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Latent Diffusion is a framework for high-resolution image synthesis that performs the denoising process within a compressed latent space. It uses variational autoencoders to encode images into a lower-dimensional representation, reducing the computational cost of noise prediction compared to operating on raw pixels. The project enables text-to-image generation by integrating natural language descriptions through cross-attention conditioning. It also supports image inpainting and restoration, filling masked or missing image areas with generated content, and example-based synthesis using retrie
CVPR 2024 - Oral, Best Paper Award Candidate Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
The main features of kprokofi/light-weight-face-anti-spoofing are: Others.
Projects with overlapping indexed features include: algolzw/daclip-uir — Project Page | Paper | Model Card 🤗. compvis/latent-diffusion — Latent Diffusion is a framework for high-resolution image synthesis that performs the denoising process within a… prs-eth/marigold — [CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular…
