Meituan’s LongCat-Image: The New AI Model Redefining Image Generation and Editing

Struggling with AI models that are too large, too slow, or just can’t get text right—especially in Chinese? A new challenger has entered the arena. Meet LongCat-Image, an open-source AI image generation model from Meituan that redefines efficiency and quality. Here at TipTinker, we’re diving deep into what makes this model a potential game-changer for developers and creators alike.

LongCat-Image isn’t just another model; it’s a comprehensive ecosystem designed to tackle some of the biggest hurdles in AI image generation. Let’s explore why it’s turning heads.

What Makes LongCat-Image Special?

LongCat-Image stands out with a few killer features that directly address common pain points.

Exceptional Efficiency: With only 6 billion parameters, LongCat-Image competes with open-source models several times its size. This means lower hardware requirements and faster inference without sacrificing quality.
Masterful Bilingual Text Rendering: This is its superpower. The model demonstrates superior accuracy and stability in rendering complex Chinese characters, a feat where many other models fail. It also boasts excellent English text capabilities.
State-of-the-Art Image Editing: The specialized LongCat-Image-Edit model delivers incredible precision. It excels at following complex instructions for local or global edits while preserving the consistency of unchanged areas.
Remarkable Photorealism: Through an innovative data strategy, LongCat-Image produces images with a high degree of realism and detail.
Truly Open-Source: Meituan has released not only the final models but also intermediate checkpoints and the full training code, empowering the community to build upon their work.

[Image: A gallery showcasing LongCat-Image’s capabilities, with examples of photorealistic portraits, complex scenes, and perfect Chinese text rendering.]

A Quick Start Guide to LongCat-Image

Ready to try it yourself? Getting started is straightforward. The LongCat-Image suite includes two primary models for inference: one for text-to-image generation and another for editing.

Step 1: Set Up Your Environment

First, clone the official repository and install the necessary dependencies.

# Clone the repository
git clone https://github.com/meituan-longcat/LongCat-Image
cd LongCat-Image

# Create a conda environment and install requirements
conda create -n longcat-image python=3.10
conda activate longcat-image
pip install -r requirements.txt
python setup.py develop

Step 2: Text-to-Image Generation

Use the LongCat-Image model to create stunning visuals from a text prompt. Note the tip about prompt rewriting, which can further boost quality.

import torch
from transformers import AutoProcessor
from longcat_image.models import LongCatImageTransformer2DModel
from longcat_image.pipelines import LongCatImagePipeline

device = torch.device('cuda')
checkpoint_dir = './weights/LongCat-Image' # Assumes you've downloaded the model here

text_processor = AutoProcessor.from_pretrained(checkpoint_dir, subfolder='tokenizer')
transformer = LongCatImageTransformer2DModel.from_pretrained(
    checkpoint_dir,
    subfolder='transformer',
    torch_dtype=torch.bfloat16
).to(device)

pipe = LongCatImagePipeline.from_pretrained(
    checkpoint_dir,
    transformer=transformer,
    text_processor=text_processor
)

# Use CPU offloading if VRAM is limited (~17 GB required)
pipe.enable_model_cpu_offload()

prompt = 'A portrait of a female warrior, "Cyberpunk" style, neon lights reflected in her eyes.'

image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=4.5,
    num_inference_steps=50,
    enable_prompt_rewrite=True # Uses the text encoder to refine the prompt
).images[0]

image.save('./my_first_longcat_image.png')

Step 3: High-Precision Image Editing

To modify an existing image, use the LongCat-Image-Edit model. It’s perfect for tasks from changing an object’s color to transforming a cat into a dog.

import torch
from PIL import Image
from longcat_image.pipelines import LongCatImageEditPipeline

# Assume the base setup (device, transformer, etc.) is loaded for the Edit model
# checkpoint_dir would be './weights/LongCat-Image-Edit'

edit_pipe = LongCatImageEditPipeline.from_pretrained(...)
edit_pipe.enable_model_cpu_offload() # Use if VRAM is limited (~19 GB required)

init_image = Image.open('assets/test.png').convert('RGB')
prompt = 'Change the cat to a dog'

image = edit_pipe(
    init_image,
    prompt,
    guidance_scale=4.5,
    num_inference_steps=50
).images[0]

image.save('./edited_image.png')

🚀 Pro-Tips for Best Results

Tip	Description
Enclose Text in Quotes	CRITICAL: For rendering text in your image, always enclose it in double quotes (`""`) in your prompt. This tells the tokenizer to use character-level encoding for the best results.
Manage Your VRAM	If you don’t have a top-tier GPU, use `pipe.enable_model_cpu_offload()`. It’s a bit slower but prevents out-of-memory errors.
Refine Your Prompts	For text-to-image, keep `enable_prompt_rewrite=True`. The model uses its powerful text encoder to improve your prompt before generation.
Use the Dev Model	For researchers, the `LongCat-Image-Dev` model is the ideal starting point for fine-tuning on custom datasets.

Conclusion

LongCat-Image is more than just another AI model—it’s a statement. By delivering exceptional performance in a highly efficient, bilingual, and truly open-source package, Meituan has provided a powerful tool for the global AI community. Its ability to render Chinese text accurately sets a new standard.

Go ahead and integrate LongCat-Image into your workflow today. We at TipTinker believe it has the potential to unlock new creative possibilities.