mirror of
https://github.com/easydiffusion/easydiffusion.git
synced 2024-12-25 16:38:55 +01:00
Refactor the default model download code, remove check_models.py, don't check in legacy paths since that's already migrated during initialization; Download CodeFormer's model only when it's used for the first time
This commit is contained in:
cmdr2
parent
0860e35d17
commit
dd95df8f02
@ -1,105 +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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"codeformer": [
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{"file_name": "codeformer.pth", "model_id": "codeformer-0.1.0"},
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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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"codeformer": [".pth"],
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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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@ -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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@ -25,12 +29,19 @@ MODEL_EXTENSIONS = {
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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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@ -39,6 +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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@ -77,7 +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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@ -85,31 +98,25 @@ 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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@ -136,7 +143,9 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
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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(DEFAULT_MODELS["realesrgan"][0], "realesrgan")
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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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@ -168,6 +177,7 @@ def resolve_model_paths(task_data: TaskData):
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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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@ -179,7 +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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@ -193,6 +227,26 @@ def set_vram_optimizations(context: Context):
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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 DEFAULT_MODELS.items():
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for model in models:
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file_name = model["file_name"]
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legacy_path = os.path.join(app.SD_DIR, file_name)
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if os.path.exists(legacy_path):
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shutil.move(legacy_path, os.path.join(app.MODELS_DIR, model_type, file_name))
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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"{app.MODELS_DIR}/{model_type}/**/*{ext}", recursive=True)):
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return True
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return False
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def set_clip_skip(context: Context, task_data: TaskData):
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clip_skip = task_data.clip_skip
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@ -255,7 +309,7 @@ def getModels():
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"vae": [],
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"hypernetwork": [],
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"lora": [],
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"codeformer": [],
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"codeformer": ["codeformer"],
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},
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}
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@ -312,14 +366,8 @@ def getModels():
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listModels(model_type="hypernetwork")
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listModels(model_type="gfpgan")
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listModels(model_type="lora")
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listModels(model_type="codeformer")
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if models_scanned > 0:
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log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")
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# legacy
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custom_weight_path = os.path.join(app.SD_DIR, "custom-model.ckpt")
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if os.path.exists(custom_weight_path):
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models["options"]["stable-diffusion"].append("custom-model")
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return models
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