license: apache-2.0
tags:
- diffusion
- pixel-diffusion
- text-to-image
- image-generation
- imagenet
pipeline_tag: unconditional-image-generation
base_model:
- zhen-nan/DiP
DiP: Taming Diffusion Models in Pixel Space
Pre-Introduction
Just looking into this model , this safetensors is only the inference weights and not the training data tensors.
I dont know how to run inference yet either, WIP
Introduction
Diffusion models face a fundamental trade-off between generation quality and computational efficiency. Latent Diffusion Models (LDMs) offer an efficient solution but suffer from potential information loss and non-end-to-end training. In contrast, existing pixel space models bypass VAEs but are computationally prohibitive for high-resolution synthesis. To resolve this dilemma, we propose DiP, an efficient pixel space diffusion framework. DiP decouples generation into a global and a local stage: a Diffusion Transformer (DiT) backbone operates on large patches for efficient global structure construction, while a co-trained lightweight Patch Detailer Head leverages contextual features to restore fine-grained local details. This synergistic design achieves computational efficiency comparable to LDMs without relying on a VAE. DiP is accomplished with up to 10x faster inference speeds than previous method while increasing the total number of parameters by only 0.3%, and achieves an 1.79 FID score on ImageNet 256x256.