SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models (2403.09055v3)

Abstract: We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each encoding prescribed semantic meaning. In order to maximize the productivity of content creators and to fully realize their artistic imagination, it requires both quick interactive interfaces and fine-grained regional controls in their tools. Despite astonishing generation quality from recent diffusion models, we find that existing approaches for regional controllability are very slow (52 seconds for $512 \times 512$ image) while not compatible with acceleration methods such as LCM, blocking their huge potential in interactive content creation. From this observation, we build our solution for interactive content creation in two steps: (1) we establish compatibility between region-based controls and acceleration techniques for diffusion models, maintaining high fidelity of multi-prompt image generation with $\times 10$ reduced number of inference steps, (2) we increase the generation throughput with our new multi-prompt stream batch pipeline, enabling low-latency generation from multiple, region-based text prompts on a single RTX 2080 Ti GPU. Our proposed framework is generalizable to any existing diffusion models and acceleration schedulers, allowing sub-second (0.64 seconds) image content creation application upon well-established image diffusion models. Our project page is: https://jaerinlee.com/research/semantic-draw.

StreamMultiDiffusion: A Real-Time Interactive Framework for Region-Based Text-to-Image Generation

Key Contributions and Findings

StreamMultiDiffusion addresses key challenges in deploying diffusion models for practical, interactive applications, specifically focusing on latency and user control. This paper presents:

• Improvement over Existing Techniques: StreamMultiDiffusion stabilizes and accelerates MultiDiffusion for compatibility with fast inference techniques such as Latent Consistency Models (LCM). This is enabled by innovations like latent pre-averaging, mask-centering bootstrapping, and quantized masks.
• Real-Time, Interactive Framework: The newly proposed multi-prompt stream batch architecture significantly increases the throughput of image generation, enabling real-time, interactive applications on a single RTX 2080 Ti GPU, achieving generation speeds of 1.57 FPS.
• Semantic Palette Paradigm: Introduces a novel interaction model, semantic palette, enabling real-time generation of complex images based on hand-drawn regions with associated semantic prompts.

Performance and Evaluation

Quantitative and qualitative assessments affirm the efficacy of StreamMultiDiffusion. Notably:

• Speed Improvement: Demonstrated a $\times 10$ speed increase in panorama generation compared to existing solutions, alongside offering real-time performance capabilities essential for end-user applications.
• High-Quality Results: Through various examples, including the generation of large format images and detailed region-specific prompts, StreamMultiDiffusion showcased its ability to maintain high fidelity and quality, aligning closely with prescriptive user inputs.
• Quantitative Metrics: Utilized Intersection over Union (IoU) to demonstrate mask fidelity, underscoring the method's precise adherence to specified regional prompts.

Theoretical Implications and Practical Applications

• Toward Seamless Model Compatibility: This work illustrates a foundational approach toward making high-potential but computationally intensive generative models like diffusion models more adaptable and usable in real-world scenarios. This is significant for further research in making AI-driven creative tools more accessible.
• Implications for Interactive AI Applications: By demonstrating the feasibility and utility of real-time interaction with complex generative models, StreamMultiDiffusion opens new avenues for AI in creative industries, including gaming, film, and digital art.
• Enabling Professional-Grade Tools: With its ability to provide real-time feedback and accept intuitive, fine-grained user inputs like semantic drawing, StreamMultiDiffusion represents a step toward professional-grade AI tools for content creation.

Future Directions

• Scalability and Efficiency: Further research can explore optimizations that allow for the scaling up of image resolutions and complexities without significantly affecting the interaction latency, making the technology more viable for high-production environments.
• User Interface and Experience Enhancement: Beyond backend optimizations, enhancing user interfaces to make the most of StreamMultiDiffusion’s capabilities will be key. This includes developing more intuitive ways for users to specify their creative intentions to the model.
• Expansion to Other Domains: Extending the principles behind StreamMultiDiffusion to other forms of media generation, such as video or 3D models, could have far-reaching implications for content creation across various digital and interactive media.

Conclusion

StreamMultiDiffusion marks an important advancement in the practical deployment of diffusion models for interactive image generation, bridging the gap between cutting-edge AI research and real-world applications. It not only addresses key technical challenges but also reimagines the interface between users and generative models, offering a glimpse into the future of AI-assisted creativity.