AiArtLab / zen-image-edit

Zen Image Edit

Qwen-Image-2.1 on a 0.8B text encoder. Text-to-image, character and scene editing, and transparent (RGBA) generation in one pipeline.

What changed

The text encoder is replaced by Qwen3.5-0.8B plus a 158M adapter, fine-tuned to reproduce what the native encoder produced — both from plain text and from text read together with the reference images ( Improved using Qwen ). The adapter lives inside the DiT as its text-fusion block, so the whole model is one self-contained diffusers folder and no 17.5 GB encoder is needed anywhere. The sampler runs a plain static shift of 5.0 instead of the original dynamic shifting.

Examples

Every image below is generated by this pipeline with 30 steps at 1024 px.

Text-to-image

Edit — one condition image (background change, subject kept)

Edit — two condition images (character replacement: identity from <image1> , pose/clothing/scene from <image2> )

Edit — three condition images (subject from <image1> , scene from <image2> , lighting from <image3> )

Transparent RGBA

Usage

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("AiArtLab/zen-image-edit", trust_remote_code=True,
                                         dtype=torch.float16)
pipe.enable_model_cpu_offload()   # 14.5 GB DiT + fp32 VAE decoder do not co-reside on 32 GB

# text-to-image
image = pipe(prompt="a red fox in a snowy forest at dusk, cinematic, 85mm",
             output_resolution=1024, num_inference_steps=30,
             generator=torch.Generator("cuda").manual_seed(1234)).images[0]

# editing: 1..N condition images, referenced in the prompt by TAG <image1>, <image2>, ...
image = pipe(prompt="Replace the woman in <image2> with the woman from <image1>; keep <image2> pose, "
                    "clothing and background unchanged.",
             image=[ref_image, scene_image],
             output_resolution=1024, num_inference_steps=30,
             generator=torch.Generator("cuda").manual_seed(1234)).images[0]

trust_remote_code=True pulls pipeline.py and transformer.py from this repo and runs them, so no clone is needed. Cloning works too and gives the class directly:

from pipeline import ZenImageEditPipeline
pipe = ZenImageEditPipeline.from_pretrained(".", dtype=torch.float16)

CLI — one image, or a whole file of prompts (one per line, # starts a comment, blank lines are skipped; the pipeline is loaded once for the whole file):

python example.py --prompt "a red fox in a snowy forest" --out fox.png
python example.py --prompts-file prompts.txt --out gens --size 1024 --steps 30
python example.py --prompt "..." --width 1280 --height 768 --out wide.png
python example.py --prompt "..." --negative "low quality, blurry, watermark" --cfg 3 --out cfg.png
python example.py --prompt "..." --scheduler-test --shift 5 --out ab.png

--scheduler-test renders every prompt twice with the same seed — the shipped static --shift (5.0) and Qwen-Image-2.1's original dynamic-shift schedule — and glues the pair with labels, so a schedule change can be judged without rerunning anything by hand.

--size sets a square frame (or the frame area when --image supplies the aspect ratio); --width / --height override it and are floored to a multiple of 32. --cfg is true_cfg_scale and defaults to 1.0 — Qwen-Image-2.1 is meant to run without guidance, and --negative only takes effect above 1.

Requirements: torch , transformers , accelerate and a diffusers built with Qwen-Image-2.1 ( pip install git+https://github.com/huggingface/diffusers ) — the transformer subclasses QwenImage21Transformer2DModel . trust_remote_code saves the clone, it does not save the 17 GB of weights.

Files

pipeline.py          ZenImageEditPipeline — one class for t2i and editing, as QwenImage21Pipeline
transformer.py       QwenImage21FusionTransformer2DModel + the text-fusion blocks
example.py           CLI for both modes
transformer/         DiT config + 2 fp16 shards, adapter merged in as text_fusion.*
text_encoder/        Qwen3.5-0.8B, fp16
processor/           its processor (image slicing + tokenization)
tokenizer/           its tokenizer
vae/                 Qwen-Image-2.1 VAE, fp32
scheduler/           FlowMatchEulerDiscreteScheduler config
media/               the examples above

QwenImage21FusionTransformer2DModel is a custom class defined in transformer.py , not registered inside diffusers , so the pipeline publishes it on the diffusers module at import time. That is what makes the trust_remote_code=True one-liner above work; without it the stock component loader would not find the DiT class.

Limitations

• English only — that is all the adapter was trained and tested on; other languages drift.
• Numerals on signage come out wrong: "OPEN 24 HOURS" renders as "OPEN 26 HOURS" on every seed tried. Words are fine.
• Batch size >1 at 1024 px peaks near 28 GB; one prompt per call is the safe mode.

NOTICE

Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.

This is a derivative work of Qwen-Image-2.1 — the full agreement is in LICENSE , the list of modified files and the remainder of the required attribution is in NOTICE . The Qwen3.5-0.8B text encoder is redistributed under the Apache License 2.0, see LICENSE-Qwen3.5-0.8B .

Contacts

Please contact with us if you may provide some GPU's or money on training

• telegram recoilme *prefered way
• mail at aiartlab.org (slow response)

Citation

@misc{zenimageedit,
  title={Zen Image Edit},
  author={recoilme and AiArtLab Team},
  url={https://huggingface.co/AiArtLab/zen-image-edit},
  year={2026}
}