Provable Maximum Entropy Manifold Exploration via Diffusion Models
Authors: Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate our approach on both synthetic and high-dimensional text-to-image diffusion, demonstrating promising results. |
| Researcher Affiliation | Academia | 1ETH Zurich, 8092 Zurich, Switzerland 2ETH AI Center, Zurich, Switzerland. Correspondence to: Riccardo De Santi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Score-based Maximum Entropy Manifold Exploration (S-MEME) Algorithm 2 LINEARFINETUNINGSOLVER (Implementation based on Adjoint Matching (Domingo-Enrich et al., 2024)) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | For this we utilize the stable diffusion (SD) 1.5 (Rombach et al., 2021) checkpoint pre-trained on the LAION-5B dataset (Schuhmann et al., 2022). |
| Dataset Splits | No | The paper uses pre-trained models and discusses fine-tuning and sampling for evaluation. It mentions pre-training on 10K samples for an illustrative setting, but does not specify dataset splits (e.g., train/test/validation) for reproducing their experiments. |
| Hardware Specification | Yes | We fine-tuned the checkpoint with K = 3 iterations of S-MEME on a single Nvidia H100 GPU for the prompt A creative architecture. |
| Software Dependencies | No | The paper mentions using "stable diffusion (SD) 1.5" but does not specify version numbers for other key software components like programming languages (e.g., Python) or libraries (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | For fine-tuning, in this experiment we ran S-MEME for 6000 gradient steps in total, for K = 1, 2, 3, 4. ... we perform an iteration of Algorithm 2 by first sampling 20 trajectories via DDPM of length 400 that are used for solving the lean adjoint ODE with the reward λ log p T (x) and λ = 0.1. Subsequently we perform 2 stochastic gradient steps by the Adam optimizer with batch size 2048, initialized with learning rate 4 10 4. |