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Research questionHow can open image generators follow fine-grained editing instructions without sacrificing image quality or inference speed?Open image generators must produce realistic images while interpreting detailed editing instructions reliably. Achieving both quality and responsiveness can require choices about training data, model design, optimization, and sampling.
Diffusion Models
Evaluation & Benchmarks
Image Generation
Inference Optimization
Multimodal Models
Latest papersRecent research connected to this question, newest first.LLaDA-Image: Building Strong Image Generators with Fully Open Training RecipesThe source presents a 6B diffusion transformer trained from scratch alongside a frozen vision-language module, with image-only pretraining and later instruction-oriented training. It reports a distilled variant using 2–4 sampling steps, benchmark results on English and Chinese tracks, and released weights, code, and training recipes; the evidence concerns this system rather than open image generators generally.research paper · Sep 3, 2026
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