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    Kiko WanWrapper - v3.0
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    🎞️ ComfyUI Image-to-Video Workflow - WAN 2.1 Wrapper (Kiko WAN v3)

    This is a high-performance, multi-pass Image-to-Video workflow for ComfyUI, powered by the WAN 2.1 Wrapper, with advanced optimizations like torch.compile and Sage Attention for faster and smarter frame generation. I tried to expose all the settings that Kijai exposes that I can understand πŸ˜‰, This is not the fastest workflow you will find on here, but it is one I use to make 20 secons videos.

    Crafted with ❀️ on Arch Linux BTW, using an RTX 4090 and 128 GB of RAMβ€”this setup is tuned for heavy-duty inference and silky-smooth video generation.

    πŸš€ Features

    • 🧠 WAN 2.1 Wrapper for cinematic image-to-video transformations

    • πŸ”‚ Two-pass generation: initial + refinement/extension

    • 🐌 Optional Slow Motion + Frame Interpolation (RIFE, FILM, etc.)

    • 🧽 Sharpening and Upscaling (e.g., RealESRGAN, SwinIR)

    • πŸ› οΈ Includes torch.compile for faster inference

    • πŸŒ€ Integrates Sage Attention for improved attention efficiency

    • πŸ“ Customizable prompts, seed, duration, and aspect ratio logic

    • πŸŒ€ Final loop polish with "Extend Last Frame"

    βš™οΈ System Specs

    • OS: Arch Linux (rolling release)

    • GPU: NVIDIA RTX 4090 (24GB VRAM)

    • RAM: 128 GB DDR5

    • Python: 3.12.9 via pyenv

    • ComfyUI: Latest build from GitHub

    • torch: 2.x with torch.compile enabled

    • Sage Attention: Enabled via patched attention mechanism

    πŸ› οΈ Workflow Overview

    πŸ”Ή Input & Resize

    • Drop an image and optionally resize to fit WAN 2.1's expected input.

    πŸ”Ή WAN 2.1 Wrapper Core

    • Uses torch.compile for speed boost

    • Enhanced with Sage Attention (set via the custom node or environment)

    πŸ”Ή Pass 1: Generate + Optional Slow Motion

    • Frame-by-frame synthesis

    • Add slow motion via interpolation node (RIFE or FILM)

    πŸ”Ή Pass 2: Extend + Merge

    • Extends the motion, ensures smoother transitions

    • Combines motion with refined prompt guidance

    πŸ”Ή Final Polish

    • Sharpening and Upscaling

    • Final interpolation if needed

    • Loop-ready output by extending the last frame

    πŸ§ͺ Performance Tips

    • Tune torch compile for you system, they are all different, my setting might not work for you.

    • For Sage Attention:

      • Use the node

    • Running on lower-end GPUs? Disable upscaling and reduce frame count.

    🧰 Requirements

    • ComfyUI

    • WAN 2.1 Wrapper Node

    • Optional:

      • RIFE, FILM, or DAIN for interpolation

      • RealESRGAN / SwinIR for upscaling

      • Sage Attention patch or node

    ▢️ How to Use

    1. Load the kiko-wan-v3.json file into ComfyUI.

    2. Drop your image into the input node.

    3. Customize prompts, duration, and frame count.

    4. Click Queue Prompt to generate.

    5. Your video will be rendered in the output folder.

    πŸ“ Files

    • kiko-wan-v3.json β€” Exported workflow (coming soon)

    • kiko-wan-v3.png β€” Workflow diagram

    🧠 Inspirations & Credits

    • ComfyUI

    • WAN 2.1 Wrapper

    • Real-ESRGAN, RIFE, FILM, Sage Attention contributors

    • Arch Linux + NVIDIA ecosystem for elite workstation performance πŸ˜‰

    πŸ’‘ Future Plans

    • Add batch image-to-video mode

    • Audio?

    βš™οΈ Custom Nodes Used in kiko-wan-wrapper-v3.json

    Description

    🎞️ ComfyUI Image-to-Video Workflow - WAN 2.1 Wrapper (Kiko WAN v3)

    This is a high-performance, multi-pass Image-to-Video workflow for ComfyUI, powered by the WAN 2.1 Wrapper, with advanced optimizations like torch.compile and Sage Attention for faster and smarter frame generation. I tried to expose all the settings that Kijai exposes that I can understand πŸ˜‰, This is not the fastest workflow you will find on here, but it is one I use to make 20 secons videos.

    Crafted with ❀️ on Arch Linux BTW, using an RTX 4090 and 128 GB of RAMβ€”this setup is tuned for heavy-duty inference and silky-smooth video generation.

    πŸš€ Features

    • 🧠 WAN 2.1 Wrapper for cinematic image-to-video transformations

    • πŸ”‚ Two-pass generation: initial + refinement/extension

    • 🐌 Optional Slow Motion + Frame Interpolation (RIFE, FILM, etc.)

    • 🧽 Sharpening and Upscaling (e.g., RealESRGAN, SwinIR)

    • πŸ› οΈ Includes torch.compile for faster inference

    • πŸŒ€ Integrates Sage Attention for improved attention efficiency

    • πŸ“ Customizable prompts, seed, duration, and aspect ratio logic

    • πŸŒ€ Final loop polish with "Extend Last Frame"

    βš™οΈ System Specs

    • OS: Arch Linux (rolling release)

    • GPU: NVIDIA RTX 4090 (24GB VRAM)

    • RAM: 128 GB DDR5

    • Python: 3.12.9 via pyenv

    • ComfyUI: Latest build from GitHub

    • torch: 2.x with torch.compile enabled

    • Sage Attention: Enabled via patched attention mechanism

    πŸ› οΈ Workflow Overview

    πŸ”Ή Input & Resize

    • Drop an image and optionally resize to fit WAN 2.1's expected input.

    πŸ”Ή WAN 2.1 Wrapper Core

    • Uses torch.compile for speed boost

    • Enhanced with Sage Attention (set via the custom node or environment)

    πŸ”Ή Pass 1: Generate + Optional Slow Motion

    • Frame-by-frame synthesis

    • Add slow motion via interpolation node (RIFE or FILM)

    πŸ”Ή Pass 2: Extend + Merge

    • Extends the motion, ensures smoother transitions

    • Combines motion with refined prompt guidance

    πŸ”Ή Final Polish

    • Sharpening and Upscaling

    • Final interpolation if needed

    • Loop-ready output by extending the last frame

    πŸ§ͺ Performance Tips

    • Tune torch compile for you system, they are all different, my setting might not work for you.

    • For Sage Attention:

      • Use the node

    • Running on lower-end GPUs? Disable upscaling and reduce frame count.

    🧰 Requirements

    • ComfyUI

    • WAN 2.1 Wrapper Node

    • Optional:

      • RIFE, FILM, or DAIN for interpolation

      • RealESRGAN / SwinIR for upscaling

      • Sage Attention patch or node

    ▢️ How to Use

    1. Load the kiko-wan-v3.json file into ComfyUI.

    2. Drop your image into the input node.

    3. Customize prompts, duration, and frame count.

    4. Click Queue Prompt to generate.

    5. Your video will be rendered in the output folder.

    πŸ“ Files

    • kiko-wan-v3.json β€” Exported workflow (coming soon)

    • kiko-wan-v3.png β€” Workflow diagram

    🧠 Inspirations & Credits

    • ComfyUI

    • WAN 2.1 Wrapper

    • Real-ESRGAN, RIFE, FILM, Sage Attention contributors

    • Arch Linux + NVIDIA ecosystem for elite workstation performance πŸ˜‰

    πŸ’‘ Future Plans

    • Add batch image-to-video mode

    • Audio?

    βš™οΈ Custom Nodes Used in kiko-wan-wrapper-v3.json

    Workflows
    Wan Video

    Details

    Downloads
    82
    Platform
    CivitAI
    Platform Status
    Available
    Created
    3/27/2025
    Updated
    1/6/2026
    Deleted
    -

    Files

    kikoWanwrapper_v30.zip

    Mirrors

    CivitAI (1 mirrors)