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..
CoDe: Blockwise Control for Denoising Diffusion Models
Authors: Anuj Singh, Sayak Mukherjee, Ahmad Beirami, Hadi J. Rad
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that, despite its simplicity, Co De offers a favorable trade-off between reward alignment, prompt instruction following, and inference cost, achieving a competitive performance against the state-of-the-art baselines. Our code is available at: https://github.com/anujinho/code |
| Researcher Affiliation | Collaboration | 1Delft University of Technology, The Netherlands 2Shell Global Solutions International B.V., Amsterdam, The Netherlands 3Massachusetts Institute of Technology, Cambridge MA, USA |
| Pseudocode | Yes | Algorithm 1: Co De Algorithm 2: Co De(η) |
| Open Source Code | Yes | Our code is available at: https://github.com/anujinho/code |
| Open Datasets | Yes | Unless otherwise mentioned, for all experiments, we use a pretrained Stable Diffusion version 1.5 (Rombach et al., 2021) as our base model, which is trained on the LAION-400M dataset (Schuhmann et al., 2021). |
| Dataset Splits | No | For quantitative evaluations, we generate 50 images per setting (i.e., prompt-reference image pair) with 500 DDPM steps. |
| Hardware Specification | Yes | To achieve this, we have used NVIDIA A100 GPUs with 80GB of RAM. |
| Software Dependencies | No | The paper mentions using a 'pretrained Stable Diffusion version 1.5' and the 'CLIP image encoder' but does not specify any version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For quantitative evaluations, we generate 50 images per setting (i.e., prompt-reference image pair) with 500 DDPM steps. ... For the guidance-based methods DPS and UG, the guidance scale is varied between 1 and 50, whereas for the sampling-based methods, Bo N the number of samples N is varied between 2 and 500, while for SVDD and Co De, the number of samples N is varied between 2 and 40. |