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import base64
import logging
import math
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras_cv.models.generative.stable_diffusion.constants import _ALPHAS_CUMPROD
from keras_cv.models.generative.stable_diffusion.diffusion_model import DiffusionModel
class EndpointHandler():
def __init__(self, path=""):
gpus = tf.config.list_physical_devices(device_type = 'GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
self.seed = None
img_height = 512
img_width = 512
self.img_height = round(img_height / 128) * 128
self.img_width = round(img_width / 128) * 128
self.MAX_PROMPT_LENGTH = 77
self.diffusion_model = DiffusionModel(self.img_height, self.img_width, self.MAX_PROMPT_LENGTH)
diffusion_model_weights_fpath = keras.utils.get_file(
origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_diffusion_model.h5",
file_hash="8799ff9763de13d7f30a683d653018e114ed24a6a819667da4f5ee10f9e805fe",
)
self.diffusion_model.load_weights(diffusion_model_weights_fpath)
def _get_initial_diffusion_noise(self, batch_size, seed):
if seed is not None:
return tf.random.stateless_normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4),
seed=[seed, seed],
)
else:
return tf.random.normal(
(batch_size, self.img_height // 8, self.img_width // 8, 4)
)
def _get_initial_alphas(self, timesteps):
alphas = [_ALPHAS_CUMPROD[t] for t in timesteps]
alphas_prev = [1.0] + alphas[:-1]
return alphas, alphas_prev
def _get_timestep_embedding(self, timestep, batch_size, dim=320, max_period=10000):
half = dim // 2
freqs = tf.math.exp(
-math.log(max_period) * tf.range(0, half, dtype=tf.float32) / half
)
args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
embedding = tf.reshape(embedding, [1, -1])
return tf.repeat(embedding, batch_size, axis=0)
def __call__(self, data: Dict[str, Any]) -> str:
# get inputs
contexts = data.pop("inputs", data)
logging.warning(contexts)
batch_size = data.pop("batch_size", 1)
context = base64.b64decode(contexts[0])
context = np.frombuffer(context, dtype="float32")
context = np.reshape(context, (batch_size, 77, 768))
print(context)
unconditional_context = base64.b64decode(contexts[1])
unconditional_context = np.frombuffer(unconditional_context, dtype="float32")
unconditional_context = np.reshape(unconditional_context, (batch_size, 77, 768))
print(unconditional_context)
num_steps = data.pop("num_steps", 25)
unconditional_guidance_scale = data.pop("unconditional_guidance_scale", 7.5)
latent = self._get_initial_diffusion_noise(batch_size, self.seed)
# Iterative reverse diffusion stage
timesteps = tf.range(1, 1000, 1000 // num_steps)
alphas, alphas_prev = self._get_initial_alphas(timesteps)
progbar = keras.utils.Progbar(len(timesteps))
iteration = 0
for index, timestep in list(enumerate(timesteps))[::-1]:
latent_prev = latent # Set aside the previous latent vector
t_emb = self._get_timestep_embedding(timestep, batch_size)
unconditional_latent = self.diffusion_model.predict_on_batch(
[latent, t_emb, unconditional_context]
)
latent = self.diffusion_model.predict_on_batch([latent, t_emb, context])
latent = unconditional_latent + unconditional_guidance_scale * (
latent - unconditional_latent
)
a_t, a_prev = alphas[index], alphas_prev[index]
pred_x0 = (latent_prev - math.sqrt(1 - a_t) * latent) / math.sqrt(a_t)
latent = latent * math.sqrt(1.0 - a_prev) + math.sqrt(a_prev) * pred_x0
iteration += 1
progbar.update(iteration)
latent_b64 = base64.b64encode(latent.numpy().tobytes())
latent_b64str = latent_b64.decode()
return latent_b64str |