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--- |
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library_name: diffusers |
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base_model: Qwen/Qwen-Image |
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base_model_relation: quantized |
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quantized_by: AlekseyCalvin |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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pipeline_tag: text-to-image |
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tags: |
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- fp4 |
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- Abliterated |
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- quantized |
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- 4-bit |
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- Qwen2.5-VL7b-Abliterated |
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- instruct |
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- Diffusers |
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- Transformers |
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- uncensored |
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- text-to-image |
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- image-to-image |
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- image-generation |
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--- |
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<p align="center"> |
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<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" width="200"/> |
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<p> |
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# QWEN-IMAGE Model |fp4|+Abliterated Qwen2.5VL-7b |
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This repo contains a variant of QWEN's **[QWEN-IMAGE](https://huggingface.co/Qwen/Qwen-Image)**, the state-of-the-art generative model with extensive and (image/)text-to-image &/or instruction/control-editing capabilities. <br> |
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To make these cutting edge capabilities more accessible to those constrained to low-end consumer-grade hardware, **we've quantized the DiT (Diffusion Transformer) component of Qwen-Image to the 4-bit FP4 format** using the Bits&Bytes toolkit.<br> |
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This optimization was derived by us directly from the BF16 base model weights released on 08/04/2025, with no other mix-ins or modifications to the DiT component. <br> |
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*NOTE: Install `bitsandbytes` prior to inference.* <br> |
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**QWEN-IMAGE** is an open-weights customization-friendly frontier model released under the highly permissive Apache 2.0 license, welcoming unrestricted (within legal limits) commercial, experimental, artistic, academic, and other uses &/or modifications. <br> |
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To help highlight horizons of possibility broadened by the **QWEN-IMAGE** release, our quantization is bundled with an "Abliterated" (aka de-censored) finetune of [Qwen2.5-VL 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), QWEN-IMAGE model's sole conditioning encoder (of prompts, instructions, input images, controls, etc), as well as a powerful Vision-Language-Model in its own right. <br> |
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As such, our repo saddles a lean & prim FP4 DiT over the **[Qwen2.5-VL-7B-Abliterated-Caption-it](https://huggingface.co/prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it/tree/main)** by [Prithiv Sakthi](https://huggingface.co/prithivMLmods) (aka [prithivMLmods](https://github.com/prithivsakthiur)). |
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<p align="center"> |
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<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.jpg" width="1600"/> |
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<p> |
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# NOTICE: |
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*Do not be alarmed by the file warning from the ClamAV automated checker.* <br> |
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*It is a clear false positive.* *In assessing one of the typical Diffusers-adapted Safetensors shards (model weights), the checker reads:* |
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``The following viruses have been found: Pickle.Malware.SysAccess.sys.STACK_GLOBAL.UNOFFICIAL`` <br> |
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*However, a Safetensors by its sheer design can not contain suchlike inserts. You may confirm for yourself thru HF's built-in weight/index viewer. <br> |
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So, to be sure, this repo does **not** contain any pickle checkpoints, or any other pickled data.* <br> |
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# TEXT-TO-IMAGE PIPELINE EXAMPLE: |
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This repo is formatted for usage with Diffusers (0.35.0.dev0+) & Transformers libraries, vis-a-vis associated pipelines & model component classes, such as the defaults listed in `model_index.json` (in this repo's root folder). <br> |
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*Sourced/adapted from [the original base model repo](https://huggingface.co/Qwen/Qwen-Image) by QWEN.* |
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**EDIT: |
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We've confronted some issues with using the below pipeline. Will update once a reliable replacement is confirmed.** <br> |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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import bitsandbytes |
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model_name = "AlekseyCalvin/QwenImage_fp4_diffusers" |
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# Load the pipeline |
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if torch.cuda.is_available(): |
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torch_dtype = torch.bfloat16 |
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device = "cuda" |
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else: |
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torch_dtype = torch.float32 |
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device = "cpu" |
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pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype) |
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pipe = pipe.to(device) |
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positive_magic = [ |
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"en": "Ultra HD, 4K, cinematic composition." # for english prompt, |
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"zh": "超清，4K，电影级构图" # for chinese prompt, |
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] |
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# Generate image |
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prompt = '''A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition''' |
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negative_prompt = " " |
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# Generate with different aspect ratios |
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aspect_ratios = { |
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"1:1": (1328, 1328), |
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"16:9": (1664, 928), |
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"9:16": (928, 1664), |
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"4:3": (1472, 1140), |
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"3:4": (1140, 1472) |
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} |
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width, height = aspect_ratios["16:9"] |
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image = pipe( |
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prompt=prompt + positive_magic["en"], |
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negative_prompt=negative_prompt, |
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width=width, |
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height=height, |
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num_inference_steps=50, |
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true_cfg_scale=4.0, |
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generator=torch.Generator(device="cuda").manual_seed(42) |
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).images[0] |
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image.save("example.png") |
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``` |
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<br> |
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# SHOWCASES FROM THE QWEN TEAM: |
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# MORE INFO: |
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- Check out the [Technical Report](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf) for QWEN-IMAGE, released by the Qwen team! <br> |
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- Find source base model weights here at [huggingface](https://huggingface.co/Qwen/Qwen-Image) and at [Modelscope](https://modelscope.cn/models/Qwen/Qwen-Image). |
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## QWEN LINKS: |
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<p align="center"> |
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💜 <a href="https://chat.qwen.ai/"><b>Qwen Chat</b></a>   |   🤗 <a href="https://huggingface.co/Qwen/Qwen-Image">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image">ModelScope</a>   |    📑 <a href="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf">Tech Report</a>    |    📑 <a href="https://qwenlm.github.io/blog/qwen-image/">Blog</a>    |
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<br> |
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🖥️ <a href="https://huggingface.co/spaces/Qwen/qwen-image">Demo</a>   |   💬 <a href="https://github.com/QwenLM/Qwen-Image/blob/main/assets/wechat.png">WeChat (微信)</a>   |   🫨 <a href="https://discord.gg/CV4E9rpNSD">Discord</a>   |
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</p> |
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## QWEN-IMAGE TECHNICAL REPORT CITATION: |
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```bibtex |
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@article{qwen-image, |
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title={Qwen-Image Technical Report}, |
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author={Qwen Team}, |
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journal={arXiv preprint}, |
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year={2025} |
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} |
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``` |
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