runpod_infer.py

'''
RunPod | serverless-ckpt-template | runpod_infer.py

Entry point for job requests from RunPod serverless platform.
'''

import os

import sd_runner

import runpod
from runpod.serverless.utils import rp_download, rp_cleanup, rp_upload
from runpod.serverless.utils.rp_validator import validate

MODEL_RUNNER = sd_runner.Predictor()
MODEL_RUNNER.setup()

INPUT_SCHEMA = {
    'prompt': {
        'type': str,
        'required': True
    },
    'negative_prompt': {
        'type': str,
        'required': False,
        'default': None
    },
    'width': {
        'type': int,
        'required': False,
        'default': 768,
        'constraints': lambda width: width in [128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024]
    },
    'height': {
        'type': int,
        'required': False,
        'default': 768,
        'constraints': lambda height: height in [128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024]
    },
    'num_outputs': {
        'type': int,
        'required': False,
        'default': 1,
        'constraints': lambda num_outputs: num_outputs in range(1, 4)
    },
    'num_inference_steps': {
        'type': int,
        'required': False,
        'default': 50,
        'constraints': lambda num_inference_steps: num_inference_steps in range(1, 500)
    },
    'guidance_scale': {
        'type': float,
        'required': False,
        'default': 7.5,
        'constraints': lambda guidance_scale: 0 <= guidance_scale <= 20
    },
    'scheduler': {
        'type': str,
        'required': False,
        'default': 'DPMSolverMultistep',
        'constraints': lambda scheduler: scheduler in ['DDIM', 'K_EULER', 'DPMSolverMultistep', 'K_EULER_ANCESTRAL', 'PNDM', 'KLMS']
    },
    'seed': {
        'type': int,
        'required': False,
        'default': int.from_bytes(os.urandom(2), "big")
    }
}


def handler(job):
    '''
    Takes in raw data from the API call, prepares it for the model.
    Passes the data to the model to get the results.
    Prepares the resulting output to be returned to the API call.
    '''
    job_input = job['input']
    job_output = []

    # -------------------------------- Validation -------------------------------- #
    validated_input = validate(job_input, INPUT_SCHEMA)
    if 'errors' in validated_input:
        return {"errors": validated_input['errors']}

    valid_input = validated_input['validated_input']

    image_paths = MODEL_RUNNER.predict(
        prompt=valid_input['prompt'],
        negative_prompt=valid_input['negative_prompt'],
        width=valid_input['width'],
        height=valid_input['height'],
        num_outputs=valid_input['num_outputs'],
        num_inference_steps=valid_input['num_inference_steps'],
        guidance_scale=valid_input['guidance_scale'],
        scheduler=valid_input['scheduler'],
        seed=valid_input['seed']
    )

    for index, img_path in enumerate(image_paths):
        image_url = rp_upload.upload_image(job['id'], img_path)

        job_output.append({
            "image": image_url,
            "prompt": job_input["prompt"],
            "negative_prompt": job_input["negative_prompt"],
            "width": job_input['width'],
            "height": job_input['height'],
            "num_inference_steps": job_input['num_inference_steps'],
            "guidance_scale": job_input['guidance_scale'],
            "scheduler": job_input['scheduler'],
            "seed": job_input['seed'] + index
        })

    # Remove downloaded input objects
    # rp_cleanup.clean(['input_objects'])

    return job_output


# ---------------------------------------------------------------------------- #
#                               Start the Worker                               #
# ---------------------------------------------------------------------------- #
runpod.serverless.start({"handler": handler})