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.

• 🌟 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.

Clone the repo:

git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Image
cd LongCat-Image

Install dependencies:

# 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

[!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.

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)  # Uncomment for high VRAM devices (Faster inference)
pipe.enable_model_cpu_offload()  # Offload to CPU to save VRAM (Required ~19 GB); slower but prevents OOM

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')