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Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models

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arxiv 2411.07126 v1 pith:OTR3LDGX submitted 2024-11-11 cs.CV cs.LG

NVIDIA: Yuval Atzmon , Maciej Bala , Yogesh Balaji , Tiffany Cai , Yin Cui , Jiaojiao Fan , Yunhao Ge , Siddharth Gururani
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Jacob Huffman Ronald Isaac Pooya Jannaty Tero Karras Grace Lam J. P. Lewis Aaron Licata Yen-Chen Lin Ming-Yu Liu Qianli Ma Arun Mallya Ashlee Martino-Tarr Doug Mendez Seungjun Nah Chris Pruett Fitsum Reda Jiaming Song Ting-Chun Wang Fangyin Wei Xiaohui Zeng Yu Zeng Qinsheng Zhang
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classification cs.CVcs.LG
keywords imagediffusionedifymodelsgenerationlaplacianaccuracyapplications
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We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. Edify Image supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama generation, and finetuning for image customization.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Layer-normalized averaging of all decoder-only LLM hidden states, rather than last-layer embeddings, improves text-to-image compositional alignment and beats T5 on GenAI-Bench.

  2. Pixel-Space Diffusion Transformers

    cs.CV 2026-07 conditional novelty 3.0 of 10

    A systematic review of pixel-space diffusion transformers, categorizing architectures and challenges for end-to-end image generation without latent compression.

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