Two-person lab releases text-to-image model trained for $21K
A two-person research lab shared an experimental text-to-image model trained for about $21,000. The model, built with a Pyramid-JiT (P-JiT) architecture, compresses image data more aggressively than typical diffusion models, reducing training costs and improving detail generation. It uses a 2B-parameter DiT backbone with a 4B-parameter text encoder (Qwen3.5-4B) and supports 1024×1024 resolution…
Key points
- Two-person lab trained a text-to-image model for ~$21K, releasing weights under Apache 2.0
- Uses P-JiT architecture to compress images aggressively, improving detail and reducing training costs
- Model lacks text-heavy data support and struggles with complex scenes like crowds or multi-person interactions
The lab, which focuses on democratizing animation tools, notes the model’s limitations: it avoids text-heavy data (no rendered text, posters, or books) and struggles with complex scenes like full-body poses or crowds. Post-training with Finite Difference Flow Optimization (FDFO) and human feedback was applied to refine outputs. The model’s weights are released under Apache 2.0, and the lab plans to iterate toward video models next.
Model page: Claude Opus 5.5 →
Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)
linum.ai · 4 November 2026Loading the full article…
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