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Introduces Representation Forcing (RF), a technique that enables unified multimodal models to perform both perception and generation end-to-end without external VAE latent spaces, matching state-of-the-art VAE-based models in image generation while improving understanding.
NVIDIA introduces PiD, a Pixel Diffusion Decoder that replaces traditional VAE/RAE decoders in latent diffusion models, enabling fast, high-resolution decoding with up to 6× speedup and improved visual fidelity.
NVIDIA Spatial Intelligence Lab proposes PiD, which redesigns the decoding stage of latent diffusion models as a conditional pixel diffusion process, unifying decoding and upsampling to achieve low-latency, high-resolution decoding.
PiD introduces a pixel diffusion decoder that reformulates latent decoding as conditional pixel diffusion, enabling fast and high-quality image synthesis at high resolutions with reduced computational requirements. It decodes latents into 4x or 8x upscaled images in under a second on consumer hardware.
L2P proposes an efficient transfer paradigm that leverages pre-trained latent diffusion models to build pixel-space diffusion models, enabling high-quality generation with minimal computational overhead and data requirements, and supporting native 4K resolution.