Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising
Authors: Gongfan Fang, Xinyin Ma, Xinchao Wang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 21,639 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,459 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |