48 lines
1.6 KiB
YAML
48 lines
1.6 KiB
YAML
# 1.Data setting
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data_txt_root: '/dataset/example/train_data_info.txt' # data csv_filepath
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resolution: 1024
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aspect_ratio_type: 'mar_1024' # data bucketing strategy, mar_256、mar_512、mar_1024
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null_text_ratio: 0.1
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dataloader_num_workers: 8
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train_batch_size: 2
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repeats: 100
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prompt_template_encode_prefix: '<|im_start|>system\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\n<|im_start|>user\n'
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prompt_template_encode_suffix: '<|im_end|>\n<|im_start|>assistant\n'
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prompt_template_encode_start_idx: 36
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prompt_template_encode_end_idx: 5
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# 2. Model setting
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text_tokenizer_max_length: 512 # tokenizer max len
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pretrained_model_name_or_path: "/xxx/weights/Longcat-Image-Dev" # root directory of the model，with vae、transformer、scheduler eta;
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diffusion_pretrain_weight: null # if a specified diffusion weight path is provided, load the model parameters from the current directory.
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use_dynamic_shifting: true # scheduler dynamic shifting
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resume_from_checkpoint: latest
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# - "latest" # Loads most recent step checkpoint
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# - "/path/to/checkpoint" # Resumes from specified directory
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# 3. Training setting
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lora_rank: 32
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use_ema: False
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ema_rate: 0.999
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mixed_precision: 'bf16'
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max_train_steps: 100000
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gradient_accumulation_steps: 1
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gradient_checkpointing: true
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gradient_clip: 1.0
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learning_rate: 1.0e-4
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adam_weight_decay: 1.0e-3
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adam_epsilon: 1.0e-8
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adam_beta1: 0.9
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adam_beta2: 0.999
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lr_num_cycles: 1
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lr_power: 1.0
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lr_scheduler: 'constant'
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lr_warmup_steps: 1000
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#4. Log setting
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log_interval: 10
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save_model_steps: 200
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work_dir: 'output/lora_model'
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seed: 43 |