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Update app.py
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app.py
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@@ -2,28 +2,51 @@ import gradio as gr
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from diffusers import StableDiffusionPipeline
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import torch
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#
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#
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe.to(device)
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image = pipe(prompt).images[0]
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return image
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# Gradio UI
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demo = gr.Interface(
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fn=generate_poster,
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inputs=gr.Textbox(
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outputs=gr.Image(type="pil", label="Generated Poster"),
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title="AI Poster Generator",
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description="
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)
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from diffusers import StableDiffusionPipeline
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import torch
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# ----------------------------------------
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# Load PosterCraft model from Hugging Face
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# ----------------------------------------
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# If your Space uses a free CPU runtime, you can leave torch_dtype=None.
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# On GPU, float16 is faster and uses less memory.
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model_id = "PosterCraft/PosterCraft-v1_RL"
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try:
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if torch.cuda.is_available() else None
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)
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except Exception as e:
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raise RuntimeError(f"Error loading model {model_id}: {e}")
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# Select device
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe.to(device)
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# ----------------------------------------
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# Generation function
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# ----------------------------------------
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def generate_poster(prompt: str):
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"""
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Generate a poster/flyer based on the user's text prompt.
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"""
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if not prompt.strip():
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return None
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image = pipe(prompt).images[0]
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return image
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# ----------------------------------------
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# Gradio UI
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# ----------------------------------------
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demo = gr.Interface(
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fn=generate_poster,
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inputs=gr.Textbox(
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label="Enter your poster prompt",
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placeholder="e.g. Modern music festival flyer with bold typography and neon lights"
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),
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outputs=gr.Image(type="pil", label="Generated Poster"),
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title="AI Poster Generator",
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description="Enter a description and get an AI-designed poster or flyer."
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)
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# Launch the app
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if __name__ == "__main__":
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demo.launch()
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