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import base64
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import torch
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from io import BytesIO
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from diffusers import StableDiffusionPipeline
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def model_fn(model_dir):
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  # Load stable diffusion and move it to the GPU
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  pipe = StableDiffusionPipeline.from_pretrained(model_dir, torch_dtype=torch.float16)
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  pipe = pipe.to("cuda")
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  return pipe
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def predict_fn(data, pipe):
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    # get prompt & parameters
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    prompt = data.pop("inputs", data)
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    # set valid HP for stable diffusion
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    num_inference_steps = data.pop("num_inference_steps", 50)
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    guidance_scale = data.pop("guidance_scale", 7.5)
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    num_images_per_prompt = data.pop("num_images_per_prompt", 4)
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    # run generation with parameters
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    generated_images = pipe(prompt,num_inference_steps=num_inference_steps,guidance_scale=guidance_scale,num_images_per_prompt=num_images_per_prompt)["images"]
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    # create response 
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    encoded_images=[]
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    for image in generated_images:
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      buffered = BytesIO()
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      image.save(buffered, format="JPEG")
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      encoded_images.append(base64.b64encode(buffered.getvalue()).decode())
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    # create response
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    return {"generated_images": encoded_images}
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