Upload lora5/config_lora-20240605-021228.toml with huggingface_hub
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        lora5/config_lora-20240605-021228.toml
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            bucket_reso_steps = 32
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            cache_latents = true
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            cache_latents_to_disk = true
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            caption_extension = ".txt"
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            clip_skip = 2
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            dynamo_backend = "no"
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            enable_bucket = true
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            epoch = 10
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            +
            gradient_accumulation_steps = 1
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            gradient_checkpointing = true
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            +
            huber_c = 0.1
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            huber_schedule = "snr"
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| 13 | 
            +
            learning_rate = 0.0002
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            +
            logging_dir = "/root/autodl-tmp/logs/example"
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            loss_type = "l2"
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            lr_scheduler = "constant"
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            lr_scheduler_args = []
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            lr_scheduler_num_cycles = 1
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            lr_scheduler_power = 1
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            max_bucket_reso = 2176
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| 21 | 
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            max_data_loader_n_workers = 0
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            max_grad_norm = 1
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            max_timestep = 1000
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            max_token_length = 75
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            max_train_epochs = 10
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            max_train_steps = 22511
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            min_bucket_reso = 384
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            mixed_precision = "fp16"
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            network_alpha = 16
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            network_args = []
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            network_dim = 32
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            network_module = "networks.lora"
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            network_weights = "/root/kohya_ss/sd-models/arknight_all_v2.safetensors"
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            no_half_vae = true
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            noise_offset = 0.035
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            noise_offset_type = "Original"
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            optimizer_args = []
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            optimizer_type = "AdamW"
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            output_dir = "/root/kohya_ss/output5"
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            output_name = "arknight_all_v2_pro"
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            pretrained_model_name_or_path = "/root/kohya_ss/sd-models/pony-v6.safetensors"
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            prior_loss_weight = 1
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            resolution = "1024,1024"
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            sample_every_n_steps = 1000
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            sample_prompts = "/root/kohya_ss/output5/prompt.txt"
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            sample_sampler = "euler_a"
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            save_every_n_epochs = 1
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            save_last_n_steps_state = 1
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            save_model_as = "safetensors"
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            save_precision = "fp16"
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            seed = 12345
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            text_encoder_lr = 0.0001
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            train_batch_size = 50
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            train_data_dir = "/root/kohya_ss/train5/arkinghts_v2"
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            training_comment = "example"
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            unet_lr = 0.0001
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            xformers = true
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