在Diffusion步数蒸馏的相关工作里,大家更加喜欢用分布匹配的概念,经典的工作比如SDS, VSD,Diff-Instruct,DMD,SiD, eta,而传统的像feature蒸馏那样去蒸馏output的做法逐渐被大家所抛弃,比如Progressive Distillation。 那么这里有两个问题,1.直接学习output有什么问题?2. 什么是分布匹配,为什么它可以? 首先思考第一个问题...
两者之间的差异代表着向着更加逼真且更少假象的方向,并将其反向传播给单步生成器。 Distribution Matching Distillation (DMD): DMD核心将预训练的扩散去噪器(diffusion denoiser)转化为快速的“一步”(one-step)图像生成器,同时保持生成图像的高质量。 预训练模型及一步模型生成器: 预训练基础模型(Pretrained base mod...
作者期待通过蒸馏更先进的模型和引入变化的指导尺度,进一步提升模型性能和灵活性。 图1展现了一下和目前SOTA方法的对比: 基线稳定扩散(Stable Diffusion, SD):生成每张图像需要约250毫秒。扩散匹配蒸馏(Diffusion Matching Distillation, DMD):生成每张图像仅需约90毫秒。强调了DMD技术生成图像的速度比SD快得多,同时还在...
作者期待通过蒸馏更先进的模型和引入变化的指导尺度,进一步提升模型性能和灵活性。 图1展现了一下和目前SOTA方法的对比: 基线稳定扩散(Stable Diffusion, SD):生成每张图像需要约250毫秒。扩散匹配蒸馏(Diffusion Matching Distillation, DMD):生成每张图像仅需约90毫秒。强调了DMD技术生成图像的速度比SD快得多,同时还在...
Generative equilibrium transformers are disclosed. Disclosed embodiments provide a simple and effective technique that can distill a multi-step diffusion process into a single-step generative model using solely noise/image pairs.ZHENGYANG GENGASHWINI POLKEJEREMY KOLTERBAHARE AZARIIVAN BATALOVFILIPE CONDESSA...
To address this limitation, we propose FluxSR, a novel one-step diffusion Real-ISR technique based on flow matching models. We use the state-of-the-art diffusion model FLUX.1-dev as both the teacher model and the base model. First, we introduce Flow Trajectory Distillation (FTD) to ...
几篇论文实现代码:《One-Step Diffusion Distillation through Score Implicit Matching》(NeurIPS 2024) GitHub: github.com/maple-research-lab/SIM 《AutoPSV: Automated Process-Supervised Verifier》(Neu...
Combined with a simple regression loss to match the output of the multi-step diffusion model, our method outperforms all published few-step diffusion approaches, reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k, comparable to Stable Diffusion but orders of magnitude ...
We propose EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality. Our approach is derived through the lens of Expectation-Maximization (EM), where the generator parameters are updated using ...
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-WorldImage Super-ResolutionLinwei Dong 1 * Qingnan Fan 2 * Yihong Guo 1 Zhonghao Wang 3Qi Zhang 2 Jinwei Chen 2 Yawei Luo 1† Changqing Zou 1,41 Zhejiang University 2 Vivo Mobile Communication Co. Ltd3 University of Chi...