Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising
Authors: Gongfan Fang, Xinyin Ma, Xinchao Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on the Image Net dataset demonstrate that Remix Di T achieves promising results compared to standard diffusion transformers and other multiple-expert methods. |
| Researcher Affiliation | Academia | Gongfan Fang Xinyin Ma Xinchao Wang National University of Singapore {gongfan,maxinyin}@u.nus.edu, xinchao@nus.edu.sg |
| Pseudocode | Yes | Algorithm 1 Mixed Linear (Py Torch-like Pseudo Code) Algorithm 2 Remix-Di T |
| Open Source Code | Yes | Code is available in the supplemental material. |
| Open Datasets | Yes | Experiments conducted on the Image Net dataset demonstrate that Remix Di T achieves promising results compared to standard diffusion transformers and other multiple-expert methods. |
| Dataset Splits | No | The paper does not explicitly provide train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper mentions 'GPU Mem. (Mi B)' in Table 3 but does not specify the exact GPU models, CPU models, or other specific hardware components used for the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch-like Pseudo Code' but does not specify any software names with version numbers for libraries or environments. |
| Experiment Setup | Yes | In our experiments, we conducted 100 K fine-tuning on Di T-S/B/L models [29], pre-trained for 2M/1M/1M steps correspondingly. |