Text-guided Controllable Diffusion for Realistic Camouflage Images Generation

The introduction of SDPose marks a significant advancement in human pose estimation by leveraging pre-trained diffusion models, specifically Stable Diffusion, to enhance the accuracy and robustness of keypoint predictions in various contexts. This framework directly predicts keypoint heatmaps in the latent space of the SD U-Net, preserving generative priors and avoiding modifications that could disrupt the model's performance.

Delta Sampling (DS) has been introduced as a novel method for enabling data-free knowledge transfer across different diffusion models, particularly addressing the challenges faced when upgrading base models like Stable Diffusion. This method operates at inference time, utilizing the delta between model predictions before and after adaptation, thus facilitating the reuse of adaptation components across varying architectures.

A recent thesis presents significant advancements in generative models, particularly focusing on normalizing flows and their applications in computer vision. Key innovations include the development of invertible convolution layers and efficient algorithms for training and inversion, enhancing the performance of these models in real-world scenarios.

Recent advancements in human preference optimization have been applied to text-to-image Diffusion Models, enhancing prompt alignment and visual appeal. The proposed method fine-tunes models like Stable Diffusion 1.5 and XL using perceptual objectives in the U-Net embedding space, significantly improving training efficiency and user preference alignment.