Fast samplers for Inverse Problems in Iterative Refinement models
Authors: Kushagra Pandey, Ruihan Yang, Stephan Mandt
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method s performance on various linear image restoration tasks across multiple datasets, employing diffusion and flow-matching models. Notably, on challenging inverse problems like 4 super-resolution on the Image Net dataset, our method can generate high-quality samples in as few as 5 conditional sampling steps and outperforms competing baselines requiring 20-1000 steps. |
| Researcher Affiliation | Academia | Kushagra Pandey Department of Computer Science University of California Irvine pandeyk1@uci.edu; Ruihan Yang Department of Computer Science University of California Irvine ruihan.yang@uci.edu; Stephan Mandt Department of Computer Science University of California Irvine mandt@uci.edu |
| Pseudocode | Yes | Algorithm 1 Conjugate ΠGDM sampling; Algorithm 2 Conjugate ΠGFM sampling |
| Open Source Code | No | Our code will be publicly available at https://github.com/mandt-lab/c-pigdm. |
| Open Datasets | Yes | For diffusion models, we utilize an unconditional pre-trained Image Net [Deng et al., 2009] checkpoint at 256 256 resolution from Open AI [Dhariwal and Nichol, 2021]3. For evaluations on the FFHQ dataset Karras et al. [2019], we use a pre-trained checkpoint from Choi et al. [2021] also at 256 256 resolution. For flow model comparisons, we utilize three publicly available model checkpoints from Liu et al. [20223]4, trained on the AFHQ-Cat [Choi et al., 2020], LSUN-Bedroom Yu et al. [2015], and Celeb A-HQ [Karras et al., 2018] datasets. |
| Dataset Splits | Yes | For flows, we conduct evaluations on the entire validation set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, nor does it provide specific GPU/CPU models, processor types, or memory details. |
| Software Dependencies | No | For numerical approximation of these integrals, we use the odeint method from the torchdiffeq package [Chen, 2018] with parameters atol=1e-5, rtol=1e-5 and the RK45 solver [Dormand and Prince, 1980]. While specific packages are mentioned, their version numbers are not provided (e.g., torchdiffeq [Chen, 2018] is cited by year but no version). |
| Experiment Setup | Yes | We conduct an extensive search to optimize the parameters w, λ and τ to identify the best-performing configuration based on sample quality. For diffusion baselines, we include DDRM [Kawar et al., 2022], DPS [Chung et al., 2022a], and ΠGDM [Song et al., 2022]. As recommended for DPS [Chung et al., 2022a], we use NFE=1000 for all tasks. For DDRM, we adhere to the original implementation and run it with ηb = 1.0 and η = 0.85 at NFE=20. We test our implementation of ΠGDM (see Section 2.2), with NFE values of 5, 10, and 20 and use the recommended guidance schedule of wt = r2 t across all tasks. For flow models, we consider the recently proposed method inspired by ΠGDM running on OT-ODE path by Pokle et al. [2024] (which we refer to as ΠGFM; see Appendix B), and similarly run it with NFE values of 5, 10, and 20. We optimize all baselines by conducting an extensive grid search over w and τ for the best performance (in terms of sample quality). |