CivArchive
    WAN 2.2 GGUF/start end frame/t2v/i2v 8gb/10 seconds to 4 minutes workflow - v1.0

    WAN 2.2 – First-to-Last-Frame Cinematic Workflow (with Radial/SparseSage Attention Patch)

    Ultra-Stable 10-Second Videos Without Backlooping

    Full Windows Guide (Triton • SpargeAttn • RadialAttn)


    Runtime Requirements:

    Windows • Python 3.10–3.11 • RTX 4060 Ti 8GB or higher
    ComfyUI Version: 0.3.6+
    WAN Version: WAN 2.2 GGUF (VAC + CLIP) / you can switch to a different model as you like


    What this workflow does

    This project creates perfectly stable 10-second videos with:

    • No ping-pong

    • No frame collapse

    • No morphing artifacts

    • Perfect start → end interpolation

    • Full world-space motion (not pixel morphing)

    • High temporal stability

    • Cinematic camera motion

    • Optional film-VFI slow-motion

    • Optional x4 upscale + sharpening

    Unlike default WAN or standard samplers, this version uses:

    SparseSageAttn + RadialAttn
    to extend WAN’s attention window from ~80 frames to 161+ frames.

    This allows WAN to render full 10 seconds as one consistent scene.


    Features

    • True First-to-Last-Frame world building

    • Support for start + end images (24 fully controlled scenes)

    • Works for image-to-video and text-to-video

    • Smooth cinematic motion

    • Film grain, fog, monochrome stability

    • Optional ClearReality x4 Upscale

    • Optional Sharpen pass

    • Compatible with 8 GB GPUs


    🔧 Installation (Windows)

    Step 1 — Install Triton for Windows

    WAN 2.2 + RadialAttn requires Triton.
    Download the correct Windows wheel here:
    https://github.com/woct0rdho/triton-windows/releases

    Install inside your venv.

    Step 2 — Install SparseSageAttn

    Download the Windows wheel from:
    https://github.com/woct0rdho/SpargeAttn/releases

    Install inside your venv.

    Step 3 — Install RadialAttn Node

    Download from:
    https://github.com/woct0rdho/ComfyUI-RadialAttn

    Place inside your ComfyUI custom_nodes folder.

    Step 4 — Restart ComfyUI

    If Sparse / Radial Attn loads correctly, startup log will say:
    “Using sparse_sage_attn as block_sparse_sage2_attn_cuda”

    Then the patch is active.


    📸 How the workflow works

    1. Shift Node (SD3-style conditioning)

    Increasing SHIFT tells WAN:
    “Keep the scene physically consistent over time.”

    This stabilizes the entire world motion but with a cost of texture consystency on fast moving scenes.


    2. First-to-Last Frame Sampler

    Takes:

    • Start Frame (Image A)

    • End Frame (Image B)

    And generates smooth world-space interpolation across 161 frames.

    The attention patch extends WAN’s temporal memory so it no longer ping-pongs.


    3. FILM VFI (Optional)

    If enabled, this doubles or quadruples FPS smoothly.
    Use after baseline render.


    4. Upscale (Optional)

    • Upscale Model = ClearReality x4


    Important Tips

    For 8GB GPUs:

    • Perform Upscale AFTER VFI
      to avoid VRAM OOM

    • Disable Upscale during testing (put Upscale + Sharpen in a Group Toggle)


    🎥 How to use the workflow

    1. Load your Start and End images

    Use a pair per scene (A → B).

    2. Insert your Cinematic Prompt

    Example prompt structure:

    “The video begins in a foggy German forest. The camera slowly glides forward along a muddy path. No morphing. This is a continuous world. At the end of the video, the camera reaches the abandoned village, keeping the same cinematic monochrome style.”

    3. Set SHIFT = HIGH

    (For 10 seconds)

    4. Render First-to-Last

    WAN will generate full 161-frame motion directly.

    5. Optional: enable FILM VFI

    For slow motion/smoother movement. Example : Wan 2.2 is trained with 16 fps - Set VFI x2 and the output to 32 fps - Set VFI x4 output 64 fps.

    6. Optional: enable Upscale & Sharpen

    For maximum clarity.


    If you need help setting anything up

    Copy-paste this entire description into ChatGPT and ask:

    “I want to recreate this WAN 2.2 First-to-Last-Frame workflow exactly as described above (with Triton, SparseSageAttn, RadialAttn, SD3 SHIFT = 50, dual KSamplers, and the optional upscale/sharpen section).
    Please help me rebuild this step by step in ComfyUI.”

    Then it will guide you .


    🏁 Final Notes

    This workflow is designed for:

    • Cinematic travel shots

    • World-building

    • Stable long sequences

    • Scene-to-scene storytelling

    • Consistent motion with minimal artifacts

    The combination of SHIFT, First-to-Last, and Attention Patching is what enables true 10-second scenes without looping.


    Bonus Tip:
    You can chain multiple scenes into one continuous film by connecting the “Last frame extractor” output of one scene to the “Start Image” input of the next scene. This ensures perfectly aligned transitions without re-loading images manually.

    Description

    FAQ

    Comments (4)

    elis_tsNov 23, 2025
    CivitAI

    Nice workflow.

    Is it supposed to use only 512x512 dimensions?
    Because when I change this to 640x512 or any other I get this error: "RuntimeError: Trying to create tensor with negative dimension -10496: [-10496, 40, 128]"

    Doomspace
    Author
    Nov 23, 2025· 2 reactions

    Thanks!

    In this specific workflow the resolution is effectively locked to 512×512 or any non-square resolution when using the RadialAttn + SparseSageAttn patch for WAN 2.2.

    The negative dimension error you see:
    RuntimeError: Trying to create tensor with negative dimension -10496: [-10496, 40, 128]
    comes from the custom attention backend (SparseSage / RadialAttn).

    That patch builds an internal block map over the latent (height × width). It assumes a 512×512 input (64×64 latent grid) and certain divisibility. When you change the image size to 640×512 or any !!!non-square resolution!!!, those internal calculations break and you end up with an invalid (negative) dimension.

    So yes – for this workflow, with the attention patch enabled, you should keep it at 512×512 or box sized and do resizing / upscaling afterwards in a separate step (e.g. Image Resize or an ESRGAN/ClearReality x4 pass).

    If you really want to use arbitrary resolutions, you’d have to either:

    disable the RadialAttn/SparseSage patch nodes (losing the super-stable 10s behavior), or

    reconfigure the attention block parameters in code, which is non-trivial and not covered by this workflow.

    TL;DR:
    512×512 is intentional here – it’s the safe setting for WAN 2.2 + RadialAttn on this graph.

    playjay78Feb 22, 2026
    CivitAI

    I keep getting error: Some Nodes Are Missing

    When loading the graph, the following node types were not found

    PatchRadialAttn

    LastFrameExtractorNode

    I tried following your directions. But they are brief on GIT. Please help a noob thank you.

    Workflows
    Wan Video 2.2 I2V-A14B

    Details

    Downloads
    730
    Platform
    CivitAI
    Platform Status
    Available
    Created
    11/23/2025
    Updated
    8/24/2026
    Deleted
    -

    Files

    wan22GGUFStartEndFrameT2v_v10.zip

    Mirrors