ComfyUI-HunyuanVideoSamplerSave

ComfyUI-HunyuanVideoSamplerSave
★ 20

视频生成运动效果优化Hunyuan兼容结果保存
ComfyUI自定义节点，针对Hunyuan文本到视频模型优化采样与运动效果，提升生成效率与质量并支持结果保存。
💡 用于基于Hunyuan模型的高效文本到视频采样与保存。
🍴 2 Forks💻 Python🔄 2025-02-05
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📄 README

ComfyUI-HunyuanVideoSamplerSave

A ComfyUI custom node implementation for optimized video generation and motion effects, designed to work with Hunyuan text-to-video models.

Image to Video:

Text to Video:

Features

HunyuanVideoSamplerSave

An optimized video sampler that extends ComfyUI’s KSampler capabilities:

  • Memory-efficient batch processing for video frames
  • Progress tracking for long video generation tasks
  • Optimized VRAM usage through sequential frame processing
  • Interrupt-safe with proper memory management
  • Compatible with all standard ComfyUI samplers and schedulers

    • ImageMotionInfluence

    A powerful tool for creating motion sequences from static images:

  • Horizontal panning effects with adjustable range
  • Progressive zoom capabilities
  • Seamless loop generation through mirror techniques
  • Configurable frame count and motion parameters

    • ResizeImageForHunyuan

    A specialized resizing tool optimized for Hunyuan video generation:

  • Predefined aspect ratios optimized for home GPUs
  • Multiple size options for each aspect ratio
  • All dimensions properly aligned to 16×16 grid
  • Multiple upscaling methods
  • Crop control options

    • EmptyVideoLatentForHunyuan

    A latent initialization tool specifically designed for Hunyuan video generation:

  • Supports multiple optimized resolutions for home GPUs
  • Common aspect ratios (16:9, 4:3, 3:2, 9:16, 3:4, 2:3)
  • Memory-efficient latent generation
  • Configurable video length and batch size
  • All dimensions automatically aligned to model requirements

    • Installation

  • Clone this repository into your ComfyUI custom nodes directory:
    • cd ComfyUI/custom_nodes/
git clone https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoSamplerSave.git
  • Download the model to models/unet folder:
    • https://drive.google.com/file/d/1BvGHjR4Mb60ZPx9tqzA1AabAwZc47ctx/view?usp=sharing
  • Restart ComfyUI to load the new nodes.

    • Usage

    Video Generation Workflow

  • Image Motion Setup
  • Input: Any source image
  • Configure motion parameters:
  • move_range_x: Controls horizontal movement (-150 to 150)
  • frame_num: Number of frames to generate (2 to 500)
  • zoom: Progressive zoom effect (0.0 to 0.5)
  • Output: Sequence of motion-affected images

  • Image Resizing
  • Use ResizeImageForHunyuan to ensure proper dimensions
  • Select from optimized presets for your GPU
  • Choose appropriate upscaling method

  • Latent Setup
  • Use EmptyVideoLatentForHunyuan to initialize latent space
  • Select resolution from optimized presets
  • Configure video length and batch size

  • Video Generation
  • Use HunyuanVideoSamplerSave with your text prompts
  • The motion-influenced latents guide the video generation
  • Adjustable parameters:
  • Steps: Generation steps per frame
  • CFG: Prompt influence strength
  • Sampler and Scheduler selection
  • Denoising strength

    • Parameters

    HunyuanVideoSamplerSave

  • model: Loaded Hunyuan model
  • positive/negative: Conditioning from text prompts
  • video_latents: Input latent sequence
  • seed: Generation seed for reproducibility
  • steps: Number of sampling steps
  • cfg: Conditioning strength
  • sampler_name: Choice of sampling algorithm
  • scheduler: Noise scheduler selection
  • denoise: Denoising strength

    • ImageMotionInfluence

  • image: Input source image
  • move_range_x: Horizontal motion range
  • frame_num: Number of frames to generate
  • zoom: Zoom effect intensity

    • ResizeImageForHunyuan

  • image: Input image to resize
  • size_preset: Selection of predefined sizes (e.g., “384×216 (16:9)”, “768×432 (16:9)”)
  • upscale_method: Choice of upscaling algorithm (nearest-exact, bilinear, area, bicubic)
  • crop: Crop method selection (disabled, center)

    • EmptyVideoLatentForHunyuan

  • resolution: Selection of optimized video resolutions
  • length: Number of frames to generate
  • batch_size: Number of videos to generate in parallel

    • Memory Optimization

    The nodes implement several memory optimization strategies:

  • Sequential frame processing
  • Active memory management
  • Intermediate result storage
  • Garbage collection during processing
  • Optimized resolution presets for home GPUs
  • Proper dimension alignment for efficient processing

    • This allows for processing of longer sequences and higher resolution outputs compared to standard sampling approaches.

    Integration

    This custom node is designed to work seamlessly with:

  • ComfyUI’s core components
  • Hunyuan text-to-video models
  • Standard VAE encoders
  • Various sampling and scheduling methods

    • Requirements

  • ComfyUI installation
  • Compatible Hunyuan model
  • Sufficient VRAM for video processing
  • Python 3.x
  • PyTorch

    • License

    MIT