ComfyUI.org

Mastering Video-to-Video Translation: A Deep Dive into Wan2.1 VACE Model and ComfyUI

CN
ComfyUI.org
2025-05-30 07:10:20

1. Workflow Overview

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This workflow uses Wan2.1 VACE Model for Video-to-Video translation, featuring:

  • Frame Reprocessing: Enhances each frame via AI model

  • Depth Control: Uses DepthAnything for spatial consistency

  • Start/End Frame Guidance: Ensures temporal coherence

  • Flux Optimization: Improves generation stability

2. Core Models

Model Name

Function

Path

VACE-Wan2.1-1.3B-Preview.safetensors

Main video translation model

ComfyUI/models/wan_video/

wan_2.1_vae.safetensors

Video VAE encoder

Same as above

depth_anything_vitl14.pth

Depth map generator

ComfyUI/models/depth_anything/

flux1-dev-fp8.safetensors

Flux optimization model

ComfyUI/models/unet/

3. Key Components

Node Name

Function

Installation

WanVideoVACEEncode

Encodes video frames

Install ComfyUI-WanVideoWrapper

DepthAnythingPreprocessor

Generates depth maps

Install ComfyUI-ControlNet-Aux

FluxGuidance

Stabilizes generation

Built-in (requires Flux model)

VHS_VideoCombine

Renders final video

Install ComfyUI-VideoHelperSuite

4. Workflow Structure

Group 1: Load Models

  • Loads Wan2.1 VACE, VAE, and T5 text encoder

Group 2: First Frame Reprocessing

  • Generates depth map from input video’s first frame

  • Applies FluxGuidance for optimized rendering

Group 3: VACE Video Generation

  • Guided by start/end frames and depth video

  • Parameters:

    • Resolution: 512x768 (adjustable)

    • Frame rate: 16fps (via VHS_VideoCombine)

Group 4: Video Export

  • Output: MP4 (H.264, CRF=19)

5. Inputs & Outputs

  • Required Inputs:

    • Source video (e.g., bc78b00a0e5776429eae83cf6aedc8d294f3031eb601476ecd3974bec50c0559.mp4)

    • Prompt (e.g., "Beautiful girl dancing")

  • Final Output:

    • Reprocessed MP4 video (e.g., AnimateDiff_00003.mp4)

6. Notes

  • ⚠️ VRAM Requirement: Minimum 16GB (24GB+ recommended)

  • 💡 Model Setup:

    • Ensure Wan2.1 VACE models are in correct paths

    • depth_anything model auto-downloads on first run (~1.5GB)

  • 🔧 Tuning Tips:

    • Adjust denoise=1 in KSampler for reprocessing strength

    • Modify 40 in FluxGuidance for detail/stability trade-off

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