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Model role in the family: text-to-image aligned / post-trained.
Shared components:text_encoder/ (Qwen3-VL, 8.875 GB) and vae/ (Mage-VAE, 0.345 GB) are
bit-identical across all six Mage-Flow repos, as are all four config files. The six models differ
only in transformer/diffusion_pytorch_model.safetensors (8.232 GB). Each mirror below is
nonetheless self-contained and independently restorable.
Mage-Flow is a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. Instead of scaling to tens of billions of parameters, Mage-Flow reaches state-of-the-art-competitive quality through careful tokenizer–backbone–system co-design, so it stays fast, memory-light, and easy to fine-tune under realistic compute budgets.
The stack is built from two shared, co-designed components:
Mage-VAE — a lightweight, high-fidelity latent tokenizer (one-step diffusion encode/decode with anchor-latent KL regularization).
NR-MMDiT — a shared 4B Native-Resolution Multimodal Diffusion Transformer, trained with rectified flow matching in the Mage-VAE latent space.
Together with native-resolution packing and a fused-kernel training infrastructure, this shared stack powers two model instantiations: Mage-Flow for text-to-image generation and Mage-Flow-Edit for instruction-based image editing. Each ships in Base, RL-aligned, and 4-step Turbo variants.
✨ Highlights
Compact & competitive. A single 4B family for generation and editing that matches or beats much larger open systems (Qwen-Image 20B, Z-Image 6B, FLUX.2 32B, FireRed-Image-Edit 20B).
Efficient tokenizer. Mage-VAE matches FLUX.2-VAE reconstruction fidelity while using ~12× / ~22× fewer encode / decode MACs per pixel, removing the VAE as the high-resolution bottleneck.
Native resolution. One checkpoint generates from 512 to 2048 on any aspect ratio, including extreme 4:1 (e.g. 512×2048, 2048×512).
System-level speed. Native-resolution packing (FlashAttention var-len + per-sample 2D RoPE) + fused CUDA kernels cut per-step training time from ~1.93 s → ~0.78 s (~2.5× faster training); CFG's conditional/unconditional branches run in one packed forward.
Full family.Base, RL-aligned, and 4-step Turbo variants for both generation and editing.
Versatile editing. Mage-Flow-Edit supports semantic content editing, appearance transformation, image restoration, and structure-aware outputs within a unified image-and-text-conditioned model. See the report's editing galleries.
Interactive latency. At 1024² on a single A100: Mage-Flow-Turbo 0.59 s/image, Mage-Flow-Edit-Turbo 1.02 s/edit, peak memory ~18–20 GB (lowest among compared systems).
One-to-many editing diversity
One-to-many editing diversity — Mage-Flow-Edit can generate diverse outputs from a single reference image.
📥 Model Zoo
Each checkpoint is a self-contained diffusers-style repo (transformer/ + shared vae/, text_encoder/, scheduler/).
Text-to-image — prompt following, fine detail, and legible English/Chinese text rendering. (The first panel is open; click a title to expand the others.)
Showcase
t2i teaser
General scenes
general scenes
Portraits
portraits
Cuisine & still life
cuisine
English text rendering
english text
Chinese text rendering
chinese text
Instruction-based editing — appearance, content, scene/subject, human-centered & creative, low-level, and restoration edits (source → result). (The first panel is open; click a title to expand the others.)
Various Editing I
editing showcase 1
Various Editing II
editing showcase 2
Localized content & object editing
content editing
Scene, subject & camera transformations
scene and subject
Appearance & artistic rendering
appearance
Human-centered & creative editing
human-centered and creative
Low-level vision & conditional reconstruction
low-level vision
Bidirectional degradation & restoration
restoration
📊 Performance
Full benchmark tables (text-to-image & image editing) — click to expand
Text-to-image — full benchmark suite: GenEval, DPG-Bench, TIIF-Bench (short/long splits), CVTG-2K, OneIG (EN/CN), LongText (EN/CN). Higher is better; GenEval / CVTG-2K / OneIG / LongText on a 0–1 scale, DPG / TIIF on 0–100. The Type column marks closed- vs open-source; bold / underline = best / second-best among open-source models (closed-source shown for reference, not ranked); – = not reported; ★ = ours.
Model
Type
#Params
Steps
GenEval
DPG
TIIF-Short
TIIF-Long
CVTG-2K
OneIG-EN
OneIG-CN
LongText-EN
LongText-CN
Seedream 3.0
Closed
–
–
0.84
88.27
86.02
84.31
0.592
0.530
0.528
0.896
0.878
Seedream 4.0
Closed
–
–
0.84
88.63
–
–
0.892
0.573
0.554
0.936
0.946
GPT-Image-1
Closed
–
–
0.84
85.15
89.15
88.29
0.857
0.533
0.474
0.956
0.619
Nano-Banana-Pro
Closed
–
–
0.83
87.16
–
–
0.779
0.580
0.570
0.981
0.949
FLUX.1-dev
Open
12B
50
0.66
83.84
71.09
71.78
0.496
0.434
0.245
0.607
0.005
FLUX.1-Krea-dev
Open
12B
50
0.72
86.59
80.36
81.67
0.444
0.443
0.271
0.693
0.002
FLUX.2-dev
Open
32B
50
0.87
87.57
88.82
88.10
0.893
0.551
0.516
0.963
0.757
FLUX.2-Klein-Base-4B
Open
4B
50
0.78
83.02
79.94
80.01
0.656
0.485
0.366
0.554
0.071
FLUX.2-Klein-Base-9B
Open
9B
50
0.83
85.29
81.47
84.52
0.655
0.544
0.400
0.872
0.227
FLUX.2-Klein-4B
Open
4B
4
0.83
85.53
78.91
79.04
0.628
0.500
0.364
0.649
0.068
FLUX.2-Klein-9B
Open
9B
4
0.86
86.20
85.22
84.13
0.424
0.538
0.406
0.872
0.226
Qwen-Image
Open
20B
50
0.87
88.32
86.14
86.83
0.829
0.539
0.548
0.943
0.946
JoyAI-Image
Open
16B
50
–
88.05
–
–
0.874
0.542
0.521
0.963
0.963
HunyuanImage-3.0
Open
80B
50
0.72
86.10
–
–
0.765
–
–
–
–
LongCat-Image
Open
6B
50
0.87
86.80
80.93
81.30
0.866
0.516
0.518
0.885
0.956
Z-Image-Base
Open
6B
50
0.84
88.14
80.20
83.04
0.867
0.546
0.535
0.935
0.936
Z-Image-Turbo
Open
6B
8
0.82
84.86
77.73
80.05
0.859
0.528
0.507
0.917
0.926
Mage-Flow-Base ★
Open
4B
30
0.79
86.26
82.50
83.19
0.851
0.542
0.509
0.904
0.792
Mage-Flow ★
Open
4B
20
0.90
86.49
82.19
84.70
0.887
0.536
0.505
0.944
0.823
Mage-Flow-Turbo ★
Open
4B
4
0.88
85.48
83.58
84.16
0.873
0.523
0.491
0.911
0.801
Image editing — ImgEdit-Bench (0–5), GEdit-Bench EN/CN (0–10), TextEdit-Bench synthetic/real (0–25). Higher is better; the Type column marks closed- vs open-source; bold / underline = best / second-best among open-source models; – = not reported; ★ = ours.
Model
Type
#Params
Steps
ImgEdit
GEdit-EN
GEdit-CN
TextEdit-Syn
TextEdit-Real
Nano-Banana
Closed
–
–
4.29
7.291
7.399
16.54
18.22
Seedream 4.0
Closed
–
–
4.30
7.701
7.692
14.90
18.54
Seedream 4.5
Closed
–
–
4.32
7.820
7.800
–
–
Nano-Banana-Pro
Closed
–
–
4.37
7.738
7.799
–
–
Step1X-Edit-v1.2
Open
19B
50
3.95
7.480
7.467
9.26
12.02
FLUX.1-Kontext-dev
Open
12B
28
3.71
6.462
1.857
12.14
14.31
FLUX.2-dev
Open
32B
50
4.35
7.413
7.278
11.86
14.71
FLUX.2-Klein-Base-4B
Open
4B
50
3.80
7.081
7.102
11.01
13.79
FLUX.2-Klein-4B
Open
4B
4
4.01
7.717
7.750
11.84
14.46
FLUX.2-Klein-Base-9B
Open
9B
50
4.05
7.740
7.745
12.76
15.65
FLUX.2-Klein-9B
Open
9B
4
4.18
8.040
8.055
12.73
15.75
Z-Image-Edit
Open
6B
50
4.30
7.570
7.540
–
–
Qwen-Image-Edit-2509
Open
20B
50
4.31
7.480
7.467
13.40
15.81
Qwen-Image-Edit-2511
Open
20B
50
4.51
7.877
7.819
13.53
16.81
LongCat-Image-Edit
Open
6B
50
4.45
7.748
7.731
12.46
14.89
FireRed-Image-Edit-1.0
Open
20B
50
4.56
7.943
7.887
15.19
17.23
JoyAI-Image-Edit
Open
16B
50
4.46
8.276
8.125
14.80
17.23
Mage-Flow-Edit-Base ★
Open
4B
30
4.28
7.860
7.970
13.63
15.57
Mage-Flow-Edit ★
Open
4B
30
4.34
8.127
8.123
14.14
16.26
Mage-Flow-Edit-Turbo ★
Open
4B
4
4.38
8.271
8.264
12.77
15.41
🏗️ Architecture
Mage-VAE — a latent tokenizer built as a symmetric one-step diffusion codec: the decoder is a fully-convolutional one-step pixel-diffusion model (no global-attention blocks), and the encoder is its architectural dual (a one-step latent generator conditioned on pixels). A standard Gaussian-prior KL is replaced with an anchor-latent KL that regularizes the posterior toward FLUX.2-VAE latents, giving a generation-ready 128-channel, 16×-downsampled latent space.
Mage-VAE architecture and training
Mage-VAE — anchor VAE (FLUX.2-VAE), the symmetric one-step encoder/decoder architecture, and the three-stage training pipeline.
Mage-Flow — a 4B Multimodal DiT that encodes prompts with Qwen3-VL and images with Mage-VAE, then processes packed variable-length image+text sequences with per-sample 2D rotary embeddings and joint self-attention. Native-resolution packing removes bucket quantization and padding, lets one checkpoint generalize to any output size, and fuses the CFG cond/uncond branches into a single forward.
Mage-Flow / Native-Resolution MMDiT architecture
Mage-Flow — native-resolution packing of variable-length image+text tokens through the Native-Resolution MMDiT (left), and the dual-stream MMDiT block (right).
Post-training — from Base, generation is aligned with Diffusion-NFT (prompt following, aesthetics, text rendering, preference) to produce the RL model, and distilled with decoupled-DMD + adversarial perceptual guidance into the 4-step Turbo. Editing models reuse the recipe, trained on a mixture of generation and editing data to keep the generative prior.
🚀 Quick Start
Installation
Install everything exceptflash-attn first, then install flash-attn separately with build isolation off — it compiles a CUDA extension against your installed torch, so torch and a matching CUDA toolkit must already be present.
bash
1cd Mage/mage_flow
2uv venv &&source .venv/bin/activate
34# 1) Pinned, tested dependency set (torch 2.13, transformers 5.5, diffusers 0.38, pillow 12.3, …).5# Recommended for reproducibility. `uv pip install -e .` also works, but its loose6# bounds may resolve to a newer torch/transformers than the code was tested against.7uv pip install -r requirements.txt
8uv pip install -e . --no-deps # the mage-flow package itself910# 2) flash-attn — needs build tools present and a CUDA toolkit whose MAJOR version11# matches your torch build (e.g. torch cu12x ↔ nvcc 12.x). A cu13/nvcc-12 mix fails.12uv pip install setuptools wheel ninja
13uv pip install --no-build-isolation flash-attn==2.8.3
Plain pip is equivalent (pip install -r requirements.txt, pip install -e . --no-deps, then the two flash-attn lines). This registers three commands: mage-flow, mage-flow-edit, mage-flow-app.
torch / CUDA: the default PyPI torch wheel targets the newest CUDA (currently cu13x). If your machine's CUDA toolkit is 12.x, install torch from the matching index first, e.g. uv pip install torch==2.13.0 torchvision==0.28.0 --index-url https://download.pytorch.org/whl/cu126, otherwise the flash-attn build will fail with a CUDA-version mismatch. (torch 2.13.0 ships cu126/cu129/cu130 wheels — pick the one matching your nvcc.)
Python API
pipe.generate(prompts, **kw) and pipe.edit(prompts, ref_images, **kw) return a list[PIL.Image] aligned with prompts. A promptslist is batched into one packed forward per denoise step (each sample can have its own resolution/seed).
Text-to-image:
python
1from mage_flow import MageFlowPipeline
23pipe = MageFlowPipeline.from_pretrained("microsoft/Mage-Flow", device="cuda")45# 1) single image6img = pipe.generate(["A close-up portrait of an elderly African man with deep wrinkles, wearing a traditional hat, soft natural lighting, ultra realistic."],7 steps=20, cfg=5.0, heights=[1024], widths=[1024])[0]8img.save("t2i.png")910# 2) batch: several prompts / resolutions / seeds in ONE packed forward per step11imgs = pipe.generate(12["the Salar de Uyuni mirror surface captured at high noon, with intimate stillness permeating the air. dew beads on every blade of grass. National Geographic editorial, cinematic depth, fine-grained natural texture.",13"A close-up portrait of an elderly African man with deep wrinkles, wearing a traditional hat, soft natural lighting, ultra realistic.",14"An immersive close-up of a steaming bowl of Sichuan mapo tofu over jasmine rice served on a hand-thrown ceramic plate, finished with a wedge of citrus. Surface oils catch a tiny specular highlight. Shot with a Hasselblad H6D-100c, ambient window light, the kind of image that makes the viewer hungry."],15 heights=[512,1024,1792], widths=[2048,1024,1024],# per-sample; 4:1 is fine16 seeds=[1,2,3], steps=20, cfg=5.0,17)
Image editing:
python
1from mage_flow import MageFlowPipeline
23pipe = MageFlowPipeline.from_pretrained("microsoft/Mage-Flow-Edit", device="cuda")45# single reference (path or PIL image)6img = pipe.edit(["Replace the background with a field of sunflowers"],["assets/dog.jpg"],7 steps=30, cfg=5.0, max_size=1024)[0]8img.save("single_edit.png")910# multi-image edit — ref_images[i] is a LIST of source images11img = pipe.edit(["blend the object from image 2 into image 1"],12[["scene.png","object.png"]], steps=30, cfg=5.0)[0]13img.save("multi_edit.png")1415# explicit output size (overrides max_size); Turbo edit = 4 steps / cfg 116img = pipe.edit(["Replace the background with a field of sunflowers"],["assets/dog.jpg"],17 heights=[1024], widths=[1024], steps=4, cfg=1.0)[0]18img.save("single_edit_1024x1024.png")
Parameters (shared by generate / edit):
Parameter
Default
Description
prompts
—
string or list of strings; a list is batched into one forward per step
ref_images(edit)
—
per prompt: one image/path, or alist of images for multi-image edit
steps
30
denoising steps — Base30, RL 20, Turbo 4
cfg
5.0
classifier-free guidance scale (Turbo:1.0)
heights, widths
[1024]
per-sample output size, multiple of 16; native resolution512–2048
max_size(edit)
source size
longest output side; short side follows the reference's aspect ratio
vl_cond_long_edge(edit)
384
cap the long edge of the reference image fed to theVL text encoder (matches training preprocessing; the VAE/generation path keeps the full output resolution). 0/None disables
neg_prompts
" "
per-sample negative prompt (applied whencfg > 1)
seeds
42
per-sample seed;-1 = random
batch_cfg
True
fuse the CFG conditional + unconditional passes into one packed forward
renormalization
False
rescale guided velocity per token (reduces over-saturation at high cfg)
static_shift
6.0
override the flow-matching sigma shift
prompt_template
mage-flow / mage-flow-edit
text-encoder prompt template
CLI
bash
1# text-to-image (two prompts in one batch)2mage-flow --prompt "A close-up portrait of an elderly African man with deep wrinkles, wearing a traditional hat, soft natural lighting, ultra realistic.""An immersive landscape of a Greenlandic icefjord at midnight sun, painted by early dawn light, crystal-clear skies adding drama. fine grains of sand carving sharp shadows. Peter Lik gallery print, moody atmosphere, museum-grade composition."\3 --model_path microsoft/Mage-Flow --steps 20 --cfg 5.0\4 --height 1024512 --width 10242048 --seed 42 --out ./outputs
567# editing (one --ref per prompt; comma-separate sources for multi-image edit)8mage-flow-edit --prompt "Replace the background with a field of sunflowers""blend these two images"\9 --ref assets/dog.jpg "scene.png,object.png"\10 --model_path microsoft/Mage-Flow-Edit --max_size 1024 --out ./outputs
Flag
Scope
Meaning
--prompt
both
one or more prompts, run as a batch (sample i uses --seed + i)
--model_path
both
local repo dir or HF Hub repo id (auto-downloaded + cached)
--steps
both
number of denoising steps
--cfg
both
classifier-free guidance scale
--height
both
output height — one value, or one per prompt for mixed resolutions
--width
both
output width — one value, or one per prompt for mixed resolutions
--seed
both
base seed (sample i uses --seed + i)
--neg_prompt
both
negative prompt
--static_shift
both
override the flow-matching sigma shift
--out
both
output directory
--ref
edit
reference image per prompt (comma-separate paths for a multi-image edit)
--max_size
edit
max size of the reference image
--vl_cond_long_edge
edit
VL-condition long edge (default 384)
Gradio app
mage-flow-app # serve on http://0.0.0.0:7860 (or: python -m mage_flow.app)
A web UI with Text → Image and Image Edit tabs; models load lazily on first use and are cached. Presets default to the microsoft/Mage-Flow*Hugging Face repos (downloaded + cached on first use); set MAGEFLOW_HF_DIR to load local checkpoint dirs instead.
Launch options:
Flag
Default
Meaning
--host
0.0.0.0
bind address
--port
7860
port
--device
cuda
inference device
--share
off
create a public Gradio share link
--preload
(lazy)
comma-separated repo ids / paths to load at startup instead of on first use
📝 Citation
bibtex
1@article{zhang2026mageflow,
2 title={Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing},
3 author={Zhang, Xinjie and Zhang, Peng and Zheng, Shicheng and Guo, Jinghao and Jia, Zhaoyang and Shen, Yifei and Guo, Xun and Luo, Yuxuan and Li, Jiahao and Xie, Wenxuan and Pu, Fanyi and Zhang, Xiaoyi and Zhang, Kaichen and Guo, Zongyu and Bi, Tianci and Gui, Dongnan and Liu, Zhening and Wen, Zimo and Zheng, Zihan and Yang, Senqiao and Li, Xiao and Wang, Jinglu and Li, Bin and Lu, Yan},
4 journal={arXiv preprint arXiv:2607.19064},
5 year={2026}
6}
