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import gradio as gr |
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import json |
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from .class_configuration_file import ConfigurationFile |
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from .class_source_model import SourceModel |
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from .class_folders import Folders |
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from .class_basic_training import BasicTraining |
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from .class_advanced_training import AdvancedTraining |
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from .class_sample_images import SampleImages |
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from library.dreambooth_folder_creation_gui import ( |
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gradio_dreambooth_folder_creation_tab, |
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) |
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from .common_gui import color_aug_changed |
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class Dreambooth: |
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def __init__( |
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self, |
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headless: bool = False, |
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): |
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self.headless = headless |
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self.dummy_db_true = gr.Label(value=True, visible=False) |
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self.dummy_db_false = gr.Label(value=False, visible=False) |
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self.dummy_headless = gr.Label(value=headless, visible=False) |
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gr.Markdown('Train a custom model using kohya dreambooth python code...') |
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self.config = ConfigurationFile(headless) |
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self.source_model = SourceModel(headless=headless) |
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with gr.Tab('Folders'): |
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self.folders = Folders(headless=headless) |
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with gr.Tab('Parameters'): |
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self.basic_training = BasicTraining( |
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learning_rate_value='1e-5', |
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lr_scheduler_value='cosine', |
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lr_warmup_value='10', |
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) |
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self.full_bf16 = gr.Checkbox( |
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label='Full bf16', value = False |
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) |
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with gr.Accordion('Advanced Configuration', open=False): |
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self.advanced_training = AdvancedTraining(headless=headless) |
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self.advanced_training.color_aug.change( |
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color_aug_changed, |
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inputs=[self.advanced_training.color_aug], |
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outputs=[self.basic_training.cache_latents], |
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) |
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self.sample = SampleImages() |
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with gr.Tab('Tools'): |
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gr.Markdown( |
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'This section provide Dreambooth tools to help setup your dataset...' |
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) |
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gradio_dreambooth_folder_creation_tab( |
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train_data_dir_input=self.folders.train_data_dir, |
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reg_data_dir_input=self.folders.reg_data_dir, |
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output_dir_input=self.folders.output_dir, |
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logging_dir_input=self.folders.logging_dir, |
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headless=headless, |
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) |
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def save_to_json(self, filepath): |
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def serialize(obj): |
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if isinstance(obj, gr.inputs.Input): |
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return obj.get() |
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if isinstance(obj, (bool, int, float, str)): |
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return obj |
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if isinstance(obj, dict): |
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return {k: serialize(v) for k, v in obj.items()} |
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if hasattr(obj, "__dict__"): |
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return serialize(vars(obj)) |
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return str(obj) |
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try: |
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with open(filepath, 'w') as outfile: |
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print(serialize(vars(self))) |
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json.dump(serialize(vars(self)), outfile) |
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except Exception as e: |
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print(f"Error saving to JSON: {str(e)}") |
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def load_from_json(self, filepath): |
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def deserialize(key, value): |
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if hasattr(self, key): |
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attr = getattr(self, key) |
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if isinstance(attr, gr.inputs.Input): |
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attr.set(value) |
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elif hasattr(attr, "__dict__"): |
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for k, v in value.items(): |
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deserialize(k, v) |
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else: |
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setattr(self, key, value) |
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else: |
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print(f"Warning: {key} not found in the object's attributes.") |
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try: |
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with open(filepath) as json_file: |
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data = json.load(json_file) |
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for key, value in data.items(): |
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deserialize(key, value) |
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except FileNotFoundError: |
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print(f"Error: The file {filepath} was not found.") |
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except json.JSONDecodeError: |
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print(f"Error: The file {filepath} could not be decoded as JSON.") |
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except Exception as e: |
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print(f"Error loading from JSON: {str(e)}") |
