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+/web/assets/** linguist-generated
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.gitignore

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+__pycache__/
+*.py[cod]
+/output/
+/input/
+!/input/example.png
+/models/
+/temp/
+/custom_nodes/
+!custom_nodes/example_node.py.example
+extra_model_paths.yaml
+/.vs
+.vscode/
+.idea/
+venv/
+.venv/
+/web/extensions/*
+!/web/extensions/logging.js.example
+!/web/extensions/core/
+/tests-ui/data/object_info.json
+/user/
+*.log
+web_custom_versions/
+.DS_Store
+openapi.yaml
+filtered-openapi.yaml
+uv.lock

CODEOWNERS

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+# Admins
+* @comfyanonymous
+
+# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
+# Inlined the team members for now.
+
+# Maintainers
+*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+
+# Python web server
+/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+
+# Node developers
+/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

CONTRIBUTING.md

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+# Contributing to ComfyUI
+
+Welcome, and thank you for your interest in contributing to ComfyUI!
+
+There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
+
+## Asking Questions
+
+Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
+
+## Providing Feedback
+
+Your comments and feedback are welcome, and the development team is available via a handful of different channels.
+
+See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
+
+## Reporting Issues
+
+Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
+
+
+### Look For an Existing Issue
+
+Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
+
+If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
+
+
+### Creating Pull Requests
+
+* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
+
+
+## Thank You
+
+Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
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+
+  12. No Surrender of Others' Freedom.
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+
+                     END OF TERMS AND CONDITIONS
+
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+
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+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
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+
+  If the program does terminal interaction, make it output a short
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+
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+
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,14 +1,14 @@
----
-title: Video Prompt Generator
-emoji: 🔥
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 5.14.0
-app_file: app.py
-pinned: true
-license: apache-2.0
-short_description: Create Prompts for your Videos
----
-
+---
+title: Video Prompt Generator
+emoji: 🔥
+colorFrom: blue
+colorTo: pink
+sdk: gradio
+sdk_version: 5.14.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+short_description: Create Prompts for your Videos
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,72 +1,183 @@
-import spaces
-import torch
-import gradio as gr
-import os
-from diffusers import FluxPipeline
-from transformers import T5EncoderModel, CLIPTextModel
-
-# Initialize model outside the function
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.bfloat16
-file_url = "https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v31.safetensors"
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-single_file_base_model = "camenduru/FLUX.1-dev-diffusers"
-
-# Initialize text encoders
-text_encoder = CLIPTextModel.from_pretrained(
-    single_file_base_model,
-    subfolder="text_encoder",
-    torch_dtype=dtype,
-    token=huggingface_token
-)
-
-text_encoder_2 = T5EncoderModel.from_pretrained(
-    single_file_base_model,
-    subfolder="text_encoder_2",
-    torch_dtype=dtype,
-    config=single_file_base_model,
-    token=huggingface_token
-)
-
-# Load the pipeline with proper configuration
-flux_pipeline = FluxPipeline.from_single_file(
-    file_url,
-    text_encoder=text_encoder,
-    text_encoder_2=text_encoder_2,
-    torch_dtype=dtype,
-    token=huggingface_token
-)
-flux_pipeline.to(device)
-
-@spaces.GPU()
-def generate_image(prompt, negative_prompt="", num_inference_steps=30, guidance_scale=7.5):
-    # Generate image
-    image = flux_pipeline(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale
-    ).images[0]
-    
-    return image
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
-        gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here...", value=""),
-        gr.Slider(minimum=1, maximum=100, value=30, step=1, label="Number of Inference Steps"),
-        gr.Slider(minimum=1.0, maximum=20.0, value=7.5, step=0.1, label="Guidance Scale")
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="Chroma Image Generator",
-    description="Generate images using the Chroma model",
-    examples=[
-        ["A beautiful sunset over mountains, photorealistic, 8k", "blurry, low quality, distorted", 30, 7.5],
-        ["A futuristic cityscape at night, neon lights, cyberpunk style", "ugly, deformed, low resolution", 30, 7.5]
-    ]
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+# Download required models
+t5_path = hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp8_e4m3fn.safetensors", local_dir="models/text_encoders/")
+vae_path = hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev", filename="ae.safetensors", local_dir="models/vae")
+unet_path = hf_hub_download(repo_id="lodestones/Chroma", filename="chroma-unlocked-v31.safetensors", local_dir="models/unet")
+
+# Import the workflow functions
+from my_workflow import (
+    get_value_at_index,
+    add_comfyui_directory_to_sys_path,
+    add_extra_model_paths,
+    import_custom_nodes,
+    NODE_CLASS_MAPPINGS,
+    CLIPTextEncode,
+    CLIPLoader,
+    VAEDecode,
+    UNETLoader,
+    VAELoader,
+    SaveImage,
+)
+
+# Initialize ComfyUI
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+import_custom_nodes()
+
+def generate_image(prompt, negative_prompt, width, height, steps, cfg, seed):
+    with torch.inference_mode():
+        # Set random seed if provided
+        if seed == -1:
+            seed = random.randint(1, 2**64)
+        random.seed(seed)
+        
+        randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+        randomnoise_68 = randomnoise.get_noise(noise_seed=seed)
+
+        emptysd3latentimage = NODE_CLASS_MAPPINGS["EmptySD3LatentImage"]()
+        emptysd3latentimage_69 = emptysd3latentimage.generate(
+            width=width, height=height, batch_size=1
+        )
+
+        ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
+        ksamplerselect_72 = ksamplerselect.get_sampler(sampler_name="euler")
+
+        cliploader = CLIPLoader()
+        cliploader_78 = cliploader.load_clip(
+            clip_name="t5xxl_fp8_e4m3fn.safetensors", type="chroma", device="default"
+        )
+
+        t5tokenizeroptions = NODE_CLASS_MAPPINGS["T5TokenizerOptions"]()
+        t5tokenizeroptions_82 = t5tokenizeroptions.set_options(
+            min_padding=1, min_length=0, clip=get_value_at_index(cliploader_78, 0)
+        )
+
+        cliptextencode = CLIPTextEncode()
+        cliptextencode_74 = cliptextencode.encode(
+            text=prompt,
+            clip=get_value_at_index(t5tokenizeroptions_82, 0),
+        )
+
+        cliptextencode_75 = cliptextencode.encode(
+            text=negative_prompt,
+            clip=get_value_at_index(t5tokenizeroptions_82, 0),
+        )
+
+        unetloader = UNETLoader()
+        unetloader_76 = unetloader.load_unet(
+            unet_name="chroma-unlocked-v31.safetensors", weight_dtype="fp8_e4m3fn"
+        )
+
+        vaeloader = VAELoader()
+        vaeloader_80 = vaeloader.load_vae(vae_name="ae.safetensors")
+
+        cfgguider = NODE_CLASS_MAPPINGS["CFGGuider"]()
+        basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
+        samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
+        vaedecode = VAEDecode()
+        saveimage = SaveImage()
+
+        cfgguider_73 = cfgguider.get_guider(
+            cfg=cfg,
+            model=get_value_at_index(unetloader_76, 0),
+            positive=get_value_at_index(cliptextencode_74, 0),
+            negative=get_value_at_index(cliptextencode_75, 0),
+        )
+
+        basicscheduler_84 = basicscheduler.get_sigmas(
+            scheduler="beta",
+            steps=steps,
+            denoise=1,
+            model=get_value_at_index(unetloader_76, 0),
+        )
+
+        samplercustomadvanced_67 = samplercustomadvanced.sample(
+            noise=get_value_at_index(randomnoise_68, 0),
+            guider=get_value_at_index(cfgguider_73, 0),
+            sampler=get_value_at_index(ksamplerselect_72, 0),
+            sigmas=get_value_at_index(basicscheduler_84, 0),
+            latent_image=get_value_at_index(emptysd3latentimage_69, 0),
+        )
+
+        vaedecode_79 = vaedecode.decode(
+            samples=get_value_at_index(samplercustomadvanced_67, 0),
+            vae=get_value_at_index(vaeloader_80, 0),
+        )
+
+        # Instead of saving to file, return the image directly
+        return get_value_at_index(vaedecode_79, 0)
+
+# Create Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("# Chroma Image Generator")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt here...",
+                lines=3
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter negative prompt here...",
+                value="low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors",
+                lines=2
+            )
+            
+            with gr.Row():
+                width = gr.Slider(
+                    minimum=512,
+                    maximum=2048,
+                    value=1024,
+                    step=64,
+                    label="Width"
+                )
+                height = gr.Slider(
+                    minimum=512,
+                    maximum=2048,
+                    value=1024,
+                    step=64,
+                    label="Height"
+                )
+            
+            with gr.Row():
+                steps = gr.Slider(
+                    minimum=1,
+                    maximum=50,
+                    value=26,
+                    step=1,
+                    label="Steps"
+                )
+                cfg = gr.Slider(
+                    minimum=1,
+                    maximum=20,
+                    value=4,
+                    step=0.5,
+                    label="CFG Scale"
+                )
+                seed = gr.Number(
+                    value=-1,
+                    label="Seed (-1 for random)"
+                )
+            
+            generate_btn = gr.Button("Generate")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+    
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, width, height, steps, cfg, seed],
+        outputs=[output_image]
+    )
+
+if __name__ == "__main__":
+    app.launch(share=True)

chroma_api.json

@@ -0,0 +1,194 @@
+{
+  "67": {
+    "inputs": {
+      "noise": [
+        "68",
+        0
+      ],
+      "guider": [
+        "73",
+        0
+      ],
+      "sampler": [
+        "72",
+        0
+      ],
+      "sigmas": [
+        "84",
+        0
+      ],
+      "latent_image": [
+        "69",
+        0
+      ]
+    },
+    "class_type": "SamplerCustomAdvanced",
+    "_meta": {
+      "title": "SamplerCustomAdvanced"
+    }
+  },
+  "68": {
+    "inputs": {
+      "noise_seed": 666
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "RandomNoise"
+    }
+  },
+  "69": {
+    "inputs": {
+      "width": 1024,
+      "height": 1024,
+      "batch_size": 1
+    },
+    "class_type": "EmptySD3LatentImage",
+    "_meta": {
+      "title": "EmptySD3LatentImage"
+    }
+  },
+  "72": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "KSamplerSelect",
+    "_meta": {
+      "title": "KSamplerSelect"
+    }
+  },
+  "73": {
+    "inputs": {
+      "cfg": 4,
+      "model": [
+        "76",
+        0
+      ],
+      "positive": [
+        "74",
+        0
+      ],
+      "negative": [
+        "75",
+        0
+      ]
+    },
+    "class_type": "CFGGuider",
+    "_meta": {
+      "title": "CFGGuider"
+    }
+  },
+  "74": {
+    "inputs": {
+      "text": "Extreme close-up photograph of a single tiger eye, direct frontal view. The iris is very detailed and the pupil resembling a dark void. The word \"Chroma\" is across the lower portion of the image in large white stylized letters, with brush strokes resembling those made with Japanese calligraphy. Each strand of the thick fur is highly detailed and distinguishable. Natural lighting to capture authentic eye shine and depth.",
+      "clip": [
+        "82",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "75": {
+    "inputs": {
+      "text": "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors",
+      "clip": [
+        "82",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "76": {
+    "inputs": {
+      "unet_name": "chroma-unlocked-v31.safetensors",
+      "weight_dtype": "fp8_e4m3fn"
+    },
+    "class_type": "UNETLoader",
+    "_meta": {
+      "title": "Load Diffusion Model"
+    }
+  },
+  "78": {
+    "inputs": {
+      "clip_name": "t5xxl_fp8_e4m3fn.safetensors",
+      "type": "chroma",
+      "device": "default"
+    },
+    "class_type": "CLIPLoader",
+    "_meta": {
+      "title": "Load CLIP"
+    }
+  },
+  "79": {
+    "inputs": {
+      "samples": [
+        "67",
+        0
+      ],
+      "vae": [
+        "80",
+        0
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "80": {
+    "inputs": {
+      "vae_name": "ae.safetensors"
+    },
+    "class_type": "VAELoader",
+    "_meta": {
+      "title": "Load VAE"
+    }
+  },
+  "81": {
+    "inputs": {
+      "filename_prefix": "2025-05-24/ComfyUI",
+      "images": [
+        "79",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  },
+  "82": {
+    "inputs": {
+      "min_padding": 1,
+      "min_length": 0,
+      "clip": [
+        "78",
+        0
+      ]
+    },
+    "class_type": "T5TokenizerOptions",
+    "_meta": {
+      "title": "T5TokenizerOptions"
+    }
+  },
+  "84": {
+    "inputs": {
+      "scheduler": "beta",
+      "steps": 26,
+      "denoise": 1,
+      "model": [
+        "76",
+        0
+      ]
+    },
+    "class_type": "BasicScheduler",
+    "_meta": {
+      "title": "BasicScheduler"
+    }
+  }
+}

comfyui_to_python.py

@@ -0,0 +1,641 @@
+import copy
+import glob
+import inspect
+import json
+import os
+import random
+import sys
+import re
+from typing import Dict, List, Any, Callable, Tuple, TextIO
+from argparse import ArgumentParser
+
+import black
+
+
+from comfyui_to_python_utils import (
+    import_custom_nodes,
+    find_path,
+    add_comfyui_directory_to_sys_path,
+    add_extra_model_paths,
+    get_value_at_index,
+)
+
+add_comfyui_directory_to_sys_path()
+from nodes import NODE_CLASS_MAPPINGS
+
+
+DEFAULT_INPUT_FILE = "workflow_api.json"
+DEFAULT_OUTPUT_FILE = "workflow_api.py"
+DEFAULT_QUEUE_SIZE = 10
+
+
+class FileHandler:
+    """Handles reading and writing files.
+
+    This class provides methods to read JSON data from an input file and write code to an output file.
+    """
+
+    @staticmethod
+    def read_json_file(file_path: str | TextIO, encoding: str = "utf-8") -> dict:
+        """
+        Reads a JSON file and returns its contents as a dictionary.
+
+        Args:
+            file_path (str): The path to the JSON file.
+
+        Returns:
+            dict: The contents of the JSON file as a dictionary.
+
+        Raises:
+            FileNotFoundError: If the file is not found, it lists all JSON files in the directory of the file path.
+            ValueError: If the file is not a valid JSON.
+        """
+
+        if hasattr(file_path, "read"):
+            return json.load(file_path)
+        with open(file_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+        return data
+
+    @staticmethod
+    def write_code_to_file(file_path: str | TextIO, code: str) -> None:
+        """Write the specified code to a Python file.
+
+        Args:
+            file_path (str): The path to the Python file.
+            code (str): The code to write to the file.
+
+        Returns:
+            None
+        """
+        if isinstance(file_path, str):
+            # Extract directory from the filename
+            directory = os.path.dirname(file_path)
+
+            # If the directory does not exist, create it
+            if directory and not os.path.exists(directory):
+                os.makedirs(directory)
+
+            # Save the code to a .py file
+            with open(file_path, "w", encoding="utf-8") as file:
+                file.write(code)
+        else:
+            file_path.write(code)
+
+
+class LoadOrderDeterminer:
+    """Determine the load order of each key in the provided dictionary.
+
+    This class places the nodes without node dependencies first, then ensures that any node whose
+    result is used in another node will be added to the list in the order it should be executed.
+
+    Attributes:
+        data (Dict): The dictionary for which to determine the load order.
+        node_class_mappings (Dict): Mappings of node classes.
+    """
+
+    def __init__(self, data: Dict, node_class_mappings: Dict):
+        """Initialize the LoadOrderDeterminer with the given data and node class mappings.
+
+        Args:
+            data (Dict): The dictionary for which to determine the load order.
+            node_class_mappings (Dict): Mappings of node classes.
+        """
+        self.data = data
+        self.node_class_mappings = node_class_mappings
+        self.visited = {}
+        self.load_order = []
+        self.is_special_function = False
+
+    def determine_load_order(self) -> List[Tuple[str, Dict, bool]]:
+        """Determine the load order for the given data.
+
+        Returns:
+            List[Tuple[str, Dict, bool]]: A list of tuples representing the load order.
+        """
+        self._load_special_functions_first()
+        self.is_special_function = False
+        for key in self.data:
+            if key not in self.visited:
+                self._dfs(key)
+        return self.load_order
+
+    def _dfs(self, key: str) -> None:
+        """Depth-First Search function to determine the load order.
+
+        Args:
+            key (str): The key from which to start the DFS.
+
+        Returns:
+            None
+        """
+        # Mark the node as visited.
+        self.visited[key] = True
+        inputs = self.data[key]["inputs"]
+        # Loop over each input key.
+        for input_key, val in inputs.items():
+            # If the value is a list and the first item in the list has not been visited yet,
+            # then recursively apply DFS on the dependency.
+            if isinstance(val, list) and val[0] not in self.visited:
+                self._dfs(val[0])
+        # Add the key and its corresponding data to the load order list.
+        self.load_order.append((key, self.data[key], self.is_special_function))
+
+    def _load_special_functions_first(self) -> None:
+        """Load functions without dependencies, loaderes, and encoders first.
+
+        Returns:
+            None
+        """
+        # Iterate over each key in the data to check for loader keys.
+        for key in self.data:
+            class_def = self.node_class_mappings[self.data[key]["class_type"]]()
+            # Check if the class is a loader class or meets specific conditions.
+            if (
+                class_def.CATEGORY == "loaders"
+                or class_def.FUNCTION in ["encode"]
+                or not any(
+                    isinstance(val, list) for val in self.data[key]["inputs"].values()
+                )
+            ):
+                self.is_special_function = True
+                # If the key has not been visited, perform a DFS from that key.
+                if key not in self.visited:
+                    self._dfs(key)
+
+
+class CodeGenerator:
+    """Generates Python code for a workflow based on the load order.
+
+    Attributes:
+        node_class_mappings (Dict): Mappings of node classes.
+        base_node_class_mappings (Dict): Base mappings of node classes.
+    """
+
+    def __init__(self, node_class_mappings: Dict, base_node_class_mappings: Dict):
+        """Initialize the CodeGenerator with given node class mappings.
+
+        Args:
+            node_class_mappings (Dict): Mappings of node classes.
+            base_node_class_mappings (Dict): Base mappings of node classes.
+        """
+        self.node_class_mappings = node_class_mappings
+        self.base_node_class_mappings = base_node_class_mappings
+
+    def generate_workflow(
+        self,
+        load_order: List,
+        queue_size: int = 10,
+    ) -> str:
+        """Generate the execution code based on the load order.
+
+        Args:
+            load_order (List): A list of tuples representing the load order.
+            queue_size (int): The number of photos that will be created by the script.
+
+        Returns:
+            str: Generated execution code as a string.
+        """
+        # Create the necessary data structures to hold imports and generated code
+        import_statements, executed_variables, special_functions_code, code = (
+            set(["NODE_CLASS_MAPPINGS"]),
+            {},
+            [],
+            [],
+        )
+        # This dictionary will store the names of the objects that we have already initialized
+        initialized_objects = {}
+
+        custom_nodes = False
+        # Loop over each dictionary in the load order list
+        for idx, data, is_special_function in load_order:
+            # Generate class definition and inputs from the data
+            inputs, class_type = data["inputs"], data["class_type"]
+            input_types = self.node_class_mappings[class_type].INPUT_TYPES()
+            class_def = self.node_class_mappings[class_type]()
+
+            # If required inputs are not present, skip the node as it will break the code if passed through to the script
+            missing_required_variable = False
+            if "required" in input_types.keys():
+                for required in input_types["required"]:
+                    if required not in inputs.keys():
+                        missing_required_variable = True
+            if missing_required_variable:
+                continue
+
+            # If the class hasn't been initialized yet, initialize it and generate the import statements
+            if class_type not in initialized_objects:
+                # No need to use preview image nodes since we are executing the script in a terminal
+                if class_type == "PreviewImage":
+                    continue
+
+                class_type, import_statement, class_code = self.get_class_info(
+                    class_type
+                )
+                initialized_objects[class_type] = self.clean_variable_name(class_type)
+                if class_type in self.base_node_class_mappings.keys():
+                    import_statements.add(import_statement)
+                if class_type not in self.base_node_class_mappings.keys():
+                    custom_nodes = True
+                special_functions_code.append(class_code)
+
+            # Get all possible parameters for class_def
+            class_def_params = self.get_function_parameters(
+                getattr(class_def, class_def.FUNCTION)
+            )
+            no_params = class_def_params is None
+
+            # Remove any keyword arguments from **inputs if they are not in class_def_params
+            inputs = {
+                key: value
+                for key, value in inputs.items()
+                if no_params or key in class_def_params
+            }
+            # Deal with hidden variables
+            if (
+                "hidden" in input_types.keys()
+                and "unique_id" in input_types["hidden"].keys()
+            ):
+                inputs["unique_id"] = random.randint(1, 2**64)
+            elif class_def_params is not None:
+                if "unique_id" in class_def_params:
+                    inputs["unique_id"] = random.randint(1, 2**64)
+
+            # Create executed variable and generate code
+            executed_variables[idx] = f"{self.clean_variable_name(class_type)}_{idx}"
+            inputs = self.update_inputs(inputs, executed_variables)
+
+            if is_special_function:
+                special_functions_code.append(
+                    self.create_function_call_code(
+                        initialized_objects[class_type],
+                        class_def.FUNCTION,
+                        executed_variables[idx],
+                        is_special_function,
+                        **inputs,
+                    )
+                )
+            else:
+                code.append(
+                    self.create_function_call_code(
+                        initialized_objects[class_type],
+                        class_def.FUNCTION,
+                        executed_variables[idx],
+                        is_special_function,
+                        **inputs,
+                    )
+                )
+
+        # Generate final code by combining imports and code, and wrap them in a main function
+        final_code = self.assemble_python_code(
+            import_statements, special_functions_code, code, queue_size, custom_nodes
+        )
+
+        return final_code
+
+    def create_function_call_code(
+        self,
+        obj_name: str,
+        func: str,
+        variable_name: str,
+        is_special_function: bool,
+        **kwargs,
+    ) -> str:
+        """Generate Python code for a function call.
+
+        Args:
+            obj_name (str): The name of the initialized object.
+            func (str): The function to be called.
+            variable_name (str): The name of the variable that the function result should be assigned to.
+            is_special_function (bool): Determines the code indentation.
+            **kwargs: The keyword arguments for the function.
+
+        Returns:
+            str: The generated Python code.
+        """
+        args = ", ".join(self.format_arg(key, value) for key, value in kwargs.items())
+
+        # Generate the Python code
+        code = f"{variable_name} = {obj_name}.{func}({args})\n"
+
+        # If the code contains dependencies and is not a loader or encoder, indent the code because it will be placed inside
+        # of a for loop
+        if not is_special_function:
+            code = f"\t{code}"
+
+        return code
+
+    def format_arg(self, key: str, value: any) -> str:
+        """Formats arguments based on key and value.
+
+        Args:
+            key (str): Argument key.
+            value (any): Argument value.
+
+        Returns:
+            str: Formatted argument as a string.
+        """
+        if key == "noise_seed" or key == "seed":
+            return f"{key}=random.randint(1, 2**64)"
+        elif isinstance(value, str):
+            value = value.replace("\n", "\\n").replace('"', "'")
+            return f'{key}="{value}"'
+        elif isinstance(value, dict) and "variable_name" in value:
+            return f'{key}={value["variable_name"]}'
+        return f"{key}={value}"
+
+    def assemble_python_code(
+        self,
+        import_statements: set,
+        speical_functions_code: List[str],
+        code: List[str],
+        queue_size: int,
+        custom_nodes=False,
+    ) -> str:
+        """Generates the final code string.
+
+        Args:
+            import_statements (set): A set of unique import statements.
+            speical_functions_code (List[str]): A list of special functions code strings.
+            code (List[str]): A list of code strings.
+            queue_size (int): Number of photos that will be generated by the script.
+            custom_nodes (bool): Whether to include custom nodes in the code.
+
+        Returns:
+            str: Generated final code as a string.
+        """
+        # Get the source code of the utils functions as a string
+        func_strings = []
+        for func in [
+            get_value_at_index,
+            find_path,
+            add_comfyui_directory_to_sys_path,
+            add_extra_model_paths,
+        ]:
+            func_strings.append(f"\n{inspect.getsource(func)}")
+        # Define static import statements required for the script
+        static_imports = (
+            [
+                "import os",
+                "import random",
+                "import sys",
+                "from typing import Sequence, Mapping, Any, Union",
+                "import torch",
+            ]
+            + func_strings
+            + ["\n\nadd_comfyui_directory_to_sys_path()\nadd_extra_model_paths()\n"]
+        )
+        # Check if custom nodes should be included
+        if custom_nodes:
+            static_imports.append(f"\n{inspect.getsource(import_custom_nodes)}\n")
+            custom_nodes = "import_custom_nodes()\n\t"
+        else:
+            custom_nodes = ""
+        # Create import statements for node classes
+        imports_code = [
+            f"from nodes import {', '.join([class_name for class_name in import_statements])}"
+        ]
+        # Assemble the main function code, including custom nodes if applicable
+        main_function_code = (
+            "def main():\n\t"
+            + f"{custom_nodes}with torch.inference_mode():\n\t\t"
+            + "\n\t\t".join(speical_functions_code)
+            + f"\n\n\t\tfor q in range({queue_size}):\n\t\t"
+            + "\n\t\t".join(code)
+        )
+        # Concatenate all parts to form the final code
+        final_code = "\n".join(
+            static_imports
+            + imports_code
+            + ["", main_function_code, "", 'if __name__ == "__main__":', "\tmain()"]
+        )
+        # Format the final code according to PEP 8 using the Black library
+        final_code = black.format_str(final_code, mode=black.Mode())
+
+        return final_code
+
+    def get_class_info(self, class_type: str) -> Tuple[str, str, str]:
+        """Generates and returns necessary information about class type.
+
+        Args:
+            class_type (str): Class type.
+
+        Returns:
+            Tuple[str, str, str]: Updated class type, import statement string, class initialization code.
+        """
+        import_statement = class_type
+        variable_name = self.clean_variable_name(class_type)
+        if class_type in self.base_node_class_mappings.keys():
+            class_code = f"{variable_name} = {class_type.strip()}()"
+        else:
+            class_code = f'{variable_name} = NODE_CLASS_MAPPINGS["{class_type}"]()'
+
+        return class_type, import_statement, class_code
+
+    @staticmethod
+    def clean_variable_name(class_type: str) -> str:
+        """
+        Remove any characters from variable name that could cause errors running the Python script.
+
+        Args:
+            class_type (str): Class type.
+
+        Returns:
+            str: Cleaned variable name with no special characters or spaces
+        """
+        # Convert to lowercase and replace spaces with underscores
+        clean_name = class_type.lower().strip().replace("-", "_").replace(" ", "_")
+
+        # Remove characters that are not letters, numbers, or underscores
+        clean_name = re.sub(r"[^a-z0-9_]", "", clean_name)
+
+        # Ensure that it doesn't start with a number
+        if clean_name[0].isdigit():
+            clean_name = "_" + clean_name
+
+        return clean_name
+
+    def get_function_parameters(self, func: Callable) -> List:
+        """Get the names of a function's parameters.
+
+        Args:
+            func (Callable): The function whose parameters we want to inspect.
+
+        Returns:
+            List: A list containing the names of the function's parameters.
+        """
+        signature = inspect.signature(func)
+        parameters = {
+            name: param.default if param.default != param.empty else None
+            for name, param in signature.parameters.items()
+        }
+        catch_all = any(
+            param.kind == inspect.Parameter.VAR_KEYWORD
+            for param in signature.parameters.values()
+        )
+        return list(parameters.keys()) if not catch_all else None
+
+    def update_inputs(self, inputs: Dict, executed_variables: Dict) -> Dict:
+        """Update inputs based on the executed variables.
+
+        Args:
+            inputs (Dict): Inputs dictionary to update.
+            executed_variables (Dict): Dictionary storing executed variable names.
+
+        Returns:
+            Dict: Updated inputs dictionary.
+        """
+        for key in inputs.keys():
+            if (
+                isinstance(inputs[key], list)
+                and inputs[key][0] in executed_variables.keys()
+            ):
+                inputs[key] = {
+                    "variable_name": f"get_value_at_index({executed_variables[inputs[key][0]]}, {inputs[key][1]})"
+                }
+        return inputs
+
+
+class ComfyUItoPython:
+    """Main workflow to generate Python code from a workflow_api.json file.
+
+    Attributes:
+        input_file (str): Path to the input JSON file.
+        output_file (str): Path to the output Python file.
+        queue_size (int): The number of photos that will be created by the script.
+        node_class_mappings (Dict): Mappings of node classes.
+        base_node_class_mappings (Dict): Base mappings of node classes.
+    """
+
+    def __init__(
+        self,
+        workflow: str = "",
+        input_file: str = "",
+        output_file: str | TextIO = "",
+        queue_size: int = 1,
+        node_class_mappings: Dict = NODE_CLASS_MAPPINGS,
+        needs_init_custom_nodes: bool = False,
+    ):
+        """Initialize the ComfyUItoPython class with the given parameters. Exactly one of workflow or input_file must be specified.
+        Args:
+            workflow (str): The workflow's JSON.
+            input_file (str): Path to the input JSON file.
+            output_file (str | TextIO): Path to the output file or a file-like object.
+            queue_size (int): The number of times a workflow will be executed by the script. Defaults to 1.
+            node_class_mappings (Dict): Mappings of node classes. Defaults to NODE_CLASS_MAPPINGS.
+            needs_init_custom_nodes (bool): Whether to initialize custom nodes. Defaults to False.
+        """
+        if input_file and workflow:
+            raise ValueError("Can't provide both input_file and workflow")
+        elif not input_file and not workflow:
+            raise ValueError("Needs input_file or workflow")
+
+        if not output_file:
+            raise ValueError("Needs output_file")
+
+        self.workflow = workflow
+        self.input_file = input_file
+        self.output_file = output_file
+        self.queue_size = queue_size
+        self.node_class_mappings = node_class_mappings
+        self.needs_init_custom_nodes = needs_init_custom_nodes
+
+        self.base_node_class_mappings = copy.deepcopy(self.node_class_mappings)
+        self.execute()
+
+    def execute(self):
+        """Execute the main workflow to generate Python code.
+
+        Returns:
+            None
+        """
+        # Step 1: Import all custom nodes if we need to
+        if self.needs_init_custom_nodes:
+            import_custom_nodes()
+        else:
+            # If they're already imported, we don't know which nodes are custom nodes, so we need to import all of them
+            self.base_node_class_mappings = {}
+
+        # Step 2: Read JSON data from the input file
+        if self.input_file:
+            data = FileHandler.read_json_file(self.input_file)
+        else:
+            data = json.loads(self.workflow)
+
+        # Step 3: Determine the load order
+        load_order_determiner = LoadOrderDeterminer(data, self.node_class_mappings)
+        load_order = load_order_determiner.determine_load_order()
+
+        # Step 4: Generate the workflow code
+        code_generator = CodeGenerator(
+            self.node_class_mappings, self.base_node_class_mappings
+        )
+        generated_code = code_generator.generate_workflow(
+            load_order, queue_size=self.queue_size
+        )
+
+        # Step 5: Write the generated code to a file
+        FileHandler.write_code_to_file(self.output_file, generated_code)
+
+        print(f"Code successfully generated and written to {self.output_file}")
+
+
+def run(
+    input_file: str = DEFAULT_INPUT_FILE,
+    output_file: str = DEFAULT_OUTPUT_FILE,
+    queue_size: int = DEFAULT_QUEUE_SIZE,
+) -> None:
+    """Generate Python code from a ComfyUI workflow_api.json file.
+
+    Args:
+        input_file (str): Path to the input JSON file. Defaults to "workflow_api.json".
+        output_file (str): Path to the output Python file.
+            Defaults to "workflow_api.py".
+        queue_size (int): The number of times a workflow will be executed by the script.
+            Defaults to 1.
+
+    Returns:
+        None
+    """
+    ComfyUItoPython(
+        input_file=input_file,
+        output_file=output_file,
+        queue_size=queue_size,
+        needs_init_custom_nodes=True,
+    )
+
+
+def main() -> None:
+    """Main function to generate Python code from a ComfyUI workflow_api.json file."""
+    parser = ArgumentParser(
+        description="Generate Python    code from a ComfyUI workflow_api.json file."
+    )
+    parser.add_argument(
+        "-f",
+        "--input_file",
+        type=str,
+        help="path to the input JSON file",
+        default=DEFAULT_INPUT_FILE,
+    )
+    parser.add_argument(
+        "-o",
+        "--output_file",
+        type=str,
+        help="path to the output Python file",
+        default=DEFAULT_OUTPUT_FILE,
+    )
+    parser.add_argument(
+        "-q",
+        "--queue_size",
+        type=int,
+        help="number of times the workflow will be executed by default",
+        default=DEFAULT_QUEUE_SIZE,
+    )
+    pargs = parser.parse_args()
+    run(**vars(pargs))
+    print("Done.")
+
+
+if __name__ == "__main__":
+    """Run the main function."""
+    main()

comfyui_to_python_utils.py

@@ -0,0 +1,106 @@
+import os
+from typing import Sequence, Mapping, Any, Union
+import sys
+
+
+def import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    # Creating an instance of PromptServer with the loop
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+
+    # Initializing custom nodes
+    init_extra_nodes()
+
+
+def find_path(name: str, path: str = None) -> str:
+    """
+    Recursively looks at parent folders starting from the given path until it finds the given name.
+    Returns the path as a Path object if found, or None otherwise.
+    """
+    # If no path is given, use the current working directory
+    if path is None:
+        path = os.getcwd()
+
+    # Check if the current directory contains the name
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(path)
+
+    # If the parent directory is the same as the current directory, we've reached the root and stop the search
+    if parent_directory == path:
+        return None
+
+    # Recursively call the function with the parent directory
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """
+    Add 'ComfyUI' to the sys.path
+    """
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """
+    Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
+    """
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print(
+            "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
+        )
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping.
+
+    If the object is a sequence (like list or string), returns the value at the given index.
+    If the object is a mapping (like a dictionary), returns the value at the index-th key.
+
+    Some return a dictionary, in these cases, we look for the "results" key
+
+    Args:
+        obj (Union[Sequence, Mapping]): The object to retrieve the value from.
+        index (int): The index of the value to retrieve.
+
+    Returns:
+        Any: The value at the given index.
+
+    Raises:
+        IndexError: If the index is out of bounds for the object and the object is not a mapping.
+    """
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]

comfyui_version.py

@@ -0,0 +1,3 @@
+# This file is automatically generated by the build process when version is
+# updated in pyproject.toml.
+__version__ = "0.3.35"

cuda_malloc.py

@@ -0,0 +1,90 @@
+import os
+import importlib.util
+from comfy.cli_args import args
+import subprocess
+
+#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
+def get_gpu_names():
+    if os.name == 'nt':
+        import ctypes
+
+        # Define necessary C structures and types
+        class DISPLAY_DEVICEA(ctypes.Structure):
+            _fields_ = [
+                ('cb', ctypes.c_ulong),
+                ('DeviceName', ctypes.c_char * 32),
+                ('DeviceString', ctypes.c_char * 128),
+                ('StateFlags', ctypes.c_ulong),
+                ('DeviceID', ctypes.c_char * 128),
+                ('DeviceKey', ctypes.c_char * 128)
+            ]
+
+        # Load user32.dll
+        user32 = ctypes.windll.user32
+
+        # Call EnumDisplayDevicesA
+        def enum_display_devices():
+            device_info = DISPLAY_DEVICEA()
+            device_info.cb = ctypes.sizeof(device_info)
+            device_index = 0
+            gpu_names = set()
+
+            while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0):
+                device_index += 1
+                gpu_names.add(device_info.DeviceString.decode('utf-8'))
+            return gpu_names
+        return enum_display_devices()
+    else:
+        gpu_names = set()
+        out = subprocess.check_output(['nvidia-smi', '-L'])
+        for l in out.split(b'\n'):
+            if len(l) > 0:
+                gpu_names.add(l.decode('utf-8').split(' (UUID')[0])
+        return gpu_names
+
+blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
+                "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
+                "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
+                "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
+                "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
+                "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60"
+                }
+
+def cuda_malloc_supported():
+    try:
+        names = get_gpu_names()
+    except:
+        names = set()
+    for x in names:
+        if "NVIDIA" in x:
+            for b in blacklist:
+                if b in x:
+                    return False
+    return True
+
+
+if not args.cuda_malloc:
+    try:
+        version = ""
+        torch_spec = importlib.util.find_spec("torch")
+        for folder in torch_spec.submodule_search_locations:
+            ver_file = os.path.join(folder, "version.py")
+            if os.path.isfile(ver_file):
+                spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
+                module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(module)
+                version = module.__version__
+        if int(version[0]) >= 2: #enable by default for torch version 2.0 and up
+            args.cuda_malloc = cuda_malloc_supported()
+    except:
+        pass
+
+
+if args.cuda_malloc and not args.disable_cuda_malloc:
+    env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
+    if env_var is None:
+        env_var = "backend:cudaMallocAsync"
+    else:
+        env_var += ",backend:cudaMallocAsync"
+
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var

execution.py

@@ -0,0 +1,1032 @@
+import sys
+import copy
+import logging
+import threading
+import heapq
+import time
+import traceback
+from enum import Enum
+import inspect
+from typing import List, Literal, NamedTuple, Optional
+
+import torch
+import nodes
+
+import comfy.model_management
+from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
+from comfy_execution.graph_utils import is_link, GraphBuilder
+from comfy_execution.caching import HierarchicalCache, LRUCache, DependencyAwareCache, CacheKeySetInputSignature, CacheKeySetID
+from comfy_execution.validation import validate_node_input
+
+class ExecutionResult(Enum):
+    SUCCESS = 0
+    FAILURE = 1
+    PENDING = 2
+
+class DuplicateNodeError(Exception):
+    pass
+
+class IsChangedCache:
+    def __init__(self, dynprompt, outputs_cache):
+        self.dynprompt = dynprompt
+        self.outputs_cache = outputs_cache
+        self.is_changed = {}
+
+    def get(self, node_id):
+        if node_id in self.is_changed:
+            return self.is_changed[node_id]
+
+        node = self.dynprompt.get_node(node_id)
+        class_type = node["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        if not hasattr(class_def, "IS_CHANGED"):
+            self.is_changed[node_id] = False
+            return self.is_changed[node_id]
+
+        if "is_changed" in node:
+            self.is_changed[node_id] = node["is_changed"]
+            return self.is_changed[node_id]
+
+        # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
+        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None)
+        try:
+            is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED")
+            node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
+        except Exception as e:
+            logging.warning("WARNING: {}".format(e))
+            node["is_changed"] = float("NaN")
+        finally:
+            self.is_changed[node_id] = node["is_changed"]
+        return self.is_changed[node_id]
+
+
+class CacheType(Enum):
+    CLASSIC = 0
+    LRU = 1
+    DEPENDENCY_AWARE = 2
+
+
+class CacheSet:
+    def __init__(self, cache_type=None, cache_size=None):
+        if cache_type == CacheType.DEPENDENCY_AWARE:
+            self.init_dependency_aware_cache()
+            logging.info("Disabling intermediate node cache.")
+        elif cache_type == CacheType.LRU:
+            if cache_size is None:
+                cache_size = 0
+            self.init_lru_cache(cache_size)
+            logging.info("Using LRU cache")
+        else:
+            self.init_classic_cache()
+
+        self.all = [self.outputs, self.ui, self.objects]
+
+    # Performs like the old cache -- dump data ASAP
+    def init_classic_cache(self):
+        self.outputs = HierarchicalCache(CacheKeySetInputSignature)
+        self.ui = HierarchicalCache(CacheKeySetInputSignature)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
+    def init_lru_cache(self, cache_size):
+        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
+    # only hold cached items while the decendents have not executed
+    def init_dependency_aware_cache(self):
+        self.outputs = DependencyAwareCache(CacheKeySetInputSignature)
+        self.ui = DependencyAwareCache(CacheKeySetInputSignature)
+        self.objects = DependencyAwareCache(CacheKeySetID)
+
+    def recursive_debug_dump(self):
+        result = {
+            "outputs": self.outputs.recursive_debug_dump(),
+            "ui": self.ui.recursive_debug_dump(),
+        }
+        return result
+
+def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
+    valid_inputs = class_def.INPUT_TYPES()
+    input_data_all = {}
+    missing_keys = {}
+    for x in inputs:
+        input_data = inputs[x]
+        _, input_category, input_info = get_input_info(class_def, x, valid_inputs)
+        def mark_missing():
+            missing_keys[x] = True
+            input_data_all[x] = (None,)
+        if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)):
+            input_unique_id = input_data[0]
+            output_index = input_data[1]
+            if outputs is None:
+                mark_missing()
+                continue # This might be a lazily-evaluated input
+            cached_output = outputs.get(input_unique_id)
+            if cached_output is None:
+                mark_missing()
+                continue
+            if output_index >= len(cached_output):
+                mark_missing()
+                continue
+            obj = cached_output[output_index]
+            input_data_all[x] = obj
+        elif input_category is not None:
+            input_data_all[x] = [input_data]
+
+    if "hidden" in valid_inputs:
+        h = valid_inputs["hidden"]
+        for x in h:
+            if h[x] == "PROMPT":
+                input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}]
+            if h[x] == "DYNPROMPT":
+                input_data_all[x] = [dynprompt]
+            if h[x] == "EXTRA_PNGINFO":
+                input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
+            if h[x] == "UNIQUE_ID":
+                input_data_all[x] = [unique_id]
+            if h[x] == "AUTH_TOKEN_COMFY_ORG":
+                input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)]
+            if h[x] == "API_KEY_COMFY_ORG":
+                input_data_all[x] = [extra_data.get("api_key_comfy_org", None)]
+    return input_data_all, missing_keys
+
+map_node_over_list = None #Don't hook this please
+
+def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
+    # check if node wants the lists
+    input_is_list = getattr(obj, "INPUT_IS_LIST", False)
+
+    if len(input_data_all) == 0:
+        max_len_input = 0
+    else:
+        max_len_input = max(len(x) for x in input_data_all.values())
+
+    # get a slice of inputs, repeat last input when list isn't long enough
+    def slice_dict(d, i):
+        return {k: v[i if len(v) > i else -1] for k, v in d.items()}
+
+    results = []
+    def process_inputs(inputs, index=None, input_is_list=False):
+        if allow_interrupt:
+            nodes.before_node_execution()
+        execution_block = None
+        for k, v in inputs.items():
+            if input_is_list:
+                for e in v:
+                    if isinstance(e, ExecutionBlocker):
+                        v = e
+                        break
+            if isinstance(v, ExecutionBlocker):
+                execution_block = execution_block_cb(v) if execution_block_cb else v
+                break
+        if execution_block is None:
+            if pre_execute_cb is not None and index is not None:
+                pre_execute_cb(index)
+            results.append(getattr(obj, func)(**inputs))
+        else:
+            results.append(execution_block)
+
+    if input_is_list:
+        process_inputs(input_data_all, 0, input_is_list=input_is_list)
+    elif max_len_input == 0:
+        process_inputs({})
+    else:
+        for i in range(max_len_input):
+            input_dict = slice_dict(input_data_all, i)
+            process_inputs(input_dict, i)
+    return results
+
+def merge_result_data(results, obj):
+    # check which outputs need concatenating
+    output = []
+    output_is_list = [False] * len(results[0])
+    if hasattr(obj, "OUTPUT_IS_LIST"):
+        output_is_list = obj.OUTPUT_IS_LIST
+
+    # merge node execution results
+    for i, is_list in zip(range(len(results[0])), output_is_list):
+        if is_list:
+            value = []
+            for o in results:
+                if isinstance(o[i], ExecutionBlocker):
+                    value.append(o[i])
+                else:
+                    value.extend(o[i])
+            output.append(value)
+        else:
+            output.append([o[i] for o in results])
+    return output
+
+def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None):
+    results = []
+    uis = []
+    subgraph_results = []
+    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
+    has_subgraph = False
+    for i in range(len(return_values)):
+        r = return_values[i]
+        if isinstance(r, dict):
+            if 'ui' in r:
+                uis.append(r['ui'])
+            if 'expand' in r:
+                # Perform an expansion, but do not append results
+                has_subgraph = True
+                new_graph = r['expand']
+                result = r.get("result", None)
+                if isinstance(result, ExecutionBlocker):
+                    result = tuple([result] * len(obj.RETURN_TYPES))
+                subgraph_results.append((new_graph, result))
+            elif 'result' in r:
+                result = r.get("result", None)
+                if isinstance(result, ExecutionBlocker):
+                    result = tuple([result] * len(obj.RETURN_TYPES))
+                results.append(result)
+                subgraph_results.append((None, result))
+        else:
+            if isinstance(r, ExecutionBlocker):
+                r = tuple([r] * len(obj.RETURN_TYPES))
+            results.append(r)
+            subgraph_results.append((None, r))
+
+    if has_subgraph:
+        output = subgraph_results
+    elif len(results) > 0:
+        output = merge_result_data(results, obj)
+    else:
+        output = []
+    ui = dict()
+    if len(uis) > 0:
+        ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
+    return output, ui, has_subgraph
+
+def format_value(x):
+    if x is None:
+        return None
+    elif isinstance(x, (int, float, bool, str)):
+        return x
+    else:
+        return str(x)
+
+def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results):
+    unique_id = current_item
+    real_node_id = dynprompt.get_real_node_id(unique_id)
+    display_node_id = dynprompt.get_display_node_id(unique_id)
+    parent_node_id = dynprompt.get_parent_node_id(unique_id)
+    inputs = dynprompt.get_node(unique_id)['inputs']
+    class_type = dynprompt.get_node(unique_id)['class_type']
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    if caches.outputs.get(unique_id) is not None:
+        if server.client_id is not None:
+            cached_output = caches.ui.get(unique_id) or {}
+            server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id)
+        return (ExecutionResult.SUCCESS, None, None)
+
+    input_data_all = None
+    try:
+        if unique_id in pending_subgraph_results:
+            cached_results = pending_subgraph_results[unique_id]
+            resolved_outputs = []
+            for is_subgraph, result in cached_results:
+                if not is_subgraph:
+                    resolved_outputs.append(result)
+                else:
+                    resolved_output = []
+                    for r in result:
+                        if is_link(r):
+                            source_node, source_output = r[0], r[1]
+                            node_output = caches.outputs.get(source_node)[source_output]
+                            for o in node_output:
+                                resolved_output.append(o)
+
+                        else:
+                            resolved_output.append(r)
+                    resolved_outputs.append(tuple(resolved_output))
+            output_data = merge_result_data(resolved_outputs, class_def)
+            output_ui = []
+            has_subgraph = False
+        else:
+            input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data)
+            if server.client_id is not None:
+                server.last_node_id = display_node_id
+                server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
+
+            obj = caches.objects.get(unique_id)
+            if obj is None:
+                obj = class_def()
+                caches.objects.set(unique_id, obj)
+
+            if hasattr(obj, "check_lazy_status"):
+                required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True)
+                required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
+                required_inputs = [x for x in required_inputs if isinstance(x,str) and (
+                    x not in input_data_all or x in missing_keys
+                )]
+                if len(required_inputs) > 0:
+                    for i in required_inputs:
+                        execution_list.make_input_strong_link(unique_id, i)
+                    return (ExecutionResult.PENDING, None, None)
+
+            def execution_block_cb(block):
+                if block.message is not None:
+                    mes = {
+                        "prompt_id": prompt_id,
+                        "node_id": unique_id,
+                        "node_type": class_type,
+                        "executed": list(executed),
+
+                        "exception_message": f"Execution Blocked: {block.message}",
+                        "exception_type": "ExecutionBlocked",
+                        "traceback": [],
+                        "current_inputs": [],
+                        "current_outputs": [],
+                    }
+                    server.send_sync("execution_error", mes, server.client_id)
+                    return ExecutionBlocker(None)
+                else:
+                    return block
+            def pre_execute_cb(call_index):
+                GraphBuilder.set_default_prefix(unique_id, call_index, 0)
+            output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
+        if len(output_ui) > 0:
+            caches.ui.set(unique_id, {
+                "meta": {
+                    "node_id": unique_id,
+                    "display_node": display_node_id,
+                    "parent_node": parent_node_id,
+                    "real_node_id": real_node_id,
+                },
+                "output": output_ui
+            })
+            if server.client_id is not None:
+                server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
+        if has_subgraph:
+            cached_outputs = []
+            new_node_ids = []
+            new_output_ids = []
+            new_output_links = []
+            for i in range(len(output_data)):
+                new_graph, node_outputs = output_data[i]
+                if new_graph is None:
+                    cached_outputs.append((False, node_outputs))
+                else:
+                    # Check for conflicts
+                    for node_id in new_graph.keys():
+                        if dynprompt.has_node(node_id):
+                            raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.")
+                    for node_id, node_info in new_graph.items():
+                        new_node_ids.append(node_id)
+                        display_id = node_info.get("override_display_id", unique_id)
+                        dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id)
+                        # Figure out if the newly created node is an output node
+                        class_type = node_info["class_type"]
+                        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+                        if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                            new_output_ids.append(node_id)
+                    for i in range(len(node_outputs)):
+                        if is_link(node_outputs[i]):
+                            from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1]
+                            new_output_links.append((from_node_id, from_socket))
+                    cached_outputs.append((True, node_outputs))
+            new_node_ids = set(new_node_ids)
+            for cache in caches.all:
+                cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused()
+            for node_id in new_output_ids:
+                execution_list.add_node(node_id)
+            for link in new_output_links:
+                execution_list.add_strong_link(link[0], link[1], unique_id)
+            pending_subgraph_results[unique_id] = cached_outputs
+            return (ExecutionResult.PENDING, None, None)
+        caches.outputs.set(unique_id, output_data)
+    except comfy.model_management.InterruptProcessingException as iex:
+        logging.info("Processing interrupted")
+
+        # skip formatting inputs/outputs
+        error_details = {
+            "node_id": real_node_id,
+        }
+
+        return (ExecutionResult.FAILURE, error_details, iex)
+    except Exception as ex:
+        typ, _, tb = sys.exc_info()
+        exception_type = full_type_name(typ)
+        input_data_formatted = {}
+        if input_data_all is not None:
+            input_data_formatted = {}
+            for name, inputs in input_data_all.items():
+                input_data_formatted[name] = [format_value(x) for x in inputs]
+
+        logging.error(f"!!! Exception during processing !!! {ex}")
+        logging.error(traceback.format_exc())
+
+        error_details = {
+            "node_id": real_node_id,
+            "exception_message": str(ex),
+            "exception_type": exception_type,
+            "traceback": traceback.format_tb(tb),
+            "current_inputs": input_data_formatted
+        }
+        if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
+            logging.error("Got an OOM, unloading all loaded models.")
+            comfy.model_management.unload_all_models()
+
+        return (ExecutionResult.FAILURE, error_details, ex)
+
+    executed.add(unique_id)
+
+    return (ExecutionResult.SUCCESS, None, None)
+
+class PromptExecutor:
+    def __init__(self, server, cache_type=False, cache_size=None):
+        self.cache_size = cache_size
+        self.cache_type = cache_type
+        self.server = server
+        self.reset()
+
+    def reset(self):
+        self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size)
+        self.status_messages = []
+        self.success = True
+
+    def add_message(self, event, data: dict, broadcast: bool):
+        data = {
+            **data,
+            "timestamp": int(time.time() * 1000),
+        }
+        self.status_messages.append((event, data))
+        if self.server.client_id is not None or broadcast:
+            self.server.send_sync(event, data, self.server.client_id)
+
+    def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
+        node_id = error["node_id"]
+        class_type = prompt[node_id]["class_type"]
+
+        # First, send back the status to the frontend depending
+        # on the exception type
+        if isinstance(ex, comfy.model_management.InterruptProcessingException):
+            mes = {
+                "prompt_id": prompt_id,
+                "node_id": node_id,
+                "node_type": class_type,
+                "executed": list(executed),
+            }
+            self.add_message("execution_interrupted", mes, broadcast=True)
+        else:
+            mes = {
+                "prompt_id": prompt_id,
+                "node_id": node_id,
+                "node_type": class_type,
+                "executed": list(executed),
+                "exception_message": error["exception_message"],
+                "exception_type": error["exception_type"],
+                "traceback": error["traceback"],
+                "current_inputs": error["current_inputs"],
+                "current_outputs": list(current_outputs),
+            }
+            self.add_message("execution_error", mes, broadcast=False)
+
+    def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
+        nodes.interrupt_processing(False)
+
+        if "client_id" in extra_data:
+            self.server.client_id = extra_data["client_id"]
+        else:
+            self.server.client_id = None
+
+        self.status_messages = []
+        self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
+
+        with torch.inference_mode():
+            dynamic_prompt = DynamicPrompt(prompt)
+            is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs)
+            for cache in self.caches.all:
+                cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
+                cache.clean_unused()
+
+            cached_nodes = []
+            for node_id in prompt:
+                if self.caches.outputs.get(node_id) is not None:
+                    cached_nodes.append(node_id)
+
+            comfy.model_management.cleanup_models_gc()
+            self.add_message("execution_cached",
+                          { "nodes": cached_nodes, "prompt_id": prompt_id},
+                          broadcast=False)
+            pending_subgraph_results = {}
+            executed = set()
+            execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
+            current_outputs = self.caches.outputs.all_node_ids()
+            for node_id in list(execute_outputs):
+                execution_list.add_node(node_id)
+
+            while not execution_list.is_empty():
+                node_id, error, ex = execution_list.stage_node_execution()
+                if error is not None:
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                    break
+
+                result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
+                self.success = result != ExecutionResult.FAILURE
+                if result == ExecutionResult.FAILURE:
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                    break
+                elif result == ExecutionResult.PENDING:
+                    execution_list.unstage_node_execution()
+                else: # result == ExecutionResult.SUCCESS:
+                    execution_list.complete_node_execution()
+            else:
+                # Only execute when the while-loop ends without break
+                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+
+            ui_outputs = {}
+            meta_outputs = {}
+            all_node_ids = self.caches.ui.all_node_ids()
+            for node_id in all_node_ids:
+                ui_info = self.caches.ui.get(node_id)
+                if ui_info is not None:
+                    ui_outputs[node_id] = ui_info["output"]
+                    meta_outputs[node_id] = ui_info["meta"]
+            self.history_result = {
+                "outputs": ui_outputs,
+                "meta": meta_outputs,
+            }
+            self.server.last_node_id = None
+            if comfy.model_management.DISABLE_SMART_MEMORY:
+                comfy.model_management.unload_all_models()
+
+
+def validate_inputs(prompt, item, validated):
+    unique_id = item
+    if unique_id in validated:
+        return validated[unique_id]
+
+    inputs = prompt[unique_id]['inputs']
+    class_type = prompt[unique_id]['class_type']
+    obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
+
+    class_inputs = obj_class.INPUT_TYPES()
+    valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
+
+    errors = []
+    valid = True
+
+    validate_function_inputs = []
+    validate_has_kwargs = False
+    if hasattr(obj_class, "VALIDATE_INPUTS"):
+        argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS)
+        validate_function_inputs = argspec.args
+        validate_has_kwargs = argspec.varkw is not None
+    received_types = {}
+
+    for x in valid_inputs:
+        input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
+        assert extra_info is not None
+        if x not in inputs:
+            if input_category == "required":
+                error = {
+                    "type": "required_input_missing",
+                    "message": "Required input is missing",
+                    "details": f"{x}",
+                    "extra_info": {
+                        "input_name": x
+                    }
+                }
+                errors.append(error)
+            continue
+
+        val = inputs[x]
+        info = (input_type, extra_info)
+        if isinstance(val, list):
+            if len(val) != 2:
+                error = {
+                    "type": "bad_linked_input",
+                    "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
+                    "details": f"{x}",
+                    "extra_info": {
+                        "input_name": x,
+                        "input_config": info,
+                        "received_value": val
+                    }
+                }
+                errors.append(error)
+                continue
+
+            o_id = val[0]
+            o_class_type = prompt[o_id]['class_type']
+            r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
+            received_type = r[val[1]]
+            received_types[x] = received_type
+            if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type):
+                details = f"{x}, received_type({received_type}) mismatch input_type({input_type})"
+                error = {
+                    "type": "return_type_mismatch",
+                    "message": "Return type mismatch between linked nodes",
+                    "details": details,
+                    "extra_info": {
+                        "input_name": x,
+                        "input_config": info,
+                        "received_type": received_type,
+                        "linked_node": val
+                    }
+                }
+                errors.append(error)
+                continue
+            try:
+                r = validate_inputs(prompt, o_id, validated)
+                if r[0] is False:
+                    # `r` will be set in `validated[o_id]` already
+                    valid = False
+                    continue
+            except Exception as ex:
+                typ, _, tb = sys.exc_info()
+                valid = False
+                exception_type = full_type_name(typ)
+                reasons = [{
+                    "type": "exception_during_inner_validation",
+                    "message": "Exception when validating inner node",
+                    "details": str(ex),
+                    "extra_info": {
+                        "input_name": x,
+                        "input_config": info,
+                        "exception_message": str(ex),
+                        "exception_type": exception_type,
+                        "traceback": traceback.format_tb(tb),
+                        "linked_node": val
+                    }
+                }]
+                validated[o_id] = (False, reasons, o_id)
+                continue
+        else:
+            try:
+                # Unwraps values wrapped in __value__ key. This is used to pass
+                # list widget value to execution, as by default list value is
+                # reserved to represent the connection between nodes.
+                if isinstance(val, dict) and "__value__" in val:
+                    val = val["__value__"]
+                    inputs[x] = val
+
+                if input_type == "INT":
+                    val = int(val)
+                    inputs[x] = val
+                if input_type == "FLOAT":
+                    val = float(val)
+                    inputs[x] = val
+                if input_type == "STRING":
+                    val = str(val)
+                    inputs[x] = val
+                if input_type == "BOOLEAN":
+                    val = bool(val)
+                    inputs[x] = val
+            except Exception as ex:
+                error = {
+                    "type": "invalid_input_type",
+                    "message": f"Failed to convert an input value to a {input_type} value",
+                    "details": f"{x}, {val}, {ex}",
+                    "extra_info": {
+                        "input_name": x,
+                        "input_config": info,
+                        "received_value": val,
+                        "exception_message": str(ex)
+                    }
+                }
+                errors.append(error)
+                continue
+
+            if x not in validate_function_inputs and not validate_has_kwargs:
+                if "min" in extra_info and val < extra_info["min"]:
+                    error = {
+                        "type": "value_smaller_than_min",
+                        "message": "Value {} smaller than min of {}".format(val, extra_info["min"]),
+                        "details": f"{x}",
+                        "extra_info": {
+                            "input_name": x,
+                            "input_config": info,
+                            "received_value": val,
+                        }
+                    }
+                    errors.append(error)
+                    continue
+                if "max" in extra_info and val > extra_info["max"]:
+                    error = {
+                        "type": "value_bigger_than_max",
+                        "message": "Value {} bigger than max of {}".format(val, extra_info["max"]),
+                        "details": f"{x}",
+                        "extra_info": {
+                            "input_name": x,
+                            "input_config": info,
+                            "received_value": val,
+                        }
+                    }
+                    errors.append(error)
+                    continue
+
+                if isinstance(input_type, list):
+                    combo_options = input_type
+                    if val not in combo_options:
+                        input_config = info
+                        list_info = ""
+
+                        # Don't send back gigantic lists like if they're lots of
+                        # scanned model filepaths
+                        if len(combo_options) > 20:
+                            list_info = f"(list of length {len(combo_options)})"
+                            input_config = None
+                        else:
+                            list_info = str(combo_options)
+
+                        error = {
+                            "type": "value_not_in_list",
+                            "message": "Value not in list",
+                            "details": f"{x}: '{val}' not in {list_info}",
+                            "extra_info": {
+                                "input_name": x,
+                                "input_config": input_config,
+                                "received_value": val,
+                            }
+                        }
+                        errors.append(error)
+                        continue
+
+    if len(validate_function_inputs) > 0 or validate_has_kwargs:
+        input_data_all, _ = get_input_data(inputs, obj_class, unique_id)
+        input_filtered = {}
+        for x in input_data_all:
+            if x in validate_function_inputs or validate_has_kwargs:
+                input_filtered[x] = input_data_all[x]
+        if 'input_types' in validate_function_inputs:
+            input_filtered['input_types'] = [received_types]
+
+        #ret = obj_class.VALIDATE_INPUTS(**input_filtered)
+        ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
+        for x in input_filtered:
+            for i, r in enumerate(ret):
+                if r is not True and not isinstance(r, ExecutionBlocker):
+                    details = f"{x}"
+                    if r is not False:
+                        details += f" - {str(r)}"
+
+                    error = {
+                        "type": "custom_validation_failed",
+                        "message": "Custom validation failed for node",
+                        "details": details,
+                        "extra_info": {
+                            "input_name": x,
+                        }
+                    }
+                    errors.append(error)
+                    continue
+
+    if len(errors) > 0 or valid is not True:
+        ret = (False, errors, unique_id)
+    else:
+        ret = (True, [], unique_id)
+
+    validated[unique_id] = ret
+    return ret
+
+def full_type_name(klass):
+    module = klass.__module__
+    if module == 'builtins':
+        return klass.__qualname__
+    return module + '.' + klass.__qualname__
+
+def validate_prompt(prompt):
+    outputs = set()
+    for x in prompt:
+        if 'class_type' not in prompt[x]:
+            error = {
+                "type": "invalid_prompt",
+                "message": "Cannot execute because a node is missing the class_type property.",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], {})
+
+        class_type = prompt[x]['class_type']
+        class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
+        if class_ is None:
+            error = {
+                "type": "invalid_prompt",
+                "message": f"Cannot execute because node {class_type} does not exist.",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], {})
+
+        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
+            outputs.add(x)
+
+    if len(outputs) == 0:
+        error = {
+            "type": "prompt_no_outputs",
+            "message": "Prompt has no outputs",
+            "details": "",
+            "extra_info": {}
+        }
+        return (False, error, [], {})
+
+    good_outputs = set()
+    errors = []
+    node_errors = {}
+    validated = {}
+    for o in outputs:
+        valid = False
+        reasons = []
+        try:
+            m = validate_inputs(prompt, o, validated)
+            valid = m[0]
+            reasons = m[1]
+        except Exception as ex:
+            typ, _, tb = sys.exc_info()
+            valid = False
+            exception_type = full_type_name(typ)
+            reasons = [{
+                "type": "exception_during_validation",
+                "message": "Exception when validating node",
+                "details": str(ex),
+                "extra_info": {
+                    "exception_type": exception_type,
+                    "traceback": traceback.format_tb(tb)
+                }
+            }]
+            validated[o] = (False, reasons, o)
+
+        if valid is True:
+            good_outputs.add(o)
+        else:
+            logging.error(f"Failed to validate prompt for output {o}:")
+            if len(reasons) > 0:
+                logging.error("* (prompt):")
+                for reason in reasons:
+                    logging.error(f"  - {reason['message']}: {reason['details']}")
+            errors += [(o, reasons)]
+            for node_id, result in validated.items():
+                valid = result[0]
+                reasons = result[1]
+                # If a node upstream has errors, the nodes downstream will also
+                # be reported as invalid, but there will be no errors attached.
+                # So don't return those nodes as having errors in the response.
+                if valid is not True and len(reasons) > 0:
+                    if node_id not in node_errors:
+                        class_type = prompt[node_id]['class_type']
+                        node_errors[node_id] = {
+                            "errors": reasons,
+                            "dependent_outputs": [],
+                            "class_type": class_type
+                        }
+                        logging.error(f"* {class_type} {node_id}:")
+                        for reason in reasons:
+                            logging.error(f"  - {reason['message']}: {reason['details']}")
+                    node_errors[node_id]["dependent_outputs"].append(o)
+            logging.error("Output will be ignored")
+
+    if len(good_outputs) == 0:
+        errors_list = []
+        for o, errors in errors:
+            for error in errors:
+                errors_list.append(f"{error['message']}: {error['details']}")
+        errors_list = "\n".join(errors_list)
+
+        error = {
+            "type": "prompt_outputs_failed_validation",
+            "message": "Prompt outputs failed validation",
+            "details": errors_list,
+            "extra_info": {}
+        }
+
+        return (False, error, list(good_outputs), node_errors)
+
+    return (True, None, list(good_outputs), node_errors)
+
+MAXIMUM_HISTORY_SIZE = 10000
+
+class PromptQueue:
+    def __init__(self, server):
+        self.server = server
+        self.mutex = threading.RLock()
+        self.not_empty = threading.Condition(self.mutex)
+        self.task_counter = 0
+        self.queue = []
+        self.currently_running = {}
+        self.history = {}
+        self.flags = {}
+
+    def put(self, item):
+        with self.mutex:
+            heapq.heappush(self.queue, item)
+            self.server.queue_updated()
+            self.not_empty.notify()
+
+    def get(self, timeout=None):
+        with self.not_empty:
+            while len(self.queue) == 0:
+                self.not_empty.wait(timeout=timeout)
+                if timeout is not None and len(self.queue) == 0:
+                    return None
+            item = heapq.heappop(self.queue)
+            i = self.task_counter
+            self.currently_running[i] = copy.deepcopy(item)
+            self.task_counter += 1
+            self.server.queue_updated()
+            return (item, i)
+
+    class ExecutionStatus(NamedTuple):
+        status_str: Literal['success', 'error']
+        completed: bool
+        messages: List[str]
+
+    def task_done(self, item_id, history_result,
+                  status: Optional['PromptQueue.ExecutionStatus']):
+        with self.mutex:
+            prompt = self.currently_running.pop(item_id)
+            if len(self.history) > MAXIMUM_HISTORY_SIZE:
+                self.history.pop(next(iter(self.history)))
+
+            status_dict: Optional[dict] = None
+            if status is not None:
+                status_dict = copy.deepcopy(status._asdict())
+
+            self.history[prompt[1]] = {
+                "prompt": prompt,
+                "outputs": {},
+                'status': status_dict,
+            }
+            self.history[prompt[1]].update(history_result)
+            self.server.queue_updated()
+
+    # Note: slow
+    def get_current_queue(self):
+        with self.mutex:
+            out = []
+            for x in self.currently_running.values():
+                out += [x]
+            return (out, copy.deepcopy(self.queue))
+
+    # read-safe as long as queue items are immutable
+    def get_current_queue_volatile(self):
+        with self.mutex:
+            running = [x for x in self.currently_running.values()]
+            queued = copy.copy(self.queue)
+            return (running, queued)
+
+    def get_tasks_remaining(self):
+        with self.mutex:
+            return len(self.queue) + len(self.currently_running)
+
+    def wipe_queue(self):
+        with self.mutex:
+            self.queue = []
+            self.server.queue_updated()
+
+    def delete_queue_item(self, function):
+        with self.mutex:
+            for x in range(len(self.queue)):
+                if function(self.queue[x]):
+                    if len(self.queue) == 1:
+                        self.wipe_queue()
+                    else:
+                        self.queue.pop(x)
+                        heapq.heapify(self.queue)
+                    self.server.queue_updated()
+                    return True
+        return False
+
+    def get_history(self, prompt_id=None, max_items=None, offset=-1):
+        with self.mutex:
+            if prompt_id is None:
+                out = {}
+                i = 0
+                if offset < 0 and max_items is not None:
+                    offset = len(self.history) - max_items
+                for k in self.history:
+                    if i >= offset:
+                        out[k] = self.history[k]
+                        if max_items is not None and len(out) >= max_items:
+                            break
+                    i += 1
+                return out
+            elif prompt_id in self.history:
+                return {prompt_id: copy.deepcopy(self.history[prompt_id])}
+            else:
+                return {}
+
+    def wipe_history(self):
+        with self.mutex:
+            self.history = {}
+
+    def delete_history_item(self, id_to_delete):
+        with self.mutex:
+            self.history.pop(id_to_delete, None)
+
+    def set_flag(self, name, data):
+        with self.mutex:
+            self.flags[name] = data
+            self.not_empty.notify()
+
+    def get_flags(self, reset=True):
+        with self.mutex:
+            if reset:
+                ret = self.flags
+                self.flags = {}
+                return ret
+            else:
+                return self.flags.copy()

extra_model_paths.yaml.example

@@ -0,0 +1,47 @@
+#Rename this to extra_model_paths.yaml and ComfyUI will load it
+
+
+#config for a1111 ui
+#all you have to do is change the base_path to where yours is installed
+a111:
+    base_path: path/to/stable-diffusion-webui/
+
+    checkpoints: models/Stable-diffusion
+    configs: models/Stable-diffusion
+    vae: models/VAE
+    loras: |
+         models/Lora
+         models/LyCORIS
+    upscale_models: |
+                  models/ESRGAN
+                  models/RealESRGAN
+                  models/SwinIR
+    embeddings: embeddings
+    hypernetworks: models/hypernetworks
+    controlnet: models/ControlNet
+
+#config for comfyui
+#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc.
+
+#comfyui:
+#     base_path: path/to/comfyui/
+#     # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads
+#     #is_default: true
+#     checkpoints: models/checkpoints/
+#     clip: models/clip/
+#     clip_vision: models/clip_vision/
+#     configs: models/configs/
+#     controlnet: models/controlnet/
+#     diffusion_models: |
+#                  models/diffusion_models
+#                  models/unet
+#     embeddings: models/embeddings/
+#     loras: models/loras/
+#     upscale_models: models/upscale_models/
+#     vae: models/vae/
+
+#other_ui:
+#    base_path: path/to/ui
+#    checkpoints: models/checkpoints
+#    gligen: models/gligen
+#    custom_nodes: path/custom_nodes

folder_paths.py

@@ -0,0 +1,396 @@
+from __future__ import annotations
+
+import os
+import time
+import mimetypes
+import logging
+from typing import Literal, List
+from collections.abc import Collection
+
+from comfy.cli_args import args
+
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+
+folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
+
+# --base-directory - Resets all default paths configured in folder_paths with a new base path
+if args.base_directory:
+    base_path = os.path.abspath(args.base_directory)
+else:
+    base_path = os.path.dirname(os.path.realpath(__file__))
+
+models_dir = os.path.join(base_path, "models")
+folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
+folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
+
+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["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)
+folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
+folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
+folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)
+
+folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
+folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
+
+folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
+
+folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set())
+
+folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
+
+folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
+
+folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
+
+output_directory = os.path.join(base_path, "output")
+temp_directory = os.path.join(base_path, "temp")
+input_directory = os.path.join(base_path, "input")
+user_directory = os.path.join(base_path, "user")
+
+filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
+
+class CacheHelper:
+    """
+    Helper class for managing file list cache data.
+    """
+    def __init__(self):
+        self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
+        self.active = False
+
+    def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]:
+        if not self.active:
+            return default
+        return self.cache.get(key, default)
+
+    def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None:
+        if self.active:
+            self.cache[key] = value
+
+    def clear(self):
+        self.cache.clear()
+
+    def __enter__(self):
+        self.active = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.active = False
+        self.clear()
+
+cache_helper = CacheHelper()
+
+extension_mimetypes_cache = {
+    "webp" : "image",
+    "fbx" : "model",
+}
+
+def map_legacy(folder_name: str) -> str:
+    legacy = {"unet": "diffusion_models",
+              "clip": "text_encoders"}
+    return legacy.get(folder_name, folder_name)
+
+if not os.path.exists(input_directory):
+    try:
+        os.makedirs(input_directory)
+    except:
+        logging.error("Failed to create input directory")
+
+def set_output_directory(output_dir: str) -> None:
+    global output_directory
+    output_directory = output_dir
+
+def set_temp_directory(temp_dir: str) -> None:
+    global temp_directory
+    temp_directory = temp_dir
+
+def set_input_directory(input_dir: str) -> None:
+    global input_directory
+    input_directory = input_dir
+
+def get_output_directory() -> str:
+    global output_directory
+    return output_directory
+
+def get_temp_directory() -> str:
+    global temp_directory
+    return temp_directory
+
+def get_input_directory() -> str:
+    global input_directory
+    return input_directory
+
+def get_user_directory() -> str:
+    return user_directory
+
+def set_user_directory(user_dir: str) -> None:
+    global user_directory
+    user_directory = user_dir
+
+
+#NOTE: used in http server so don't put folders that should not be accessed remotely
+def get_directory_by_type(type_name: str) -> str | None:
+    if type_name == "output":
+        return get_output_directory()
+    if type_name == "temp":
+        return get_temp_directory()
+    if type_name == "input":
+        return get_input_directory()
+    return None
+
+def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]:
+    """
+    Example:
+        files = os.listdir(folder_paths.get_input_directory())
+        videos = filter_files_content_types(files, ["video"])
+
+    Note:
+        - 'model' in MIME context refers to 3D models, not files containing trained weights and parameters
+    """
+    global extension_mimetypes_cache
+    result = []
+    for file in files:
+        extension = file.split('.')[-1]
+        if extension not in extension_mimetypes_cache:
+            mime_type, _ = mimetypes.guess_type(file, strict=False)
+            if not mime_type:
+                continue
+            content_type = mime_type.split('/')[0]
+            extension_mimetypes_cache[extension] = content_type
+        else:
+            content_type = extension_mimetypes_cache[extension]
+
+        if content_type in content_types:
+            result.append(file)
+    return result
+
+# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
+# otherwise use default_path as base_dir
+def annotated_filepath(name: str) -> tuple[str, str | None]:
+    if name.endswith("[output]"):
+        base_dir = get_output_directory()
+        name = name[:-9]
+    elif name.endswith("[input]"):
+        base_dir = get_input_directory()
+        name = name[:-8]
+    elif name.endswith("[temp]"):
+        base_dir = get_temp_directory()
+        name = name[:-7]
+    else:
+        return name, None
+
+    return name, base_dir
+
+
+def get_annotated_filepath(name: str, default_dir: str | None=None) -> str:
+    name, base_dir = annotated_filepath(name)
+
+    if base_dir is None:
+        if default_dir is not None:
+            base_dir = default_dir
+        else:
+            base_dir = get_input_directory()  # fallback path
+
+    return os.path.join(base_dir, name)
+
+
+def exists_annotated_filepath(name) -> bool:
+    name, base_dir = annotated_filepath(name)
+
+    if base_dir is None:
+        base_dir = get_input_directory()  # fallback path
+
+    filepath = os.path.join(base_dir, name)
+    return os.path.exists(filepath)
+
+
+def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None:
+    global folder_names_and_paths
+    folder_name = map_legacy(folder_name)
+    if folder_name in folder_names_and_paths:
+        paths, _exts = folder_names_and_paths[folder_name]
+        if full_folder_path in paths:
+            if is_default and paths[0] != full_folder_path:
+                # If the path to the folder is not the first in the list, move it to the beginning.
+                paths.remove(full_folder_path)
+                paths.insert(0, full_folder_path)
+        else:
+            if is_default:
+                paths.insert(0, full_folder_path)
+            else:
+                paths.append(full_folder_path)
+    else:
+        folder_names_and_paths[folder_name] = ([full_folder_path], set())
+
+def get_folder_paths(folder_name: str) -> list[str]:
+    folder_name = map_legacy(folder_name)
+    return folder_names_and_paths[folder_name][0][:]
+
+def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]:
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {}
+
+    # Attempt to add the initial directory to dirs with error handling
+    try:
+        dirs[directory] = os.path.getmtime(directory)
+    except FileNotFoundError:
+        logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
+
+    logging.debug("recursive file list on directory {}".format(directory))
+    dirpath: str
+    subdirs: list[str]
+    filenames: list[str]
+
+    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+        for file_name in filenames:
+            try:
+                relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                result.append(relative_path)
+            except:
+                logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
+                continue
+
+        for d in subdirs:
+            path: str = os.path.join(dirpath, d)
+            try:
+                dirs[path] = os.path.getmtime(path)
+            except FileNotFoundError:
+                logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
+                continue
+    logging.debug("found {} files".format(len(result)))
+    return result, dirs
+
+def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]:
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
+
+
+
+def get_full_path(folder_name: str, filename: str) -> str | None:
+    global folder_names_and_paths
+    folder_name = map_legacy(folder_name)
+    if folder_name not in folder_names_and_paths:
+        return None
+    folders = folder_names_and_paths[folder_name]
+    filename = os.path.relpath(os.path.join("/", filename), "/")
+    for x in folders[0]:
+        full_path = os.path.join(x, filename)
+        if os.path.isfile(full_path):
+            return full_path
+        elif os.path.islink(full_path):
+            logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path))
+
+    return None
+
+
+def get_full_path_or_raise(folder_name: str, filename: str) -> str:
+    full_path = get_full_path(folder_name, filename)
+    if full_path is None:
+        raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.")
+    return full_path
+
+
+def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]:
+    folder_name = map_legacy(folder_name)
+    global folder_names_and_paths
+    output_list = set()
+    folders = folder_names_and_paths[folder_name]
+    output_folders = {}
+    for x in folders[0]:
+        files, folders_all = recursive_search(x, excluded_dir_names=[".git"])
+        output_list.update(filter_files_extensions(files, folders[1]))
+        output_folders = {**output_folders, **folders_all}
+
+    return sorted(list(output_list)), output_folders, time.perf_counter()
+
+def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None:
+    strong_cache = cache_helper.get(folder_name)
+    if strong_cache is not None:
+        return strong_cache
+
+    global filename_list_cache
+    global folder_names_and_paths
+    folder_name = map_legacy(folder_name)
+    if folder_name not in filename_list_cache:
+        return None
+    out = filename_list_cache[folder_name]
+
+    for x in out[1]:
+        time_modified = out[1][x]
+        folder = x
+        if os.path.getmtime(folder) != time_modified:
+            return None
+
+    folders = folder_names_and_paths[folder_name]
+    for x in folders[0]:
+        if os.path.isdir(x):
+            if x not in out[1]:
+                return None
+
+    return out
+
+def get_filename_list(folder_name: str) -> list[str]:
+    folder_name = map_legacy(folder_name)
+    out = cached_filename_list_(folder_name)
+    if out is None:
+        out = get_filename_list_(folder_name)
+        global filename_list_cache
+        filename_list_cache[folder_name] = out
+    cache_helper.set(folder_name, out)
+    return list(out[0])
+
+def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]:
+    def map_filename(filename: str) -> tuple[int, str]:
+        prefix_len = len(os.path.basename(filename_prefix))
+        prefix = filename[:prefix_len + 1]
+        try:
+            digits = int(filename[prefix_len + 1:].split('_')[0])
+        except:
+            digits = 0
+        return digits, prefix
+
+    def compute_vars(input: str, image_width: int, image_height: int) -> str:
+        input = input.replace("%width%", str(image_width))
+        input = input.replace("%height%", str(image_height))
+        now = time.localtime()
+        input = input.replace("%year%", str(now.tm_year))
+        input = input.replace("%month%", str(now.tm_mon).zfill(2))
+        input = input.replace("%day%", str(now.tm_mday).zfill(2))
+        input = input.replace("%hour%", str(now.tm_hour).zfill(2))
+        input = input.replace("%minute%", str(now.tm_min).zfill(2))
+        input = input.replace("%second%", str(now.tm_sec).zfill(2))
+        return input
+
+    if "%" in filename_prefix:
+        filename_prefix = compute_vars(filename_prefix, image_width, image_height)
+
+    subfolder = os.path.dirname(os.path.normpath(filename_prefix))
+    filename = os.path.basename(os.path.normpath(filename_prefix))
+
+    full_output_folder = os.path.join(output_dir, subfolder)
+
+    if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir:
+        err = "**** ERROR: Saving image outside the output folder is not allowed." + \
+              "\n full_output_folder: " + os.path.abspath(full_output_folder) + \
+              "\n         output_dir: " + output_dir + \
+              "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder)))
+        logging.error(err)
+        raise Exception(err)
+
+    try:
+        counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+    except ValueError:
+        counter = 1
+    except FileNotFoundError:
+        os.makedirs(full_output_folder, exist_ok=True)
+        counter = 1
+    return full_output_folder, filename, counter, subfolder, filename_prefix

hook_breaker_ac10a0.py

@@ -0,0 +1,17 @@
+# Prevent custom nodes from hooking anything important
+import comfy.model_management
+
+HOOK_BREAK = [(comfy.model_management, "cast_to")]
+
+
+SAVED_FUNCTIONS = []
+
+
+def save_functions():
+    for f in HOOK_BREAK:
+        SAVED_FUNCTIONS.append((f[0], f[1], getattr(f[0], f[1])))
+
+
+def restore_functions():
+    for f in SAVED_FUNCTIONS:
+        setattr(f[0], f[1], f[2])

latent_preview.py

@@ -0,0 +1,108 @@
+import torch
+from PIL import Image
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import comfy.model_management
+import folder_paths
+import comfy.utils
+import logging
+
+MAX_PREVIEW_RESOLUTION = args.preview_size
+
+def preview_to_image(latent_image):
+        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            )
+        if comfy.model_management.directml_enabled:
+                latents_ubyte = latents_ubyte.to(dtype=torch.uint8)
+        latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+class LatentPreviewer:
+    def decode_latent_to_preview(self, x0):
+        pass
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        preview_image = self.decode_latent_to_preview(x0)
+        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
+
+class TAESDPreviewerImpl(LatentPreviewer):
+    def __init__(self, taesd):
+        self.taesd = taesd
+
+    def decode_latent_to_preview(self, x0):
+        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
+        return preview_to_image(x_sample)
+
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = None
+        if latent_rgb_factors_bias is not None:
+            self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
+
+    def decode_latent_to_preview(self, x0):
+        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+        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)
+
+        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)
+
+
+def get_previewer(device, latent_format):
+    previewer = None
+    method = args.preview_method
+    if method != LatentPreviewMethod.NoPreviews:
+        # TODO previewer methods
+        taesd_decoder_path = None
+        if latent_format.taesd_decoder_name is not None:
+            taesd_decoder_path = next(
+                (fn for fn in folder_paths.get_filename_list("vae_approx")
+                    if fn.startswith(latent_format.taesd_decoder_name)),
+                ""
+            )
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+
+        if method == LatentPreviewMethod.TAESD:
+            if taesd_decoder_path:
+                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
+                previewer = TAESDPreviewerImpl(taesd)
+            else:
+                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+
+        if previewer is None:
+            if latent_format.latent_rgb_factors is not None:
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
+    return previewer
+
+def prepare_callback(model, steps, x0_output_dict=None):
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = get_previewer(model.load_device, model.model.latent_format)
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        if x0_output_dict is not None:
+            x0_output_dict["x0"] = x0
+
+        preview_bytes = None
+        if previewer:
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
+        pbar.update_absolute(step + 1, total_steps, preview_bytes)
+    return callback
+

main.py

@@ -0,0 +1,311 @@
+import comfy.options
+comfy.options.enable_args_parsing()
+
+import os
+import importlib.util
+import folder_paths
+import time
+from comfy.cli_args import args
+from app.logger import setup_logger
+import itertools
+import utils.extra_config
+import logging
+import sys
+
+if __name__ == "__main__":
+    #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
+    os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
+    os.environ['DO_NOT_TRACK'] = '1'
+
+
+setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
+
+def apply_custom_paths():
+    # extra model paths
+    extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
+    if os.path.isfile(extra_model_paths_config_path):
+        utils.extra_config.load_extra_path_config(extra_model_paths_config_path)
+
+    if args.extra_model_paths_config:
+        for config_path in itertools.chain(*args.extra_model_paths_config):
+            utils.extra_config.load_extra_path_config(config_path)
+
+    # --output-directory, --input-directory, --user-directory
+    if args.output_directory:
+        output_dir = os.path.abspath(args.output_directory)
+        logging.info(f"Setting output directory to: {output_dir}")
+        folder_paths.set_output_directory(output_dir)
+
+    # These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
+    folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints"))
+    folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
+    folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
+    folder_paths.add_model_folder_path("diffusion_models",
+                                       os.path.join(folder_paths.get_output_directory(), "diffusion_models"))
+    folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras"))
+
+    if args.input_directory:
+        input_dir = os.path.abspath(args.input_directory)
+        logging.info(f"Setting input directory to: {input_dir}")
+        folder_paths.set_input_directory(input_dir)
+
+    if args.user_directory:
+        user_dir = os.path.abspath(args.user_directory)
+        logging.info(f"Setting user directory to: {user_dir}")
+        folder_paths.set_user_directory(user_dir)
+
+
+def execute_prestartup_script():
+    def execute_script(script_path):
+        module_name = os.path.splitext(script_path)[0]
+        try:
+            spec = importlib.util.spec_from_file_location(module_name, script_path)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+            return True
+        except Exception as e:
+            logging.error(f"Failed to execute startup-script: {script_path} / {e}")
+        return False
+
+    if args.disable_all_custom_nodes:
+        return
+
+    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    for custom_node_path in node_paths:
+        possible_modules = os.listdir(custom_node_path)
+        node_prestartup_times = []
+
+        for possible_module in possible_modules:
+            module_path = os.path.join(custom_node_path, possible_module)
+            if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__":
+                continue
+
+            script_path = os.path.join(module_path, "prestartup_script.py")
+            if os.path.exists(script_path):
+                time_before = time.perf_counter()
+                success = execute_script(script_path)
+                node_prestartup_times.append((time.perf_counter() - time_before, module_path, success))
+    if len(node_prestartup_times) > 0:
+        logging.info("\nPrestartup times for custom nodes:")
+        for n in sorted(node_prestartup_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (PRESTARTUP FAILED)"
+            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
+        logging.info("")
+
+apply_custom_paths()
+execute_prestartup_script()
+
+
+# Main code
+import asyncio
+import shutil
+import threading
+import gc
+
+
+if os.name == "nt":
+    logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+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.oneapi_device_selector is not None:
+        os.environ['ONEAPI_DEVICE_SELECTOR'] = args.oneapi_device_selector
+        logging.info("Set oneapi device selector to: {}".format(args.oneapi_device_selector))
+
+    if args.deterministic:
+        if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
+            os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8"
+
+    import cuda_malloc
+
+import comfy.utils
+
+import execution
+import server
+from server import BinaryEventTypes
+import nodes
+import comfy.model_management
+import comfyui_version
+import app.logger
+import hook_breaker_ac10a0
+
+def cuda_malloc_warning():
+    device = comfy.model_management.get_torch_device()
+    device_name = comfy.model_management.get_torch_device_name(device)
+    cuda_malloc_warning = False
+    if "cudaMallocAsync" in device_name:
+        for b in cuda_malloc.blacklist:
+            if b in device_name:
+                cuda_malloc_warning = True
+        if cuda_malloc_warning:
+            logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
+
+
+def prompt_worker(q, server_instance):
+    current_time: float = 0.0
+    cache_type = execution.CacheType.CLASSIC
+    if args.cache_lru > 0:
+        cache_type = execution.CacheType.LRU
+    elif args.cache_none:
+        cache_type = execution.CacheType.DEPENDENCY_AWARE
+
+    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
+    last_gc_collect = 0
+    need_gc = False
+    gc_collect_interval = 10.0
+
+    while True:
+        timeout = 1000.0
+        if need_gc:
+            timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0)
+
+        queue_item = q.get(timeout=timeout)
+        if queue_item is not None:
+            item, item_id = queue_item
+            execution_start_time = time.perf_counter()
+            prompt_id = item[1]
+            server_instance.last_prompt_id = prompt_id
+
+            e.execute(item[2], prompt_id, item[3], item[4])
+            need_gc = True
+            q.task_done(item_id,
+                        e.history_result,
+                        status=execution.PromptQueue.ExecutionStatus(
+                            status_str='success' if e.success else 'error',
+                            completed=e.success,
+                            messages=e.status_messages))
+            if server_instance.client_id is not None:
+                server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
+
+            current_time = time.perf_counter()
+            execution_time = current_time - execution_start_time
+            logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
+
+        flags = q.get_flags()
+        free_memory = flags.get("free_memory", False)
+
+        if flags.get("unload_models", free_memory):
+            comfy.model_management.unload_all_models()
+            need_gc = True
+            last_gc_collect = 0
+
+        if free_memory:
+            e.reset()
+            need_gc = True
+            last_gc_collect = 0
+
+        if need_gc:
+            current_time = time.perf_counter()
+            if (current_time - last_gc_collect) > gc_collect_interval:
+                gc.collect()
+                comfy.model_management.soft_empty_cache()
+                last_gc_collect = current_time
+                need_gc = False
+                hook_breaker_ac10a0.restore_functions()
+
+
+async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None):
+    addresses = []
+    for addr in address.split(","):
+        addresses.append((addr, port))
+    await asyncio.gather(
+        server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop()
+    )
+
+
+def hijack_progress(server_instance):
+    def hook(value, total, preview_image):
+        comfy.model_management.throw_exception_if_processing_interrupted()
+        progress = {"value": value, "max": total, "prompt_id": server_instance.last_prompt_id, "node": server_instance.last_node_id}
+
+        server_instance.send_sync("progress", progress, server_instance.client_id)
+        if preview_image is not None:
+            server_instance.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server_instance.client_id)
+
+    comfy.utils.set_progress_bar_global_hook(hook)
+
+
+def cleanup_temp():
+    temp_dir = folder_paths.get_temp_directory()
+    if os.path.exists(temp_dir):
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+def start_comfyui(asyncio_loop=None):
+    """
+    Starts the ComfyUI server using the provided asyncio event loop or creates a new one.
+    Returns the event loop, server instance, and a function to start the server asynchronously.
+    """
+    if args.temp_directory:
+        temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
+        logging.info(f"Setting temp directory to: {temp_dir}")
+        folder_paths.set_temp_directory(temp_dir)
+    cleanup_temp()
+
+    if args.windows_standalone_build:
+        try:
+            import new_updater
+            new_updater.update_windows_updater()
+        except:
+            pass
+
+    if not asyncio_loop:
+        asyncio_loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(asyncio_loop)
+    prompt_server = server.PromptServer(asyncio_loop)
+
+    hook_breaker_ac10a0.save_functions()
+    nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes)
+    hook_breaker_ac10a0.restore_functions()
+
+    cuda_malloc_warning()
+
+    prompt_server.add_routes()
+    hijack_progress(prompt_server)
+
+    threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start()
+
+    if args.quick_test_for_ci:
+        exit(0)
+
+    os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+    call_on_start = None
+    if args.auto_launch:
+        def startup_server(scheme, address, port):
+            import webbrowser
+            if os.name == 'nt' and address == '0.0.0.0':
+                address = '127.0.0.1'
+            if ':' in address:
+                address = "[{}]".format(address)
+            webbrowser.open(f"{scheme}://{address}:{port}")
+        call_on_start = startup_server
+
+    async def start_all():
+        await prompt_server.setup()
+        await run(prompt_server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)
+
+    # Returning these so that other code can integrate with the ComfyUI loop and server
+    return asyncio_loop, prompt_server, start_all
+
+
+if __name__ == "__main__":
+    # Running directly, just start ComfyUI.
+    logging.info("Python version: {}".format(sys.version))
+    logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
+
+    event_loop, _, start_all_func = start_comfyui()
+    try:
+        x = start_all_func()
+        app.logger.print_startup_warnings()
+        event_loop.run_until_complete(x)
+    except KeyboardInterrupt:
+        logging.info("\nStopped server")
+
+    cleanup_temp()

my_workflow.py

@@ -0,0 +1,213 @@
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping.
+
+    If the object is a sequence (like list or string), returns the value at the given index.
+    If the object is a mapping (like a dictionary), returns the value at the index-th key.
+
+    Some return a dictionary, in these cases, we look for the "results" key
+
+    Args:
+        obj (Union[Sequence, Mapping]): The object to retrieve the value from.
+        index (int): The index of the value to retrieve.
+
+    Returns:
+        Any: The value at the given index.
+
+    Raises:
+        IndexError: If the index is out of bounds for the object and the object is not a mapping.
+    """
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+
+def find_path(name: str, path: str = None) -> str:
+    """
+    Recursively looks at parent folders starting from the given path until it finds the given name.
+    Returns the path as a Path object if found, or None otherwise.
+    """
+    # If no path is given, use the current working directory
+    if path is None:
+        path = os.getcwd()
+
+    # Check if the current directory contains the name
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(path)
+
+    # If the parent directory is the same as the current directory, we've reached the root and stop the search
+    if parent_directory == path:
+        return None
+
+    # Recursively call the function with the parent directory
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """
+    Add 'ComfyUI' to the sys.path
+    """
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """
+    Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
+    """
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print(
+            "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
+        )
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+
+
+def import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    from main import apply_custom_paths, execute_prestartup_script
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    # Creating an instance of PromptServer with the loop
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+
+    # Initializing custom nodes
+    init_extra_nodes()
+
+    apply_custom_paths()
+    execute_prestartup_script()
+
+
+from nodes import (
+    CLIPTextEncode,
+    NODE_CLASS_MAPPINGS,
+    CLIPLoader,
+    VAEDecode,
+    UNETLoader,
+    VAELoader,
+    SaveImage,
+)
+
+
+def main():
+    import_custom_nodes()
+    with torch.inference_mode():
+        randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+        randomnoise_68 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
+
+        emptysd3latentimage = NODE_CLASS_MAPPINGS["EmptySD3LatentImage"]()
+        emptysd3latentimage_69 = emptysd3latentimage.generate(
+            width=1024, height=1024, batch_size=1
+        )
+
+        ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
+        ksamplerselect_72 = ksamplerselect.get_sampler(sampler_name="euler")
+
+        cliploader = CLIPLoader()
+        cliploader_78 = cliploader.load_clip(
+            clip_name="t5xxl_fp8_e4m3fn.safetensors", type="chroma", device="default"
+        )
+
+        t5tokenizeroptions = NODE_CLASS_MAPPINGS["T5TokenizerOptions"]()
+        t5tokenizeroptions_82 = t5tokenizeroptions.set_options(
+            min_padding=1, min_length=0, clip=get_value_at_index(cliploader_78, 0)
+        )
+
+        cliptextencode = CLIPTextEncode()
+        cliptextencode_74 = cliptextencode.encode(
+            text="Extreme close-up photograph of a single tiger eye, direct frontal view. The iris is very detailed and the pupil resembling a dark void. The word 'Chroma' is across the lower portion of the image in large white stylized letters, with brush strokes resembling those made with Japanese calligraphy. Each strand of the thick fur is highly detailed and distinguishable. Natural lighting to capture authentic eye shine and depth.",
+            clip=get_value_at_index(t5tokenizeroptions_82, 0),
+        )
+
+        cliptextencode_75 = cliptextencode.encode(
+            text="low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors",
+            clip=get_value_at_index(t5tokenizeroptions_82, 0),
+        )
+
+        unetloader = UNETLoader()
+        unetloader_76 = unetloader.load_unet(
+            unet_name="chroma-unlocked-v31.safetensors", weight_dtype="fp8_e4m3fn"
+        )
+
+        vaeloader = VAELoader()
+        vaeloader_80 = vaeloader.load_vae(vae_name="ae.safetensors")
+
+        cfgguider = NODE_CLASS_MAPPINGS["CFGGuider"]()
+        basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
+        samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
+        vaedecode = VAEDecode()
+        saveimage = SaveImage()
+
+        for q in range(1):
+            cfgguider_73 = cfgguider.get_guider(
+                cfg=4,
+                model=get_value_at_index(unetloader_76, 0),
+                positive=get_value_at_index(cliptextencode_74, 0),
+                negative=get_value_at_index(cliptextencode_75, 0),
+            )
+
+            basicscheduler_84 = basicscheduler.get_sigmas(
+                scheduler="beta",
+                steps=26,
+                denoise=1,
+                model=get_value_at_index(unetloader_76, 0),
+            )
+
+            samplercustomadvanced_67 = samplercustomadvanced.sample(
+                noise=get_value_at_index(randomnoise_68, 0),
+                guider=get_value_at_index(cfgguider_73, 0),
+                sampler=get_value_at_index(ksamplerselect_72, 0),
+                sigmas=get_value_at_index(basicscheduler_84, 0),
+                latent_image=get_value_at_index(emptysd3latentimage_69, 0),
+            )
+
+            vaedecode_79 = vaedecode.decode(
+                samples=get_value_at_index(samplercustomadvanced_67, 0),
+                vae=get_value_at_index(vaeloader_80, 0),
+            )
+
+            saveimage_81 = saveimage.save_images(
+                filename_prefix="2025-05-24/ComfyUI",
+                images=get_value_at_index(vaedecode_79, 0),
+            )
+
+
+if __name__ == "__main__":
+    main()

new_updater.py

@@ -0,0 +1,35 @@
+import os
+import shutil
+
+base_path = os.path.dirname(os.path.realpath(__file__))
+
+
+def update_windows_updater():
+    top_path = os.path.dirname(base_path)
+    updater_path = os.path.join(base_path, ".ci/update_windows/update.py")
+    bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat")
+
+    dest_updater_path = os.path.join(top_path, "update/update.py")
+    dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat")
+    dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat")
+
+    try:
+        with open(dest_bat_path, 'rb') as f:
+            contents = f.read()
+    except:
+        return
+
+    if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"):
+        return
+
+    shutil.copy(updater_path, dest_updater_path)
+    try:
+        with open(dest_bat_deps_path, 'rb') as f:
+            contents = f.read()
+            contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause')
+        with open(dest_bat_deps_path, 'wb') as f:
+            f.write(contents)
+    except:
+        pass
+    shutil.copy(bat_path, dest_bat_path)
+    print("Updated the windows standalone package updater.")  # noqa: T201

node_helpers.py

@@ -0,0 +1,60 @@
+import hashlib
+import torch
+
+from comfy.cli_args import args
+
+from PIL import ImageFile, UnidentifiedImageError
+
+def conditioning_set_values(conditioning, values={}, append=False):
+    c = []
+    for t in conditioning:
+        n = [t[0], t[1].copy()]
+        for k in values:
+            val = values[k]
+            if append:
+                old_val = n[1].get(k, None)
+                if old_val is not None:
+                    val = old_val + val
+
+            n[1][k] = val
+        c.append(n)
+
+    return c
+
+def pillow(fn, arg):
+    prev_value = None
+    try:
+        x = fn(arg)
+    except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
+        prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
+        ImageFile.LOAD_TRUNCATED_IMAGES = True
+        x = fn(arg)
+    finally:
+        if prev_value is not None:
+            ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
+    return x
+
+def hasher():
+    hashfuncs = {
+        "md5": hashlib.md5,
+        "sha1": hashlib.sha1,
+        "sha256": hashlib.sha256,
+        "sha512": hashlib.sha512
+    }
+    return hashfuncs[args.default_hashing_function]
+
+def string_to_torch_dtype(string):
+    if string == "fp32":
+        return torch.float32
+    if string == "fp16":
+        return torch.float16
+    if string == "bf16":
+        return torch.bfloat16
+
+def image_alpha_fix(destination, source):
+    if destination.shape[-1] < source.shape[-1]:
+        source = source[...,:destination.shape[-1]]
+    elif destination.shape[-1] > source.shape[-1]:
+        destination = torch.nn.functional.pad(destination, (0, 1))
+        destination[..., -1] = 1.0
+    return destination, source

nodes.py

@@ -0,0 +1,2331 @@
+from __future__ import annotations
+import torch
+
+import os
+import sys
+import json
+import hashlib
+import traceback
+import math
+import time
+import random
+import logging
+
+from PIL import Image, ImageOps, ImageSequence
+from PIL.PngImagePlugin import PngInfo
+
+import numpy as np
+import safetensors.torch
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+import comfy.diffusers_load
+import comfy.samplers
+import comfy.sample
+import comfy.sd
+import comfy.utils
+import comfy.controlnet
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator
+
+import comfy.clip_vision
+
+import comfy.model_management
+from comfy.cli_args import args
+
+import importlib
+
+import folder_paths
+import latent_preview
+import node_helpers
+
+def before_node_execution():
+    comfy.model_management.throw_exception_if_processing_interrupted()
+
+def interrupt_processing(value=True):
+    comfy.model_management.interrupt_current_processing(value)
+
+MAX_RESOLUTION=16384
+
+class CLIPTextEncode(ComfyNodeABC):
+    @classmethod
+    def INPUT_TYPES(s) -> InputTypeDict:
+        return {
+            "required": {
+                "text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
+                "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
+            }
+        }
+    RETURN_TYPES = (IO.CONDITIONING,)
+    OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+    DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
+
+    def encode(self, clip, text):
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
+        tokens = clip.tokenize(text)
+        return (clip.encode_from_tokens_scheduled(tokens), )
+
+
+class ConditioningCombine:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "combine"
+
+    CATEGORY = "conditioning"
+
+    def combine(self, conditioning_1, conditioning_2):
+        return (conditioning_1 + conditioning_2, )
+
+class ConditioningAverage :
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
+                              "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "addWeighted"
+
+    CATEGORY = "conditioning"
+
+    def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
+        out = []
+
+        if len(conditioning_from) > 1:
+            logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+        cond_from = conditioning_from[0][0]
+        pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
+
+        for i in range(len(conditioning_to)):
+            t1 = conditioning_to[i][0]
+            pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from)
+            t0 = cond_from[:,:t1.shape[1]]
+            if t0.shape[1] < t1.shape[1]:
+                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
+
+            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
+            t_to = conditioning_to[i][1].copy()
+            if pooled_output_from is not None and pooled_output_to is not None:
+                t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))
+            elif pooled_output_from is not None:
+                t_to["pooled_output"] = pooled_output_from
+
+            n = [tw, t_to]
+            out.append(n)
+        return (out, )
+
+class ConditioningConcat:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning_to": ("CONDITIONING",),
+            "conditioning_from": ("CONDITIONING",),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "concat"
+
+    CATEGORY = "conditioning"
+
+    def concat(self, conditioning_to, conditioning_from):
+        out = []
+
+        if len(conditioning_from) > 1:
+            logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+        cond_from = conditioning_from[0][0]
+
+        for i in range(len(conditioning_to)):
+            t1 = conditioning_to[i][0]
+            tw = torch.cat((t1, cond_from),1)
+            n = [tw, conditioning_to[i][1].copy()]
+            out.append(n)
+
+        return (out, )
+
+class ConditioningSetArea:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, width, height, x, y, strength):
+        c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
+                                                                "strength": strength,
+                                                                "set_area_to_bounds": False})
+        return (c, )
+
+class ConditioningSetAreaPercentage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+                              "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+                              "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+                              "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, width, height, x, y, strength):
+        c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
+                                                                "strength": strength,
+                                                                "set_area_to_bounds": False})
+        return (c, )
+
+class ConditioningSetAreaStrength:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, strength):
+        c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
+        return (c, )
+
+
+class ConditioningSetMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "mask": ("MASK", ),
+                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "set_cond_area": (["default", "mask bounds"],),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, mask, set_cond_area, strength):
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask.shape) < 3:
+            mask = mask.unsqueeze(0)
+
+        c = node_helpers.conditioning_set_values(conditioning, {"mask": mask,
+                                                                "set_area_to_bounds": set_area_to_bounds,
+                                                                "mask_strength": strength})
+        return (c, )
+
+class ConditioningZeroOut:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "zero_out"
+
+    CATEGORY = "advanced/conditioning"
+
+    def zero_out(self, conditioning):
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            pooled_output = d.get("pooled_output", None)
+            if pooled_output is not None:
+                d["pooled_output"] = torch.zeros_like(pooled_output)
+            conditioning_lyrics = d.get("conditioning_lyrics", None)
+            if conditioning_lyrics is not None:
+                d["conditioning_lyrics"] = torch.zeros_like(conditioning_lyrics)
+            n = [torch.zeros_like(t[0]), d]
+            c.append(n)
+        return (c, )
+
+class ConditioningSetTimestepRange:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "set_range"
+
+    CATEGORY = "advanced/conditioning"
+
+    def set_range(self, conditioning, start, end):
+        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
+                                                                "end_percent": end})
+        return (c, )
+
+class VAEDecode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
+                "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."})
+            }
+        }
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_TOOLTIPS = ("The decoded image.",)
+    FUNCTION = "decode"
+
+    CATEGORY = "latent"
+    DESCRIPTION = "Decodes latent images back into pixel space images."
+
+    def decode(self, vae, samples):
+        images = vae.decode(samples["samples"])
+        if len(images.shape) == 5: #Combine batches
+            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+        return (images, )
+
+class VAEDecodeTiled:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time."}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                            }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    CATEGORY = "_for_testing"
+
+    def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
+        if tile_size < overlap * 4:
+            overlap = tile_size // 4
+        if temporal_size < temporal_overlap * 2:
+            temporal_overlap = temporal_overlap // 2
+        temporal_compression = vae.temporal_compression_decode()
+        if temporal_compression is not None:
+            temporal_size = max(2, temporal_size // temporal_compression)
+            temporal_overlap = max(1, min(temporal_size // 2, temporal_overlap // temporal_compression))
+        else:
+            temporal_size = None
+            temporal_overlap = None
+
+        compression = vae.spacial_compression_decode()
+        images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression, tile_t=temporal_size, overlap_t=temporal_overlap)
+        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
+    def INPUT_TYPES(s):
+        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "latent"
+
+    def encode(self, vae, pixels):
+        t = vae.encode(pixels[:,:,:,:3])
+        return ({"samples":t}, )
+
+class VAEEncodeTiled:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ),
+                             "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                            }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "_for_testing"
+
+    def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
+        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+        return ({"samples": t}, )
+
+class VAEEncodeForInpaint:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "latent/inpaint"
+
+    def encode(self, vae, pixels, mask, grow_mask_by=6):
+        x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio
+        y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+
+        pixels = pixels.clone()
+        if pixels.shape[1] != x or pixels.shape[2] != y:
+            x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2
+            y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2
+            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+        #grow mask by a few pixels to keep things seamless in latent space
+        if grow_mask_by == 0:
+            mask_erosion = mask
+        else:
+            kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+            padding = math.ceil((grow_mask_by - 1) / 2)
+
+            mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
+
+        m = (1.0 - mask.round()).squeeze(1)
+        for i in range(3):
+            pixels[:,:,:,i] -= 0.5
+            pixels[:,:,:,i] *= m
+            pixels[:,:,:,i] += 0.5
+        t = vae.encode(pixels)
+
+        return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
+
+
+class InpaintModelConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "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")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
+        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")
+
+        orig_pixels = pixels
+        pixels = orig_pixels.clone()
+        if pixels.shape[1] != x or pixels.shape[2] != y:
+            x_offset = (pixels.shape[1] % 8) // 2
+            y_offset = (pixels.shape[2] % 8) // 2
+            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+        m = (1.0 - mask.round()).squeeze(1)
+        for i in range(3):
+            pixels[:,:,:,i] -= 0.5
+            pixels[:,:,:,i] *= m
+            pixels[:,:,:,i] += 0.5
+        concat_latent = vae.encode(pixels)
+        orig_latent = vae.encode(orig_pixels)
+
+        out_latent = {}
+
+        out_latent["samples"] = orig_latent
+        if noise_mask:
+            out_latent["noise_mask"] = mask
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent,
+                                                                    "concat_mask": mask})
+            out.append(c)
+        return (out[0], out[1], out_latent)
+
+
+class SaveLatent:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ),
+                              "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        # support save metadata for latent sharing
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = None
+        if not args.disable_metadata:
+            metadata = {"prompt": prompt_info}
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        file = f"{filename}_{counter:05}_.latent"
+
+        results: list[FileLocator] = []
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": "output"
+        })
+
+        file = os.path.join(full_output_folder, file)
+
+        output = {}
+        output["latent_tensor"] = samples["samples"].contiguous()
+        output["latent_format_version_0"] = torch.tensor([])
+
+        comfy.utils.save_torch_file(output, file, metadata=metadata)
+        return { "ui": { "latents": results } }
+
+
+class LoadLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
+        return {"required": {"latent": [sorted(files), ]}, }
+
+    CATEGORY = "_for_testing"
+
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "load"
+
+    def load(self, latent):
+        latent_path = folder_paths.get_annotated_filepath(latent)
+        latent = safetensors.torch.load_file(latent_path, device="cpu")
+        multiplier = 1.0
+        if "latent_format_version_0" not in latent:
+            multiplier = 1.0 / 0.18215
+        samples = {"samples": latent["latent_tensor"].float() * multiplier}
+        return (samples, )
+
+    @classmethod
+    def IS_CHANGED(s, latent):
+        image_path = folder_paths.get_annotated_filepath(latent)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, latent):
+        if not folder_paths.exists_annotated_filepath(latent):
+            return "Invalid latent file: {}".format(latent)
+        return True
+
+
+class CheckpointLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
+                              "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "advanced/loaders"
+    DEPRECATED = True
+
+    def load_checkpoint(self, config_name, ckpt_name):
+        config_path = folder_paths.get_full_path("configs", config_name)
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+
+class CheckpointLoaderSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
+            }
+        }
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    OUTPUT_TOOLTIPS = ("The model used for denoising latents.",
+                       "The CLIP model used for encoding text prompts.",
+                       "The VAE model used for encoding and decoding images to and from latent space.")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders"
+    DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
+
+    def load_checkpoint(self, ckpt_name):
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return out[:3]
+
+class DiffusersLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        paths = []
+        for search_path in folder_paths.get_folder_paths("diffusers"):
+            if os.path.exists(search_path):
+                for root, subdir, files in os.walk(search_path, followlinks=True):
+                    if "model_index.json" in files:
+                        paths.append(os.path.relpath(root, start=search_path))
+
+        return {"required": {"model_path": (paths,), }}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "advanced/loaders/deprecated"
+
+    def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
+        for search_path in folder_paths.get_folder_paths("diffusers"):
+            if os.path.exists(search_path):
+                path = os.path.join(search_path, model_path)
+                if os.path.exists(path):
+                    model_path = path
+                    break
+
+        return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+
+
+class unCLIPCheckpointLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                             }}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders"
+
+    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return out
+
+class CLIPSetLastLayer:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip": ("CLIP", ),
+                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "set_last_layer"
+
+    CATEGORY = "conditioning"
+
+    def set_last_layer(self, clip, stop_at_clip_layer):
+        clip = clip.clone()
+        clip.clip_layer(stop_at_clip_layer)
+        return (clip,)
+
+class LoraLoader:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
+                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+    DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                self.loaded_lora = None
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        return (model_lora, clip_lora)
+
+class LoraLoaderModelOnly(LoraLoader):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_lora_model_only"
+
+    def load_lora_model_only(self, model, lora_name, strength_model):
+        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
+
+class VAELoader:
+    @staticmethod
+    def vae_list():
+        vaes = folder_paths.get_filename_list("vae")
+        approx_vaes = folder_paths.get_filename_list("vae_approx")
+        sdxl_taesd_enc = False
+        sdxl_taesd_dec = False
+        sd1_taesd_enc = False
+        sd1_taesd_dec = False
+        sd3_taesd_enc = False
+        sd3_taesd_dec = False
+        f1_taesd_enc = False
+        f1_taesd_dec = False
+
+        for v in approx_vaes:
+            if v.startswith("taesd_decoder."):
+                sd1_taesd_dec = True
+            elif v.startswith("taesd_encoder."):
+                sd1_taesd_enc = True
+            elif v.startswith("taesdxl_decoder."):
+                sdxl_taesd_dec = True
+            elif v.startswith("taesdxl_encoder."):
+                sdxl_taesd_enc = True
+            elif v.startswith("taesd3_decoder."):
+                sd3_taesd_dec = True
+            elif v.startswith("taesd3_encoder."):
+                sd3_taesd_enc = True
+            elif v.startswith("taef1_encoder."):
+                f1_taesd_dec = True
+            elif v.startswith("taef1_decoder."):
+                f1_taesd_enc = True
+        if sd1_taesd_dec and sd1_taesd_enc:
+            vaes.append("taesd")
+        if sdxl_taesd_dec and sdxl_taesd_enc:
+            vaes.append("taesdxl")
+        if sd3_taesd_dec and sd3_taesd_enc:
+            vaes.append("taesd3")
+        if f1_taesd_dec and f1_taesd_enc:
+            vaes.append("taef1")
+        return vaes
+
+    @staticmethod
+    def load_taesd(name):
+        sd = {}
+        approx_vaes = folder_paths.get_filename_list("vae_approx")
+
+        encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
+        decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))
+
+        enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder))
+        for k in enc:
+            sd["taesd_encoder.{}".format(k)] = enc[k]
+
+        dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder))
+        for k in dec:
+            sd["taesd_decoder.{}".format(k)] = dec[k]
+
+        if name == "taesd":
+            sd["vae_scale"] = torch.tensor(0.18215)
+            sd["vae_shift"] = torch.tensor(0.0)
+        elif name == "taesdxl":
+            sd["vae_scale"] = torch.tensor(0.13025)
+            sd["vae_shift"] = torch.tensor(0.0)
+        elif name == "taesd3":
+            sd["vae_scale"] = torch.tensor(1.5305)
+            sd["vae_shift"] = torch.tensor(0.0609)
+        elif name == "taef1":
+            sd["vae_scale"] = torch.tensor(0.3611)
+            sd["vae_shift"] = torch.tensor(0.1159)
+        return sd
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "vae_name": (s.vae_list(), )}}
+    RETURN_TYPES = ("VAE",)
+    FUNCTION = "load_vae"
+
+    CATEGORY = "loaders"
+
+    #TODO: scale factor?
+    def load_vae(self, vae_name):
+        if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]:
+            sd = self.load_taesd(vae_name)
+        else:
+            vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
+            sd = comfy.utils.load_torch_file(vae_path)
+        vae = comfy.sd.VAE(sd=sd)
+        vae.throw_exception_if_invalid()
+        return (vae,)
+
+class ControlNetLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+
+    RETURN_TYPES = ("CONTROL_NET",)
+    FUNCTION = "load_controlnet"
+
+    CATEGORY = "loaders"
+
+    def load_controlnet(self, control_net_name):
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
+        controlnet = comfy.controlnet.load_controlnet(controlnet_path)
+        if controlnet is None:
+            raise RuntimeError("ERROR: controlnet file is invalid and does not contain a valid controlnet model.")
+        return (controlnet,)
+
+class DiffControlNetLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
+
+    RETURN_TYPES = ("CONTROL_NET",)
+    FUNCTION = "load_controlnet"
+
+    CATEGORY = "loaders"
+
+    def load_controlnet(self, model, control_net_name):
+        controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
+        controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
+        return (controlnet,)
+
+
+class ControlNetApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "image": ("IMAGE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_controlnet"
+
+    DEPRECATED = True
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_controlnet(self, conditioning, control_net, image, strength):
+        if strength == 0:
+            return (conditioning, )
+
+        c = []
+        control_hint = image.movedim(-1,1)
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            c_net = control_net.copy().set_cond_hint(control_hint, strength)
+            if 'control' in t[1]:
+                c_net.set_previous_controlnet(t[1]['control'])
+            n[1]['control'] = c_net
+            n[1]['control_apply_to_uncond'] = True
+            c.append(n)
+        return (c, )
+
+
+class ControlNetApplyAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "image": ("IMAGE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             },
+                "optional": {"vae": ("VAE", ),
+                             }
+    }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "apply_controlnet"
+
+    CATEGORY = "conditioning/controlnet"
+
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
+        if strength == 0:
+            return (positive, negative)
+
+        control_hint = image.movedim(-1,1)
+        cnets = {}
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+
+                prev_cnet = d.get('control', None)
+                if prev_cnet in cnets:
+                    c_net = cnets[prev_cnet]
+                else:
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
+                    c_net.set_previous_controlnet(prev_cnet)
+                    cnets[prev_cnet] = c_net
+
+                d['control'] = c_net
+                d['control_apply_to_uncond'] = False
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1])
+
+
+class UNETLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_unet(self, unet_name, weight_dtype):
+        model_options = {}
+        if weight_dtype == "fp8_e4m3fn":
+            model_options["dtype"] = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
+        elif weight_dtype == "fp8_e5m2":
+            model_options["dtype"] = torch.float8_e5m2
+
+        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name)
+        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
+        return (model,)
+
+class CLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace"], ),
+                              },
+                "optional": {
+                              "device": (["default", "cpu"], {"advanced": True}),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5"
+
+    def load_clip(self, clip_name, type="stable_diffusion", device="default"):
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        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, model_options=model_options)
+        return (clip,)
+
+class DualCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
+                              "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream"], ),
+                              },
+                "optional": {
+                              "device": (["default", "cpu"], {"advanced": True}),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    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\nhidream: at least one of t5 or llama, recommended t5 and llama"
+
+    def load_clip(self, clip_name1, clip_name2, type, device="default"):
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+
+        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)
+
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
+        return (clip,)
+
+class CLIPVisionLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),
+                             }}
+    RETURN_TYPES = ("CLIP_VISION",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "loaders"
+
+    def load_clip(self, clip_name):
+        clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name)
+        clip_vision = comfy.clip_vision.load(clip_path)
+        if clip_vision is None:
+            raise RuntimeError("ERROR: clip vision file is invalid and does not contain a valid vision model.")
+        return (clip_vision,)
+
+class CLIPVisionEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "image": ("IMAGE",),
+                              "crop": (["center", "none"],)
+                             }}
+    RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+
+    def encode(self, clip_vision, image, crop):
+        crop_image = True
+        if crop != "center":
+            crop_image = False
+        output = clip_vision.encode_image(image, crop=crop_image)
+        return (output,)
+
+class StyleModelLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}
+
+    RETURN_TYPES = ("STYLE_MODEL",)
+    FUNCTION = "load_style_model"
+
+    CATEGORY = "loaders"
+
+    def load_style_model(self, style_model_name):
+        style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name)
+        style_model = comfy.sd.load_style_model(style_model_path)
+        return (style_model,)
+
+
+class StyleModelApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "style_model": ("STYLE_MODEL", ),
+                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+                             "strength_type": (["multiply", "attn_bias"], ),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_stylemodel"
+
+    CATEGORY = "conditioning/style_model"
+
+    def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type):
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+        if strength_type == "multiply":
+            cond *= strength
+
+        n = cond.shape[1]
+        c_out = []
+        for t in conditioning:
+            (txt, keys) = t
+            keys = keys.copy()
+            # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it
+            if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0):
+                # math.log raises an error if the argument is zero
+                # torch.log returns -inf, which is what we want
+                attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0]))
+                # get the size of the mask image
+                mask_ref_size = keys.get("attention_mask_img_shape", (1, 1))
+                n_ref = mask_ref_size[0] * mask_ref_size[1]
+                n_txt = txt.shape[1]
+                # grab the existing mask
+                mask = keys.get("attention_mask", None)
+                # create a default mask if it doesn't exist
+                if mask is None:
+                    mask = torch.zeros((txt.shape[0], n_txt + n_ref, n_txt + n_ref), dtype=torch.float16)
+                # convert the mask dtype, because it might be boolean
+                # we want it to be interpreted as a bias
+                if mask.dtype == torch.bool:
+                    # log(True) = log(1) = 0
+                    # log(False) = log(0) = -inf
+                    mask = torch.log(mask.to(dtype=torch.float16))
+                # now we make the mask bigger to add space for our new tokens
+                new_mask = torch.zeros((txt.shape[0], n_txt + n + n_ref, n_txt + n + n_ref), dtype=torch.float16)
+                # copy over the old mask, in quandrants
+                new_mask[:, :n_txt, :n_txt] = mask[:, :n_txt, :n_txt]
+                new_mask[:, :n_txt, n_txt+n:] = mask[:, :n_txt, n_txt:]
+                new_mask[:, n_txt+n:, :n_txt] = mask[:, n_txt:, :n_txt]
+                new_mask[:, n_txt+n:, n_txt+n:] = mask[:, n_txt:, n_txt:]
+                # now fill in the attention bias to our redux tokens
+                new_mask[:, :n_txt, n_txt:n_txt+n] = attn_bias
+                new_mask[:, n_txt+n:, n_txt:n_txt+n] = attn_bias
+                keys["attention_mask"] = new_mask.to(txt.device)
+                keys["attention_mask_img_shape"] = mask_ref_size
+
+            c_out.append([torch.cat((txt, cond), dim=1), keys])
+
+        return (c_out,)
+
+class unCLIPConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                             "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_adm"
+
+    CATEGORY = "conditioning"
+
+    def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
+        if strength == 0:
+            return (conditioning, )
+
+        c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True)
+        return (c, )
+
+class GLIGENLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}
+
+    RETURN_TYPES = ("GLIGEN",)
+    FUNCTION = "load_gligen"
+
+    CATEGORY = "loaders"
+
+    def load_gligen(self, gligen_name):
+        gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name)
+        gligen = comfy.sd.load_gligen(gligen_path)
+        return (gligen,)
+
+class GLIGENTextBoxApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                              "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning/gligen"
+
+    def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
+        c = []
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected")
+        for t in conditioning_to:
+            n = [t[0], t[1].copy()]
+            position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+
+            n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+            c.append(n)
+        return (c, )
+
+class EmptyLatentImage:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}),
+                "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."})
+            }
+        }
+    RETURN_TYPES = ("LATENT",)
+    OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent"
+    DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling."
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
+        return ({"samples":latent}, )
+
+
+class LatentFromBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
+                              "length": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "frombatch"
+
+    CATEGORY = "latent/batch"
+
+    def frombatch(self, samples, batch_index, length):
+        s = samples.copy()
+        s_in = samples["samples"]
+        batch_index = min(s_in.shape[0] - 1, batch_index)
+        length = min(s_in.shape[0] - batch_index, length)
+        s["samples"] = s_in[batch_index:batch_index + length].clone()
+        if "noise_mask" in samples:
+            masks = samples["noise_mask"]
+            if masks.shape[0] == 1:
+                s["noise_mask"] = masks.clone()
+            else:
+                if masks.shape[0] < s_in.shape[0]:
+                    masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
+                s["noise_mask"] = masks[batch_index:batch_index + length].clone()
+        if "batch_index" not in s:
+            s["batch_index"] = [x for x in range(batch_index, batch_index+length)]
+        else:
+            s["batch_index"] = samples["batch_index"][batch_index:batch_index + length]
+        return (s,)
+
+class RepeatLatentBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "latent/batch"
+
+    def repeat(self, samples, amount):
+        s = samples.copy()
+        s_in = samples["samples"]
+
+        s["samples"] = s_in.repeat((amount, 1,1,1))
+        if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1:
+            masks = samples["noise_mask"]
+            if masks.shape[0] < s_in.shape[0]:
+                masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
+            s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1))
+        if "batch_index" in s:
+            offset = max(s["batch_index"]) - min(s["batch_index"]) + 1
+            s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]]
+        return (s,)
+
+class LatentUpscale:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
+    crop_methods = ["disabled", "center"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
+                              "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "crop": (s.crop_methods,)}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "latent"
+
+    def upscale(self, samples, upscale_method, width, height, crop):
+        if width == 0 and height == 0:
+            s = samples
+        else:
+            s = samples.copy()
+
+            if width == 0:
+                height = max(64, height)
+                width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2]))
+            elif height == 0:
+                width = max(64, width)
+                height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1]))
+            else:
+                width = max(64, width)
+                height = max(64, height)
+
+            s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
+        return (s,)
+
+class LatentUpscaleBy:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
+                              "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "latent"
+
+    def upscale(self, samples, upscale_method, scale_by):
+        s = samples.copy()
+        width = round(samples["samples"].shape[-1] * scale_by)
+        height = round(samples["samples"].shape[-2] * scale_by)
+        s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
+        return (s,)
+
+class LatentRotate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "rotate"
+
+    CATEGORY = "latent/transform"
+
+    def rotate(self, samples, rotation):
+        s = samples.copy()
+        rotate_by = 0
+        if rotation.startswith("90"):
+            rotate_by = 1
+        elif rotation.startswith("180"):
+            rotate_by = 2
+        elif rotation.startswith("270"):
+            rotate_by = 3
+
+        s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2])
+        return (s,)
+
+class LatentFlip:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "flip_method": (["x-axis: vertically", "y-axis: horizontally"],),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "flip"
+
+    CATEGORY = "latent/transform"
+
+    def flip(self, samples, flip_method):
+        s = samples.copy()
+        if flip_method.startswith("x"):
+            s["samples"] = torch.flip(samples["samples"], dims=[2])
+        elif flip_method.startswith("y"):
+            s["samples"] = torch.flip(samples["samples"], dims=[3])
+
+        return (s,)
+
+class LatentComposite:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples_to": ("LATENT",),
+                              "samples_from": ("LATENT",),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "composite"
+
+    CATEGORY = "latent"
+
+    def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
+        x =  x // 8
+        y = y // 8
+        feather = feather // 8
+        samples_out = samples_to.copy()
+        s = samples_to["samples"].clone()
+        samples_to = samples_to["samples"]
+        samples_from = samples_from["samples"]
+        if feather == 0:
+            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
+        else:
+            samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
+            mask = torch.ones_like(samples_from)
+            for t in range(feather):
+                if y != 0:
+                    mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
+
+                if y + samples_from.shape[2] < samples_to.shape[2]:
+                    mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
+                if x != 0:
+                    mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
+                if x + samples_from.shape[3] < samples_to.shape[3]:
+                    mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
+            rev_mask = torch.ones_like(mask) - mask
+            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
+        samples_out["samples"] = s
+        return (samples_out,)
+
+class LatentBlend:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "samples1": ("LATENT",),
+            "samples2": ("LATENT",),
+            "blend_factor": ("FLOAT", {
+                "default": 0.5,
+                "min": 0,
+                "max": 1,
+                "step": 0.01
+            }),
+        }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "blend"
+
+    CATEGORY = "_for_testing"
+
+    def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"):
+
+        samples_out = samples1.copy()
+        samples1 = samples1["samples"]
+        samples2 = samples2["samples"]
+
+        if samples1.shape != samples2.shape:
+            samples2.permute(0, 3, 1, 2)
+            samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
+            samples2.permute(0, 2, 3, 1)
+
+        samples_blended = self.blend_mode(samples1, samples2, blend_mode)
+        samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor)
+        samples_out["samples"] = samples_blended
+        return (samples_out,)
+
+    def blend_mode(self, img1, img2, mode):
+        if mode == "normal":
+            return img2
+        else:
+            raise ValueError(f"Unsupported blend mode: {mode}")
+
+class LatentCrop:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "crop"
+
+    CATEGORY = "latent/transform"
+
+    def crop(self, samples, width, height, x, y):
+        s = samples.copy()
+        samples = samples['samples']
+        x =  x // 8
+        y = y // 8
+
+        #enfonce minimum size of 64
+        if x > (samples.shape[3] - 8):
+            x = samples.shape[3] - 8
+        if y > (samples.shape[2] - 8):
+            y = samples.shape[2] - 8
+
+        new_height = height // 8
+        new_width = width // 8
+        to_x = new_width + x
+        to_y = new_height + y
+        s['samples'] = samples[:,:,y:to_y, x:to_x]
+        return (s,)
+
+class SetLatentNoiseMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT",),
+                              "mask": ("MASK",),
+                              }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "set_mask"
+
+    CATEGORY = "latent/inpaint"
+
+    def set_mask(self, samples, mask):
+        s = samples.copy()
+        s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+        return (s,)
+
+def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
+    latent_image = latent["samples"]
+    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+
+    if disable_noise:
+        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+    else:
+        batch_inds = latent["batch_index"] if "batch_index" in latent else None
+        noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
+
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = latent["noise_mask"]
+
+    callback = latent_preview.prepare_callback(model, steps)
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+                                  denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
+                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+    out = latent.copy()
+    out["samples"] = samples
+    return (out, )
+
+class KSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
+                "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
+                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
+                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
+                "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
+                "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
+                "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
+                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    OUTPUT_TOOLTIPS = ("The denoised latent.",)
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling"
+    DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
+
+    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
+        return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
+
+class KSamplerAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "add_noise": (["enable", "disable"], ),
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "latent_image": ("LATENT", ),
+                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
+                    "return_with_leftover_noise": (["disable", "enable"], ),
+                     }
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling"
+
+    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
+        force_full_denoise = True
+        if return_with_leftover_noise == "enable":
+            force_full_denoise = False
+        disable_noise = False
+        if add_noise == "disable":
+            disable_noise = True
+        return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
+
+class SaveImage:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save."}),
+                "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."})
+            },
+            "hidden": {
+                "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image"
+    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+
+    def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        for (batch_number, image) in enumerate(images):
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "images": results } }
+
+class PreviewImage(SaveImage):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+        self.compress_level = 1
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ), },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+class LoadImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        files = folder_paths.filter_files_content_types(files, ["image"])
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True})},
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "load_image"
+    def load_image(self, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+
+        img = node_helpers.pillow(Image.open, image_path)
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return (output_image, output_mask)
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True
+
+class LoadImageMask:
+    _color_channels = ["alpha", "red", "green", "blue"]
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True}),
+                     "channel": (s._color_channels, ), }
+                }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "load_image"
+    def load_image(self, image, channel):
+        image_path = folder_paths.get_annotated_filepath(image)
+        i = node_helpers.pillow(Image.open, image_path)
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
+        if i.getbands() != ("R", "G", "B", "A"):
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            i = i.convert("RGBA")
+        mask = None
+        c = channel[0].upper()
+        if c in i.getbands():
+            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
+            mask = torch.from_numpy(mask)
+            if c == 'A':
+                mask = 1. - mask
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (mask.unsqueeze(0),)
+
+    @classmethod
+    def IS_CHANGED(s, image, channel):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True
+
+
+class LoadImageOutput(LoadImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("COMBO", {
+                    "image_upload": True,
+                    "image_folder": "output",
+                    "remote": {
+                        "route": "/internal/files/output",
+                        "refresh_button": True,
+                        "control_after_refresh": "first",
+                    },
+                }),
+            }
+        }
+
+    DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration."
+    EXPERIMENTAL = True
+    FUNCTION = "load_image"
+
+
+class ImageScale:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    crop_methods = ["disabled", "center"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
+                              "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              "crop": (s.crop_methods,)}}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image/upscaling"
+
+    def upscale(self, image, upscale_method, width, height, crop):
+        if width == 0 and height == 0:
+            s = image
+        else:
+            samples = image.movedim(-1,1)
+
+            if width == 0:
+                width = max(1, round(samples.shape[3] * height / samples.shape[2]))
+            elif height == 0:
+                height = max(1, round(samples.shape[2] * width / samples.shape[3]))
+
+            s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)
+            s = s.movedim(1,-1)
+        return (s,)
+
+class ImageScaleBy:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
+                              "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image/upscaling"
+
+    def upscale(self, image, upscale_method, scale_by):
+        samples = image.movedim(-1,1)
+        width = round(samples.shape[3] * scale_by)
+        height = round(samples.shape[2] * scale_by)
+        s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = s.movedim(1,-1)
+        return (s,)
+
+class ImageInvert:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",)}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "invert"
+
+    CATEGORY = "image"
+
+    def invert(self, image):
+        s = 1.0 - image
+        return (s,)
+
+class ImageBatch:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "batch"
+
+    CATEGORY = "image"
+
+    def batch(self, image1, image2):
+        if image1.shape[1:] != image2.shape[1:]:
+            image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
+        s = torch.cat((image1, image2), dim=0)
+        return (s,)
+
+class EmptyImage:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                              "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate"
+
+    CATEGORY = "image"
+
+    def generate(self, width, height, batch_size=1, color=0):
+        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF)
+        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF)
+        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)
+        return (torch.cat((r, g, b), dim=-1), )
+
+class ImagePadForOutpaint:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, image, left, top, right, bottom, feathering):
+        d1, d2, d3, d4 = image.size()
+
+        new_image = torch.ones(
+            (d1, d2 + top + bottom, d3 + left + right, d4),
+            dtype=torch.float32,
+        ) * 0.5
+
+        new_image[:, top:top + d2, left:left + d3, :] = image
+
+        mask = torch.ones(
+            (d2 + top + bottom, d3 + left + right),
+            dtype=torch.float32,
+        )
+
+        t = torch.zeros(
+            (d2, d3),
+            dtype=torch.float32
+        )
+
+        if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3:
+
+            for i in range(d2):
+                for j in range(d3):
+                    dt = i if top != 0 else d2
+                    db = d2 - i if bottom != 0 else d2
+
+                    dl = j if left != 0 else d3
+                    dr = d3 - j if right != 0 else d3
+
+                    d = min(dt, db, dl, dr)
+
+                    if d >= feathering:
+                        continue
+
+                    v = (feathering - d) / feathering
+
+                    t[i, j] = v * v
+
+        mask[top:top + d2, left:left + d3] = t
+
+        return (new_image, mask.unsqueeze(0))
+
+
+NODE_CLASS_MAPPINGS = {
+    "KSampler": KSampler,
+    "CheckpointLoaderSimple": CheckpointLoaderSimple,
+    "CLIPTextEncode": CLIPTextEncode,
+    "CLIPSetLastLayer": CLIPSetLastLayer,
+    "VAEDecode": VAEDecode,
+    "VAEEncode": VAEEncode,
+    "VAEEncodeForInpaint": VAEEncodeForInpaint,
+    "VAELoader": VAELoader,
+    "EmptyLatentImage": EmptyLatentImage,
+    "LatentUpscale": LatentUpscale,
+    "LatentUpscaleBy": LatentUpscaleBy,
+    "LatentFromBatch": LatentFromBatch,
+    "RepeatLatentBatch": RepeatLatentBatch,
+    "SaveImage": SaveImage,
+    "PreviewImage": PreviewImage,
+    "LoadImage": LoadImage,
+    "LoadImageMask": LoadImageMask,
+    "LoadImageOutput": LoadImageOutput,
+    "ImageScale": ImageScale,
+    "ImageScaleBy": ImageScaleBy,
+    "ImageInvert": ImageInvert,
+    "ImageBatch": ImageBatch,
+    "ImagePadForOutpaint": ImagePadForOutpaint,
+    "EmptyImage": EmptyImage,
+    "ConditioningAverage": ConditioningAverage ,
+    "ConditioningCombine": ConditioningCombine,
+    "ConditioningConcat": ConditioningConcat,
+    "ConditioningSetArea": ConditioningSetArea,
+    "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
+    "ConditioningSetAreaStrength": ConditioningSetAreaStrength,
+    "ConditioningSetMask": ConditioningSetMask,
+    "KSamplerAdvanced": KSamplerAdvanced,
+    "SetLatentNoiseMask": SetLatentNoiseMask,
+    "LatentComposite": LatentComposite,
+    "LatentBlend": LatentBlend,
+    "LatentRotate": LatentRotate,
+    "LatentFlip": LatentFlip,
+    "LatentCrop": LatentCrop,
+    "LoraLoader": LoraLoader,
+    "CLIPLoader": CLIPLoader,
+    "UNETLoader": UNETLoader,
+    "DualCLIPLoader": DualCLIPLoader,
+    "CLIPVisionEncode": CLIPVisionEncode,
+    "StyleModelApply": StyleModelApply,
+    "unCLIPConditioning": unCLIPConditioning,
+    "ControlNetApply": ControlNetApply,
+    "ControlNetApplyAdvanced": ControlNetApplyAdvanced,
+    "ControlNetLoader": ControlNetLoader,
+    "DiffControlNetLoader": DiffControlNetLoader,
+    "StyleModelLoader": StyleModelLoader,
+    "CLIPVisionLoader": CLIPVisionLoader,
+    "VAEDecodeTiled": VAEDecodeTiled,
+    "VAEEncodeTiled": VAEEncodeTiled,
+    "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
+    "GLIGENLoader": GLIGENLoader,
+    "GLIGENTextBoxApply": GLIGENTextBoxApply,
+    "InpaintModelConditioning": InpaintModelConditioning,
+
+    "CheckpointLoader": CheckpointLoader,
+    "DiffusersLoader": DiffusersLoader,
+
+    "LoadLatent": LoadLatent,
+    "SaveLatent": SaveLatent,
+
+    "ConditioningZeroOut": ConditioningZeroOut,
+    "ConditioningSetTimestepRange": ConditioningSetTimestepRange,
+    "LoraLoaderModelOnly": LoraLoaderModelOnly,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Sampling
+    "KSampler": "KSampler",
+    "KSamplerAdvanced": "KSampler (Advanced)",
+    # Loaders
+    "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)",
+    "CheckpointLoaderSimple": "Load Checkpoint",
+    "VAELoader": "Load VAE",
+    "LoraLoader": "Load LoRA",
+    "CLIPLoader": "Load CLIP",
+    "ControlNetLoader": "Load ControlNet Model",
+    "DiffControlNetLoader": "Load ControlNet Model (diff)",
+    "StyleModelLoader": "Load Style Model",
+    "CLIPVisionLoader": "Load CLIP Vision",
+    "UpscaleModelLoader": "Load Upscale Model",
+    "UNETLoader": "Load Diffusion Model",
+    # Conditioning
+    "CLIPVisionEncode": "CLIP Vision Encode",
+    "StyleModelApply": "Apply Style Model",
+    "CLIPTextEncode": "CLIP Text Encode (Prompt)",
+    "CLIPSetLastLayer": "CLIP Set Last Layer",
+    "ConditioningCombine": "Conditioning (Combine)",
+    "ConditioningAverage ": "Conditioning (Average)",
+    "ConditioningConcat": "Conditioning (Concat)",
+    "ConditioningSetArea": "Conditioning (Set Area)",
+    "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
+    "ConditioningSetMask": "Conditioning (Set Mask)",
+    "ControlNetApply": "Apply ControlNet (OLD)",
+    "ControlNetApplyAdvanced": "Apply ControlNet",
+    # Latent
+    "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
+    "SetLatentNoiseMask": "Set Latent Noise Mask",
+    "VAEDecode": "VAE Decode",
+    "VAEEncode": "VAE Encode",
+    "LatentRotate": "Rotate Latent",
+    "LatentFlip": "Flip Latent",
+    "LatentCrop": "Crop Latent",
+    "EmptyLatentImage": "Empty Latent Image",
+    "LatentUpscale": "Upscale Latent",
+    "LatentUpscaleBy": "Upscale Latent By",
+    "LatentComposite": "Latent Composite",
+    "LatentBlend": "Latent Blend",
+    "LatentFromBatch" : "Latent From Batch",
+    "RepeatLatentBatch": "Repeat Latent Batch",
+    # Image
+    "SaveImage": "Save Image",
+    "PreviewImage": "Preview Image",
+    "LoadImage": "Load Image",
+    "LoadImageMask": "Load Image (as Mask)",
+    "LoadImageOutput": "Load Image (from Outputs)",
+    "ImageScale": "Upscale Image",
+    "ImageScaleBy": "Upscale Image By",
+    "ImageUpscaleWithModel": "Upscale Image (using Model)",
+    "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)",
+}
+
+EXTENSION_WEB_DIRS = {}
+
+# Dictionary of successfully loaded module names and associated directories.
+LOADED_MODULE_DIRS = {}
+
+
+def get_module_name(module_path: str) -> str:
+    """
+    Returns the module name based on the given module path.
+    Examples:
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes
+    Args:
+        module_path (str): The path of the module.
+    Returns:
+        str: The module name.
+    """
+    base_path = os.path.basename(module_path)
+    if os.path.isfile(module_path):
+        base_path = os.path.splitext(base_path)[0]
+    return base_path
+
+
+def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool:
+    module_name = get_module_name(module_path)
+    if os.path.isfile(module_path):
+        sp = os.path.splitext(module_path)
+        module_name = sp[0]
+        sys_module_name = module_name
+    elif os.path.isdir(module_path):
+        sys_module_name = module_path.replace(".", "_x_")
+
+    try:
+        logging.debug("Trying to load custom node {}".format(module_path))
+        if os.path.isfile(module_path):
+            module_spec = importlib.util.spec_from_file_location(sys_module_name, module_path)
+            module_dir = os.path.split(module_path)[0]
+        else:
+            module_spec = importlib.util.spec_from_file_location(sys_module_name, os.path.join(module_path, "__init__.py"))
+            module_dir = module_path
+
+        module = importlib.util.module_from_spec(module_spec)
+        sys.modules[sys_module_name] = module
+        module_spec.loader.exec_module(module)
+
+        LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir)
+
+        if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None:
+            web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY")))
+            if os.path.isdir(web_dir):
+                EXTENSION_WEB_DIRS[module_name] = web_dir
+
+        if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
+            for name, node_cls in module.NODE_CLASS_MAPPINGS.items():
+                if name not in ignore:
+                    NODE_CLASS_MAPPINGS[name] = node_cls
+                    node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path))
+            if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
+                NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
+            return True
+        else:
+            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
+            return False
+    except Exception as e:
+        logging.warning(traceback.format_exc())
+        logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
+        return False
+
+def init_external_custom_nodes():
+    """
+    Initializes the external custom nodes.
+
+    This function loads custom nodes from the specified folder paths and imports them into the application.
+    It measures the import times for each custom node and logs the results.
+
+    Returns:
+        None
+    """
+    base_node_names = set(NODE_CLASS_MAPPINGS.keys())
+    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    node_import_times = []
+    for custom_node_path in node_paths:
+        possible_modules = os.listdir(os.path.realpath(custom_node_path))
+        if "__pycache__" in possible_modules:
+            possible_modules.remove("__pycache__")
+
+        for possible_module in possible_modules:
+            module_path = os.path.join(custom_node_path, possible_module)
+            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
+            if module_path.endswith(".disabled"): continue
+            time_before = time.perf_counter()
+            success = load_custom_node(module_path, base_node_names, module_parent="custom_nodes")
+            node_import_times.append((time.perf_counter() - time_before, module_path, success))
+
+    if len(node_import_times) > 0:
+        logging.info("\nImport times for custom nodes:")
+        for n in sorted(node_import_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (IMPORT FAILED)"
+            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
+        logging.info("")
+
+def init_builtin_extra_nodes():
+    """
+    Initializes the built-in extra nodes in ComfyUI.
+
+    This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI.
+    If any of the extra node files fail to import, a warning message is logged.
+
+    Returns:
+        None
+    """
+    extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")
+    extras_files = [
+        "nodes_latent.py",
+        "nodes_hypernetwork.py",
+        "nodes_upscale_model.py",
+        "nodes_post_processing.py",
+        "nodes_mask.py",
+        "nodes_compositing.py",
+        "nodes_rebatch.py",
+        "nodes_model_merging.py",
+        "nodes_tomesd.py",
+        "nodes_clip_sdxl.py",
+        "nodes_canny.py",
+        "nodes_freelunch.py",
+        "nodes_custom_sampler.py",
+        "nodes_hypertile.py",
+        "nodes_model_advanced.py",
+        "nodes_model_downscale.py",
+        "nodes_images.py",
+        "nodes_video_model.py",
+        "nodes_sag.py",
+        "nodes_perpneg.py",
+        "nodes_stable3d.py",
+        "nodes_sdupscale.py",
+        "nodes_photomaker.py",
+        "nodes_pixart.py",
+        "nodes_cond.py",
+        "nodes_morphology.py",
+        "nodes_stable_cascade.py",
+        "nodes_differential_diffusion.py",
+        "nodes_ip2p.py",
+        "nodes_model_merging_model_specific.py",
+        "nodes_pag.py",
+        "nodes_align_your_steps.py",
+        "nodes_attention_multiply.py",
+        "nodes_advanced_samplers.py",
+        "nodes_webcam.py",
+        "nodes_audio.py",
+        "nodes_sd3.py",
+        "nodes_gits.py",
+        "nodes_controlnet.py",
+        "nodes_hunyuan.py",
+        "nodes_flux.py",
+        "nodes_lora_extract.py",
+        "nodes_torch_compile.py",
+        "nodes_mochi.py",
+        "nodes_slg.py",
+        "nodes_mahiro.py",
+        "nodes_lt.py",
+        "nodes_hooks.py",
+        "nodes_load_3d.py",
+        "nodes_cosmos.py",
+        "nodes_video.py",
+        "nodes_lumina2.py",
+        "nodes_wan.py",
+        "nodes_lotus.py",
+        "nodes_hunyuan3d.py",
+        "nodes_primitive.py",
+        "nodes_cfg.py",
+        "nodes_optimalsteps.py",
+        "nodes_hidream.py",
+        "nodes_fresca.py",
+        "nodes_apg.py",
+        "nodes_preview_any.py",
+        "nodes_ace.py",
+        "nodes_string.py",
+        "nodes_camera_trajectory.py",
+    ]
+
+    import_failed = []
+    for node_file in extras_files:
+        if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"):
+            import_failed.append(node_file)
+
+    return import_failed
+
+
+def init_builtin_api_nodes():
+    api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes")
+    api_nodes_files = [
+        "nodes_ideogram.py",
+        "nodes_openai.py",
+        "nodes_minimax.py",
+        "nodes_veo2.py",
+        "nodes_kling.py",
+        "nodes_bfl.py",
+        "nodes_luma.py",
+        "nodes_recraft.py",
+        "nodes_pixverse.py",
+        "nodes_stability.py",
+        "nodes_pika.py",
+    ]
+
+    if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"):
+        return api_nodes_files
+
+    import_failed = []
+    for node_file in api_nodes_files:
+        if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"):
+            import_failed.append(node_file)
+
+    return import_failed
+
+
+def init_extra_nodes(init_custom_nodes=True, init_api_nodes=True):
+    import_failed = init_builtin_extra_nodes()
+
+    import_failed_api = []
+    if init_api_nodes:
+        import_failed_api = init_builtin_api_nodes()
+
+    if init_custom_nodes:
+        init_external_custom_nodes()
+    else:
+        logging.info("Skipping loading of custom nodes")
+
+    if len(import_failed_api) > 0:
+        logging.warning("WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
+        for node in import_failed_api:
+            logging.warning("IMPORT FAILED: {}".format(node))
+        logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.")
+        if args.windows_standalone_build:
+            logging.warning("Please run the update script: update/update_comfyui.bat")
+        else:
+            logging.warning("Please do a: pip install -r requirements.txt")
+        logging.warning("")
+
+    if len(import_failed) > 0:
+        logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
+        for node in import_failed:
+            logging.warning("IMPORT FAILED: {}".format(node))
+        logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.")
+        if args.windows_standalone_build:
+            logging.warning("Please run the update script: update/update_comfyui.bat")
+        else:
+            logging.warning("Please do a: pip install -r requirements.txt")
+        logging.warning("")
+
+    return import_failed

pyproject.toml

@@ -0,0 +1,24 @@
+[project]
+name = "ComfyUI"
+version = "0.3.35"
+readme = "README.md"
+license = { file = "LICENSE" }
+requires-python = ">=3.9"
+
+[project.urls]
+homepage = "https://www.comfy.org/"
+repository = "https://github.com/comfyanonymous/ComfyUI"
+documentation = "https://docs.comfy.org/"
+
+[tool.ruff]
+lint.select = [
+  "N805", # invalid-first-argument-name-for-method
+  "S307", # suspicious-eval-usage
+  "S102", # exec
+  "T",    # print-usage
+  "W",
+  # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
+  # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
+  "F",
+]
+exclude = ["*.ipynb"]

pytest.ini

@@ -0,0 +1,9 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')
+  execution: mark as execution test (deselect with '-m "not execution"')
+testpaths =
+  tests
+  tests-unit
+addopts = -s
+pythonpath = .

requirements.txt

@@ -1,9 +1,9 @@
-accelerate
-git+https://github.com/huggingface/diffusers.git
-torch
-gradio
-transformers
-xformers
-sentencepiece
-bitsandbytes
+accelerate
+git+https://github.com/huggingface/diffusers.git
+torch
+gradio
+transformers
+xformers
+sentencepiece
+bitsandbytes
 peft

server.py

@@ -0,0 +1,893 @@
+import os
+import sys
+import asyncio
+import traceback
+
+import nodes
+import folder_paths
+import execution
+import uuid
+import urllib
+import json
+import glob
+import struct
+import ssl
+import socket
+import ipaddress
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+from io import BytesIO
+
+import aiohttp
+from aiohttp import web
+import logging
+
+import mimetypes
+from comfy.cli_args import args
+import comfy.utils
+import comfy.model_management
+import node_helpers
+from comfyui_version import __version__
+from app.frontend_management import FrontendManager
+
+from app.user_manager import UserManager
+from app.model_manager import ModelFileManager
+from app.custom_node_manager import CustomNodeManager
+from typing import Optional, Union
+from api_server.routes.internal.internal_routes import InternalRoutes
+
+class BinaryEventTypes:
+    PREVIEW_IMAGE = 1
+    UNENCODED_PREVIEW_IMAGE = 2
+    TEXT = 3
+
+async def send_socket_catch_exception(function, message):
+    try:
+        await function(message)
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err:
+        logging.warning("send error: {}".format(err))
+
+@web.middleware
+async def cache_control(request: web.Request, handler):
+    response: web.Response = await handler(request)
+    if request.path.endswith('.js') or request.path.endswith('.css') or request.path.endswith('index.json'):
+        response.headers.setdefault('Cache-Control', 'no-cache')
+    return response
+
+
+@web.middleware
+async def compress_body(request: web.Request, handler):
+    accept_encoding = request.headers.get("Accept-Encoding", "")
+    response: web.Response = await handler(request)
+    if not isinstance(response, web.Response):
+        return response
+    if response.content_type not in ["application/json", "text/plain"]:
+        return response
+    if response.body and "gzip" in accept_encoding:
+        response.enable_compression()
+    return response
+
+
+def create_cors_middleware(allowed_origin: str):
+    @web.middleware
+    async def cors_middleware(request: web.Request, handler):
+        if request.method == "OPTIONS":
+            # Pre-flight request. Reply successfully:
+            response = web.Response()
+        else:
+            response = await handler(request)
+
+        response.headers['Access-Control-Allow-Origin'] = allowed_origin
+        response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS'
+        response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
+        response.headers['Access-Control-Allow-Credentials'] = 'true'
+        return response
+
+    return cors_middleware
+
+def is_loopback(host):
+    if host is None:
+        return False
+    try:
+        if ipaddress.ip_address(host).is_loopback:
+            return True
+        else:
+            return False
+    except:
+        pass
+
+    loopback = False
+    for family in (socket.AF_INET, socket.AF_INET6):
+        try:
+            r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM)
+            for family, _, _, _, sockaddr in r:
+                if not ipaddress.ip_address(sockaddr[0]).is_loopback:
+                    return loopback
+                else:
+                    loopback = True
+        except socket.gaierror:
+            pass
+
+    return loopback
+
+
+def create_origin_only_middleware():
+    @web.middleware
+    async def origin_only_middleware(request: web.Request, handler):
+        #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason.
+        #in that case the Host and Origin hostnames won't match
+        #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime
+        if 'Host' in request.headers and 'Origin' in request.headers:
+            host = request.headers['Host']
+            origin = request.headers['Origin']
+            host_domain = host.lower()
+            parsed = urllib.parse.urlparse(origin)
+            origin_domain = parsed.netloc.lower()
+            host_domain_parsed = urllib.parse.urlsplit('//' + host_domain)
+
+            #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit
+            loopback = is_loopback(host_domain_parsed.hostname)
+
+            if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host
+                host_domain = host_domain_parsed.hostname
+            if host_domain_parsed.port is None:
+                origin_domain = parsed.hostname
+
+            if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0:
+                if host_domain != origin_domain:
+                    logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain))
+                    return web.Response(status=403)
+
+        if request.method == "OPTIONS":
+            response = web.Response()
+        else:
+            response = await handler(request)
+
+        return response
+
+    return origin_only_middleware
+
+class PromptServer():
+    def __init__(self, loop):
+        PromptServer.instance = self
+
+        mimetypes.init()
+        mimetypes.add_type('application/javascript; charset=utf-8', '.js')
+        mimetypes.add_type('image/webp', '.webp')
+
+        self.user_manager = UserManager()
+        self.model_file_manager = ModelFileManager()
+        self.custom_node_manager = CustomNodeManager()
+        self.internal_routes = InternalRoutes(self)
+        self.supports = ["custom_nodes_from_web"]
+        self.prompt_queue = execution.PromptQueue(self)
+        self.loop = loop
+        self.messages = asyncio.Queue()
+        self.client_session:Optional[aiohttp.ClientSession] = None
+        self.number = 0
+
+        middlewares = [cache_control]
+        if args.enable_compress_response_body:
+            middlewares.append(compress_body)
+
+        if args.enable_cors_header:
+            middlewares.append(create_cors_middleware(args.enable_cors_header))
+        else:
+            middlewares.append(create_origin_only_middleware())
+
+        max_upload_size = round(args.max_upload_size * 1024 * 1024)
+        self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
+        self.sockets = dict()
+        self.web_root = (
+            FrontendManager.init_frontend(args.front_end_version)
+            if args.front_end_root is None
+            else args.front_end_root
+        )
+        logging.info(f"[Prompt Server] web root: {self.web_root}")
+        routes = web.RouteTableDef()
+        self.routes = routes
+        self.last_node_id = None
+        self.client_id = None
+
+        self.on_prompt_handlers = []
+
+        @routes.get('/ws')
+        async def websocket_handler(request):
+            ws = web.WebSocketResponse()
+            await ws.prepare(request)
+            sid = request.rel_url.query.get('clientId', '')
+            if sid:
+                # Reusing existing session, remove old
+                self.sockets.pop(sid, None)
+            else:
+                sid = uuid.uuid4().hex
+
+            self.sockets[sid] = ws
+
+            try:
+                # Send initial state to the new client
+                await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid)
+                # On reconnect if we are the currently executing client send the current node
+                if self.client_id == sid and self.last_node_id is not None:
+                    await self.send("executing", { "node": self.last_node_id }, sid)
+
+                async for msg in ws:
+                    if msg.type == aiohttp.WSMsgType.ERROR:
+                        logging.warning('ws connection closed with exception %s' % ws.exception())
+            finally:
+                self.sockets.pop(sid, None)
+            return ws
+
+        @routes.get("/")
+        async def get_root(request):
+            response = web.FileResponse(os.path.join(self.web_root, "index.html"))
+            response.headers['Cache-Control'] = 'no-cache'
+            response.headers["Pragma"] = "no-cache"
+            response.headers["Expires"] = "0"
+            return response
+
+        @routes.get("/embeddings")
+        def get_embeddings(request):
+            embeddings = folder_paths.get_filename_list("embeddings")
+            return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
+
+        @routes.get("/models")
+        def list_model_types(request):
+            model_types = list(folder_paths.folder_names_and_paths.keys())
+
+            return web.json_response(model_types)
+
+        @routes.get("/models/{folder}")
+        async def get_models(request):
+            folder = request.match_info.get("folder", None)
+            if not folder in folder_paths.folder_names_and_paths:
+                return web.Response(status=404)
+            files = folder_paths.get_filename_list(folder)
+            return web.json_response(files)
+
+        @routes.get("/extensions")
+        async def get_extensions(request):
+            files = glob.glob(os.path.join(
+                glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True)
+
+            extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files))
+
+            for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+                files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True)
+                extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote(
+                    name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files)))
+
+            return web.json_response(extensions)
+
+        def get_dir_by_type(dir_type):
+            if dir_type is None:
+                dir_type = "input"
+
+            if dir_type == "input":
+                type_dir = folder_paths.get_input_directory()
+            elif dir_type == "temp":
+                type_dir = folder_paths.get_temp_directory()
+            elif dir_type == "output":
+                type_dir = folder_paths.get_output_directory()
+
+            return type_dir, dir_type
+
+        def compare_image_hash(filepath, image):
+            hasher = node_helpers.hasher()
+
+            # function to compare hashes of two images to see if it already exists, fix to #3465
+            if os.path.exists(filepath):
+                a = hasher()
+                b = hasher()
+                with open(filepath, "rb") as f:
+                    a.update(f.read())
+                    b.update(image.file.read())
+                    image.file.seek(0)
+                return a.hexdigest() == b.hexdigest()
+            return False
+
+        def image_upload(post, image_save_function=None):
+            image = post.get("image")
+            overwrite = post.get("overwrite")
+            image_is_duplicate = False
+
+            image_upload_type = post.get("type")
+            upload_dir, image_upload_type = get_dir_by_type(image_upload_type)
+
+            if image and image.file:
+                filename = image.filename
+                if not filename:
+                    return web.Response(status=400)
+
+                subfolder = post.get("subfolder", "")
+                full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder))
+                filepath = os.path.abspath(os.path.join(full_output_folder, filename))
+
+                if os.path.commonpath((upload_dir, filepath)) != upload_dir:
+                    return web.Response(status=400)
+
+                if not os.path.exists(full_output_folder):
+                    os.makedirs(full_output_folder)
+
+                split = os.path.splitext(filename)
+
+                if overwrite is not None and (overwrite == "true" or overwrite == "1"):
+                    pass
+                else:
+                    i = 1
+                    while os.path.exists(filepath):
+                        if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465
+                            image_is_duplicate = True
+                            break
+                        filename = f"{split[0]} ({i}){split[1]}"
+                        filepath = os.path.join(full_output_folder, filename)
+                        i += 1
+
+                if not image_is_duplicate:
+                    if image_save_function is not None:
+                        image_save_function(image, post, filepath)
+                    else:
+                        with open(filepath, "wb") as f:
+                            f.write(image.file.read())
+
+                return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type})
+            else:
+                return web.Response(status=400)
+
+        @routes.post("/upload/image")
+        async def upload_image(request):
+            post = await request.post()
+            return image_upload(post)
+
+
+        @routes.post("/upload/mask")
+        async def upload_mask(request):
+            post = await request.post()
+
+            def image_save_function(image, post, filepath):
+                original_ref = json.loads(post.get("original_ref"))
+                filename, output_dir = folder_paths.annotated_filepath(original_ref['filename'])
+
+                if not filename:
+                    return web.Response(status=400)
+
+                # validation for security: prevent accessing arbitrary path
+                if filename[0] == '/' or '..' in filename:
+                    return web.Response(status=400)
+
+                if output_dir is None:
+                    type = original_ref.get("type", "output")
+                    output_dir = folder_paths.get_directory_by_type(type)
+
+                if output_dir is None:
+                    return web.Response(status=400)
+
+                if original_ref.get("subfolder", "") != "":
+                    full_output_dir = os.path.join(output_dir, original_ref["subfolder"])
+                    if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:
+                        return web.Response(status=403)
+                    output_dir = full_output_dir
+
+                file = os.path.join(output_dir, filename)
+
+                if os.path.isfile(file):
+                    with Image.open(file) as original_pil:
+                        metadata = PngInfo()
+                        if hasattr(original_pil,'text'):
+                            for key in original_pil.text:
+                                metadata.add_text(key, original_pil.text[key])
+                        original_pil = original_pil.convert('RGBA')
+                        mask_pil = Image.open(image.file).convert('RGBA')
+
+                        # alpha copy
+                        new_alpha = mask_pil.getchannel('A')
+                        original_pil.putalpha(new_alpha)
+                        original_pil.save(filepath, compress_level=4, pnginfo=metadata)
+
+            return image_upload(post, image_save_function)
+
+        @routes.get("/view")
+        async def view_image(request):
+            if "filename" in request.rel_url.query:
+                filename = request.rel_url.query["filename"]
+                filename,output_dir = folder_paths.annotated_filepath(filename)
+
+                if not filename:
+                    return web.Response(status=400)
+
+                # validation for security: prevent accessing arbitrary path
+                if filename[0] == '/' or '..' in filename:
+                    return web.Response(status=400)
+
+                if output_dir is None:
+                    type = request.rel_url.query.get("type", "output")
+                    output_dir = folder_paths.get_directory_by_type(type)
+
+                if output_dir is None:
+                    return web.Response(status=400)
+
+                if "subfolder" in request.rel_url.query:
+                    full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"])
+                    if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir:
+                        return web.Response(status=403)
+                    output_dir = full_output_dir
+
+                filename = os.path.basename(filename)
+                file = os.path.join(output_dir, filename)
+
+                if os.path.isfile(file):
+                    if 'preview' in request.rel_url.query:
+                        with Image.open(file) as img:
+                            preview_info = request.rel_url.query['preview'].split(';')
+                            image_format = preview_info[0]
+                            if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''):
+                                image_format = 'webp'
+
+                            quality = 90
+                            if preview_info[-1].isdigit():
+                                quality = int(preview_info[-1])
+
+                            buffer = BytesIO()
+                            if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb':
+                                img = img.convert("RGB")
+                            img.save(buffer, format=image_format, quality=quality)
+                            buffer.seek(0)
+
+                            return web.Response(body=buffer.read(), content_type=f'image/{image_format}',
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+
+                    if 'channel' not in request.rel_url.query:
+                        channel = 'rgba'
+                    else:
+                        channel = request.rel_url.query["channel"]
+
+                    if channel == 'rgb':
+                        with Image.open(file) as img:
+                            if img.mode == "RGBA":
+                                r, g, b, a = img.split()
+                                new_img = Image.merge('RGB', (r, g, b))
+                            else:
+                                new_img = img.convert("RGB")
+
+                            buffer = BytesIO()
+                            new_img.save(buffer, format='PNG')
+                            buffer.seek(0)
+
+                            return web.Response(body=buffer.read(), content_type='image/png',
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+
+                    elif channel == 'a':
+                        with Image.open(file) as img:
+                            if img.mode == "RGBA":
+                                _, _, _, a = img.split()
+                            else:
+                                a = Image.new('L', img.size, 255)
+
+                            # alpha img
+                            alpha_img = Image.new('RGBA', img.size)
+                            alpha_img.putalpha(a)
+                            alpha_buffer = BytesIO()
+                            alpha_img.save(alpha_buffer, format='PNG')
+                            alpha_buffer.seek(0)
+
+                            return web.Response(body=alpha_buffer.read(), content_type='image/png',
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
+                    else:
+                        # Get content type from mimetype, defaulting to 'application/octet-stream'
+                        content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'
+
+                        # For security, force certain extensions to download instead of display
+                        file_extension = os.path.splitext(filename)[1].lower()
+                        if file_extension in {'.html', '.htm', '.js', '.css'}:
+                            content_type = 'application/octet-stream'  # Forces download
+
+                        return web.FileResponse(
+                            file,
+                            headers={
+                                "Content-Disposition": f"filename=\"{filename}\"",
+                                "Content-Type": content_type
+                            }
+                        )
+
+            return web.Response(status=404)
+
+        @routes.get("/view_metadata/{folder_name}")
+        async def view_metadata(request):
+            folder_name = request.match_info.get("folder_name", None)
+            if folder_name is None:
+                return web.Response(status=404)
+            if not "filename" in request.rel_url.query:
+                return web.Response(status=404)
+
+            filename = request.rel_url.query["filename"]
+            if not filename.endswith(".safetensors"):
+                return web.Response(status=404)
+
+            safetensors_path = folder_paths.get_full_path(folder_name, filename)
+            if safetensors_path is None:
+                return web.Response(status=404)
+            out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024)
+            if out is None:
+                return web.Response(status=404)
+            dt = json.loads(out)
+            if not "__metadata__" in dt:
+                return web.Response(status=404)
+            return web.json_response(dt["__metadata__"])
+
+        @routes.get("/system_stats")
+        async def system_stats(request):
+            device = comfy.model_management.get_torch_device()
+            device_name = comfy.model_management.get_torch_device_name(device)
+            cpu_device = comfy.model_management.torch.device("cpu")
+            ram_total = comfy.model_management.get_total_memory(cpu_device)
+            ram_free = comfy.model_management.get_free_memory(cpu_device)
+            vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True)
+            vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True)
+
+            system_stats = {
+                "system": {
+                    "os": os.name,
+                    "ram_total": ram_total,
+                    "ram_free": ram_free,
+                    "comfyui_version": __version__,
+                    "python_version": sys.version,
+                    "pytorch_version": comfy.model_management.torch_version,
+                    "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded",
+                    "argv": sys.argv
+                },
+                "devices": [
+                    {
+                        "name": device_name,
+                        "type": device.type,
+                        "index": device.index,
+                        "vram_total": vram_total,
+                        "vram_free": vram_free,
+                        "torch_vram_total": torch_vram_total,
+                        "torch_vram_free": torch_vram_free,
+                    }
+                ]
+            }
+            return web.json_response(system_stats)
+
+        @routes.get("/prompt")
+        async def get_prompt(request):
+            return web.json_response(self.get_queue_info())
+
+        def node_info(node_class):
+            obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
+            info = {}
+            info['input'] = obj_class.INPUT_TYPES()
+            info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
+            info['output'] = obj_class.RETURN_TYPES
+            info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
+            info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']
+            info['name'] = node_class
+            info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class
+            info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else ''
+            info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes")
+            info['category'] = 'sd'
+            if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True:
+                info['output_node'] = True
+            else:
+                info['output_node'] = False
+
+            if hasattr(obj_class, 'CATEGORY'):
+                info['category'] = obj_class.CATEGORY
+
+            if hasattr(obj_class, 'OUTPUT_TOOLTIPS'):
+                info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS
+
+            if getattr(obj_class, "DEPRECATED", False):
+                info['deprecated'] = True
+            if getattr(obj_class, "EXPERIMENTAL", False):
+                info['experimental'] = True
+
+            if hasattr(obj_class, 'API_NODE'):
+                info['api_node'] = obj_class.API_NODE
+            return info
+
+        @routes.get("/object_info")
+        async def get_object_info(request):
+            with folder_paths.cache_helper:
+                out = {}
+                for x in nodes.NODE_CLASS_MAPPINGS:
+                    try:
+                        out[x] = node_info(x)
+                    except Exception:
+                        logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
+                        logging.error(traceback.format_exc())
+                return web.json_response(out)
+
+        @routes.get("/object_info/{node_class}")
+        async def get_object_info_node(request):
+            node_class = request.match_info.get("node_class", None)
+            out = {}
+            if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS):
+                out[node_class] = node_info(node_class)
+            return web.json_response(out)
+
+        @routes.get("/history")
+        async def get_history(request):
+            max_items = request.rel_url.query.get("max_items", None)
+            if max_items is not None:
+                max_items = int(max_items)
+            return web.json_response(self.prompt_queue.get_history(max_items=max_items))
+
+        @routes.get("/history/{prompt_id}")
+        async def get_history_prompt_id(request):
+            prompt_id = request.match_info.get("prompt_id", None)
+            return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id))
+
+        @routes.get("/queue")
+        async def get_queue(request):
+            queue_info = {}
+            current_queue = self.prompt_queue.get_current_queue_volatile()
+            queue_info['queue_running'] = current_queue[0]
+            queue_info['queue_pending'] = current_queue[1]
+            return web.json_response(queue_info)
+
+        @routes.post("/prompt")
+        async def post_prompt(request):
+            logging.info("got prompt")
+            json_data =  await request.json()
+            json_data = self.trigger_on_prompt(json_data)
+
+            if "number" in json_data:
+                number = float(json_data['number'])
+            else:
+                number = self.number
+                if "front" in json_data:
+                    if json_data['front']:
+                        number = -number
+
+                self.number += 1
+
+            if "prompt" in json_data:
+                prompt = json_data["prompt"]
+                valid = execution.validate_prompt(prompt)
+                extra_data = {}
+                if "extra_data" in json_data:
+                    extra_data = json_data["extra_data"]
+
+                if "client_id" in json_data:
+                    extra_data["client_id"] = json_data["client_id"]
+                if valid[0]:
+                    prompt_id = str(uuid.uuid4())
+                    outputs_to_execute = valid[2]
+                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+                    response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
+                    return web.json_response(response)
+                else:
+                    logging.warning("invalid prompt: {}".format(valid[1]))
+                    return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
+            else:
+                error = {
+                    "type": "no_prompt",
+                    "message": "No prompt provided",
+                    "details": "No prompt provided",
+                    "extra_info": {}
+                }
+                return web.json_response({"error": error, "node_errors": {}}, status=400)
+
+        @routes.post("/queue")
+        async def post_queue(request):
+            json_data =  await request.json()
+            if "clear" in json_data:
+                if json_data["clear"]:
+                    self.prompt_queue.wipe_queue()
+            if "delete" in json_data:
+                to_delete = json_data['delete']
+                for id_to_delete in to_delete:
+                    delete_func = lambda a: a[1] == id_to_delete
+                    self.prompt_queue.delete_queue_item(delete_func)
+
+            return web.Response(status=200)
+
+        @routes.post("/interrupt")
+        async def post_interrupt(request):
+            nodes.interrupt_processing()
+            return web.Response(status=200)
+
+        @routes.post("/free")
+        async def post_free(request):
+            json_data = await request.json()
+            unload_models = json_data.get("unload_models", False)
+            free_memory = json_data.get("free_memory", False)
+            if unload_models:
+                self.prompt_queue.set_flag("unload_models", unload_models)
+            if free_memory:
+                self.prompt_queue.set_flag("free_memory", free_memory)
+            return web.Response(status=200)
+
+        @routes.post("/history")
+        async def post_history(request):
+            json_data =  await request.json()
+            if "clear" in json_data:
+                if json_data["clear"]:
+                    self.prompt_queue.wipe_history()
+            if "delete" in json_data:
+                to_delete = json_data['delete']
+                for id_to_delete in to_delete:
+                    self.prompt_queue.delete_history_item(id_to_delete)
+
+            return web.Response(status=200)
+
+    async def setup(self):
+        timeout = aiohttp.ClientTimeout(total=None) # no timeout
+        self.client_session = aiohttp.ClientSession(timeout=timeout)
+
+    def add_routes(self):
+        self.user_manager.add_routes(self.routes)
+        self.model_file_manager.add_routes(self.routes)
+        self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items())
+        self.app.add_subapp('/internal', self.internal_routes.get_app())
+
+        # Prefix every route with /api for easier matching for delegation.
+        # This is very useful for frontend dev server, which need to forward
+        # everything except serving of static files.
+        # Currently both the old endpoints without prefix and new endpoints with
+        # prefix are supported.
+        api_routes = web.RouteTableDef()
+        for route in self.routes:
+            # Custom nodes might add extra static routes. Only process non-static
+            # routes to add /api prefix.
+            if isinstance(route, web.RouteDef):
+                api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
+        self.app.add_routes(api_routes)
+        self.app.add_routes(self.routes)
+
+        # Add routes from web extensions.
+        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+            self.app.add_routes([web.static('/extensions/' + name, dir)])
+
+        workflow_templates_path = FrontendManager.templates_path()
+        if workflow_templates_path:
+            self.app.add_routes([
+                web.static('/templates', workflow_templates_path)
+            ])
+
+        self.app.add_routes([
+            web.static('/', self.web_root),
+        ])
+
+    def get_queue_info(self):
+        prompt_info = {}
+        exec_info = {}
+        exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining()
+        prompt_info['exec_info'] = exec_info
+        return prompt_info
+
+    async def send(self, event, data, sid=None):
+        if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE:
+            await self.send_image(data, sid=sid)
+        elif isinstance(data, (bytes, bytearray)):
+            await self.send_bytes(event, data, sid)
+        else:
+            await self.send_json(event, data, sid)
+
+    def encode_bytes(self, event, data):
+        if not isinstance(event, int):
+            raise RuntimeError(f"Binary event types must be integers, got {event}")
+
+        packed = struct.pack(">I", event)
+        message = bytearray(packed)
+        message.extend(data)
+        return message
+
+    async def send_image(self, image_data, sid=None):
+        image_type = image_data[0]
+        image = image_data[1]
+        max_size = image_data[2]
+        if max_size is not None:
+            if hasattr(Image, 'Resampling'):
+                resampling = Image.Resampling.BILINEAR
+            else:
+                resampling = Image.ANTIALIAS
+
+            image = ImageOps.contain(image, (max_size, max_size), resampling)
+        type_num = 1
+        if image_type == "JPEG":
+            type_num = 1
+        elif image_type == "PNG":
+            type_num = 2
+
+        bytesIO = BytesIO()
+        header = struct.pack(">I", type_num)
+        bytesIO.write(header)
+        image.save(bytesIO, format=image_type, quality=95, compress_level=1)
+        preview_bytes = bytesIO.getvalue()
+        await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
+
+    async def send_bytes(self, event, data, sid=None):
+        message = self.encode_bytes(event, data)
+
+        if sid is None:
+            sockets = list(self.sockets.values())
+            for ws in sockets:
+                await send_socket_catch_exception(ws.send_bytes, message)
+        elif sid in self.sockets:
+            await send_socket_catch_exception(self.sockets[sid].send_bytes, message)
+
+    async def send_json(self, event, data, sid=None):
+        message = {"type": event, "data": data}
+
+        if sid is None:
+            sockets = list(self.sockets.values())
+            for ws in sockets:
+                await send_socket_catch_exception(ws.send_json, message)
+        elif sid in self.sockets:
+            await send_socket_catch_exception(self.sockets[sid].send_json, message)
+
+    def send_sync(self, event, data, sid=None):
+        self.loop.call_soon_threadsafe(
+            self.messages.put_nowait, (event, data, sid))
+
+    def queue_updated(self):
+        self.send_sync("status", { "status": self.get_queue_info() })
+
+    async def publish_loop(self):
+        while True:
+            msg = await self.messages.get()
+            await self.send(*msg)
+
+    async def start(self, address, port, verbose=True, call_on_start=None):
+        await self.start_multi_address([(address, port)], call_on_start=call_on_start)
+
+    async def start_multi_address(self, addresses, call_on_start=None, verbose=True):
+        runner = web.AppRunner(self.app, access_log=None)
+        await runner.setup()
+        ssl_ctx = None
+        scheme = "http"
+        if args.tls_keyfile and args.tls_certfile:
+                ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE)
+                ssl_ctx.load_cert_chain(certfile=args.tls_certfile,
+                                keyfile=args.tls_keyfile)
+                scheme = "https"
+
+        if verbose:
+            logging.info("Starting server\n")
+        for addr in addresses:
+            address = addr[0]
+            port = addr[1]
+            site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
+            await site.start()
+
+            if not hasattr(self, 'address'):
+                self.address = address #TODO: remove this
+                self.port = port
+
+            if ':' in address:
+                address_print = "[{}]".format(address)
+            else:
+                address_print = address
+
+            if verbose:
+                logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port))
+
+        if call_on_start is not None:
+            call_on_start(scheme, self.address, self.port)
+
+    def add_on_prompt_handler(self, handler):
+        self.on_prompt_handlers.append(handler)
+
+    def trigger_on_prompt(self, json_data):
+        for handler in self.on_prompt_handlers:
+            try:
+                json_data = handler(json_data)
+            except Exception:
+                logging.warning("[ERROR] An error occurred during the on_prompt_handler processing")
+                logging.warning(traceback.format_exc())
+
+        return json_data
+
+    def send_progress_text(
+        self, text: Union[bytes, bytearray, str], node_id: str, sid=None
+    ):
+        if isinstance(text, str):
+            text = text.encode("utf-8")
+        node_id_bytes = str(node_id).encode("utf-8")
+
+        # Pack the node_id length as a 4-byte unsigned integer, followed by the node_id bytes
+        message = struct.pack(">I", len(node_id_bytes)) + node_id_bytes + text
+
+        self.send_sync(BinaryEventTypes.TEXT, message, sid)