In this article, we explore the use of scaling autoregressive models to enable controllable image synthesis from user-scribble based semantic segmentation maps. We discuss how these models can be used to generate high-quality images that meet specific criteria, such as object placement and layout. Our proposed approach leverages the power of text-to-image diffusion models, which have shown promising results in generating images from textual descriptions.
Methodology
Our method involves training a scaling autoregressive model to generate images based on user-scribbled semantic segmentation maps. The model is trained using a combination of text-to-image diffusion models and conditional control networks, which allow users to specify the desired layout and objects in the generated image. We propose a versatile ControlNet model that enables users to fine-tune the output layout on a more fine-grained level through an input semantic map.
Advantages
The proposed method offers several advantages over traditional text-to-image synthesis methods. Firstly, it allows for controllable image generation, enabling users to specify the desired objects and layout in the generated image. Secondly, it can handle complex scenes with multiple objects and fine-grained details, resulting in high-quality images that meet the user’s specifications. Finally, our approach is trained using a combination of text-to-image diffusion models and conditional control networks, which enables us to generate images that are both visually appealing and semantically consistent with the input text.
Conclusion
In conclusion, scaling autoregressive models offer a promising solution for enabling controllable image synthesis from user-scribble based semantic segmentation maps. Our proposed approach leverages the power of text-to-image diffusion models and conditional control networks to generate high-quality images that meet specific criteria, such as object placement and layout. By demystifying complex concepts and using everyday language and engaging metaphors or analogies, we hope to provide a clear understanding of this innovative method in the field of computer vision.
