File size: 8,723 Bytes
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import os
class BasicTraining:
def __init__(
self,
sdxl_checkbox: gr.Checkbox,
learning_rate_value="1e-6",
lr_scheduler_value="constant",
lr_warmup_value="0",
finetuning: bool = False,
dreambooth: 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
self.dreambooth = dreambooth
self.sdxl_checkbox = sdxl_checkbox
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.max_train_steps = gr.Textbox(
label="Max train steps",
placeholder="(Optional) Enforce number of steps",
)
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.lr_scheduler = gr.Dropdown(
label="LR Scheduler",
choices=[
"adafactor",
"constant",
"constant_with_warmup",
"cosine",
"cosine_with_restarts",
"linear",
"polynomial",
],
value=lr_scheduler_value,
)
self.optimizer = gr.Dropdown(
label="Optimizer",
choices=[
"AdamW",
"AdamW8bit",
"Adafactor",
"DAdaptation",
"DAdaptAdaGrad",
"DAdaptAdam",
"DAdaptAdan",
"DAdaptAdanIP",
"DAdaptAdamPreprint",
"DAdaptLion",
"DAdaptSGD",
"Lion",
"Lion8bit",
"PagedAdamW8bit",
"PagedAdamW32bit",
"PagedLion8bit",
"Prodigy",
"SGDNesterov",
"SGDNesterov8bit",
],
value="AdamW8bit",
interactive=True,
)
with gr.Row():
self.max_grad_norm = gr.Slider(
label="Max grad norm",
value=1.0,
minimum=0.0,
maximum=1.0
)
self.lr_scheduler_args = gr.Textbox(
label="LR scheduler extra arguments",
placeholder='(Optional) eg: "milestones=[1,10,30,50]" "gamma=0.1"',
)
self.optimizer_args = gr.Textbox(
label="Optimizer extra arguments",
placeholder="(Optional) eg: relative_step=True scale_parameter=True warmup_init=True",
)
with gr.Row():
# Original GLOBAL LR
if finetuning or dreambooth:
self.learning_rate = gr.Number(
label="Learning rate Unet", value=learning_rate_value,
minimum=0,
maximum=1,
info="Set to 0 to not train the Unet"
)
else:
self.learning_rate = gr.Number(
label="Learning rate", value=learning_rate_value,
minimum=0,
maximum=1
)
# New TE LR for non SDXL models
self.learning_rate_te = gr.Number(
label="Learning rate TE",
value=learning_rate_value,
visible=finetuning or dreambooth,
minimum=0,
maximum=1,
info="Set to 0 to not train the Text Encoder"
)
# New TE LR for SDXL models
self.learning_rate_te1 = gr.Number(
label="Learning rate TE1",
value=learning_rate_value,
visible=False,
minimum=0,
maximum=1,
info="Set to 0 to not train the Text Encoder 1"
)
# New TE LR for SDXL models
self.learning_rate_te2 = gr.Number(
label="Learning rate TE2",
value=learning_rate_value,
visible=False,
minimum=0,
maximum=1,
info="Set to 0 to not train the Text Encoder 2"
)
self.lr_warmup = gr.Slider(
label="LR warmup (% of steps)",
value=lr_warmup_value,
minimum=0,
maximum=100,
step=1,
)
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(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)",
)
def update_learning_rate_te(sdxl_checkbox, finetuning, dreambooth):
return (
gr.Number.update(visible=(not sdxl_checkbox and (finetuning or dreambooth))),
gr.Number.update(visible=(sdxl_checkbox and (finetuning or dreambooth))),
gr.Number.update(visible=(sdxl_checkbox and (finetuning or dreambooth))),
)
self.sdxl_checkbox.change(
update_learning_rate_te,
inputs=[self.sdxl_checkbox, gr.Checkbox(value=finetuning, visible=False), gr.Checkbox(value=dreambooth, visible=False)],
outputs=[
self.learning_rate_te,
self.learning_rate_te1,
self.learning_rate_te2,
],
)
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