Paper · arXiv 2404.04478
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Zhengcong Fei, Mingyuan Fan, Changqian Yu, Debang Li, Junshi Huang
Diffusion-RWKV, an architecture adapted from RWKV for image generation, efficiently handles high-resolution images with reduced computational complexity compared to CNN or Transformer-based models.
Abstract
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields. However, substantial computational complexity poses limitations for their application in long-context tasks, such as high-resolution image generation. This paper introduces a series of architectures adapted from the RWKV model used in the NLP, with requisite modifications tailored for diffusion model applied to image generation tasks, referred to as Diffusion-RWKV. Similar to the diffusion with Transformers, our model is designed to efficiently handle patchnified inputs in a sequence with extra conditions, while also scaling up effectively, accommodating both large-scale parameters and extensive datasets. Its distinctive advantage manifests in its reduced spatial aggregation complexity, rendering it exceptionally adept at processing high-resolution images, thereby eliminating the necessity for windowing or group cached operations. Experimental results on both condition and unconditional image generation tasks demonstrate that Diffison-RWKV achieves performance on par with or surpasses existing CNN or Transformer-based diffusion models in FID and IS metrics while significantly reducing total computation FLOP usage.