Spaces:
Runtime error
Runtime error
| import argparse | |
| import inspect | |
| from . import gaussian_diffusion as gd | |
| from .respace import SpacedDiffusion, space_timesteps | |
| def create_gaussian_diffusion( | |
| *, | |
| steps=1000, | |
| learn_sigma=False, | |
| sigma_small=False, | |
| noise_schedule="linear", | |
| use_kl=False, | |
| predict_xstart=False, | |
| rescale_timesteps=False, | |
| rescale_learned_sigmas=False, | |
| timestep_respacing="", | |
| ): | |
| betas = gd.get_named_beta_schedule(noise_schedule, steps) | |
| if use_kl: | |
| loss_type = gd.LossType.RESCALED_KL | |
| elif rescale_learned_sigmas: | |
| loss_type = gd.LossType.RESCALED_MSE | |
| else: | |
| loss_type = gd.LossType.MSE | |
| if not timestep_respacing: | |
| timestep_respacing = [steps] | |
| return SpacedDiffusion( | |
| use_timesteps=space_timesteps(steps, timestep_respacing), | |
| betas=betas, | |
| model_mean_type=( | |
| gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X | |
| ), | |
| model_var_type=( | |
| ( | |
| gd.ModelVarType.FIXED_LARGE | |
| if not sigma_small | |
| else gd.ModelVarType.FIXED_SMALL | |
| ) | |
| if not learn_sigma | |
| else gd.ModelVarType.LEARNED_RANGE | |
| ), | |
| loss_type=loss_type, | |
| rescale_timesteps=rescale_timesteps, | |
| ) | |