arXiv Computer Vision
Data-free Multistep Diffusion Distillation
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Consistent Distribution Matching for Data-Free Diffusion Distillation
Consistent Distribution Matching is a data‑free distillation technique that accelerates diffusion and flow models by unifying sample generation and score estimation in a single student network. The method requires only a frozen teacher and a trainable student, optimizing a single objective that provably leads to Wasserstein convergence of the student’s flow‑map to the teacher’s marginals. On ImageNet 256×256, it achieves an FID of 2.04 with one function evaluation and 1.37 with four evaluations after just 40 training epochs, outperforming existing data‑free baselines.
Hugging Face Trending Papers
Oct 6
Consistent Distribution Matching for Data-Free Diffusion Distillation
Flow and diffusion models suffer from slow inference due to computationally expensive numerical integration. Distillation provides a promising way for a student model to learn from a teacher's dynamic...
arXiv Computer Vision
Oct 7
Two Halves are More than One: Phase-wise Velocity Distillation for Fast and High-Quality Image Generation
The paper introduces Phase-wise Velocity Distillation (PVD), a method that splits the diffusion generation timeline into coarse and fine phases, each handled by a half-sized expert. By decoupling structural composition from detail refinement, PVD achieves higher quality image generation within a single full-backbone compute budget, outperforming monolithic student models. Experiments on ImageNet and text-to-image tasks show competitive FID scores and significant reductions in parameters and VRAM usage.
arXiv Machine Learning
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Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models
Omni‑Diffusion‑Distill is a two‑stage distillation framework that compresses unified multimodal diffusion large language models (dLLMs) into a single efficient student while preserving both image generation and multimodal understanding. In the first stage the student learns to skip decoding steps by replaying cached teacher trajectories, and in the second stage it is refined on intermediate states along its own rollouts. The method introduces a pairwise collision penalty to reduce repetition in parallel text decoding and entropy‑matched guidance to prevent entropy collapse in image generation, achieving state‑of‑the‑art trade‑offs: reducing image generation from 128 to 8 steps and multimodal understanding from 512 to 64 steps, yielding 18.2× and 21.2× wall‑clock speedups and strong performance on GenEval, DPG‑Bench, MM‑Vet, and COCO captioning.
arXiv Machine Learning
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Improved Distributional Diffusion Models
arXiv:2609.37147v1 Announce Type: cross Abstract: Distributional Diffusion Models (DDMs) replace the standard mean-prediction denoiser with a \emph{distributional} denoiser trained via a scoring rule...
arXiv Computer Vision
Sep 28
PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution
arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while...