---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-to-image
library_name: transformers
---
## Introduction
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.
### Key Features
- 🌟 **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.
- 🌟 **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.
- 🌟 **Remarkable Photorealism**: Through an innovative data strategy and training framework, LongCat-Image achieves remarkable photorealism in generated images.
[//]: # (For more details, please refer to the comprehensive [***LongCat-Image Technical Report***](https://arxiv.org/abs/2412.11963).)
## 🎨 Showcase
## Quick Start
### Installation
Clone the repo:
```shell
git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Image
cd LongCat-Image
```
Install dependencies:
```shell
# create conda environment
conda create -n longcat-image python=3.10
conda activate longcat-image
# install other requirements
pip install -r requirements.txt
python setup.py develop
```
### Run Text-to-Image Generation
> [!TIP]
> 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.
> [!CAUTION]
> **Special Handling for Text Rendering**
>
> For both Text-to-Image and Image Editing tasks involving text generation, **you must enclose the target text within quotes (`""`)**.
>
> **Reason:** The tokenizer applies **character-level encoding** specifically to content found inside quotes. Failure to use explicit quotation marks will result in a significant degradation of text rendering quality.
```python
import torch
from transformers import AutoProcessor
from longcat_image.models import LongCatImageTransformer2DModel
from longcat_image.pipelines import LongCatImagePipeline
device = torch.device('cuda')
checkpoint_dir = './weights/LongCat-Image'
text_processor = AutoProcessor.from_pretrained( checkpoint_dir, subfolder = 'tokenizer' )
transformer = LongCatImageTransformer2DModel.from_pretrained( checkpoint_dir , subfolder = 'transformer',
torch_dtype=torch.bfloat16, use_safetensors=True).to(device)
pipe = LongCatImagePipeline.from_pretrained(
checkpoint_dir,
transformer=transformer,
text_processor=text_processor
)
# pipe.to(device, torch.bfloat16) # Uncomment for high VRAM devices (Faster inference)
pipe.enable_model_cpu_offload() # Offload to CPU to save VRAM (Required ~17 GB); slower but prevents OOM
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。'
image = pipe(
prompt,
height=768,
width=1344,
guidance_scale=4.5,
num_inference_steps=50,
num_images_per_prompt=1,
generator=torch.Generator("cpu").manual_seed(43),
enable_cfg_renorm=True,
enable_prompt_rewrite=True # Reusing the text encoder as a built-in prompt rewriter
).images[0]
image.save('./t2i_example.png')
```
)
