4 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…
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models
Tripti Shukla, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introd…
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…