AdjointDEIS: Efficient Gradients for Diffusion Models
Authors: Zander W. Blasingame, Chen Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we demonstrate the effectiveness of Adjoint DEIS for guided generation with an adversarial attack in the form of the face morphing problem. 5 Experiments To illustrate the efficacy of our technique, we examine an application of guided generation in the form of the face morphing attack. |
| Researcher Affiliation | Academia | Zander W. Blasingame Clarkson University blasinzw@clarkson.edu Chen Liu Clarkson University cliu@clarkson.edu |
| Pseudocode | Yes | Algorithm 1 Adjoint DEIS-2M. Algorithm 2 Di M Framework. |
| Open Source Code | No | Our code will be released at https: //github.com/zblasingame/Adjoint DEIS. Our code for Adjoint DEIS will be available here at https://github.com/zblasingame/ Adjoint DEIS. |
| Open Datasets | Yes | We run our experiments on SYN-MAD 2022 [51] morphed pairs that are constructed from the Face Research Lab London dataset [52], more details in Appendix H.4. The SYN-MAD 2022 dataset used in this paper can be found at https://github.com/ marcohuber/SYN-MAD-2022. |
| Dataset Splits | No | The paper describes the datasets used and how a subset was selected (489 bona fide image pairs) but does not provide explicit train/validation/test splits (e.g., percentages or counts) for their experiments. |
| Hardware Specification | Yes | All of the main experiments were done on a single NVIDIA Tesla V100 32GB GPU. On average, the guided generation experiments for our approach took between 6 8 hours for the whole dataset of face morphs with a batch size of 8. Some additional follow-up work for the camera-ready version used an NVIDIA H100 Tensor Core 80GB GPU with a batch size of 16. |
| Software Dependencies | No | The paper mentions various models and tools used (e.g., DDIM solver, Arc Face, dlib) but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In our experiments, we used a learning rate of 0.01, N = 20 sampling steps, M = 20 steps for Adjoint DEIS, and 50 optimization steps for gradient descent. |