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--- |
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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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library_name: transformers |
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--- |
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<div align="center"> |
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<img src="assets/longcat-image_logo.svg" width="45%" alt="LongCat-Image" /> |
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</div> |
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<hr> |
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<div align="center" style="line-height: 1;"> |
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<a href='https://arxiv.org/pdf/2512.07584'><img src='https://img.shields.io/badge/Technical-Report-red'></a> |
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<a href='https://github.com/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/GitHub-Code-black'></a> |
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<a href='https://github.com/meituan-longcat/LongCat-Flash-Chat/blob/main/figures/wechat_official_accounts.png'><img src='https://img.shields.io/badge/WeChat-LongCat-brightgreen?logo=wechat&logoColor=white'></a> |
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<a href='https://x.com/Meituan_LongCat'><img src='https://img.shields.io/badge/Twitter-LongCat-white?logo=x&logoColor=white'></a> |
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</div> |
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<div align="center" style="line-height: 1;"> |
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[//]: # ( <a href='https://meituan-longcat.github.io/LongCat-Image/'><img src='https://img.shields.io/badge/Project-Page-green'></a>) |
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image-blue'></a> |
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image-Dev'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Dev-blue'></a> |
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image-Edit'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Edit-blue'></a> |
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</div> |
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## Introduction |
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We introduce **LongCat-Image**, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models. |
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<div align="center"> |
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<img src="assets/model_struct.jpg" width="90%" alt="LongCat-Image Generation Examples" /> |
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</div> |
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### Key Features |
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- 🌟 **Exceptional Efficiency and Performance**: With only **6B parameters**, LongCat-Image surpasses numerous open-source models that are several times larger across multiple benchmarks, demonstrating the immense potential of efficient model design. |
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- 🌟 **Powerful Chinese Text Rendering**: LongCat-Image demonstrates superior accuracy and stability in rendering common Chinese characters compared to existing SOTA open-source models and achieves industry-leading coverage of the Chinese dictionary. |
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- 🌟 **Remarkable Photorealism**: Through an innovative data strategy and training framework, LongCat-Image achieves remarkable photorealism in generated images. |
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[//]: # (For more details, please refer to the comprehensive [***LongCat-Image Technical Report***](https://arxiv.org/abs/2412.11963).) |
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## 🎨 Showcase |
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<div align="center"> |
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<img src="assets/gallery.jpeg" width="90%" alt="LongCat-Image Generation Examples" /> |
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</div> |
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## Quick Start |
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### Installation |
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```shell |
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pip install git+https://github.com/huggingface/diffusers |
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``` |
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### Run Text-to-Image Generation |
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> [!TIP] |
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> Leveraging a stronger LLM for prompt refinement can further enhance image generation quality. Please refer to [inference_t2i.py](https://github.com/meituan-longcat/LongCat-Image/blob/main/scripts/inference_t2i.py#L28) for detailed usage instructions. |
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> [!CAUTION] |
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> **📝 Special Handling for Text Rendering** |
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> |
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> For both Text-to-Image and Image Editing tasks involving text generation, **you must enclose the target text within single or double quotation marks** (both English '...' / "..." and Chinese ‘...’ / “...” styles are supported). |
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> |
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> **Reasoning:** The model utilizes a specialized **character-level encoding** strategy specifically for quoted content. Failure to use explicit quotation marks prevents this mechanism from triggering, which will severely compromise the text rendering capability. |
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```python |
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import torch |
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from diffusers import LongCatImagePipeline |
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if __name__ == '__main__': |
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device = torch.device('cuda') |
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pipe = LongCatImagePipeline.from_pretrained("meituan-longcat/LongCat-Image", torch_dtype= torch.bfloat16 ) |
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# pipe.to(device, torch.bfloat16) # Uncomment for high VRAM devices (Faster inference) |
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pipe.enable_model_cpu_offload() # Offload to CPU to save VRAM (Required ~17 GB); slower but prevents OOM |
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prompt = '一个年轻的亚裔女性，身穿黄色针织衫，搭配白色项链。她的双手放在膝盖上，表情恬静。背景是一堵粗糙的砖墙，午后的阳光温暖地洒在她身上，营造出一种宁静而温馨的氛围。镜头采用中距离视角，突出她的神态和服饰的细节。光线柔和地打在她的脸上，强调她的五官和饰品的质感，增加画面的层次感与亲和力。整个画面构图简洁，砖墙的纹理与阳光的光影效果相得益彰，突显出人物的优雅与从容。' |
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image = pipe( |
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prompt, |
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height=768, |
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width=1344, |
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guidance_scale=4.0, |
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num_inference_steps=50, |
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num_images_per_prompt=1, |
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generator=torch.Generator("cpu").manual_seed(43), |
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enable_cfg_renorm=True, |
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enable_prompt_rewrite=True |
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).images[0] |
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image.save('./t2i_example.png') |
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``` |
