README.md

---
license: mit
language:
- en
- zh
- ja
library_name: comfyui
pipeline_tag: image-to-image
base_model: microsoft/Mage-Flow-Edit-Base
tags:
- comfyui
- mage-flow
- image-editing
- text-to-image
- safetensors
- all-in-one
---

# Mage-Flow-Edit-Base All-in-One · ComfyUI

![Mage Flow All-in-One cover](assets/cover.png)

A byte-preserving All-in-One Safetensors compatibility repack of
[Microsoft Mage-Flow-Edit-Base](https://huggingface.co/microsoft/Mage-Flow-Edit-Base)
for [ComfyUI-MageFlow](https://github.com/zhaotututu/ComfyUI-MageFlow).

This repository does **not** contain a newly trained or fine-tuned model.
Transformer, Qwen3-VL text-encoder, and VAE tensors are merged into one
checkpoint without changing their names, shapes, dtypes, or raw bytes.

## Files

```text
TUTU_Mage-Flow-Edit-Base-AllInOne-BF16.safetensors
```

- Size: `17,452,410,112` bytes (`17.452 GB`, `16.254 GiB`)
- Format: `tutu.mage-flow.all-in-one.v1`
- Precision: original BF16/compatible upstream tensor dtypes
- SHA256: `30bd77f2cec6e3e6bec078dedc63920c5611730e508ef9a33fe1aaa1612c701b`
- Tensor count: `1,949`

Every tensor was checked against the official Diffusers source files for key,
shape, dtype, byte length, and SHA256 of the raw tensor data.

## Local regression result

One strict RTX 5090 / Windows / SDPA regression used the same source image,
prompt, seed (`230803`), CFG (`5.0`), and `30` steps:

| Package | Cold load | Edit inference | Peak allocated VRAM |
|---|---:|---:|---:|
| Official Diffusers directory | 40.10 s | 22.11 s | 18.251 GiB |
| All-in-One checkpoint | 28.87 s | 21.44 s | 18.252 GiB |

The two saved RGB outputs were pixel-exact: maximum difference `0`, mean
difference `0`, and MSE `0`.

This is a compatibility regression, not a multi-run performance benchmark.
The All-in-One layout simplifies deployment and was faster to cold-load in
this run; it does not reduce the model's inference-time VRAM requirement.

## Installation

1. Install
   [ComfyUI-MageFlow](https://github.com/zhaotututu/ComfyUI-MageFlow)
   in `ComfyUI/custom_nodes`.
2. Put the Safetensors file in:

   ```text
   ComfyUI/models/checkpoints/
   ```

3. Restart ComfyUI.
4. Add `Mage Flow Edit (All-in-One)` or
   `Mage Flow Generate (All-in-One)`.
5. Select this checkpoint and start with `sdpa`, `30` steps, and CFG `5.0`.

Enable `cache_model` when processing several queued jobs. Run
`Mage Flow Unload` before switching to another large model.

## Why a dedicated node is required

The file layout is simpler, but the architecture is still Mage Flow:

- Mage Flow DiT;
- Qwen3-VL text encoder and processor;
- Mage VAE;
- FlowMatch Euler scheduler;
- model-specific packed generation/editing pipeline.

ComfyUI's generic checkpoint loader does not currently construct this complete
pipeline. The custom node hides that architecture-specific loading behind one
user-facing checkpoint selector.

## 中文说明

这是微软 `Mage-Flow-Edit-Base` 的 ComfyUI 单文件兼容封装，不是重新训练的新模型。
Transformer、Qwen3-VL 文本编码器和 VAE 被合并进同一个 Safetensors，张量名称、
形状、数据类型和原始字节均未改变。

安装 `ComfyUI-MageFlow` 后，把模型放入 `ComfyUI/models/checkpoints/`，在
`Mage Flow Edit (All-in-One)` 节点中选择它即可。连续排队时开启
`cache_model`；准备切换其他大模型时运行 `Mage Flow Unload` 释放显存。

## 日本語

Microsoft `Mage-Flow-Edit-Base` の重みを変更せず、ComfyUI 用の単一
Safetensors にまとめた互換パッケージです。再学習・量子化・ファインチューニング
は行っていません。`ComfyUI-MageFlow` を導入し、チェックポイントを
`ComfyUI/models/checkpoints/` に配置してください。

## License, provenance, and responsible use

- Base model and upstream code: Microsoft Mage / Mage-Flow-Edit-Base.
- Upstream license: MIT.
- This compatibility repack: TUTU, 2026.
- No claim of original model authorship is made.

Microsoft's upstream safety screening, watermarking behavior, model card, and
responsible-use guidance remain applicable. Review the
[official model card](https://huggingface.co/microsoft/Mage-Flow-Edit-Base)
before use.