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ComfyDeploy: How wanvideo - seamless flow works in ComfyUI?
What is wanvideo - seamless flow?
experimental wanvideo comfyui node with a singular goal - visually seamless transitions between context windows
How to install it in ComfyDeploy?
Head over to the machine page
- Click on the "Create a new machine" button
- Select the
Edit
build steps - Add a new step -> Custom Node
- Search for
wanvideo - seamless flow
and select it - Close the build step dialig and then click on the "Save" button to rebuild the machine
wanvideo - seamless flow
ComfyUI-WanSeamlessFlow/
├── __init__.py # Registry and imports
├── blending.py # Core embedding interpolation functions
├── nodes.py # ComfyUI node definitions
├── visualization.py # Diagnostic visualization utilities
├── README.md # Documentation and examples
└── utils/ # Support utilities
└── optimization.py # Embedding optimization algorithms
key notes - needs modifications, for now, to Kijai's wanvideo wrapper
- see
./reference/nodes.py
for current patches made:
architecture / data flow map
[Architecture Map]
┌─────────────────────┐ ┌───────────────────────┐ ┌─────────────────────┐
│ WanSeamlessFlow │ ──→ │ Context Window Engine │ ──→ │ Rendering Pipeline │
│ • Embedding Order │ │ • Window Transition │ │ • Composite Output │
│ • Blend Parameters │ │ • Interpolation │ │ • Visual Smoothing │
└─────────────────────┘ └───────────────────────┘ └─────────────────────┘
integration with Kijai's ComfyUI-WanVideoWrapper
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ LoadWanVideo │ │ WanVideoText │ │ WanSmartBlend │ │ WanVideoSampler │
│ T5TextEncoder │───▶│ Encode │───▶│ │───▶│ │
└──────────────────┘ └──────────────────┘ └──────────────────┘ └──────────────────┘
│
▼
┌──────────────────┐
│ WanBlendVisualize│
│ (Optional) │
└──────────────────┘
usage
Multi-Prompt Usage: For optimal results with prompt transitions:
Modify your WanVideoTextEncode to use multiple prompts separated by | characters:
high quality nature video featuring a red panda balancing on a bamboo stem | high quality nature video focusing on the bird perched on the panda's head | high quality nature video showcasing the waterfall in the background
- Adjust the blend_width parameter based on your number of frames:
- With 257 frames and 3 prompts → 85.6 frames per prompt
- Recommended blend_width: 8-16 frames
- Higher values create wider transition zones
Compatibility Notes: This setup is fully compatible with your existing components:
- TeaCache: Works alongside WanSmartBlend, both optimizing different parts
- Context Windowing: Seamless transitions work at context window boundaries
- Torch Compilation: No interference, remains performance-enhancing
Parameter Recommendations:
- for your particular setup with 257 frames:
- blend_width: 8 # Start conservative, increase for smoother transitions
- blend_method: "smooth" # Provides natural transitions without obvious linear interpolation
- optimize_order: true # Automatically orders prompts for minimal semantic distance
- verbosity: 1 # Basic logging without overwhelming console output
Extended Analysis: This integration creates a multi-dimensional benefits matrix:
⎡ TeaCache Compatibility ⎤ ⎡ High | Compatible with caching mechanisms ⎤
⎢ Context Window Flow ⎥ = ⎢ High | Works with all scheduler types ⎥
⎢ Smooth Transitions ⎥ ⎢ High | Creates gradual prompt blending ⎥
⎢ Performance Impact ⎥ ⎢ Low | Minimal computational overhead ⎥
⎣ Implementation Effort ⎦ ⎣ Low | Non-invasive integration ⎦
logical flow
Integration point: context window embedding selection logic
WindowProcessingPipeline {
window_context → embedding_selection → model_forward → window_composition
↑ ↑ ↑
| (context info) | (embedding selection) | (output compositing)
↓ ↓ ↓
context_scheduler [INTERVENTION POINT] window_blending
}