| from typing import Dict, List, Any |
|
|
| import base64 |
| import numpy as np |
| import tensorflow as tf |
| from tensorflow import keras |
| from keras_cv.models.stable_diffusion.decoder import Decoder |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| img_height = 512 |
| img_width = 512 |
| img_height = round(img_height / 128) * 128 |
| img_width = round(img_width / 128) * 128 |
| |
| self.decoder = Decoder(img_height, img_width) |
| decoder_weights_fpath = keras.utils.get_file( |
| origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_decoder.h5", |
| file_hash="ad350a65cc8bc4a80c8103367e039a3329b4231c2469a1093869a345f55b1962", |
| ) |
| self.decoder.load_weights(decoder_weights_fpath) |
|
|
| def __call__(self, data: Dict[str, Any]) -> str: |
| |
| latent = data.pop("inputs", data) |
| batch_size = data.pop("batch_size", 1) |
|
|
| latent = base64.b64decode(latent) |
| latent = np.frombuffer(latent, dtype="float32") |
| latent = np.reshape(latent, (batch_size, 64, 64, 4)) |
|
|
| decoded = self.decoder.predict_on_batch(latent) |
| decoded = ((decoded + 1) / 2) * 255 |
| images = np.clip(decoded, 0, 255).astype("uint8") |
|
|
| images_b64 = base64.b64encode(images.tobytes()) |
| images_b64str = images_b64.decode() |
|
|
| return images_b64str |
|
|
