Pixel-Space Diffusion Transformers
Latent diffusion models (LDMs) enable efficient high-resolution image synthesis by denoising in a VAE-compressed latent space. However, fixed visual tokenizers can discard fine textures and structural details, while separate representation and diffusion training creates a mismatch between reconstruction and generation objectives. These limitations have renewed interest in pixel-space diffusion, which models raw pixels directly, removes the VAE bottleneck, and supports end-to-end optimization. This formulation better matches the demands of high-fidelity generation but introduces challenges in high-dimensional modeling, including noise scheduling, loss weighting, token efficiency, and scala...
| Authors | Renye Yan, Jikang Cheng, You Wu et al. |
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| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 12 August 2026 |
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