Wan 2.1 | Revolutionary Video Generation

ComfyUI Wan 2.1 Workflow Description

1. What is Wan 2.1?

The ComfyUI Wan 2.1 workflow is a cutting-edge video generation pipeline that leverages the latest Wan 2.1 models to create high-quality videos from text prompts or/and base images. Wan 2.1 supports Text-to-Video (T2V) and Image-to-Video (I2V) generation, producing 5-second videos with natural motion and professional-grade quality. Wan 2.1 sets a new benchmark for AI video generation, outperforming open-source and commercial alternatives. The Wan 2.1 14B model pushes the limits further, delivering exceptional results up to 720P.

2. Benefits and Capabilities of Wan 2.1

• High-quality output: Generates 480P to 720P videos with realistic motion and high-fidelity textures.
• Hardware accessibility: The lightweight Wan 2.1 1.3B model requires only 8.19GB VRAM, making it compatible with most modern GPUs (which are provided by RunComfy here!).
• Versatile generation: Wan 2.1 Supports both Text-to-Video (T2V) and Image-to-Video (I2V) workflows.
• Multilingual support: Wan 2.1 is the first video model capable of generating both Chinese and English text within videos.
• VAE efficiency: The Wan-VAE backend efficiently handles 1080P videos while preserving temporal consistency.
• Fast processing: The Wan 2.1 1.3B model delivers quick results while maintaining quality.

3. How to Use Wan 2.1

3.1 Wan 2.1 Generation Methods

Primary Wan 2.1 Generation Method (disabled by default): Text-to-Video

• Inputs: Text prompt
• Best for: Creating videos from scratch using textual descriptions
• Characteristics:
  • Uses the Wan 2.1 1.3B model for faster generation
  • Creates 33-frame (5-second) videos at 480P resolution
  • Optimized for smooth motion in short clips

Advanced Wan 2.1 Method (enabled by default): Image-to-Video with Text Prompt

• Inputs: Base image + text prompt
• Best for: Animating still images while guiding motion with a prompt
• Characteristics:
  • Preserves visual elements of the input image
  • Allows text control over motion direction
  • Uses the Wan 2.1 14B model for higher fidelity
  • Creates 33-frame videos at 512x512 resolution

Example Workflow:

1. In CLIPTextEncode (Positive Prompt / Negative Prompt): Enter your scene description (e.g., "a fox moving quickly in a beautiful winter landscape with trees and mountains during daytime, tracking camera").
2. In Load Image: Upload your base image.
3. For further refinement (optional):
   • In KSampler: Adjust steps (default: 30) for a quality vs. speed balance.
   • In ModelSamplingSD3: Modify scale value (default: 8) for prompt adherence.
4. Click Queue Prompt to start the generation.
5. In SaveAnimatedWEBP find your output preview (also saved in ComfyUI > Output folder).

3.2 Parameter Reference for Wan 2.1

• KSampler:
  • steps: 20-30 (higher values improve quality but increase time)
  • cfg: 6.0 (controls prompt adherence strength)
  • scheduler: "simple" (determines noise scheduling approach)
  • sampler_name: "uni_pc" (recommended sampler for Wan 2.1)

  

• WanImageToVideo:
  • width/height: 512 (output resolution)
  • length: 33 (frames per video)
  • batch_size: 1 (number of videos per run)
• ModelSamplingSD3:
  • scale: 8 (controls guidance adherence)
• EmptyHunyuanLatentVideo:
  • width/height: 832/480 (T2V output resolution)
  • length: 33 (frames per video)
  • batch_size: 1 (number of videos per run)

  

3.3 Advanced Optimization with Wan 2.1

• Memory Optimization:
  • Use the Wan 2.1 1.3B model for faster generation with lower VRAM requirements.
  • Reduce resolution (e.g., 512x320) for quicker processing.
  • Decrease frame count for shorter and faster renders.
• Quality Optimization:
  • Use the Wan 2.1 14B model for higher-quality output.
  • Increase KSampler steps to 30-40 for more refined results.
  • Utilize Image-to-Video with a high-quality base image for the best fidelity.

More Information

For additional details on Wan 2.1, visit the .

Credits

The Wan 2.1 model was developed by the Wan Team, and the ComfyUI integration was created by the original developers. Full credit goes to these innovators for advancing AI-powered video generation.