Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models

1 code implementation • 8 Feb 2024 • Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang

However, these models struggle to effectively suppress the generation of undesired content, which is explicitly requested to be omitted from the generated image in the prompt.

Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models

1 code implementation • 15 Dec 2023 • Senmao Li, Taihang Hu, Fahad Shahbaz Khan, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, Jian Yang

This finding inspired us to omit the encoder at certain adjacent time-steps and reuse cyclically the encoder features in the previous time-steps for the decoder.

Knowledge Distillation

StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

1 code implementation • 28 Mar 2023 • Senmao Li, Joost Van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang

A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.

Text-based Image Editing