Instructions to use rootlocalghost/LongCat-Image-Edit-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootlocalghost/LongCat-Image-Edit-Turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="rootlocalghost/LongCat-Image-Edit-Turbo")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rootlocalghost/LongCat-Image-Edit-Turbo", dtype="auto") - Notebooks
- Google Colab
- Kaggle
clone README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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- zh
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pipeline_tag: image-to-image
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library_name: transformers
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---
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<div align="center">
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<img src="assets/longcat-image_logo.svg" width="45%" alt="LongCat-Image" />
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</div>
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<hr>
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<div align="center" style="line-height: 1;">
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<a href='https://arxiv.org/pdf/2512.07584'><img src='https://img.shields.io/badge/Technical-Report-red'></a>
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<a href='https://github.com/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/GitHub-Code-black'></a>
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<a href='https://github.com/meituan-longcat/LongCat-Flash-Chat/blob/main/figures/wechat_official_accounts.png'><img src='https://img.shields.io/badge/WeChat-LongCat-brightgreen?logo=wechat&logoColor=white'></a>
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<a href='https://x.com/Meituan_LongCat'><img src='https://img.shields.io/badge/Twitter-LongCat-white?logo=x&logoColor=white'></a>
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</div>
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<div align="center" style="line-height: 1;">
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[//]: # ( <a href='https://meituan-longcat.github.io/LongCat-Image/'><img src='https://img.shields.io/badge/Project-Page-green'></a>)
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image-blue'></a>
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image-Dev'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Dev-blue'></a>
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image-Edit'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Edit-blue'></a>
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<a href='https://huggingface.co/meituan-longcat/LongCat-Image-Edit-Turbo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Edit--Turbo-blue'></a>
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</div>
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## Introduction
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We introduce **LongCat-Image-Edit-Turbo**, the distilled version of LongCat-Image-Edit. It achieves high-quality image editing with only 8 NFEs (Number of Function Evaluations) , offering extremely low inference latency.
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<div align="center">
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<img src="assets/model_struct_edit.png" width="90%" alt="LongCat-Image-Edit model" />
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</div>
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### Installation
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```shell
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pip install git+https://github.com/huggingface/diffusers
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```
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### Run Image Editing
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> [!CAUTION]
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> **📝 Special Handling for Text Rendering**
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>
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> 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).
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>
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> **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.
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>
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```python
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import torch
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from PIL import Image
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from diffusers import LongCatImageEditPipeline
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if __name__ == '__main__':
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device = torch.device('cuda')
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pipe = LongCatImageEditPipeline.from_pretrained("meituan-longcat/LongCat-Image-Edit-Turbo", torch_dtype= torch.bfloat16 )
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# pipe.to(device, torch.bfloat16) # Uncomment for high VRAM devices (Faster inference)
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pipe.enable_model_cpu_offload() # Offload to CPU to save VRAM (Required ~18 GB); slower but prevents OOM
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img = Image.open('assets/test.png').convert('RGB')
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prompt = '将猫变成狗'
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image = pipe(
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img,
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prompt,
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negative_prompt='',
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guidance_scale=1,
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num_inference_steps=8,
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num_images_per_prompt=1,
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generator=torch.Generator("cpu").manual_seed(43)
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).images[0]
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image.save('./edit_example.png')
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```
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