Commit
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ba42139
1
Parent(s):
e731c90
Upload web-ui.py
Browse files
web-ui.py
CHANGED
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@@ -60,7 +60,7 @@ def convert_model(checkpoint_path, output_path):
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def load_model(model_name):
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if model_name in model_cache:
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return model_cache[model_name]
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-
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# Limit cache size
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if len(model_cache) >= max_cache_size:
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model_cache.pop(next(iter(model_cache)))
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@@ -96,9 +96,10 @@ def load_model(model_name):
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return ip_model
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# Function to process image and generate output
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def generate_image(input_image, positive_prompt, negative_prompt, width, height, model_name, num_inference_steps, seed, randomize_seed, num_images, batch_size, enable_shortcut, s_scale):
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saved_images = []
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# Load and prepare the model
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ip_model = load_model(model_name)
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@@ -170,6 +171,7 @@ with gr.Blocks() as demo:
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negative_prompt = gr.Textbox(label="Negative Prompt")
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with gr.Row():
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model_selector = gr.Dropdown(label="Select Model", choices=static_model_names, value=static_model_names[0])
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with gr.Column():
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output_gallery = gr.Gallery(label="Generated Images")
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@@ -184,8 +186,8 @@ with gr.Blocks() as demo:
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generate_btn.click(
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generate_image,
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inputs=[input_image, positive_prompt, negative_prompt, width, height, model_selector, num_inference_steps, seed, randomize_seed, num_images, batch_size, enable_shortcut, s_scale],
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outputs=[output_gallery, output_text, display_seed]
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)
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convert_btn.click(
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def load_model(model_name):
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if model_name in model_cache:
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return model_cache[model_name]
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print(f"loading model {model_name}")
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# Limit cache size
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if len(model_cache) >= max_cache_size:
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model_cache.pop(next(iter(model_cache)))
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return ip_model
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# Function to process image and generate output
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def generate_image(input_image, positive_prompt, negative_prompt, width, height, model_name, num_inference_steps, seed, randomize_seed, num_images, batch_size, enable_shortcut, s_scale, custom_model_path):
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saved_images = []
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if custom_model_path:
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model_name = custom_model_path
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# Load and prepare the model
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ip_model = load_model(model_name)
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negative_prompt = gr.Textbox(label="Negative Prompt")
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with gr.Row():
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model_selector = gr.Dropdown(label="Select Model", choices=static_model_names, value=static_model_names[0])
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custom_model_path = gr.Textbox(label="Custom Model Path (Optional)")
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with gr.Column():
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output_gallery = gr.Gallery(label="Generated Images")
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generate_btn.click(
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generate_image,
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inputs=[input_image, positive_prompt, negative_prompt, width, height, model_selector, num_inference_steps, seed, randomize_seed, num_images, batch_size, enable_shortcut, s_scale, custom_model_path],
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outputs=[output_gallery, output_text, display_seed]
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)
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convert_btn.click(
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