Gradient Guidance for Diffusion Models: An Optimization Perspective
Authors: Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation and image experiments are provided in Section 7 to support theoretical findings on latent structure-preserving and optimization convergence. |
| Researcher Affiliation | Academia | Yingqing Guo Hui Yuan Yukang Yang Minshuo Chen Mengdi Wang Princeton University Department of Electrical and Computer Engineering, Princeton University. Authors emails are: {yg6736, huiyuan, yy1325, minshuochen, mengdiw}@princeton.edu. |
| Pseudocode | Yes | Algorithm 1 Gradient-Guided Diffusion for Generative Optimization and Algorithm 2 Gradient-Guided Diffusion with Adaptive Fine-tuning. |
| Open Source Code | Yes | Our code is released at https://github.com/yukang123/GGDMOptim.git. |
| Open Datasets | Yes | We independently sample a total of 65536 data points as our pre-training data set. We employ the Stable Diffusion v1.5 model [53] as the pre-trained model. For the reward model...Image Net [21]. |
| Dataset Splits | No | The paper mentions a 'pre-training data set' and 'batch size' but does not provide specific training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | We summarize the time cost of our experiments on one NVIDIA A100 GPU in Table 1. |
| Software Dependencies | No | The paper mentions using U-Net, Adam optimizer, and DDPM implementation, but does not provide specific version numbers for software dependencies like PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | For linear data structure, we set the data s ambient dimension as D = 64 and the linear subspace dimension as d = 16... We discretize the backward process to have 200 time steps... the U-Net is trained using our generated data set for 20 epochs. We use Adam as the optimizer, set the batch size as 32, and set the learning rate to be 10 4. |