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from typing import Dict, List, Any
import base64
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 GroupNormalization(tf.keras.layers.Layer):
"""GroupNormalization layer.
This layer is only here temporarily and will be removed
as we introduce GroupNormalization in core Keras.
"""
def __init__(
self,
groups=32,
axis=-1,
epsilon=1e-5,
**kwargs,
):
super().__init__(**kwargs)
self.groups = groups
self.axis = axis
self.epsilon = epsilon
def build(self, input_shape):
dim = input_shape[self.axis]
self.gamma = self.add_weight(
shape=(dim,),
name="gamma",
initializer="ones",
)
self.beta = self.add_weight(
shape=(dim,),
name="beta",
initializer="zeros",
)
def call(self, inputs):
input_shape = tf.shape(inputs)
reshaped_inputs = self._reshape_into_groups(inputs, input_shape)
normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape)
return tf.reshape(normalized_inputs, input_shape)
def _reshape_into_groups(self, inputs, input_shape):
group_shape = [input_shape[i] for i in range(inputs.shape.rank)]
group_shape[self.axis] = input_shape[self.axis] // self.groups
group_shape.insert(self.axis, self.groups)
group_shape = tf.stack(group_shape)
return tf.reshape(inputs, group_shape)
def _apply_normalization(self, reshaped_inputs, input_shape):
group_reduction_axes = list(range(1, reshaped_inputs.shape.rank))
axis = -2 if self.axis == -1 else self.axis - 1
group_reduction_axes.pop(axis)
mean, variance = tf.nn.moments(
reshaped_inputs, group_reduction_axes, keepdims=True
)
gamma, beta = self._get_reshaped_weights(input_shape)
return tf.nn.batch_normalization(
reshaped_inputs,
mean=mean,
variance=variance,
scale=gamma,
offset=beta,
variance_epsilon=self.epsilon,
)
def _get_reshaped_weights(self, input_shape):
broadcast_shape = self._create_broadcast_shape(input_shape)
gamma = tf.reshape(self.gamma, broadcast_shape)
beta = tf.reshape(self.beta, broadcast_shape)
return gamma, beta
def _create_broadcast_shape(self, input_shape):
broadcast_shape = [1] * input_shape.shape.rank
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(self.axis, self.groups)
return broadcast_shape
class PaddedConv2D(keras.layers.Layer):
def __init__(self, filters, kernel_size, padding=0, strides=1, **kwargs):
super().__init__(**kwargs)
self.padding2d = keras.layers.ZeroPadding2D(padding)
self.conv2d = keras.layers.Conv2D(filters, kernel_size, strides=strides)
def call(self, inputs):
x = self.padding2d(inputs)
return self.conv2d(x)
class AttentionBlock(keras.layers.Layer):
def __init__(self, output_dim, **kwargs):
super().__init__(**kwargs)
self.output_dim = output_dim
self.norm = GroupNormalization(epsilon=1e-5)
self.q = PaddedConv2D(output_dim, 1)
self.k = PaddedConv2D(output_dim, 1)
self.v = PaddedConv2D(output_dim, 1)
self.proj_out = PaddedConv2D(output_dim, 1)
def call(self, inputs):
x = self.norm(inputs)
q, k, v = self.q(x), self.k(x), self.v(x)
# Compute attention
_, h, w, c = q.shape
q = tf.reshape(q, (-1, h * w, c)) # b, hw, c
k = tf.transpose(k, (0, 3, 1, 2))
k = tf.reshape(k, (-1, c, h * w)) # b, c, hw
y = q @ k
y = y * (c**-0.5)
y = keras.activations.softmax(y)
# Attend to values
v = tf.transpose(v, (0, 3, 1, 2))
v = tf.reshape(v, (-1, c, h * w))
y = tf.transpose(y, (0, 2, 1))
x = v @ y
x = tf.transpose(x, (0, 2, 1))
x = tf.reshape(x, (-1, h, w, c))
return self.proj_out(x) + inputs
class ResnetBlock(keras.layers.Layer):
def __init__(self, output_dim, **kwargs):
super().__init__(**kwargs)
self.output_dim = output_dim
self.norm1 = GroupNormalization(epsilon=1e-5)
self.conv1 = PaddedConv2D(output_dim, 3, padding=1)
self.norm2 = GroupNormalization(epsilon=1e-5)
self.conv2 = PaddedConv2D(output_dim, 3, padding=1)
def build(self, input_shape):
if input_shape[-1] != self.output_dim:
self.residual_projection = PaddedConv2D(self.output_dim, 1)
else:
self.residual_projection = lambda x: x
def call(self, inputs):
x = self.conv1(keras.activations.swish(self.norm1(inputs)))
x = self.conv2(keras.activations.swish(self.norm2(x)))
return x + self.residual_projection(inputs)
class ImageEncoder(keras.Sequential):
"""ImageEncoder is the VAE Encoder for StableDiffusion."""
def __init__(self, img_height=512, img_width=512, download_weights=True):
super().__init__(
[
keras.layers.Input((img_height, img_width, 3)),
PaddedConv2D(128, 3, padding=1),
ResnetBlock(128),
ResnetBlock(128),
PaddedConv2D(128, 3, padding=1, strides=2),
ResnetBlock(256),
ResnetBlock(256),
PaddedConv2D(256, 3, padding=1, strides=2),
ResnetBlock(512),
ResnetBlock(512),
PaddedConv2D(512, 3, padding=1, strides=2),
ResnetBlock(512),
ResnetBlock(512),
ResnetBlock(512),
AttentionBlock(512),
ResnetBlock(512),
GroupNormalization(epsilon=1e-5),
keras.layers.Activation("swish"),
PaddedConv2D(8, 3, padding=1),
PaddedConv2D(8, 1),
# TODO(lukewood): can this be refactored to be a Rescaling layer?
# Perhaps some sort of rescale and gather?
# Either way, we may need a lambda to gather the first 4 dimensions.
keras.layers.Lambda(lambda x: x[..., :4] * 0.18215),
]
)
if download_weights:
image_encoder_weights_fpath = keras.utils.get_file(
origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/vae_encoder.h5",
file_hash="c60fb220a40d090e0f86a6ab4c312d113e115c87c40ff75d11ffcf380aab7ebb",
)
self.load_weights(image_encoder_weights_fpath)
class EndpointHandler():
def __init__(self, path=""):
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)
self.image_encoder = ImageEncoder()
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 _prepare_img_mask(self, image, mask, batch_size):
image = base64.b64decode(image)
image = np.frombuffer(image, dtype="uint8")
image = np.reshape(image, (512, 512, 3))
image = tf.convert_to_tensor(image)
image = tf.squeeze(image)
image = tf.cast(image, dtype=tf.float32) / 255.0 * 2.0 - 1.0
image = tf.expand_dims(image, axis=0)
known_x0 = self.image_encoder(image)
if image.shape.rank == 3:
known_x0 = tf.repeat(known_x0, batch_size, axis=0)
mask = base64.b64decode(mask)
mask = np.frombuffer(mask, dtype="uint8")
mask = np.reshape(mask, (512, 512, 1))
mask = tf.convert_to_tensor(mask)
mask = tf.expand_dims(mask, axis=0)
mask = tf.cast(
tf.nn.max_pool2d(mask, ksize=8, strides=8, padding="SAME"),
dtype=tf.float32,
)
mask = tf.squeeze(mask)
if mask.shape.rank == 2:
mask = tf.repeat(tf.expand_dims(mask, axis=0), batch_size, axis=0)
mask = tf.expand_dims(mask, axis=-1)
return known_x0, mask
def __call__(self, data: Dict[str, Any]) -> str:
# get inputs
inputs = data.pop("inputs", data)
batch_size = data.pop("batch_size", 1)
context = base64.b64decode(inputs[0])
context = np.frombuffer(context, dtype="float32")
context = np.reshape(context, (batch_size, 77, 768))
unconditional_context = base64.b64decode(inputs[1])
unconditional_context = np.frombuffer(unconditional_context, dtype="float32")
unconditional_context = np.reshape(unconditional_context, (batch_size, 77, 768))
num_steps = data.pop("num_steps", 25)
unconditional_guidance_scale = data.pop("unconditional_guidance_scale", 7.5)
num_resamples = data.pop("num_resamples", 1)
known_x0, mask = self._prepare_img_mask(inputs[2], inputs[3], batch_size)
latent = self._get_initial_diffusion_noise(batch_size, self.seed)
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]:
a_t, a_prev = alphas[index], alphas_prev[index]
latent_prev = latent # Set aside the previous latent vector
t_emb = self._get_timestep_embedding(timestep, batch_size)
for resample_index in range(num_resamples):
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
)
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
# Use known image (x0) to compute latent
if timestep > 1:
noise = tf.random.normal(tf.shape(known_x0), seed=self.seed)
else:
noise = 0.0
known_latent = (
math.sqrt(a_prev) * known_x0 + math.sqrt(1 - a_prev) * noise
)
# Use known latent in unmasked regions
latent = mask * known_latent + (1 - mask) * latent
# Resample latent
if resample_index < num_resamples - 1 and timestep > 1:
beta_prev = 1 - (a_t / a_prev)
latent_prev = tf.random.normal(
tf.shape(latent),
mean=latent * math.sqrt(1 - beta_prev),
stddev=math.sqrt(beta_prev),
seed=self.seed,
)
iteration += 1
progbar.update(iteration)
latent_b64 = base64.b64encode(latent.numpy().tobytes())
latent_b64str = latent_b64.decode()
return latent_b64str
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