Text-to-image diffusion models have made significant advances in generating and editing
high-quality images. As a result, numerous approaches have explored the ability of diffusion …

Abstract We present Prompt Diffusion, a framework for enabling in-context learning in
diffusion-based generative models. Given a pair of task-specific example images, such as …

Abstract Text-to-image (T2I) research has grown explosively in the past year owing to the
large-scale pre-trained diffusion models and many emerging personalization and editing …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Text-to-image diffusion models produce high quality images but do not offer control over
individual instances in the image. We introduce InstanceDiffusion that adds precise instance …

Modern generative models exhibit unprecedented capabilities to generate extremely
realistic data. However, given the inherent compositionality of real world, reliable use of …

Recently diffusion models have made remarkable progress in text-to-image (T2I) generation
synthesizing images with high fidelity and diverse contents. Despite this advancement latent …

Recent advances in vision-language models like Stable Diffusion have shown remarkable
power in creative image synthesis and editing. However most existing text-to-image editing …

Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability
to generate creative content, specialize to user data through few-shot fine-tuning, and …

Recent advancements in large-scale pre-trained text-to-image models have led to
remarkable progress in semantic image synthesis. Nevertheless synthesizing high-quality …