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71 lines
1.4 KiB
TOML
71 lines
1.4 KiB
TOML
script = "finetune-ldm-lora.py" # not used for now
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[wandb]
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mode = "offline" # "online", "offline", "disabled"
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entity = "acme"
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project = "test-lora-training"
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[models]
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unet = {checkpoint = "/path/to/stable-diffusion-1-5/unet.safetensors"}
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text_encoder = {checkpoint = "/path/to/stable-diffusion-1-5/CLIPTextEncoderL.safetensors"}
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lda = {checkpoint = "/path/to/stable-diffusion-1-5/lda.safetensors"}
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[latent_diffusion]
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unconditional_sampling_probability = 0.05
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offset_noise = 0.1
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[lora]
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rank = 16
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trigger_phrase = "a spsh photo,"
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use_only_trigger_probability = 1.0
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unet_targets = ["CrossAttentionBlock2d"]
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text_encoder_targets = ["TransformerLayer"]
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lda_targets = []
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[training]
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duration = "1000:epoch"
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seed = 0
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gpu_index = 0
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batch_size = 4
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gradient_accumulation = "4:step"
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clip_grad_norm = 1.0
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# clip_grad_value = 1.0
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evaluation_interval = "5:epoch"
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evaluation_seed = 1
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[optimizer]
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optimizer = "Prodigy" # "SGD", "Adam", "AdamW", "AdamW8bit", "Lion8bit"
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learning_rate = 1
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betas = [0.9, 0.999]
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eps = 1e-8
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weight_decay = 1e-2
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[scheduler]
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scheduler_type = "ConstantLR"
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update_interval = "1:step"
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warmup = "500:step"
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[dropout]
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dropout_probability = 0.2
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use_gyro_dropout = false
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[dataset]
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hf_repo = "acme/images"
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revision = "main"
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[checkpointing]
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# save_folder = "/path/to/ckpts"
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save_interval = "1:step"
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[test_diffusion]
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num_inference_steps = 30
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use_short_prompts = false
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prompts = [
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"a cute cat",
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"a cute dog",
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"a cute bird",
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"a cute horse",
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]
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