File size: 5,561 Bytes
2fdce3c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import gradio as gr
import os
class BasicTraining:
def __init__(
self,
learning_rate_value='1e-6',
lr_scheduler_value='constant',
lr_warmup_value='0',
finetuning: bool = False,
):
self.learning_rate_value = learning_rate_value
self.lr_scheduler_value = lr_scheduler_value
self.lr_warmup_value = lr_warmup_value
self.finetuning = finetuning
with gr.Row():
self.train_batch_size = gr.Slider(
minimum=1,
maximum=64,
label='Train batch size',
value=1,
step=1,
)
self.epoch = gr.Number(label='Epoch', value=1, precision=0)
self.max_train_epochs = gr.Textbox(
label='Max train epoch',
placeholder='(Optional) Enforce number of epoch',
)
self.save_every_n_epochs = gr.Number(
label='Save every N epochs', value=1, precision=0
)
self.caption_extension = gr.Textbox(
label='Caption Extension',
placeholder='(Optional) Extension for caption files. default: .caption',
)
with gr.Row():
self.mixed_precision = gr.Dropdown(
label='Mixed precision',
choices=[
'no',
'fp16',
'bf16',
],
value='fp16',
)
self.save_precision = gr.Dropdown(
label='Save precision',
choices=[
'float',
'fp16',
'bf16',
],
value='fp16',
)
self.num_cpu_threads_per_process = gr.Slider(
minimum=1,
maximum=os.cpu_count(),
step=1,
label='Number of CPU threads per core',
value=2,
)
self.seed = gr.Textbox(
label='Seed', placeholder='(Optional) eg:1234'
)
self.cache_latents = gr.Checkbox(label='Cache latents', value=True)
self.cache_latents_to_disk = gr.Checkbox(
label='Cache latents to disk', value=False
)
with gr.Row():
self.learning_rate = gr.Number(
label='Learning rate', value=learning_rate_value
)
self.lr_scheduler = gr.Dropdown(
label='LR Scheduler',
choices=[
'adafactor',
'constant',
'constant_with_warmup',
'cosine',
'cosine_with_restarts',
'linear',
'polynomial',
],
value=lr_scheduler_value,
)
self.lr_warmup = gr.Slider(
label='LR warmup (% of steps)',
value=lr_warmup_value,
minimum=0,
maximum=100,
step=1,
)
self.optimizer = gr.Dropdown(
label='Optimizer',
choices=[
'AdamW',
'AdamW8bit',
'Adafactor',
'DAdaptation',
'DAdaptAdaGrad',
'DAdaptAdam',
'DAdaptAdan',
'DAdaptAdanIP',
'DAdaptAdamPreprint',
'DAdaptLion',
'DAdaptSGD',
'Lion',
'Lion8bit',
"PagedAdamW8bit",
"PagedLion8bit",
'Prodigy',
'SGDNesterov',
'SGDNesterov8bit',
],
value='AdamW8bit',
interactive=True,
)
with gr.Row(visible=not finetuning):
self.lr_scheduler_num_cycles = gr.Textbox(
label='LR number of cycles',
placeholder='(Optional) For Cosine with restart and polynomial only',
)
self.lr_scheduler_power = gr.Textbox(
label='LR power',
placeholder='(Optional) For Cosine with restart and polynomial only',
)
with gr.Row():
self.optimizer_args = gr.Textbox(
label='Optimizer extra arguments',
placeholder='(Optional) eg: relative_step=True scale_parameter=True warmup_init=True',
)
with gr.Row(visible=not finetuning):
self.max_resolution = gr.Textbox(
label='Max resolution',
value='512,512',
placeholder='512,512',
)
self.stop_text_encoder_training = gr.Slider(
minimum=-1,
maximum=100,
value=0,
step=1,
label='Stop text encoder training',
)
with gr.Row(visible=not finetuning):
self.enable_bucket = gr.Checkbox(label='Enable buckets', value=True)
self.min_bucket_reso = gr.Slider(label='Minimum bucket resolution', value=256, minimum=64, maximum=4096, step=64, info='Minimum size in pixel a bucket can be (>= 64)')
self.max_bucket_reso = gr.Slider(label='Maximum bucket resolution', value=2048, minimum=64, maximum=4096, step=64, info='Maximum size in pixel a bucket can be (>= 64)')
|