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

    ๐Ÿš€ 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

    ๐ŸŽญ 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

    ๐Ÿ› ๏ธ 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

    FAQ

    Comments (8)

    zerocool22Sep 23, 2025
    CivitAI

    So this WF could work in this setup?:

    Ryzen 5900x
    4070 TI SUPER 16 VRAM
    64 RAM

    blobby99Sep 24, 2025ยท 1 reaction
    CivitAI

    Spotted that sample WITHOUT the sound track - took a moment. Very nice (unlike the book on which the film is based).

    cougarmountainashSep 24, 2025ยท 1 reaction
    CivitAI

    Really cool, works well. Thanks!

    zardozai
    Author
    Sep 24, 2025

    I'm pleased to hear that it was helpful.

    GerdoOct 7, 2025ยท 1 reaction
    CivitAI

    Excellent result. However, with the original fps, it generates only 1 second instead of 4 seconds. And if I set 16 fps, it generates the same 1 second, but in slow motion. I don't understand what I'm doing wrong.

    rivalsdreamOct 31, 2025ยท 1 reaction
    CivitAI

    Thank you! this works really well, at least as well as it can work on 12gb vram. I'm using the Q4 model and man it still takes some serious time, I don't know if going lower with the model will worth it.

    Anyway the workflow works great and is easy to use, thank you.

    RedditUser981Nov 25, 2025
    CivitAI

    Too slower

    davemullen68699Jan 21, 2026
    CivitAI

    "Native"

    Requires 4 nodes sets to be downloaded.

    So not "native" then?

    Workflows
    Wan Video 2.2 I2V-A14B

    Details

    Downloads
    783
    Platform
    CivitAI
    Platform Status
    Available
    Created
    9/23/2025
    Updated
    5/13/2026
    Deleted
    -

    Files

    wan22AnimateNative_v10.zip

    Mirrors

    CivitAI (1 mirrors)