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Classifier Guidance

Technique using a pre-trained classifier to guide the diffusion process toward specific attributes or classes. Guidance is applied via the classifier's gradient during denoising to precisely control generation.

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Conditional Diffusion

Extension of diffusion models that incorporates conditions or labels to generate samples meeting specific criteria. Conditioning can be applied at each step of the diffusion or reverse process.

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Diffusion Scheduler

Strategy defining the evolution of noise variance through the forward diffusion steps. The scheduler influences the quality and convergence speed of the reverse generation process.

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Energy-Based Diffusion

Formulation of diffusion models as energy-based models where training minimizes an energy function. Allows natural integration with gradient guidance techniques.

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Iterative Denoising

Progressive refinement process where each iteration applies a slight correction to reduce residual noise. Diffusion models typically use hundreds to thousands of iterations for high-quality generation.

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Guidance Interpolation

Technique allowing the combination of multiple guidance vectors to generate smooth transitions between different concepts or attributes. Useful for creating controlled variations in the generation space.

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Diffusion Architecture

Specific neural structure designed for the diffusion process, typically based on U-Nets with attention mechanisms. The architecture must efficiently handle spatial and temporal dependencies of noise.

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