| import gradio as gr |
| 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(): |
| |
| 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 |
| ) |
| |
| 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" |
| ) |
| |
| 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" |
| ) |
| |
| 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, |
| ], |
| ) |
|
|
