![]() 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. Besides the standard text-to-image task, we also validate our approach on other tasks: text-to-video, personalized generation and reference-guided generation. Additionally, we introduce a prior noise injection method to improve the texture details in the generated image. By benefiting from our propagation scheme, we are able to perform in parallel the decoder at certain adjacent time-steps. 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. 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. Just use the microphone or headset input on your iPhone or iPad for the audio signal input, and watch decoded text appear. The HotPaw Morse Code Decoder translates Morse Code sound into text. ![]() In particular, we find that encoder features change gently, whereas the decoder features exhibit substantial variations across different time-steps. Download MorseDecoder and enjoy it on your iPhone, iPad, and iPod touch. We empirically analyze the encoder features and provide insights to important questions regarding their changes at the inference process. In this work, we conduct the first comprehensive study of the UNet encoder. ![]() While several works have explored basic properties of the UNet decoder, its encoder largely remains unexplored. ![]() Download a PDF of the paper titled Faster Diffusion: Rethinking the Role of UNet Encoder in Diffusion Models, by Senmao Li and 6 other authors Download PDF Abstract:One of the key components within diffusion models is the UNet for noise prediction. ![]()
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