Geek7/dftrztxi

1import gradio as gr2from diffusers import DiffusionPipeline3import dask4from dask import delayed5 6# Load model7pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")8 9def generate_image(prompt, num_inference_steps=50):10    """11    Generate an image based on a text prompt using diffusion with optimizations.12    The number of inference steps is reduced for faster generation.13    """14    # Reduce steps for faster processing15    image = pipe(prompt, num_inference_steps=num_inference_steps).images[0]16    return image17 18# Dask-delayed function to utilize multi-core CPU processing19@delayed20def dask_generate(prompt):21    return generate_image(prompt)22 23def parallel_generate(prompt):24    # Execute the generation using Dask to potentially improve processing speed25    image = dask.compute(dask_generate(prompt))[0]26    return image27 28# Gradio interface29iface = gr.Interface(30    fn=parallel_generate,31    inputs=gr.Textbox(label="Prompt", placeholder="Enter your prompt here"),32    outputs=gr.Image(type="pil"),33    title="CPU Optimized Image Generation",34    description="Enter a prompt to generate an image efficiently using CPU optimization."35)36 37# Launch the Gradio app38if __name__ == "__main__":39    iface.launch()