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ComfyUI-AIToolkit-MiniMaxH3
Reference-video preprocessing for MiniMax-H3 ref2va that matches ai-toolkit exactly, so LoRAs trained there behave the same in ComfyUI.
Node: AI-Toolkit H3 Reference Video
Takes a VIDEO (any fps) and outputs everything
MiniMaxH3ReferenceToVideo needs:
| output | wire to | what it is |
|---|---|---|
| frames | ref_video_1 | 24 fps frames, real-time paced from frame 0, tail trimmed, count snapped DOWN to the model's 17n+5 grid, resized to the ref's own aspect at target_megapixels on the /32 grid |
| audio | ref_video_audio_1 | soundtrack trimmed to the exact window the kept frames cover (disconnect if the clip is silent — a silent placeholder is emitted) |
| width | width | generation width (= ref aspect at the target area) |
| height | height | generation height |
| length | length | the snapped 24 fps frame count |
Inputs
- video – the reference clip. Any source fps works; frames are resampled
to 24 fps by frame selection (
frame i <- source round(i * src_fps / 24)). The stock H3 node assumes 24 fps input; feeding it a 48/60/90 fps clip directly produces stretched / half-speed motion. - target_megapixels – pixel area of the generation.
0.258= 672x384 = ai-toolkit's res-512 bucket;~0.59= res 768. - max_length – optional cap on the 24 fps frame count (0 = whole clip).
Example
example_workflow.json — load video → this node → H3 Reference to Video →
KSampler (cfg 1.0, euler — the model is guidance-distilled, positive is wired
to both conditioning inputs) → separate AV latent → video+audio decode → save.
Sigma shifts 12 (video) / 3 (audio). Add a LoraLoaderModelOnly between the
UNET loader and ModelSamplingMiniMaxH3 to use an ai-toolkit LoRA.
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1 commits
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README
ComfyUI-AIToolkit-MiniMaxH3
Reference-video preprocessing for MiniMax-H3 ref2va that matches ai-toolkit exactly, so LoRAs trained there behave the same in ComfyUI.
Node: AI-Toolkit H3 Reference Video
Takes a VIDEO (any fps) and outputs everything
MiniMaxH3ReferenceToVideo needs:
| output | wire to | what it is |
|---|---|---|
| frames | ref_video_1 | 24 fps frames, real-time paced from frame 0, tail trimmed, count snapped DOWN to the model's 17n+5 grid, resized to the ref's own aspect at target_megapixels on the /32 grid |
| audio | ref_video_audio_1 | soundtrack trimmed to the exact window the kept frames cover (disconnect if the clip is silent — a silent placeholder is emitted) |
| width | width | generation width (= ref aspect at the target area) |
| height | height | generation height |
| length | length | the snapped 24 fps frame count |
Inputs
- video – the reference clip. Any source fps works; frames are resampled
to 24 fps by frame selection (
frame i <- source round(i * src_fps / 24)). The stock H3 node assumes 24 fps input; feeding it a 48/60/90 fps clip directly produces stretched / half-speed motion. - target_megapixels – pixel area of the generation.
0.258= 672x384 = ai-toolkit's res-512 bucket;~0.59= res 768. - max_length – optional cap on the 24 fps frame count (0 = whole clip).
Example
example_workflow.json — load video → this node → H3 Reference to Video →
KSampler (cfg 1.0, euler — the model is guidance-distilled, positive is wired
to both conditioning inputs) → separate AV latent → video+audio decode → save.
Sigma shifts 12 (video) / 3 (audio). Add a LoraLoaderModelOnly between the
UNET loader and ModelSamplingMiniMaxH3 to use an ai-toolkit LoRA.
Similar repos
Contributors
1 commits
Languages
Python
100.0%