meituan-longcat/LongCat-Image-Edit

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

Installation

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. >

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')