One-step and few-step text-to-image generation must accurately render the details specified in the prompt despite limited opportunities for refinement. Distribution matching distillation (DMD) uses sc
Intrinsic Distribution Matching improves few-step text-to-image generation by adding explicit moment constraints to distribution matching distillation, achieving state-of-the-art long-text rendering with minimal network evaluations.
One-step and few-step text-to-image generation must accurately render the details specified in the prompt despite limited opportunities for refinement. Distribution matching distillation (DMD) uses score differences to bring the generated distribution closer to a reference distribution. Following prior work, we estimate this difference with internal velocity branches, avoiding a separate fake-score network. We ask whether an explicit moment constraint improves this learned score supervision. A decomposition of the reverse-KL field into a Gaussian moment component and a non-Gaussian residual identifies statistics that can be constrained explicitly. We introduce Intrinsic Distribution…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Intrinsic Distribution Matching was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #1 of 30, reached on Oct 30, 09:28 UTC. It is #1 right now, holding steady at #1 over the last 6 checks.
Position on Hugging Face · Daily papers at every check (lower is better).
Best rank per day (UTC).
▲ as reported by Hugging Face.
Yes. As of Oct 31, 07:28 UTC it is #1, holding steady at #1 over the last 6 checks.
#1 on Hugging Face · Daily papers, on Oct 30, 09:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 2 days (13 checks) since.