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model:
base_learning_rate: 1.0e-4
target: sgm.models.diffusion.DiffusionEngine
params:
denoiser_config:
target: sgm.modules.diffusionmodules.denoiser.Denoiser
params:
scaling_config:
target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
params:
sigma_data: 1.0
network_config:
target: sgm.modules.diffusionmodules.openaimodel.UNetModel
params:
in_channels: 1
out_channels: 1
model_channels: 32
attention_resolutions: []
num_res_blocks: 4
channel_mult: [1, 2, 2]
num_head_channels: 32
first_stage_config:
target: sgm.models.autoencoder.IdentityFirstStage
loss_fn_config:
target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
params:
loss_weighting_config:
target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
params:
sigma_data: 1.0
sigma_sampler_config:
target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
sampler_config:
target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
params:
num_steps: 50
discretization_config:
target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
data:
target: sgm.data.mnist.MNISTLoader
params:
batch_size: 512
num_workers: 1
lightning:
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
metrics_over_trainsteps_checkpoint:
params:
every_n_train_steps: 25000
image_logger:
target: main.ImageLogger
params:
disabled: False
batch_frequency: 1000
max_images: 64
increase_log_steps: False
log_first_step: False
log_images_kwargs:
use_ema_scope: False
N: 64
n_rows: 8
trainer:
devices: 0,
benchmark: True
num_sanity_val_steps: 0
accumulate_grad_batches: 1
max_epochs: 10 |