77 lines
2.3 KiB
Markdown
77 lines
2.3 KiB
Markdown
# ComfyUI FASHN VTON v1.5 Custom Nodes
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This custom node set implements the [FASHN VTON v1.5](https://github.com/fashn-AI/fashn-vton-1.5) model for virtual try-on in ComfyUI.
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## Installation
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**Via ComfyUI-Manager:**
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Search for "ComfyUI-FASHN-VTON" in the manager and install it directly.
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**Manually:**
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1. Clone this repo into `custom_nodes` folder.
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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Note: If you are using a portable version of ComfyUI, use the corresponding python executable.
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## How to Use
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<img width="980" height="496" alt="example" src="https://github.com/user-attachments/assets/b82241d9-4976-4a89-8922-b347c367a335" />
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### 1. (Down)load FASHN VTON
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Use the **(Down)load FASHN VTON** node.
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The model **downloads automatically on first use**. In most cases, **no manual download is required**.
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When this node runs, it will:
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- Download the **FASHN VTON v1.5** model weights
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- Download the required **DWPose** models
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- Store everything under `ComfyUI/models/fashn-vton/`
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- Load the pipeline automatically
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If the files already exist locally, the download step is skipped.
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#### Automatic Downloads
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The following files are fetched automatically from Hugging Face:
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- **FASHN VTON model**
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- `model.safetensors`
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- Source: https://huggingface.co/fashn-ai/fashn-vton-1.5
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- **DWPose models**
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- `yolox_l.onnx`
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- `dw-ll_ucoco_384.onnx`
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- Source: https://huggingface.co/fashn-ai/DWPose
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#### Manual Download (Optional)
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If you prefer to download the models manually (e.g. for offline use), place the files in the following directory structure:
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```
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ComfyUI/
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└── models/
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└── fashn-vton/
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├── model.safetensors
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└── dwpose/
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├── yolox_l.onnx
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└── dw-ll_ucoco_384.onnx
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```
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### 2. Inference
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Use the **FASHN VTON Inference** node:
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- **pipeline**: Connect from the Loader node.
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- **person_image**: The image of the person.
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- **garment_image**: The image of the garment.
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- **category**: `tops`, `bottoms`, or `one-pieces`.
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- **num_timesteps**: Recommended 30–50.
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- **guidance_scale**: Recommended 1.5–3.0.
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- **keep_model_loaded**: If set to `false`, the model will be moved to CPU after each inference to save VRAM.
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## Credits
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Model by [FASHN AI](https://fashn.ai/). Implementation based on their open-source repository.
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