1 citations · 2 across the 5 of their papers we have counts for
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CM-EVS: Sparse Panoramic RGB-D-Pose Data for Complete Scene Coverage
Jiale Liu, Jungang Li, Jieming Yu +13
Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a…
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
Hao Li, Shamit Lal, Zhiheng Li +9
We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training…
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models
Benjamin Biggs, Arjun Seshadri, Yang Zou +6
We present Diffusion Soup, a compartmentalization method for Text-to-Image Generation that averages the weights of diffusion models trained on sharded data. By construction, our ap…
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
Robik Shrestha, Yang Zou, Qiuyu Chen +3
Existing text-to-image generative models reflect or even amplify societal biases ingrained in their training data. This is especially concerning for human image generation where mo…
On the Scalability of Diffusion-based Text-to-Image Generation
Hao Li, Yang Zou, Ying Wang +7
Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explore…