  One of the key components within diffusion models is the UNet for noise
prediction. While several works have explored basic properties of the UNet
decoder, its encoder largely remains unexplored. In this work, we conduct the
first comprehensive study of the UNet encoder. We empirically analyze the
encoder features and provide insights to important questions regarding their
changes at the inference process. In particular, we find that encoder features
change gently, whereas the decoder features exhibit substantial variations
across different time-steps. This finding inspired us to omit the encoder at
certain adjacent time-steps and reuse cyclically the encoder features in the
previous time-steps for the decoder. Further based on this observation, we
introduce a simple yet effective encoder propagation scheme to accelerate the
diffusion sampling for a diverse set of tasks. By benefiting from our
propagation scheme, we are able to perform in parallel the decoder at certain
adjacent time-steps. Additionally, we introduce a prior noise injection method
to improve the texture details in the generated image. Besides the standard
text-to-image task, we also validate our approach on other tasks:
text-to-video, personalized generation and reference-guided generation. Without
utilizing any knowledge distillation technique, our approach accelerates both
the Stable Diffusion (SD) and the DeepFloyd-IF models sampling by 41$\%$ and
24$\%$ respectively, while maintaining high-quality generation performance. Our
code is available in
\href{https://github.com/hutaiHang/Faster-Diffusion}{FasterDiffusion}.
