Comfy-Org Makes NVIDIA's PixelDiT Run Locally Without a VAE

Comfy-Org has published repackaged weights for NVIDIA's PixelDiT on Hugging Face, reformatting the model files so they drop straight into a ComfyUI install with no custom code required. The underlying model is a research release from NVIDIA that earned an Oral presentation at CVPR 2026 , and it works in a fundamentally different way from every latent diffusion model you've used before.

No VAE. Seriously.

Almost every popular image generation model today , Stable Diffusion, FLUX, you name it , compresses images into a compact "latent" space using a Variational Autoencoder (VAE), runs the diffusion process there, then decodes back to pixels. That compression step is lossy: fine details like text, sharp textures, and high-frequency patterns get blurred or distorted before the model even starts generating.

PixelDiT is a single-stage, end-to-end model that eliminates the need for the autoencoder and learns the diffusion process directly in pixel space. The practical upshot: PixelDiT keeps fine lettering intact because it denoises directly in pixel space , no lossy VAE, no baked-in artifacts , while latent models like FLUX smear characters after VAE reconstruction.

The architecture doing the heavy lifting

Working directly in pixel space sounds simple, but it's computationally brutal. Every pixel is a token, and attention over millions of tokens is quadratically expensive. NVIDIA's solution is a two-level design that splits the work intelligently.

• Patch-level DiT: Operates on large image patches to capture global layout and semantics efficiently, using short token sequences.