---
license: apache-2.0
base_model:
- Comfy-Org/MiniMax-H3
- larryvrh/MiniMax-H3-Turbo-Lora
base_model_relation: quantized
pipeline_tag: text-to-video
tags:
- cmf
- cortiq
- video
- audio
- 4-bit
---

# MiniMax-H3 Turbo — one 23.5 GB file, no Python

[MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3) renders video and
synchronized stereo audio from one prompt, in one transformer, on two flow
schedules. [larryvrh's Turbo LoRA](https://huggingface.co/larryvrh/MiniMax-H3-Turbo-Lora)
brings it to four sampling steps. This is both of them in the
[CMF container](https://github.com/infosave2007/cmf) — the DiT, the Qwen3-VL
prompt encoder, the video VAE decoder and the audio vocoder in a single
memory-mapped file — running on `cortiq`, a Rust binary with no ML framework
underneath.

| | reference checkout | here |
|---|---|---|
| diffusion model | 66.3 GB (bf16) | — |
| prompt encoder | 51.5 GB (bf16) | — |
| video + audio VAE | 5.8 GB | — |
| Turbo LoRA | 0.8 GB | — |
| **total** | **124.4 GB, four files + a ComfyUI checkout** | **23.5 GB, one file** |

47.83 B parameters, 2 361 tensors, `cortiq verify` clean.

The LoRA is not a separate download: it is merged into the weights, so the file
IS the 4-step model.

**Text-to-video only.** The release also takes first/last keyframes (`fl2va`)
and reference images, videos and audio (`ref2va`); those paths are not ported
and the vision tower is not packed. What is here is `t2va`: prompt in, video
and audio out.

## Getting a file

```bash
huggingface-cli download infosave/MiniMax-H3-Turbo-cmf \
  --include 'parts-q4tp/part_*' --local-dir .
cat parts-q4tp/part_* > mmh3-turbo-q4tp.cmf

cortiq animate mmh3-turbo-q4tp.cmf \
  --prompt "A corgi in a chef hat flipping a pancake, sizzling sounds and a cheerful bark." \
  --width 512 --height 288 --frames 39 --out corgi.avi
```

`cortiq animate` writes an MJPEG+PCM AVI and the stereo track beside it as
`.wav`. There is no ffmpeg in the loop and nothing to install: the JPEG encoder
and the RIFF muxer are part of the binary, because a pipeline that ends in a
shell-out to a 20 MB dependency is not a pipeline you can ship.

Width and height are multiples of 32. Frame counts snap **up** to the model's
17k+5 grid (5, 22, 39, 56, … 124), which is the grid the temporal VAE and the
DiT's frame-span pattern agree on. 4 steps is what the LoRA is trained for;
more still helps a little.

## What it costs to run

The file is memory-mapped, so plan on RAM at least its size or every step
touches non-resident pages.

Measured on 48 CPU cores, 4 steps:

| | tokens in the pack | denoise (4 steps) | decode | total |
|---|---|---|---|---|
| 256×160, 22 frames (0.9 s) | 375 | 45.0 s | 10.7 s | **55.7 s** |
| 512×288, 39 frames (1.6 s) | 1 879 | 220.8 s | 150.0 s | **371.3 s** |

Nearly all of the decode is the video VAE — the vocoder is 4 s of the 150.

The packed sequence is `[text | audio | video]` and everything attends to
everything, so cost grows with the token count and then with its square. A
512×288 second is five times the tokens of a 256×160 one.

**The GPU path is off, on purpose.** `cortiq`'s wide-GEMM arm on wgpu is
measured three times faster than the host here and it is also wrong: on an
RTX PRO 6000 Blackwell the DiT's first step disagrees with the CPU (video
velocity rms 1.32 against 1.73, audio 0.16 against 1.01) and the second step
returns NaN — a flat grey frame and a clipped waveform. The op probe cannot
notice: it only times the two arms, and it runs the bad one for real while it
is deciding. So `cortiq animate` pins `CMF_GPU=0` before the backend comes up,
which is early enough to be sure. `CMF_MMH3_GPU=1` opts back in if you want to
watch it fail. Fixing that kernel is the next thing worth doing to this model —
it is the whole difference between minutes and hours.

## What the conversion did

**The adaLN collapse.** Forty per cent of the released DiT is one matrix per
block: `adaln_proj.linear` is `[96768, 2688]`, 520 MB at bf16, **13 B of the
model's 33 B parameters** — for a map whose input is one number, the timestep.
Its output over the whole schedule is a one-dimensional curve in R^96768, and
Comfy-Org's `pruned` checkpoints already ship it as one: an `adaln_t_table` of
`[1025, 8]` shared by every block and per-block weights of `[96768, 8]`.

Measured against the full matrix on block 0 (`tools/mmh3_fetch.py check`, which
range-reads 520 MB out of the 66 GB file rather than downloading it):

```
adaln  max|Δ| 8.0e-4   rms 8.7e-5   against a signal of rms 0.464
time-curve singular values 1..12, relative:
  1.00e0 2.96e-1 1.05e-1 6.63e-2 6.60e-3 2.11e-3 5.61e-4 2.92e-4
  3.67e-5 2.73e-5 1.32e-5 1.34e-6
```

The ninth singular value is already 3.7e-5 of the first. Rank eight is not an
approximation anyone should feel nervous about; the 26 GB is redundant.

The Turbo LoRA is written against the FULL matrix (`lora_A` is `[16, 2688]`),
which is why the ComfyUI node re-injects the time conditioning at run time when
the base is pruned. `cortiq animate-pack` does it once, at conversion:

```
adaln(t) = W_p · u(t) + b + B · (A · silu(e(t)))
         = [W_p | B] · [u(t) ; A · silu(e(t))]
```

— a rank-24 curve, driven by a `[1025, 24]` table per block. 4.6 MB a block
instead of 520, with the LoRA already inside it.

**The rest.**

- **Backbone** — bf16 → `q4tp`, 4.16 bits a weight with a predicted per-row
  scale ladder. The LoRA's rank-64 update is merged before quantizing.
- **Prompt encoder** — Qwen3-VL-32B truncated to 50 layers, 51.5 GB → 12.2 GB.
  It is the largest single component of the file and it runs once per
  generation.
- **Video VAE — decoder only.** It is a ViT3D, not a conv stack: 36 transformer
  blocks over the latent grid and one linear that expands each cell into a
  4×16×16 block of pixels. The 3-D causal CNN encoder is a third of the
  checkpoint and text-to-video never runs it.
- **Audio VAE — decoder only**, f16. Quantizing a vocoder buys 45 MB and costs
  audible hiss. Its 254 kaiser-sinc resampling filters are read from the
  checkpoint rather than re-derived — the design formula is in the code as a
  fallback, but a filter you compute is a filter that can drift from the one
  the weights were trained against.
- Integrity: 47.83 B parameters over 2 361 tensors; `cortiq verify` checks
  every one against the directory's hashes.

## On parity

Established, not assumed, and separately for each of the four stacks. The
reference is ComfyUI's own module, run on a toy checkpoint carrying the
release's real tensor names and the release's real schedules — `tools/`
builds them, `tools/mmh3_toy_gate.sh` runs the diff. The packs are exact f32
on purpose: `q4tp`'s noise floor sits an order of magnitude above the
arithmetic difference these are looking for, so quantizing here would pass a
broken port.

| stack | worst | rms | signal rms |
|---|---|---|---|
| DiT — video velocity | 8.8e-5 | 2.1e-5 | 0.515 |
| DiT — audio velocity | 5.2e-5 | 2.5e-5 | 0.409 |
| DiT — token refiner | 8.3e-7 | 2.6e-7 | 1.003 |
| Qwen3-VL encoder | 1.1e-6 | 3.3e-7 | 0.812 |
| video VAE decoder | 4.2e-7 | 4.0e-8 | 0.470 |
| audio VAE decoder | 1.7e-9 | 3.5e-10 | 8.9e-4 |

A dozen conventions in this model pass at one token and fail differently at a
hundred, which is why the toys are not one-vector unit tests: the packed
layout's cursor, the video time axis's 1,4,4,4,4 span pattern, which 96 of 128
head dimensions rotate, the adaLN row order (timestep-major, modality-minor),
the video VAE's 256-pixel tiling — global attention makes a tile a different
computation from a whole frame, so the tiling is part of the output, not a
memory strategy — and the audio stream's separate clock.

## Two clocks

The video and audio latents ride different flow schedules (shift 12 and 3).
The sampler walks the video grid, which at four steps is
`1, 0.973, 0.923, 0.8, 0`, and integrates the audio on its own remap of it.
Stepping both on the video grid is what a stock sampler does; it is fine at
twenty steps and wrong at four, because over the last interval Δσ_a and Δσ_v
differ by a factor of three and no per-step slope correction survives a step
that large. `--stock-sampler` reproduces the broken behaviour if you want to
hear it.

## Provenance

Weights derive from MiniMax's H3 release as repackaged by Comfy-Org, and from
larryvrh's Turbo LoRA; both remain under their own licences. The Turbo LoRA is
a **preview** — its own card notes plastic-looking skin and over-sharp grain at
`ckpt850`, and nothing here changes that. The CMF container and the cortiq
runtime are Apache-2.0 (see the repository's LICENSE and PATENTS.md).