Want smaller files? There is a second set built from MiniMax's pruned
checkpoints, ~40% smaller at the same quant tier, in
MiniMax-H3-curve-GGUF.
They need ComfyUI 0.30.0+, and they are a different trade, not a straight
replacement - so this repo stays exactly as it is. Stay here if you are on an
older ComfyUI, or if you would rather run the larger quants.
File
Size
Bits/weight
For
fl2va Q5_1
25.9 GB
6.0
24-32 GB cards (near-Q6 quality)
fl2va Q4_0
19.9 GB
4.5
16 GB cards (streams the overflow)
ref2va Q5_1
25.9 GB
6.0
reference/identity workflows
ref2va Q4_0
19.9 GB
4.5
same, low-VRAM
fl2va = text/first-last-frame to video+audio (T2V and I2V).
ref2va = reference-conditioned generation (identity from up to 9 images,
3 videos with soundtracks, 3 voice clips).
Troubleshooting: "Unexpected architecture type in GGUF file: 'minimax_h3'"
If ComfyUI throws this the moment it tries to load the file, the quant is
fine - ComfyUI-GGUF checks a GGUF's architecture against a fixed list and
rejects anything not on it before reading a single tensor, and upstream's list
does not yet include minimax_h3.
The fix is to install (or update to) ComfyUI-H3-Multishot v1.5.2 or newer:
From v1.5.2 the pack teaches ComfyUI-GGUF that architecture automatically, in
memory, at every ComfyUI start - no files are modified and the fix survives
ComfyUI-GGUF updates. You should see this in the ComfyUI console on startup:
[H3] taught ComfyUI-GGUF the 'minimax_h3' architecture
If you still hit the error, the pack is not being loaded. As a fallback you can
patch ComfyUI-GGUF on disk with python apply_gguf_arch_patch.py from the
pack's folder, then restart.
(Earlier pack versions required that script as a manual install step. It was
too easy to miss, which is why several people reasonably concluded these quants
were broken. Sorry about that - it is automatic now.)
Sample
30 seconds, three chained shots, one script, rendered on the fl2va Q5_1
quant - same presenter, same voice, across both seams:
K-quants are architecturally impossible for this model. H3's hidden width
is 2688, and K-quants require weight rows divisible by 256
(2688 % 256 = 128), so requesting one just quantizes something else with
the wrong name on it.
The ladder for pure quants is the classic family: Q8_0, Q5_1, Q5_0,
Q4_0. Q8_0 is not built for these original-form files on size grounds
it lands at about the size of MiniMax's own int8_convrot release, so it would
give you a similar download for no obvious reason to prefer it.
To be clear, since this has been read the other way: that is a statement about
file size, not a claim that Q8_0 is better or worse than the official int8.
They are closer than the names suggest. int8_convrot averages 8.22 bits per
parameter and leaves 272 tensors at BF16, while Q8_0 is 8.5 bits per weight - so
Q8_0 actually carries slightly more bits. Q8_0 has much finer granularity (a
scale every 32 weights, against per-tensor scales), while the rotation in
convrot spreads outliers before quantizing, which is exactly why an official
release can ship at 8 bits flat. Those pull in opposite directions and I have not
measured which wins.
That caveat is worth spelling out, because it stops being true in the pruned
form: factored down, a Q8_0 fits in 21.5 GB, which is smaller than the Q5_1
in this repo. If you want it, it is in
MiniMax-H3-curve-GGUF.
One useful exception: H3's FFN down-projections have 256-divisible rows,
so a mixed file (real K-quants on those tensors, classic types on
everything else) can land below the pure-classic floor: Q3mix, 17.3 GB -
light streaming on 16 GB cards, fully resident with headroom on 20 GB+.
Publishing after it passes a quality gate against Q4_0.
Quantization notes
~40% of this model is per-block adaln modulation (96768x2688 per block).
It is quantized here, as it is in the official int8_convrot release.
Kept at full precision: patch projections, time embedder, condition
projection, final layer, token refiner (all genuinely small).
Built with a patched llama.cpp adding a first-class minimax_h3
architecture (no mislabeled arch strings in the metadata).
VRAM guidance
The DiT does not need to fit entirely in VRAM - ComfyUI streams what does
not fit. Measured on an RTX 5090 (32 GB): Q5_1 runs with ~22 GB resident,
~10.3 min for a 243-frame 544x960 shot at 20 steps. On 16 GB cards use
Q4_0 and expect slower steps from streaming, not failure.
You also need the text encoder
A GGUF here contains the transformer and nothing else. The DiT alone will
not generate anything.
Qwen3-VL-32B, Q4_K_M (19.8 GB) or Q5_K_M (23.2 GB). Take the mmproj
file with it. It is required for image conditioning and for multi-shot
chaining, which feeds the previous shot's last frame through the encoder's
vision path. Keep both filenames as downloaded so the loader pairs them.
Also on Civitai: Text Encoder GGUF.
