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from genmo.diffusion_utils import gaussian_diffusion as gd
from genmo.diffusion_utils.respace import SpacedDiffusion, space_timesteps
def create_gaussian_diffusion(cfg, training):
# default params
predict_xstart = True # we always predict x_start (a.k.a. x0), that's our deal!
steps = 1000
scale_beta = 1.0 # no scaling
timestep_respacing = (
cfg.train_timestep_respacing if training else cfg.test_timestep_respacing
) # '' # can be used for ddim sampling, we don't use it.
if type(timestep_respacing) is not str:
timestep_respacing = str(timestep_respacing)
learn_sigma = False
rescale_timesteps = False
betas = gd.get_named_beta_schedule(cfg.noise_schedule, steps, scale_beta)
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 cfg.sigma_small
else gd.ModelVarType.FIXED_SMALL
)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
),
loss_type=loss_type,
rescale_timesteps=rescale_timesteps,
)
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