Working on DGX

config/base.py

@@ -1,56 +0,0 @@
-import ml_collections
-
-def get_config():
-
-    config = ml_collections.ConfigDict()
-
-    # misc
-    config.seed = 42
-    config.logdir = "logs"
-    config.num_epochs = 100
-    config.mixed_precision = "fp16"
-    config.allow_tf32 = True
-
-    # pretrained model initialization
-    config.pretrained = pretrained = ml_collections.ConfigDict()
-    pretrained.model = "runwayml/stable-diffusion-v1-5"
-    pretrained.revision = "main"
-
-    # training
-    config.train = train = ml_collections.ConfigDict()
-    train.mixed_precision = "fp16"
-    train.batch_size = 1
-    train.use_8bit_adam = False
-    train.scale_lr = False
-    train.learning_rate = 1e-4
-    train.adam_beta1 = 0.9
-    train.adam_beta2 = 0.999
-    train.adam_weight_decay = 1e-4
-    train.adam_epsilon = 1e-8
-    train.gradient_accumulation_steps = 32
-    train.max_grad_norm = 1.0
-    train.num_inner_epochs = 1
-    train.cfg = True
-    train.adv_clip_max = 10
-    train.clip_range = 1e-4
-
-    # sampling
-    config.sample = sample = ml_collections.ConfigDict()
-    sample.num_steps = 30
-    sample.eta = 1.0
-    sample.guidance_scale = 5.0
-    sample.batch_size = 4
-    sample.num_batches_per_epoch = 8
-
-    # prompting
-    config.prompt_fn = "imagenet_animals"
-    config.prompt_fn_kwargs = {}
-
-    # rewards
-    config.reward_fn = "jpeg_compressibility"
-
-    config.per_prompt_stat_tracking = ml_collections.ConfigDict()
-    config.per_prompt_stat_tracking.buffer_size = 64
-    config.per_prompt_stat_tracking.min_count = 16
-
-    return config