22 citations · 26 across the 12 of their papers we have counts for
5 papers · 1 filter
CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis
Aravindan Sundaram, Ujjayan Pal, Abhimanyu Chauhan +2
Despite recent advancements in text-to-image models, achieving semantically accurate images in text-to-image diffusion models is a persistent challenge. While existing initial late…
TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
We consider the problem of single-source domain generalization. Existing methods typically rely on extensive augmentations to synthetically cover diverse domains during training. H…
Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of independently, in a disentangled fashion, controlling the outputs of text-to-image diffusion models with color and style attributes of a user-supplied re…
AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of customizing text-to-image diffusion models with user-supplied reference images. Given new prompts, the existing methods can capture the key concept from…
Composing Parts for Expressive Object Generation
Harsh Rangwani, Aishwarya Agarwal, Kuldeep Kulkarni +2
Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, l…