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import gradio as gr
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from os import path
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from backend.lora import (
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get_lora_models,
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get_active_lora_weights,
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update_lora_weights,
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load_lora_weight,
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)
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from state import get_settings, get_context
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from frontend.utils import get_valid_lora_model
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from models.interface_types import InterfaceType
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_MAX_LORA_WEIGHTS = 5
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_custom_lora_sliders = []
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_custom_lora_names = []
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_custom_lora_columns = []
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app_settings = get_settings()
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def on_click_update_weight(*lora_weights):
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update_weights = []
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active_weights = get_active_lora_weights()
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if not len(active_weights):
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gr.Warning("No active LoRAs, first you need to load LoRA model")
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return
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for idx, lora in enumerate(active_weights):
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update_weights.append(
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(
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lora[0],
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lora_weights[idx],
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)
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)
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if len(update_weights) > 0:
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update_lora_weights(
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get_context(InterfaceType.WEBUI).lcm_text_to_image.pipeline,
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app_settings.settings.lcm_diffusion_setting,
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update_weights,
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)
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def on_click_load_lora(lora_name, lora_weight):
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if app_settings.settings.lcm_diffusion_setting.use_openvino:
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gr.Warning("Currently LoRA is not supported in OpenVINO.")
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return
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lora_models_map = get_lora_models(
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app_settings.settings.lcm_diffusion_setting.lora.models_dir
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)
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settings = app_settings.settings.lcm_diffusion_setting
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settings.lora.fuse = False
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settings.lora.enabled = False
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print(f"Selected Lora Model :{lora_name}")
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print(f"Lora weight :{lora_weight}")
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settings.lora.path = lora_models_map[lora_name]
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settings.lora.weight = lora_weight
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if not path.exists(settings.lora.path):
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gr.Warning("Invalid LoRA model path!")
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return
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pipeline = get_context(InterfaceType.WEBUI).lcm_text_to_image.pipeline
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if not pipeline:
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gr.Warning("Pipeline not initialized. Please generate an image first.")
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return
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settings.lora.enabled = True
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load_lora_weight(
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get_context(InterfaceType.WEBUI).lcm_text_to_image.pipeline,
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settings,
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)
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global _MAX_LORA_WEIGHTS
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values = []
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labels = []
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rows = []
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active_weights = get_active_lora_weights()
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for idx, lora in enumerate(active_weights):
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labels.append(f"{lora[0]}: ")
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values.append(lora[1])
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rows.append(gr.Row.update(visible=True))
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for i in range(len(active_weights), _MAX_LORA_WEIGHTS):
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labels.append(f"Update weight")
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values.append(0.0)
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rows.append(gr.Row.update(visible=False))
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return labels + values + rows
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def get_lora_models_ui() -> None:
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with gr.Blocks() as ui:
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gr.HTML(
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"Download and place your LoRA model weights in <b>lora_models</b> folders and restart App"
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)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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lora_models_map = get_lora_models(
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app_settings.settings.lcm_diffusion_setting.lora.models_dir
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)
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valid_model = get_valid_lora_model(
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list(lora_models_map.values()),
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app_settings.settings.lcm_diffusion_setting.lora.path,
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app_settings.settings.lcm_diffusion_setting.lora.models_dir,
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)
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if valid_model != "":
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valid_model_path = lora_models_map[valid_model]
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app_settings.settings.lcm_diffusion_setting.lora.path = (
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valid_model_path
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)
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else:
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app_settings.settings.lcm_diffusion_setting.lora.path = ""
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lora_model = gr.Dropdown(
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lora_models_map.keys(),
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label="LoRA model",
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info="LoRA model weight to load (You can use Lora models from Civitai or Hugging Face .safetensors format)",
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value=valid_model,
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interactive=True,
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)
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lora_weight = gr.Slider(
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0.0,
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1.0,
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value=app_settings.settings.lcm_diffusion_setting.lora.weight,
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step=0.05,
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label="Initial Lora weight",
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interactive=True,
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)
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load_lora_btn = gr.Button(
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"Load selected LoRA",
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elem_id="load_lora_button",
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scale=0,
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)
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with gr.Row():
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gr.Markdown(
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"## Loaded LoRA models",
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show_label=False,
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)
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update_lora_weights_btn = gr.Button(
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"Update LoRA weights",
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elem_id="load_lora_button",
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scale=0,
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)
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global _MAX_LORA_WEIGHTS
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global _custom_lora_sliders
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global _custom_lora_names
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global _custom_lora_columns
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for i in range(0, _MAX_LORA_WEIGHTS):
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new_row = gr.Column(visible=False)
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_custom_lora_columns.append(new_row)
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with new_row:
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lora_name = gr.Markdown(
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"Lora Name",
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show_label=True,
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)
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lora_slider = gr.Slider(
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0.0,
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1.0,
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step=0.05,
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label="LoRA weight",
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interactive=True,
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visible=True,
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)
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_custom_lora_names.append(lora_name)
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_custom_lora_sliders.append(lora_slider)
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load_lora_btn.click(
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fn=on_click_load_lora,
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inputs=[lora_model, lora_weight],
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outputs=[
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*_custom_lora_names,
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*_custom_lora_sliders,
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*_custom_lora_columns,
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],
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)
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update_lora_weights_btn.click(
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fn=on_click_update_weight,
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inputs=[*_custom_lora_sliders],
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outputs=None,
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)
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