I used ChatGPT to explain some new parameters in SUPIR:
--edm_steps: Number of steps for the EDM Sampling Scheduler. This determines how many iterations the sampling process will take. A higher number generally means more refinement but takes longer.
--s_stage1: Strength control for the first stage of processing. A negative value indicates that this stage is not used.
--s_churn: A hyperparameter of the EDM process, influencing the variability or diversity in the generated samples.
--s_noise: Another hyperparameter for EDM, affecting the amount of noise introduced during the diffusion process, which can impact the clarity or smoothness of the output.
--s_cfg: Classifier-free guidance scale for prompts. This parameter adjusts the influence of textual prompts on the generation process, affecting how closely the output matches the given textual description.
--s_stage2: Strength control for the second stage of processing. Adjusts the intensity of modifications in the second stage.
linear_CFG and --linear_s_stage2: These settings allow for a linear increase of the s_cfg and s_stage2 parameters from their start points (--spt_linear_CFG and --spt_linear_s_stage2) to their specified values through the process, providing a way to dynamically adjust the guidance strength.
--spt_linear_CFG and --spt_linear_s_stage2: Define the starting points for the linear increase of s_cfg and s_stage2, respectively.
--ae_dtype and --diff_dtype: Specify the data types (precision) used during inference for the autoencoder and diffusion processes, respectively, affecting memory usage and computational speed.
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