fashion.yaml

# @package _global_

# to execute this experiment run:
# python eval.py experiment=generation/diffusion/eval/fashion

defaults:
  - override /data: fashion.yaml
  - override /model: diffusion/diffusion_module.yaml
  - override /callbacks: generation_diffusion.yaml
  - override /trainer: ddp.yaml
  - override /logger: wandb.yaml

# all parameters below will be merged with parameters from default configurations set above
# this allows you to overwrite only specified parameters

seed: 12345

task_name: generation/diffusion/eval

trainer:
  devices: 1
  
data:
  batch_size: 512 # for 1 GPU 16G
  num_workers: 10
  train_val_test_split: [10_000, 10_000, 50_000] # test with 50.000 samples

model:
  net:
    denoise_net:
      base_channels: 128
      n_layer_blocks: 2
      channel_multipliers: [1, 2, 2, 2]
      attention_levels: [1] # resolution 16
    sampler:
      _target_: src.models.diffusion.sampler.DDPMSampler
      n_train_steps: ${model.net.n_train_steps}
      beta_schedule: linear
      
logger:                                                                                                                                                                                                                                                                                                                                         
  wandb:
    name: ${now:%Y-%m-%d}_${now:%H-%M-%S}
    project: generation
    tags: ${tags}
    group: diffusion
    id: null # pass correct id to resume experiment! (if ckpt_path is not null)

ckpt_path: null # simply provide checkpoint path to resume training