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 [1].
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
Authors: Anh-Dung Dinh, Daochang Liu, Chang Xu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results evidently improve over different baselines on datasets with various resolutions. (Abstract) and Section 6. Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Sydney. Correspondence to: Anh-Dung Dinh <EMAIL>, Chang Xu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DDPM denoising process with guidance and Algorithm 2 Pixel As Params Denoising Process (Px P) |
| Open Source Code | Yes | The code for the proposed sampling process is available here: https://github.com/dungdinhanh/pxpguided-diffusion |
| Open Datasets | Yes | All experiments are conducted on the Image Net dataset at 64x64, 128x128, 256x256, and CIFAR10 to evaluate our method since these datasets offer classification classes and pretrained diffusion models. |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly provide details about training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All the experiments are run with cluster nodes with 8GPUs NVIDIA A-100. |
| Software Dependencies | No | The paper does not explicitly provide a list of software dependencies with specific version numbers (e.g., Python, PyTorch, or library versions). |
| Experiment Setup | Yes | All the hyperparameters for reproducing all the results are available in Table 7. (Table 7 provides specific values for hyperparameters like δ1, δ2, δ3, δ4, s, and TIME-STEPS for various models and datasets). |