Post
49
I built Inko end to end: a 0.5B bilingual text-to-image model with a focus on fine art, from data curation and pretraining to RL post-training and evaluation.
It competes with DreamLite, SANA-0.6B and PixArt-Σ, and approaches SD3.5 Medium on some benchmarks.
The journey involves four rounds of cleaning the Chinese-painting dataset, a cross-hardware loss-spike investigation, and a reward redesign after early RL experiments collapased diversity.
Behind the release, a four-domain bilingual corpus, 600k pretraining updates on 16 Ascend NPUs, and DiffusionART with a custom 24.6k-prompt pool and five pairwise rubric channels.
Dense prompt alignment is Inko's strongest general result. Anatomy, counting, reasoning, text rendering, and exact spatial relations remain limitations.
kaupane/Inko
kaupane/Inko
It competes with DreamLite, SANA-0.6B and PixArt-Σ, and approaches SD3.5 Medium on some benchmarks.
The journey involves four rounds of cleaning the Chinese-painting dataset, a cross-hardware loss-spike investigation, and a reward redesign after early RL experiments collapased diversity.
Behind the release, a four-domain bilingual corpus, 600k pretraining updates on 16 Ascend NPUs, and DiffusionART with a custom 24.6k-prompt pool and five pairwise rubric channels.
Dense prompt alignment is Inko's strongest general result. Anatomy, counting, reasoning, text rendering, and exact spatial relations remain limitations.
kaupane/Inko
kaupane/Inko
