JoaoZaokk/HunyuanVideo-1.5-720p-T2V-Quantized

HunyuanVideo 1.5 720p T2V — quantized, with the failures kept

Quantized builds of HunyuanVideo 1.5 720p T2V in ComfyUI's native asym_w4a8_int8 and convrot_w4a4 formats. One is usable. Two are here on purpose as measured negative results.

⚠️ Licence — read this first

These are modified files redistributed under the Tencent Hunyuan Community License Agreement, not a permissive licence. The full Agreement is in this repository as LICENSE_TENCENT_HUNYUANVIDEO15.txt and the required notice, statement of modifications and non-affiliation statement are in NOTICE.txt.

The Agreement does not apply in the European Union, the United Kingdom, or South Korea, and the rights it grants are limited to a Territory that excludes all three. If your monthly active users exceed 100 million you must request a separate licence from Tencent. There is an Acceptable Use Policy in Exhibit A of the Agreement. Read it.

Tencent is not affiliated with, sponsoring, or endorsing this repository or anything measured in it.

Method, tools and the full measurement log: https://github.com/JoaoZaokk/comfy-quant-bench

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The files

file	median effective error	format	GiB	verdict
hv15_w4a8.safetensors	—	432 × asym_w4a8_int8	8.24	use this one
hunyuan15-misto-t025.safetensors	0.1837	282 × 4-bit / 150 × 8-bit	8.03	correct, visibly grainy
hunyuan15-misto-t040.safetensors	0.2147	402 × 4-bit / 30 × 8-bit	7.95	DESTROYED — AN EXAMPLE OF WHAT NOT TO DO

Source: 15.51 GiB FP16. The usable build is 1.88x lighter.

"Effective error" is, per layer, the measured relative error of the format that layer actually received, on the real activations that layer saw during sampling.

What they look like

Same prompt, same seed (12345), same steps, resolution, sampler and scheduler:

FP16 reference
misto-t025 — 0.1837, correct but grainy
misto-t040 — 0.2147, destroyed
pure ConvRot W4A4 — destroyed

images/ carries the whole ladder that was measured — t015, t021, t022 and the pure ConvRot W4A4 build as well. Their weights are not published here: they sit between or beyond the three files above and add tens of gigabytes without adding a finding. The pictures are the evidence; the three checkpoints are what is worth downloading.

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Why this model is the interesting one

A 15% gap in effective error separates "ship it" from "unusable". 0.1837 renders a correct if grainy picture; 0.2147 renders nothing recognizable. Between those two builds sits 0.08 GiB of disk.

That line is not a property of the format. Measured across three architecture families:

model	parameters	tolerated	not tolerated
Wan 2.1 VACE	1.3 B	0.0546	0.0793
Z-Image v2	~6 B	0.1421	0.1848
HunyuanVideo 1.5 family	~13 B	0.1837	0.2147, and 0.2163 on capybara_v0.1

Monotone in the tolerated column — so a threshold chosen on one model is not transferable to another. Three families make that a hypothesis, not a law.

The Z-Image cell was filled on 2026-09-03, and the mechanism that was going to fill it was backwards. The only axis left was convrot_groupsize — the model was already 170/170 layers at 4 bits, so there was nothing left to promote. The written prediction was that a larger rotation group would give more error ("coarser rotation, less able to spread outliers"). Measured over the intersection of layers every value accepts, four points, monotone in the opposite direction: cg 16 0.1926, cg 64 0.1516, cg 256 0.1312, cg 1024 lower still. A Hadamard rotation of size N spreads each outlier across N channels, so a larger N mixes more. The pre-written control — a smaller group must reduce the error — is what caught it.

Rendered four arms, three seeds: BF16 good (the reference control), cg 256 (0.1216) good, cg 64 (0.1421) good, cg 16 (0.1848) destroyed in 3 of 3.

Note where that lands: 0.1848 destroys a ~6 B model while 0.1837 is tolerated on a ~13 B one. The bands do not overlap and they sit 0.6% apart, which is what you would expect if model size sets the line. Three families are still three families. Nothing was measured between 0.1421 and 0.1848, so the exact turning point is not a fact — the two ends are.

capybara_v0.1 is a community checkpoint on this architecture, not a Z-Image one: read from the file it carries 1364 tensors and 54 double_blocks, against Z-Image's 453 and zero. An earlier version of this table put its 0.2163 break in the Z-Image row. Moving it here is the second independent break measured on this architecture, and it leaves Z-Image's upper bound unmeasured: what is known there is that 0.1241 works, and nothing more.

Latent divergence does not decide it either. 0.8255 (destroyed) against 0.7173 (fine) is a 15% gap on that axis too, so no cut on latent distance separates usable from unusable. Only a render does. A checkpoint that converts cleanly, resolves the CUDA backend, and passes every structural check can still produce garbage.

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Speed

Pure ConvRot W4A4 against FP16, same card, re-measured over three seeds after an earlier single-run measurement was found to have inverted the sign:

FP16          0.990 s/step
ConvRot W4A4  0.536 s/step      1.85x faster

An earlier version of this bench published "1.055x slower", from one run per arm with 1 runs is a small sample printed on the screen. It agreed with a previous result, so agreement with expectation was mistaken for confirmation by measurement. Three seeds inverted it. The correction is recorded rather than quietly edited.

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Verified, not assumed

The converter hard-refuses to run unless both quantize_convrot_w4a4_weight and convrot_w4a4_linear resolve to comfy_kitchen.backends.cuda — the eager backend declares the same capabilities and would silently produce numbers describing dequantized math. Dispatch is separately counted on a real load and a real forward: quantized modules, quantized forwards, zero dequantize calls.

Built and verified with:

comfy-kitchen  0.2.31        ComfyUI  c1739380 (0.33.0)
torch          2.13.0+cu130  CUDA     13.0
GPU            RTX 3090 (sm_86)

Native INT4 MMA requires major == 8 (Ampere / Ada). Hopper and Blackwell are routed to an INT8 branch deliberately.

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Format

Standard safetensors, ComfyUI-native, mixed precision in one file — the format's own behaviour, not a trick played on it. Per quantized layer, <layer>.weight as an INT8 container plus <layer>.weight_scale as FP32, and a per-layer entry in __metadata__._quantization_metadata that ComfyUI turns into a <layer>.comfy_quant tensor at load and dispatches on individually. convrot_groupsize 256. Every non-quantized tensor is preserved byte for byte from the source and verified as such. The .quant.json sidecars record full conversion provenance.

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Not covered

One prompt, one seed for the image ladder (three for the timing), 480x480, one frame, one scheduler, one card. No perceptual metric — "correct", "grainy" and "destroyed" are the judgement of someone who looked at them. No SASS. Only convrot_groupsize 256 in the ladder. The monotonicity across model sizes rests on three points.

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Credits

• Tencent — tencent/HunyuanVideo-1.5. These files are derivatives of their model, redistributed under their Community License Agreement, a full copy of which is included here. All trademark rights in "Tencent Hunyuan" are theirs. Tencent does not endorse this work.
• Comfy-Org / comfyanonymous and the ComfyUI contributors — the QuantizedTensor / Layout / MixedPrecisionOps model this format plugs into.
• comfy-kitchen — the ConvRot W4A4 and W4A8 CUDA kernels.
• Quantized by JoaoZaokk with comfy-quant-bench.