Diffusion Inference with Dynamic Classifier-free Guidance
Published in International Conference on Inventive Computing and Informatics, 2024
This research work proposes varying the CFG scale values across the inference steps by making use of various scheduling functions, which not only results in better images but also unlocks the full potential of the rich latent space representation of diffusion models by allowing for the sampling of various different images from the same initial conditions by only varying the CFG scale and keeping all other parameters constant.
Recommended citation: V. S., A., Kulkarni, A., Chawla, D., Rawther, A., & Rangareddy, J. (2024). Diffusion Inference with Dynamic Classifier-free Guidance. In International Conference on Inventive Computing and Informatics (2nd ed., pp. 53–59). IEEE. https://doi.org/10.1109/icici62254.2024.00018
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