mirror of
https://github.com/easydiffusion/easydiffusion.git
synced 2024-11-25 09:44:25 +01:00
sdkit 1.0.49; Use a test_diffusers flag to gate access to the new renderer and LoRA model selection
This commit is contained in:
cmdr2
parent
8aead029a8
commit
f8bc50871a
@ -95,7 +95,7 @@ if "%ERRORLEVEL%" EQU "0" (
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set PYTHONNOUSERSITE=1
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set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
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call python -m pip install --upgrade sdkit==1.0.48 -q || (
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call python -m pip install --upgrade sdkit==1.0.49 -q || (
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echo "Error updating sdkit"
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)
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)
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@ -106,7 +106,7 @@ if "%ERRORLEVEL%" EQU "0" (
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set PYTHONNOUSERSITE=1
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set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
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call python -m pip install sdkit==1.0.48 || (
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call python -m pip install sdkit==1.0.49 || (
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echo "Error installing sdkit. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 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" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
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pause
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exit /b
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@ -103,7 +103,7 @@ if python ../scripts/check_modules.py sdkit sdkit.models ldm transformers numpy
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export PYTHONNOUSERSITE=1
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export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
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python -m pip install --upgrade sdkit==1.0.48 -q
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python -m pip install --upgrade sdkit==1.0.49 -q
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fi
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else
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echo "Installing sdkit: https://pypi.org/project/sdkit/"
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@ -111,7 +111,7 @@ else
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export PYTHONNOUSERSITE=1
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export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
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if python -m pip install sdkit==1.0.48 ; then
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if python -m pip install sdkit==1.0.49 ; then
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echo "Installed."
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else
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fail "sdkit install failed"
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@ -7,13 +7,14 @@ from easydiffusion.utils import log
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from sdkit import Context
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from sdkit.models import load_model, unload_model, scan_model
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KNOWN_MODEL_TYPES = ["stable-diffusion", "vae", "hypernetwork", "gfpgan", "realesrgan"]
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KNOWN_MODEL_TYPES = ["stable-diffusion", "vae", "hypernetwork", "gfpgan", "realesrgan", "lora"]
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MODEL_EXTENSIONS = {
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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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DEFAULT_MODELS = {
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"stable-diffusion": [ # needed to support the legacy installations
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@ -23,7 +24,7 @@ DEFAULT_MODELS = {
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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"]
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MODELS_TO_LOAD_ON_START = ["stable-diffusion", "vae", "hypernetwork", "lora"]
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known_models = {}
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@ -102,6 +103,7 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
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"gfpgan": task_data.use_face_correction,
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"realesrgan": task_data.use_upscale,
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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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models_to_reload = {
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model_type: path
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@ -125,6 +127,7 @@ def resolve_model_paths(task_data: TaskData):
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)
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task_data.use_vae_model = resolve_model_to_use(task_data.use_vae_model, model_type="vae")
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task_data.use_hypernetwork_model = resolve_model_to_use(task_data.use_hypernetwork_model, model_type="hypernetwork")
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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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@ -184,11 +187,13 @@ def getModels():
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"stable-diffusion": "sd-v1-4",
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"vae": "",
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"hypernetwork": "",
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"lora": "",
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},
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"options": {
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"stable-diffusion": ["sd-v1-4"],
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"vae": [],
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"hypernetwork": [],
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"lora": [],
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},
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}
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@ -243,6 +248,7 @@ def getModels():
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listModels(model_type="vae")
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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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if models_scanned > 0:
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log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")
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@ -10,7 +10,7 @@ from easydiffusion.utils import get_printable_request, save_images_to_disk, log
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from sdkit import Context
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from sdkit.generate import generate_images
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from sdkit.filter import apply_filters
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from sdkit.utils import img_to_buffer, img_to_base64_str, latent_samples_to_images, gc
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from sdkit.utils import img_to_buffer, img_to_base64_str, latent_samples_to_images, diffusers_latent_samples_to_images
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context = Context() # thread-local
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"""
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@ -26,6 +26,11 @@ def init(device):
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context.temp_images = {}
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context.partial_x_samples = None
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from easydiffusion import app
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app_config = app.getConfig()
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context.test_diffusers = app_config.get("test_diffusers", False)
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device_manager.device_init(context, device)
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@ -57,7 +62,13 @@ def make_images_internal(
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):
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images, user_stopped = generate_images_internal(
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req, task_data, data_queue, task_temp_images, step_callback, task_data.stream_image_progress, task_data.stream_image_progress_interval
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req,
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task_data,
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data_queue,
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task_temp_images,
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step_callback,
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task_data.stream_image_progress,
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task_data.stream_image_progress_interval,
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)
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filtered_images = filter_images(task_data, images, user_stopped)
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@ -82,10 +93,18 @@ def generate_images_internal(
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):
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context.temp_images.clear()
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callback = make_step_callback(req, task_data, data_queue, task_temp_images, step_callback, stream_image_progress, stream_image_progress_interval)
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callback = make_step_callback(
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req,
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task_data,
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data_queue,
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task_temp_images,
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step_callback,
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stream_image_progress,
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stream_image_progress_interval,
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)
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try:
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if req.init_image is not None:
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if req.init_image is not None and not context.test_diffusers:
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req.sampler_name = "ddim"
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images = generate_images(context, callback=callback, **req.dict())
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@ -94,9 +113,13 @@ def generate_images_internal(
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images = []
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user_stopped = True
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if context.partial_x_samples is not None:
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if context.test_diffusers:
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images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
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else:
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images = latent_samples_to_images(context, context.partial_x_samples)
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finally:
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if hasattr(context, "partial_x_samples") and context.partial_x_samples is not None:
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if not context.test_diffusers:
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del context.partial_x_samples
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context.partial_x_samples = None
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@ -145,7 +168,12 @@ def make_step_callback(
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def update_temp_img(x_samples, task_temp_images: list):
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partial_images = []
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if context.test_diffusers:
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images = diffusers_latent_samples_to_images(context, x_samples)
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else:
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images = latent_samples_to_images(context, x_samples)
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if task_data.block_nsfw:
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images = apply_filters(context, "nsfw_checker", images)
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@ -158,17 +186,21 @@ def make_step_callback(
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del images
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return partial_images
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def on_image_step(x_samples, i):
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def on_image_step(x_samples, i, *args):
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nonlocal last_callback_time
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if context.test_diffusers:
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context.partial_x_samples = (x_samples, args[0])
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else:
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context.partial_x_samples = x_samples
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step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
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last_callback_time = time.time()
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progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
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if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
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progress["output"] = update_temp_img(x_samples, task_temp_images)
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progress["output"] = update_temp_img(context.partial_x_samples, task_temp_images)
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data_queue.put(json.dumps(progress))
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@ -29,7 +29,7 @@ NOCACHE_HEADERS = {"Cache-Control": "no-cache, no-store, must-revalidate", "Prag
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class NoCacheStaticFiles(StaticFiles):
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def __init__(self, directory: str):
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# follow_symlink is only available on fastapi >= 0.92.0
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if (os.path.islink(directory)):
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if os.path.islink(directory):
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super().__init__(directory=os.path.realpath(directory))
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else:
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super().__init__(directory=directory)
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@ -51,11 +51,12 @@ class SetAppConfigRequest(BaseModel):
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ui_open_browser_on_start: bool = None
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listen_to_network: bool = None
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listen_port: int = None
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test_diffusers: bool = False
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def init():
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mimetypes.init()
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mimetypes.add_type('text/css', '.css')
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mimetypes.add_type("text/css", ".css")
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if os.path.isdir(app.CUSTOM_MODIFIERS_DIR):
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server_api.mount(
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@ -132,6 +133,9 @@ def set_app_config_internal(req: SetAppConfigRequest):
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if "net" not in config:
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config["net"] = {}
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config["net"]["listen_port"] = int(req.listen_port)
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config["test_diffusers"] = req.test_diffusers
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try:
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app.setConfig(config)
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@ -21,6 +21,7 @@ class GenerateImageRequest(BaseModel):
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sampler_name: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
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hypernetwork_strength: float = 0
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lora_alpha: float = 0
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class TaskData(BaseModel):
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@ -36,6 +37,7 @@ class TaskData(BaseModel):
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# use_stable_diffusion_config: str = "v1-inference"
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use_vae_model: str = None
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use_hypernetwork_model: str = None
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use_lora_model: str = None
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show_only_filtered_image: bool = False
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block_nsfw: bool = False
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@ -162,7 +162,7 @@
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<option value="unipc_snr">UniPC SNR</option>
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<option value="unipc_tu">UniPC TU</option>
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<option value="unipc_snr_2">UniPC SNR 2</option>
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<option value="unipc_tu_2">UniPC TC 2</option>
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<option value="unipc_tu_2">UniPC TU 2</option>
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<option value="unipc_tq">UniPC TQ</option>
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</select>
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<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/How-to-Use#samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
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@ -217,6 +217,13 @@
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<tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" size="4" value="25" onkeypress="preventNonNumericalInput(event)"></td></tr>
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<tr class="pl-5"><td><label for="guidance_scale_slider">Guidance Scale:</label></td><td> <input id="guidance_scale_slider" name="guidance_scale_slider" class="editor-slider" value="75" type="range" min="11" max="500"> <input id="guidance_scale" name="guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"></td></tr>
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<tr id="prompt_strength_container" class="pl-5"><td><label for="prompt_strength_slider">Prompt Strength:</label></td><td> <input id="prompt_strength_slider" name="prompt_strength_slider" class="editor-slider" value="80" type="range" min="0" max="99"> <input id="prompt_strength" name="prompt_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td></tr>
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<tr id="lora_model_container" class="pl-5"><td><label for="lora_model">LoRA:</i></label></td><td>
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<input id="lora_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
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</td></tr>
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<tr id="lora_alpha_container" class="pl-5">
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<td><label for="lora_alpha_slider">LoRA strength:</label></td>
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<td> <input id="lora_alpha_slider" name="lora_alpha_slider" class="editor-slider" value="100" type="range" min="0" max="100"> <input id="lora_alpha" name="lora_alpha" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td>
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</tr>
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<tr class="pl-5"><td><label for="hypernetwork_model">Hypernetwork:</i></label></td><td>
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<input id="hypernetwork_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
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</td></tr>
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@ -15,6 +15,7 @@ const SETTINGS_IDS_LIST = [
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"stable_diffusion_model",
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"vae_model",
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"hypernetwork_model",
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"lora_model",
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"sampler_name",
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"width",
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"height",
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@ -22,6 +23,7 @@ const SETTINGS_IDS_LIST = [
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"guidance_scale",
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"prompt_strength",
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"hypernetwork_strength",
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"lora_alpha",
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"output_format",
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"output_quality",
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"negative_prompt",
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@ -46,6 +46,9 @@ let vaeModelField = new ModelDropdown(document.querySelector('#vae_model'), 'vae
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let hypernetworkModelField = new ModelDropdown(document.querySelector('#hypernetwork_model'), 'hypernetwork', 'None')
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let hypernetworkStrengthSlider = document.querySelector('#hypernetwork_strength_slider')
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let hypernetworkStrengthField = document.querySelector('#hypernetwork_strength')
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let loraModelField = new ModelDropdown(document.querySelector('#lora_model'), 'lora', 'None')
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let loraAlphaSlider = document.querySelector('#lora_alpha_slider')
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let loraAlphaField = document.querySelector('#lora_alpha')
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let outputFormatField = document.querySelector('#output_format')
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let blockNSFWField = document.querySelector('#block_nsfw')
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let showOnlyFilteredImageField = document.querySelector("#show_only_filtered_image")
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@ -931,6 +934,9 @@ function createTask(task) {
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taskConfig += `, <b>Hypernetwork:</b> ${task.reqBody.use_hypernetwork_model}`
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taskConfig += `, <b>Hypernetwork Strength:</b> ${task.reqBody.hypernetwork_strength}`
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}
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if (task.reqBody.use_lora_model) {
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taskConfig += `, <b>LoRA:</b> ${task.reqBody.use_lora_model}`
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}
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if (task.reqBody.preserve_init_image_color_profile) {
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taskConfig += `, <b>Preserve Color Profile:</b> true`
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}
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@ -1041,9 +1047,11 @@ function getCurrentUserRequest() {
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height: parseInt(heightField.value),
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// allow_nsfw: allowNSFWField.checked,
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vram_usage_level: vramUsageLevelField.value,
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sampler_name: samplerField.value,
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//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
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use_stable_diffusion_model: stableDiffusionModelField.value,
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use_vae_model: vaeModelField.value,
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use_lora_model: loraModelField.value,
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stream_progress_updates: true,
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stream_image_progress: (numOutputsTotal > 50 ? false : streamImageProgressField.checked),
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show_only_filtered_image: showOnlyFilteredImageField.checked,
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@ -1067,9 +1075,9 @@ function getCurrentUserRequest() {
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newTask.reqBody.mask = imageInpainter.getImg()
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}
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newTask.reqBody.preserve_init_image_color_profile = applyColorCorrectionField.checked
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if (!testDiffusers.checked) {
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newTask.reqBody.sampler_name = 'ddim'
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} else {
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newTask.reqBody.sampler_name = samplerField.value
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}
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}
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if (saveToDiskField.checked && diskPathField.value.trim() !== '') {
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newTask.reqBody.save_to_disk_path = diskPathField.value.trim()
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@ -1458,6 +1466,34 @@ function updateHypernetworkStrengthContainer() {
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hypernetworkModelField.addEventListener('change', updateHypernetworkStrengthContainer)
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updateHypernetworkStrengthContainer()
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/********************* LoRA alpha **********************/
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function updateLoraAlpha() {
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loraAlphaField.value = loraAlphaSlider.value / 100
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loraAlphaField.dispatchEvent(new Event("change"))
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}
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function updateLoraAlphaSlider() {
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if (loraAlphaField.value < 0) {
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loraAlphaField.value = 0
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} else if (loraAlphaField.value > 0.99) {
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loraAlphaField.value = 0.99
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}
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loraAlphaSlider.value = loraAlphaField.value * 100
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loraAlphaSlider.dispatchEvent(new Event("change"))
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}
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loraAlphaSlider.addEventListener('input', updateLoraAlpha)
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loraAlphaField.addEventListener('input', updateLoraAlphaSlider)
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updateLoraAlpha()
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// function updateLoraAlphaContainer() {
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// document.querySelector("#lora_alpha_container").style.display = (loraModelField.value === "" ? 'none' : '')
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// }
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// loraModelField.addEventListener('change', updateLoraAlphaContainer)
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// updateLoraAlphaContainer()
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document.querySelector("#lora_alpha_container").style.display = 'none'
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|
||||
/********************* JPEG/WEBP Quality **********************/
|
||||
function updateOutputQuality() {
|
||||
outputQualityField.value = 0 | outputQualitySlider.value
|
||||
@ -1550,7 +1586,9 @@ loadImg2ImgFromFile()
|
||||
|
||||
function img2imgLoad() {
|
||||
promptStrengthContainer.style.display = 'table-row'
|
||||
if (!testDiffusers.checked) {
|
||||
samplerSelectionContainer.style.display = "none"
|
||||
}
|
||||
initImagePreviewContainer.classList.add("has-image")
|
||||
colorCorrectionSetting.style.display = ''
|
||||
|
||||
@ -1565,7 +1603,9 @@ function img2imgUnload() {
|
||||
maskSetting.checked = false
|
||||
|
||||
promptStrengthContainer.style.display = "none"
|
||||
if (!testDiffusers.checked) {
|
||||
samplerSelectionContainer.style.display = ""
|
||||
}
|
||||
initImagePreviewContainer.classList.remove("has-image")
|
||||
colorCorrectionSetting.style.display = 'none'
|
||||
imageEditor.setImage(null, parseInt(widthField.value), parseInt(heightField.value))
|
||||
|
||||
@ -190,6 +190,14 @@ var PARAMETERS = [
|
||||
icon: "fa-fire",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "test_diffusers",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Test Diffusers",
|
||||
note: "<b>Experimental! Can have bugs!</b> Use upcoming features (like LoRA) in our new engine. Please press Save, then restart the program after changing this.",
|
||||
icon: "fa-bolt",
|
||||
default: false,
|
||||
},
|
||||
];
|
||||
|
||||
function getParameterSettingsEntry(id) {
|
||||
@ -263,6 +271,7 @@ let listenPortField = document.querySelector("#listen_port")
|
||||
let useBetaChannelField = document.querySelector("#use_beta_channel")
|
||||
let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
|
||||
let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
|
||||
let testDiffusers = document.querySelector("#test_diffusers")
|
||||
|
||||
let saveSettingsBtn = document.querySelector('#save-system-settings-btn')
|
||||
|
||||
@ -302,6 +311,10 @@ async function getAppConfig() {
|
||||
if (config.net && config.net.listen_port !== undefined) {
|
||||
listenPortField.value = config.net.listen_port
|
||||
}
|
||||
if (config.test_diffusers !== undefined) {
|
||||
testDiffusers.checked = config.test_diffusers
|
||||
document.querySelector("#lora_model_container").style.display = (testDiffusers.checked ? '' : 'none')
|
||||
}
|
||||
|
||||
console.log('get config status response', config)
|
||||
} catch (e) {
|
||||
@ -471,7 +484,8 @@ saveSettingsBtn.addEventListener('click', function() {
|
||||
'update_branch': updateBranch,
|
||||
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
|
||||
'listen_to_network': listenToNetworkField.checked,
|
||||
'listen_port': listenPortField.value
|
||||
'listen_port': listenPortField.value,
|
||||
'test_diffusers': testDiffusers.checked
|
||||
})
|
||||
saveSettingsBtn.classList.add('active')
|
||||
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user