Representative Guidance: Diffusion Model Sampling with Coherence
Authors: Anh-Dung Dinh, Daochang Liu, Chang Xu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted to evaluate on Image Net Deng et al. (2009) dataset with two resolutions 64x64 and 256x256 with 50000 generated samples. We first verify our claims that our proposed Rep G helped to improve the details and fix the faulty information in the images qualitatively in section5.1 and quantitatively in section5.2. After that, we will compare quantitatively with other state-of-the-art methods such as Big GAN Brock et al. (2018), ADM Dhariwal & Nichol (2021), Px P Dinh et al. (2023a), Pro G Dinh et al. (2023b), EDS Zheng et al. (2022), IDDPM Nichol & Dhariwal (2021), VAQ-VAE-2 Razavi et al. (2019) and Classifier-free guidance (CLSFree) Ho & Salimans (2022). |
| Researcher Affiliation | Academia | Anh-Dung Dinh School of Computer Science The University of Sydney EMAIL Daochang Liu School of Physics, Mathematics and Computing The University of Western Australia EMAIL Chang Xu School of Computer Science The University of Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 DDPM denoising process with representative guidance |
| Open Source Code | Yes | source code: https://github.com/dungdinhanh/rep-guidance. |
| Open Datasets | Yes | Experiments are conducted to evaluate on Image Net Deng et al. (2009) dataset with two resolutions 64x64 and 256x256 with 50000 generated samples. |
| Dataset Splits | Yes | Experiments are conducted to evaluate on Image Net Deng et al. (2009) dataset with two resolutions 64x64 and 256x256 with 50000 generated samples. |
| Hardware Specification | Yes | All the experiments in this paper are conducted on A100 GPUs 40GB. |
| Software Dependencies | No | The paper mentions self-supervised models like Mo Co-v2, Sim Siam, and Moco-v3 by their scientific papers but does not specify any general software dependencies (e.g., Python, PyTorch, CUDA) with version numbers. |
| Experiment Setup | Yes | Table 6: All hyperparameters for producing the results are shown in this table. Model Datasets K H γ IDDPM + Rep G Image Net64x64 5 1 10.0 ADM + Rep G Image Net64x64 5 1 10.0 ADM-CLSFree + Rep G Image Net64x64 5 1 8.0 ADM + Rep G Image Net256x256 10 2 20.0 ADM-CLSFree + Rep G Image Net256x256 10 2 20.0 Di T-CLSFree + Rep G Image Net256x256 10 2 15.0 |