StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control
Arxiv 2024
Abstract
The enormous success of diffusion models in text-to-image synthesis has made them promising candidates for the next generation of end-user applications for image generation and editing. Previous works have focused on improving the usability of diffusion models by reducing the inference time or increasing user interactivity by allowing new, fine-grained controls such as region-based text prompts. However, we empirically find that integrating both branches of works is nontrivial, limiting the potential of diffusion models. To solve this incompatibility, we present StreamMultiDiffusion, the first real-time region-based text-to-image generation framework. By stabilizing fast inference techniques and restructuring the model into a newly proposed multi-prompt stream batch architecture, we achieve ×10 faster panorama generation than existing solutions, and the generation speed of 1.57 FPS in region-based text-to-image synthesis on a single RTX 2080 Ti GPU. Our solution opens up a new paradigm for interactive image generation named semantic palette, where high-quality images are generated in real-time from given multiple hand-drawn regions, encoding prescribed semantic meanings (e.g., eagle, girl).
Stable Acceleration of Region-Based Image Generation
Semantic Palette
Multi-Prompt Stream Batch Architecture
Real-Time Semantic Palette
More Examples
Accelerated Text-to-Panorama Generation
Accelerated Region-Based Text-to-Image Generation
BibTex
@article{lee2024streammultidiffusion, title="{StreamMultiDiffusion:} Real-Time Interactive Generation with Region-Based Semantic Control", author={Lee, Jaerin and Jung, Daniel Sungho and Lee, Kanggeon and Lee, Kyoung Mu}, journal={arXiv preprint arXiv:2403.09055}, year={2024} }
Reference
[1] Bar-Tal, O., Yariv, L., Lipman, Y., Dekel, T.: MultiDiffusion: Fusing diffusion paths for controlled image generation. In ICML 2023.
[2] Luo, S., Tan, Y., Huang, L., Li, J., Zhao, H.: Latent Consistency Models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023.
[3] Kodaira, A., Xu, C., Hazama, T., Yoshimoto, T., Ohno, K., Mitsuhori, S., Sugano, S., Cho, H., Liu, Z., Keutzer, K.: StreamDiffusion: A pipeline-level solution for real-time interactive generation. arXiv preprint arXiv:2312.12491, 2023.