SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
Authors: Jaihoon Kim, Juil Koo, Kyeongmin Yeo, Minhyuk Sung
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify that Sync Tweedies offers the broadest applicability to diverse applications and superior performance compared to the previous state-of-the-art for each application. Our project page is at https://synctweedies.github.io. ... We present quantitative and qualitative results in Table 3 and Figure 4, respectively. |
| Researcher Affiliation | Academia | Jaihoon Kim Juil Koo Kyeongmin Yeo Minhyuk Sung KAIST {jh27kim,63days,aaaaa,mhsung}@kaist.ac.kr |
| Pseudocode | No | No explicit pseudocode or algorithm blocks labeled 'Algorithm' or 'Pseudocode' were found in the paper. |
| Open Source Code | Yes | Our project page is at https://synctweedies.github.io. |
| Open Datasets | Yes | We generate 360 panorama images from 360 depth maps obtained from the 360Mono Depth [43] dataset. ... We use 429 pairs of meshes and prompts used in TEXTure [44] and Text2Tex [10]. ... We use 3D Gaussian splats trained with multi-view images from the Synthetic Ne RF dataset [39]... MVDiffusion [56], which is finetuned using 3D scenes in the Scan Net [13] dataset. |
| Dataset Splits | No | The paper describes splits for generation/evaluation (e.g., '50 views for texture generation and evaluate the results from 150 unseen views') but does not specify explicit training, validation, and test dataset percentages or counts in a general sense for their method's training. |
| Hardware Specification | Yes | We use the NVIDIA RTX A6000 for the runtime comparisons. ... Additionally, we present qualitative results of arbitrary-sized image generation using Intel Gaudi-v2 in Figure 11, along with a comparison of computation times between Intel Gaudi-v2 and NVIDIA A6000 in Figure 12. |
| Software Dependencies | No | The paper mentions specific models like 'Depth-conditioned Control Net [64]', 'Stable Diffusion [47]', and 'Deep Floyd [14]', but does not list specific software dependencies with version numbers (e.g., PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | For all diffusion synchronization processes, we use a fully deterministic DDIM [53] sampling with 30 steps, unless specified otherwise. ... We set the CFG weight [22] to 30 and t to 0.8. For other settings, we follow the 3D mesh texture generation experiment presented in Section 5.1. |