Cvpr 2023 Self Guided Diffusion Models Cees Snoek We present a framework for large image synthesis, based on self supervised guided diffusion, that maintains con textual integrity and image realism over large areas. We introduce a novel approach that trains diffusion models conditioned on embeddings from ssl. our diffusion models successfully project these features back to high quality histopathology and remote sensing images.
Cvpr Poster Image Neural Field Diffusion Models Official code for our cvpr 2024 publication learned representation guided diffusion models for large image generation. this codebase builds heavily on compvis latent diffusion and pathldm. To synthesize high fidelity samples, diffusion models typically require auxiliary data to guide the generation process. however, it is impractical to procure th. We introduce a novel approach that trains diffusion models conditioned on embeddings from ssl. our diffusion models successfully project these features back to high quality histopathology and remote sensing images. We present a framework for large image synthesis, based on self supervised guided diffusion, that maintains contextual integrity and image realism over large areas.
Cvpr Poster Generative Image Dynamics We introduce a novel approach that trains diffusion models conditioned on embeddings from ssl. our diffusion models successfully project these features back to high quality histopathology and remote sensing images. We present a framework for large image synthesis, based on self supervised guided diffusion, that maintains contextual integrity and image realism over large areas. Official code for our cvpr 2024 publication learned representation guided diffusion models for large image generation. this codebase builds heavily on compvis latent diffusion and pathldm.
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