数据集 / meituan-longcat/LongCat-Image

meituan-longcat/LongCat-Image 已完整同步

Introduction

We introduce LongCat-Image, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models.

Key Features

  • 🌟 Exceptional Efficiency and Performance: With only 6B parameters, LongCat-Image surpasses numerous open-source models that are several times larger across multiple benchmarks, demonstrating the immense potential of efficient model design.
  • 🌟 Powerful Chinese Text Rendering: LongCat-Image demonstrates superior accuracy and stability in rendering common Chinese characters compared to existing SOTA open-source models and achieves industry-leading coverage of the Chinese dictionary.
  • 🌟 Remarkable Photorealism: Through an innovative data strategy and training framework, LongCat-Image achieves remarkable photorealism in generated images.

🎨 Showcase

Quick Start

Installation

pip install git+https://github.com/huggingface/diffusers

Run Text-to-Image Generation

Tip

Leveraging a stronger LLM for prompt refinement can further enhance image generation quality. Please refer to inference_t2i.py for detailed usage instructions.

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 diffusers import LongCatImagePipeline

if __name__ == '__main__':
    device = torch.device('cuda')

    pipe = LongCatImagePipeline.from_pretrained

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数据集版权信息

本数据集的许可证为 Apache License 2.0。如有违反相关条款，请联系 WEHUB，我们将及时处理。 查看许可证