config/base.py

import ml_collections


def get_config():
    config = ml_collections.ConfigDict()

    ###### General ######
    # run name for wandb logging and checkpoint saving -- if not provided, will be auto-generated based on the datetime.
    config.run_name = ""
    # random seed for reproducibility.
    config.seed = 42
    # top-level logging directory for checkpoint saving.
    config.logdir = "logs"
    # number of epochs between saving model checkpoints.
    config.save_freq = 20
    # number of epochs between evaluating the model.
    config.eval_freq = 20
    # number of checkpoints to keep before overwriting old ones.
    config.num_checkpoint_limit = 5
    # mixed precision training. options are "fp16", "bf16", and "no". half-precision speeds up training significantly.
    config.mixed_precision = "fp16"
    # allow tf32 on Ampere GPUs, which can speed up training.
    config.allow_tf32 = True
    # whether or not to use LoRA.
    config.use_lora = True
    config.dataset = ""
    config.resolution = 768

    ###### Pretrained Model ######
    config.pretrained = pretrained = ml_collections.ConfigDict()
    # base model to load. either a path to a local directory, or a model name from the HuggingFace model hub.
    pretrained.model = "runwayml/stable-diffusion-v1-5"
    # revision of the model to load.
    pretrained.revision = "main"

    ###### Sampling ######
    config.sample = sample = ml_collections.ConfigDict()
    # number of sampler inference steps for collecting dataset.
    sample.num_steps = 40
    # number of sampler inference steps for evaluation.
    sample.eval_num_steps = 40
    # classifier-free guidance weight. 1.0 is no guidance.
    sample.guidance_scale = 4.5
    # batch size (per GPU!) to use for sampling.
    sample.train_batch_size = 1
    sample.num_image_per_prompt = 1
    sample.test_batch_size = 1
    # number of batches to sample per epoch. the total number of samples per epoch is `num_batches_per_epoch *
    # batch_size * num_gpus`.
    sample.num_batches_per_epoch = 2
    # Whether use all samples in a batch to compute std
    sample.global_std = True
    # noise level
    sample.noise_level = 0.7
    # Whether to use the same noise for the same prompt
    sample.same_latent = False
    
    ###### Training ######
    config.train = train = ml_collections.ConfigDict()
    # batch size (per GPU!) to use for training.
    train.batch_size = 1
    # whether to use the 8bit Adam optimizer from bitsandbytes.
    train.use_8bit_adam = False
    # learning rate.
    train.learning_rate = 3e-4
    # Adam beta1.
    train.adam_beta1 = 0.9
    # Adam beta2.
    train.adam_beta2 = 0.999
    # Adam weight decay.
    train.adam_weight_decay = 1e-4
    # Adam epsilon.
    train.adam_epsilon = 1e-8
    # number of gradient accumulation steps. the effective batch size is `batch_size * num_gpus *
    # gradient_accumulation_steps`.
    train.gradient_accumulation_steps = 1
    # maximum gradient norm for gradient clipping.
    train.max_grad_norm = 1.0
    # number of inner epochs per outer epoch. each inner epoch is one iteration through the data collected during one
    # outer epoch's round of sampling.
    train.num_inner_epochs = 1
    # whether or not to use classifier-free guidance during training. if enabled, the same guidance scale used during
    # sampling will be used during training.
    train.cfg = True
    # clip advantages to the range [-adv_clip_max, adv_clip_max].
    train.adv_clip_max = 5
    # the PPO clip range.
    train.clip_range = 1e-4
    # the fraction of timesteps to train on. if set to less than 1.0, the model will be trained on a subset of the
    # timesteps for each sample. this will speed up training but reduce the accuracy of policy gradient estimates.
    train.timestep_fraction = 1.0
    # kl ratio
    train.beta = 0.0
    # pretrained lora path
    train.lora_path = None
    # save ema model
    train.ema = False

    ###### Prompt Function ######
    # prompt function to use. see `prompts.py` for available prompt functions.
    config.prompt_fn = "imagenet_animals"
    # kwargs to pass to the prompt function.
    config.prompt_fn_kwargs = {}

    ###### Reward Function ######
    # reward function to use. see `rewards.py` for available reward functions.
    config.reward_fn = ml_collections.ConfigDict()
    config.save_dir = ''

    ###### Per-Prompt Stat Tracking ######
    config.per_prompt_stat_tracking = True

    return config