The research investigates the UNet encoder in diffusion models, identifying changes in encoder and decoder features. It introduces an innovative encoder propagation scheme for accelerated sampling and a noise injection method for texture enhancement. Validation across tasks shows significant speed gains for specific models while maintaining high-quality generation. The FasterDiffusion code release aims to encourage further research in the field.
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UNet Encoder in Diffusion Models: Impact on Image and Video Generation
Overview
Diffusion models offer a cutting-edge approach to image generation, capturing temporal changes in data. The UNet encoder within diffusion models has been under scrutiny, revealing intriguing patterns in feature transformations during inference. These models use an encoder propagation scheme to revolutionize diffusion sampling by reusing past features, enabling efficient parallel processing.
Research Findings
The study thoroughly investigates the UNet encoder in diffusion models, revealing gentle changes in encoder features and substantial variations in decoder features during inference. Introducing an encoder propagation scheme accelerates diffusion sampling and enables parallel processing. A prior noise injection method enhances texture details in generated images. The approach achieves notable acceleration in model sampling while maintaining high-quality generation.
Practical Solutions and Value
The research pioneers the first comprehensive study of the UNet encoder in diffusion models, examining changes in encoder features during inference. The innovative encoder propagation scheme accelerates diffusion sampling, while the noise injection method enhances texture details in generated images. The approach has been validated across diverse tasks and exhibits significant sampling acceleration for models without knowledge distillation while maintaining high-quality generation.
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