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dataset:
dataset: MNIST
image_size: 32
channels: 1
sample:
method: ${method}
model_path: YOUR PATH of pretrained ckpt
image_output_path: out
batch_size: 5
total_num: 100
work_dir: ${hydra:runtime.cwd}
method: F-PNDM
sample_speed: 50
seed: 42
model:
_target_: src.model.DDIM.Model
type: simple
resolution: 32
in_channels: 1
out_ch: 1
ch: 128
ch_mult:
- 1
- 1
- 1
- 1
num_res_blocks: 2
attn_resolutions:
- 16
dropout: 0.1
resamp_with_conv: true
scheduler:
_target_: src.trainer.scheduler.Schedule
beta_start: 0.0001
beta_end: 0.02
diffusion_step: 1000
train:
optim_cfg:
weight_decay: 0.0
optimizer: adam
lr: 0.0002
beta1: 0.9
amsgrad: false
eps: 1.0e-08
grad_clip: 1.0
epochs: 100
ema_rate: 0.9999
use_ema: true
batch_size: 2048
num_workers: 6
save_path: ./save_ckpt
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