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app.py
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@@ -5,12 +5,13 @@ import gradio as gr
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipeline = DiffusionPipeline.from_pretrained("anusha-bhambore/live-eventful")
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pipeline = pipeline.to(device)
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def generate_image_interface(prompt, negative_prompt,num_inference_steps=50, weight=640):
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params = {'prompt': prompt, 'num_inference_steps': num_inference_steps, 'num_images_per_prompt':2, 'height':int(1.2*weight),
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'weight': weight, 'negative_prompt': negative_prompt}
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img = pipeline(**params).images
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return img[0],img[1]
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description = "Experience the magic of personalized birthday event design with our innovative web app! Simply input your preferences and prompts, and watch as your creative ideas transform into stunning, one-of-a-kind birthday event images. "
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demo = gr.Interface(generate_image_interface,title="Birthday Events", inputs=["text","text","text", "text",gr.Slider(1,100),gr.Slider(512,640)], outputs=["image","image"], description=description)
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demo.launch()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipeline = DiffusionPipeline.from_pretrained("anusha-bhambore/live-eventful")
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pipeline = pipeline.to(device)
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def generate_image_interface(prompt, negative_prompt, gender, age, num_inference_steps=50, weight=640):
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params = {'prompt': prompt, 'num_inference_steps': num_inference_steps, 'num_images_per_prompt':2, 'height':int(1.2*weight),
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'weight': weight, 'negative_prompt': negative_prompt,'gender': gender, 'age': age }
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img = pipeline(**params).images
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return img[0],img[1]
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description = "Experience the magic of personalized birthday event design with our innovative web app! Simply input your preferences and prompts, and watch as your creative ideas transform into stunning, one-of-a-kind birthday event images. "
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demo = gr.Interface(generate_image_interface,title="Birthday Events", inputs=["text","text","text", "text",gr.Slider(1,100),gr.Slider(512,640)], outputs=["image","image"], description=description)
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demo.launch()
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