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..
TFG: Unified Training-Free Guidance for Diffusion Models
Authors: Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Y. Zou, Stefano Ermon
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We systematically benchmark across 7 diffusion models on 16 tasks with 40 targets, and improve performance by 8.5% on average. |
| Researcher Affiliation | Academia | 1Stanford University 2Peking University 3Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Training-Free Guidance |
| Open Source Code | Yes | 1Code is available at https://github.com/YWolfeee/Training-Free-Guidance. |
| Open Datasets | Yes | We conduct a case study on CIFAR10 [30]... (1) CIFAR10-DDPM [48] is a U-Net [54] model trained on CIFAR10 [30] images. (2) Image Net-DDPM [7] is an larger U-Net model trained on Image Net-1k [55] images. (3) Cat-DDPM is trained on Cat [12] images. (4) Celeb A-DDPM is trained on Celeb A-HQ dataset [26]... (5) Molecule-EDM [24] is an equivariant diffusion model pretrained on molecule dataset QM9 [50]... |
| Dataset Splits | Yes | For dataset, we employ QM9 [50] and adopt the split in [24] with 100,000 training samples. Following [24] and [3], the training set is further split into two halves that guarantees there is no data leakage. The first half is leveraged to train a property prediction network... The second half is used to train the diffusion model as well as the guidance network. |
| Hardware Specification | Yes | We run most of the experiments on clusters using NVIDIA A100s. |
| Software Dependencies | No | We implemented our experiments using Py Torch [49] and the Hugging Face library. (Appendix E.5) |
| Experiment Setup | Yes | We consistently set the time step T = 100 and the DDIM parameter ฮท = 1. We consider Nrecur = 1, Niter = 4 and use a single sample for Implicit Dynamic (Line 4) throughout all experiments and methods for fair comparison. For TFG, the structures of ฯ and ยต are set to increase and the scalars ฯ, ยต, ฮณ are determined via our searching strategy. |