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CODEOWNERS

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+*       @comfyanonymous

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    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.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,212 @@
+ComfyUI
+=======
+A powerful and modular stable diffusion GUI and backend.
+-----------
+![ComfyUI Screenshot](comfyui_screenshot.png)
+
+This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
+### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+
+### [Installing ComfyUI](#installing)
+
+## Features
+- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
+- Fully supports SD1.x, SD2.x and SDXL
+- Asynchronous Queue system
+- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
+- Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)
+- Works even if you don't have a GPU with: ```--cpu``` (slow)
+- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
+- Embeddings/Textual inversion
+- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
+- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
+- Loading full workflows (with seeds) from generated PNG files.
+- Saving/Loading workflows as Json files.
+- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
+- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
+- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
+- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
+- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
+- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
+- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
+- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
+- Latent previews with [TAESD](#how-to-show-high-quality-previews)
+- Starts up very fast.
+- Works fully offline: will never download anything.
+- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
+
+Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
+
+## Shortcuts
+
+| Keybind                   | Explanation                                                                                                        |
+|---------------------------|--------------------------------------------------------------------------------------------------------------------|
+| Ctrl + Enter              | Queue up current graph for generation                                                                              |
+| Ctrl + Shift + Enter      | Queue up current graph as first for generation                                                                     |
+| Ctrl + S                  | Save workflow                                                                                                      |
+| Ctrl + O                  | Load workflow                                                                                                      |
+| Ctrl + A                  | Select all nodes                                                                                                   |
+| Ctrl + M                  | Mute/unmute selected nodes                                                                                         |
+| Ctrl + B                  | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
+| Delete/Backspace          | Delete selected nodes                                                                                              |
+| Ctrl + Delete/Backspace   | Delete the current graph                                                                                           |
+| Space                     | Move the canvas around when held and moving the cursor                                                             |
+| Ctrl/Shift + Click        | Add clicked node to selection                                                                                      |
+| Ctrl + C/Ctrl + V         | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
+| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| Shift + Drag              | Move multiple selected nodes at the same time                                                                      |
+| Ctrl + D                  | Load default graph                                                                                                 |
+| Q                         | Toggle visibility of the queue                                                                                     |
+| H                         | Toggle visibility of history                                                                                       |
+| R                         | Refresh graph                                                                                                      |
+| Double-Click LMB          | Open node quick search palette                                                                                     |
+
+Ctrl can also be replaced with Cmd instead for macOS users
+
+# Installing
+
+## Windows
+
+There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
+
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z)
+
+Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
+
+#### How do I share models between another UI and ComfyUI?
+
+See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
+
+## Jupyter Notebook
+
+To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
+
+## Manual Install (Windows, Linux)
+
+Git clone this repo.
+
+Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
+
+Put your VAE in: models/vae
+
+### AMD GPUs (Linux only)
+AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
+
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```
+
+This is the command to install the nightly with ROCm 5.6 that supports the 7000 series and might have some performance improvements:
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.6```
+
+### NVIDIA
+
+Nvidia users should install torch and xformers using this command:
+
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers```
+
+#### Troubleshooting
+
+If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
+
+```pip uninstall torch```
+
+And install it again with the command above.
+
+### Dependencies
+
+Install the dependencies by opening your terminal inside the ComfyUI folder and:
+
+```pip install -r requirements.txt```
+
+After this you should have everything installed and can proceed to running ComfyUI.
+
+### Others:
+
+#### [Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476)
+
+#### Apple Mac silicon
+
+You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
+
+1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
+1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
+1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
+1. Launch ComfyUI by running `python main.py --force-fp16`. Note that --force-fp16 will only work if you installed the latest pytorch nightly.
+
+> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
+
+#### DirectML (AMD Cards on Windows)
+
+```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
+
+### I already have another UI for Stable Diffusion installed do I really have to install all of these dependencies?
+
+You don't. If you have another UI installed and working with its own python venv you can use that venv to run ComfyUI. You can open up your favorite terminal and activate it:
+
+```source path_to_other_sd_gui/venv/bin/activate```
+
+or on Windows:
+
+With Powershell: ```"path_to_other_sd_gui\venv\Scripts\Activate.ps1"```
+
+With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
+
+And then you can use that terminal to run ComfyUI without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
+
+# Running
+
+```python main.py```
+
+### For AMD cards not officially supported by ROCm
+
+Try running it with this command if you have issues:
+
+For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
+
+For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
+
+# Notes
+
+Only parts of the graph that have an output with all the correct inputs will be executed.
+
+Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
+
+Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
+
+You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
+
+You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
+
+Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
+
+To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
+
+```embedding:embedding_filename.pt```
+
+
+## How to increase generation speed?
+
+Make sure you use the regular loaders/Load Checkpoint node to load checkpoints. It will auto pick the right settings depending on your GPU.
+
+You can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers this option does not do anything.
+
+```--dont-upcast-attention```
+
+## How to show high-quality previews?
+
+Use ```--preview-method auto``` to enable previews.
+
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+
+## Support and dev channel
+
+[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
+
+# QA
+
+### Why did you make this?
+
+I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions.
+
+### Who is this for?
+
+This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs.

cuda_malloc.py

@@ -0,0 +1,84 @@
+import os
+import importlib.util
+from comfy.cli_args import args
+
+#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:
+        return set()
+
+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"
+                }
+
+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 +1,764 @@
+import os
+import sys
+import copy
+import json
+import threading
+import heapq
+import traceback
+import gc
+
+import torch
+import nodes
+
+import comfy.model_management
+
+def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
+    valid_inputs = class_def.INPUT_TYPES()
+    input_data_all = {}
+    for x in inputs:
+        input_data = inputs[x]
+        if isinstance(input_data, list):
+            input_unique_id = input_data[0]
+            output_index = input_data[1]
+            if input_unique_id not in outputs:
+                input_data_all[x] = (None,)
+                continue
+            obj = outputs[input_unique_id][output_index]
+            input_data_all[x] = obj
+        else:
+            if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
+                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] = [prompt]
+            if h[x] == "EXTRA_PNGINFO":
+                if "extra_pnginfo" in extra_data:
+                    input_data_all[x] = [extra_data['extra_pnginfo']]
+            if h[x] == "UNIQUE_ID":
+                input_data_all[x] = [unique_id]
+    return input_data_all
+
+def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
+    # check if node wants the lists
+    input_is_list = False
+    if hasattr(obj, "INPUT_IS_LIST"):
+        input_is_list = obj.INPUT_IS_LIST
+
+    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):
+        d_new = dict()
+        for k,v in d.items():
+            d_new[k] = v[i if len(v) > i else -1]
+        return d_new
+    
+    results = []
+    if input_is_list:
+        if allow_interrupt:
+            nodes.before_node_execution()
+        results.append(getattr(obj, func)(**input_data_all))
+    elif max_len_input == 0:
+        if allow_interrupt:
+            nodes.before_node_execution()
+        results.append(getattr(obj, func)())
+    else:
+        for i in range(max_len_input):
+            if allow_interrupt:
+                nodes.before_node_execution()
+            results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
+    return results
+
+def get_output_data(obj, input_data_all):
+    
+    results = []
+    uis = []
+    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
+
+    for r in return_values:
+        if isinstance(r, dict):
+            if 'ui' in r:
+                uis.append(r['ui'])
+            if 'result' in r:
+                results.append(r['result'])
+        else:
+            results.append(r)
+    
+    output = []
+    if len(results) > 0:
+        # check which outputs need concatenating
+        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:
+                output.append([x for o in results for x in o[i]])
+            else:
+                output.append([o[i] for o in results])
+
+    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
+
+def format_value(x):
+    if x is None:
+        return None
+    elif isinstance(x, (int, float, bool, str)):
+        return x
+    else:
+        return str(x)
+
+def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage):
+    unique_id = current_item
+    inputs = prompt[unique_id]['inputs']
+    class_type = prompt[unique_id]['class_type']
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    if unique_id in outputs:
+        return (True, None, None)
+
+    for x in inputs:
+        input_data = inputs[x]
+
+        if isinstance(input_data, list):
+            input_unique_id = input_data[0]
+            output_index = input_data[1]
+            if input_unique_id not in outputs:
+                result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
+                if result[0] is not True:
+                    # Another node failed further upstream
+                    return result
+
+    input_data_all = None
+    try:
+        input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
+        if server.client_id is not None:
+            server.last_node_id = unique_id
+            server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
+
+        obj = object_storage.get((unique_id, class_type), None)
+        if obj is None:
+            obj = class_def()
+            object_storage[(unique_id, class_type)] = obj
+
+        output_data, output_ui = get_output_data(obj, input_data_all)
+        outputs[unique_id] = output_data
+        if len(output_ui) > 0:
+            outputs_ui[unique_id] = output_ui
+            if server.client_id is not None:
+                server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
+    except comfy.model_management.InterruptProcessingException as iex:
+        print("Processing interrupted")
+
+        # skip formatting inputs/outputs
+        error_details = {
+            "node_id": unique_id,
+        }
+
+        return (False, 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]
+
+        output_data_formatted = {}
+        for node_id, node_outputs in outputs.items():
+            output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
+
+        print("!!! Exception during processing !!!")
+        print(traceback.format_exc())
+
+        error_details = {
+            "node_id": unique_id,
+            "exception_message": str(ex),
+            "exception_type": exception_type,
+            "traceback": traceback.format_tb(tb),
+            "current_inputs": input_data_formatted,
+            "current_outputs": output_data_formatted
+        }
+        return (False, error_details, ex)
+
+    executed.add(unique_id)
+
+    return (True, None, None)
+
+def recursive_will_execute(prompt, outputs, current_item):
+    unique_id = current_item
+    inputs = prompt[unique_id]['inputs']
+    will_execute = []
+    if unique_id in outputs:
+        return []
+
+    for x in inputs:
+        input_data = inputs[x]
+        if isinstance(input_data, list):
+            input_unique_id = input_data[0]
+            output_index = input_data[1]
+            if input_unique_id not in outputs:
+                will_execute += recursive_will_execute(prompt, outputs, input_unique_id)
+
+    return will_execute + [unique_id]
+
+def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
+    unique_id = current_item
+    inputs = prompt[unique_id]['inputs']
+    class_type = prompt[unique_id]['class_type']
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+
+    is_changed_old = ''
+    is_changed = ''
+    to_delete = False
+    if hasattr(class_def, 'IS_CHANGED'):
+        if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
+            is_changed_old = old_prompt[unique_id]['is_changed']
+        if 'is_changed' not in prompt[unique_id]:
+            input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
+            if input_data_all is not None:
+                try:
+                    #is_changed = class_def.IS_CHANGED(**input_data_all)
+                    is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
+                    prompt[unique_id]['is_changed'] = is_changed
+                except:
+                    to_delete = True
+        else:
+            is_changed = prompt[unique_id]['is_changed']
+
+    if unique_id not in outputs:
+        return True
+
+    if not to_delete:
+        if is_changed != is_changed_old:
+            to_delete = True
+        elif unique_id not in old_prompt:
+            to_delete = True
+        elif inputs == old_prompt[unique_id]['inputs']:
+            for x in inputs:
+                input_data = inputs[x]
+
+                if isinstance(input_data, list):
+                    input_unique_id = input_data[0]
+                    output_index = input_data[1]
+                    if input_unique_id in outputs:
+                        to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
+                    else:
+                        to_delete = True
+                    if to_delete:
+                        break
+        else:
+            to_delete = True
+
+    if to_delete:
+        d = outputs.pop(unique_id)
+        del d
+    return to_delete
+
+class PromptExecutor:
+    def __init__(self, server):
+        self.outputs = {}
+        self.object_storage = {}
+        self.outputs_ui = {}
+        self.old_prompt = {}
+        self.server = server
+
+    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.server.send_sync("execution_interrupted", mes, self.server.client_id)
+        else:
+            if self.server.client_id is not None:
+                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": error["current_outputs"],
+                }
+                self.server.send_sync("execution_error", mes, self.server.client_id)
+
+        # Next, remove the subsequent outputs since they will not be executed
+        to_delete = []
+        for o in self.outputs:
+            if (o not in current_outputs) and (o not in executed):
+                to_delete += [o]
+                if o in self.old_prompt:
+                    d = self.old_prompt.pop(o)
+                    del d
+        for o in to_delete:
+            d = self.outputs.pop(o)
+            del d
+
+    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
+
+        if self.server.client_id is not None:
+            self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id)
+
+        with torch.inference_mode():
+            #delete cached outputs if nodes don't exist for them
+            to_delete = []
+            for o in self.outputs:
+                if o not in prompt:
+                    to_delete += [o]
+            for o in to_delete:
+                d = self.outputs.pop(o)
+                del d
+            to_delete = []
+            for o in self.object_storage:
+                if o[0] not in prompt:
+                    to_delete += [o]
+                else:
+                    p = prompt[o[0]]
+                    if o[1] != p['class_type']:
+                        to_delete += [o]
+            for o in to_delete:
+                d = self.object_storage.pop(o)
+                del d
+
+            for x in prompt:
+                recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
+
+            current_outputs = set(self.outputs.keys())
+            for x in list(self.outputs_ui.keys()):
+                if x not in current_outputs:
+                    d = self.outputs_ui.pop(x)
+                    del d
+
+            comfy.model_management.cleanup_models()
+            if self.server.client_id is not None:
+                self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id)
+            executed = set()
+            output_node_id = None
+            to_execute = []
+
+            for node_id in list(execute_outputs):
+                to_execute += [(0, node_id)]
+
+            while len(to_execute) > 0:
+                #always execute the output that depends on the least amount of unexecuted nodes first
+                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
+                output_node_id = to_execute.pop(0)[-1]
+
+                # This call shouldn't raise anything if there's an error deep in
+                # the actual SD code, instead it will report the node where the
+                # error was raised
+                success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage)
+                if success is not True:
+                    self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
+                    break
+
+            for x in executed:
+                self.old_prompt[x] = copy.deepcopy(prompt[x])
+            self.server.last_node_id = None
+
+
+
+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()
+    required_inputs = class_inputs['required']
+
+    errors = []
+    valid = True
+
+    for x in required_inputs:
+        if x not in inputs:
+            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 = required_inputs[x]
+        type_input = info[0]
+        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
+            if r[val[1]] != type_input:
+                received_type = r[val[1]]
+                details = f"{x}, {received_type} != {type_input}"
+                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:
+                if type_input == "INT":
+                    val = int(val)
+                    inputs[x] = val
+                if type_input == "FLOAT":
+                    val = float(val)
+                    inputs[x] = val
+                if type_input == "STRING":
+                    val = str(val)
+                    inputs[x] = val
+            except Exception as ex:
+                error = {
+                    "type": "invalid_input_type",
+                    "message": f"Failed to convert an input value to a {type_input} 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 len(info) > 1:
+                if "min" in info[1] and val < info[1]["min"]:
+                    error = {
+                        "type": "value_smaller_than_min",
+                        "message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
+                        "details": f"{x}",
+                        "extra_info": {
+                            "input_name": x,
+                            "input_config": info,
+                            "received_value": val,
+                        }
+                    }
+                    errors.append(error)
+                    continue
+                if "max" in info[1] and val > info[1]["max"]:
+                    error = {
+                        "type": "value_bigger_than_max",
+                        "message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
+                        "details": f"{x}",
+                        "extra_info": {
+                            "input_name": x,
+                            "input_config": info,
+                            "received_value": val,
+                        }
+                    }
+                    errors.append(error)
+                    continue
+
+            if hasattr(obj_class, "VALIDATE_INPUTS"):
+                input_data_all = get_input_data(inputs, obj_class, unique_id)
+                #ret = obj_class.VALIDATE_INPUTS(**input_data_all)
+                ret = map_node_over_list(obj_class, input_data_all, "VALIDATE_INPUTS")
+                for i, r in enumerate(ret):
+                    if r is not True:
+                        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,
+                                "input_config": info,
+                                "received_value": val,
+                            }
+                        }
+                        errors.append(error)
+                        continue
+            else:
+                if isinstance(type_input, list):
+                    if val not in type_input:
+                        input_config = info
+                        list_info = ""
+
+                        # Don't send back gigantic lists like if they're lots of
+                        # scanned model filepaths
+                        if len(type_input) > 20:
+                            list_info = f"(list of length {len(type_input)})"
+                            input_config = None
+                        else:
+                            list_info = str(type_input)
+
+                        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(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:
+        class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
+        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == 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:
+            print(f"Failed to validate prompt for output {o}:")
+            if len(reasons) > 0:
+                print("* (prompt):")
+                for reason in reasons:
+                    print(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
+                        }
+                        print(f"* {class_type} {node_id}:")
+                        for reason in reasons:
+                            print(f"  - {reason['message']}: {reason['details']}")
+                    node_errors[node_id]["dependent_outputs"].append(o)
+            print("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)
+
+
+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 = {}
+        server.prompt_queue = self
+
+    def put(self, item):
+        with self.mutex:
+            heapq.heappush(self.queue, item)
+            self.server.queue_updated()
+            self.not_empty.notify()
+
+    def get(self):
+        with self.not_empty:
+            while len(self.queue) == 0:
+                self.not_empty.wait()
+            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)
+
+    def task_done(self, item_id, outputs):
+        with self.mutex:
+            prompt = self.currently_running.pop(item_id)
+            self.history[prompt[1]] = { "prompt": prompt, "outputs": {} }
+            for o in outputs:
+                self.history[prompt[1]]["outputs"][o] = outputs[o]
+            self.server.queue_updated()
+
+    def get_current_queue(self):
+        with self.mutex:
+            out = []
+            for x in self.currently_running.values():
+                out += [x]
+            return (out, copy.deepcopy(self.queue))
+
+    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):
+        with self.mutex:
+            if prompt_id is None:
+                return copy.deepcopy(self.history)
+            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)

extra_model_paths.yaml.example

@@ -0,0 +1,26 @@
+#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
+
+#other_ui:
+#    base_path: path/to/ui
+#    checkpoints: models/checkpoints
+#    gligen: models/gligen
+#    custom_nodes: path/custom_nodes

folder_paths.py

@@ -0,0 +1,235 @@
+import os
+import time
+
+supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])
+
+folder_names_and_paths = {}
+
+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["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], 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")], [])
+
+folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
+
+output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
+temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+
+filename_list_cache = {}
+
+if not os.path.exists(input_directory):
+    os.makedirs(input_directory)
+
+def set_output_directory(output_dir):
+    global output_directory
+    output_directory = output_dir
+
+def set_temp_directory(temp_dir):
+    global temp_directory
+    temp_directory = temp_dir
+
+def get_output_directory():
+    global output_directory
+    return output_directory
+
+def get_temp_directory():
+    global temp_directory
+    return temp_directory
+
+def get_input_directory():
+    global input_directory
+    return input_directory
+
+
+#NOTE: used in http server so don't put folders that should not be accessed remotely
+def get_directory_by_type(type_name):
+    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
+
+
+# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
+# otherwise use default_path as base_dir
+def annotated_filepath(name):
+    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, default_dir=None):
+    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):
+    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, full_folder_path):
+    global folder_names_and_paths
+    if folder_name in folder_names_and_paths:
+        folder_names_and_paths[folder_name][0].append(full_folder_path)
+    else:
+        folder_names_and_paths[folder_name] = ([full_folder_path], set())
+
+def get_folder_paths(folder_name):
+    return folder_names_and_paths[folder_name][0][:]
+
+def recursive_search(directory, excluded_dir_names=None):
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {directory: os.path.getmtime(directory)}
+    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+        for file_name in filenames:
+            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            result.append(relative_path)
+        for d in subdirs:
+            path = os.path.join(dirpath, d)
+            dirs[path] = os.path.getmtime(path)
+    return result, dirs
+
+def filter_files_extensions(files, extensions):
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
+
+
+
+def get_full_path(folder_name, filename):
+    global folder_names_and_paths
+    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
+
+    return None
+
+def get_filename_list_(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):
+    global filename_list_cache
+    global folder_names_and_paths
+    if folder_name not in filename_list_cache:
+        return None
+    out = filename_list_cache[folder_name]
+    if time.perf_counter() < (out[2] + 0.5):
+        return out
+    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):
+    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
+    return list(out[0])
+
+def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):
+    def map_filename(filename):
+        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, image_width, image_height):
+        input = input.replace("%width%", str(image_width))
+        input = input.replace("%height%", str(image_height))
+        return input
+
+    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:
+        print("Saving image outside the output folder is not allowed.")
+        return {}
+
+    try:
+        counter = max(filter(lambda a: a[1][:-1] == 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

latent_preview.py

@@ -0,0 +1,77 @@
+import torch
+from PIL import Image
+import struct
+import numpy as np
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import folder_paths
+
+MAX_PREVIEW_RESOLUTION = 512
+
+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.decoder(x0)[0].detach()
+        # x_sample = self.taesd.unscale_latents(x_sample).div(4).add(0.5)  # returns value in [-2, 2]
+        x_sample = x_sample.sub(0.5).mul(2)
+
+        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+
+        preview_image = Image.fromarray(x_sample)
+        return preview_image
+
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self, latent_rgb_factors):
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
+
+    def decode_latent_to_preview(self, x0):
+        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
+
+        latents_ubyte = (((latent_image + 1) / 2)
+                            .clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            .byte()).cpu()
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+
+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 = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+            if taesd_decoder_path:
+                method = LatentPreviewMethod.TAESD
+
+        if method == LatentPreviewMethod.TAESD:
+            if taesd_decoder_path:
+                taesd = TAESD(None, taesd_decoder_path).to(device)
+                previewer = TAESDPreviewerImpl(taesd)
+            else:
+                print("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)
+    return previewer
+
+

main.py

@@ -0,0 +1,195 @@
+import comfy.options
+comfy.options.enable_args_parsing()
+
+import os
+import importlib.util
+import folder_paths
+import time
+
+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:
+            print(f"Failed to execute startup-script: {script_path} / {e}")
+        return False
+
+    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:
+        print("\nPrestartup times for custom nodes:")
+        for n in sorted(node_prestartup_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (PRESTARTUP FAILED)"
+            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
+        print()
+
+execute_prestartup_script()
+
+
+# Main code
+import asyncio
+import itertools
+import shutil
+import threading
+import gc
+
+from comfy.cli_args import args
+
+if os.name == "nt":
+    import logging
+    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)
+        print("Set cuda device to:", args.cuda_device)
+
+    import cuda_malloc
+
+import comfy.utils
+import yaml
+
+import execution
+import server
+from server import BinaryEventTypes
+from nodes import init_custom_nodes
+import comfy.model_management
+
+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:
+            print("\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):
+    e = execution.PromptExecutor(server)
+    while True:
+        item, item_id = q.get()
+        execution_start_time = time.perf_counter()
+        prompt_id = item[1]
+        e.execute(item[2], prompt_id, item[3], item[4])
+        q.task_done(item_id, e.outputs_ui)
+        if server.client_id is not None:
+            server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, server.client_id)
+
+        print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
+        gc.collect()
+        comfy.model_management.soft_empty_cache()
+
+async def run(server, address='', port=8188, verbose=True, call_on_start=None):
+    await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
+
+
+def hijack_progress(server):
+    def hook(value, total, preview_image):
+        comfy.model_management.throw_exception_if_processing_interrupted()
+        server.send_sync("progress", {"value": value, "max": total}, server.client_id)
+        if preview_image is not None:
+            server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server.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 load_extra_path_config(yaml_path):
+    with open(yaml_path, 'r') as stream:
+        config = yaml.safe_load(stream)
+    for c in config:
+        conf = config[c]
+        if conf is None:
+            continue
+        base_path = None
+        if "base_path" in conf:
+            base_path = conf.pop("base_path")
+        for x in conf:
+            for y in conf[x].split("\n"):
+                if len(y) == 0:
+                    continue
+                full_path = y
+                if base_path is not None:
+                    full_path = os.path.join(base_path, full_path)
+                print("Adding extra search path", x, full_path)
+                folder_paths.add_model_folder_path(x, full_path)
+
+
+if __name__ == "__main__":
+    if args.temp_directory:
+        temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
+        print(f"Setting temp directory to: {temp_dir}")
+        folder_paths.set_temp_directory(temp_dir)
+    cleanup_temp()
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server = server.PromptServer(loop)
+    q = execution.PromptQueue(server)
+
+    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):
+        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):
+            load_extra_path_config(config_path)
+
+    init_custom_nodes()
+
+    cuda_malloc_warning()
+
+    server.add_routes()
+    hijack_progress(server)
+
+    threading.Thread(target=prompt_worker, daemon=True, args=(q, server,)).start()
+
+    if args.output_directory:
+        output_dir = os.path.abspath(args.output_directory)
+        print(f"Setting output directory to: {output_dir}")
+        folder_paths.set_output_directory(output_dir)
+
+    if args.quick_test_for_ci:
+        exit(0)
+
+    call_on_start = None
+    if args.auto_launch:
+        def startup_server(address, port):
+            import webbrowser
+            if os.name == 'nt' and address == '0.0.0.0':
+                address = '127.0.0.1'
+            webbrowser.open(f"http://{address}:{port}")
+        call_on_start = startup_server
+
+    try:
+        loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start))
+    except KeyboardInterrupt:
+        print("\nStopped server")
+
+    cleanup_temp()

nodes.py

@@ -0,0 +1,1786 @@
+import torch
+
+import os
+import sys
+import json
+import hashlib
+import traceback
+import math
+import time
+import random
+
+from PIL import Image, ImageOps
+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
+
+import comfy.clip_vision
+
+import comfy.model_management
+from comfy.cli_args import args
+
+import importlib
+
+import folder_paths
+import latent_preview
+
+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=8192
+
+class CLIPTextEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+
+    def encode(self, clip, text):
+        tokens = clip.tokenize(text)
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        return ([[cond, {"pooled_output": pooled}]], )
+
+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:
+            print("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:
+            print("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 = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
+            n[1]['strength'] = strength
+            n[1]['set_area_to_bounds'] = False
+            c.append(n)
+        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 = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            n[1]['area'] = ("percentage", height, width, y, x)
+            n[1]['strength'] = strength
+            n[1]['set_area_to_bounds'] = False
+            c.append(n)
+        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):
+        c = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask.shape) < 3:
+            mask = mask.unsqueeze(0)
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            _, h, w = mask.shape
+            n[1]['mask'] = mask
+            n[1]['set_area_to_bounds'] = set_area_to_bounds
+            n[1]['mask_strength'] = strength
+            c.append(n)
+        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()
+            if "pooled_output" in d:
+                d["pooled_output"] = torch.zeros_like(d["pooled_output"])
+            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 = []
+        for t in conditioning:
+            d = t[1].copy()
+            d['start_percent'] = 1.0 - start
+            d['end_percent'] = 1.0 - end
+            n = [t[0], d]
+            c.append(n)
+        return (c, )
+
+class VAEDecode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    CATEGORY = "latent"
+
+    def decode(self, vae, samples):
+        return (vae.decode(samples["samples"]), )
+
+class VAEDecodeTiled:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
+                             "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64})
+                            }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    CATEGORY = "_for_testing"
+
+    def decode(self, vae, samples, tile_size):
+        return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), )
+
+class VAEEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "latent"
+
+    @staticmethod
+    def vae_encode_crop_pixels(pixels):
+        x = (pixels.shape[1] // 8) * 8
+        y = (pixels.shape[2] // 8) * 8
+        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, :]
+        return pixels
+
+    def encode(self, vae, pixels):
+        pixels = self.vae_encode_crop_pixels(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": 320, "max": 4096, "step": 64})
+                            }}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "_for_testing"
+
+    def encode(self, vae, pixels, tile_size):
+        pixels = VAEEncode.vae_encode_crop_pixels(pixels)
+        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, )
+        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] // 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")
+
+        pixels = 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]
+
+        #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 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()
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": "output"
+        })
+
+        file = os.path.join(full_output_folder, file)
+
+        output = {}
+        output["latent_tensor"] = samples["samples"]
+        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"
+
+    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+        config_path = folder_paths.get_full_path("configs", config_name)
+        ckpt_path = folder_paths.get_full_path("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"), ),
+                             }}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "loaders"
+
+    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+        ckpt_path = folder_paths.get_full_path("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("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",),
+                              "clip": ("CLIP", ),
+                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+
+    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("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:
+                temp = self.loaded_lora
+                self.loaded_lora = None
+                del temp
+
+        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 VAELoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
+    RETURN_TYPES = ("VAE",)
+    FUNCTION = "load_vae"
+
+    CATEGORY = "loaders"
+
+    #TODO: scale factor?
+    def load_vae(self, vae_name):
+        vae_path = folder_paths.get_full_path("vae", vae_name)
+        vae = comfy.sd.VAE(ckpt_path=vae_path)
+        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("controlnet", control_net_name)
+        controlnet = comfy.controlnet.load_controlnet(controlnet_path)
+        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("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"
+
+    CATEGORY = "conditioning"
+
+    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})
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "apply_controlnet"
+
+    CATEGORY = "conditioning"
+
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
+        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, (1.0 - start_percent, 1.0 - end_percent))
+                    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("unet"), ),
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_unet(self, unet_name):
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        model = comfy.sd.load_unet(unet_path)
+        return (model,)
+
+class CLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return (clip,)
+
+class DualCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name1, clip_name2):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        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("clip_vision", clip_name)
+        clip_vision = comfy.clip_vision.load(clip_path)
+        return (clip_vision,)
+
+class CLIPVisionEncode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "image": ("IMAGE",)
+                             }}
+    RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+
+    def encode(self, clip_vision, image):
+        output = clip_vision.encode_image(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("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", ),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_stylemodel"
+
+    CATEGORY = "conditioning/style_model"
+
+    def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+        c = []
+        for t in conditioning:
+            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
+            c.append(n)
+        return (c, )
+
+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 = []
+        for t in conditioning:
+            o = t[1].copy()
+            x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}
+            if "unclip_conditioning" in o:
+                o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
+            else:
+                o["unclip_conditioning"] = [x]
+            n = [t[0], o]
+            c.append(n)
+        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("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}),
+                              "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=True)
+        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, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent"
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        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": 64, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 64, "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):
+        s = samples.copy()
+        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[3] * 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):
+    device = comfy.model_management.get_torch_device()
+    latent_image = latent["samples"]
+
+    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"]
+
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = latent_preview.get_previewer(device, model.model.latent_format)
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        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)
+
+    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, seed=seed)
+    out = latent.copy()
+    out["samples"] = samples
+    return (out, )
+
+class KSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "latent_image": ("LATENT", ),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     }
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling"
+
+    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}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "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 = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"images": ("IMAGE", ),
+                     "filename_prefix": ("STRING", {"default": "ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image"
+
+    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 image in 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]))
+
+            file = f"{filename}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+            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))
+
+    @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))]
+        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)
+        i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGB")
+        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)
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (image, 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 = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        if i.getbands() != ("R", "G", "B", "A"):
+            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,)
+
+    @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, channel):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        if channel not in s._color_channels:
+            return "Invalid color channel: {}".format(channel)
+
+        return True
+
+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": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 1, "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):
+        samples = image.movedim(-1,1)
+        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": 64}),
+                              "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.zeros(
+            (d1, d2 + top + bottom, d3 + left + right, d4),
+            dtype=torch.float32,
+        )
+        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)
+
+
+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,
+    "ImageScale": ImageScale,
+    "ImageScaleBy": ImageScaleBy,
+    "ImageInvert": ImageInvert,
+    "ImageBatch": ImageBatch,
+    "ImagePadForOutpaint": ImagePadForOutpaint,
+    "EmptyImage": EmptyImage,
+    "ConditioningAverage ": ConditioningAverage ,
+    "ConditioningCombine": ConditioningCombine,
+    "ConditioningConcat": ConditioningConcat,
+    "ConditioningSetArea": ConditioningSetArea,
+    "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
+    "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,
+
+    "CheckpointLoader": CheckpointLoader,
+    "DiffusersLoader": DiffusersLoader,
+
+    "LoadLatent": LoadLatent,
+    "SaveLatent": SaveLatent,
+
+    "ConditioningZeroOut": ConditioningZeroOut,
+    "ConditioningSetTimestepRange": ConditioningSetTimestepRange,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Sampling
+    "KSampler": "KSampler",
+    "KSamplerAdvanced": "KSampler (Advanced)",
+    # Loaders
+    "CheckpointLoader": "Load Checkpoint (With Config)",
+    "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",
+    # 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",
+    "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)",
+    # 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)",
+    "ImageScale": "Upscale Image",
+    "ImageScaleBy": "Upscale Image By",
+    "ImageUpscaleWithModel": "Upscale Image (using Model)",
+    "ImageInvert": "Invert Image",
+    "ImagePadForOutpaint": "Pad Image for Outpainting",
+    "ImageBatch": "Batch Images",
+    # _for_testing
+    "VAEDecodeTiled": "VAE Decode (Tiled)",
+    "VAEEncodeTiled": "VAE Encode (Tiled)",
+}
+
+EXTENSION_WEB_DIRS = {}
+
+def load_custom_node(module_path, ignore=set()):
+    module_name = os.path.basename(module_path)
+    if os.path.isfile(module_path):
+        sp = os.path.splitext(module_path)
+        module_name = sp[0]
+    try:
+        if os.path.isfile(module_path):
+            module_spec = importlib.util.spec_from_file_location(module_name, module_path)
+            module_dir = os.path.split(module_path)[0]
+        else:
+            module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
+            module_dir = module_path
+
+        module = importlib.util.module_from_spec(module_spec)
+        sys.modules[module_name] = module
+        module_spec.loader.exec_module(module)
+
+        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 in module.NODE_CLASS_MAPPINGS:
+                if name not in ignore:
+                    NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name]
+            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:
+            print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
+            return False
+    except Exception as e:
+        print(traceback.format_exc())
+        print(f"Cannot import {module_path} module for custom nodes:", e)
+        return False
+
+def load_custom_nodes():
+    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(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)
+            node_import_times.append((time.perf_counter() - time_before, module_path, success))
+
+    if len(node_import_times) > 0:
+        print("\nImport times for custom nodes:")
+        for n in sorted(node_import_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (IMPORT FAILED)"
+            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
+        print()
+
+def init_custom_nodes():
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_latent.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_clip_sdxl.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_canny.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_freelunch.py"))
+    load_custom_nodes()

pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')
+testpaths = tests
+addopts = -s

requirements.txt

@@ -0,0 +1,12 @@
+torch
+torchsde
+einops
+transformers>=4.25.1
+safetensors>=0.3.0
+aiohttp
+accelerate
+pyyaml
+Pillow
+scipy
+tqdm
+psutil

server.py

@@ -0,0 +1,630 @@
+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
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+from io import BytesIO
+
+try:
+    import aiohttp
+    from aiohttp import web
+except ImportError:
+    print("Module 'aiohttp' not installed. Please install it via:")
+    print("pip install aiohttp")
+    print("or")
+    print("pip install -r requirements.txt")
+    sys.exit()
+
+import mimetypes
+from comfy.cli_args import args
+import comfy.utils
+import comfy.model_management
+
+
+class BinaryEventTypes:
+    PREVIEW_IMAGE = 1
+    UNENCODED_PREVIEW_IMAGE = 2
+
+async def send_socket_catch_exception(function, message):
+    try:
+        await function(message)
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
+        print("send error:", 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'):
+        response.headers.setdefault('Cache-Control', 'no-cache')
+    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
+
+class PromptServer():
+    def __init__(self, loop):
+        PromptServer.instance = self
+
+        mimetypes.init()
+        mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
+
+        self.supports = ["custom_nodes_from_web"]
+        self.prompt_queue = None
+        self.loop = loop
+        self.messages = asyncio.Queue()
+        self.number = 0
+
+        middlewares = [cache_control]
+        if args.enable_cors_header:
+            middlewares.append(create_cors_middleware(args.enable_cors_header))
+
+        self.app = web.Application(client_max_size=104857600, middlewares=middlewares)
+        self.sockets = dict()
+        self.web_root = os.path.join(os.path.dirname(
+            os.path.realpath(__file__)), "web")
+        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:
+                        print('ws connection closed with exception %s' % ws.exception())
+            finally:
+                self.sockets.pop(sid, None)
+            return ws
+
+        @routes.get("/")
+        async def get_root(request):
+            return web.FileResponse(os.path.join(self.web_root, "index.html"))
+
+        @routes.get("/embeddings")
+        def get_embeddings(self):
+            embeddings = folder_paths.get_filename_list("embeddings")
+            return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
+
+        @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 image_upload(post, image_save_function=None):
+            image = post.get("image")
+            overwrite = post.get("overwrite")
+
+            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):
+                        filename = f"{split[0]} ({i}){split[1]}"
+                        filepath = os.path.join(full_output_folder, filename)
+                        i += 1
+
+                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'])
+
+                # 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)
+
+                # 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:
+                        return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""})
+
+            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 get_queue(request):
+            device = comfy.model_management.get_torch_device()
+            device_name = comfy.model_management.get_torch_device_name(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,
+                    "python_version": sys.version,
+                    "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded"
+                },
+                "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['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['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
+            return info
+
+        @routes.get("/object_info")
+        async def get_object_info(request):
+            out = {}
+            for x in nodes.NODE_CLASS_MAPPINGS:
+                out[x] = node_info(x)
+            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):
+            return web.json_response(self.prompt_queue.get_history())
+
+        @routes.get("/history/{prompt_id}")
+        async def get_history(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()
+            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):
+            print("got prompt")
+            resp_code = 200
+            out_string = ""
+            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:
+                    print("invalid prompt:", valid[1])
+                    return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
+            else:
+                return web.json_response({"error": "no prompt", "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("/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)
+        
+    def add_routes(self):
+        self.app.add_routes(self.routes)
+
+        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+            self.app.add_routes([
+                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
+            ])
+
+        self.app.add_routes([
+            web.static('/', self.web_root, follow_symlinks=True),
+        ])
+
+    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=4)
+        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:
+            for ws in self.sockets.values():
+                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:
+            for ws in self.sockets.values():
+                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):
+        runner = web.AppRunner(self.app, access_log=None)
+        await runner.setup()
+        site = web.TCPSite(runner, address, port)
+        await site.start()
+
+        if address == '':
+            address = '0.0.0.0'
+        if verbose:
+            print("Starting server\n")
+            print("To see the GUI go to: http://{}:{}".format(address, port))
+        if call_on_start is not None:
+            call_on_start(address, 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 as e:
+                print(f"[ERROR] An error occurred during the on_prompt_handler processing")
+                traceback.print_exc()
+
+        return json_data