Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Authors: Kexun Zhang, Xianjun Yang, William Yang Wang, Lei Li
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that REDI improves the model inference efficiency by 2 speedup. Furthermore, REDI is able to generalize well in zero-shot cross-domain image genreation such as image stylization. |
| Researcher Affiliation | Academia | Kexun Zhang 1 Xianjun Yang 1 William Yang Wang 1 Lei Li 1 1Department of Computer Science, University of California, Santa Barbara. Correspondence to: Kexun Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 REDI Knowledge Base Construction |
| Open Source Code | Yes | The code and demo for REDI is available at https://github.com/ zkx06111/Re Diffusion. |
| Open Datasets | Yes | We build the knowledge base B upon MS-COCO (Lin et al., 2014) training set (with 82k imagecaption pairs) |
| Dataset Splits | Yes | We build the knowledge base B upon MS-COCO (Lin et al., 2014) training set (with 82k imagecaption pairs) and evaluate the generation quality on 4k random samples from the MS-COCO validation set. |
| Hardware Specification | Yes | For example, the basic sampler takes 336 seconds on av-erage to run on an NVIDIA 1080Ti |
| Software Dependencies | No | The paper mentions that 'Our inference code is based on Huggingface Diffusers', but it does not specify version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For PNDM, we generate 50-sample trajectories to build the knowledge base. We choose the key step k to be 40, making the first 10 samples in the trajectory the key, and alternate the value step v from 30 to 10. For DPM-solver, we choose the length of the trajectory to be 20 and conduct experiments on k = 5, v = 8/10/13/15. in this experiment, we choose K = 47 and V = 40. |