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CIVITAI IS RENAMING EVERY DOWNLOADED FILE THE SAME THING. MAKE SURE YOU SAVE-AS AND NAME THE FILE SO YOU KNOW WHAT IT IS.
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CIVITAI IS RENAMING EVERY DOWNLOADED FILE THE SAME THING. MAKE SURE YOU SAVE-AS AND NAME THE FILE SO YOU KNOW WHAT IT IS.
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Fontes
2 fontesArtefatos de modelo
4 artefatosTrechos de fonte
2 trechosSource context: 914 downloads · Type Checkpoint · Base model MiniMax H3
MiniMax-H3's 33B video+audio DiTs, quantized for 16-32 GB cards. Both flavors — fl2va (text / first-last-frame to video+audio) and ref2va (identity from reference images, videos, and voice clips) — quantized from the Comfy-Org bf16 releases with a first-class minimax_h3 architecture in the metadata.
The files
fl2va Q5_1 — 25.9 GB · 6.0 bits/weight · the 24-32 GB tier, near-Q6 quality. The demo on the companion workflow page rendered on this file.
fl2va Q4_0 — 19.9 GB · 4.5 bits/weight · the 16 GB tier (streams the overflow).
ref2va Q5_1 / Q4_0 — same sizes, for reference-identity workflows.
Coming after validation: Q3mix (17.3 GB) — real 3-bit K-quants on the tensors whose geometry allows it, classic types elsewhere. Named for what it is.
Quick fixes — read this first
“unknown model architecture: minimax_h3” Why: ComfyUI-GGUF doesn't know this architecture out of the box. Fix: Install ComfyUI-GGUF , then run the one-line apply_gguf_arch_patch.py from the attached loader pack (idempotent, takes two seconds), restart ComfyUI.
Where is the text encoder? Where are the VAEs? Why: A GGUF here contains the transformer and nothing else . Fix: The text encoder is now quantized too — MiniMax-H3 Text Encoder GGUF (also on Hugging Face ) (take the mmproj file with it, it is required for reference images AND for multi-shot chaining). VAEs come from Comfy-Org/MiniMax-H3 . Load the GGUF with UnetLoaderGGUF — or with the one-dropdown dual-format loader in the companion workflow pack.
It's 19.9 GB and I have a 16 GB card Why: That is the wrong mental model — the file does not need to fit. Fix: Nothing. ComfyUI streams what doesn't fit; you pay some speed, not failure. Measured reference: Q5_1 on an RTX 5090 runs ~22 GB resident, about 10 minutes for a 243-frame 544x960 shot at 20 steps.
Which flavor do I want? Why: Two different conditioning systems, two checkpoints. Fix: fl2va for text-to-video, image-to-video, and last-frame chaining (the multishot workflow). ref2va when identity comes from reference images or voice clips instead.
Why there is no Q6_K
K-quants (Q6_K, Q5_K, Q4_K, Q3_K...) require weight rows divisible by 256, and H3's hidden width is 2688 — which is not. Requesting one just quantizes something else with the wrong name on it. The ladder for pure quants here is the classic family: Q5_1 and Q4_0 above. One useful exception exists — the FFN down-projections ARE 256-divisible, which is what makes the mixed Q3mix tier possible at 17.3 GB.
Quantization notes
About 40% of this model is per-block adaln modulation; it is quantized here, exactly as the official int8 release quantizes it.
Kept at full precision: patch projections, time embedder, condition projection, final layer, token refiner.
Built with a patched llama.cpp adding minimax_h3 as a first-class architecture — the metadata says what the file actually is.
Everything else I've published
These weights on Hugging Face
The multishot workflow on Hugging Face
Node pack source on GitHub
All models on Hugging Face
Verificado 14 de out.
gguf · 18,5 GB · SHA-256 9644176146d3…b165 · Civitai
Source context: 635 downloads · Type Checkpoint · Base model MiniMax H3
