  We introduce StreamDiffusion, a real-time diffusion pipeline designed for
interactive image generation. Existing diffusion models are adept at creating
images from text or image prompts, yet they often fall short in real-time
interaction. This limitation becomes particularly evident in scenarios
involving continuous input, such as Metaverse, live video streaming, and
broadcasting, where high throughput is imperative. To address this, we present
a novel approach that transforms the original sequential denoising into the
batching denoising process. Stream Batch eliminates the conventional
wait-and-interact approach and enables fluid and high throughput streams. To
handle the frequency disparity between data input and model throughput, we
design a novel input-output queue for parallelizing the streaming process.
Moreover, the existing diffusion pipeline uses classifier-free guidance(CFG),
which requires additional U-Net computation. To mitigate the redundant
computations, we propose a novel residual classifier-free guidance (RCFG)
algorithm that reduces the number of negative conditional denoising steps to
only one or even zero. Besides, we introduce a stochastic similarity
filter(SSF) to optimize power consumption. Our Stream Batch achieves around
1.5x speedup compared to the sequential denoising method at different denoising
levels. The proposed RCFG leads to speeds up to 2.05x higher than the
conventional CFG. Combining the proposed strategies and existing mature
acceleration tools makes the image-to-image generation achieve up-to 91.07fps
on one RTX4090, improving the throughputs of AutoPipline developed by Diffusers
over 59.56x. Furthermore, our proposed StreamDiffusion also significantly
reduces the energy consumption by 2.39x on one RTX3060 and 1.99x on one
RTX4090, respectively.

Computer Vision and Pattern Recognition;Graphics;Machine Learning