---
license: other
license_name: minimax-h3-community-license-agreement
license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE
tags:
- comfyui
- nvfp4
- quantized
- video
- text-to-video
base_model: MiniMaxAI/MiniMax-H3
base_model_relation: quantized
---

# MiniMax H3 — NVFP4

NVFP4 quantizations of the **MiniMax-H3** diffusion transformer for ComfyUI — both task
variants, `ref2va` (reference images → video) and `fl2va` (first/last frame → video).

- Original model: https://huggingface.co/MiniMaxAI/MiniMax-H3
- ComfyUI repackage these derive from: https://huggingface.co/Comfy-Org/MiniMax-H3

**NVFP4 requires an NVIDIA Blackwell GPU** (RTX 50-series, RTX PRO 6000, B200). On Ada,
Hopper or older the NVFP4 path is emulated — use Comfy-Org's `int8_convrot` files instead.

## Which file do I want?

First pick the **task**, then the file:

- **`ref2va`** — up to 9 reference images (+ videos/audio) → video. Identity-driven.
- **`fl2va`** — first and/or last frame → video. Interpolates between endpoints; also how
  you chain clips past one generation by feeding the previous clip's last frame in.

| file | size | s/it | VRAM (DiT) | notes |
|---|---|---|---|---|
| **`minimax_h3_ref2va_pruned_nvfp4.safetensors`** | **12.5 GB** | **1.90** | 11.9 GB | smallest/fastest ref2va |
| **`minimax_h3_fl2va_pruned_nvfp4.safetensors`** | **12.5 GB** | — | ~11.9 GB | smallest/fastest fl2va |
| `minimax_h3_ref2va_nvfp4_mixed.safetensors` | 24.4 GB | 1.92 | ~20 GB | from unpruned bf16 |
| `minimax_h3_ref2va_nvfp4_full.safetensors` | 18.7 GB | 1.91 | ~16 GB | experimental |

Take a **`pruned_nvfp4`** file when footprint and speed matter: half the size of the
alternatives at equal-or-better speed, with the modulation path left at full precision. If
you have VRAM to spare and want maximum fidelity, read "Honest limitations" first — 4-bit
weights appear to cost some motion quality relative to Comfy-Org's 8-bit `int8_convrot`.

The two pruned files are the same size and were produced by the same script over the same
200 quantized layers — they differ only in task head. `fl2va` s/it is unmeasured but should
match `ref2va`; the architectures are identical.

## Why the pruned base is the right one to quantize

Comfy-Org's `pruned` checkpoint is not lossily pruned — it is a **structural refactor of
AdaLN**, and understanding it explains the whole table above.

In the bf16 model, AdaLN modulation dominates the parameter count:

| group | bf16 | pruned |
|---|---|---|
| `adaln_proj` | **13.04B (39.4%)** | **0.04B (0.2%)** |
| `mlp` | 12.02B | 11.56B |
| `attn` | 8.02B | 7.71B |
| token_refiner / norms / embedders | 0.05B | 0.80B |
| **total** | **33.12B** | **20.11B** |

The bf16 model projects a 5376-dim conditioning vector into modulation parameters per block.
The pruned model replaces this with an 8-dim timestep table (`adaln_t_table`, shape
`[1025, 8]`) feeding `adaln_proj.linear` of shape `[96768, 8]`. Because modulation depends
only on the timestep, that 5376-wide projection was almost entirely redundant — 13.04B
parameters collapse to 0.04B, a ~326x reduction.

This matters for quantization because AdaLN is the part you least want to quantize: it emits
the scale and shift applied to every residual stream, so error there is multiplicative and
compounds across all 50 blocks and every sampling step. In the bf16 model you face a bad
choice — protect AdaLN and produce a ~36 GB file (larger than the 34 GB int8 it should beat),
or quantize 39% of the model and hope. **In the pruned model the problem disappears**: AdaLN
is already tiny, so you keep it at full precision for free and quantize only attn+mlp, which
are error-tolerant.

Comfy-Org's `pruned_int8_convrot` quantizes exactly those 200 attn/mlp layers to
int8_convrot and leaves everything else alone. `pruned_nvfp4` takes that same set to NVFP4.

## Measured

RTX PRO 6000 Blackwell (96 GB), ComfyUI 0.30.0, ref2va, 864x480, 39 frames, 20 steps,
`res_multistep` / `beta`, three matched seeds. **These are speed/size numbers only** — the
quality comparison from the same runs is retracted (see "Honest limitations"). Note also that
`beta` was the wrong scheduler; ComfyUI's official H3 templates all use `simple`.

| model | size | staged VRAM | s/it |
|---|---|---|---|
| `pruned_int8_convrot` (Comfy-Org) | 21.0 GB | 19,995 MB | 2.17 |
| **`pruned_nvfp4` (this repo)** | **12.5 GB** | **11,944 MB** | **1.90** |

**-40% file size, -8.0 GB VRAM, -12.4% sampling time.**

At ~12 GB for the DiT, a 32 GB card (RTX 5090) becomes viable if the text encoder is
offloaded to CPU after encoding — it runs once per prompt, not once per sampling step.

## How `pruned_nvfp4` was built

The obvious tool does **not** work. The StarNodes model converter passes non-floating-point
tensors through untouched:

```python
if not tensor.dtype.is_floating_point:
    return tensor          # already-int8 weights are copied verbatim
```

so running it on an int8 checkpoint silently produces a byte-identical file. A real
dequantize -> requantize is required. `comfy-kitchen` exposes both halves:

```python
params = ckt.TensorWiseINT8Layout.Params(
    scale=weight_scale, orig_dtype=torch.bfloat16, orig_shape=qdata.shape,
    is_weight=True, convrot=True, convrot_groupsize=256)
deq = ckt.TensorWiseINT8Layout.dequantize(qdata, params)              # -> bf16
nq, nparams = ckt.TensorCoreNVFP4Layout.quantize(deq.contiguous())    # -> NVFP4
tensors = ckt.TensorCoreNVFP4Layout.state_dict_tensors(nq, nparams)
```

Per-layer config lives in a `comfy_quant` uint8 tensor holding JSON, e.g.
`{"format": "int8_tensorwise", "convrot": true, "convrot_groupsize": 256}`, rewritten to
`{"format": "nvfp4"}` on output. Note `quantize()` requires bf16/fp16 — float32 raises
`Unsupported dtype code`.

Full script: `pruned_to_nvfp4.py` in this repo. 200 layers, ~6 seconds on one GPU.

## Prompting: H3 wants a structured IR, not prose

**Read this before blaming the weights for bad output.** H3 was trained on the structured
output of **H3-Context-IR**, a preprocessing model that rewrites a plain request into
labelled sections; MiniMax's model card calls it "critical to the quality of the final
output". ComfyUI passes your raw string straight to the DiT, so you must write that
structure yourself.

Official guides:
[base](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md) ·
[ref](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.md)

```text
For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.

integrated_multimodal_description: [Shot 1] Live-action, cinematic, the young woman shown in
<Picture 1> remains beside the rain-covered train window, preserving her appearance and the
carriage layout. The camera trucks right with small amplitude at slow speed as she lifts her
gaze toward the passing city lights. The quiet, breathy young woman (S1) says:
<d>[English] I get off at the next station.</d> She folds the letter along its existing crease.

overall_soundscape: The train wheels produce a steady metallic rhythm beneath a low
ventilation hum. Rain ticks against the window while paper rustles softly in her hands.

non_diegetic_music: Sustained cello notes at a slow tempo with widely spaced piano tones.
```

**Dialogue must be explicit or you get gibberish.** Speech is generated jointly with video,
so saying *that* someone speaks without giving the words yields correct prosody and mouth
shapes with no lexical content. Speaker identity, action and delivery go *outside* `<d>`;
only the language tag and verbatim words go *inside*. Use stable IDs `(S1)`, `(S2)`, and
`(S1,S2)` for simultaneous speech.

Other essentials: `[Shot 1]` carries no timestamp, later shots use `[Shot N] At MM:SS.mmm`;
aim for 350-500 words of description; write camera motion as type + amplitude + speed;
reference tags must appear in the order the inputs were connected. ref2va accepts up to 9
reference images, and 3-4 varied shots hold identity far better than one.

## Companion files (mirrored, not ours)

| file | precision | origin |
|---|---|---|
| `text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors` | NVFP4-AWQ | **Comfy-Org, unmodified** |
| `vae/minimax_h3_video_vae_fp16.safetensors` | FP16 | **Comfy-Org, unmodified** |
| `vae/minimax_h3_audio_vae_fp32.safetensors` | FP32 | **Comfy-Org, unmodified** |

**The NVFP4 text encoder is Comfy-Org's work, not ours.** Only the
`minimax_h3_*_nvfp4*.safetensors` diffusion models here are new.

VAEs are deliberately **not** quantized: they are small, run once per generation rather than
per step, and decode straight to pixels and audio samples where error is immediately
visible. The text encoder being NVFP4 buys VRAM, not speed — it also runs once per prompt.

## Usage

```
📂 ComfyUI/models/
├── 📂 diffusion_models/
│   └── minimax_h3_ref2va_pruned_nvfp4.safetensors   (or the fl2va file)
├── 📂 text_encoders/
│   └── qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
└── 📂 vae/
    ├── minimax_h3_video_vae_fp16.safetensors
    └── minimax_h3_audio_vae_fp32.safetensors
```

Recommended stack total: **33.7 GB**. Use the official
[R2V template](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json),
swapping the diffusion model — or the
[FL2V template](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_fl2v.json)
for the `fl2va` file. Requires ComfyUI >= 0.30.0 (native H3 support in
`comfy/ldm/minimax/`). `CLIPLoader` type must be `minimax`; sampler `res_multistep`;
frame `length` must satisfy 17n+5.

## Honest limitations

- **NVFP4 is 4 bits/weight and that appears to cost visible quality vs int8_convrot.** In
  side-by-side playback of 15s clips at 1152x640, the int8 build showed noticeably less
  mid-motion artifacting and held object shape better through fast pans. If you have the
  VRAM (~20 GB vs ~12 GB) and can spend ~14% more time per step, Comfy-Org's
  `pruned_int8_convrot` may be the better choice. This repo's value is smallest-footprint
  and fastest, not highest-fidelity.
- That comparison is **not controlled**: the two runs also differed in text encoder
  (nvfp4_awq vs int8_convrot), it was n=1, and matched seeds do not produce matched
  trajectories across different quantization. Treat it as a lead, not a result.
- The earlier claim of "no visible degradation across three matched seeds" is **retracted** —
  it was measured at 480x864 / 39 frames, too small and too short to show what appears at
  768p over 15s. Contact sheets in particular hide motion artifacts; judge on playback.
- 15s (362 frames) generates fine; see the VRAM note below.
- The **`fl2va` file is unbenchmarked** — it was produced by the same script over the same
  layer set as `ref2va`, but has not been run end-to-end.
- `pruned_nvfp4` is **doubly quantized** (bf16 -> int8_convrot by Comfy-Org -> NVFP4 here).
  Error from both passes compounds. It held up in testing, but that is a real caveat.
- Benchmarks are single-GPU, one card, one resolution.

Failure cases are welcome in the discussions tab — concrete artifacts beat aggregate scores.

## VRAM / length ceiling

Measured on RTX PRO 6000 (96 GB), NVFP4 pruned DiT, 20 steps. The budget that matters is
**pixels x frames**, not resolution alone:

| size | px | frames | result |
|---|---|---|---|
| 768x960 | 737,280 | 362 | ok, 29.51 s/it |
| 1152x640 | 737,280 | 362 | ok, 28.73 s/it |
| **1344x768** | **1,032,192** | **362** | **OOM — container hard-killed** |

The ComfyUI template default (1344x768) is safe at its default 124 frames but **not** at 362.
OOM here is a SIGKILL: no traceback, no `/history` entry, and ComfyUI restarts with its queue
wiped. A job that silently vanishes from `/queue` is this.

## License

Inherits the MiniMax-H3 Community License Agreement from the original model.