---
license: other
library_name: diffusers
pipeline_tag: text-to-image
base_model: microsoft/Mage-Flow
base_model_relation: quantized
tags:
  - ajh
  - mage-flow
  - mage-flow-nvfp4-ajh
  - nvfp4
  - blackwell
  - qwen3-vl
  - text-to-image
  - quantization
---

# Mage-Flow-NVFP4-AJH

**Mage-Flow-NVFP4-AJH** is a portable, runnable NVFP4 package for
[`microsoft/Mage-Flow`](https://huggingface.co/microsoft/Mage-Flow).
It combines a native NVFP4 Mage transformer with a complete mixed
NVFP4/FP8 Qwen3-VL text encoder.

## ComfyUI

Ready-to-use custom nodes are available at
[`AJH-Code/ComfyUI-MageFlow-NVFP4-AJH`](https://github.com/AJH-Code/ComfyUI-MageFlow-NVFP4-AJH).
The plugin provides loader and generation nodes, downloads this complete model
repository, and returns a standard ComfyUI `IMAGE`. Its initial release targets
Linux x86-64, Python 3.11, and NVIDIA Blackwell SM120 GPUs; follow the exact
runtime requirements documented in the plugin repository.

## Showcase

![Cyborg woman gazing toward a star-filled sky, generated by Mage-Flow-NVFP4-AJH](examples/cyborg_stargaze_nvfp4.png)

Generated directly with the released full NVFP4 package, without upscaling or
post-processing.

> A 4K resolution high detail photo realistic image of the top half of a
> cyborg woman with dark black hair, striking blue eyes that have a very subtle
> glow in the iris, standing side profile, head tilted up towards the sky with
> a questioning expression, she has subtle gaps in her skin that hint at a
> robotic nature, outdoor forest night setting, sky filled with bright
> brilliant stars that glow against the dark setting, nebula visible

Settings: 1280×1280, 20 steps, CFG 5, static shift 6, seed `3334072683`.

This is a complete Hugging Face component-layout repository, not an overlay:

```text
model_index.json
transformer/
  config.json
  diffusion_pytorch_model-00001-of-00004.safetensors
  diffusion_pytorch_model.safetensors.index.json
text_encoder/
  config.json
  model.safetensors
vae/
scheduler/
```

The transformer shards include every retained BF16 tensor alongside the
NVFP4 module state. The VAE, scheduler, text-encoder configuration, tokenizer,
and processor are also included. Running the downloaded repository does not
fetch BF16 weights from the base model.

## Quantization policy

Mage transformer:

- 48 image/text MLP up/down projections use resident W4A4 NVFP4.
- Attention, modulation, normalization, and all non-target tensors remain
  BF16.
- The 48 original BF16 target weight matrices are absent. Their biases remain
  BF16 and are stored normally inside the quantized modules.
- The complete transformer is stored as four indexed standard Safetensors
  shards. Packed NVFP4 buffers use their module state-dict names:
  `packed_weight`, `weight_scales`, `weight_scale`, and `bias`.

Qwen3-VL text encoder:

- Blocks 2–33: 224 NVFP4 projections.
- Blocks 1 and 34: 14 FP8 projections.
- Blocks 0 and 35, embeddings, norms, biases, and the vision tower remain
  BF16.
- Packaged text checkpoint size: `4,031,376,064` bytes.
- The packaged loader constructs the model directly from this file: 475
  non-quantized tensors and 238 packed projections, with no unresolved meta
  tensors.

## Requirements

The prebuilt runtime was tested on:

- NVIDIA RTX 50-series / SM120
- Linux x86-64
- CUDA 13.1
- Python 3.11
- PyTorch `2.13.0+cu130`
- `comfy-kitchen==0.2.22`
- `flash-attn==2.8.3`

Create a local environment:

```bash
python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
CUDA_HOME=/usr/local/cuda-13.1 \
  python -m pip install --no-build-isolation flash-attn==2.8.3
```

The included binaries are for the exact tested stack. Rebuild them after
changing PyTorch, CUDA, or the C++ ABI:

```bash
CUDA_HOME=/usr/local/cuda-13.1 \
PYTHON_BIN="$PWD/.venv/bin/python" \
  ./build_native.sh
```

## Generate an image

Expose exactly one SM120 GPU:

```bash
CUDA_VISIBLE_DEVICES=0 .venv/bin/python generate.py \
  --prompt 'A detailed watercolor fox reading under an old oak tree' \
  --output fox.png \
  --height 1024 \
  --width 1024 \
  --steps 20 \
  --seed 1
```

When running from another copy of the scripts, `--model` also accepts the Hub
repository id or a downloaded standard-layout directory:

```bash
CUDA_VISIBLE_DEVICES=0 .venv/bin/python generate.py \
  --model ajh-code/Mage-Flow-NVFP4-AJH \
  --prompt 'A lighthouse poster reading "ARCTIC LOOP"' \
  --output lighthouse.png
```

The command refuses to overwrite an existing output and writes a companion
JSON report containing coverage, memory, timing, and environment information.

## Measured results

Transformer:

- Loaded packed transformer allocation: `5,628,438,016` bytes.
- Measured transformer allocation saving: `2,604,638,208` bytes
  (`2.4258 GiB`).
- Target BF16 source reads: `0`.
- Representative real up-projection: `3.26x` eager and `3.25x` compiled
  speedup over BF16.

Text encoder:

- BF16 language-stack residency: `7.5453 GiB`.
- Packed mixed residency: `3.0427 GiB`.
- Measured saving: `4.5026 GiB`.

Combined validation on an RTX 5060 Ti 16 GB:

- All seven coverage, source-access, allocation, finite, pixel, latent, and
  text-policy gates passed.
- Peak text stage with the packed transformer still resident:
  `10,437,891,584` bytes (`9.72 GiB`).
- VAL-07 pixel cosine/NRMSE versus frozen BF16:
  `0.9927827428` / `0.1200022063`.
- VAL-07 latent cosine/NRMSE versus frozen BF16:
  `0.9339298065` / `0.3618087155`.
- The requested strings `ARCTIC LOOP` and `NORTHERN COAST` were both rendered
  correctly.

![Combined native NVFP4 VAL-07 output](examples/VAL-07_combined_native.png)

The standard-layout packaged loader is validated independently of the
research-tree loader. It installs 48 native Mage projections and 238 packed
Qwen projections and rejects any checkpoint containing the replaced BF16
transformer targets.

## Quality and speed caveats

The mixed text encoder is functional and produced visually strong downstream
images, but it does not meet our unusually strict embedding-similarity gate:
mean token/pooled cosine was `0.9531366898` / `0.9751400001`. All ten content
screening verdicts and category lists remained unchanged.

The ten-case text workload was slower with the packed runtime (`30.36 s`)
than BF16 (`18.29 s`). This package therefore claims major text-encoder VRAM
savings, not a text-encoding speedup. Transformer projections are materially
faster, but repeated matched end-to-end timing has not been completed.

Additional limitations:

- Native execution is currently SM120-only.
- The repository follows the Hugging Face component and Safetensors layout,
  but the new `mage_flow_nvfp4` runtime is not yet built into stock Diffusers.
  Use the included loader.
- CUDA graph compatibility is not claimed.
- Generation and text-to-image are tested; Base, Turbo, and editing variants
  are not.
- A controlled photorealistic 1024×1024 comparison found that the current
  NVFP4 transformer can produce fuzzy hair or skin microtexture for some
  seeds. Using the original BF16 text encoder did not remove it; the
  transformer quantization is the primary associated variable.
- Four transformer-only held-out cases and one combined held-out case have
  been evaluated. This is not a broad benchmark.

## Validate the download

`MANIFEST.json` records every distributed file except itself:

```bash
.venv/bin/python validate_release.py
```

Hashing the transformer shards and text checkpoint can take a little while.

## License and attribution

- Mage-Flow and the vendored Mage inference source are Copyright (c) 2026
  Microsoft and licensed under MIT. See `LICENSE` and
  `licenses/MAGE-MIT.txt`.
- Qwen3-VL and the mixed NVFP4/FP8 text checkpoint are licensed under
  Apache-2.0. See `licenses/QWEN-APACHE-2.0.txt`.
- The text checkpoint was produced by
  [`InsecureErasure/Qwen3-VL-4B-Instruct-NVFP4`](https://huggingface.co/InsecureErasure/Qwen3-VL-4B-Instruct-NVFP4)
  using learned rounding and `comfy-kitchen`.