Unlock Advanced Video Depth Control with Wan Model-Based Workflow
Unlock AI-powered video depth control with our Wan model-based workflow. Discover how to extract depth maps, stylize videos with text guidance, and more. Dive into the details now!
- Use Case
- Video
- Best For
- Video
- Models
- Wan2.1ControlnetLora
- Key Nodes
- Controlnet
- VRAM
- Medium VRAM (12–16GB)
- Reading Time
- 3 min
Workflow Overview
Unlock AI-powered video depth control with our Wan model-based workflow. Discover how to extract depth maps, stylize videos with text guidance, and more. Dive into the details now!
Content type: Workflow
Primary intent: Download
Required Models
- Wan2.1
- Controlnet
- Lora
Required Nodes
- Controlnet
Setup Notes
- Install the required models before opening the workflow template.
- Recommended hardware: Medium VRAM (12–16GB).
1. Workflow Overview
This is a Wan model-based video depth control workflow specialized for video-to-video conversion. Key features:
Depth map extraction from video frames
Text-guided video stylization
Two-stage sampling pipeline
Automatic multilingual prompt translation
Core Models:
Wan 2.1 T2V 1.3B: Video-optimized base model
DepthAnythingV2: Depth preprocessor
Florence-2-base: For auto captioning
Wan Control LoRA: Depth adapter
2. Node Breakdown
Critical Components:
VHS_LoadVideo
Function: Load input video and extract frames
Requires:
comfyui-videohelpersuiteParams: 16fps, 480x720 resolution
AIO_Preprocessor
Function: Depth extraction using DepthAnythingV2
Install:
comfyui_controlnet_auxextensionOutput: 512x512 normalized depth map
SamplerCustom (Dual-stage)
Process: 10-step high sigma + 15-step low sigma
Uses: Euler sampler
Special Dependencies:
wan_2.1_vae.safetensors: From Wan model hub
umt5_xxl_fp8: Multilingual text encoder
3. Workflow Structure
Group Logic:
Video Input Group:
Nodes: VHS_LoadVideo → ImageResizeKJ
Function: Frame loading & normalization
Depth Processing:
Nodes: AIO_Preprocessor → ImageScale
Output: Standardized depth maps
Generation Control:
Contains: UNETLoader + LoRA loader + TeaCache
Key: 0.8 strength depth LoRA
Two-Stage Sampling:
SplitSigmas → Dual SamplerCustom
4. Inputs & Outputs
Parameters:
Required: Input video (e.g. "自动写提示词2.mp4")
Optional: Positive prompts (auto-translated)
Advanced: Depth control strength (0.08)
Output:
MP4 video (16fps, H.264)
Frame previews
Translated prompts
5. Notes
Hardware: Minimum 12GB VRAM
Must install: VideoHelperSuite + ControlNet-Aux
Model paths: All Wan models in
wan/subfolderCommon issue: Frame rate mismatch causes audio sync problems
Tuning: Lower CRF (current 19) for better quality