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Improving image synthesis with diffusion-negative sampling
Alakh Desai, Nuno Vasconcelos
For image generation with diffusion models (DMs), a negative prompt n can be used to complement the text prompt p, helping define properties not desired in the synthesized image. W…
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
Deepak Sridhar, Abhishek Peri, Rohith Rachala +1
Recent advances in generative modeling with diffusion processes (DPs) enabled breakthroughs in image synthesis. Despite impressive image quality, these models have various prompt c…
Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models
Deepak Sridhar, Nuno Vasconcelos
Diffusion models have recently surpassed GANs in image synthesis and editing, offering superior image quality and diversity. However, achieving precise control over attributes in g…
Editable Image Elements for Controllable Synthesis
Jiteng Mu, Michaël Gharbi, Richard Zhang +4
Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space…