Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -598,72 +598,78 @@ def infer_upscale(
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with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as app:
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loras_state = gr.State(loras)
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selected_indices = gr.State([])
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-
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with gr.Row():
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with gr.Column(scale=3):
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prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
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with gr.Column(scale=1):
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generate_button = gr.Button("Generate", variant="primary")
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with gr.Row():
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with gr.Column(scale=1, min_width=25):
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randomize_button = gr.Button("🎲", variant="secondary")
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with gr.Column(scale=8):
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with gr.Row():
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with gr.Column(scale=0, min_width=50):
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lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, width=50, height=50)
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with gr.Column(scale=3, min_width=100):
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selected_info_1 = gr.Markdown("Select a LoRA 1")
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with gr.Column(scale=5, min_width=50):
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lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
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with gr.Column(scale=8):
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with gr.Row():
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with gr.Column(scale=0, min_width=50):
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lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, width=50, height=50)
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with gr.Column(scale=3, min_width=100):
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selected_info_2 = gr.Markdown("Select a LoRA 2")
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with gr.Column(scale=5, min_width=50):
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lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
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with gr.Row():
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with gr.Column():
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with gr.Group():
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with gr.Row():
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custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3)
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add_custom_lora_button = gr.Button("Add Custom LoRA", scale=2)
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remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
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gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)")
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gallery = gr.Gallery(
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[(item["image"], item["title"]) for item in loras],
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label="Or pick from the LoRA Explorer gallery",
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columns=4,
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)
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with gr.Column():
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progress_bar = gr.Markdown(visible=False)
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result = gr.Image(label="Generated Image", interactive=False)
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with gr.Accordion("History", open=False):
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history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
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with gr.
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with gr.
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with gr.Row():
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upscale_button = gr.Button("Upscale")
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@@ -671,10 +677,34 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as app:
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with gr.Column(scale=4):
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upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
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with gr.Column(scale=1):
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upscale_steps = gr.Slider(
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upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)
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with gr.Row():
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@@ -711,15 +741,29 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as app:
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inputs=[selected_indices, loras_state],
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outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
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)
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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)
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upscale_button.click(
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infer_upscale,
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inputs=[
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)
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if __name__ == "__main__":
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with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as app:
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loras_state = gr.State(loras)
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selected_indices = gr.State([])
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with gr.Row():
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with gr.Column(scale=3):
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prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
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with gr.Column(scale=1):
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generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
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with gr.Row(elem_id="loaded_loras"):
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with gr.Column(scale=1, min_width=25):
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randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
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with gr.Column(scale=8):
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with gr.Row():
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with gr.Column(scale=0, min_width=50):
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lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
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with gr.Column(scale=3, min_width=100):
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selected_info_1 = gr.Markdown("Select a LoRA 1")
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with gr.Column(scale=5, min_width=50):
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lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
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with gr.Row():
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remove_button_1 = gr.Button("Remove", size="sm")
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with gr.Column(scale=8):
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with gr.Row():
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with gr.Column(scale=0, min_width=50):
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lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
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with gr.Column(scale=3, min_width=100):
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selected_info_2 = gr.Markdown("Select a LoRA 2")
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with gr.Column(scale=5, min_width=50):
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lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
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with gr.Row():
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remove_button_2 = gr.Button("Remove", size="sm")
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with gr.Row():
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with gr.Column():
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with gr.Group():
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with gr.Row(elem_id="custom_lora_structure"):
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custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
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add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
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remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
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gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
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gallery = gr.Gallery(
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[(item["image"], item["title"]) for item in loras],
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label="Or pick from the LoRA Explorer gallery",
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allow_preview=False,
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columns=4,
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elem_id="gallery"
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)
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with gr.Column():
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progress_bar = gr.Markdown(elem_id="progress", visible=False)
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result = gr.Image(label="Generated Image", interactive=False)
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with gr.Accordion("History", open=False):
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history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
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with gr.Row():
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with gr.Accordion("Advanced Settings", open=False):
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with gr.Row():
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input_image = gr.Image(label="Input image", type="filepath")
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image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
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with gr.Column():
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with gr.Row():
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
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with gr.Row():
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width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
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height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
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with gr.Row():
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randomize_seed = gr.Checkbox(True, label="Randomize seed")
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
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# 업스케일 관련 UI 추가
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with gr.Row():
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upscale_button = gr.Button("Upscale")
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with gr.Column(scale=4):
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upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
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with gr.Column(scale=1):
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upscale_steps = gr.Slider(
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label="Number of Inference Steps for Upscaling",
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minimum=8,
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maximum=50,
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step=1,
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value=28,
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)
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upscale_factor = gr.Slider(
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label="Upscale Factor",
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minimum=1,
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maximum=4,
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step=1,
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value=4,
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)
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controlnet_conditioning_scale = gr.Slider(
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label="Controlnet Conditioning Scale",
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minimum=0.1,
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maximum=1.5,
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step=0.1,
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value=0.6,
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)
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upscale_seed = gr.Slider(
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label="Seed for Upscaling",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=42,
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)
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upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)
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with gr.Row():
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inputs=[selected_indices, loras_state],
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outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
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)
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gr.on(
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triggers=[generate_button.click, prompt.submit],
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fn=run_lora,
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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).then( # Update the history gallery
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fn=lambda x, history: update_history(x, history),
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inputs=[result, history_gallery],
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outputs=history_gallery,
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)
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# 업스케일 버튼 이벤트 추가
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upscale_button.click(
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infer_upscale,
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inputs=[
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upscale_seed,
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upscale_randomize_seed,
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upscale_input,
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upscale_steps,
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upscale_factor,
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controlnet_conditioning_scale,
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],
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outputs=upscale_result,
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)
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if __name__ == "__main__":
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