LongCat-Image is an open-source bilingual (Chinese/English) text-to-image foundation model that aims to improve a few "hard" diffusion problems at once: reliable multilingual text rendering, strong photorealism, and deployment efficiency. At ~6B parameters, it’s also small enough to be realistic for local or cost-sensitive inference, which makes it a good candidate for benchmarking in real products.
If you work with Chinese or mixed-language content, the model’s text-rendering emphasis is the main reason to try it first. The authors call out an important prompting detail: wrap any text you want rendered inside quotation marks so the model’s character-level encoding mechanism triggers. A quick sanity check is to generate poster-style images with short quoted slogans (both Chinese and English) and compare legibility and layout consistency to your current baseline.
Quick stats from the listing feed: pipeline: text-to-image · 208 likes · 3199 downloads.
Source listing: https://huggingface.co/models?sort=modified