Support new flux model variants.

Add a way to reshape lora weights.

Allow weight patches to all weight not just .weight and .bias

Add a way for a lora to set a weight to a specific value.

README.md

@@ -28,7 +28,7 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-![ComfyUI Screenshot](comfyui_screenshot.png)
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
 </div>
 
 This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
@@ -40,6 +40,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
@@ -139,7 +140,7 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
 
 This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
 

api_server/routes/internal/internal_routes.py

@@ -2,6 +2,7 @@ from aiohttp import web
 from typing import Optional
 from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
 from api_server.services.file_service import FileService
+from api_server.services.terminal_service import TerminalService
 import app.logger
 
 class InternalRoutes:
@@ -11,7 +12,8 @@ class InternalRoutes:
     Check README.md for more information.
     
     '''
-    def __init__(self):
+
+    def __init__(self, prompt_server):
         self.routes: web.RouteTableDef = web.RouteTableDef()
         self._app: Optional[web.Application] = None
         self.file_service = FileService({
@@ -19,6 +21,8 @@ class InternalRoutes:
             "user": user_directory,
             "output": output_directory
         })
+        self.prompt_server = prompt_server
+        self.terminal_service = TerminalService(prompt_server)
 
     def setup_routes(self):
         @self.routes.get('/files')
@@ -34,7 +38,28 @@ class InternalRoutes:
 
         @self.routes.get('/logs')
         async def get_logs(request):
-            return web.json_response(app.logger.get_logs())
+            return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
+
+        @self.routes.get('/logs/raw')
+        async def get_logs(request):
+            self.terminal_service.update_size()
+            return web.json_response({
+                "entries": list(app.logger.get_logs()),
+                "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
+            })
+
+        @self.routes.patch('/logs/subscribe')
+        async def subscribe_logs(request):
+            json_data = await request.json()
+            client_id = json_data["clientId"]
+            enabled = json_data["enabled"]
+            if enabled:
+                self.terminal_service.subscribe(client_id)
+            else:
+                self.terminal_service.unsubscribe(client_id)
+
+            return web.Response(status=200)
+
 
         @self.routes.get('/folder_paths')
         async def get_folder_paths(request):

api_server/services/terminal_service.py

@@ -0,0 +1,60 @@
+from app.logger import on_flush
+import os
+import shutil
+
+
+class TerminalService:
+    def __init__(self, server):
+        self.server = server
+        self.cols = None
+        self.rows = None
+        self.subscriptions = set()
+        on_flush(self.send_messages)
+
+    def get_terminal_size(self):
+        try:
+            size = os.get_terminal_size()
+            return (size.columns, size.lines)
+        except OSError:
+            try:
+                size = shutil.get_terminal_size()
+                return (size.columns, size.lines)
+            except OSError:
+                return (80, 24)  # fallback to 80x24
+
+    def update_size(self):
+        columns, lines = self.get_terminal_size()
+        changed = False
+        
+        if columns != self.cols:
+            self.cols = columns
+            changed = True 
+
+        if lines != self.rows:
+            self.rows = lines
+            changed = True
+
+        if changed:
+            return {"cols": self.cols, "rows": self.rows}
+
+        return None
+
+    def subscribe(self, client_id):
+        self.subscriptions.add(client_id)
+
+    def unsubscribe(self, client_id):
+        self.subscriptions.discard(client_id)
+
+    def send_messages(self, entries):
+        if not len(entries) or not len(self.subscriptions):
+            return
+        
+        new_size = self.update_size()
+        
+        for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
+            if client_id not in self.server.sockets:
+                # Automatically unsub if the socket has disconnected
+                self.unsubscribe(client_id)
+                continue
+
+            self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)

app/logger.py

@@ -1,20 +1,69 @@
-import logging
-from logging.handlers import MemoryHandler
 from collections import deque
+from datetime import datetime
+import io
+import logging
+import sys
+import threading
 
 logs = None
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+stdout_interceptor = None
+stderr_interceptor = None
+
+
+class LogInterceptor(io.TextIOWrapper):
+    def __init__(self, stream,  *args, **kwargs):
+        buffer = stream.buffer
+        encoding = stream.encoding
+        super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)
+        self._lock = threading.Lock()
+        self._flush_callbacks = []
+        self._logs_since_flush = []
+
+    def write(self, data):
+        entry = {"t": datetime.now().isoformat(), "m": data}
+        with self._lock:
+            self._logs_since_flush.append(entry)
+
+            # Simple handling for cr to overwrite the last output if it isnt a full line
+            # else logs just get full of progress messages
+            if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"):
+                logs.pop()
+            logs.append(entry)
+        super().write(data)
+
+    def flush(self):
+        super().flush()
+        for cb in self._flush_callbacks:
+            cb(self._logs_since_flush)
+            self._logs_since_flush = []
+
+    def on_flush(self, callback):
+        self._flush_callbacks.append(callback)
 
 
 def get_logs():
-    return "\n".join([formatter.format(x) for x in logs])
+    return logs
 
 
+def on_flush(callback):
+    if stdout_interceptor is not None:
+        stdout_interceptor.on_flush(callback)
+    if stderr_interceptor is not None:
+        stderr_interceptor.on_flush(callback)
+
 def setup_logger(log_level: str = 'INFO', capacity: int = 300):
     global logs
     if logs:
         return
 
+    # Override output streams and log to buffer
+    logs = deque(maxlen=capacity)
+
+    global stdout_interceptor
+    global stderr_interceptor
+    stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)
+    stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)
+
     # Setup default global logger
     logger = logging.getLogger()
     logger.setLevel(log_level)
@@ -22,10 +71,3 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300):
     stream_handler = logging.StreamHandler()
     stream_handler.setFormatter(logging.Formatter("%(message)s"))
     logger.addHandler(stream_handler)
-
-    # Create a memory handler with a deque as its buffer
-    logs = deque(maxlen=capacity)
-    memory_handler = MemoryHandler(capacity, flushLevel=logging.INFO)
-    memory_handler.buffer = logs
-    memory_handler.setFormatter(formatter)
-    logger.addHandler(memory_handler)

app/user_manager.py

@@ -1,18 +1,35 @@
+from __future__ import annotations
 import json
 import os
 import re
 import uuid
 import glob
 import shutil
+import logging
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
 import folder_paths
 from .app_settings import AppSettings
+from typing import TypedDict
 
 default_user = "default"
 
 
+class FileInfo(TypedDict):
+    path: str
+    size: int
+    modified: int
+
+
+def get_file_info(path: str, relative_to: str) -> FileInfo:
+    return {
+        "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
+        "size": os.path.getsize(path),
+        "modified": os.path.getmtime(path)
+    }
+
+
 class UserManager():
     def __init__(self):
         user_directory = folder_paths.get_user_directory()
@@ -154,6 +171,7 @@ class UserManager():
 
             recurse = request.rel_url.query.get('recurse', '').lower() == "true"
             full_info = request.rel_url.query.get('full_info', '').lower() == "true"
+            split_path = request.rel_url.query.get('split', '').lower() == "true"
 
             # Use different patterns based on whether we're recursing or not
             if recurse:
@@ -161,26 +179,21 @@ class UserManager():
             else:
                 pattern = os.path.join(glob.escape(path), '*')
 
-            results = glob.glob(pattern, recursive=recurse)
+            def process_full_path(full_path: str) -> FileInfo | str | list[str]:
+                if full_info:
+                    return get_file_info(full_path, path)
 
-            if full_info:
-                results = [
-                    {
-                        'path': os.path.relpath(x, path).replace(os.sep, '/'),
-                        'size': os.path.getsize(x),
-                        'modified': os.path.getmtime(x)
-                    } for x in results if os.path.isfile(x)
-                ]
-            else:
-                results = [
-                    os.path.relpath(x, path).replace(os.sep, '/')
-                    for x in results
-                    if os.path.isfile(x)
-                ]
+                rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')
+                if split_path:
+                    return [rel_path] + rel_path.split('/')
 
-            split_path = request.rel_url.query.get('split', '').lower() == "true"
-            if split_path and not full_info:
-                results = [[x] + x.split('/') for x in results]
+                return rel_path
+
+            results = [
+                process_full_path(full_path)
+                for full_path in glob.glob(pattern, recursive=recurse)
+                if os.path.isfile(full_path)
+            ]
 
             return web.json_response(results)
 
@@ -208,20 +221,51 @@ class UserManager():
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):
+            """
+            Upload or update a user data file.
+
+            This endpoint handles file uploads to a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Path Parameters:
+            - file: The target file path (URL encoded if necessary).
+
+            Returns:
+            - 400: If 'file' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 409: If overwrite=false and the file already exists.
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+
+            The request body should contain the raw file content to be written.
+            """
             path = get_user_data_path(request)
             if not isinstance(path, str):
                 return path
 
-            overwrite = request.query["overwrite"] != "false"
+            overwrite = request.query.get("overwrite", 'true') != "false"
+            full_info = request.query.get('full_info', 'false').lower() == "true"
+
             if not overwrite and os.path.exists(path):
-                return web.Response(status=409)
+                return web.Response(status=409, text="File already exists")
 
             body = await request.read()
 
             with open(path, "wb") as f:
                 f.write(body)
 
-            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(path, user_path)
+            else:
+                resp = os.path.relpath(path, user_path)
+
             return web.json_response(resp)
 
         @routes.delete("/userdata/{file}")
@@ -236,6 +280,30 @@ class UserManager():
 
         @routes.post("/userdata/{file}/move/{dest}")
         async def move_userdata(request):
+            """
+            Move or rename a user data file.
+
+            This endpoint handles moving or renaming files within a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Path Parameters:
+            - file: The source file path (URL encoded if necessary)
+            - dest: The destination file path (URL encoded if necessary)
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Returns:
+            - 400: If either 'file' or 'dest' parameter is missing
+            - 403: If either requested path is not allowed
+            - 404: If the source file does not exist
+            - 409: If overwrite=false and the destination file already exists
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+            """
             source = get_user_data_path(request, check_exists=True)
             if not isinstance(source, str):
                 return source
@@ -244,12 +312,19 @@ class UserManager():
             if not isinstance(source, str):
                 return dest
 
-            overwrite = request.query["overwrite"] != "false"
-            if not overwrite and os.path.exists(dest):
-                return web.Response(status=409)
+            overwrite = request.query.get("overwrite", 'true') != "false"
+            full_info = request.query.get('full_info', 'false').lower() == "true"
 
-            print(f"moving '{source}' -> '{dest}'")
+            if not overwrite and os.path.exists(dest):
+                return web.Response(status=409, text="File already exists")
+
+            logging.info(f"moving '{source}' -> '{dest}'")
             shutil.move(source, dest)
 
-            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(dest, user_path)
+            else:
+                resp = os.path.relpath(dest, user_path)
+
             return web.json_response(resp)

comfy/clip_model.py

@@ -23,6 +23,7 @@ class CLIPAttention(torch.nn.Module):
 
 ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
                "gelu": torch.nn.functional.gelu,
+               "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
 }
 
 class CLIPMLP(torch.nn.Module):
@@ -139,27 +140,35 @@ class CLIPTextModel(torch.nn.Module):
 
 
 class CLIPVisionEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):
+    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
         super().__init__()
-        self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
+
+        num_patches = (image_size // patch_size) ** 2
+        if model_type == "siglip_vision_model":
+            self.class_embedding = None
+            patch_bias = True
+        else:
+            num_patches = num_patches + 1
+            self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
+            patch_bias = False
 
         self.patch_embedding = operations.Conv2d(
             in_channels=num_channels,
             out_channels=embed_dim,
             kernel_size=patch_size,
             stride=patch_size,
-            bias=False,
+            bias=patch_bias,
             dtype=dtype,
             device=device
         )
 
-        num_patches = (image_size // patch_size) ** 2
-        num_positions = num_patches + 1
-        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
+        if self.class_embedding is not None:
+            embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)
+        return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):
@@ -170,9 +179,15 @@ class CLIPVision(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        model_type = config_dict["model_type"]
 
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=dtype, device=device, operations=operations)
-        self.pre_layrnorm = operations.LayerNorm(embed_dim)
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
+        if model_type == "siglip_vision_model":
+            self.pre_layrnorm = lambda a: a
+            self.output_layernorm = True
+        else:
+            self.pre_layrnorm = operations.LayerNorm(embed_dim)
+            self.output_layernorm = False
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.post_layernorm = operations.LayerNorm(embed_dim)
 
@@ -181,14 +196,21 @@ class CLIPVision(torch.nn.Module):
         x = self.pre_layrnorm(x)
         #TODO: attention_mask?
         x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
-        pooled_output = self.post_layernorm(x[:, 0, :])
+        if self.output_layernorm:
+            x = self.post_layernorm(x)
+            pooled_output = x
+        else:
+            pooled_output = self.post_layernorm(x[:, 0, :])
         return x, i, pooled_output
 
 class CLIPVisionModelProjection(torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         self.vision_model = CLIPVision(config_dict, dtype, device, operations)
-        self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
+        if "projection_dim" in config_dict:
+            self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
+        else:
+            self.visual_projection = lambda a: a
 
     def forward(self, *args, **kwargs):
         x = self.vision_model(*args, **kwargs)

comfy/clip_vision.py

@@ -16,9 +16,9 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224):
-    mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype)
-    std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype)
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]):
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
     image = image.movedim(-1, 1)
     if not (image.shape[2] == size and image.shape[3] == size):
         scale = (size / min(image.shape[2], image.shape[3]))
@@ -35,6 +35,8 @@ class ClipVisionModel():
             config = json.load(f)
 
         self.image_size = config.get("image_size", 224)
+        self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
+        self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
@@ -51,7 +53,7 @@ class ClipVisionModel():
 
     def encode_image(self, image):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std).float()
         out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()
@@ -94,7 +96,9 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        if sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
+        if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+        elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")

comfy/clip_vision_siglip_384.json

@@ -0,0 +1,13 @@
+{
+  "num_channels": 3,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": 384,
+  "intermediate_size": 4304,
+  "model_type": "siglip_vision_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 27,
+  "patch_size": 14,
+  "image_mean": [0.5, 0.5, 0.5],
+  "image_std": [0.5, 0.5, 0.5]
+}

comfy/extra_samplers/uni_pc.py

@@ -16,7 +16,7 @@ class NoiseScheduleVP:
             continuous_beta_0=0.1,
             continuous_beta_1=20.,
         ):
-        """Create a wrapper class for the forward SDE (VP type).
+        r"""Create a wrapper class for the forward SDE (VP type).
 
         ***
         Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.

comfy/latent_formats.py

@@ -190,7 +190,21 @@ class Mochi(LatentFormat):
                                          0.9294154431013696, 1.3720942357788521, 0.881393668867029,
                                          0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)
 
-        self.latent_rgb_factors = None #TODO
+        self.latent_rgb_factors =[
+            [-0.0069, -0.0045,  0.0018],
+            [ 0.0154, -0.0692, -0.0274],
+            [ 0.0333,  0.0019,  0.0206],
+            [-0.1390,  0.0628,  0.1678],
+            [-0.0725,  0.0134, -0.1898],
+            [ 0.0074, -0.0270, -0.0209],
+            [-0.0176, -0.0277, -0.0221],
+            [ 0.5294,  0.5204,  0.3852],
+            [-0.0326, -0.0446, -0.0143],
+            [-0.0659,  0.0153, -0.0153],
+            [ 0.0185, -0.0217,  0.0014],
+            [-0.0396, -0.0495, -0.0281]
+        ]
+        self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]
         self.taesd_decoder_name = None #TODO
 
     def process_in(self, latent):

comfy/ldm/audio/dit.py

@@ -612,7 +612,9 @@ class ContinuousTransformer(nn.Module):
         return_info = False,
         **kwargs
     ):
+        patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
         batch, seq, device = *x.shape[:2], x.device
+        context = kwargs["context"]
 
         info = {
             "hidden_states": [],
@@ -643,9 +645,19 @@ class ContinuousTransformer(nn.Module):
         if self.use_sinusoidal_emb or self.use_abs_pos_emb:
             x = x + self.pos_emb(x)
 
+        blocks_replace = patches_replace.get("dit", {})
         # Iterate over the transformer layers
-        for layer in self.layers:
-            x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+        for i, layer in enumerate(self.layers):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
+                    return out
+
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
             # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
 
             if return_info:
@@ -874,7 +886,6 @@ class AudioDiffusionTransformer(nn.Module):
         mask=None,
         return_info=False,
         control=None,
-        transformer_options={},
         **kwargs):
             return self._forward(
                 x,

comfy/ldm/aura/mmdit.py

@@ -437,7 +437,8 @@ class MMDiT(nn.Module):
         pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
         return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
 
-    def forward(self, x, timestep, context, **kwargs):
+    def forward(self, x, timestep, context, transformer_options={}, **kwargs):
+        patches_replace = transformer_options.get("patches_replace", {})
         # patchify x, add PE
         b, c, h, w = x.shape
 
@@ -458,15 +459,36 @@ class MMDiT(nn.Module):
 
         global_cond = self.t_embedder(t, x.dtype)  # B, D
 
+        blocks_replace = patches_replace.get("dit", {})
         if len(self.double_layers) > 0:
-            for layer in self.double_layers:
-                c, x = layer(c, x, global_cond, **kwargs)
+            for i, layer in enumerate(self.double_layers):
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["txt"], out["img"] = layer(args["txt"],
+                                                       args["img"],
+                                                       args["vec"])
+                        return out
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
+                    c = out["txt"]
+                    x = out["img"]
+                else:
+                    c, x = layer(c, x, global_cond, **kwargs)
 
         if len(self.single_layers) > 0:
             c_len = c.size(1)
             cx = torch.cat([c, x], dim=1)
-            for layer in self.single_layers:
-                cx = layer(cx, global_cond, **kwargs)
+            for i, layer in enumerate(self.single_layers):
+                if ("single_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"] = layer(args["img"], args["vec"])
+                        return out
+
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
+                    cx = out["img"]
+                else:
+                    cx = layer(cx, global_cond, **kwargs)
 
             x = cx[:, c_len:]
 

comfy/ldm/flux/model.py

@@ -20,6 +20,7 @@ import comfy.ldm.common_dit
 @dataclass
 class FluxParams:
     in_channels: int
+    out_channels: int
     vec_in_dim: int
     context_in_dim: int
     hidden_size: int
@@ -29,6 +30,7 @@ class FluxParams:
     depth_single_blocks: int
     axes_dim: list
     theta: int
+    patch_size: int
     qkv_bias: bool
     guidance_embed: bool
 
@@ -43,8 +45,9 @@ class Flux(nn.Module):
         self.dtype = dtype
         params = FluxParams(**kwargs)
         self.params = params
-        self.in_channels = params.in_channels * 2 * 2
-        self.out_channels = self.in_channels
+        self.patch_size = params.patch_size
+        self.in_channels = params.in_channels * params.patch_size * params.patch_size
+        self.out_channels = params.out_channels * params.patch_size * params.patch_size
         if params.hidden_size % params.num_heads != 0:
             raise ValueError(
                 f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
@@ -96,7 +99,9 @@ class Flux(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         control=None,
+        transformer_options={},
     ) -> Tensor:
+        patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
 
@@ -114,8 +119,19 @@ class Flux(nn.Module):
         ids = torch.cat((txt_ids, img_ids), dim=1)
         pe = self.pe_embedder(ids)
 
+        blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.double_blocks):
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"])
+                    return out
+
+                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe}, {"original_block": block_wrap})
+                txt = out["txt"]
+                img = out["img"]
+            else:
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -127,7 +143,16 @@ class Flux(nn.Module):
         img = torch.cat((txt, img), 1)
 
         for i, block in enumerate(self.single_blocks):
-            img = block(img, vec=vec, pe=pe)
+            if ("single_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"])
+                    return out
+
+                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe}, {"original_block": block_wrap})
+                img = out["img"]
+            else:
+                img = block(img, vec=vec, pe=pe)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -141,9 +166,9 @@ class Flux(nn.Module):
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def forward(self, x, timestep, context, y, guidance, control=None, **kwargs):
+    def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
-        patch_size = 2
+        patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
 
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
@@ -151,10 +176,10 @@ class Flux(nn.Module):
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 1] = torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control)
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options)
         return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]

comfy/ldm/flux/redux.py

@@ -0,0 +1,25 @@
+import torch
+import comfy.ops
+
+ops = comfy.ops.manual_cast
+
+class ReduxImageEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        redux_dim: int = 1152,
+        txt_in_features: int = 4096,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__()
+
+        self.redux_dim = redux_dim
+        self.device = device
+        self.dtype = dtype
+
+        self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype)
+        self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype)
+
+    def forward(self, sigclip_embeds) -> torch.Tensor:
+        projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds)))
+        return projected_x

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -494,8 +494,9 @@ class AsymmDiTJoint(nn.Module):
         packed_indices: Dict[str, torch.Tensor] = None,
         rope_cos: torch.Tensor = None,
         rope_sin: torch.Tensor = None,
-        control=None, **kwargs
+        control=None, transformer_options={}, **kwargs
     ):
+        patches_replace = transformer_options.get("patches_replace", {})
         y_feat = context
         y_mask = attention_mask
         sigma = timestep
@@ -515,15 +516,32 @@ class AsymmDiTJoint(nn.Module):
         )
         del y_mask
 
+        blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.blocks):
-            x, y_feat = block(
-                x,
-                c,
-                y_feat,
-                rope_cos=rope_cos,
-                rope_sin=rope_sin,
-                crop_y=num_tokens,
-            )  # (B, M, D), (B, L, D)
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"], out["txt"] = block(
+                                                    args["img"],
+                                                    args["vec"],
+                                                    args["txt"],
+                                                    rope_cos=args["rope_cos"],
+                                                    rope_sin=args["rope_sin"],
+                                                    crop_y=args["num_tokens"]
+                                                    )
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
+                y_feat = out["txt"]
+                x = out["img"]
+            else:
+                x, y_feat = block(
+                    x,
+                    c,
+                    y_feat,
+                    rope_cos=rope_cos,
+                    rope_sin=rope_sin,
+                    crop_y=num_tokens,
+                )  # (B, M, D), (B, L, D)
         del y_feat  # Final layers don't use dense text features.
 
         x = self.final_layer(x, c)  # (B, M, patch_size ** 2 * out_channels)

comfy/ldm/genmo/vae/model.py

@@ -2,12 +2,16 @@
 #adapted to ComfyUI
 
 from typing import Callable, List, Optional, Tuple, Union
+from functools import partial
+import math
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
 
+from comfy.ldm.modules.attention import optimized_attention
+
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
@@ -158,8 +162,10 @@ class ResBlock(nn.Module):
         *,
         affine: bool = True,
         attn_block: Optional[nn.Module] = None,
-        padding_mode: str = "replicate",
         causal: bool = True,
+        prune_bottleneck: bool = False,
+        padding_mode: str,
+        bias: bool = True,
     ):
         super().__init__()
         self.channels = channels
@@ -170,23 +176,23 @@ class ResBlock(nn.Module):
             nn.SiLU(inplace=True),
             PConv3d(
                 in_channels=channels,
-                out_channels=channels,
+                out_channels=channels // 2 if prune_bottleneck else channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
-                # causal=causal,
+                bias=bias,
+                causal=causal,
             ),
             norm_fn(channels, affine=affine),
             nn.SiLU(inplace=True),
             PConv3d(
-                in_channels=channels,
+                in_channels=channels // 2 if prune_bottleneck else channels,
                 out_channels=channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
-                # causal=causal,
+                bias=bias,
+                causal=causal,
             ),
         )
 
@@ -206,6 +212,81 @@ class ResBlock(nn.Module):
         return self.attn_block(x)
 
 
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        out_bias: bool = True,
+        qk_norm: bool = True,
+    ) -> None:
+        super().__init__()
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.qk_norm = qk_norm
+
+        self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
+        self.out = nn.Linear(dim, dim, bias=out_bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute temporal self-attention.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+            chunk_size: Chunk size for large tensors.
+
+        Returns:
+            x: Output tensor. Shape: [B, C, T, H, W].
+        """
+        B, _, T, H, W = x.shape
+
+        if T == 1:
+            # No attention for single frame.
+            x = x.movedim(1, -1)  # [B, C, T, H, W] -> [B, T, H, W, C]
+            qkv = self.qkv(x)
+            _, _, x = qkv.chunk(3, dim=-1)  # Throw away queries and keys.
+            x = self.out(x)
+            return x.movedim(-1, 1)  # [B, T, H, W, C] -> [B, C, T, H, W]
+
+        # 1D temporal attention.
+        x = rearrange(x, "B C t h w -> (B h w) t C")
+        qkv = self.qkv(x)
+
+        # Input: qkv with shape [B, t, 3 * num_heads * head_dim]
+        # Output: x with shape [B, num_heads, t, head_dim]
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)
+
+        if self.qk_norm:
+            q = F.normalize(q, p=2, dim=-1)
+            k = F.normalize(k, p=2, dim=-1)
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
+
+        assert x.size(0) == q.size(0)
+
+        x = self.out(x)
+        x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
+        return x
+
+
+class AttentionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        **attn_kwargs,
+    ) -> None:
+        super().__init__()
+        self.norm = norm_fn(dim)
+        self.attn = Attention(dim, **attn_kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.attn(self.norm(x))
+
+
 class CausalUpsampleBlock(nn.Module):
     def __init__(
         self,
@@ -244,14 +325,9 @@ class CausalUpsampleBlock(nn.Module):
         return x
 
 
-def block_fn(channels, *, has_attention: bool = False, **block_kwargs):
-    assert has_attention is False #NOTE: if this is ever true add back the attention code.
-
-    attn_block = None #AttentionBlock(channels) if has_attention else None
-
-    return ResBlock(
-        channels, affine=True, attn_block=attn_block, **block_kwargs
-    )
+def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):
+    attn_block = AttentionBlock(channels) if has_attention else None
+    return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)
 
 
 class DownsampleBlock(nn.Module):
@@ -288,8 +364,9 @@ class DownsampleBlock(nn.Module):
                 out_channels=out_channels,
                 kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
                 stride=(temporal_reduction, spatial_reduction, spatial_reduction),
+                # First layer in each block always uses replicate padding
                 padding_mode="replicate",
-                bias=True,
+                bias=block_kwargs["bias"],
             )
         )
 
@@ -382,7 +459,7 @@ class Decoder(nn.Module):
         blocks = []
 
         first_block = [
-            nn.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
+            ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
         ]  # Input layer.
         # First set of blocks preserve channel count.
         for _ in range(num_res_blocks[-1]):
@@ -452,11 +529,165 @@ class Decoder(nn.Module):
 
         return self.output_proj(x).contiguous()
 
+class LatentDistribution:
+    def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+        """Initialize latent distribution.
+
+        Args:
+            mean: Mean of the distribution. Shape: [B, C, T, H, W].
+            logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].
+        """
+        assert mean.shape == logvar.shape
+        self.mean = mean
+        self.logvar = logvar
+
+    def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):
+        if temperature == 0.0:
+            return self.mean
+
+        if noise is None:
+            noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)
+        else:
+            assert noise.device == self.mean.device
+            noise = noise.to(self.mean.dtype)
+
+        if temperature != 1.0:
+            raise NotImplementedError(f"Temperature {temperature} is not supported.")
+
+        # Just Gaussian sample with no scaling of variance.
+        return noise * torch.exp(self.logvar * 0.5) + self.mean
+
+    def mode(self):
+        return self.mean
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        base_channels: int,
+        channel_multipliers: List[int],
+        num_res_blocks: List[int],
+        latent_dim: int,
+        temporal_reductions: List[int],
+        spatial_reductions: List[int],
+        prune_bottlenecks: List[bool],
+        has_attentions: List[bool],
+        affine: bool = True,
+        bias: bool = True,
+        input_is_conv_1x1: bool = False,
+        padding_mode: str,
+    ):
+        super().__init__()
+        self.temporal_reductions = temporal_reductions
+        self.spatial_reductions = spatial_reductions
+        self.base_channels = base_channels
+        self.channel_multipliers = channel_multipliers
+        self.num_res_blocks = num_res_blocks
+        self.latent_dim = latent_dim
+
+        self.fourier_features = FourierFeatures()
+        ch = [mult * base_channels for mult in channel_multipliers]
+        num_down_blocks = len(ch) - 1
+        assert len(num_res_blocks) == num_down_blocks + 2
+
+        layers = (
+            [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]
+            if not input_is_conv_1x1
+            else [Conv1x1(in_channels, ch[0])]
+        )
+
+        assert len(prune_bottlenecks) == num_down_blocks + 2
+        assert len(has_attentions) == num_down_blocks + 2
+        block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)
+
+        for _ in range(num_res_blocks[0]):
+            layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))
+        prune_bottlenecks = prune_bottlenecks[1:]
+        has_attentions = has_attentions[1:]
+
+        assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1
+        for i in range(num_down_blocks):
+            layer = DownsampleBlock(
+                ch[i],
+                ch[i + 1],
+                num_res_blocks=num_res_blocks[i + 1],
+                temporal_reduction=temporal_reductions[i],
+                spatial_reduction=spatial_reductions[i],
+                prune_bottleneck=prune_bottlenecks[i],
+                has_attention=has_attentions[i],
+                affine=affine,
+                bias=bias,
+                padding_mode=padding_mode,
+            )
+
+            layers.append(layer)
+
+        # Additional blocks.
+        for _ in range(num_res_blocks[-1]):
+            layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))
+
+        self.layers = nn.Sequential(*layers)
+
+        # Output layers.
+        self.output_norm = norm_fn(ch[-1])
+        self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)
+
+    @property
+    def temporal_downsample(self):
+        return math.prod(self.temporal_reductions)
+
+    @property
+    def spatial_downsample(self):
+        return math.prod(self.spatial_reductions)
+
+    def forward(self, x) -> LatentDistribution:
+        """Forward pass.
+
+        Args:
+            x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]
+
+        Returns:
+            means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].
+                   h = H // 8, w = W // 8, t - 1 = (T - 1) // 6
+            logvar: Shape: [B, latent_dim, t, h, w].
+        """
+        assert x.ndim == 5, f"Expected 5D input, got {x.shape}"
+        x = self.fourier_features(x)
+
+        x = self.layers(x)
+
+        x = self.output_norm(x)
+        x = F.silu(x, inplace=True)
+        x = self.output_proj(x)
+
+        means, logvar = torch.chunk(x, 2, dim=1)
+
+        assert means.ndim == 5
+        assert logvar.shape == means.shape
+        assert means.size(1) == self.latent_dim
+
+        return LatentDistribution(means, logvar)
+
 
 class VideoVAE(nn.Module):
     def __init__(self):
         super().__init__()
-        self.encoder = None #TODO once the model releases
+        self.encoder = Encoder(
+            in_channels=15,
+            base_channels=64,
+            channel_multipliers=[1, 2, 4, 6],
+            num_res_blocks=[3, 3, 4, 6, 3],
+            latent_dim=12,
+            temporal_reductions=[1, 2, 3],
+            spatial_reductions=[2, 2, 2],
+            prune_bottlenecks=[False, False, False, False, False],
+            has_attentions=[False, True, True, True, True],
+            affine=True,
+            bias=True,
+            input_is_conv_1x1=True,
+            padding_mode="replicate"
+        )
         self.decoder = Decoder(
             out_channels=3,
             base_channels=128,
@@ -474,7 +705,7 @@ class VideoVAE(nn.Module):
         )
 
     def encode(self, x):
-        return self.encoder(x)
+        return self.encoder(x).mode()
 
     def decode(self, x):
         return self.decoder(x)

comfy/ldm/modules/attention.py

@@ -372,10 +372,10 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
         )
 
     if mask is not None:
-        pad = 8 - q.shape[1] % 8
-        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
-        mask_out[:, :, :mask.shape[-1]] = mask
-        mask = mask_out[:, :, :mask.shape[-1]]
+        pad = 8 - mask.shape[-1] % 8
+        mask_out = torch.empty([q.shape[0], q.shape[2], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device)
+        mask_out[..., :mask.shape[-1]] = mask
+        mask = mask_out[..., :mask.shape[-1]]
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
@@ -393,6 +393,13 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     return out
 
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+    SDP_BATCH_LIMIT = 2**15
+else:
+    #TODO: other GPUs ?
+    SDP_BATCH_LIMIT = 2**31
+
+
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
@@ -404,10 +411,15 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             (q, k, v),
         )
 
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-    out = (
-        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-    )
+    if SDP_BATCH_LIMIT >= q.shape[0]:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        out = (
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+        for i in range(0, q.shape[0], SDP_BATCH_LIMIT):
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
 

comfy/lora.py

@@ -49,6 +49,15 @@ def load_lora(lora, to_load):
             dora_scale = lora[dora_scale_name]
             loaded_keys.add(dora_scale_name)
 
+        reshape_name = "{}.reshape_weight".format(x)
+        reshape = None
+        if reshape_name in lora.keys():
+            try:
+                reshape = lora[reshape_name].tolist()
+                loaded_keys.add(reshape_name)
+            except:
+                pass
+
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
         diffusers2_lora = "{}.lora_B.weight".format(x)
@@ -82,7 +91,7 @@ def load_lora(lora, to_load):
             if mid_name is not None and mid_name in lora.keys():
                 mid = lora[mid_name]
                 loaded_keys.add(mid_name)
-            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
+            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape))
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
 
@@ -193,6 +202,12 @@ def load_lora(lora, to_load):
             patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,))
             loaded_keys.add(diff_bias_name)
 
+        set_weight_name = "{}.set_weight".format(x)
+        set_weight = lora.get(set_weight_name, None)
+        if set_weight is not None:
+            patch_dict[to_load[x]] = ("set", (set_weight,))
+            loaded_keys.add(set_weight_name)
+
     for x in lora.keys():
         if x not in loaded_keys:
             logging.warning("lora key not loaded: {}".format(x))
@@ -282,11 +297,14 @@ def model_lora_keys_unet(model, key_map={}):
     sdk = sd.keys()
 
     for k in sdk:
-        if k.startswith("diffusion_model.") and k.endswith(".weight"):
-            key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-            key_map["lora_unet_{}".format(key_lora)] = k
-            key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
-            key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
+        if k.startswith("diffusion_model."):
+            if k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+                key_map["lora_unet_{}".format(key_lora)] = k
+                key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
+                key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
+            else:
+                key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names
 
     diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
     for k in diffusers_keys:
@@ -440,10 +458,17 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
                     logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape))
                 else:
                     weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype))
+        elif patch_type == "set":
+            weight.copy_(v[0])
         elif patch_type == "lora": #lora/locon
             mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
             mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
             dora_scale = v[4]
+            reshape = v[5]
+
+            if reshape is not None:
+                weight = pad_tensor_to_shape(weight, reshape)
+
             if v[2] is not None:
                 alpha = v[2] / mat2.shape[0]
             else:

comfy/lora_convert.py

@@ -0,0 +1,17 @@
+import torch
+
+
+def convert_lora_bfl_control(sd): #BFL loras for Flux
+    sd_out = {}
+    for k in sd:
+        k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight"))
+        sd_out[k_to] = sd[k]
+
+    sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]])
+    return sd_out
+
+
+def convert_lora(sd):
+    if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
+        return convert_lora_bfl_control(sd)
+    return sd

comfy/model_base.py

@@ -153,8 +153,7 @@ class BaseModel(torch.nn.Module):
     def encode_adm(self, **kwargs):
         return None
 
-    def extra_conds(self, **kwargs):
-        out = {}
+    def concat_cond(self, **kwargs):
         if len(self.concat_keys) > 0:
             cond_concat = []
             denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
@@ -193,7 +192,14 @@ class BaseModel(torch.nn.Module):
                     elif ck == "masked_image":
                         cond_concat.append(self.blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
-            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
+            return data
+        return None
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        concat_cond = self.concat_cond(**kwargs)
+        if concat_cond is not None:
+            out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond)
 
         adm = self.encode_adm(**kwargs)
         if adm is not None:
@@ -523,9 +529,7 @@ class SD_X4Upscaler(BaseModel):
         return out
 
 class IP2P:
-    def extra_conds(self, **kwargs):
-        out = {}
-
+    def concat_cond(self, **kwargs):
         image = kwargs.get("concat_latent_image", None)
         noise = kwargs.get("noise", None)
         device = kwargs["device"]
@@ -537,18 +541,15 @@ class IP2P:
             image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
 
         image = utils.resize_to_batch_size(image, noise.shape[0])
+        return self.process_ip2p_image_in(image)
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-        return out
 
 class SD15_instructpix2pix(IP2P, BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)
         self.process_ip2p_image_in = lambda image: image
 
+
 class SDXL_instructpix2pix(IP2P, SDXL):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)
@@ -709,6 +710,38 @@ class Flux(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
 
+    def concat_cond(self, **kwargs):
+        num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size)
+        out_channels = self.model_config.unet_config["out_channels"]
+
+        if num_channels <= out_channels:
+            return None
+
+        image = kwargs.get("concat_latent_image", None)
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        if image is None:
+            image = torch.zeros_like(noise)
+
+        image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+        image = utils.resize_to_batch_size(image, noise.shape[0])
+        image = self.process_latent_in(image)
+        if num_channels <= out_channels * 2:
+            return image
+
+        #inpaint model
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.ones_like(noise)[:, :1]
+
+        mask = torch.mean(mask, dim=1, keepdim=True)
+        print(mask.shape)
+        mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
+        mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
+        mask = utils.resize_to_batch_size(mask, noise.shape[0])
+        return torch.cat((image, mask), dim=1)
+
     def encode_adm(self, **kwargs):
         return kwargs["pooled_output"]
 

comfy/model_detection.py

@@ -137,6 +137,12 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config = {}
         dit_config["image_model"] = "flux"
         dit_config["in_channels"] = 16
+        patch_size = 2
+        dit_config["patch_size"] = patch_size
+        in_key = "{}img_in.weight".format(key_prefix)
+        if in_key in state_dict_keys:
+            dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size)
+        dit_config["out_channels"] = 16
         dit_config["vec_in_dim"] = 768
         dit_config["context_in_dim"] = 4096
         dit_config["hidden_size"] = 3072
@@ -321,8 +327,9 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 
-    scaled_fp8_weight = state_dict.get("{}scaled_fp8".format(unet_key_prefix), None)
-    if scaled_fp8_weight is not None:
+    scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
+    if scaled_fp8_key in state_dict:
+        scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
         model_config.scaled_fp8 = scaled_fp8_weight.dtype
         if model_config.scaled_fp8 == torch.float32:
             model_config.scaled_fp8 = torch.float8_e4m3fn
@@ -540,7 +547,11 @@ def model_config_from_diffusers_unet(state_dict):
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
     out_sd = {}
 
-    if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
+    if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
         hidden_size = state_dict["x_embedder.bias"].shape[0]
@@ -549,10 +560,6 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
         sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
-    elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
-        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
-        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
-        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
     else:
         return None
 

comfy/model_management.py

@@ -896,7 +896,7 @@ 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 <= (15, 0, 1):  # black image bug on recent versions of macOS
+        if (14, 5) <= macos_version <= (15, 2):  # black image bug on recent versions of macOS
             upcast = True
     except:
         pass

comfy/model_patcher.py

@@ -373,14 +373,18 @@ class ModelPatcher:
         lowvram_counter = 0
         loading = []
         for n, m in self.model.named_modules():
-            if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"):
-                loading.append((comfy.model_management.module_size(m), n, m))
+            params = []
+            for name, param in m.named_parameters(recurse=False):
+                params.append(name)
+            if hasattr(m, "comfy_cast_weights") or len(params) > 0:
+                loading.append((comfy.model_management.module_size(m), n, m, params))
 
         load_completely = []
         loading.sort(reverse=True)
         for x in loading:
             n = x[1]
             m = x[2]
+            params = x[3]
             module_mem = x[0]
 
             lowvram_weight = False
@@ -416,22 +420,21 @@ class ModelPatcher:
                     if m.comfy_cast_weights:
                         wipe_lowvram_weight(m)
 
-                if hasattr(m, "weight"):
-                    mem_counter += module_mem
-                    load_completely.append((module_mem, n, m))
+                mem_counter += module_mem
+                load_completely.append((module_mem, n, m, params))
 
         load_completely.sort(reverse=True)
         for x in load_completely:
             n = x[1]
             m = x[2]
-            weight_key = "{}.weight".format(n)
-            bias_key = "{}.bias".format(n)
+            params = x[3]
             if hasattr(m, "comfy_patched_weights"):
                 if m.comfy_patched_weights == True:
                     continue
 
-            self.patch_weight_to_device(weight_key, device_to=device_to)
-            self.patch_weight_to_device(bias_key, device_to=device_to)
+            for param in params:
+                self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
+
             logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
             m.comfy_patched_weights = True
 

comfy/model_sampling.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
+def rescale_zero_terminal_snr_sigmas(sigmas):
+    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas_bar[-1] = 4.8973451890853435e-08
+    return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
 class EPS:
     def calculate_input(self, sigma, noise):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
@@ -48,7 +67,7 @@ class CONST:
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):
-    def __init__(self, model_config=None):
+    def __init__(self, model_config=None, zsnr=None):
         super().__init__()
 
         if model_config is not None:
@@ -61,11 +80,14 @@ class ModelSamplingDiscrete(torch.nn.Module):
         linear_end = sampling_settings.get("linear_end", 0.012)
         timesteps = sampling_settings.get("timesteps", 1000)
 
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
+        if zsnr is None:
+            zsnr = sampling_settings.get("zsnr", False)
+
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr)
         self.sigma_data = 1.0
 
     def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False):
         if given_betas is not None:
             betas = given_betas
         else:
@@ -83,6 +105,9 @@ class ModelSamplingDiscrete(torch.nn.Module):
         # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
 
         sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        if zsnr:
+            sigmas = rescale_zero_terminal_snr_sigmas(sigmas)
+
         self.set_sigmas(sigmas)
 
     def set_sigmas(self, sigmas):

comfy/sampler_helpers.py

@@ -1,14 +1,10 @@
 import torch
 import comfy.model_management
 import comfy.conds
+import comfy.utils
 
 def prepare_mask(noise_mask, shape, device):
-    """ensures noise mask is of proper dimensions"""
-    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
-    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
-    noise_mask = noise_mask.to(device)
-    return noise_mask
+    return comfy.utils.reshape_mask(noise_mask, shape).to(device)
 
 def get_models_from_cond(cond, model_type):
     models = []

comfy/sd.py

@@ -30,9 +30,12 @@ import comfy.text_encoders.genmo
 
 import comfy.model_patcher
 import comfy.lora
+import comfy.lora_convert
 import comfy.t2i_adapter.adapter
 import comfy.taesd.taesd
 
+import comfy.ldm.flux.redux
+
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     key_map = {}
     if model is not None:
@@ -40,6 +43,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     if clip is not None:
         key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
 
+    lora = comfy.lora_convert.convert_lora(lora)
     loaded = comfy.lora.load_lora(lora, key_map)
     if model is not None:
         new_modelpatcher = model.clone()
@@ -171,6 +175,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.latent_dim = 2
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
@@ -240,16 +245,22 @@ class VAE:
                 self.output_channels = 2
                 self.upscale_ratio = 2048
                 self.downscale_ratio =  2048
+                self.latent_dim = 1
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd: #genmo mochi vae
+            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae
                 if "blocks.2.blocks.3.stack.5.weight" in sd:
                     sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
+                if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
+                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
                 self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
                 self.latent_channels = 12
+                self.latent_dim = 3
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
+                self.working_dtypes = [torch.float16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -354,24 +365,45 @@ class VAE:
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
 
-    def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        model_management.load_model_gpu(self.patcher)
-        output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
-        return output.movedim(1,-1)
+    def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None):
+        memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
+        dims = samples.ndim - 2
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+
+        if dims == 1:
+            args.pop("tile_y")
+            output = self.decode_tiled_1d(samples, **args)
+        elif dims == 2:
+            output = self.decode_tiled_(samples, **args)
+        elif dims == 3:
+            output = self.decode_tiled_3d(samples, **args)
+        return output.movedim(1, -1)
 
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        pixel_samples = pixel_samples.movedim(-1,1)
+        pixel_samples = pixel_samples.movedim(-1, 1)
+        if self.latent_dim == 3:
+            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             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 / 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)
+            samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                if samples is None:
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                samples[x:x + batch_number] = out
 
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
@@ -405,6 +437,8 @@ def load_style_model(ckpt_path):
     keys = model_data.keys()
     if "style_embedding" in keys:
         model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
+    elif "redux_down.weight" in keys:
+        model = comfy.ldm.flux.redux.ReduxImageEncoder()
     else:
         raise Exception("invalid style model {}".format(ckpt_path))
     model.load_state_dict(model_data)

comfy/supported_models.py

@@ -197,6 +197,8 @@ class SDXL(supported_models_base.BASE):
                 self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
             return model_base.ModelType.V_PREDICTION_EDM
         elif "v_pred" in state_dict:
+            if "ztsnr" in state_dict: #Some zsnr anime checkpoints
+                self.sampling_settings["zsnr"] = True
             return model_base.ModelType.V_PREDICTION
         else:
             return model_base.ModelType.EPS

comfy/text_encoders/genmo.py

@@ -12,7 +12,7 @@ class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
 
 class MochiT5XXL(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
 
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):

comfy/utils.py

@@ -316,10 +316,18 @@ MMDIT_MAP_BLOCK = {
     ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
     ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
     ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"),
+    ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"),
     ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
     ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
     ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
     ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.attn.ln_q.weight", "attn.norm_q.weight"),
+    ("x_block.attn.ln_k.weight", "attn.norm_k.weight"),
+    ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"),
+    ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"),
+    ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"),
+    ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"),
     ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
     ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
     ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
@@ -349,6 +357,12 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
             key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
             key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
 
+            k = "{}.attn2.".format(block_from)
+            qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
         for k in MMDIT_MAP_BLOCK:
             key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
 
@@ -834,3 +848,24 @@ class ProgressBar:
 
     def update(self, value):
         self.update_absolute(self.current + value)
+
+def reshape_mask(input_mask, output_shape):
+    dims = len(output_shape) - 2
+
+    if dims == 1:
+        scale_mode = "linear"
+
+    if dims == 2:
+        input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "bilinear"
+
+    if dims == 3:
+        if len(input_mask.shape) < 5:
+            input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "trilinear"
+
+    mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+    if mask.shape[1] < output_shape[1]:
+        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+    mask = comfy.utils.repeat_to_batch_size(mask, output_shape[0])
+    return mask

comfy_extras/nodes_mochi.py

@@ -3,9 +3,6 @@ import torch
 import comfy.model_management
 
 class EmptyMochiLatentVideo:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
@@ -15,10 +12,10 @@ class EmptyMochiLatentVideo:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
-    CATEGORY = "latent/mochi"
+    CATEGORY = "latent/video"
 
     def generate(self, width, height, length, batch_size=1):
-        latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=self.device)
+        latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
         return ({"samples":latent}, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_model_advanced.py

@@ -26,8 +26,8 @@ class X0(comfy.model_sampling.EPS):
 class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
     original_timesteps = 50
 
-    def __init__(self, model_config=None):
-        super().__init__(model_config)
+    def __init__(self, model_config=None, zsnr=None):
+        super().__init__(model_config, zsnr=zsnr)
 
         self.skip_steps = self.num_timesteps // self.original_timesteps
 
@@ -51,25 +51,6 @@ class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete)
         return log_sigma.exp().to(timestep.device)
 
 
-def rescale_zero_terminal_snr_sigmas(sigmas):
-    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas_bar[-1] = 4.8973451890853435e-08
-    return ((1 - alphas_bar) / alphas_bar) ** 0.5
-
 class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
@@ -100,9 +81,7 @@ class ModelSamplingDiscrete:
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
 
-        model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        if zsnr:
-            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
+        model_sampling = ModelSamplingAdvanced(model.model.model_config, zsnr=zsnr)
 
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )

comfy_extras/nodes_model_merging_model_specific.py

@@ -75,6 +75,34 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+
+class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["init_x_linear."] = argument
+        arg_dict["positional_encoding"] = argument
+        arg_dict["cond_seq_linear."] = argument
+        arg_dict["register_tokens"] = argument
+        arg_dict["t_embedder."] = argument
+
+        for i in range(4):
+            arg_dict["double_layers.{}.".format(i)] = argument
+
+        for i in range(32):
+            arg_dict["single_layers.{}.".format(i)] = argument
+
+        arg_dict["modF."] = argument
+        arg_dict["final_linear."] = argument
+
+        return {"required": arg_dict}
+
 class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
     CATEGORY = "advanced/model_merging/model_specific"
 
@@ -124,11 +152,35 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_frequencies."] = argument
+        arg_dict["t_embedder."] = argument
+        arg_dict["t5_y_embedder."] = argument
+        arg_dict["t5_yproj."] = argument
+
+        for i in range(48):
+            arg_dict["blocks.{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
     "ModelMergeSDXL": ModelMergeSDXL,
     "ModelMergeSD3_2B": ModelMergeSD3_2B,
+    "ModelMergeAuraflow": ModelMergeAuraflow,
     "ModelMergeFlux1": ModelMergeFlux1,
     "ModelMergeSD35_Large": ModelMergeSD35_Large,
+    "ModelMergeMochiPreview": ModelMergeMochiPreview,
 }

comfy_extras/nodes_sag.py

@@ -57,12 +57,24 @@ def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
     attn = attn.reshape(b, -1, hw1, hw2)
     # Global Average Pool
     mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
-    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
-    mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
+
+    total = mask.shape[-1]
+    x = round(math.sqrt((lh / lw) * total))
+    xx = None
+    for i in range(0, math.floor(math.sqrt(total) / 2)):
+        for j in [(x + i), max(1, x - i)]:
+            if total % j == 0:
+                xx = j
+                break
+        if xx is not None:
+            break
+
+    x = xx
+    y = total // x
 
     # Reshape
     mask = (
-        mask.reshape(b, *mid_shape)
+        mask.reshape(b, x, y)
         .unsqueeze(1)
         .type(attn.dtype)
     )

comfy_extras/nodes_sd3.py

@@ -3,24 +3,29 @@ import comfy.sd
 import comfy.model_management
 import nodes
 import torch
+import comfy_extras.nodes_slg
+
 
 class TripleCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), )
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nsd3: clip-l, clip-g, t5"
+
     def load_clip(self, clip_name1, clip_name2, clip_name3):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
-        clip_path3 = folder_paths.get_full_path_or_raise("clip", clip_name3)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
+        clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
+
 class EmptySD3LatentImage:
     def __init__(self):
         self.device = comfy.model_management.intermediate_device()
@@ -39,6 +44,7 @@ class EmptySD3LatentImage:
         latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )
 
+
 class CLIPTextEncodeSD3:
     @classmethod
     def INPUT_TYPES(s):
@@ -95,11 +101,36 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
     CATEGORY = "conditioning/controlnet"
     DEPRECATED = True
 
+
+class SkipLayerGuidanceSD3(comfy_extras.nodes_slg.SkipLayerGuidanceDiT):
+    '''
+    Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
+    Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
+    Experimental implementation by Dango233@StabilityAI.
+    '''
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "skip_guidance_sd3"
+
+    CATEGORY = "advanced/guidance"
+
+    def skip_guidance_sd3(self, model, layers, scale, start_percent, end_percent):
+        return self.skip_guidance(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers)
+
+
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
     "EmptySD3LatentImage": EmptySD3LatentImage,
     "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
     "ControlNetApplySD3": ControlNetApplySD3,
+    "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

comfy_extras/nodes_slg.py

@@ -0,0 +1,78 @@
+import comfy.model_patcher
+import comfy.samplers
+import re
+
+
+class SkipLayerGuidanceDiT:
+    '''
+    Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
+    Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
+    Original experimental implementation for SD3 by Dango233@StabilityAI.
+    '''
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "skip_guidance"
+    EXPERIMENTAL = True
+
+    DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model."
+
+    CATEGORY = "advanced/guidance"
+
+    def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""):
+        # check if layer is comma separated integers
+        def skip(args, extra_args):
+            return args
+
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        double_layers = re.findall(r'\d+', double_layers)
+        double_layers = [int(i) for i in double_layers]
+
+        single_layers = re.findall(r'\d+', single_layers)
+        single_layers = [int(i) for i in single_layers]
+
+        if len(double_layers) == 0 and len(single_layers) == 0:
+            return (model, )
+
+        def post_cfg_function(args):
+            model = args["model"]
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            x = args["input"]
+            model_options = args["model_options"].copy()
+
+            for layer in double_layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
+
+            for layer in single_layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer)
+
+            model_sampling.percent_to_sigma(start_percent)
+
+            sigma_ = sigma[0].item()
+            if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
+                (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
+                cfg_result = cfg_result + (cond_pred - slg) * scale
+            return cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT,
+}

folder_paths.py

@@ -18,7 +18,7 @@ folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".y
 
 folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
 folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions)
 folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions)
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
@@ -81,7 +81,8 @@ extension_mimetypes_cache = {
 }
 
 def map_legacy(folder_name: str) -> str:
-    legacy = {"unet": "diffusion_models"}
+    legacy = {"unet": "diffusion_models",
+              "clip": "text_encoders"}
     return legacy.get(folder_name, folder_name)
 
 if not os.path.exists(input_directory):

latent_preview.py

@@ -47,7 +47,12 @@ class Latent2RGBPreviewer(LatentPreviewer):
         if self.latent_rgb_factors_bias is not None:
             self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
 
-        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        if x0.ndim == 5:
+            x0 = x0[0, :, 0]
+        else:
+            x0 = x0[0]
+
+        latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
         # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
 
         return preview_to_image(latent_image)

main.py

@@ -71,6 +71,7 @@ if os.name == "nt":
 if __name__ == "__main__":
     if args.cuda_device is not None:
         os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
+        os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device)
         logging.info("Set cuda device to: {}".format(args.cuda_device))
 
     if args.deterministic:

nodes.py

@@ -290,15 +290,21 @@ class VAEDecodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64})
+                             "tile_size": ("INT", {"default": 512, "min": 128, "max": 4096, "step": 32}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
                             }}
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
     CATEGORY = "_for_testing"
 
-    def decode(self, vae, samples, tile_size):
-        return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), )
+    def decode(self, vae, samples, tile_size, overlap=64):
+        if tile_size < overlap * 4:
+            overlap = tile_size // 4
+        images = vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, overlap=overlap // 8)
+        if len(images.shape) == 5: #Combine batches
+            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+        return (images, )
 
 class VAEEncode:
     @classmethod
@@ -376,6 +382,7 @@ class InpaintModelConditioning:
                              "vae": ("VAE", ),
                              "pixels": ("IMAGE", ),
                              "mask": ("MASK", ),
+                             "noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}),
                              }}
 
     RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
@@ -384,7 +391,7 @@ class InpaintModelConditioning:
 
     CATEGORY = "conditioning/inpaint"
 
-    def encode(self, positive, negative, pixels, vae, mask):
+    def encode(self, positive, negative, pixels, vae, mask, noise_mask):
         x = (pixels.shape[1] // 8) * 8
         y = (pixels.shape[2] // 8) * 8
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
@@ -408,7 +415,8 @@ class InpaintModelConditioning:
         out_latent = {}
 
         out_latent["samples"] = orig_latent
-        out_latent["noise_mask"] = mask
+        if noise_mask:
+            out_latent["noise_mask"] = mask
 
         out = []
         for conditioning in [positive, negative]:
@@ -888,7 +896,7 @@ class UNETLoader:
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -896,6 +904,8 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5"
+
     def load_clip(self, clip_name, type="stable_diffusion"):
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
@@ -908,15 +918,15 @@ class CLIPLoader:
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
-        clip_path = folder_paths.get_full_path_or_raise("clip", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
-                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -924,9 +934,11 @@ class DualCLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
+
     def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         if type == "sdxl":
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
@@ -1957,6 +1969,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageInvert": "Invert Image",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
     "ImageBatch": "Batch Images",
+    "ImageCrop": "Image Crop",
+    "ImageBlend": "Image Blend",
+    "ImageBlur": "Image Blur",
+    "ImageQuantize": "Image Quantize",
+    "ImageSharpen": "Image Sharpen",
+    "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
@@ -2117,6 +2135,7 @@ def init_builtin_extra_nodes():
         "nodes_lora_extract.py",
         "nodes_torch_compile.py",
         "nodes_mochi.py",
+        "nodes_slg.py",
     ]
 
     import_failed = []

server.py

@@ -152,7 +152,7 @@ class PromptServer():
         mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
 
         self.user_manager = UserManager()
-        self.internal_routes = InternalRoutes()
+        self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = None
         self.loop = loop

tests-unit/prompt_server_test/user_manager_test.py

@@ -14,7 +14,7 @@ def user_manager(tmp_path):
     um = UserManager()
     um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join(
         tmp_path, file
-    )
+    ) if file else tmp_path
     return um
 
 
@@ -80,9 +80,7 @@ async def test_listuserdata_split_path(aiohttp_client, app, tmp_path):
     client = await aiohttp_client(app)
     resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true")
     assert resp.status == 200
-    assert await resp.json() == [
-        ["subdir/file1.txt", "subdir", "file1.txt"]
-    ]
+    assert await resp.json() == [["subdir/file1.txt", "subdir", "file1.txt"]]
 
 
 async def test_listuserdata_invalid_directory(aiohttp_client, app):
@@ -118,3 +116,116 @@ async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path):
             assert "/" in result[0]["path"]  # Ensure forward slash is used
             assert "\\" not in result[0]["path"]  # Ensure backslash is not present
             assert result[0]["path"] == "subdir/file1.txt"
+
+
+async def test_post_userdata_new_file(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    content = b"test content"
+    resp = await client.post("/userdata/test.txt", data=content)
+
+    assert resp.status == 200
+    assert await resp.text() == '"test.txt"'
+
+    # Verify file was created with correct content
+    with open(tmp_path / "test.txt", "rb") as f:
+        assert f.read() == content
+
+
+async def test_post_userdata_overwrite_existing(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "test.txt", "w") as f:
+        f.write("initial content")
+
+    client = await aiohttp_client(app)
+    new_content = b"updated content"
+    resp = await client.post("/userdata/test.txt", data=new_content)
+
+    assert resp.status == 200
+    assert await resp.text() == '"test.txt"'
+
+    # Verify file was overwritten
+    with open(tmp_path / "test.txt", "rb") as f:
+        assert f.read() == new_content
+
+
+async def test_post_userdata_no_overwrite(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "test.txt", "w") as f:
+        f.write("initial content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/test.txt?overwrite=false", data=b"new content")
+
+    assert resp.status == 409
+
+    # Verify original content unchanged
+    with open(tmp_path / "test.txt", "r") as f:
+        assert f.read() == "initial content"
+
+
+async def test_post_userdata_full_info(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    content = b"test content"
+    resp = await client.post("/userdata/test.txt?full_info=true", data=content)
+
+    assert resp.status == 200
+    result = await resp.json()
+    assert result["path"] == "test.txt"
+    assert result["size"] == len(content)
+    assert "modified" in result
+
+
+async def test_move_userdata(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt")
+
+    assert resp.status == 200
+    assert await resp.text() == '"dest.txt"'
+
+    # Verify file was moved
+    assert not os.path.exists(tmp_path / "source.txt")
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "test content"
+
+
+async def test_move_userdata_no_overwrite(aiohttp_client, app, tmp_path):
+    # Create source and destination files
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("source content")
+    with open(tmp_path / "dest.txt", "w") as f:
+        f.write("destination content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt?overwrite=false")
+
+    assert resp.status == 409
+
+    # Verify files remain unchanged
+    with open(tmp_path / "source.txt", "r") as f:
+        assert f.read() == "source content"
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "destination content"
+
+
+async def test_move_userdata_full_info(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt?full_info=true")
+
+    assert resp.status == 200
+    result = await resp.json()
+    assert result["path"] == "dest.txt"
+    assert result["size"] == len("test content")
+    assert "modified" in result
+
+    # Verify file was moved
+    assert not os.path.exists(tmp_path / "source.txt")
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "test content"

tests-unit/server/routes/internal_routes_test.py

@@ -8,7 +8,7 @@ from folder_paths import models_dir, user_directory, output_directory
 
 @pytest.fixture
 def internal_routes():
-    return InternalRoutes()
+    return InternalRoutes(None)
 
 @pytest.fixture
 def aiohttp_client_factory(aiohttp_client, internal_routes):
@@ -102,7 +102,7 @@ async def test_file_service_initialization():
         # Create a mock instance
         mock_file_service_instance = MagicMock(spec=FileService)
         MockFileService.return_value = mock_file_service_instance
-        internal_routes = InternalRoutes()
+        internal_routes = InternalRoutes(None)
 
         # Check if FileService was initialized with the correct parameters
         MockFileService.assert_called_once_with({
@@ -112,4 +112,4 @@ async def test_file_service_initialization():
         })
 
         # Verify that the file_service attribute of InternalRoutes is set
-        assert internal_routes.file_service == mock_file_service_instance
+        assert internal_routes.file_service == mock_file_service_instance

web/assets/ExtensionPanel-CfMfcLgI.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, bK 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, bL as script$1, A as createBaseVNode, x as createBlock, M as Fragment, N as renderList, am as toDisplayString, ap as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-CgU1oKZt.js";
-import { s as script, a as script$2, b as script$3 } from "./index-DBWDcZsl.js";
-import "./index-DYEEBf64.js";
+import { d as defineComponent, c6 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, bT as script$1, A as createBaseVNode, x as createBlock, N as Fragment, O as renderList, a6 as toDisplayString, aw as createTextVNode, bR as script$3, j as createCommentVNode, D as script$4 } from "./index-B6dYHNhg.js";
+import { s as script, a as script$2 } from "./index-CjwCGacA.js";
+import "./index-MX9DEi8Q.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-DZLYjWBj.js.map
+//# sourceMappingURL=ExtensionPanel-CfMfcLgI.js.map

web/assets/ExtensionPanel-CfMfcLgI.js.map

@@ -1 +1 @@
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+{"version":3,"file":"ExtensionPanel-CfMfcLgI.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;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/GraphView-CghYAxkP.css

@@ -1,13 +1,13 @@
 
-.group-title-editor.node-title-editor[data-v-fc3f26e3] {
+.group-title-editor.node-title-editor[data-v-8a100d5a] {
   z-index: 9999;
   padding: 0.25rem;
 }
-[data-v-fc3f26e3] .editable-text {
+[data-v-8a100d5a] .editable-text {
   width: 100%;
   height: 100%;
 }
-[data-v-fc3f26e3] .editable-text input {
+[data-v-8a100d5a] .editable-text input {
   width: 100%;
   height: 100%;
   /* Override the default font size */
@@ -45,7 +45,7 @@
   --sidebar-icon-size: 1rem;
 }
 
-.side-tool-bar-container[data-v-b6bfc188] {
+.side-tool-bar-container[data-v-e0812a25] {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -58,36 +58,43 @@
   background-color: var(--comfy-menu-bg);
   color: var(--fg-color);
 }
-.side-tool-bar-end[data-v-b6bfc188] {
+.side-tool-bar-end[data-v-e0812a25] {
   align-self: flex-end;
   margin-top: auto;
 }
 
-.p-splitter-gutter {
+[data-v-7c3279c1] .p-splitter-gutter {
   pointer-events: auto;
 }
-.gutter-hidden {
-  display: none !important;
+[data-v-7c3279c1] .p-splitter-gutter:hover,[data-v-7c3279c1] .p-splitter-gutter[data-p-gutter-resizing='true'] {
+  transition: background-color 0.2s ease 300ms;
+  background-color: var(--p-primary-color);
 }
-
-.side-bar-panel[data-v-b9df3042] {
+.side-bar-panel[data-v-7c3279c1] {
   background-color: var(--bg-color);
   pointer-events: auto;
 }
-.splitter-overlay[data-v-b9df3042] {
-  width: 100%;
-  height: 100%;
-  position: absolute;
-  top: 0;
-  left: 0;
-  background-color: transparent;
+.bottom-panel[data-v-7c3279c1] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.splitter-overlay[data-v-7c3279c1] {
   pointer-events: none;
+  border-style: none;
+  background-color: transparent;
+}
+.splitter-overlay-root[data-v-7c3279c1] {
+  position: absolute;
+  top: 0px;
+  left: 0px;
+  height: 100%;
+  width: 100%;
+
   /* Set it the same as the ComfyUI menu */
   /* Note: Lite-graph DOM widgets have the same z-index as the node id, so
   999 should be sufficient to make sure splitter overlays on node's DOM
   widgets */
   z-index: 999;
-  border: none;
 }
 
 [data-v-37f672ab] .highlight {
@@ -143,7 +150,7 @@
   align-items: flex-start !important;
 }
 
-.node-tooltip[data-v-79ec8c53] {
+.node-tooltip[data-v-c2e0098f] {
   background: var(--comfy-input-bg);
   border-radius: 5px;
   box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
@@ -159,22 +166,28 @@
   z-index: 99999;
 }
 
-.p-buttongroup-vertical[data-v-444d3768] {
+.p-buttongroup-vertical[data-v-94481f39] {
   display: flex;
   flex-direction: column;
   border-radius: var(--p-button-border-radius);
   overflow: hidden;
   border: 1px solid var(--p-panel-border-color);
 }
-.p-buttongroup-vertical .p-button[data-v-444d3768] {
+.p-buttongroup-vertical .p-button[data-v-94481f39] {
   margin: 0;
   border-radius: 0;
 }
 
-[data-v-84e785b8] .p-togglebutton::before {
+.comfy-menu-hamburger[data-v-2ddd26e8] {
+  pointer-events: auto;
+  position: fixed;
+  z-index: 9999;
+}
+
+[data-v-9eb975c3] .p-togglebutton::before {
   display: none
 }
-[data-v-84e785b8] .p-togglebutton {
+[data-v-9eb975c3] .p-togglebutton {
   position: relative;
   flex-shrink: 0;
   border-radius: 0px;
@@ -182,14 +195,14 @@
   padding-left: 0.5rem;
   padding-right: 0.5rem
 }
-[data-v-84e785b8] .p-togglebutton.p-togglebutton-checked {
+[data-v-9eb975c3] .p-togglebutton.p-togglebutton-checked {
   border-bottom-width: 2px;
   border-bottom-color: var(--p-button-text-primary-color)
 }
-[data-v-84e785b8] .p-togglebutton-checked .close-button,[data-v-84e785b8] .p-togglebutton:hover .close-button {
+[data-v-9eb975c3] .p-togglebutton-checked .close-button,[data-v-9eb975c3] .p-togglebutton:hover .close-button {
   visibility: visible
 }
-.status-indicator[data-v-84e785b8] {
+.status-indicator[data-v-9eb975c3] {
   position: absolute;
   font-weight: 700;
   font-size: 1.5rem;
@@ -197,10 +210,10 @@
   left: 50%;
   transform: translate(-50%, -50%)
 }
-[data-v-84e785b8] .p-togglebutton:hover .status-indicator {
+[data-v-9eb975c3] .p-togglebutton:hover .status-indicator {
   display: none
 }
-[data-v-84e785b8] .p-togglebutton .close-button {
+[data-v-9eb975c3] .p-togglebutton .close-button {
   visibility: hidden
 }
 
@@ -223,35 +236,35 @@
   border-bottom-left-radius: 0;
 }
 
-.comfyui-queue-button[data-v-2b80bf74] .p-splitbutton-dropdown {
+.comfyui-queue-button[data-v-95bc9be0] .p-splitbutton-dropdown {
   border-top-right-radius: 0;
   border-bottom-right-radius: 0;
 }
 
-.actionbar[data-v-2e54db00] {
+.actionbar[data-v-eb6e9acf] {
   pointer-events: all;
   position: fixed;
   z-index: 1000;
 }
-.actionbar.is-docked[data-v-2e54db00] {
+.actionbar.is-docked[data-v-eb6e9acf] {
   position: static;
   border-style: none;
   background-color: transparent;
   padding: 0px;
 }
-.actionbar.is-dragging[data-v-2e54db00] {
+.actionbar.is-dragging[data-v-eb6e9acf] {
   -webkit-user-select: none;
      -moz-user-select: none;
           user-select: none;
 }
-[data-v-2e54db00] .p-panel-content {
+[data-v-eb6e9acf] .p-panel-content {
   padding: 0.25rem;
 }
-[data-v-2e54db00] .p-panel-header {
+[data-v-eb6e9acf] .p-panel-header {
   display: none;
 }
 
-.comfyui-menu[data-v-b13fdc92] {
+.comfyui-menu[data-v-d84a704d] {
   width: 100vw;
   background: var(--comfy-menu-bg);
   color: var(--fg-color);
@@ -263,13 +276,13 @@
   grid-column: 1/-1;
   max-height: 90vh;
 }
-.comfyui-menu.dropzone[data-v-b13fdc92] {
+.comfyui-menu.dropzone[data-v-d84a704d] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-b13fdc92] {
+.comfyui-menu.dropzone-active[data-v-d84a704d] {
   background: var(--p-highlight-background-focus);
 }
-.comfyui-logo[data-v-b13fdc92] {
+.comfyui-logo[data-v-d84a704d] {
   font-size: 1.2em;
   -webkit-user-select: none;
      -moz-user-select: none;

web/assets/InstallView-CN3CA9Fk.css

@@ -0,0 +1,4 @@
+
+[data-v-53e62b05] .p-steppanel {
+    background-color: transparent
+}

web/assets/KeybindingPanel-BNYKhW1k.css

@@ -1,8 +0,0 @@
-
-[data-v-e5724e4d] .p-datatable-tbody > tr > td {
-  padding: 1px;
-  min-height: 2rem;
-}
-[data-v-e5724e4d] .p-datatable-row-selected .actions,[data-v-e5724e4d] .p-datatable-selectable-row:hover .actions {
-  visibility: visible;
-}

web/assets/KeybindingPanel-CB_wEOHl.css

@@ -0,0 +1,8 @@
+
+[data-v-2d8b3a76] .p-datatable-tbody > tr > td {
+  padding: 0.25rem;
+  min-height: 2rem
+}
+[data-v-2d8b3a76] .p-datatable-row-selected .actions,[data-v-2d8b3a76] .p-datatable-selectable-row:hover .actions {
+  visibility: visible
+}

web/assets/KeybindingPanel-DcEfyPZZ.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, ap as createTextVNode, am as toDisplayString, z as unref, at as script, j as createCommentVNode, r as ref, bH as FilterMatchMode, K as useKeybindingStore, F as useCommandStore, aC as watchEffect, aZ as useToast, t as resolveDirective, bI as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, af as script$4, b2 as withModifiers, aA as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, by as KeyComboImpl, bJ as KeybindingImpl, _ as _export_sfc } from "./index-CgU1oKZt.js";
-import { s as script$1, a as script$3, b as script$5 } from "./index-DBWDcZsl.js";
-import "./index-DYEEBf64.js";
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, c3 as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, be as useToast, t as resolveDirective, c4 as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ao as script$4, bi as withModifiers, bR as script$5, aH as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, b$ as KeyComboImpl, c5 as KeybindingImpl, _ as _export_sfc } from "./index-B6dYHNhg.js";
+import { s as script$1, a as script$3 } from "./index-CjwCGacA.js";
+import "./index-MX9DEi8Q.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -35,10 +35,11 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-e5724e4d"), n = n(), popScopeId(), n), "_withScopeId");
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2d8b3a76"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "keybinding-panel" };
-const _hoisted_2 = { class: "actions invisible" };
-const _hoisted_3 = { key: 1 };
+const _hoisted_2 = { class: "actions invisible flex flex-row" };
+const _hoisted_3 = ["title"];
+const _hoisted_4 = { key: 1 };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "KeybindingPanel",
   setup(__props) {
@@ -177,7 +178,16 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
             createVNode(unref(script$1), {
               field: "id",
               header: "Command ID",
-              sortable: ""
+              sortable: "",
+              class: "max-w-64 2xl:max-w-full"
+            }, {
+              body: withCtx((slotProps) => [
+                createBaseVNode("div", {
+                  class: "overflow-hidden text-ellipsis whitespace-nowrap",
+                  title: slotProps.data.id
+                }, toDisplayString(slotProps.data.id), 9, _hoisted_3)
+              ]),
+              _: 1
             }),
             createVNode(unref(script$1), {
               field: "keybinding",
@@ -188,7 +198,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   key: 0,
                   keyCombo: slotProps.data.keybinding.combo,
                   isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
-                }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
+                }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_4, "-"))
               ]),
               _: 1
             })
@@ -257,8 +267,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e5724e4d"]]);
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-2d8b3a76"]]);
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-YkUFoiMw.js.map
+//# sourceMappingURL=KeybindingPanel-DcEfyPZZ.js.map

web/assets/ServerStartView-e57oVZ6V.js

@@ -0,0 +1,102 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, r as ref, o as onMounted, w as watch, I as onBeforeUnmount, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, A as createBaseVNode, a6 as toDisplayString, z as unref, bK as script, bL as electronAPI } from "./index-B6dYHNhg.js";
+import { t, s } from "./index-B4gmhi99.js";
+const _hoisted_1$1 = { class: "p-terminal rounded-none h-full w-full" };
+const _hoisted_2$1 = { class: "px-4 whitespace-pre-wrap" };
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "LogTerminal",
+  props: {
+    fetchLogs: { type: Function },
+    fetchInterval: {}
+  },
+  setup(__props) {
+    const props = __props;
+    const log = ref("");
+    const scrollPanelRef = ref(null);
+    const scrolledToBottom = ref(false);
+    let intervalId = 0;
+    onMounted(async () => {
+      const element = scrollPanelRef.value?.$el;
+      const scrollContainer = element?.querySelector(".p-scrollpanel-content");
+      if (scrollContainer) {
+        scrollContainer.addEventListener("scroll", () => {
+          scrolledToBottom.value = scrollContainer.scrollTop + scrollContainer.clientHeight === scrollContainer.scrollHeight;
+        });
+      }
+      const scrollToBottom = /* @__PURE__ */ __name(() => {
+        if (scrollContainer) {
+          scrollContainer.scrollTop = scrollContainer.scrollHeight;
+        }
+      }, "scrollToBottom");
+      watch(log, () => {
+        if (scrolledToBottom.value) {
+          scrollToBottom();
+        }
+      });
+      const fetchLogs = /* @__PURE__ */ __name(async () => {
+        log.value = await props.fetchLogs();
+      }, "fetchLogs");
+      await fetchLogs();
+      scrollToBottom();
+      intervalId = window.setInterval(fetchLogs, props.fetchInterval);
+    });
+    onBeforeUnmount(() => {
+      window.clearInterval(intervalId);
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$1, [
+        createVNode(unref(script), {
+          class: "h-full w-full",
+          ref_key: "scrollPanelRef",
+          ref: scrollPanelRef
+        }, {
+          default: withCtx(() => [
+            createBaseVNode("pre", _hoisted_2$1, toDisplayString(log.value), 1)
+          ]),
+          _: 1
+        }, 512)
+      ]);
+    };
+  }
+});
+const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
+const _hoisted_2 = { class: "text-2xl font-bold" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerStartView",
+  setup(__props) {
+    const electron = electronAPI();
+    const status = ref(t.INITIAL_STATE);
+    const logs = ref([]);
+    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
+      status.value = newStatus;
+      logs.value = [];
+    }, "updateProgress");
+    const addLogMessage = /* @__PURE__ */ __name((message) => {
+      logs.value = [...logs.value, message];
+    }, "addLogMessage");
+    const fetchLogs = /* @__PURE__ */ __name(async () => {
+      return logs.value.join("\n");
+    }, "fetchLogs");
+    onMounted(() => {
+      electron.sendReady();
+      electron.onProgressUpdate(updateProgress);
+      electron.onLogMessage((message) => {
+        addLogMessage(message);
+      });
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createBaseVNode("h2", _hoisted_2, toDisplayString(unref(s)[status.value]), 1),
+        createVNode(_sfc_main$1, {
+          "fetch-logs": fetchLogs,
+          "fetch-interval": 500
+        })
+      ]);
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ServerStartView-e57oVZ6V.js.map

web/assets/ServerStartView-e57oVZ6V.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"ServerStartView-e57oVZ6V.js","sources":["../../src/components/common/LogTerminal.vue","../../src/views/ServerStartView.vue"],"sourcesContent":["<!-- A simple read-only terminal component that displays logs. -->\n<template>\n  <div class=\"p-terminal rounded-none h-full w-full\">\n    <ScrollPanel class=\"h-full w-full\" ref=\"scrollPanelRef\">\n      <pre class=\"px-4 whitespace-pre-wrap\">{{ log }}</pre>\n    </ScrollPanel>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport ScrollPanel from 'primevue/scrollpanel'\nimport { onBeforeUnmount, onMounted, ref, watch } from 'vue'\n\nconst props = defineProps<{\n  fetchLogs: () => Promise<string>\n  fetchInterval: number\n}>()\n\nconst log = ref<string>('')\nconst scrollPanelRef = ref<InstanceType<typeof ScrollPanel> | null>(null)\n/**\n * Whether the user has scrolled to the bottom of the terminal.\n * This is used to prevent the terminal from scrolling to the bottom\n * when new logs are fetched.\n */\nconst scrolledToBottom = ref(false)\n\nlet intervalId: number = 0\n\nonMounted(async () => {\n  const element = scrollPanelRef.value?.$el\n  const scrollContainer = element?.querySelector('.p-scrollpanel-content')\n\n  if (scrollContainer) {\n    scrollContainer.addEventListener('scroll', () => {\n      scrolledToBottom.value =\n        scrollContainer.scrollTop + scrollContainer.clientHeight ===\n        scrollContainer.scrollHeight\n    })\n  }\n\n  const scrollToBottom = () => {\n    if (scrollContainer) {\n      scrollContainer.scrollTop = scrollContainer.scrollHeight\n    }\n  }\n\n  watch(log, () => {\n    if (scrolledToBottom.value) {\n      scrollToBottom()\n    }\n  })\n\n  const fetchLogs = async () => {\n    log.value = await props.fetchLogs()\n  }\n\n  await fetchLogs()\n  scrollToBottom()\n  intervalId = window.setInterval(fetchLogs, props.fetchInterval)\n})\n\nonBeforeUnmount(() => {\n  window.clearInterval(intervalId)\n})\n</script>\n","<template>\n  <div\n    class=\"font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto\"\n  >\n    <h2 class=\"text-2xl font-bold\">{{ ProgressMessages[status] }}</h2>\n    <LogTerminal :fetch-logs=\"fetchLogs\" :fetch-interval=\"500\" />\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, onMounted } from 'vue'\nimport LogTerminal from '@/components/common/LogTerminal.vue'\nimport {\n  ProgressStatus,\n  ProgressMessages\n} from '@comfyorg/comfyui-electron-types'\nimport { electronAPI } from '@/utils/envUtil'\n\nconst electron = electronAPI()\n\nconst status = ref<ProgressStatus>(ProgressStatus.INITIAL_STATE)\nconst logs = ref<string[]>([])\n\nconst updateProgress = ({ status: newStatus }: { status: ProgressStatus }) => {\n  status.value = newStatus\n  logs.value = [] // Clear logs when status changes\n}\n\nconst addLogMessage = (message: string) => {\n  logs.value = [...logs.value, message]\n}\n\nconst fetchLogs = async () => {\n  return logs.value.join('\\n')\n}\n\nonMounted(() => {\n  electron.sendReady()\n  electron.onProgressUpdate(updateProgress)\n  electron.onLogMessage((message: string) => {\n    addLogMessage(message)\n  })\n})\n</script>\n"],"names":["ProgressStatus"],"mappings":";;;;;;;;;;;;;AAaA,UAAM,QAAQ;AAKR,UAAA,MAAM,IAAY,EAAE;AACpB,UAAA,iBAAiB,IAA6C,IAAI;AAMlE,UAAA,mBAAmB,IAAI,KAAK;AAElC,QAAI,aAAqB;AAEzB,cAAU,YAAY;AACd,YAAA,UAAU,eAAe,OAAO;AAChC,YAAA,kBAAkB,SAAS,cAAc,wBAAwB;AAEvE,UAAI,iBAAiB;AACH,wBAAA,iBAAiB,UAAU,MAAM;AAC/C,2BAAiB,QACf,gBAAgB,YAAY,gBAAgB,iBAC5C,gBAAgB;AAAA,QAAA,CACnB;AAAA,MACH;AAEA,YAAM,iBAAiB,6BAAM;AAC3B,YAAI,iBAAiB;AACnB,0BAAgB,YAAY,gBAAgB;AAAA,QAC9C;AAAA,MAAA,GAHqB;AAMvB,YAAM,KAAK,MAAM;AACf,YAAI,iBAAiB,OAAO;AACX;QACjB;AAAA,MAAA,CACD;AAED,YAAM,YAAY,mCAAY;AACxB,YAAA,QAAQ,MAAM,MAAM,UAAU;AAAA,MAAA,GADlB;AAIlB,YAAM,UAAU;AACD;AACf,mBAAa,OAAO,YAAY,WAAW,MAAM,aAAa;AAAA,IAAA,CAC/D;AAED,oBAAgB,MAAM;AACpB,aAAO,cAAc,UAAU;AAAA,IAAA,CAChC;;;;;;;;;;;;;;;;;;;;;;AC9CD,UAAM,WAAW;AAEX,UAAA,SAAS,IAAoBA,EAAe,aAAa;AACzD,UAAA,OAAO,IAAc,CAAA,CAAE;AAE7B,UAAM,iBAAiB,wBAAC,EAAE,QAAQ,gBAA4C;AAC5E,aAAO,QAAQ;AACf,WAAK,QAAQ;IAAC,GAFO;AAKjB,UAAA,gBAAgB,wBAAC,YAAoB;AACzC,WAAK,QAAQ,CAAC,GAAG,KAAK,OAAO,OAAO;AAAA,IAAA,GADhB;AAItB,UAAM,YAAY,mCAAY;AACrB,aAAA,KAAK,MAAM,KAAK,IAAI;AAAA,IAAA,GADX;AAIlB,cAAU,MAAM;AACd,eAAS,UAAU;AACnB,eAAS,iBAAiB,cAAc;AAC/B,eAAA,aAAa,CAAC,YAAoB;AACzC,sBAAc,OAAO;AAAA,MAAA,CACtB;AAAA,IAAA,CACF;;;;;;;;;;;;"}

web/assets/WelcomeView-DQQgHnsr.css

@@ -0,0 +1,36 @@
+
+.animated-gradient-text[data-v-12b8b11b] {
+  font-weight: 700;
+  font-size: clamp(2rem, 8vw, 4rem);
+  background: linear-gradient(to right, #12c2e9, #c471ed, #f64f59, #12c2e9);
+  background-size: 300% auto;
+  background-clip: text;
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  animation: gradient-12b8b11b 8s linear infinite;
+}
+.text-glow[data-v-12b8b11b] {
+  filter: drop-shadow(0 0 8px rgba(255, 255, 255, 0.3));
+}
+@keyframes gradient-12b8b11b {
+0% {
+    background-position: 0% center;
+}
+100% {
+    background-position: 300% center;
+}
+}
+.fade-in-up[data-v-12b8b11b] {
+  animation: fadeInUp-12b8b11b 1.5s ease-out;
+  animation-fill-mode: both;
+}
+@keyframes fadeInUp-12b8b11b {
+0% {
+    opacity: 0;
+    transform: translateY(20px);
+}
+100% {
+    opacity: 1;
+    transform: translateY(0);
+}
+}

web/assets/WelcomeView-DT4bj-QV.js

@@ -0,0 +1,33 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, g as openBlock, h as createElementBlock, A as createBaseVNode, a6 as toDisplayString, i as createVNode, z as unref, D as script, P as pushScopeId, Q as popScopeId, _ as _export_sfc } from "./index-B6dYHNhg.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-12b8b11b"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
+const _hoisted_2 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
+const _hoisted_3 = { class: "animated-gradient-text text-glow select-none" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "WelcomeView",
+  setup(__props) {
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createBaseVNode("div", _hoisted_2, [
+          createBaseVNode("h1", _hoisted_3, toDisplayString(_ctx.$t("welcome.title")), 1),
+          createVNode(unref(script), {
+            label: _ctx.$t("welcome.getStarted"),
+            icon: "pi pi-arrow-right",
+            iconPos: "right",
+            size: "large",
+            rounded: "",
+            onClick: _cache[0] || (_cache[0] = ($event) => _ctx.$router.push("/install")),
+            class: "p-4 text-lg fade-in-up"
+          }, null, 8, ["label"])
+        ])
+      ]);
+    };
+  }
+});
+const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-12b8b11b"]]);
+export {
+  WelcomeView as default
+};
+//# sourceMappingURL=WelcomeView-DT4bj-QV.js.map

web/assets/WelcomeView-DT4bj-QV.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"WelcomeView-DT4bj-QV.js","sources":[],"sourcesContent":[],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/index-B1vRdV2i.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bu as ComfyDialog, bv as $el, bw as ComfyApp, c as app, k as LiteGraph, aP as LGraphCanvas, bx as DraggableList, a_ as useToastStore, ax as useNodeDefStore, bq as api, L as LGraphGroup, by as KeyComboImpl, K as useKeybindingStore, F as useCommandStore, e as LGraphNode, bz as ComfyWidgets, bA as applyTextReplacements, av as NodeSourceType, bB as NodeBadgeMode, u as useSettingStore, q as computed, bC as getColorPalette, w as watch, bD as BadgePosition, aR as LGraphBadge, bE as _, bF as defaultColorPalette } from "./index-CgU1oKZt.js";
-import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-DNVvusS1.js";
+import { bV as ComfyDialog, bW as $el, bX as ComfyApp, c as app, k as LiteGraph, b2 as LGraphCanvas, bY as DraggableList, bf as useToastStore, bZ as serialise, aE as useNodeDefStore, b_ as deserialiseAndCreate, bH as api, L as LGraphGroup, b$ as KeyComboImpl, M as useKeybindingStore, F as useCommandStore, e as LGraphNode, c0 as ComfyWidgets, c1 as applyTextReplacements } from "./index-B6dYHNhg.js";
+import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-BJ21PG7d.js";
 class ClipspaceDialog extends ComfyDialog {
   static {
     __name(this, "ClipspaceDialog");
@@ -37,7 +37,9 @@ class ClipspaceDialog extends ComfyDialog {
         ...self.createButtons()
       ]);
       if (self.element) {
-        self.element.removeChild(self.element.firstChild);
+        if (self.element.firstChild) {
+          self.element.removeChild(self.element.firstChild);
+        }
         self.element.appendChild(children);
       } else {
         self.element = $el("div.comfy-modal", { parent: document.body }, [
@@ -76,7 +78,7 @@ class ClipspaceDialog extends ComfyDialog {
     return buttons;
   }
   createImgSettings() {
-    if (ComfyApp.clipspace.imgs) {
+    if (ComfyApp.clipspace?.imgs) {
       const combo_items = [];
       const imgs = ComfyApp.clipspace.imgs;
       for (let i = 0; i < imgs.length; i++) {
@@ -87,8 +89,10 @@ class ClipspaceDialog extends ComfyDialog {
         {
           id: "clipspace_img_selector",
           onchange: /* @__PURE__ */ __name((event) => {
-            ComfyApp.clipspace["selectedIndex"] = event.target.selectedIndex;
-            ClipspaceDialog.invalidatePreview();
+            if (event.target && ComfyApp.clipspace) {
+              ComfyApp.clipspace["selectedIndex"] = event.target.selectedIndex;
+              ClipspaceDialog.invalidatePreview();
+            }
           }, "onchange")
         },
         combo_items
@@ -102,7 +106,9 @@ class ClipspaceDialog extends ComfyDialog {
         {
           id: "clipspace_img_paste_mode",
           onchange: /* @__PURE__ */ __name((event) => {
-            ComfyApp.clipspace["img_paste_mode"] = event.target.value;
+            if (event.target && ComfyApp.clipspace) {
+              ComfyApp.clipspace["img_paste_mode"] = event.target.value;
+            }
           }, "onchange")
         },
         [
@@ -127,7 +133,7 @@ class ClipspaceDialog extends ComfyDialog {
     }
   }
   createImgPreview() {
-    if (ComfyApp.clipspace.imgs) {
+    if (ComfyApp.clipspace?.imgs) {
       return $el("img", { id: "clipspace_preview", ondragstart: /* @__PURE__ */ __name(() => false, "ondragstart") });
     } else return [];
   }
@@ -154,7 +160,7 @@ app.registerExtension({
 window.comfyAPI = window.comfyAPI || {};
 window.comfyAPI.clipspace = window.comfyAPI.clipspace || {};
 window.comfyAPI.clipspace.ClipspaceDialog = ClipspaceDialog;
-const ext$2 = {
+const ext$1 = {
   name: "Comfy.ContextMenuFilter",
   init() {
     const ctxMenu = LiteGraph.ContextMenu;
@@ -172,10 +178,10 @@ const ext$2 = {
         let itemCount = displayedItems.length;
         requestAnimationFrame(() => {
           const currentNode = LGraphCanvas.active_canvas.current_node;
-          const clickedComboValue = currentNode.widgets?.filter(
-            (w) => w.type === "combo" && w.options.values.length === values.length
+          const clickedComboValue = currentNode?.widgets?.filter(
+            (w) => w.type === "combo" && w.options.values?.length === values.length
           ).find(
-            (w) => w.options.values.every((v, i) => v === values[i])
+            (w) => w.options.values?.every((v, i) => v === values[i])
           )?.value;
           let selectedIndex = clickedComboValue ? values.findIndex((v) => v === clickedComboValue) : 0;
           if (selectedIndex < 0) {
@@ -244,7 +250,7 @@ const ext$2 = {
           filter.addEventListener("input", () => {
             const term = filter.value.toLocaleLowerCase();
             displayedItems = items.filter((item) => {
-              const isVisible = !term || item.textContent.toLocaleLowerCase().includes(term);
+              const isVisible = !term || item.textContent?.toLocaleLowerCase().includes(term);
               item.style.display = isVisible ? "block" : "none";
               return isVisible;
             });
@@ -278,7 +284,7 @@ const ext$2 = {
     LiteGraph.ContextMenu.prototype = ctxMenu.prototype;
   }
 };
-app.registerExtension(ext$2);
+app.registerExtension(ext$1);
 function stripComments(str) {
   return str.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, "");
 }
@@ -960,17 +966,13 @@ class GroupNodeBuilder {
         }
       }
     }, "storeExternalLinks");
-    const backup = localStorage.getItem("litegrapheditor_clipboard");
     try {
-      app.canvas.copyToClipboard(this.nodes);
-      const config = JSON.parse(
-        localStorage.getItem("litegrapheditor_clipboard")
-      );
+      const serialised = serialise(this.nodes, app.canvas.graph);
+      const config = JSON.parse(serialised);
       storeLinkTypes(config);
       storeExternalLinks(config);
       return config;
     } finally {
-      localStorage.setItem("litegrapheditor_clipboard", backup);
     }
   }
 }
@@ -1224,7 +1226,7 @@ class GroupNodeConfig {
   checkPrimitiveConnection(link, inputName, inputs) {
     const sourceNode = this.nodeData.nodes[link[0]];
     if (sourceNode.type === "PrimitiveNode") {
-      const [sourceNodeId, _2, targetNodeId, __] = link;
+      const [sourceNodeId, _, targetNodeId, __] = link;
       const primitiveDef = this.primitiveDefs[sourceNodeId];
       const targetWidget = inputs[inputName];
       const primitiveConfig = primitiveDef.input.required.value;
@@ -1511,7 +1513,6 @@ class GroupNodeHandler {
     };
     this.node.convertToNodes = () => {
       const addInnerNodes = /* @__PURE__ */ __name(() => {
-        const backup = localStorage.getItem("litegrapheditor_clipboard");
         const c = { ...this.groupData.nodeData };
         c.nodes = [...c.nodes];
         const innerNodes = this.node.getInnerNodes();
@@ -1525,9 +1526,7 @@ class GroupNodeHandler {
           }
           c.nodes[i] = { ...c.nodes[i], id: id2 };
         }
-        localStorage.setItem("litegrapheditor_clipboard", JSON.stringify(c));
-        app.canvas.pasteFromClipboard();
-        localStorage.setItem("litegrapheditor_clipboard", backup);
+        deserialiseAndCreate(JSON.stringify(c), app.canvas);
         const [x, y] = this.node.pos;
         let top;
         let left;
@@ -1574,10 +1573,8 @@ class GroupNodeHandler {
           }
         }
         for (const newNode of newNodes2) {
-          newNode.pos = [
-            newNode.pos[0] - (left - x),
-            newNode.pos[1] - (top - y)
-          ];
+          newNode.pos[0] -= left - x;
+          newNode.pos[1] -= top - y;
         }
         return { newNodes: newNodes2, selectedIds: selectedIds2 };
       }, "addInnerNodes");
@@ -1612,14 +1609,16 @@ class GroupNodeHandler {
           }
         }
       }, "reconnectOutputs");
+      app.canvas.emitBeforeChange();
       const { newNodes, selectedIds } = addInnerNodes();
       reconnectInputs(selectedIds);
       reconnectOutputs(selectedIds);
       app.graph.remove(this.node);
+      app.canvas.emitAfterChange();
       return newNodes;
     };
     const getExtraMenuOptions = this.node.getExtraMenuOptions;
-    this.node.getExtraMenuOptions = function(_2, options) {
+    this.node.getExtraMenuOptions = function(_, options) {
       getExtraMenuOptions?.apply(this, arguments);
       let optionIndex = options.findIndex((o) => o.content === "Outputs");
       if (optionIndex === -1) optionIndex = options.length;
@@ -1793,7 +1792,7 @@ class GroupNodeHandler {
       } else if (innerNode.type === "Reroute") {
         const rerouteLinks = this.groupData.linksFrom[old.node.index];
         if (rerouteLinks) {
-          for (const [_2, , targetNodeId, targetSlot] of rerouteLinks["0"]) {
+          for (const [_, , targetNodeId, targetSlot] of rerouteLinks["0"]) {
             const node = this.innerNodes[targetNodeId];
             const input = node.inputs[targetSlot];
             if (input.widget) {
@@ -2024,10 +2023,10 @@ function manageGroupNodes() {
   new ManageGroupDialog(app).show();
 }
 __name(manageGroupNodes, "manageGroupNodes");
-const id$3 = "Comfy.GroupNode";
+const id$2 = "Comfy.GroupNode";
 let globalDefs;
-const ext$1 = {
-  name: id$3,
+const ext = {
+  name: id$2,
   commands: [
     {
       id: "Comfy.GroupNode.ConvertSelectedNodesToGroupNode",
@@ -2097,56 +2096,18 @@ const ext$1 = {
     }
   }
 };
-app.registerExtension(ext$1);
+app.registerExtension(ext);
 window.comfyAPI = window.comfyAPI || {};
 window.comfyAPI.groupNode = window.comfyAPI.groupNode || {};
 window.comfyAPI.groupNode.GroupNodeConfig = GroupNodeConfig;
 window.comfyAPI.groupNode.GroupNodeHandler = GroupNodeHandler;
 function setNodeMode(node, mode) {
   node.mode = mode;
-  node.graph.change();
+  node.graph?.change();
 }
 __name(setNodeMode, "setNodeMode");
-function addNodesToGroup(group, nodes = []) {
-  var x1, y1, x2, y2;
-  var nx1, ny1, nx2, ny2;
-  var node;
-  x1 = y1 = x2 = y2 = -1;
-  nx1 = ny1 = nx2 = ny2 = -1;
-  for (var n of [group.nodes, nodes]) {
-    for (var i in n) {
-      node = n[i];
-      nx1 = node.pos[0];
-      ny1 = node.pos[1];
-      nx2 = node.pos[0] + node.size[0];
-      ny2 = node.pos[1] + node.size[1];
-      if (node.type != "Reroute") {
-        ny1 -= LiteGraph.NODE_TITLE_HEIGHT;
-      }
-      if (node.flags?.collapsed) {
-        ny2 = ny1 + LiteGraph.NODE_TITLE_HEIGHT;
-        if (node?._collapsed_width) {
-          nx2 = nx1 + Math.round(node._collapsed_width);
-        }
-      }
-      if (x1 == -1 || nx1 < x1) {
-        x1 = nx1;
-      }
-      if (y1 == -1 || ny1 < y1) {
-        y1 = ny1;
-      }
-      if (x2 == -1 || nx2 > x2) {
-        x2 = nx2;
-      }
-      if (y2 == -1 || ny2 > y2) {
-        y2 = ny2;
-      }
-    }
-  }
-  var padding = 10;
-  y1 = y1 - Math.round(group.font_size * 1.4);
-  group.pos = [x1 - padding, y1 - padding];
-  group.size = [x2 - x1 + padding * 2, y2 - y1 + padding * 2];
+function addNodesToGroup(group, items) {
+  group.resizeTo([...group.children, ...items]);
 }
 __name(addNodesToGroup, "addNodesToGroup");
 app.registerExtension({
@@ -2162,11 +2123,11 @@ app.registerExtension({
       if (!group) {
         options.push({
           content: "Add Group For Selected Nodes",
-          disabled: !Object.keys(app.canvas.selected_nodes || {}).length,
+          disabled: !this.selectedItems?.size,
           callback: /* @__PURE__ */ __name(() => {
             const group2 = new LGraphGroup();
-            addNodesToGroup(group2, this.selected_nodes);
-            app.canvas.graph.add(group2);
+            addNodesToGroup(group2, this.selectedItems);
+            this.graph.add(group2);
             this.graph.change();
           }, "callback")
         });
@@ -2176,9 +2137,9 @@ app.registerExtension({
       const nodesInGroup = group.nodes;
       options.push({
         content: "Add Selected Nodes To Group",
-        disabled: !Object.keys(app.canvas.selected_nodes || {}).length,
+        disabled: !this.selectedItems?.size,
         callback: /* @__PURE__ */ __name(() => {
-          addNodesToGroup(group, this.selected_nodes);
+          addNodesToGroup(group, this.selectedItems);
           this.graph.change();
         }, "callback")
       });
@@ -2197,7 +2158,8 @@ app.registerExtension({
       options.push({
         content: "Fit Group To Nodes",
         callback: /* @__PURE__ */ __name(() => {
-          addNodesToGroup(group);
+          group.recomputeInsideNodes();
+          group.resizeTo(group.children);
           this.graph.change();
         }, "callback")
       });
@@ -2323,9 +2285,9 @@ app.registerExtension({
     };
   }
 });
-const id$2 = "Comfy.InvertMenuScrolling";
+const id$1 = "Comfy.InvertMenuScrolling";
 app.registerExtension({
-  name: id$2,
+  name: id$1,
   init() {
     const ctxMenu = LiteGraph.ContextMenu;
     const replace = /* @__PURE__ */ __name(() => {
@@ -2341,7 +2303,7 @@ app.registerExtension({
       LiteGraph.ContextMenu.prototype = ctxMenu.prototype;
     }, "replace");
     app.ui.settings.addSetting({
-      id: id$2,
+      id: id$1,
       category: ["Comfy", "Graph", "InvertMenuScrolling"],
       name: "Invert Context Menu Scrolling",
       type: "boolean",
@@ -2373,8 +2335,8 @@ app.registerExtension({
       const commandStore = useCommandStore();
       const keybinding = keybindingStore.getKeybinding(keyCombo);
       if (keybinding && keybinding.targetSelector !== "#graph-canvas") {
-        await commandStore.execute(keybinding.commandId);
         event.preventDefault();
+        await commandStore.execute(keybinding.commandId);
         return;
       }
       if (event.ctrlKey || event.altKey || event.metaKey) {
@@ -2397,35 +2359,6 @@ app.registerExtension({
     window.addEventListener("keydown", keybindListener);
   }
 });
-const id$1 = "Comfy.LinkRenderMode";
-const ext = {
-  name: id$1,
-  async setup(app2) {
-    app2.ui.settings.addSetting({
-      id: id$1,
-      category: ["Comfy", "Graph", "LinkRenderMode"],
-      name: "Link Render Mode",
-      defaultValue: 2,
-      type: "combo",
-      options: [
-        { value: LiteGraph.STRAIGHT_LINK, text: "Straight" },
-        { value: LiteGraph.LINEAR_LINK, text: "Linear" },
-        { value: LiteGraph.SPLINE_LINK, text: "Spline" },
-        { value: LiteGraph.HIDDEN_LINK, text: "Hidden" }
-      ],
-      onChange(value) {
-        app2.canvas.links_render_mode = +value;
-        app2.canvas.setDirty(
-          /* fg */
-          false,
-          /* bg */
-          true
-        );
-      }
-    });
-  }
-};
-app.registerExtension(ext);
 function dataURLToBlob(dataURL) {
   const parts = dataURL.split(";base64,");
   const contentType = parts[0].split(":")[1];
@@ -3708,8 +3641,12 @@ app.registerExtension({
             clipboardAction(async () => {
               const data = JSON.parse(t.data);
               await GroupNodeConfig.registerFromWorkflow(data.groupNodes, {});
-              localStorage.setItem("litegrapheditor_clipboard", t.data);
-              app.canvas.pasteFromClipboard();
+              if (!data.reroutes) {
+                deserialiseAndCreate(t.data, app.canvas);
+              } else {
+                localStorage.setItem("litegrapheditor_clipboard", t.data);
+                app.canvas.pasteFromClipboard();
+              }
             });
           }, "callback")
         };
@@ -3934,7 +3871,7 @@ app.registerExtension({
         };
         this.isVirtualNode = true;
       }
-      getExtraMenuOptions(_2, options) {
+      getExtraMenuOptions(_, options) {
         options.unshift(
           {
             content: (this.properties.showOutputText ? "Hide" : "Show") + " Type",
@@ -4043,9 +3980,10 @@ let touchCount = 0;
 app.registerExtension({
   name: "Comfy.SimpleTouchSupport",
   setup() {
-    let zoomPos;
+    let touchDist;
     let touchTime;
     let lastTouch;
+    let lastScale;
     function getMultiTouchPos(e) {
       return Math.hypot(
         e.touches[0].clientX - e.touches[1].clientX,
@@ -4053,63 +3991,90 @@ app.registerExtension({
       );
     }
     __name(getMultiTouchPos, "getMultiTouchPos");
-    app.canvasEl.addEventListener(
+    function getMultiTouchCenter(e) {
+      return {
+        clientX: (e.touches[0].clientX + e.touches[1].clientX) / 2,
+        clientY: (e.touches[0].clientY + e.touches[1].clientY) / 2
+      };
+    }
+    __name(getMultiTouchCenter, "getMultiTouchCenter");
+    app.canvasEl.parentElement.addEventListener(
       "touchstart",
       (e) => {
         touchCount++;
         lastTouch = null;
+        lastScale = null;
         if (e.touches?.length === 1) {
           touchTime = /* @__PURE__ */ new Date();
           lastTouch = e.touches[0];
         } else {
           touchTime = null;
           if (e.touches?.length === 2) {
-            zoomPos = getMultiTouchPos(e);
+            lastScale = app.canvas.ds.scale;
+            lastTouch = getMultiTouchCenter(e);
+            touchDist = getMultiTouchPos(e);
             app.canvas.pointer_is_down = false;
           }
         }
       },
       true
     );
-    app.canvasEl.addEventListener("touchend", (e) => {
-      touchZooming = false;
-      touchCount = e.touches?.length ?? touchCount - 1;
+    app.canvasEl.parentElement.addEventListener("touchend", (e) => {
+      touchCount--;
+      if (e.touches?.length !== 1) touchZooming = false;
       if (touchTime && !e.touches?.length) {
         if ((/* @__PURE__ */ new Date()).getTime() - touchTime > 600) {
-          try {
-            e.constructor = CustomEvent;
-          } catch (error) {
+          if (e.target === app.canvasEl) {
+            app.canvasEl.dispatchEvent(
+              new PointerEvent("pointerdown", {
+                button: 2,
+                clientX: e.changedTouches[0].clientX,
+                clientY: e.changedTouches[0].clientY
+              })
+            );
+            e.preventDefault();
           }
-          e.clientX = lastTouch.clientX;
-          e.clientY = lastTouch.clientY;
-          app.canvas.pointer_is_down = true;
-          app.canvas._mousedown_callback(e);
         }
         touchTime = null;
       }
     });
-    app.canvasEl.addEventListener(
+    app.canvasEl.parentElement.addEventListener(
       "touchmove",
       (e) => {
         touchTime = null;
-        if (e.touches?.length === 2) {
+        if (e.touches?.length === 2 && lastTouch && !e.ctrlKey && !e.shiftKey) {
+          e.preventDefault();
           app.canvas.pointer_is_down = false;
           touchZooming = true;
-          LiteGraph.closeAllContextMenus();
+          LiteGraph.closeAllContextMenus(window);
           app.canvas.search_box?.close();
-          const newZoomPos = getMultiTouchPos(e);
-          const midX = (e.touches[0].clientX + e.touches[1].clientX) / 2;
-          const midY = (e.touches[0].clientY + e.touches[1].clientY) / 2;
-          let scale = app.canvas.ds.scale;
-          const diff = zoomPos - newZoomPos;
-          if (diff > 0.5) {
-            scale *= 1 / 1.07;
-          } else if (diff < -0.5) {
-            scale *= 1.07;
+          const newTouchDist = getMultiTouchPos(e);
+          const center = getMultiTouchCenter(e);
+          let scale = lastScale * newTouchDist / touchDist;
+          const newX = (center.clientX - lastTouch.clientX) / scale;
+          const newY = (center.clientY - lastTouch.clientY) / scale;
+          if (scale < app.canvas.ds.min_scale) {
+            scale = app.canvas.ds.min_scale;
+          } else if (scale > app.canvas.ds.max_scale) {
+            scale = app.canvas.ds.max_scale;
           }
-          app.canvas.ds.changeScale(scale, [midX, midY]);
+          const oldScale = app.canvas.ds.scale;
+          app.canvas.ds.scale = scale;
+          if (Math.abs(app.canvas.ds.scale - 1) < 0.01) {
+            app.canvas.ds.scale = 1;
+          }
+          const newScale = app.canvas.ds.scale;
+          const convertScaleToOffset = /* @__PURE__ */ __name((scale2) => [
+            center.clientX / scale2 - app.canvas.ds.offset[0],
+            center.clientY / scale2 - app.canvas.ds.offset[1]
+          ], "convertScaleToOffset");
+          var oldCenter = convertScaleToOffset(oldScale);
+          var newCenter = convertScaleToOffset(newScale);
+          app.canvas.ds.offset[0] += newX + newCenter[0] - oldCenter[0];
+          app.canvas.ds.offset[1] += newY + newCenter[1] - oldCenter[1];
+          lastTouch.clientX = center.clientX;
+          lastTouch.clientY = center.clientY;
           app.canvas.setDirty(true, true);
-          zoomPos = newZoomPos;
         }
       },
       true
@@ -4121,6 +4086,7 @@ LGraphCanvas.prototype.processMouseDown = function(e) {
   if (touchZooming || touchCount) {
     return;
   }
+  app.canvas.pointer_is_down = false;
   return processMouseDown.apply(this, arguments);
 };
 const processMouseMove = LGraphCanvas.prototype.processMouseMove;
@@ -4157,7 +4123,7 @@ app.registerExtension({
   slot_types_default_in: {},
   async beforeRegisterNodeDef(nodeType, nodeData, app2) {
     var nodeId = nodeData.name;
-    const inputs = nodeData["input"]["required"];
+    const inputs = nodeData["input"]?.["required"];
     for (const inputKey in inputs) {
       var input = inputs[inputKey];
       if (typeof input[0] !== "string") continue;
@@ -4179,19 +4145,19 @@ app.registerExtension({
         nodeType.comfyClass
       );
     }
-    var outputs = nodeData["output"];
-    for (const key in outputs) {
-      var type = outputs[key];
-      if (!(type in this.slot_types_default_in)) {
-        this.slot_types_default_in[type] = ["Reroute"];
+    var outputs = nodeData["output"] ?? [];
+    for (const el of outputs) {
+      const type2 = el;
+      if (!(type2 in this.slot_types_default_in)) {
+        this.slot_types_default_in[type2] = ["Reroute"];
       }
-      this.slot_types_default_in[type].push(nodeId);
-      if (!(type in LiteGraph.registered_slot_out_types)) {
-        LiteGraph.registered_slot_out_types[type] = { nodes: [] };
+      this.slot_types_default_in[type2].push(nodeId);
+      if (!(type2 in LiteGraph.registered_slot_out_types)) {
+        LiteGraph.registered_slot_out_types[type2] = { nodes: [] };
       }
-      LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass);
-      if (!LiteGraph.slot_types_out.includes(type)) {
-        LiteGraph.slot_types_out.push(type);
+      LiteGraph.registered_slot_out_types[type2].nodes.push(nodeType.comfyClass);
+      if (!LiteGraph.slot_types_out.includes(type2)) {
+        LiteGraph.slot_types_out.push(type2);
       }
     }
     var maxNum = this.suggestionsNumber.value;
@@ -4276,7 +4242,7 @@ app.registerExtension({
         } else {
           w = node.size[0];
           h = node.size[1];
-          let titleMode = node.constructor.title_mode;
+          const titleMode = node.constructor.title_mode;
           if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) {
             h += LiteGraph.NODE_TITLE_HEIGHT;
             shiftY -= LiteGraph.NODE_TITLE_HEIGHT;
@@ -4533,7 +4499,9 @@ app.registerExtension({
           /* name=*/
           "audioUI",
           audio,
-          { serialize: false }
+          {
+            serialize: false
+          }
         );
         const isOutputNode = node.constructor.nodeData.output_node;
         if (isOutputNode) {
@@ -4627,108 +4595,4 @@ app.registerExtension({
     };
   }
 });
-function getNodeSource(node) {
-  const nodeDef = node.constructor.nodeData;
-  if (!nodeDef) {
-    return null;
-  }
-  const nodeDefStore = useNodeDefStore();
-  return nodeDefStore.nodeDefsByName[nodeDef.name]?.nodeSource ?? null;
-}
-__name(getNodeSource, "getNodeSource");
-function isCoreNode(node) {
-  return getNodeSource(node)?.type === NodeSourceType.Core;
-}
-__name(isCoreNode, "isCoreNode");
-function badgeTextVisible(node, badgeMode) {
-  return badgeMode === NodeBadgeMode.None || isCoreNode(node) && badgeMode === NodeBadgeMode.HideBuiltIn;
-}
-__name(badgeTextVisible, "badgeTextVisible");
-function getNodeIdBadgeText(node, nodeIdBadgeMode) {
-  return badgeTextVisible(node, nodeIdBadgeMode) ? "" : `#${node.id}`;
-}
-__name(getNodeIdBadgeText, "getNodeIdBadgeText");
-function getNodeSourceBadgeText(node, nodeSourceBadgeMode) {
-  const nodeSource = getNodeSource(node);
-  return badgeTextVisible(node, nodeSourceBadgeMode) ? "" : nodeSource?.badgeText ?? "";
-}
-__name(getNodeSourceBadgeText, "getNodeSourceBadgeText");
-function getNodeLifeCycleBadgeText(node, nodeLifeCycleBadgeMode) {
-  let text = "";
-  const nodeDef = node.constructor.nodeData;
-  if (!nodeDef) {
-    return "";
-  }
-  if (nodeDef.deprecated) {
-    text = "[DEPR]";
-  }
-  if (nodeDef.experimental) {
-    text = "[BETA]";
-  }
-  return badgeTextVisible(node, nodeLifeCycleBadgeMode) ? "" : text;
-}
-__name(getNodeLifeCycleBadgeText, "getNodeLifeCycleBadgeText");
-class NodeBadgeExtension {
-  static {
-    __name(this, "NodeBadgeExtension");
-  }
-  constructor(nodeIdBadgeMode = null, nodeSourceBadgeMode = null, nodeLifeCycleBadgeMode = null, colorPalette = null) {
-    this.nodeIdBadgeMode = nodeIdBadgeMode;
-    this.nodeSourceBadgeMode = nodeSourceBadgeMode;
-    this.nodeLifeCycleBadgeMode = nodeLifeCycleBadgeMode;
-    this.colorPalette = colorPalette;
-  }
-  name = "Comfy.NodeBadge";
-  init(app2) {
-    const settingStore = useSettingStore();
-    this.nodeSourceBadgeMode = computed(
-      () => settingStore.get("Comfy.NodeBadge.NodeSourceBadgeMode")
-    );
-    this.nodeIdBadgeMode = computed(
-      () => settingStore.get("Comfy.NodeBadge.NodeIdBadgeMode")
-    );
-    this.nodeLifeCycleBadgeMode = computed(
-      () => settingStore.get(
-        "Comfy.NodeBadge.NodeLifeCycleBadgeMode"
-      )
-    );
-    this.colorPalette = computed(
-      () => getColorPalette(settingStore.get("Comfy.ColorPalette"))
-    );
-    watch(this.nodeSourceBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-    watch(this.nodeIdBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-    watch(this.nodeLifeCycleBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-  }
-  nodeCreated(node, app2) {
-    node.badgePosition = BadgePosition.TopRight;
-    node.badge_enabled = true;
-    const badge = computed(
-      () => new LGraphBadge({
-        text: _.truncate(
-          [
-            getNodeIdBadgeText(node, this.nodeIdBadgeMode.value),
-            getNodeLifeCycleBadgeText(
-              node,
-              this.nodeLifeCycleBadgeMode.value
-            ),
-            getNodeSourceBadgeText(node, this.nodeSourceBadgeMode.value)
-          ].filter((s) => s.length > 0).join(" "),
-          {
-            length: 31
-          }
-        ),
-        fgColor: this.colorPalette.value.colors.litegraph_base?.BADGE_FG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_FG_COLOR,
-        bgColor: this.colorPalette.value.colors.litegraph_base?.BADGE_BG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_BG_COLOR
-      })
-    );
-    node.badges.push(() => badge.value);
-  }
-}
-app.registerExtension(new NodeBadgeExtension());
-//# sourceMappingURL=index-D36_Nnai.js.map
+//# sourceMappingURL=index-B1vRdV2i.js.map

web/assets/index-B4gmhi99.js

@@ -0,0 +1,62 @@
+const o = {
+  LOADING_PROGRESS: "loading-progress",
+  IS_PACKAGED: "is-packaged",
+  RENDERER_READY: "renderer-ready",
+  RESTART_APP: "restart-app",
+  REINSTALL: "reinstall",
+  LOG_MESSAGE: "log-message",
+  OPEN_DIALOG: "open-dialog",
+  DOWNLOAD_PROGRESS: "download-progress",
+  START_DOWNLOAD: "start-download",
+  PAUSE_DOWNLOAD: "pause-download",
+  RESUME_DOWNLOAD: "resume-download",
+  CANCEL_DOWNLOAD: "cancel-download",
+  DELETE_MODEL: "delete-model",
+  GET_ALL_DOWNLOADS: "get-all-downloads",
+  GET_ELECTRON_VERSION: "get-electron-version",
+  SEND_ERROR_TO_SENTRY: "send-error-to-sentry",
+  GET_BASE_PATH: "get-base-path",
+  GET_MODEL_CONFIG_PATH: "get-model-config-path",
+  OPEN_PATH: "open-path",
+  OPEN_LOGS_PATH: "open-logs-path",
+  OPEN_DEV_TOOLS: "open-dev-tools",
+  IS_FIRST_TIME_SETUP: "is-first-time-setup",
+  GET_SYSTEM_PATHS: "get-system-paths",
+  VALIDATE_INSTALL_PATH: "validate-install-path",
+  VALIDATE_COMFYUI_SOURCE: "validate-comfyui-source",
+  SHOW_DIRECTORY_PICKER: "show-directory-picker",
+  INSTALL_COMFYUI: "install-comfyui"
+};
+var t = /* @__PURE__ */ ((e) => (e.INITIAL_STATE = "initial-state", e.PYTHON_SETUP = "python-setup", e.STARTING_SERVER = "starting-server", e.READY = "ready", e.ERROR = "error", e.ERROR_INSTALL_PATH = "error-install-path", e))(t || {});
+const s = {
+  "initial-state": "Loading...",
+  "python-setup": "Setting up Python Environment...",
+  "starting-server": "Starting ComfyUI server...",
+  ready: "Finishing...",
+  error: "Was not able to start ComfyUI. Please check the logs for more details. You can open it from the Help menu. Please report issues to: https://forum.comfy.org",
+  "error-install-path": "Installation path does not exist. Please reset the installation location."
+}, a = "electronAPI", n = "https://942cadba58d247c9cab96f45221aa813@o4507954455314432.ingest.us.sentry.io/4508007940685824", r = [
+  {
+    id: "user_files",
+    label: "User Files",
+    description: "Settings and user-created workflows"
+  },
+  {
+    id: "models",
+    label: "Models",
+    description: "Reference model files from existing ComfyUI installations. (No copy)"
+  }
+  // TODO: Decide whether we want to auto-migrate custom nodes, and install their dependencies.
+  // huchenlei: This is a very essential thing for migration experience.
+  // {
+  //   id: 'custom_nodes',
+  //   label: 'Custom Nodes',
+  //   description: 'Reference custom node files from existing ComfyUI installations. (No copy)',
+  // },
+];
+export {
+  r,
+  s,
+  t
+};
+//# sourceMappingURL=index-B4gmhi99.js.map

web/assets/index-B4gmhi99.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"index-B4gmhi99.js","sources":["../../node_modules/@comfyorg/comfyui-electron-types/index.mjs"],"sourcesContent":["const o = {\n  LOADING_PROGRESS: \"loading-progress\",\n  IS_PACKAGED: \"is-packaged\",\n  RENDERER_READY: \"renderer-ready\",\n  RESTART_APP: \"restart-app\",\n  REINSTALL: \"reinstall\",\n  LOG_MESSAGE: \"log-message\",\n  OPEN_DIALOG: \"open-dialog\",\n  DOWNLOAD_PROGRESS: \"download-progress\",\n  START_DOWNLOAD: \"start-download\",\n  PAUSE_DOWNLOAD: \"pause-download\",\n  RESUME_DOWNLOAD: \"resume-download\",\n  CANCEL_DOWNLOAD: \"cancel-download\",\n  DELETE_MODEL: \"delete-model\",\n  GET_ALL_DOWNLOADS: \"get-all-downloads\",\n  GET_ELECTRON_VERSION: \"get-electron-version\",\n  SEND_ERROR_TO_SENTRY: \"send-error-to-sentry\",\n  GET_BASE_PATH: \"get-base-path\",\n  GET_MODEL_CONFIG_PATH: \"get-model-config-path\",\n  OPEN_PATH: \"open-path\",\n  OPEN_LOGS_PATH: \"open-logs-path\",\n  OPEN_DEV_TOOLS: \"open-dev-tools\",\n  IS_FIRST_TIME_SETUP: \"is-first-time-setup\",\n  GET_SYSTEM_PATHS: \"get-system-paths\",\n  VALIDATE_INSTALL_PATH: \"validate-install-path\",\n  VALIDATE_COMFYUI_SOURCE: \"validate-comfyui-source\",\n  SHOW_DIRECTORY_PICKER: \"show-directory-picker\",\n  INSTALL_COMFYUI: \"install-comfyui\"\n};\nvar t = /* @__PURE__ */ ((e) => (e.INITIAL_STATE = \"initial-state\", e.PYTHON_SETUP = \"python-setup\", e.STARTING_SERVER = \"starting-server\", e.READY = \"ready\", e.ERROR = \"error\", e.ERROR_INSTALL_PATH = \"error-install-path\", e))(t || {});\nconst s = {\n  \"initial-state\": \"Loading...\",\n  \"python-setup\": \"Setting up Python Environment...\",\n  \"starting-server\": \"Starting ComfyUI server...\",\n  ready: \"Finishing...\",\n  error: \"Was not able to start ComfyUI. Please check the logs for more details. You can open it from the Help menu. Please report issues to: https://forum.comfy.org\",\n  \"error-install-path\": \"Installation path does not exist. Please reset the installation location.\"\n}, a = \"electronAPI\", n = \"https://942cadba58d247c9cab96f45221aa813@o4507954455314432.ingest.us.sentry.io/4508007940685824\", r = [\n  {\n    id: \"user_files\",\n    label: \"User Files\",\n    description: \"Settings and user-created workflows\"\n  },\n  {\n    id: \"models\",\n    label: \"Models\",\n    description: \"Reference model files from existing ComfyUI installations. (No copy)\"\n  }\n  // TODO: Decide whether we want to auto-migrate custom nodes, and install their dependencies.\n  // huchenlei: This is a very essential thing for migration experience.\n  // {\n  //   id: 'custom_nodes',\n  //   label: 'Custom Nodes',\n  //   description: 'Reference custom node files from existing ComfyUI installations. (No copy)',\n  // },\n];\nexport {\n  a as ELECTRON_BRIDGE_API,\n  o as IPC_CHANNELS,\n  r as MigrationItems,\n  s as ProgressMessages,\n  t as ProgressStatus,\n  n as SENTRY_URL_ENDPOINT\n};\n"],"names":[],"mappings":"AAAA,MAAM,IAAI;AAAA,EACR,kBAAkB;AAAA,EAClB,aAAa;AAAA,EACb,gBAAgB;AAAA,EAChB,aAAa;AAAA,EACb,WAAW;AAAA,EACX,aAAa;AAAA,EACb,aAAa;AAAA,EACb,mBAAmB;AAAA,EACnB,gBAAgB;AAAA,EAChB,gBAAgB;AAAA,EAChB,iBAAiB;AAAA,EACjB,iBAAiB;AAAA,EACjB,cAAc;AAAA,EACd,mBAAmB;AAAA,EACnB,sBAAsB;AAAA,EACtB,sBAAsB;AAAA,EACtB,eAAe;AAAA,EACf,uBAAuB;AAAA,EACvB,WAAW;AAAA,EACX,gBAAgB;AAAA,EAChB,gBAAgB;AAAA,EAChB,qBAAqB;AAAA,EACrB,kBAAkB;AAAA,EAClB,uBAAuB;AAAA,EACvB,yBAAyB;AAAA,EACzB,uBAAuB;AAAA,EACvB,iBAAiB;AACnB;AACG,IAAC,IAAqB,kBAAC,OAAO,EAAE,gBAAgB,iBAAiB,EAAE,eAAe,gBAAgB,EAAE,kBAAkB,mBAAmB,EAAE,QAAQ,SAAS,EAAE,QAAQ,SAAS,EAAE,qBAAqB,sBAAsB,IAAI,KAAK,CAAA,CAAE;AACrO,MAAC,IAAI;AAAA,EACR,iBAAiB;AAAA,EACjB,gBAAgB;AAAA,EAChB,mBAAmB;AAAA,EACnB,OAAO;AAAA,EACP,OAAO;AAAA,EACP,sBAAsB;AACxB,GAAG,IAAI,eAAe,IAAI,mGAAmG,IAAI;AAAA,EAC/H;AAAA,IACE,IAAI;AAAA,IACJ,OAAO;AAAA,IACP,aAAa;AAAA,EACd;AAAA,EACD;AAAA,IACE,IAAI;AAAA,IACJ,OAAO;AAAA,IACP,aAAa;AAAA,EACd;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAQH;","x_google_ignoreList":[0]}

web/assets/index-DYEEBf64.js

@@ -1,102 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bM as script$4, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-CgU1oKZt.js";
-var script$3 = {
-  name: "BarsIcon",
-  "extends": script$4
-};
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-    fill: "none",
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-script$1.render = render$1;
-var script = {
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-  fill: "currentColor"
-}, null, -1);
-var _hoisted_2 = [_hoisted_1];
-function render(_ctx, _cache, $props, $setup, $data, $options) {
-  return openBlock(), createElementBlock("svg", mergeProps({
-    width: "14",
-    height: "14",
-    viewBox: "0 0 14 14",
-    fill: "none",
-    xmlns: "http://www.w3.org/2000/svg"
-  }, _ctx.pti()), _hoisted_2, 16);
-}
-__name(render, "render");
-script.render = render;
-export {
-  script$1 as a,
-  script$3 as b,
-  script$2 as c,
-  script as s
-};
-//# sourceMappingURL=index-DYEEBf64.js.map

web/assets/index-MX9DEi8Q.js

@@ -0,0 +1,50 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { c7 as script$2, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-B6dYHNhg.js";
+var script$1 = {
+  name: "BarsIcon",
+  "extends": script$2
+};
+var _hoisted_1$1 = /* @__PURE__ */ createBaseVNode("path", {
+  "fill-rule": "evenodd",
+  "clip-rule": "evenodd",
+  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",
+  fill: "currentColor"
+}, null, -1);
+var _hoisted_2$1 = [_hoisted_1$1];
+function render$1(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _hoisted_2$1, 16);
+}
+__name(render$1, "render$1");
+script$1.render = render$1;
+var script = {
+  name: "PlusIcon",
+  "extends": script$2
+};
+var _hoisted_1 = /* @__PURE__ */ createBaseVNode("path", {
+  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",
+  fill: "currentColor"
+}, null, -1);
+var _hoisted_2 = [_hoisted_1];
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _hoisted_2, 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as a,
+  script$1 as s
+};
+//# sourceMappingURL=index-MX9DEi8Q.js.map

web/assets/index-MX9DEi8Q.js.map

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web/assets/userSelection-BSkuSZyR.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bq as api, bv as $el } from "./index-CgU1oKZt.js";
+import { bH as api, bW as $el } from "./index-B6dYHNhg.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-DVDwxLD5.js.map
+//# sourceMappingURL=userSelection-BSkuSZyR.js.map

web/assets/widgetInputs-BJ21PG7d.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, bA as applyTextReplacements, bz as ComfyWidgets, bG as addValueControlWidgets, k as LiteGraph } from "./index-CgU1oKZt.js";
+import { e as LGraphNode, c as app, c1 as applyTextReplacements, c0 as ComfyWidgets, c2 as addValueControlWidgets, k as LiteGraph } from "./index-B6dYHNhg.js";
 const CONVERTED_TYPE = "converted-widget";
 const VALID_TYPES = [
   "STRING",
@@ -171,7 +171,7 @@ class PrimitiveNode extends LGraphNode {
     if (type instanceof Array) {
       type = "COMBO";
     }
-    const size = this.size;
+    const [oldWidth, oldHeight] = this.size;
     let widget;
     if (type in ComfyWidgets) {
       widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget;
@@ -218,8 +218,8 @@ class PrimitiveNode extends LGraphNode {
       return r;
     };
     this.size = [
-      Math.max(this.size[0], size[0]),
-      Math.max(this.size[1], size[1])
+      Math.max(this.size[0], oldWidth),
+      Math.max(this.size[1], oldHeight)
     ];
     if (!recreating) {
       const sz = this.computeSize();
@@ -320,7 +320,7 @@ class PrimitiveNode extends LGraphNode {
   }
 }
 function getWidgetConfig(slot) {
-  return slot.widget[CONFIG] ?? slot.widget[GET_CONFIG]();
+  return slot.widget[CONFIG] ?? slot.widget[GET_CONFIG]?.() ?? ["*", {}];
 }
 __name(getWidgetConfig, "getWidgetConfig");
 function getConfig(widgetName) {
@@ -373,7 +373,7 @@ __name(showWidget, "showWidget");
 function convertToInput(node, widget, config) {
   hideWidget(node, widget);
   const { type } = getWidgetType(config);
-  const sz = node.size;
+  const [oldWidth, oldHeight] = node.size;
   const inputIsOptional = !!widget.options?.inputIsOptional;
   const input = node.addInput(widget.name, type, {
     widget: { name: widget.name, [GET_CONFIG]: () => config },
@@ -382,18 +382,24 @@ function convertToInput(node, widget, config) {
   for (const widget2 of node.widgets) {
     widget2.last_y += LiteGraph.NODE_SLOT_HEIGHT;
   }
-  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]);
+  node.setSize([
+    Math.max(oldWidth, node.size[0]),
+    Math.max(oldHeight, node.size[1])
+  ]);
   return input;
 }
 __name(convertToInput, "convertToInput");
 function convertToWidget(node, widget) {
   showWidget(widget);
-  const sz = node.size;
+  const [oldWidth, oldHeight] = node.size;
   node.removeInput(node.inputs.findIndex((i) => i.widget?.name === widget.name));
   for (const widget2 of node.widgets) {
     widget2.last_y -= LiteGraph.NODE_SLOT_HEIGHT;
   }
-  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]);
+  node.setSize([
+    Math.max(oldWidth, node.size[0]),
+    Math.max(oldHeight, node.size[1])
+  ]);
 }
 __name(convertToWidget, "convertToWidget");
 function getWidgetType(config) {
@@ -450,7 +456,7 @@ function setWidgetConfig(slot, config, target) {
 __name(setWidgetConfig, "setWidgetConfig");
 function mergeIfValid(output, config2, forceUpdate, recreateWidget, config1) {
   if (!config1) {
-    config1 = output.widget[CONFIG] ?? output.widget[GET_CONFIG]();
+    config1 = getWidgetConfig(output);
   }
   if (config1[0] instanceof Array) {
     if (!isValidCombo(config1[0], config2[0])) return;
@@ -753,4 +759,4 @@ export {
   mergeIfValid,
   setWidgetConfig
 };
-//# sourceMappingURL=widgetInputs-DNVvusS1.js.map
+//# sourceMappingURL=widgetInputs-BJ21PG7d.js.map

web/extensions/core/vintageClipboard.js

@@ -0,0 +1,3 @@
+// Shim for extensions/core/vintageClipboard.ts
+export const serialise = window.comfyAPI.vintageClipboard.serialise;
+export const deserialiseAndCreate = window.comfyAPI.vintageClipboard.deserialiseAndCreate;

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-CgU1oKZt.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-BDQCPKeJ.css">
+    <script type="module" crossorigin src="./assets/index-B6dYHNhg.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-BCoLUtIt.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>

web/scripts/changeTracker.js

@@ -1,3 +1,2 @@
 // Shim for scripts/changeTracker.ts
 export const ChangeTracker = window.comfyAPI.changeTracker.ChangeTracker;
-export const globalTracker = window.comfyAPI.changeTracker.globalTracker;

web/scripts/defaultGraph.js

@@ -1,2 +1,4 @@
 // Shim for scripts/defaultGraph.ts
 export const defaultGraph = window.comfyAPI.defaultGraph.defaultGraph;
+export const defaultGraphJSON = window.comfyAPI.defaultGraph.defaultGraphJSON;
+export const blankGraph = window.comfyAPI.defaultGraph.blankGraph;

web/scripts/domWidget.js

@@ -1,2 +0,0 @@
-// Shim for scripts/domWidget.ts
-export const addDomClippingSetting = window.comfyAPI.domWidget.addDomClippingSetting;

web/scripts/workflows.js

@@ -1,3 +0,0 @@
-// Shim for scripts/workflows.ts
-export const ComfyWorkflowManager = window.comfyAPI.workflows.ComfyWorkflowManager;
-export const ComfyWorkflow = window.comfyAPI.workflows.ComfyWorkflow;