On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
Authors: Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
ICLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that considerably better FID scores can be achieved by using IIA-DDIM, IIA-DPM-Solver++, and IIA-EDM than the original counterparts when the neural function evaluation (NFE) is small (i.e., less than 25). |
| Researcher Affiliation | Collaboration | Guoqiang Zhang Dept. of Computer Science University of Exeter United Kingdom EMAIL Kenta Niwa Communication Science Labs, NTT Japan EMAIL W. Bastiaan Kleijn School of ECS Victoria Univ. of Wellington New Zealand EMAIL |
| Pseudocode | Yes | Algorithm 1 IIA-EDM as an extension of EDM in Karras et al. (2022) |
| Open Source Code | No | The paper mentions 'the EDM official open-source repository' (https://github.com/NVlabs/edm) which refers to a third-party implementation they used, not their own source code for IIA-DDIM, IIA-DPM-Solver++, or IIA-EDM. |
| Open Datasets | Yes | In this experiment, we tested four pre-trained models for four datasets: CIFAR10, FFHQ, AFHQV2, and Image Net64 (see Table 2 in Appendix D.1). ... using the validation set of COCO2014 over Stable Diffusion V2. |
| Dataset Splits | No | The paper mentions 'using the validation set of COCO2014' and states 'The set-size |B| for computing the optimal stepsizes... was |B| = 200', but it does not provide specific percentages, sample counts, or explicit instructions for how the overall datasets were split into training, validation, and test sets for all experiments. |
| Hardware Specification | Yes | The GPU (NVIDIA RTX 2080Ti) was utilized for measuring the processing time (in seconds). |
| Software Dependencies | No | The paper mentions 'Stable Diffusion V2' and pre-trained models, but does not provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The set-size |B| for computing the optimal stepsizes when employing the IIA techniques was |B| = 200, which is also the default minibatch size for sampling in the EDM official open-source repository. ... The parameter M in IIA-DDIM was set to M = 10... The set-size |B| for approximating the expectation operation in (7) was set to 16, which is also the mini-batch size for sampling in the computation of the FID scores. The hyper-parameter M in (7) was set to M = 3. |