27 citations · 39 across the 7 of their papers we have counts for
9 papers
FlowFixer: Towards Detail-Preserving Subject-Driven Generation
Jinyoung Jun, Won-Dong Jang, Wenbin Ouyang +2
We present FlowFixer, a refinement framework for subject-driven generation (SDG) that restores fine details lost during generation caused by changes in scale and perspective of a s…
DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers
Dahye Kim, Deepti Ghadiyaram, Raghudeep Gadde
Diffusion Transformers (DiTs) have achieved state-of-the-art performance in image and video generation, but their success comes at the cost of heavy computation. This inefficiency…
Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states
Samarth Gupta, Raghudeep Gadde, Rui Chen +1
We challenge a fundamental assumption of diffusion models, namely, that a large number of latent-states or time-steps is required for training so that the reverse generative proces…
Near Perfect GAN Inversion
Qianli Feng, Viraj Shah, Raghudeep Gadde +2
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it. Unfortunately, wher…
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features
Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez +1
We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike p…
Semantic Video CNNs through Representation Warping
Raghudeep Gadde, Varun Jampani, Peter V. Gehler
In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to aug…