1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2024
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…
cs.CV2024
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…
cs.CV2024★ 1 cited
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…