5 citations · 6 across the 4 of their papers we have counts for
6 papers
ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
Shubhang Bhatnagar, Ishan Bhatnagar, Viraj Shah +1
Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images. Increasingly, these images come from a diffusion model rather tha…
ReaDiT Guidance: Control for Image and Video Generation using Diffusion Transformer Features
Jay Mahajan, Chang Liu, Rauf Makharov +3
We present DiT Readout (ReaDiT) Guidance, a lightweight framework for controlling generation with Diffusion Transformer (DiT) models via their internal feature representations. Rea…
Reference-Guided Identity Preserving Face Restoration
Mo Zhou, Keren Ye, Viraj Shah +5
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods o…
UnZipLoRA: Separating Content and Style from a Single Image
Chang Liu, Viraj Shah, Aiyu Cui +1
This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Rank Adaptations). Unlike existing…
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images
Aiyu Cui, Jay Mahajan, Viraj Shah +3
Most virtual try-on research is motivated to serve the fashion business by generating images to demonstrate garments on studio models at a lower cost. However, virtual try-on shoul…
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Viraj Shah, Nataniel Ruiz, Forrester Cole +4
Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adapt…