CivArchive
    Wan2.2 Animate Native ComfyUI Fast GGUF - v1.0

    Wan2.2 Animate GGUF - Video Animation Workflow

    Overview

    This ComfyUI workflow enables high-quality video animation and character motion transfer using the Wan2.2-Animate-14B model in GGUF format. It's specifically designed for creating animated videos from reference images and motion source videos.

    Key Features

    🚀 GGUF Model Optimization

    • Uses GGUF format for efficient memory usage and faster loading

    • Compatible with various hardware configurations

    • Includes separate GGUF loaders for model, CLIP, and VAE components

    🎭 Dual Operation Modes

    • Character Replacement Mode: Replace characters in existing videos while preserving background

    • Motion Transfer Mode: Apply character poses to new scenes and environments

    🛠️ Advanced Preprocessing

    • Interactive point-based segmentation using SAM2

    • Automatic pose detection with DWPreprocessor

    • Facial feature extraction for better character preservation

    • Smart video scaling and frame management

    Workflow Structure

    Step 1: Model Loading

    • Loads Wan2.2-Animate-14B GGUF model

    • Configures CLIP text encoder and VAE decoder

    • Applies optional LoRA enhancements for improved results

    Step 2: Input Setup

    • Reference image upload for character appearance

    • Source video for motion capture

    • Positive/Negative prompt configuration

    Step 3: Video Preprocessing

    • Extracts frames, audio, and FPS from source video

    • Resizes video to optimal dimensions (must be multiples of 16)

    • Generates pose and facial reference data

    Step 4: Character Masking

    • Interactive Points Editor for precise character selection

    • SAM2 segmentation with positive/negative point guidance

    • Mask refinement with GrowMask and BlockifyMask nodes

    Step 5: Animation Generation

    • Dual KSampler setup for flexible video generation

    • WanAnimateToVideo nodes handle core animation logic

    • Support for video length extension through batch processing

    Step 6: Video Output

    • Recombines generated frames with original audio

    • Maintains original FPS for seamless playback

    • Multiple output options with SaveVideo nodes

    Technical Requirements

    Hardware

    • Compatible with various GPU/CPU configurations thanks to GGUF format

    • Lower VRAM requirements compared to standard model formats

    • Recommended: 8GB+ RAM for optimal performance

    Software

    • ComfyUI with required custom nodes:

      • ComfyUI-segment-anything-2 (SAM2)

      • comfyui-controlnet-aux (preprocessors)

      • comfyui-kjnodes (utility nodes)

      • GGUF loader nodes

    Usage Instructions

    1. Load Models: Ensure all GGUF model files are in correct directories

    2. Set Dimensions: Configure width/height as multiples of 16 (e.g., 640x640)

    3. Input Media: Upload reference image and source video

    4. Mask Creation: Use Points Editor to mark character areas (Shift+click for positive points)

    5. Configure Prompts: Set positive and negative text prompts

    6. Execute: Run the workflow and monitor progress through preview nodes

    Ideal For

    • Character animation from still images

    • Motion transfer between videos

    • Video style transfer with character preservation

    • Content creation for short films and social media

    This workflow represents a sophisticated pipeline for video animation that balances quality with computational efficiency through the use of GGUF model format.

    Description

    Workflows
    Wan Video 2.2 I2V-A14B

    Details

    Downloads
    210
    Platform
    CivitAI
    Platform Status
    Available
    Created
    9/24/2025
    Updated
    9/28/2025
    Deleted
    -

    Files

    wan22AnimateNative_v10.zip

    Mirrors

    CivitAI (1 mirrors)