ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Authors: Kexun Zhang, Xianjun Yang, William Yang Wang, Lei Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <kexun@ucsb.edu>. |
| 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. |