README.md
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# ComfyUI-HunyuanVideoSamplerSave
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A ComfyUI custom node implementation for optimized video generation and motion effects, designed to work with Hunyuan text-to-video models.
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## Features
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### HunyuanVideoSamplerSave
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An optimized video sampler that extends ComfyUI's KSampler capabilities:
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- Memory-efficient batch processing for video frames
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- Progress tracking for long video generation tasks
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- Optimized VRAM usage through sequential frame processing
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- Interrupt-safe with proper memory management
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- Compatible with all standard ComfyUI samplers and schedulers
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### ImageMotionInfluence
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A powerful tool for creating motion sequences from static images:
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- Horizontal panning effects with adjustable range
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- Progressive zoom capabilities
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- Seamless loop generation through mirror techniques
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- Configurable frame count and motion parameters
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### ResizeImageForHunyuan
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A specialized resizing tool optimized for Hunyuan video generation:
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- Predefined aspect ratios optimized for home GPUs
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- Multiple size options for each aspect ratio
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- All dimensions properly aligned to 16x16 grid
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- Multiple upscaling methods
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- Crop control options
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### EmptyVideoLatentForHunyuan
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A latent initialization tool specifically designed for Hunyuan video generation:
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- Supports multiple optimized resolutions for home GPUs
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- Common aspect ratios (16:9, 4:3, 3:2, 9:16, 3:4, 2:3)
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- Memory-efficient latent generation
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- Configurable video length and batch size
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- All dimensions automatically aligned to model requirements
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## Installation
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1. Clone this repository into your ComfyUI custom nodes directory:
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```bash
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cd ComfyUI/custom_nodes/
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git clone https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoSamplerSave.git
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```
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2. Restart ComfyUI to load the new nodes.
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## Usage
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### Video Generation Workflow
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1. **Image Motion Setup**
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- Input: Any source image
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- Configure motion parameters:
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- move_range_x: Controls horizontal movement (-150 to 150)
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- frame_num: Number of frames to generate (2 to 500)
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- zoom: Progressive zoom effect (0.0 to 0.5)
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- Output: Sequence of motion-affected images
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2. **Image Resizing**
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- Use ResizeImageForHunyuan to ensure proper dimensions
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- Select from optimized presets for your GPU
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- Choose appropriate upscaling method
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3. **Latent Setup**
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- Use EmptyVideoLatentForHunyuan to initialize latent space
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- Select resolution from optimized presets
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- Configure video length and batch size
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4. **Video Generation**
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- Use HunyuanVideoSamplerSave with your text prompts
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- The motion-influenced latents guide the video generation
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- Adjustable parameters:
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- Steps: Generation steps per frame
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- CFG: Prompt influence strength
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- Sampler and Scheduler selection
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- Denoising strength
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## Parameters
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### HunyuanVideoSamplerSave
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- **model**: Loaded Hunyuan model
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- **positive/negative**: Conditioning from text prompts
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- **video_latents**: Input latent sequence
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- **seed**: Generation seed for reproducibility
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- **steps**: Number of sampling steps
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- **cfg**: Conditioning strength
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- **sampler_name**: Choice of sampling algorithm
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- **scheduler**: Noise scheduler selection
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- **denoise**: Denoising strength
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### ImageMotionInfluence
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- **image**: Input source image
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- **move_range_x**: Horizontal motion range
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- **frame_num**: Number of frames to generate
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- **zoom**: Zoom effect intensity
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### ResizeImageForHunyuan
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- **image**: Input image to resize
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- **size_preset**: Selection of predefined sizes (e.g., "384x216 (16:9)", "768x432 (16:9)")
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- **upscale_method**: Choice of upscaling algorithm (nearest-exact, bilinear, area, bicubic)
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- **crop**: Crop method selection (disabled, center)
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### EmptyVideoLatentForHunyuan
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- **resolution**: Selection of optimized video resolutions
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- **length**: Number of frames to generate
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- **batch_size**: Number of videos to generate in parallel
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## Memory Optimization
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The nodes implement several memory optimization strategies:
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- Sequential frame processing
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- Active memory management
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- Intermediate result storage
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- Garbage collection during processing
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- Optimized resolution presets for home GPUs
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- Proper dimension alignment for efficient processing
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This allows for processing of longer sequences and higher resolution outputs compared to standard sampling approaches.
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## Integration
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This custom node is designed to work seamlessly with:
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- ComfyUI's core components
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- Hunyuan text-to-video models
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- Standard VAE encoders
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- Various sampling and scheduling methods
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## Requirements
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- ComfyUI installation
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- Compatible Hunyuan model
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- Sufficient VRAM for video processing
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- Python 3.x
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- PyTorch
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## License
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MIT
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