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Longcat Image: High-Resolution Text-to-Image & Editing Model on playground and API | RunComfy

meituan/longcat-image/text-to-image

Generate high-resolution, multilingual images from text with fast-speed generation, and API access for professional creators and teams seeking studio-quality visual output.

Choose a preset size or select Custom to provide width and height.
The number of inference steps to perform.
The guidance scale used for image generation.
Specifies the format of the generated image.

Introduction to Longcat Image Model

Developed by Meituan as part of the LongCat AI suite, Longcat Image is a powerful 6-billion-parameter text-to-image and editing model built for creators and teams who demand studio-quality visuals with unmatched efficiency. Longcat Image delivers high-resolution, multilingual generation—excelling in both Chinese and English—while maintaining precise consistency across multiple edits. For developers, Longcat Image on RunComfy can be used both in the browser and via an HTTP API, so you don’t need to host or scale the model yourself.

Examples of Longcat Image Results

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Frequently Asked Questions

Can I use Longcat Image for commercial projects through RunComfy?

Longcat Image, as a text-to-image model developed by Meituan, is distributed under the Open RAIL license. This means commercial use is permitted only if it aligns with the license conditions specified by the model creator. Using Longcat Image via RunComfy does not override or bypass those original terms—you must still comply with the model’s explicit commercial rights and attribution policies listed on longcatai.org.

What are the technical limitations of Longcat Image when generating or editing content?

Longcat Image currently supports output resolutions up to approximately 4 megapixels (e.g., 2048×2048). Aspect ratios can vary but are constrained to a 1:2 to 2:1 range, and prompts are limited to 512 tokens per text-to-image job. Control references (such as ControlNet or IP-Adapter inputs) are capped at two simultaneous sources per generation to preserve GPU memory efficiency.

How can I transition from testing Longcat Image in the RunComfy Playground to deploying it via API?

Once you are satisfied with your text-to-image experiments in the RunComfy Playground, you can export your setup into code snippets provided in Python or NodeJS directly from the interface. The Longcat Image API mirrors the same parameters and generation pipeline as the playground. You will need to use your RunComfy API key, manage usage credits (usd), and implement error handling for production-grade reliability.

What new capabilities make Longcat Image superior to earlier models?

Longcat Image introduces a DiT-based hybrid architecture and a VLM encoder that boosts its text-to-image precision, especially for complex multilingual prompts and Chinese typography. It also integrates generation and editing seamlessly within the same workflow, producing studio-quality results with consistent lighting and textures across multiple edit rounds.

How do I manage usage credits when running Longcat Image on RunComfy?

RunComfy operates on a credit-based system called usd. New users receive free trial credits to explore the Longcat Image text-to-image features, after which additional usd can be purchased as per the Generation section in your dashboard. API and Playground both consume credits proportionally to resolution and complexity.

What should I do if I experience slow rendering or job queuing with Longcat Image?

If Longcat Image text-to-image requests take longer to process, it may be due to high concurrency periods. RunComfy auto-queues jobs and scales instances, but for high-volume or low-latency production needs, you can upgrade to a dedicated GPU plan. Contact hi@runcomfy.com for infrastructure-level assistance or to reserve faster GPU tiers.

Does the RunComfy API provide the same results as the web playground version of Longcat Image?

Yes. The Longcat Image text-to-image API replicates the exact same inference graph and sampling parameters as the playground. This ensures that visual outputs remain consistent when moving from prototype to automated production environments.