script = "finetune-ldm.py" # not used for now [wandb] offline = "offline" entity = "acme" project = "test-ldm-training" [models] lda = {checkpoint="/path/to/stable-diffusion-1-5/lda.safetensors", train=false} text_encoder = {checkpoint="/path/to/stable-diffusion-1-5/text_encoder.safetensors", train=true} unet = {checkpoint="/path/to/stable-diffusion-1-5/unet.safetensors", train=true} [latent_diffusion] unconditional_sampling_probability = 0.2 offset_noise = 0.1 [training] duration = "1:epoch" seed = 0 gpu_index = 0 num_epochs = 1 batch_size = 1 gradient_accumulation = "1:step" clip_grad_norm = 2.0 clip_grad_value = 1.0 evaluation_interval = "1:epoch" evaluation_seed = 0 [optimizer] optimizer = "AdamW" # "AdamW", "AdamW8bit", "Lion8bit", "Prodigy", "SGD", "Adam" learning_rate = 1e-5 betas = [0.9, 0.999] eps = 1e-8 weight_decay = 1e-2 [scheduler] [dropout] dropout_probability = 0.2 [dataset] hf_repo = "acme/images" revision = "main" [checkpointing] # save_folder = "/path/to/ckpts" save_interval = "1:epoch" [test_diffusion] prompts = [ "A cute cat", ]