2021年1月12日 · Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample.

2023年2月1日 · We present an interpretation of the accelerating effects of DDIM that also explains the advantages of a deterministic sampling scheme over the stochastic one for fast sampling. Building on this insight, we extend DDIM to general DMs, coined generalized DDIM (gDDIM), with a small but delicate modification in parameterizing the score network.

2025年1月22日 · Classifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse.

els (DDIM) (Song et al.,2020a) and DiffWave (Kong et al., 2020b). In detail, FastDPM offers two ways to construct the approximate diffusion process: selecting Ssteps in the original diffusion process, or more flexibly, choosing Svari-ances. FastDPM also offers ways to construct the approxi-mate reverse process: using the stochastic DDPM reverse

2023年2月1日 · ddim encoder is almost equal to optimal transport. Open Peer Review. Open Publishing. Open Access. Open Discussion.

Elucidating the Design Space of Diffusion-Based Generative Models ... d (σ)

2024年9月26日 · This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose conditional diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output.

marginals in Eq. 2. The ODE perspective of DDIM and other related works on accelerated sampling from diffusion models are discussed in Appendix A.1. 3 APPROACH We propose using a Gaussian Mixture Model (GMM) within the reverse transition kernels of the DDIM generative process. Specifically, the form of transition kernels in Eq. 6 is given by q ...

2022年12月5日 · The proposed method also improves upon the speed vs quality tradeoff exhibited in standard unconditional DDPM/DDIM models (for instance, \textbf{FID of 16.47 vs 34.36} using a standard DDIM on the CelebA-HQ-128 benchmark using \textbf{T=10} reverse process steps) without having explicitly trained for such an objective.

student DDIM step match 2 teacher DDIM steps. We calculate this target value by running 2 DDIM sampling steps using the teacher, starting from z tand ending at z 1=N, with Nbeing the number of student sampling steps. By inverting a single step of …