CoDe: Blockwise Control for Denoising Diffusion Models
Authors: Anuj Singh, Sayak Mukherjee, Ahmad Beirami, Hadi J. Rad
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |