Unlock the Power of Aging Timelapse Videos with Wan2.1 Model Workflow
Generate stunning 'Aging Timelapse' videos with Wan2.1 Model! Transform images into dynamic aging process videos with wrinkles, hair whitening, and posture changes. Get started now!
- Use Case
- Video
- Best For
- Video
- VRAM
- Low VRAM (≤8GB)
- Reading Time
- 4 min
Workflow Overview
Generate stunning 'Aging Timelapse' videos with Wan2.1 Model! Transform images into dynamic aging process videos with wrinkles, hair whitening, and posture changes. Get started now!
Content type: Workflow
Primary intent: Download
Required Models
- Wan2.1
- Lora
Setup Notes
- Install the required models before opening the workflow template.
- Recommended hardware: Low VRAM (≤8GB).
1. Workflow Overview
This workflow leverages Wan2.1 Model to generate "Aging Timelapse" effect videos, featuring:
Image-to-Video Conversion: Transform input images into dynamic aging process videos
Time-Lapse Effects: Simulate wrinkles, hair whitening, and posture changes
Frame Interpolation: Boost smoothness via GIMM-VFI (16fps→32fps)
Multi-Stage Control: Combines T5 text encoding, CLIP vision encoding, and LoRA fine-tuning
2. Core Models
Model Name | Function | Path |
|---|---|---|
Wan2_1-I2V-14B-480P_fp8_e4m3fn.safetensors | Main video generation model |
|
Aging Timelapse (Wan2.1 I2V LoRA)_v1.0 | Aging effect adapter |
|
umt5-xxl-enc-fp8_e4m3fn.safetensors | Multilingual text encoder |
|
gimmvfi_r_arb_lpips_fp32.safetensors | Frame interpolation model | Auto-download to |
3. Key Components
Node Name | Function | Installation |
|---|---|---|
WanVideoModelLoader | Loads Wan2.1 video model | Install |
WanVideoTextEncode | Processes aging effect prompts | Same as above |
GIMMVFI_interpolate | Frame interpolation (2x) | Install |
WanVideoTeaCache | VRAM optimization | Built-in with |
VHS_VideoCombine | Video rendering & export | Install |
4. Workflow Structure
Group 1: Image-to-Video (Aging Effects)
Inputs:
Source image (e.g.,
ComfyUI_05329_.png)Aging prompt (e.g., "fine wrinkles, gradual hair whitening")
Process:
Extract visual features via
WanVideoImageClipEncodeEncode text descriptions with
umt5Enhance details using LoRA
Output: 512x768 latent video
Group 2: Interpolation & Export
Process:
Upsample 16fps to 32fps with
GIMMVFIRender MP4 via
VHS_VideoCombine(H.264)
Params:
CRF=19 (high quality)
Frame rate: 32fps
5. Inputs & Outputs
Required Inputs:
1024x1536 portrait image (PNG)
Aging description text (Chinese/English)
Seed value (default: random)
Final Output:
MP4 video (e.g.,
wanvideo_00007.mp4)Saved to:
ComfyUI/output/
6. Notes
⚠️ VRAM Requirement: Minimum 16GB (24GB+ recommended)
💡 Model Downloads:
Frame interpolation model (~4GB) auto-downloads on first run
Wan2.1 models require manual placement if missing
🔧 Tuning Tips:
Adjust
0.26inWanVideoTeaCachefor speed/quality trade-offModify interpolation multiplier in
GIMMVFI(default: 2x)