import base64
from io import BytesIO
from diffusers import StableDiffusionPipeline


def model_fn(model_dir):
    pipe = StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-torch"
    )

    return pipe


def predict_fn(data, pipe):
    # get prompt & parameters
    prompt = data.pop("inputs", data)
    # set valid HP for stable diffusion
    num_inference_steps = data.pop("num_inference_steps", 50)
    guidance_scale = data.pop("guidance_scale", 7.5)
    num_images_per_prompt = data.pop("num_images_per_prompt", 4)

    # run generation with parameters
    generated_images = pipe(
        prompt,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
    )["images"]

    # create response
    encoded_images = []
    for image in generated_images:
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        encoded_images.append(base64.b64encode(buffered.getvalue()).decode())

    # create response
    return {"generated_images": encoded_images}