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app.py
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from diffusers import StableDiffusionPipeline
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import matplotlib.pyplot as plt
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import torch
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model_id1 = "dreamlike-art/dreamlike-diffusion-1.0"
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pipe = StableDiffusionPipeline.from_pretrained(model_id1, use_safetensors=True)
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pipe = pipe.to("cpu")
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def generate_image_interface(prompt):
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# Assuming `pipe` is correctly defined elsewhere for image generation
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params = {
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'prompt': prompt,
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'num_inference_steps': 100,
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'num_images_per_prompt': 2, # Assuming this is a valid parameter
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'height': int(1.2 * 640) # Assuming height is calculated based on weight
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}
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# Assuming `pipe` is correctly defined elsewhere
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img = pipe(**params).images
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return img[0], img[1]
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import gradio as gr
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demo = gr.Interface(
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fn=generate_image_interface,
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inputs=["text"],
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outputs=["image", "image"],
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title="Image Generation Interface",
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description="Generate images based on prompts."
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)
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demo.launch()
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