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Found 5 papers, 2 papers with code
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
To tackle this challenge, we propose bootstrap supervision; leveraging our object removal model trained on a small counterfactual dataset, we synthetically expand this dataset considerably.
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
Style Aligned Image Generation via Shared Attention
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts.
AnyLens: A Generative Diffusion Model with Any Rendering Lens
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study.
