This paper introduces a way to control the output of diffusion transformers, a type of AI model used for image generation, by providing it with guidance on specific regions of the image. This can be useful for creative professionals who need to generate images with precise control over details like materials, objects, and spatial arrangements.
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This paper proposes a new way to improve the performance of diffusion models by aligning their inference process with the forward statistical structure of the model. This can lead to more realistic and diverse generated images, which is important for applications like image generation.