---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-to-image
library_name: transformers
---
## Introduction
We introduce **LongCat-Image-Edit**, the image editing version of Longcat-Image. LongCat-Image-Edit supports bilingual (Chinese-English) editing, achieves state-of-the-art performance among open-source image editing models, delivering leading instruction-following and image quality with superior visual consistency.
### Key Features
- 🌟 **Superior Precise Editing**: LongCat-Image-Edit supports various editing tasks, such as global editing, local editing, text modification, and reference-guided editing. It has strong semantic understanding capabilities and can perform precise editing according to instructions.
- 🌟 **Consistency Preservation**: LongCat-Image-Edit has strong consistency preservation capabilities, specifically scrutinizes whether attributes in non-edited regions, such as layout, texture, color tone, and subject identity, remain invariant unless targeted by the instruction, is well demonstrated in multi-turn editing.
- 🌟 **Strong Benchmark Performance**: LongCat-Image-Edit achieves state-of-the-art (SOTA) performance in image editing tasks while significantly improving model inference efficiency, especially among open-source image editing models.
## 🎨 Showcase
## Quick Start
[Hugging Face app](https://huggingface.co/spaces/anycoderapps/LongCat-Image-Edit)
### Installation
```shell
pip install git+https://github.com/huggingface/diffusers
```
### Run Image Editing
> [!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 single or double quotation marks** (both English '...' / "..." and Chinese ‘...’ / “...” styles are supported).
>
> **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.
>
```python
import torch
from PIL import Image
from diffusers import LongCatImageEditPipeline
if __name__ == '__main__':
device = torch.device('cuda')
pipe = LongCatImageEditPipeline.from_pretrained("meituan-longcat/LongCat-Image-Edit", torch_dtype= torch.bfloat16 )
# pipe.to(device, torch.bfloat16) # Uncomment for high VRAM devices (Faster inference)
pipe.enable_model_cpu_offload() # Offload to CPU to save VRAM (Required ~18 GB); slower but prevents OOM
img = Image.open('assets/test.png').convert('RGB')
prompt = '将猫变成狗'
image = pipe(
img,
prompt,
negative_prompt='',
guidance_scale=4.5,
num_inference_steps=50,
num_images_per_prompt=1,
generator=torch.Generator("cpu").manual_seed(43)
).images[0]
image.save('./edit_example.png')
```
)
