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BudCache redefines caching for diffusion models by prioritizing output quality over traditional heuristic approaches, achieving superior results within fixed compute budgets.
Instance-specific timestep schedules can significantly boost diffusion model performance, challenging the reliance on global discretization strategies.
Ditch the handcrafted coefficients: DyWeight learns how to dynamically weight gradients in diffusion model sampling, slashing compute while boosting image quality.
Content leakage in text-to-image style transfer can be dramatically reduced by simply suppressing the tail components of the style embedding using SVD and a time-aware schedule, without any retraining.
