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Image generation using diffusion can be controlled in multiple ways. In thispaper, we systematically analyze the equations of modern generative diffusionnetworks to propose a framework, called MDP, that explains the design space ofsuitable manipulations. We identify 5 different manipulations, includingintermediate latent, conditional embedding, cross attention maps, guidance, andpredicted noise. We analyze the corresponding parameters of these manipulationsand the manipulation schedule. We show that some previous editing methods fitnicely into our framework. Particularly, we identified one specificconfiguration as a new type of control by manipulating the predicted noise,which can perform higher-quality edits than previous work for a variety oflocal and global edits.

MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path