StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation (2312.12491v1)

Abstract: 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.

• The paper introduces a pipeline-level optimization that transforms sequential denoising into batched processing, significantly increasing throughput and reducing latency.
• It employs Residual Classifier-Free Guidance and an input-output queuing system to cut redundant computations and boost GPU efficiency up to 2.05x.
• The approach achieves a generation speed of 91 fps on an NVIDIA RTX 4090, offering practical benefits for interactive graphics and sustainable AI.

StreamDiffusion: Enhancing Real-time Image Generation through Efficient Diffusion Pipelines

The paper "StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation" presents a novel approach to improving the efficiency of diffusion models, particularly in scenarios necessitating real-time interaction. Traditional diffusion models, while effective in generating images from text or image prompts, do not naturally lend themselves to high-throughput, low-latency environments such as those encountered in video game graphics and live streaming applications. StreamDiffusion addresses these limitations with a series of strategic innovations at the pipeline level.

Key Innovations

The authors of the paper identify several core components that collectively enhance the throughput and reduce energy consumption in diffusion models:

1. Stream Batch Strategy: One of the paper's most notable contributions is the transformation of the sequential denoising step into a batching process. This allows for overlapping the denoising stages of different image inputs, thereby maximizing GPU utilization and reducing the overall processing time.
2. Residual Classifier-Free Guidance (RCFG): The paper introduces a novel variant of classifier-free guidance to address the redundancy in standard implementations. By limiting the number of negative conditional denoising computations, RCFG enhances processing efficiency and increases speed significantly—up to 2.05x compared to traditional methods.
3. Input-Output Queue for Parallelization: To manage the discrepancy between data input rates and model throughput, StreamDiffusion employs a sophisticated queuing system. This allows pre-processing and post-processing to occur asynchronously and in parallel with the main denoising computations, further enhancing overall throughput.
4. Stochastic Similarity Filter: This technique dynamically reduces GPU usage by intermittently skipping redundant processing of frames with high similarity to prior inputs. This reduction in computational load leads to a substantial decrease in energy consumption.
5. Pre-computation: StreamDiffusion pre-computes key variables such as prompt embeddings and noise samples, ensuring that each frame doesn’t need to independently compute these, thus saving valuable processing time.
6. Model Acceleration Tools: By leveraging TensorRT and a lightweight AutoEncoder, the system achieves remarkable speed improvements. The use of static batch sizes and input dimensions optimizes the pipeline for the specific computational graph and memory layouts.

These innovations result in a pipeline capable of generating images at a rate of up to 91.07 fps on an NVIDIA RTX 4090 GPU, marking a substantial improvement—up to 59.56x over previous pipelines.

Implications and Future Directions

The efficiency and potential applicability of StreamDiffusion extend beyond traditional diffusion model applications. By addressing both throughput and energy consumption issues, it becomes a valuable tool for interactive media platforms, the Metaverse, and similar domains where real-time generation is crucial.

From a practical perspective, the reductions in computational delay and energy costs suggest a significant impact on sustainable AI deployment, particularly as cloud-based GPU resources continue to proliferate. Theoretically, the concepts introduced could inspire further research into pipeline-level optimizations for other machine learning models.

Future work might explore alternative architectures that integrate seamlessly with StreamDiffusion or even extend it beyond image-to-image tasks to encompass broader multimedia interactions. Furthermore, investigating how these optimizations impact other types of diffusion models could yield productive cross-pollination of ideas in the domains of text and video generation.

In conclusion, StreamDiffusion offers a compelling solution for enhancing the efficiency of diffusion models, aligning well with the need for responsive, high-throughput interactive applications. The advancements in pipeline optimization detailed in the paper can be pivotal for AI’s integration into real-time environments, marking a significant stride in AI-driven graphics and interaction technology.