---
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
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 Image Editing
```python
import torch
from PIL import Image
from transformers import AutoProcessor
from longcat_image.models import LongCatImageTransformer2DModel
from longcat_image.pipelines import LongCatImageEditPipeline
device = torch.device('cuda')
checkpoint_dir = './weights/LongCat-Image-Edit'
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 = LongCatImageEditPipeline.from_pretrained(
checkpoint_dir,
transformer=transformer,
text_processor=text_processor,
)
pipe.to(device, torch.bfloat16)
generator = torch.Generator("cpu").manual_seed(43)
img = Image.open('assets/test.png')
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=generator
).images[0]
image.save('./edit_example.png')
```
)
