||||
transition: bottom 0.5s ease; /* Add a transition to smoothly reposition the toasts */
|
||||
}
|
||||
|
||||
.toast-notification-error {
|
||||
|
||||
@ -87,7 +87,7 @@ let promptStrengthField = document.querySelector("#prompt_strength")
|
||||
let samplerField = document.querySelector("#sampler_name")
|
||||
let samplerSelectionContainer = document.querySelector("#samplerSelection")
|
||||
let useFaceCorrectionField = document.querySelector("#use_face_correction")
|
||||
let gfpganModelField = new ModelDropdown(document.querySelector("#gfpgan_model"), "gfpgan")
|
||||
let gfpganModelField = new ModelDropdown(document.querySelector("#gfpgan_model"), ["codeformer", "gfpgan"])
|
||||
let useUpscalingField = document.querySelector("#use_upscale")
|
||||
let upscaleModelField = document.querySelector("#upscale_model")
|
||||
let upscaleAmountField = document.querySelector("#upscale_amount")
|
||||
@ -270,7 +270,9 @@ function shiftOrConfirm(e, prompt, fn) {
|
||||
confirm(
|
||||
'<small>Tip: To skip this dialog, use shift-click or disable the "Confirm dangerous actions" setting in the Settings tab.</small>',
|
||||
prompt,
|
||||
() => { fn(e) }
|
||||
() => {
|
||||
fn(e)
|
||||
}
|
||||
)
|
||||
}
|
||||
}
|
||||
@ -1261,6 +1263,10 @@ function getCurrentUserRequest() {
|
||||
}
|
||||
if (useFaceCorrectionField.checked) {
|
||||
newTask.reqBody.use_face_correction = gfpganModelField.value
|
||||
|
||||
if (gfpganModelField.value.includes("codeformer")) {
|
||||
newTask.reqBody.codeformer_upscale_faces = document.querySelector("#codeformer_upscale_faces").checked
|
||||
}
|
||||
}
|
||||
if (useUpscalingField.checked) {
|
||||
newTask.reqBody.use_upscale = upscaleModelField.value
|
||||
@ -1574,18 +1580,33 @@ metadataOutputFormatField.disabled = !saveToDiskField.checked
|
||||
gfpganModelField.disabled = !useFaceCorrectionField.checked
|
||||
useFaceCorrectionField.addEventListener("change", function(e) {
|
||||
gfpganModelField.disabled = !this.checked
|
||||
|
||||
onFixFaceModelChange()
|
||||
})
|
||||
|
||||
function onFixFaceModelChange() {
|
||||
let codeformerSettings = document.querySelector("#codeformer_settings")
|
||||
if (gfpganModelField.value === "codeformer" && !gfpganModelField.disabled) {
|
||||
codeformerSettings.classList.remove("displayNone")
|
||||
} else {
|
||||
codeformerSettings.classList.add("displayNone")
|
||||
}
|
||||
}
|
||||
gfpganModelField.addEventListener("change", onFixFaceModelChange)
|
||||
onFixFaceModelChange()
|
||||
|
||||
upscaleModelField.disabled = !useUpscalingField.checked
|
||||
upscaleAmountField.disabled = !useUpscalingField.checked
|
||||
useUpscalingField.addEventListener("change", function(e) {
|
||||
upscaleModelField.disabled = !this.checked
|
||||
upscaleAmountField.disabled = !this.checked
|
||||
|
||||
onUpscaleModelChange()
|
||||
})
|
||||
|
||||
function onUpscaleModelChange() {
|
||||
let upscale4x = document.querySelector("#upscale_amount_4x")
|
||||
if (upscaleModelField.value === "latent_upscaler") {
|
||||
if (upscaleModelField.value === "latent_upscaler" && !upscaleModelField.disabled) {
|
||||
upscale4x.disabled = true
|
||||
upscaleAmountField.value = "2"
|
||||
latentUpscalerSettings.classList.remove("displayNone")
|
||||
|
||||
@ -90,7 +90,12 @@ class ModelDropdown {
|
||||
|
||||
if (modelsOptions !== undefined) {
|
||||
// reuse models from cache (only useful for plugins, which are loaded after models)
|
||||
this.inputModels = modelsOptions[this.modelKey]
|
||||
this.inputModels = []
|
||||
let modelKeys = Array.isArray(this.modelKey) ? this.modelKey : [this.modelKey]
|
||||
for (let i = 0; i < modelKeys.length; i++) {
|
||||
let key = modelKeys[i]
|
||||
this.inputModels.push(...modelsOptions[key])
|
||||
}
|
||||
this.populateModels()
|
||||
}
|
||||
document.addEventListener(
|
||||
@ -98,6 +103,12 @@ class ModelDropdown {
|
||||
this.bind(function(e) {
|
||||
// reload the models
|
||||
this.inputModels = modelsOptions[this.modelKey]
|
||||
this.inputModels = []
|
||||
let modelKeys = Array.isArray(this.modelKey) ? this.modelKey : [this.modelKey]
|
||||
for (let i = 0; i < modelKeys.length; i++) {
|
||||
let key = modelKeys[i]
|
||||
this.inputModels.push(...modelsOptions[key])
|
||||
}
|
||||
this.populateModels()
|
||||
}, this)
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user