Merge branch 'master' of github.com:comfyanonymous/ComfyUI

comfy/cli_args.py

@@ -161,7 +161,7 @@ def _create_parser() -> EnhancedConfigArgParser:
                         help="This name will be used by the frontends and workers to exchange prompt requests and replies. Progress updates will be prefixed by the queue name, followed by a '.', then the user ID")
     parser.add_argument("--external-address", required=False,
                         help="Specifies a base URL for external addresses reported by the API, such as for image paths.")
-    parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
+    parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
     parser.add_argument("--disable-known-models", action="store_true", help="Disables automatic downloads of known models and prevents them from appearing in the UI.")
     parser.add_argument("--max-queue-size", type=int, default=65536, help="The API will reject prompt requests if the queue's size exceeds this value.")
     # tracing

comfy/cli_args_types.py

@@ -103,7 +103,7 @@ class Configuration(dict):
         distributed_queue_worker (bool): Workers will pull requests off the AMQP URL.
         distributed_queue_name (str): This name will be used by the frontends and workers to exchange prompt requests and replies. Progress updates will be prefixed by the queue name, followed by a '.', then the user ID.
         external_address (str): Specifies a base URL for external addresses reported by the API, such as for image paths.
-        verbose (bool): Shows extra output for debugging purposes such as import errors of custom nodes.
+        verbose (bool | str): Shows extra output for debugging purposes such as import errors of custom nodes; or, specifies a log level
         disable_known_models (bool): Disables automatic downloads of known models and prevents them from appearing in the UI.
         max_queue_size (int): The API will reject prompt requests if the queue's size exceeds this value.
         otel_service_name (str): The name of the service or application that is generating telemetry data. Default: "comfyui".
@@ -192,6 +192,7 @@ class Configuration(dict):
         self.force_channels_last: bool = False
         self.force_hf_local_dir_mode = False
         self.preview_size: int = 512
+        self.verbose: str | bool = "INFO"
 
         # from guill
         self.cache_lru: int = 0

comfy/clip_vision.py

@@ -117,8 +117,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     keys = list(sd.keys())
     for k in keys:
         if k not in u:
-            t = sd.pop(k)
-            del t
+            sd.pop(k)
     return clip
 
 def load(ckpt_path):

comfy/cmd/folder_paths.py

@@ -24,7 +24,7 @@ def _base_path():
 
 
 models_dir = os.path.join(get_base_path(), "models")
-folder_names_and_paths = FolderNames(models_dir)
+folder_names_and_paths: Final[FolderNames] = FolderNames(models_dir)
 folder_names_and_paths["checkpoints"] = FolderPathsTuple("checkpoints", [os.path.join(models_dir, "checkpoints")], set(supported_pt_extensions))
 folder_names_and_paths["configs"] = FolderPathsTuple("configs", [os.path.join(models_dir, "configs"), get_package_as_path("comfy.configs")], {".yaml"})
 folder_names_and_paths["loras"] = FolderPathsTuple("loras", [os.path.join(models_dir, "loras")], set(supported_pt_extensions))
@@ -239,8 +239,12 @@ def recursive_search(directory, excluded_dir_names=None):
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
-            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
-            result.append(relative_path)
+            try:
+                relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                result.append(relative_path)
+            except:
+                logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
+                continue
 
         for d in subdirs:
             path = os.path.join(dirpath, d)

comfy/cmd/main_pre.py

@@ -111,9 +111,12 @@ def _create_tracer():
 
 
 def _configure_logging():
-    logging_level = logging.INFO
-    if args.verbose:
+    if isinstance(args.verbose, str):
+        logging_level = args.verbose
+    elif args.verbose == True:
         logging_level = logging.DEBUG
+    else:
+        logging_level = logging.ERROR
 
     logging.basicConfig(format="%(message)s", level=logging_level)
 

comfy/cmd/server.py

@@ -700,6 +700,7 @@ class PromptServer(ExecutorToClientProgress):
 
         # Internal route. Should not be depended upon and is subject to change at any time.
         # TODO(robinhuang): Move to internal route table class once we refactor PromptServer to pass around Websocket.
+        # NOTE: This was an experiment and WILL BE REMOVED
         @routes.post("/internal/models/download")
         async def download_handler(request):
             async def report_progress(filename: str, status: DownloadModelStatus):
@@ -710,10 +711,11 @@ class PromptServer(ExecutorToClientProgress):
             data = await request.json()
             url = data.get('url')
             model_directory = data.get('model_directory')
+            folder_path = data.get('folder_path')
             model_filename = data.get('model_filename')
             progress_interval = data.get('progress_interval', 1.0)  # In seconds, how often to report download progress.
 
-            if not url or not model_directory or not model_filename:
+            if not url or not model_directory or not model_filename or not folder_path:
                 return web.json_response({"status": "error", "message": "Missing URL or folder path or filename"}, status=400)
 
             session = self.client_session
@@ -721,7 +723,7 @@ class PromptServer(ExecutorToClientProgress):
                 logging.error("Client session is not initialized")
                 return web.Response(status=500)
 
-            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, report_progress, progress_interval))
+            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, folder_path, report_progress, progress_interval))
             await task
 
             return web.json_response(task.result().to_dict())

comfy/component_model/folder_path_types.py

@@ -2,9 +2,10 @@ from __future__ import annotations
 
 import dataclasses
 import os
+import typing
 from typing import List, Set, Any, Iterator, Sequence, Dict, NamedTuple
 
-supported_pt_extensions = frozenset(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'])
+supported_pt_extensions = frozenset(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft', ".index.json"])
 extension_mimetypes_cache = {
     "webp": "image",
 }
@@ -37,6 +38,31 @@ class FolderPathsTuple:
 
 
 class FolderNames:
+    @staticmethod
+    def from_dict(folder_paths_dict: dict[str, tuple[typing.Sequence[str], Sequence[str]]] = None) -> FolderNames:
+        """
+        Turns a dictionary of
+        {
+          "folder_name": (["folder/paths"], {".supported.extensions"})
+        }
+
+        into a FolderNames object
+        :param folder_paths_dict: A dictionary
+        :return: A FolderNames object
+        """
+        if folder_paths_dict is None:
+            return FolderNames(os.getcwd())
+
+        fn = FolderNames(os.getcwd())
+        for folder_name, (paths, extensions) in folder_paths_dict.items():
+            paths_tuple = FolderPathsTuple(folder_name=folder_name, paths=list(paths), supported_extensions=set(extensions))
+
+            if folder_name in fn:
+                fn[folder_name] += paths_tuple
+            else:
+                fn[folder_name] = paths_tuple
+        return fn
+
     def __init__(self, default_new_folder_path: str):
         self.contents: Dict[str, FolderPathsTuple] = dict()
         self.default_new_folder_path = default_new_folder_path
@@ -66,6 +92,9 @@ class FolderNames:
     def __delitem__(self, key):
         del self.contents[key]
 
+    def __contains__(self, item):
+        return item in self.contents
+
     def items(self):
         return self.contents.items()
 

comfy/controlnet.py

@@ -239,6 +239,7 @@ class ControlNet(ControlBase):
             if len(self.extra_concat_orig) > 0:
                 to_concat = []
                 for c in self.extra_concat_orig:
+                    c = c.to(self.cond_hint.device)
                     c = utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
                     to_concat.append(utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
                 self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)

comfy/float.py

@@ -1,5 +1,4 @@
 import torch
-import math
 
 def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):
     mantissa_scaled = torch.where(

comfy/k_diffusion/sampling.py

@@ -1198,3 +1198,36 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    """DPM-Solver++(2M)."""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    t_fn = lambda sigma: sigma.log().neg()
+
+    old_uncond_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+    
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+        h = t_next - t
+        if old_uncond_denoised is None or sigmas[i + 1] == 0:
+            denoised_mix = -torch.exp(-h) * uncond_denoised
+        else:
+            h_last = t - t_fn(sigmas[i - 1])
+            r = h_last / h
+            denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+        x = denoised + denoised_mix + torch.exp(-h) * x
+        old_uncond_denoised = uncond_denoised
+    return x

comfy/latent_formats.py

@@ -31,11 +31,13 @@ class SDXL(LatentFormat):
     def __init__(self):
         self.latent_rgb_factors = [
                     #   R        G        B
-                    [ 0.3920,  0.4054,  0.4549],
-                    [-0.2634, -0.0196,  0.0653],
-                    [ 0.0568,  0.1687, -0.0755],
-                    [-0.3112, -0.2359, -0.2076]
+                    [ 0.3651,  0.4232,  0.4341],
+                    [-0.2533, -0.0042,  0.1068],
+                    [ 0.1076,  0.1111, -0.0362],
+                    [-0.3165, -0.2492, -0.2188]
                 ]
+        self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]
+
         self.taesd_decoder_name = "taesdxl_decoder"
 
 class SDXL_Playground_2_5(LatentFormat):
@@ -113,23 +115,24 @@ class SD3(LatentFormat):
         self.scale_factor = 1.5305
         self.shift_factor = 0.0609
         self.latent_rgb_factors = [
-            [-0.0645,  0.0177,  0.1052],
-            [ 0.0028,  0.0312,  0.0650],
-            [ 0.1848,  0.0762,  0.0360],
-            [ 0.0944,  0.0360,  0.0889],
-            [ 0.0897,  0.0506, -0.0364],
-            [-0.0020,  0.1203,  0.0284],
-            [ 0.0855,  0.0118,  0.0283],
-            [-0.0539,  0.0658,  0.1047],
-            [-0.0057,  0.0116,  0.0700],
-            [-0.0412,  0.0281, -0.0039],
-            [ 0.1106,  0.1171,  0.1220],
-            [-0.0248,  0.0682, -0.0481],
-            [ 0.0815,  0.0846,  0.1207],
-            [-0.0120, -0.0055, -0.0867],
-            [-0.0749, -0.0634, -0.0456],
-            [-0.1418, -0.1457, -0.1259]
+            [-0.0922, -0.0175,  0.0749],
+            [ 0.0311,  0.0633,  0.0954],
+            [ 0.1994,  0.0927,  0.0458],
+            [ 0.0856,  0.0339,  0.0902],
+            [ 0.0587,  0.0272, -0.0496],
+            [-0.0006,  0.1104,  0.0309],
+            [ 0.0978,  0.0306,  0.0427],
+            [-0.0042,  0.1038,  0.1358],
+            [-0.0194,  0.0020,  0.0669],
+            [-0.0488,  0.0130, -0.0268],
+            [ 0.0922,  0.0988,  0.0951],
+            [-0.0278,  0.0524, -0.0542],
+            [ 0.0332,  0.0456,  0.0895],
+            [-0.0069, -0.0030, -0.0810],
+            [-0.0596, -0.0465, -0.0293],
+            [-0.1448, -0.1463, -0.1189]
         ]
+        self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]
         self.taesd_decoder_name = "taesd3_decoder"
 
     def process_in(self, latent):

comfy/ldm/flux/model.py

@@ -109,7 +109,7 @@ class Flux(nn.Module):
                 raise ValueError("Didn't get guidance strength for guidance distilled model.")
             vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y)
+        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
         ids = torch.cat((txt_ids, img_ids), dim=1)

comfy/ldm/flux/weight_dtypes.py

@@ -1 +1 @@
-FLUX_WEIGHT_DTYPES = ["default", "fp8_e4m3fn", "fp8_e5m2"]
+FLUX_WEIGHT_DTYPES = ["default", "fp8_e4m3fn", "fp8_e5m2", "fp8_e4m3fn_fast"]

comfy/lora.py

@@ -292,6 +292,7 @@ def model_lora_keys_unet(model, key_map={}):
             unet_key = "diffusion_model.{}".format(diffusers_keys[k])
             key_lora = k[:-len(".weight")].replace(".", "_")
             key_map["lora_unet_{}".format(key_lora)] = unet_key
+            key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format
 
             diffusers_lora_prefix = ["", "unet."]
             for p in diffusers_lora_prefix:

comfy/model_base.py

@@ -108,7 +108,7 @@ class BaseModel(torch.nn.Module):
 
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
-                operations = ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype)
+                operations = ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=model_config.optimizations.get("fp8", False))
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)

comfy/model_filemanager/__init__.py

@@ -1,2 +1,2 @@
 # model_manager/__init__.py
-from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_model_subdirectory, validate_filename
+from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_filename

comfy/model_filemanager/download_models.py

@@ -1,3 +1,4 @@
+# NOTE: This was an experiment and WILL BE REMOVED
 from __future__ import annotations
 
 import logging
@@ -11,7 +12,7 @@ from typing import Callable, Any, Optional, Awaitable, Dict
 
 import aiohttp
 
-from ..cmd.folder_paths import models_dir
+from ..cmd.folder_paths import folder_names_and_paths, get_folder_paths
 
 
 class DownloadStatusType(Enum):
@@ -46,36 +47,31 @@ class DownloadModelStatus():
 async def download_model(model_download_request: Callable[[str], Awaitable[aiohttp.ClientResponse]],
                          model_name: str,
                          model_url: str,
-                         model_sub_directory: str,
+                         model_directory: str,
+                         folder_path: str,
                          progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
                          progress_interval: float = 1.0) -> DownloadModelStatus:
     """
     Download a model file from a given URL into the models directory.
 
     Args:
-        model_download_request (Callable[[str], Awaitable[aiohttp.ClientResponse]]): 
+        model_download_request (Callable[[str], Awaitable[aiohttp.ClientResponse]]):
             A function that makes an HTTP request. This makes it easier to mock in unit tests.
-        model_name (str): 
+        model_name (str):
             The name of the model file to be downloaded. This will be the filename on disk.
-        model_url (str): 
+        model_url (str):
             The URL from which to download the model.
-        model_sub_directory (str): 
-            The subdirectory within the main models directory where the model 
+        model_directory (str):
+            The subdirectory within the main models directory where the model
             should be saved (e.g., 'checkpoints', 'loras', etc.).
-        progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]): 
+        progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]):
             An asynchronous function to call with progress updates.
+        folder_path (str);
+            Path to which model folder should be used as the root.
 
     Returns:
         DownloadModelStatus: The result of the download operation.
     """
-    if not validate_model_subdirectory(model_sub_directory):
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR,
-            0,
-            "Invalid model subdirectory",
-            False
-        )
-
     if not validate_filename(model_name):
         return DownloadModelStatus(
             DownloadStatusType.ERROR,
@@ -84,52 +80,67 @@ async def download_model(model_download_request: Callable[[str], Awaitable[aioht
             False
         )
 
-    file_path, relative_path = create_model_path(model_name, model_sub_directory, models_dir)
-    existing_file = await check_file_exists(file_path, model_name, progress_callback, relative_path)
+    if not model_directory in folder_names_and_paths:
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            "Invalid or unrecognized model directory. model_directory must be a known model type (eg 'checkpoints'). If you are seeing this error for a custom model type, ensure the relevant custom nodes are installed and working.",
+            False
+        )
+
+    if not folder_path in get_folder_paths(model_directory):
+        return DownloadModelStatus(
+            DownloadStatusType.ERROR,
+            0,
+            f"Invalid folder path '{folder_path}', does not match the list of known directories ({get_folder_paths(model_directory)}). If you're seeing this in the downloader UI, you may need to refresh the page.",
+            False
+        )
+
+    file_path = create_model_path(model_name, folder_path)
+    existing_file = await check_file_exists(file_path, model_name, progress_callback)
     if existing_file:
         return existing_file
 
     try:
+        logging.info(f"Downloading {model_name} from {model_url}")
         status = DownloadModelStatus(DownloadStatusType.PENDING, 0, f"Starting download of {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         response = await model_download_request(model_url)
         if response.status != 200:
             error_message = f"Failed to download {model_name}. Status code: {response.status}"
             logging.error(error_message)
             status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             return DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
 
-        return await track_download_progress(response, file_path, model_name, progress_callback, relative_path, progress_interval)
+        return await track_download_progress(response, file_path, model_name, progress_callback, progress_interval)
 
     except Exception as e:
         logging.error(f"Error in downloading model: {e}")
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
 
 
-def create_model_path(model_name: str, model_directory: str, models_base_dir: str) -> tuple[str, str]:
-    full_model_dir = os.path.join(models_base_dir, model_directory)
-    os.makedirs(full_model_dir, exist_ok=True)
-    file_path = os.path.join(full_model_dir, model_name)
+def create_model_path(model_name: str, folder_path: str) -> str:
+    os.makedirs(folder_path, exist_ok=True)
+    file_path = os.path.join(folder_path, model_name)
 
     # Ensure the resulting path is still within the base directory
     abs_file_path = os.path.abspath(file_path)
-    abs_base_dir = os.path.abspath(str(models_base_dir))
+    abs_base_dir = os.path.abspath(folder_path)
     if os.path.commonprefix([abs_file_path, abs_base_dir]) != abs_base_dir:
-        raise Exception(f"Invalid model directory: {model_directory}/{model_name}")
+        raise Exception(f"Invalid model directory: {folder_path}/{model_name}")
 
-    relative_path = '/'.join([model_directory, model_name])
-    return file_path, relative_path
+    return file_path
 
 
 async def check_file_exists(file_path: str,
                             model_name: str,
-                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                            relative_path: str) -> Optional[DownloadModelStatus]:
+                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]]
+                            ) -> Optional[DownloadModelStatus]:
     if os.path.exists(file_path):
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"{model_name} already exists", True)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
         return status
     return None
 
@@ -138,7 +149,7 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                                   file_path: str,
                                   model_name: str,
                                   progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                                  relative_path: str,
+
                                   interval: float = 1.0) -> DownloadModelStatus:
     try:
         total_size = int(response.headers.get('Content-Length', 0))
@@ -149,10 +160,11 @@ async def track_download_progress(response: aiohttp.ClientResponse,
             nonlocal last_update_time
             progress = (downloaded / total_size) * 100 if total_size > 0 else 0
             status = DownloadModelStatus(DownloadStatusType.IN_PROGRESS, progress, f"Downloading {model_name}", False)
-            await progress_callback(relative_path, status)
+            await progress_callback(model_name, status)
             last_update_time = time.time()
 
-        with open(file_path, 'wb') as f:
+        temp_file_path = file_path + '.tmp'
+        with open(temp_file_path, 'wb') as f:
             chunk_iterator = response.content.iter_chunked(8192)
             while True:
                 try:
@@ -165,57 +177,35 @@ async def track_download_progress(response: aiohttp.ClientResponse,
                 if time.time() - last_update_time >= interval:
                     await update_progress()
 
+        os.rename(temp_file_path, file_path)
+
         await update_progress()
 
         logging.info(f"Successfully downloaded {model_name}. Total downloaded: {downloaded}")
         status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"Successfully downloaded {model_name}", False)
-        await progress_callback(relative_path, status)
+        await progress_callback(model_name, status)
 
         return status
     except Exception as e:
         logging.error(f"Error in track_download_progress: {e}")
         logging.error(traceback.format_exc())
-        return await handle_download_error(e, model_name, progress_callback, relative_path)
+        return await handle_download_error(e, model_name, progress_callback)
 
 
 async def handle_download_error(e: Exception,
                                 model_name: str,
-                                progress_callback: Callable[[str, DownloadModelStatus], Any],
-                                relative_path: str) -> DownloadModelStatus:
+                                progress_callback: Callable[[str, DownloadModelStatus], Any]
+                                ) -> DownloadModelStatus:
     error_message = f"Error downloading {model_name}: {str(e)}"
     status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-    await progress_callback(relative_path, status)
+    await progress_callback(model_name, status)
     return status
 
 
-def validate_model_subdirectory(model_subdirectory: str) -> bool:
-    """
-    Validate that the model subdirectory is safe to install into. 
-    Must not contain relative paths, nested paths or special characters
-    other than underscores and hyphens.
-
-    Args:
-        model_subdirectory (str): The subdirectory for the specific model type.
-
-    Returns:
-        bool: True if the subdirectory is safe, False otherwise.
-    """
-    if len(model_subdirectory) > 50:
-        return False
-
-    if '..' in model_subdirectory or '/' in model_subdirectory:
-        return False
-
-    if not re.match(r'^[a-zA-Z0-9_-]+$', model_subdirectory):
-        return False
-
-    return True
-
-
 def validate_filename(filename: str) -> bool:
     """
     Validate a filename to ensure it's safe and doesn't contain any path traversal attempts.
-    
+
     Args:
     filename (str): The filename to validate
 

comfy/model_management.py

@@ -173,7 +173,7 @@ total_ram = psutil.virtual_memory().total / (1024 * 1024)
 logging.debug("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
 try:
-    logging.debug("pytorch version: {}".format(torch.version.__version__))
+    logging.debug("pytorch version: {}".format(torch_version))
 except:
     pass
 
@@ -1059,7 +1059,7 @@ def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
     try:
         macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
-        if (14, 5) <= macos_version < (14, 7):  # black image bug on recent versions of MacOS
+        if (14, 5) <= macos_version <= (15, 0, 1):  # black image bug on recent versions of macOS
             upcast = True
     except:
         pass
@@ -1244,6 +1244,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
 
 
 def supports_fp8_compute(device=None):
+    if not is_nvidia():
+        return False
+
     props = torch.cuda.get_device_properties(device)
     if props.major >= 9:
         return True
@@ -1251,6 +1254,14 @@ def supports_fp8_compute(device=None):
         return False
     if props.minor < 9:
         return False
+
+    if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+        return False
+
+    if WINDOWS:
+        if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+            return False
+
     return True
 
 

comfy/model_patcher.py

@@ -97,7 +97,11 @@ class LowVramPatch:
         self.patches = patches
 
     def __call__(self, weight):
-        return lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
+        intermediate_dtype = weight.dtype
+        if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]:  # intermediate_dtype has to be one that is supported in math ops
+            intermediate_dtype = torch.float32
+            return stochastic_rounding(lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+        return lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
 
 
 class ModelPatcher(ModelManageable):

comfy/nodes/base_nodes.py

@@ -773,11 +773,7 @@ class ControlNetLoaderWeights:
 
     def load_controlnet(self, control_net_name, weight_dtype):
         controlnet_path = get_or_download("controlnet", control_net_name, KNOWN_CONTROLNETS)
-        model_options = {}
-        if weight_dtype == "float8_e5m2":
-            model_options["dtype"] = torch.float8_e5m2
-        elif weight_dtype == "float8_e4m3fn":
-            model_options["dtype"] = torch.float8_e4m3fn
+        model_options = get_model_options_for_dtype(weight_dtype)
 
         controlnet_ = controlnet.load_controlnet(controlnet_path, model_options=model_options)
         return (controlnet_,)
@@ -885,6 +881,9 @@ def get_model_options_for_dtype(weight_dtype):
         model_options["dtype"] = torch.float8_e4m3fn
     elif weight_dtype == "fp8_e5m2":
         model_options["dtype"] = torch.float8_e5m2
+    elif weight_dtype == "fp8_e4m3fn_fast":
+        model_options["dtype"] = torch.float8_e4m3fn
+        model_options["fp8_optimizations"] = True
     return model_options
 
 

comfy/ops.py

@@ -350,10 +350,13 @@ class fp8_ops(manual_cast):
             return torch.nn.functional.linear(input, weight, bias)
 
 
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, inference_mode: Optional[bool] = None):
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, inference_mode: Optional[bool] = None):
     if inference_mode is None:
         # todo: check a context here, since this isn't being used by any callers yet
         inference_mode = current_execution_context().inference_mode
+    if model_management.supports_fp8_compute(load_device):
+        if (fp8_optimizations or args.fast) and not disable_fast_fp8:
+            return fp8_ops
     if compute_dtype is None or weight_dtype == compute_dtype:
         # disable_weight_init seems to interact poorly with some other optimization code
         return disable_weight_init if inference_mode else skip_init

comfy/samplers.py

@@ -583,7 +583,7 @@ class Sampler:
 
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+                  "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis"]
 
 class KSAMPLER(Sampler):

comfy/sd.py

@@ -30,10 +30,10 @@ from .taesd import taesd
 from .text_encoders import aura_t5
 from .text_encoders import flux
 from .text_encoders import hydit
+from .text_encoders import long_clipl
 from .text_encoders import sa_t5
 from .text_encoders import sd2_clip
 from .text_encoders import sd3_clip
-from .text_encoders import long_clipl
 
 
 def load_lora_for_models(model, clip, _lora, strength_model, strength_clip):
@@ -359,7 +359,7 @@ class VAE:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
+            batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
             samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
@@ -430,8 +430,49 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         clip_data.append(utils.load_torch_file(p, safe_load=True))
     return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options, textmodel_json_config=textmodel_json_config)
 
+
+class TEModel(Enum):
+    CLIP_L = 1
+    CLIP_H = 2
+    CLIP_G = 3
+    T5_XXL = 4
+    T5_XL = 5
+    T5_BASE = 6
+
+
+def detect_te_model(sd):
+    if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_G
+    if "text_model.encoder.layers.22.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_H
+    if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
+        return TEModel.CLIP_L
+    if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
+        weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+        if weight.shape[-1] == 4096:
+            return TEModel.T5_XXL
+        elif weight.shape[-1] == 2048:
+            return TEModel.T5_XL
+    if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
+        return TEModel.T5_BASE
+    return None
+
+
+def t5xxl_weight_dtype(clip_data):
+    weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+
+    dtype_t5 = None
+    for sd in clip_data:
+        weight = sd.get(weight_name, None)
+        if weight is not None:
+            dtype_t5 = weight.dtype
+            break
+    return dtype_t5
+
+
 def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, textmodel_json_config=None):
     clip_data = state_dicts
+
     class EmptyClass:
         pass
 
@@ -445,53 +486,52 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
     clip_target = CLIPTarget()
     clip_target.params = {}
     if len(clip_data) == 1:
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
+        te_model = detect_te_model(clip_data[0])
+        if te_model == TEModel.CLIP_G:
             if clip_type == CLIPType.STABLE_CASCADE:
                 clip_target.clip = sdxl_clip.StableCascadeClipModel
                 clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
+            elif clip_type == CLIPType.SD3:
+                clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
+                clip_target.tokenizer = sd3_clip.SD3Tokenizer
             else:
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                 clip_target.tokenizer = sdxl_clip.SDXLTokenizer
-        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
+        elif te_model == TEModel.CLIP_H:
             clip_target.clip = sd2_clip.SD2ClipModel
             clip_target.tokenizer = sd2_clip.SD2Tokenizer
-        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
-            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
-            dtype_t5 = weight.dtype
-            if weight.shape[-1] == 4096:
-                clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
-                clip_target.tokenizer = sd3_clip.SD3Tokenizer
-            elif weight.shape[-1] == 2048:
-                clip_target.clip = aura_t5.AuraT5Model
-                clip_target.tokenizer = aura_t5.AuraT5Tokenizer
-        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
+        elif te_model == TEModel.T5_XXL:
+            clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=t5xxl_weight_dtype(clip_data))
+            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        elif te_model == TEModel.T5_XL:
+            clip_target.clip = aura_t5.AuraT5Model
+            clip_target.tokenizer = aura_t5.AuraT5Tokenizer
+        elif te_model == TEModel.T5_BASE:
             clip_target.clip = sa_t5.SAT5Model
             clip_target.tokenizer = sa_t5.SAT5Tokenizer
         else:
-            w = clip_data[0].get("text_model.embeddings.position_embedding.weight", None)
-            clip_target.clip = sd1_clip.SD1ClipModel
-            clip_target.tokenizer = sd1_clip.SD1Tokenizer
+            if clip_type == CLIPType.SD3:
+                clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
+                clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            else:
+                clip_target.clip = sd1_clip.SD1ClipModel
+                clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
         if clip_type == CLIPType.SD3:
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
+            clip_target.clip = sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, dtype_t5=t5xxl_weight_dtype(clip_data))
             clip_target.tokenizer = sd3_clip.SD3Tokenizer
         elif clip_type == CLIPType.HUNYUAN_DIT:
             clip_target.clip = hydit.HyditModel
             clip_target.tokenizer = hydit.HyditTokenizer
         elif clip_type == CLIPType.FLUX:
-            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
-            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
-            dtype_t5 = None
-            if weight is not None:
-                dtype_t5 = weight.dtype
-
-            clip_target.clip = flux.flux_clip(dtype_t5=dtype_t5)
+            clip_target.clip = flux.flux_clip(dtype_t5=t5xxl_weight_dtype(clip_data))
             clip_target.tokenizer = flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
-        clip_target.clip = sd3_clip.SD3ClipModel
+        clip_target.clip = sd3_clip.sd3_clip(dtype_t5=t5xxl_weight_dtype(clip_data))
         clip_target.tokenizer = sd3_clip.SD3Tokenizer
 
     parameters = 0
@@ -546,6 +586,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (model, clip, vae)
 
+
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options=None, te_model_options=None):
     if te_model_options is None:
         te_model_options = {}
@@ -557,6 +598,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         raise RuntimeError("Could not detect model type of: {}".format(ckpt_path))
     return out
 
+
 def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options=None, te_model_options=None, ckpt_path=""):
     if te_model_options is None:
         te_model_options = {}
@@ -583,7 +625,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         unet_weight_dtype.append(weight_dtype)
 
     model_config.custom_operations = model_options.get("custom_operations", None)
-    unet_dtype = model_options.get("weight_dtype", None)
+    unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
 
     if unet_dtype is None:
         unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
@@ -597,7 +639,6 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
 
     if output_model:
         inital_load_device = model_management.unet_initial_load_device(parameters, unet_dtype)
-        offload_device = model_management.unet_offload_device()
         model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
         model.load_model_weights(sd, diffusion_model_prefix)
 
@@ -638,7 +679,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     return (_model_patcher, clip, vae, clipvision)
 
 
-def load_diffusion_model_state_dict(sd, model_options: dict = None, ckpt_path: Optional[str]=""):  # load unet in diffusers or regular format
+def load_diffusion_model_state_dict(sd, model_options: dict = None, ckpt_path: Optional[str] = ""):  # load unet in diffusers or regular format
     if model_options is None:
         model_options = {}
     dtype = model_options.get("dtype", None)
@@ -684,6 +725,9 @@ def load_diffusion_model_state_dict(sd, model_options: dict = None, ckpt_path: O
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+    if model_options.get("fp8_optimizations", False):
+        model_config.optimizations["fp8"] = True
+
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")

comfy/sd1_clip.py

@@ -100,7 +100,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         "hidden"
     ]
 
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
+    def __init__(self, device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config: str | dict | None = None, dtype=None, model_class=clip_model.CLIPTextModel,
                  special_tokens=None, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
                  return_projected_pooled=True, return_attention_masks=False, model_options={}):  # clip-vit-base-patch32

comfy/supported_models_base.py

@@ -49,6 +49,7 @@ class BASE:
 
     manual_cast_dtype = None
     custom_operations = None
+    optimizations = {"fp8": False}
 
     @classmethod
     def matches(s, unet_config, state_dict=None):
@@ -71,6 +72,7 @@ class BASE:
         self.unet_config = unet_config.copy()
         self.sampling_settings = self.sampling_settings.copy()
         self.latent_format = self.latent_format()
+        self.optimizations = self.optimizations.copy()
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
 

comfy/text_encoders/flux.py

@@ -21,7 +21,7 @@ class T5XXLTokenizer(sd1_clip.SDTokenizer):
         if tokenizer_data is None:
             tokenizer_data = dict()
         tokenizer_path = files.get_package_as_path("comfy.text_encoders.t5_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
 
 
 class FluxTokenizer:

comfy/text_encoders/sd3_clip.py

@@ -11,7 +11,9 @@ from ..component_model import files
 
 
 class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, textmodel_json_config=None, model_options={}):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, textmodel_json_config=None, model_options=None):
+        if model_options is None:
+            model_options = {}
         textmodel_json_config = files.get_path_as_dict(textmodel_json_config, "t5_config_xxl.json", package=__package__)
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=T5, model_options=model_options)
 
@@ -51,7 +53,7 @@ class SD3Tokenizer:
 
 
 class SD3ClipModel(torch.nn.Module):
-    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None, model_options={}):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
         super().__init__()
         self.dtypes = set()
         if clip_l:
@@ -69,7 +71,8 @@ class SD3ClipModel(torch.nn.Module):
 
         if t5:
             dtype_t5 = model_management.pick_weight_dtype(dtype_t5, dtype, device)
-            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+            self.t5_attention_mask = t5_attention_mask
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
             self.dtypes.add(dtype_t5)
         else:
             self.t5xxl = None
@@ -99,6 +102,7 @@ class SD3ClipModel(torch.nn.Module):
         lg_out = None
         pooled = None
         out = None
+        extra = {}
 
         if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
             if self.clip_l is not None:
@@ -123,7 +127,11 @@ class SD3ClipModel(torch.nn.Module):
             pooled = torch.cat((l_pooled, g_pooled), dim=-1)
 
         if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+            t5_out, t5_pooled = t5_output[:2]
+            if self.t5_attention_mask:
+                extra["attention_mask"] = t5_output[2]["attention_mask"]
+
             if lg_out is not None:
                 out = torch.cat([lg_out, t5_out], dim=-2)
             else:
@@ -135,7 +143,7 @@ class SD3ClipModel(torch.nn.Module):
         if pooled is None:
             pooled = torch.zeros((1, 768 + 1280), device=model_management.intermediate_device())
 
-        return out, pooled
+        return out, pooled, extra
 
     def load_sd(self, sd):
         if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -146,11 +154,10 @@ class SD3ClipModel(torch.nn.Module):
             return self.t5xxl.load_sd(sd)
 
 
-def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None):
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False):
     class SD3ClipModel_(SD3ClipModel):
         def __init__(self, device="cpu", dtype=None, model_options=None):
             if model_options is None:
                 model_options = {}
-            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
-
+            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
     return SD3ClipModel_

comfy/web/assets/ExtensionPanel-DSQ2O8Z9.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, bJ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bK as script$1, A as createBaseVNode, x as createBlock, M as Fragment, N as renderList, ak as toDisplayString, an as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-BX7lxWeb.js";
-import { s as script, a as script$2, b as script$3 } from "./index-DC9toz42.js";
-import "./index-Dwxl_tUs.js";
+import { d as defineComponent, bJ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bK as script$1, A as createBaseVNode, x as createBlock, M as Fragment, N as renderList, ak as toDisplayString, an as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-CFrRuGBA.js";
+import { s as script, a as script$2, b as script$3 } from "./index-CN90wNx3.js";
+import "./index-CaUteDIK.js";
 const _hoisted_1 = { class: "extension-panel" };
 const _hoisted_2 = { class: "mt-4" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
@@ -100,4 +100,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-BL3a6wkg.js.map
+//# sourceMappingURL=ExtensionPanel-DSQ2O8Z9.js.map

comfy/web/assets/ExtensionPanel-DSQ2O8Z9.js.map

@@ -1 +1 @@
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+{"version":3,"file":"ExtensionPanel-DSQ2O8Z9.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <div class=\"extension-panel\">\n    <DataTable :value=\"extensionStore.extensions\" stripedRows size=\"small\">\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n    <div class=\"mt-4\">\n      <Message v-if=\"hasChanges\" severity=\"info\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n      </Message>\n      <Button\n        :label=\"$t('reloadToApplyChanges')\"\n        icon=\"pi pi-refresh\"\n        @click=\"applyChanges\"\n        :disabled=\"!hasChanges\"\n        text\n        fluid\n        severity=\"danger\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

comfy/web/assets/GraphView-vodtEF4p.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, u as useSettingStore, r as ref, a as useTitleEditorStore, b as useCanvasStore, w as watch, L as LGraphGroup, c as app, e as LGraphNode, o as onMounted, f as onUnmounted, g as openBlock, h as createElementBlock, i as createVNode, E as EditableText, n as normalizeStyle, j as createCommentVNode, k as LiteGraph, _ as _export_sfc, B as BaseStyle, s as script$e, l as resolveComponent, m as mergeProps, p as renderSlot, q as computed, t as resolveDirective, v as withDirectives, x as createBlock, y as withCtx, z as unref, A as createBaseVNode, C as normalizeClass, D as script$f, F as useCommandStore, G as useDialogStore, S as SettingDialogHeader, H as SettingDialogContent, I as useWorkspaceStore, J as onBeforeUnmount, K as useKeybindingStore, M as Fragment, N as renderList, T as Teleport, O as resolveDynamicComponent, P as script$g, Q as getWidth, R as getHeight, U as getOuterWidth, V as getOuterHeight, W as getVNodeProp, X as isArray, Y as vShow, Z as isNotEmpty, $ as UniqueComponentId, a0 as ZIndex, a1 as resolveFieldData, a2 as focus, a3 as OverlayEventBus, a4 as isEmpty, a5 as addStyle, a6 as relativePosition, a7 as absolutePosition, a8 as ConnectedOverlayScrollHandler, a9 as isTouchDevice, aa as equals, ab as findLastIndex, ac as findSingle, ad as script$h, ae as script$i, af as script$j, ag as script$k, ah as script$l, ai as script$m, aj as Ripple, ak as toDisplayString, al as Transition, am as createSlots, an as createTextVNode, ao as useNodeFrequencyStore, ap as useNodeBookmarkStore, aq as highlightQuery, ar as script$n, as as formatNumberWithSuffix, at as NodeSourceType, au as useI18n, av as useNodeDefStore, aw as NodePreview, ax as NodeSearchFilter, ay as script$o, az as SearchFilterChip, aA as watchEffect, aB as toRaw, aC as LinkReleaseTriggerAction, aD as useEventListener, aE as nextTick, aF as markRaw, aG as useModelToNodeStore, aH as usePragmaticDroppable, aI as ComfyNodeDefImpl, aJ as ComfyModelDef, aK as LGraph, aL as LLink, aM as DragAndScale, aN as LGraphCanvas, aO as ContextMenu, aP as LGraphBadge, aQ as script$p, aR as script$q, aS as script$r, aT as script$s, aU as normalizeProps, aV as ToastEventBus, aW as setAttribute, aX as TransitionGroup, aY as useToast, aZ as useToastStore, a_ as useExecutionStore, a$ as useWorkflowStore, b0 as useTitle, b1 as withModifiers, b2 as script$t, b3 as resolve, b4 as script$u, b5 as script$v, b6 as isPrintableCharacter, b7 as guardReactiveProps, b8 as useMenuItemStore, b9 as script$y, ba as nestedPosition, bb as useQueueSettingsStore, bc as storeToRefs, bd as isRef, be as script$z, bf as useQueuePendingTaskCountStore, bg as useLocalStorage, bh as useDraggable, bi as watchDebounced, bj as inject, bk as useElementBounding, bl as script$A, bm as lodashExports, bn as useEventBus, bo as provide, bp as api, bq as i18n, br as useWorkflowBookmarkStore, bs as useSidebarTabStore } from "./index-BX7lxWeb.js";
-import { s as script$w, a as script$x } from "./index-Dwxl_tUs.js";
+import { d as defineComponent, u as useSettingStore, r as ref, a as useTitleEditorStore, b as useCanvasStore, w as watch, L as LGraphGroup, c as app, e as LGraphNode, o as onMounted, f as onUnmounted, g as openBlock, h as createElementBlock, i as createVNode, E as EditableText, n as normalizeStyle, j as createCommentVNode, k as LiteGraph, _ as _export_sfc, B as BaseStyle, s as script$e, l as resolveComponent, m as mergeProps, p as renderSlot, q as computed, t as resolveDirective, v as withDirectives, x as createBlock, y as withCtx, z as unref, A as createBaseVNode, C as normalizeClass, D as script$f, F as useCommandStore, G as useDialogStore, S as SettingDialogHeader, H as SettingDialogContent, I as useWorkspaceStore, J as onBeforeUnmount, K as useKeybindingStore, M as Fragment, N as renderList, T as Teleport, O as resolveDynamicComponent, P as script$g, Q as getWidth, R as getHeight, U as getOuterWidth, V as getOuterHeight, W as getVNodeProp, X as isArray, Y as vShow, Z as isNotEmpty, $ as UniqueComponentId, a0 as ZIndex, a1 as resolveFieldData, a2 as focus, a3 as OverlayEventBus, a4 as isEmpty, a5 as addStyle, a6 as relativePosition, a7 as absolutePosition, a8 as ConnectedOverlayScrollHandler, a9 as isTouchDevice, aa as equals, ab as findLastIndex, ac as findSingle, ad as script$h, ae as script$i, af as script$j, ag as script$k, ah as script$l, ai as script$m, aj as Ripple, ak as toDisplayString, al as Transition, am as createSlots, an as createTextVNode, ao as useNodeFrequencyStore, ap as useNodeBookmarkStore, aq as highlightQuery, ar as script$n, as as formatNumberWithSuffix, at as NodeSourceType, au as useI18n, av as useNodeDefStore, aw as NodePreview, ax as NodeSearchFilter, ay as script$o, az as SearchFilterChip, aA as watchEffect, aB as toRaw, aC as LinkReleaseTriggerAction, aD as useEventListener, aE as nextTick, aF as markRaw, aG as useModelToNodeStore, aH as usePragmaticDroppable, aI as ComfyNodeDefImpl, aJ as ComfyModelDef, aK as LGraph, aL as LLink, aM as DragAndScale, aN as LGraphCanvas, aO as ContextMenu, aP as LGraphBadge, aQ as script$p, aR as script$q, aS as script$r, aT as script$s, aU as normalizeProps, aV as ToastEventBus, aW as setAttribute, aX as TransitionGroup, aY as useToast, aZ as useToastStore, a_ as useExecutionStore, a$ as useWorkflowStore, b0 as useTitle, b1 as withModifiers, b2 as script$t, b3 as resolve, b4 as script$u, b5 as script$v, b6 as isPrintableCharacter, b7 as guardReactiveProps, b8 as useMenuItemStore, b9 as script$y, ba as nestedPosition, bb as useQueueSettingsStore, bc as storeToRefs, bd as isRef, be as script$z, bf as useQueuePendingTaskCountStore, bg as useLocalStorage, bh as useDraggable, bi as watchDebounced, bj as inject, bk as useElementBounding, bl as script$A, bm as lodashExports, bn as useEventBus, bo as provide, bp as api, bq as i18n, br as useWorkflowBookmarkStore, bs as useSidebarTabStore } from "./index-CFrRuGBA.js";
+import { s as script$w, a as script$x } from "./index-CaUteDIK.js";
 const _sfc_main$m = /* @__PURE__ */ defineComponent({
   __name: "TitleEditor",
   setup(__props) {
@@ -7417,4 +7417,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=GraphView-BzS8s24m.js.map
+//# sourceMappingURL=GraphView-vodtEF4p.js.map

comfy/web/assets/KeybindingPanel-Cwwh2R-I.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, M as Fragment, N as renderList, i as createVNode, y as withCtx, an as createTextVNode, ak as toDisplayString, z as unref, ar as script, j as createCommentVNode, r as ref, bG as FilterMatchMode, K as useKeybindingStore, F as useCommandStore, aA as watchEffect, aY as useToast, t as resolveDirective, bH as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ad as script$4, b1 as withModifiers, ay as script$6, v as withDirectives, bx as KeyComboImpl, bI as KeybindingImpl, _ as _export_sfc } from "./index-BX7lxWeb.js";
-import { s as script$1, a as script$3, b as script$5 } from "./index-DC9toz42.js";
-import "./index-Dwxl_tUs.js";
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, M as Fragment, N as renderList, i as createVNode, y as withCtx, an as createTextVNode, ak as toDisplayString, z as unref, ar as script, j as createCommentVNode, r as ref, bG as FilterMatchMode, K as useKeybindingStore, F as useCommandStore, aA as watchEffect, aY as useToast, t as resolveDirective, bH as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ad as script$4, b1 as withModifiers, ay as script$6, v as withDirectives, bx as KeyComboImpl, bI as KeybindingImpl, _ as _export_sfc } from "./index-CFrRuGBA.js";
+import { s as script$1, a as script$3, b as script$5 } from "./index-CN90wNx3.js";
+import "./index-CaUteDIK.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -260,4 +260,4 @@ const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "d
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-bEYceDa7.js.map
+//# sourceMappingURL=KeybindingPanel-Cwwh2R-I.js.map

comfy/web/assets/index-CFrRuGBA.js

@@ -1,4 +1,4 @@
-const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["./KeybindingPanel-bEYceDa7.js","./index-DC9toz42.js","./index-Dwxl_tUs.js","./KeybindingPanel-BNYKhW1k.css","./ExtensionPanel-BL3a6wkg.js","./index-Dq1J85oq.js","./widgetInputs-D_n9bPiG.js","./index-DjWyclij.css","./userSelection-DTtYMe59.js","./userSelection-5XIfeQVB.css","./GraphView-BzS8s24m.js","./GraphView-DCFfls4D.css"])))=>i.map(i=>d[i]);
+const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["./KeybindingPanel-Cwwh2R-I.js","./index-CN90wNx3.js","./index-CaUteDIK.js","./KeybindingPanel-BNYKhW1k.css","./ExtensionPanel-DSQ2O8Z9.js","./index-DTOGNau5.js","./widgetInputs-B4bHTYzE.js","./index-DjWyclij.css","./userSelection-vhU1ykfH.js","./userSelection-5XIfeQVB.css","./GraphView-vodtEF4p.js","./GraphView-DCFfls4D.css"])))=>i.map(i=>d[i]);
 var __defProp2 = Object.defineProperty;
 var __name = (target, value3) => __defProp2(target, "name", { value: value3, configurable: true });
 (/* @__PURE__ */ __name(function polyfill() {
@@ -13153,7 +13153,7 @@ const vue_runtime_esmBundler = /* @__PURE__ */ Object.freeze(/* @__PURE__ */ Obj
 }, Symbol.toStringTag, { value: "Module" }));
 const config = {
   app_title: "ComfyUI",
-  app_version: "1.3.17"
+  app_version: "1.3.19"
 };
 var isVue2 = false;
 var isVue3 = true;
@@ -29532,7 +29532,7 @@ function buildTree(items2, key) {
     root: root23
   };
   for (const item2 of items2) {
-    const keys2 = typeof key === "string" ? item2[key] : key(item2);
+    const keys2 = key(item2);
     let parent = root23;
     for (let i2 = 0; i2 < keys2.length; i2++) {
       const k = keys2[i2];
@@ -29546,7 +29546,7 @@ function buildTree(items2, key) {
           children: []
         };
         map2[id3] = node3;
-        parent.children.push(node3);
+        parent.children?.push(node3);
       }
       parent = map2[id3];
     }
@@ -29573,7 +29573,7 @@ function sortedTree(node3) {
   };
   if (node3.children) {
     const sortedChildren = [...node3.children].sort(
-      (a, b) => a.label.localeCompare(b.label)
+      (a, b) => (a.label ?? "").localeCompare(b.label ?? "")
     );
     newNode.children = [];
     for (const child of sortedChildren) {
@@ -30446,8 +30446,9 @@ const useDialogStore = /* @__PURE__ */ defineStore("dialog", {
     showDialog(options3) {
       this.isVisible = true;
       nextTick(() => {
-        this.title = options3.title;
+        this.title = options3.title ?? "";
         this.headerComponent = options3.headerComponent ? markRaw(options3.headerComponent) : null;
+        this.component = markRaw(options3.component);
         this.props = options3.props || {};
         this.dialogComponentProps = options3.dialogComponentProps || {};
       });
@@ -43686,10 +43687,10 @@ const _sfc_main$v = /* @__PURE__ */ defineComponent({
   __name: "SettingDialogContent",
   setup(__props) {
     const KeybindingPanel = /* @__PURE__ */ defineAsyncComponent(
-      () => __vitePreload(() => import("./KeybindingPanel-bEYceDa7.js"), true ? __vite__mapDeps([0,1,2,3]) : void 0, import.meta.url)
+      () => __vitePreload(() => import("./KeybindingPanel-Cwwh2R-I.js"), true ? __vite__mapDeps([0,1,2,3]) : void 0, import.meta.url)
     );
     const ExtensionPanel = /* @__PURE__ */ defineAsyncComponent(
-      () => __vitePreload(() => import("./ExtensionPanel-BL3a6wkg.js"), true ? __vite__mapDeps([4,1,2]) : void 0, import.meta.url)
+      () => __vitePreload(() => import("./ExtensionPanel-DSQ2O8Z9.js"), true ? __vite__mapDeps([4,1,2]) : void 0, import.meta.url)
     );
     const aboutPanelNode = {
       key: "about",
@@ -59830,7 +59831,6 @@ const messages = {
     searchKeybindings: "Search Keybindings",
     noResultsFound: "No Results Found",
     searchFailedMessage: "We couldn't find any settings matching your search. Try adjusting your search terms.",
-    noContent: "(No Content)",
     noTasksFound: "No Tasks Found",
     noTasksFoundMessage: "There are no tasks in the queue.",
     newFolder: "New Folder",
@@ -59926,7 +59926,7 @@ const messages = {
     delete: "删除",
     rename: "重命名",
     customize: "定制",
-    experimental: "测试",
+    experimental: "BETA",
     deprecated: "弃用",
     loadWorkflow: "加载工作流",
     goToNode: "前往节点",
@@ -78288,13 +78288,17 @@ class ComfyModelDef {
     __name(this, "ComfyModelDef");
   }
   /** Proper filename of the model */
-  file_name = "";
+  file_name;
+  /** Normalized filename of the model, with all backslashes replaced with forward slashes */
+  normalized_file_name;
   /** Directory containing the model, eg 'checkpoints' */
-  directory = "";
+  directory;
   /** Simplified copy of name, used as a default title. Excludes the directory and the '.safetensors' file extension */
-  simplified_file_name = "";
+  simplified_file_name;
+  /** Key for the model, used to uniquely identify the model. */
+  key;
   /** Title / display name of the model, sometimes same as the name but not always */
-  title = "";
+  title;
   /** Metadata: architecture ID for the model, such as 'stable-diffusion-xl-v1-base' */
   architecture_id = "";
   /** Metadata: author of the model */
@@ -78321,7 +78325,8 @@ class ComfyModelDef {
   searchable = "";
   constructor(name, directory) {
     this.file_name = name;
-    this.simplified_file_name = name.replaceAll("\\", "/").split("/").pop();
+    this.normalized_file_name = name.replaceAll("\\", "/");
+    this.simplified_file_name = this.normalized_file_name.split("/").pop() ?? "";
     if (this.simplified_file_name.endsWith(".safetensors")) {
       this.simplified_file_name = this.simplified_file_name.slice(
         0,
@@ -78330,6 +78335,7 @@ class ComfyModelDef {
     }
     this.title = this.simplified_file_name;
     this.directory = directory;
+    this.key = `${directory}/${this.normalized_file_name}`;
     this.updateSearchable();
   }
   updateSearchable() {
@@ -78423,7 +78429,7 @@ const useModelStore = /* @__PURE__ */ defineStore("modelStore", {
         }
         const store = new ModelStore(folder, models);
         this.modelStoreMap[folder] = store;
-        this.isLoading[folder] = false;
+        this.isLoading[folder] = null;
         return store;
       });
       this.isLoading[folder] = promise;
@@ -80405,7 +80411,7 @@ class ComfyApp {
       async (e) => {
         if (e.detail.subType === "connectingWidgetLink") {
           const { convertToInput } = await __vitePreload(async () => {
-            const { convertToInput: convertToInput2 } = await import("./widgetInputs-D_n9bPiG.js");
+            const { convertToInput: convertToInput2 } = await import("./widgetInputs-B4bHTYzE.js");
             return { convertToInput: convertToInput2 };
           }, true ? [] : void 0, import.meta.url);
           const { node: node3, link, widget } = e.detail;
@@ -80447,7 +80453,7 @@ class ComfyApp {
     useExtensionStore().loadDisabledExtensionNames();
     const extensions = await api.getExtensions();
     this.logging.addEntry("Comfy.App", "debug", { Extensions: extensions });
-    await __vitePreload(() => import("./index-Dq1J85oq.js"), true ? __vite__mapDeps([5,6,7]) : void 0, import.meta.url);
+    await __vitePreload(() => import("./index-DTOGNau5.js"), true ? __vite__mapDeps([5,6,7]) : void 0, import.meta.url);
     await Promise.all(
       extensions.filter((extension) => !extension.includes("extensions/core")).map(async (ext) => {
         try {
@@ -80490,7 +80496,7 @@ class ComfyApp {
     if (!user || !users[user]) {
       if (this.vueAppReady) useWorkspaceStore().spinner = false;
       const { UserSelectionScreen } = await __vitePreload(async () => {
-        const { UserSelectionScreen: UserSelectionScreen2 } = await import("./userSelection-DTtYMe59.js");
+        const { UserSelectionScreen: UserSelectionScreen2 } = await import("./userSelection-vhU1ykfH.js");
         return { UserSelectionScreen: UserSelectionScreen2 };
       }, true ? __vite__mapDeps([8,9]) : void 0, import.meta.url);
       this.ui.menuContainer.style.display = "none";
@@ -86783,7 +86789,7 @@ const _sfc_main$j = /* @__PURE__ */ defineComponent({
           if (Object.values(models.models).length) {
             modelList.push(...Object.values(models.models));
           } else {
-            const fakeModel = new ComfyModelDef("(No Content)", folder);
+            const fakeModel = new ComfyModelDef("", folder);
             fakeModel.is_fake_object = true;
             modelList.push(fakeModel);
           }
@@ -86799,12 +86805,10 @@ const _sfc_main$j = /* @__PURE__ */ defineComponent({
           return model.searchable.includes(search2);
         });
       }
-      const tree = buildTree(modelList, (model) => {
-        return [
-          model.directory,
-          ...model.file_name.replaceAll("\\", "/").split("/")
-        ];
-      });
+      const tree = buildTree(
+        modelList,
+        (model) => model.key.split("/")
+      );
       return tree;
     });
     const renderedRoot = computed(() => {
@@ -86813,18 +86817,7 @@ const _sfc_main$j = /* @__PURE__ */ defineComponent({
         const children = node3.children?.map(fillNodeInfo);
         const model = node3.leaf && node3.data ? node3.data : null;
         if (model?.is_fake_object) {
-          if (model.file_name === "(No Content)") {
-            return {
-              key: node3.key,
-              label: t("noContent"),
-              leaf: true,
-              data: node3.data,
-              getIcon: /* @__PURE__ */ __name((node22) => {
-                return "pi pi-file";
-              }, "getIcon"),
-              children: []
-            };
-          } else {
+          if (model.file_name === "Loading") {
             return {
               key: node3.key,
               label: t("loading") + "...",
@@ -86857,9 +86850,7 @@ const _sfc_main$j = /* @__PURE__ */ defineComponent({
             if (node22.children?.length === 1) {
               const onlyChild = node22.children[0];
               if (onlyChild.data?.is_fake_object) {
-                if (onlyChild.data.file_name === "(No Content)") {
-                  return "0";
-                } else if (onlyChild.data.file_name === "Loading") {
+                if (onlyChild.data.file_name === "Loading") {
                   return "";
                 }
               }
@@ -86943,7 +86934,7 @@ const _sfc_main$j = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const ModelLibrarySidebarTab = /* @__PURE__ */ _export_sfc(_sfc_main$j, [["__scopeId", "data-v-6867e466"]]);
+const ModelLibrarySidebarTab = /* @__PURE__ */ _export_sfc(_sfc_main$j, [["__scopeId", "data-v-db5d3bcc"]]);
 const useModelLibrarySidebarTab = /* @__PURE__ */ __name(() => {
   const { t } = useI18n();
   return {
@@ -97329,7 +97320,7 @@ const router = createRouter({
       children: [
         {
           path: "",
-          component: /* @__PURE__ */ __name(() => __vitePreload(() => import("./GraphView-BzS8s24m.js"), true ? __vite__mapDeps([10,2,11]) : void 0, import.meta.url), "component")
+          component: /* @__PURE__ */ __name(() => __vitePreload(() => import("./GraphView-vodtEF4p.js"), true ? __vite__mapDeps([10,2,11]) : void 0, import.meta.url), "component")
         }
       ]
     }
@@ -103483,4 +103474,4 @@ export {
   withCtx as y,
   unref as z
 };
-//# sourceMappingURL=index-BX7lxWeb.js.map
+//# sourceMappingURL=index-CFrRuGBA.js.map

comfy/web/assets/index-CKl4cvVy.css

@@ -725,7 +725,7 @@
   vertical-align: top;
 }
 
-[data-v-6867e466] .pi-fake-spacer {
+[data-v-db5d3bcc] .pi-fake-spacer {
   height: 1px;
   width: 16px;
 }

comfy/web/assets/index-CN90wNx3.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { aQ as script$s, g as openBlock, h as createElementBlock, m as mergeProps, A as createBaseVNode, B as BaseStyle, P as script$t, ak as toDisplayString, aj as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, O as resolveDynamicComponent, bL as script$u, l as resolveComponent, C as normalizeClass, am as createSlots, y as withCtx, be as script$v, b4 as script$w, M as Fragment, N as renderList, an as createTextVNode, aW as setAttribute, aU as normalizeProps, p as renderSlot, j as createCommentVNode, aa as equals, aR as script$x, bM as script$y, bN as getFirstFocusableElement, a3 as OverlayEventBus, W as getVNodeProp, a1 as resolveFieldData, bO as invokeElementMethod, bP as getAttribute, bQ as getNextElementSibling, U as getOuterWidth, bR as getPreviousElementSibling, D as script$z, ag as script$A, bS as script$B, aT as script$D, Z as isNotEmpty, b1 as withModifiers, V as getOuterHeight, $ as UniqueComponentId, bT as _default, a0 as ZIndex, a2 as focus, a5 as addStyle, a7 as absolutePosition, a8 as ConnectedOverlayScrollHandler, a9 as isTouchDevice, bU as FilterOperator, af as script$E, bV as FocusTrap, i as createVNode, al as Transition, bW as withKeys, bX as getIndex, s as script$G, bY as isClickable, bZ as clearSelection, b_ as localeComparator, b$ as sort, c0 as FilterService, bG as FilterMatchMode, ac as findSingle, c1 as findIndexInList, c2 as find, c3 as exportCSV, c4 as getOffset, c5 as getHiddenElementOuterWidth, c6 as getHiddenElementOuterHeight, c7 as reorderArray, c8 as removeClass, c9 as addClass, a4 as isEmpty, ae as script$H, ah as script$I, Y as vShow } from "./index-BX7lxWeb.js";
-import { s as script$C, a as script$F } from "./index-Dwxl_tUs.js";
+import { aQ as script$s, g as openBlock, h as createElementBlock, m as mergeProps, A as createBaseVNode, B as BaseStyle, P as script$t, ak as toDisplayString, aj as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, O as resolveDynamicComponent, bL as script$u, l as resolveComponent, C as normalizeClass, am as createSlots, y as withCtx, be as script$v, b4 as script$w, M as Fragment, N as renderList, an as createTextVNode, aW as setAttribute, aU as normalizeProps, p as renderSlot, j as createCommentVNode, aa as equals, aR as script$x, bM as script$y, bN as getFirstFocusableElement, a3 as OverlayEventBus, W as getVNodeProp, a1 as resolveFieldData, bO as invokeElementMethod, bP as getAttribute, bQ as getNextElementSibling, U as getOuterWidth, bR as getPreviousElementSibling, D as script$z, ag as script$A, bS as script$B, aT as script$D, Z as isNotEmpty, b1 as withModifiers, V as getOuterHeight, $ as UniqueComponentId, bT as _default, a0 as ZIndex, a2 as focus, a5 as addStyle, a7 as absolutePosition, a8 as ConnectedOverlayScrollHandler, a9 as isTouchDevice, bU as FilterOperator, af as script$E, bV as FocusTrap, i as createVNode, al as Transition, bW as withKeys, bX as getIndex, s as script$G, bY as isClickable, bZ as clearSelection, b_ as localeComparator, b$ as sort, c0 as FilterService, bG as FilterMatchMode, ac as findSingle, c1 as findIndexInList, c2 as find, c3 as exportCSV, c4 as getOffset, c5 as getHiddenElementOuterWidth, c6 as getHiddenElementOuterHeight, c7 as reorderArray, c8 as removeClass, c9 as addClass, a4 as isEmpty, ae as script$H, ah as script$I, Y as vShow } from "./index-CFrRuGBA.js";
+import { s as script$C, a as script$F } from "./index-CaUteDIK.js";
 var script$r = {
   name: "ArrowDownIcon",
   "extends": script$s
@@ -8930,4 +8930,4 @@ export {
   script as b,
   script$2 as s
 };
-//# sourceMappingURL=index-DC9toz42.js.map
+//# sourceMappingURL=index-CN90wNx3.js.map

comfy/web/assets/index-CaUteDIK.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { aQ as script$2, g as openBlock, h as createElementBlock, m as mergeProps, A as createBaseVNode } from "./index-BX7lxWeb.js";
+import { aQ as script$2, g as openBlock, h as createElementBlock, m as mergeProps, A as createBaseVNode } from "./index-CFrRuGBA.js";
 var script$1 = {
   name: "BarsIcon",
   "extends": script$2
@@ -43,4 +43,4 @@ export {
   script as a,
   script$1 as s
 };
-//# sourceMappingURL=index-Dwxl_tUs.js.map
+//# sourceMappingURL=index-CaUteDIK.js.map

comfy/web/assets/index-CaUteDIK.js.map

@@ -1 +1 @@
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+{"version":3,"file":"index-CaUteDIK.js","sources":["../../../../../node_modules/@primevue/icons/bars/index.mjs","../../../../../node_modules/@primevue/icons/plus/index.mjs"],"sourcesContent":["import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'BarsIcon',\n  \"extends\": BaseIcon\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _cache[0] || (_cache[0] = [createElementVNode(\"path\", {\n    \"fill-rule\": \"evenodd\",\n    \"clip-rule\": \"evenodd\",\n    d: \"M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z\",\n    fill: \"currentColor\"\n  }, null, -1)]), 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'PlusIcon',\n  \"extends\": BaseIcon\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _cache[0] || (_cache[0] = [createElementVNode(\"path\", {\n    d: \"M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z\",\n    fill: \"currentColor\"\n  }, null, -1)]), 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n"],"names":["script","BaseIcon","render","createElementVNode"],"mappings":";;;AAGG,IAACA,WAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWC;AACb;AAEA,SAASC,SAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,KAAK,GAAG,OAAO,CAAC,MAAM,OAAO,CAAC,IAAI,CAACC,gBAAmB,QAAQ;AAAA,IACpE,aAAa;AAAA,IACb,aAAa;AAAA,IACb,GAAG;AAAA,IACH,MAAM;AAAA,EACP,GAAE,MAAM,EAAE,CAAC,IAAI,EAAE;AACpB;AAbSD;AAeTF,SAAO,SAASE;ACpBb,IAAC,SAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWD;AACb;AAEA,SAAS,OAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,KAAK,GAAG,OAAO,CAAC,MAAM,OAAO,CAAC,IAAI,CAACE,gBAAmB,QAAQ;AAAA,IACpE,GAAG;AAAA,IACH,MAAM;AAAA,EACP,GAAE,MAAM,EAAE,CAAC,IAAI,EAAE;AACpB;AAXS;AAaT,OAAO,SAAS;","x_google_ignoreList":[0,1]}

comfy/web/assets/index-DTOGNau5.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bt as ComfyDialog, bu as $el, bv as ComfyApp, c as app, k as LiteGraph, aN as LGraphCanvas, bw as DraggableList, aZ as useToastStore, av as useNodeDefStore, bp as api, L as LGraphGroup, bx as KeyComboImpl, K as useKeybindingStore, F as useCommandStore, e as LGraphNode, by as ComfyWidgets, bz as applyTextReplacements, at as NodeSourceType, bA as NodeBadgeMode, u as useSettingStore, q as computed, bB as getColorPalette, w as watch, bC as BadgePosition, aP as LGraphBadge, bD as _, bE as defaultColorPalette } from "./index-BX7lxWeb.js";
-import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-D_n9bPiG.js";
+import { bt as ComfyDialog, bu as $el, bv as ComfyApp, c as app, k as LiteGraph, aN as LGraphCanvas, bw as DraggableList, aZ as useToastStore, av as useNodeDefStore, bp as api, L as LGraphGroup, bx as KeyComboImpl, K as useKeybindingStore, F as useCommandStore, e as LGraphNode, by as ComfyWidgets, bz as applyTextReplacements, at as NodeSourceType, bA as NodeBadgeMode, u as useSettingStore, q as computed, bB as getColorPalette, w as watch, bC as BadgePosition, aP as LGraphBadge, bD as _, bE as defaultColorPalette } from "./index-CFrRuGBA.js";
+import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-B4bHTYzE.js";
 class ClipspaceDialog extends ComfyDialog {
   static {
     __name(this, "ClipspaceDialog");
@@ -4731,4 +4731,4 @@ class NodeBadgeExtension {
   }
 }
 app.registerExtension(new NodeBadgeExtension());
-//# sourceMappingURL=index-Dq1J85oq.js.map
+//# sourceMappingURL=index-DTOGNau5.js.map

comfy/web/assets/userSelection-vhU1ykfH.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bp as api, bu as $el } from "./index-BX7lxWeb.js";
+import { bp as api, bu as $el } from "./index-CFrRuGBA.js";
 function createSpinner() {
   const div = document.createElement("div");
   div.innerHTML = `<div class="lds-ring"><div></div><div></div><div></div><div></div></div>`;
@@ -126,4 +126,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
   UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-DTtYMe59.js.map
+//# sourceMappingURL=userSelection-vhU1ykfH.js.map

comfy/web/assets/widgetInputs-B4bHTYzE.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { e as LGraphNode, c as app, bz as applyTextReplacements, by as ComfyWidgets, bF as addValueControlWidgets, k as LiteGraph } from "./index-BX7lxWeb.js";
+import { e as LGraphNode, c as app, bz as applyTextReplacements, by as ComfyWidgets, bF as addValueControlWidgets, k as LiteGraph } from "./index-CFrRuGBA.js";
 const CONVERTED_TYPE = "converted-widget";
 const VALID_TYPES = [
   "STRING",
@@ -753,4 +753,4 @@ export {
   mergeIfValid,
   setWidgetConfig
 };
-//# sourceMappingURL=widgetInputs-D_n9bPiG.js.map
+//# sourceMappingURL=widgetInputs-B4bHTYzE.js.map

comfy/web/index.html

@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
     <link rel="stylesheet" type="text/css" href="user.css" />
     <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-BX7lxWeb.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-D6olYiyq.css">
+    <script type="module" crossorigin src="./assets/index-CFrRuGBA.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-CKl4cvVy.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>

comfy_extras/nodes/nodes_audio.py

@@ -22,15 +22,16 @@ class EmptyLatentAudio:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
-
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}), },
+                # mark as optional to not break existing workflows
+                "optional": {"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
+                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
     CATEGORY = "latent/audio"
 
-    def generate(self, seconds):
-        batch_size = 1
+    def generate(self, seconds:float, batch_size:int=1):
         length = round((seconds * 44100 / 2048) / 2) * 2
         latent = torch.zeros([batch_size, 64, length], device=self.device)
         return ({"samples": latent, "type": "audio"},)

comfy_extras/nodes/nodes_model_downscale.py

@@ -17,7 +17,7 @@ class PatchModelAddDownscale:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
         model_sampling = model.get_model_object("model_sampling")

comfy_extras/nodes/nodes_stable_cascade.py

@@ -116,6 +116,7 @@ class StableCascade_SuperResolutionControlnet:
     RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
     FUNCTION = "generate"
 
+    EXPERIMENTAL = True
     CATEGORY = "_for_testing/stable_cascade"
 
     def generate(self, image, vae):

comfy_extras/nodes/nodes_tomesd.py

@@ -154,7 +154,7 @@ class TomePatchModel:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, ratio):
         self.u = None

comfy_extras/nodes/nodes_video_model.py

@@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance:
         return (m, )
 
 class ImageOnlyCheckpointSave(nodes_model_merging.CheckpointSave):
-    CATEGORY = "_for_testing"
+    CATEGORY = "advanced/model_merging"
 
     @classmethod
     def INPUT_TYPES(s):

comfy_extras/nodes_torch_compile.py

@@ -0,0 +1,21 @@
+import torch
+
+class TorchCompileModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+    EXPERIMENTAL = True
+
+    def patch(self, model):
+        m = model.clone()
+        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model")))
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "TorchCompileModel": TorchCompileModel,
+}

tests/inference/test_workflows.py

@@ -7,7 +7,7 @@ import pytest
 from comfy.api.components.schema.prompt import Prompt
 from comfy.client.embedded_comfy_client import EmbeddedComfyClient
 from comfy.model_downloader import add_known_models, KNOWN_LORAS
-from comfy.model_downloader_types import CivitFile
+from comfy.model_downloader_types import CivitFile, HuggingFile
 from comfy_extras.nodes.nodes_audio import TorchAudioNotFoundError
 from comfy_extras.nodes.nodes_nf4 import BitsAndBytesNotFoundError
 from . import workflows
@@ -22,6 +22,7 @@ async def client(tmp_path_factory) -> EmbeddedComfyClient:
 
 def _prepare_for_workflows() -> dict[str, Traversable]:
     add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
+    add_known_models("checkpoints", HuggingFile("autismanon/modeldump", "cardosAnime_v20.safetensors"))
 
     return {f.name: f for f in importlib.resources.files(workflows).iterdir() if f.is_file() and f.name.endswith(".json")}
 

tests/unit/prompt_server_test/download_models_test.py

@@ -1,15 +1,24 @@
 import itertools
 import os
+import tempfile
 from unittest.mock import AsyncMock, patch, MagicMock
 
 import aiohttp
 import pytest
 from aiohttp import ClientResponse
 
-from comfy.model_filemanager import download_model, validate_model_subdirectory, track_download_progress, \
+from comfy.cmd import folder_paths
+from comfy.component_model.folder_path_types import FolderNames, FolderPathsTuple, supported_pt_extensions
+from comfy.model_filemanager import download_model, track_download_progress, \
     create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
 
 
+@pytest.fixture
+def temp_dir():
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        yield tmpdirname
+
+
 class AsyncIteratorMock:
     """
     A mock class that simulates an asynchronous iterator.
@@ -50,7 +59,7 @@ class ContentMock:
 
 
 @pytest.mark.asyncio
-async def test_download_model_success():
+async def test_download_model_success(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.status = 200
     mock_response.headers = {'Content-Length': '1000'}
@@ -61,15 +70,13 @@ async def test_download_model_success():
     mock_make_request = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
 
-    # Mock file operations
-    mock_open = MagicMock()
-    mock_file = MagicMock()
-    mock_open.return_value.__enter__.return_value = mock_file
     time_values = itertools.count(0, 0.1)
 
-    with patch('comfy.model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'checkpoints/model.sft')), \
+    fake_paths = FolderNames.from_dict({'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)})
+
+    with patch('comfy.model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'model.sft')), \
             patch('comfy.model_filemanager.check_file_exists', return_value=None), \
-            patch('builtins.open', mock_open), \
+            patch('comfy.cmd.folder_paths.folder_names_and_paths', fake_paths), \
             patch('time.time', side_effect=time_values):  # Simulate time passing
 
         result = await download_model(
@@ -77,6 +84,7 @@ async def test_download_model_success():
             'model.sft',
             'http://example.com/model.sft',
             'checkpoints',
+            temp_dir,
             mock_progress_callback
         )
 
@@ -91,45 +99,49 @@ async def test_download_model_success():
 
     # Check initial call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.PENDING, 0, "Starting download of model.sft", False)
     )
 
     # Check final call
     mock_progress_callback.assert_any_call(
-        'checkpoints/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "Successfully downloaded model.sft", False)
     )
 
-    # Verify file writing
-    mock_file.write.assert_any_call(b'a' * 500)
-    mock_file.write.assert_any_call(b'b' * 300)
-    mock_file.write.assert_any_call(b'c' * 200)
+    mock_file_path = os.path.join(temp_dir, 'model.sft')
+    assert os.path.exists(mock_file_path)
+    with open(mock_file_path, 'rb') as mock_file:
+        assert mock_file.read() == b''.join(chunks)
+    os.remove(mock_file_path)
 
     # Verify request was made
     mock_make_request.assert_called_once_with('http://example.com/model.sft')
 
 
 @pytest.mark.asyncio
-async def test_download_model_url_request_failure():
+async def test_download_model_url_request_failure(temp_dir):
     # Mock dependencies
     mock_response = AsyncMock(spec=ClientResponse)
     mock_response.status = 404  # Simulate a "Not Found" error
     mock_get = AsyncMock(return_value=mock_response)
     mock_progress_callback = AsyncMock()
 
+    fake_paths = FolderNames.from_dict({'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)})
+
     # Mock the create_model_path function
-    with patch('comfy.model_filemanager.create_model_path', return_value=('/mock/path/model.safetensors', 'mock/path/model.safetensors')):
-        # Mock the check_file_exists function to return None (file doesn't exist)
-        with patch('comfy.model_filemanager.check_file_exists', return_value=None):
-            # Call the function
-            result = await download_model(
-                mock_get,
-                'model.safetensors',
-                'http://example.com/model.safetensors',
-                'mock_directory',
-                mock_progress_callback
-            )
+    with patch('comfy.model_filemanager.create_model_path', return_value='/mock/path/model.safetensors'), \
+            patch('comfy.model_filemanager.check_file_exists', return_value=None), \
+            patch('comfy.cmd.folder_paths.folder_names_and_paths', fake_paths):
+        # Call the function
+        result = await download_model(
+            mock_get,
+            'model.safetensors',
+            'http://example.com/model.safetensors',
+            'checkpoints',
+            temp_dir,
+            mock_progress_callback
+        )
 
     # Assert the expected behavior
     assert isinstance(result, DownloadModelStatus)
@@ -139,7 +151,7 @@ async def test_download_model_url_request_failure():
 
     # Check that progress_callback was called with the correct arguments
     mock_progress_callback.assert_any_call(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.PENDING,
             progress_percentage=0,
@@ -148,7 +160,7 @@ async def test_download_model_url_request_failure():
         )
     )
     mock_progress_callback.assert_called_with(
-        'mock_directory/model.safetensors',
+        'model.safetensors',
         DownloadModelStatus(
             status=DownloadStatusType.ERROR,
             progress_percentage=0,
@@ -171,30 +183,53 @@ async def test_download_model_invalid_model_subdirectory():
         'model.sft',
         'http://example.com/model.sft',
         '../bad_path',
+        '../bad_path',
         mock_progress_callback
     )
 
     # Assert the result
     assert isinstance(result, DownloadModelStatus)
-    assert result.message == 'Invalid model subdirectory'
+    assert result.message.startswith('Invalid or unrecognized model directory')
+    assert result.status == 'error'
+    assert result.already_existed is False
+
+
+@pytest.mark.asyncio
+async def test_download_model_invalid_folder_path():
+    mock_make_request = AsyncMock()
+    mock_progress_callback = AsyncMock()
+
+    result = await download_model(
+        mock_make_request,
+        'model.sft',
+        'http://example.com/model.sft',
+        'checkpoints',
+        'invalid_path',
+        mock_progress_callback
+    )
+
+    # Assert the result
+    assert isinstance(result, DownloadModelStatus)
+    assert result.message.startswith("Invalid folder path")
     assert result.status == 'error'
     assert result.already_existed is False
 
 
-# For create_model_path function
 def test_create_model_path(tmp_path, monkeypatch):
-    mock_models_dir = tmp_path / "models"
-    monkeypatch.setattr('folder_paths.models_dir', str(mock_models_dir))
+    model_name = "model.safetensors"
+    folder_path = os.path.join(tmp_path, "mock_dir")
 
-    model_name = "test_model.sft"
-    model_directory = "test_dir"
+    file_path = create_model_path(model_name, folder_path)
 
-    file_path, relative_path = create_model_path(model_name, model_directory, mock_models_dir)
-
-    assert file_path == str(mock_models_dir / model_directory / model_name)
-    assert relative_path == f"{model_directory}/{model_name}"
+    assert file_path == os.path.join(folder_path, "model.safetensors")
     assert os.path.exists(os.path.dirname(file_path))
 
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("../path_traversal.safetensors", folder_path)
+
+    with pytest.raises(Exception, match="Invalid model directory"):
+        create_model_path("/etc/some_root_path", folder_path)
+
 
 @pytest.mark.asyncio
 async def test_check_file_exists_when_file_exists(tmp_path):
@@ -203,15 +238,14 @@ async def test_check_file_exists_when_file_exists(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback, "test/existing_model.sft")
-
+    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback)
     assert result is not None
     assert result.status == "completed"
     assert result.message == "existing_model.sft already exists"
     assert result.already_existed is True
 
     mock_callback.assert_called_once_with(
-        "test/existing_model.sft",
+        "existing_model.sft",
         DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "existing_model.sft already exists", already_existed=True)
     )
 
@@ -222,40 +256,47 @@ async def test_check_file_exists_when_file_does_not_exist(tmp_path):
 
     mock_callback = AsyncMock()
 
-    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback, "test/non_existing_model.sft")
-
+    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback)
     assert result is None
     mock_callback.assert_not_called()
 
 
 @pytest.mark.asyncio
-async def test_track_download_progress_no_content_length():
+async def test_track_download_progress_no_content_length(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {}  # No Content-Length header
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 500, b'b' * 500])
+    chunks = [b'a' * 500, b'b' * 500]
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
-    mock_open = MagicMock(return_value=MagicMock())
 
-    with patch('builtins.open', mock_open):
-        result = await track_download_progress(
-            mock_response, '/mock/path/model.sft', 'model.sft',
-            mock_callback, 'models/model.sft', interval=0.1
-        )
+    full_path = os.path.join(temp_dir, 'model.sft')
+
+    result = await track_download_progress(
+        mock_response, full_path, 'model.sft',
+        mock_callback, interval=0.1
+    )
 
     assert result.status == "completed"
+
+    assert os.path.exists(full_path)
+    with open(full_path, 'rb') as f:
+        assert f.read() == b''.join(chunks)
+    os.remove(full_path)
+
     # Check that progress was reported even without knowing the total size
     mock_callback.assert_any_call(
-        'models/model.sft',
+        'model.sft',
         DownloadModelStatus(DownloadStatusType.IN_PROGRESS, 0, "Downloading model.sft", already_existed=False)
     )
 
 
 @pytest.mark.asyncio
-async def test_track_download_progress_interval():
+async def test_track_download_progress_interval(temp_dir):
     mock_response = AsyncMock(spec=aiohttp.ClientResponse)
     mock_response.headers = {'Content-Length': '1000'}
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock([b'a' * 100] * 10)
+    chunks = [b'a' * 100] * 10
+    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
 
     mock_callback = AsyncMock()
     mock_open = MagicMock(return_value=MagicMock())
@@ -264,18 +305,18 @@ async def test_track_download_progress_interval():
     mock_time = MagicMock()
     mock_time.side_effect = [i * 0.5 for i in range(30)]  # This should be enough for 10 chunks
 
-    with patch('builtins.open', mock_open), \
-            patch('time.time', mock_time):
+    full_path = os.path.join(temp_dir, 'model.sft')
+
+    with patch('time.time', mock_time):
         await track_download_progress(
-            mock_response, '/mock/path/model.sft', 'model.sft',
-            mock_callback, 'models/model.sft', interval=1.0
+            mock_response, full_path, 'model.sft',
+            mock_callback, interval=1.0
         )
 
-    # Print out the actual call count and the arguments of each call for debugging
-    print(f"mock_callback was called {mock_callback.call_count} times")
-    for i, call in enumerate(mock_callback.call_args_list):
-        args, kwargs = call
-        print(f"Call {i + 1}: {args[1].status}, Progress: {args[1].progress_percentage:.2f}%")
+    assert os.path.exists(full_path)
+    with open(full_path, 'rb') as f:
+        assert f.read() == b''.join(chunks)
+    os.remove(full_path)
 
     # Assert that progress was updated at least 3 times (start, at least one interval, and end)
     assert mock_callback.call_count >= 3, f"Expected at least 3 calls, but got {mock_callback.call_count}"
@@ -291,34 +332,6 @@ async def test_track_download_progress_interval():
     assert last_call[0][1].progress_percentage == 100
 
 
-def test_valid_subdirectory():
-    assert validate_model_subdirectory("valid-model123") is True
-
-
-def test_subdirectory_too_long():
-    assert validate_model_subdirectory("a" * 51) is False
-
-
-def test_subdirectory_with_double_dots():
-    assert validate_model_subdirectory("model/../unsafe") is False
-
-
-def test_subdirectory_with_slash():
-    assert validate_model_subdirectory("model/unsafe") is False
-
-
-def test_subdirectory_with_special_characters():
-    assert validate_model_subdirectory("model@unsafe") is False
-
-
-def test_subdirectory_with_underscore_and_dash():
-    assert validate_model_subdirectory("valid_model-name") is True
-
-
-def test_empty_subdirectory():
-    assert validate_model_subdirectory("") is False
-
-
 @pytest.mark.parametrize("filename, expected", [
     ("valid_model.safetensors", True),
     ("valid_model.sft", True),

web/scripts/changeTracker.js

@@ -0,0 +1,3 @@
+// Shim for scripts/changeTracker.ts
+export const ChangeTracker = window.comfyAPI.changeTracker.ChangeTracker;
+export const globalTracker = window.comfyAPI.changeTracker.globalTracker;

web/templates/default.json

@@ -0,0 +1,356 @@
+{
+  "last_node_id": 9,
+  "last_link_id": 9,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        413,
+        389
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        415,
+        186
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        473,
+        609
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        863,
+        186
+      ],
+      "size": [
+        315,
+        262
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        156680208700286,
+        true,
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1209,
+        188
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {}
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
+        1451,
+        189
+      ],
+      "size": [
+        210,
+        26
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {}
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        26,
+        474
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "v1-5-pruned-emaonly.safetensors"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      4,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "models": [{
+    "name": "v1-5-pruned-emaonly.safetensors",
+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "directory": "checkpoints"
+  }]
+}

web/templates/flux_schnell.json

@@ -0,0 +1,420 @@
+{
+    "last_node_id": 36,
+    "last_link_id": 58,
+    "nodes": [
+      {
+        "id": 33,
+        "type": "CLIPTextEncode",
+        "pos": [
+          390,
+          400
+        ],
+        "size": {
+          "0": 422.84503173828125,
+          "1": 164.31304931640625
+        },
+        "flags": {
+          "collapsed": true
+        },
+        "order": 4,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "clip",
+            "type": "CLIP",
+            "link": 54,
+            "slot_index": 0
+          }
+        ],
+        "outputs": [
+          {
+            "name": "CONDITIONING",
+            "type": "CONDITIONING",
+            "links": [
+              55
+            ],
+            "slot_index": 0
+          }
+        ],
+        "title": "CLIP Text Encode (Negative Prompt)",
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
+        "widgets_values": [
+          ""
+        ],
+        "color": "#322",
+        "bgcolor": "#533"
+      },
+      {
+        "id": 27,
+        "type": "EmptySD3LatentImage",
+        "pos": [
+          471,
+          455
+        ],
+        "size": {
+          "0": 315,
+          "1": 106
+        },
+        "flags": {},
+        "order": 0,
+        "mode": 0,
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              51
+            ],
+            "shape": 3,
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "EmptySD3LatentImage"
+        },
+        "widgets_values": [
+          1024,
+          1024,
+          1
+        ],
+        "color": "#323",
+        "bgcolor": "#535"
+      },
+      {
+        "id": 8,
+        "type": "VAEDecode",
+        "pos": [
+          1151,
+          195
+        ],
+        "size": {
+          "0": 210,
+          "1": 46
+        },
+        "flags": {},
+        "order": 6,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "samples",
+            "type": "LATENT",
+            "link": 52
+          },
+          {
+            "name": "vae",
+            "type": "VAE",
+            "link": 46
+          }
+        ],
+        "outputs": [
+          {
+            "name": "IMAGE",
+            "type": "IMAGE",
+            "links": [
+              9
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "VAEDecode"
+        }
+      },
+      {
+        "id": 9,
+        "type": "SaveImage",
+        "pos": [
+          1375,
+          194
+        ],
+        "size": {
+          "0": 985.3012084960938,
+          "1": 1060.3828125
+        },
+        "flags": {},
+        "order": 7,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "images",
+            "type": "IMAGE",
+            "link": 9
+          }
+        ],
+        "properties": {},
+        "widgets_values": [
+          "ComfyUI"
+        ]
+      },
+      {
+        "id": 31,
+        "type": "KSampler",
+        "pos": [
+          816,
+          192
+        ],
+        "size": {
+          "0": 315,
+          "1": 262
+        },
+        "flags": {},
+        "order": 5,
+        "mode": 0,
+        "inputs": [
+          {
+            "name": "model",
+            "type": "MODEL",
+            "link": 47
+          },
+          {
+            "name": "positive",
+            "type": "CONDITIONING",
+            "link": 58
+          },
+          {
+            "name": "negative",
+            "type": "CONDITIONING",
+            "link": 55
+          },
+          {
+            "name": "latent_image",
+            "type": "LATENT",
+            "link": 51
+          }
+        ],
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              52
+            ],
+            "shape": 3,
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "KSampler"
+        },
+        "widgets_values": [
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+        "outputs": [
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+            "type": "MODEL",
+            "links": [
+              47
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+            "slot_index": 0
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+            "type": "CLIP",
+            "links": [
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+              54
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+            "slot_index": 1
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+            "links": [
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+            "slot_index": 2
+          }
+        ],
+        "properties": {
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+        },
+        "widgets_values": [
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+      {
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+        "type": "CLIPTextEncode",
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+        "inputs": [
+          {
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+            "link": 45
+          }
+        ],
+        "outputs": [
+          {
+            "name": "CONDITIONING",
+            "type": "CONDITIONING",
+            "links": [
+              58
+            ],
+            "slot_index": 0
+          }
+        ],
+        "title": "CLIP Text Encode (Positive Prompt)",
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
+        "widgets_values": [
+          "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it"
+        ],
+        "color": "#232",
+        "bgcolor": "#353"
+      },
+      {
+        "id": 34,
+        "type": "Note",
+        "pos": [
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+          "1": 164.08004760742188
+        },
+        "flags": {},
+        "order": 2,
+        "mode": 0,
+        "properties": {
+          "text": ""
+        },
+        "widgets_values": [
+          "Note that Flux dev and schnell do not have any negative prompt so CFG should be set to 1.0. Setting CFG to 1.0 means the negative prompt is ignored.\n\nThe schnell model is a distilled model that can generate a good image with only 4 steps."
+        ],
+        "color": "#432",
+        "bgcolor": "#653"
+      }
+    ],
+    "links": [
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+      [
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+      [
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+    "config": {},
+    "extra": {
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+          1.8290357611967831
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+    "models": [
+      {
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+        "url": "https://huggingface.co/Comfy-Org/flux1-schnell/resolve/main/flux1-schnell-fp8.safetensors?download=true",
+        "directory": "checkpoints"
+      }
+    ],
+    "version": 0.4
+  }

web/templates/image2image.json

@@ -0,0 +1,447 @@
+{
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+  "last_link_id": 17,
+  "nodes": [
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+          "type": "CONDITIONING",
+          "links": [
+            6
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+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "watermark, text\n"
+      ]
+    },
+    {
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+      "type": "CLIPTextEncode",
+      "pos": [
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+        186
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+          "links": [
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+          "slot_index": 0
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+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "photograph of victorian woman with wings, sky clouds, meadow grass\n"
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+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
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+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
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+          "slot_index": 0
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+          "link": 9
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+      "properties": {},
+      "widgets_values": [
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+          "type": "MASK",
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+          "shape": 3
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+          "type": "LATENT",
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+          "link": 4
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+          "name": "negative",
+          "type": "CONDITIONING",
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+          "type": "LATENT",
+          "link": 11
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+          "type": "LATENT",
+          "links": [
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+      },
+      "widgets_values": [
+        "v1-5-pruned-emaonly.safetensors"
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+      8,
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+    ]
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+  "groups": [
+    {
+      "title": "Loading images",
+      "bounding": [
+        150,
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+        726,
+        171
+      ],
+      "color": "#3f789e"
+    }
+  ],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "models": [{
+    "name": "v1-5-pruned-emaonly.safetensors",
+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "directory": "checkpoints"
+  }]
+}

web/templates/upscale.json

@@ -0,0 +1,652 @@
+{
+    "last_node_id": 16,
+    "last_link_id": 23,
+    "nodes": [
+      {
+        "id": 8,
+        "type": "VAEDecode",
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+          577.1878720703122
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+        "mode": 0,
+        "inputs": [
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+            "type": "LATENT",
+            "link": 7
+          },
+          {
+            "name": "vae",
+            "type": "VAE",
+            "link": 21
+          }
+        ],
+        "outputs": [
+          {
+            "name": "IMAGE",
+            "type": "IMAGE",
+            "links": [
+              9
+            ],
+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "VAEDecode"
+        }
+      },
+      {
+        "id": 10,
+        "type": "LatentUpscale",
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+          170
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+          "1": 130
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+        "flags": {},
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+        "mode": 0,
+        "inputs": [
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+            "type": "LATENT",
+            "link": 10
+          }
+        ],
+        "outputs": [
+          {
+            "name": "LATENT",
+            "type": "LATENT",
+            "links": [
+              14
+            ]
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "LatentUpscale"
+        },
+        "widgets_values": [
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+          1152,
+          "disabled"
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+        "type": "VAEDecode",
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+          {
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+            "type": "VAE",
+            "link": 22
+          }
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+        "outputs": [
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+            "name": "IMAGE",
+            "type": "IMAGE",
+            "links": [
+              17
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+            "slot_index": 0
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+        ],
+        "properties": {
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+            "type": "CONDITIONING",
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+              12
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+            "slot_index": 0
+          }
+        ],
+        "properties": {
+          "Node name for S&R": "CLIPTextEncode"
+        },
+        "widgets_values": [
+          "masterpiece HDR victorian portrait painting of woman, blonde hair, mountain nature, blue sky\n"
+        ]
+      },
+      {
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+        "type": "CLIPTextEncode",
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+        "widgets_values": [
+          "bad hands, text, watermark\n"
+        ]
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+      {
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+        "type": "EmptyLatentImage",
+        "pos": [
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+        "outputs": [
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+            "type": "LATENT",
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+            "slot_index": 0
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+        ],
+        "properties": {
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+          {
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+            "slot_index": 1
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+          {
+            "name": "negative",
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+            "slot_index": 2
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+          {
+            "name": "latent_image",
+            "type": "LATENT",
+            "link": 14,
+            "slot_index": 3
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+            "type": "LATENT",
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+        ],
+        "properties": {},
+        "widgets_values": [
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+        ]
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+        "mode": 0,
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+          {
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+            "link": 4
+          },
+          {
+            "name": "negative",
+            "type": "CONDITIONING",
+            "link": 6
+          },
+          {
+            "name": "latent_image",
+            "type": "LATENT",
+            "link": 2
+          }
+        ],
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