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ddpm_tf.py
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ddpm_tf.py
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import tensorflow as tf
from tensorflow.keras import layers
import math
from PIL import Image
import numpy as np
import requests
from inspect import isfunction
#from IPython.display import display
import matplotlib.pyplot as plt
image_size = 64
timesteps = 200
batch_size = 32
num_epochs = 1 # Just for the sake of demonstration
total_timesteps = 1000
norm_groups = 8 # Number of groups used in GroupNormalization layer
learning_rate = 2e-4
img_channels = 3
clip_min = -1.0
clip_max = 1.0
first_conv_channels = 64
channel_multiplier = [1, 2, 4, 8]
widths = [first_conv_channels * mult for mult in channel_multiplier]
has_attention = [False, False, True, True]
num_res_blocks = 2 # Number of residual blocks
# define various schedules for the TT timesteps
def cosine_beta_schedule(timesteps, s=0.008):
"""
cosine schedule as proposed in https://arxiv.org/abs/2102.09672
"""
steps = timesteps + 1
x = tf.linspace(0., timesteps, steps)
alphas_cumprod = tf.cos(((x / timesteps) + s) / (1 + s) * tf.math.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
return tf.clip_by_value(betas, 0.0001, 0.9999)
def linear_beta_schedule(timesteps):
beta_start = 0.0001
beta_end = 0.02
return tf.linspace(beta_start, beta_end, timesteps)
def quadratic_beta_schedule(timesteps):
beta_start = 0.0001
beta_end = 0.02
return tf.linspace(beta_start**0.5, beta_end**0.5, timesteps) ** 2
def sigmoid_beta_schedule(timesteps):
beta_start = 0.0001
beta_end = 0.02
betas = tf.linspace(-6, 6, timesteps)
return tf.sigmoid(betas) * (beta_end - beta_start) + beta_start
# define beta schedule
betas = linear_beta_schedule(timesteps=timesteps)
# define alphas
alphas = 1. - betas
alphas_cumprod = tf.math.cumprod(alphas, axis=0)
alphas_cumprod_prev = tf.pad(alphas_cumprod[:-1], [[1, 0]], constant_values=1.0)
sqrt_recip_alphas = tf.sqrt(1.0 / alphas)
# calculations for diffusion q(x_t | x_{t-1}) and others
sqrt_alphas_cumprod = tf.sqrt(alphas_cumprod)
sqrt_one_minus_alphas_cumprod = tf.sqrt(1. - alphas_cumprod)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
def extract(a, t, x_shape):
batch_size = tf.shape(t)[0]
out = tf.gather(a, t, axis=-1)
return tf.reshape(out, [batch_size, *((1,) * (len(x_shape) - 1))])
# define the forward transform
# 1.Rescaling and cropping
# 2.Dividing pixel values by 255. converting them from a range of [0, 255] to [0, 1]
# 3.Multiplies the pixel values by 2 and subtracts 1, converting them from a range of [0, 1] to [-1, 1].
transform_seq = tf.keras.Sequential([
tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=0)), # [H,W,C] to [1,H,W,C]
tf.keras.layers.Resizing(image_size, image_size, crop_to_aspect_ratio=True), # [1,H,W,C]
tf.keras.layers.Permute((3, 1, 2)), # [1,H,W,C] to [1,C,H,W]
tf.keras.layers.Lambda(lambda x: tf.squeeze((x/255.)*2 - 1, axis=0)) # [1,C,H,W] to [C,H,W]
])
def transform(img):
data = tf.keras.utils.img_to_array(img) # [H,W,C]
data = transform_seq(data) # [1,C,H,W]
return data
# define the reverse transform
reverse_transform_seq = tf.keras.Sequential([
tf.keras.layers.Lambda(lambda x: tf.expand_dims((x + 1)*255/2,axis=0) ), # [C,H,W] to [1,C,H,W]
tf.keras.layers.Permute((2, 3, 1)), # [1,C,H,W] to [1,H,W,C]
tf.keras.layers.Lambda(lambda x: tf.cast(tf.squeeze(x,axis=0), tf.uint8) ), # [1,H,W,C] to [H,W,C]
])
def reverse_transform(data): #[C,H,W]
data = reverse_transform_seq(data)
return tf.keras.utils.array_to_img(data)
# forward diffusion
def q_sample(x_start, t, noise=None):
if noise is None:
noise = tf.random.normal(x_start.shape)
sqrt_alphas_cumprod_t = extract(sqrt_alphas_cumprod, t, x_start.shape)
sqrt_one_minus_alphas_cumprod_t = extract(
sqrt_one_minus_alphas_cumprod, t, x_start.shape
)
return sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise
def get_noisy_image(x_start, t):
t = tf.convert_to_tensor([t])
# add noise
x_noisy = q_sample(x_start, t=t)
noisy_image = reverse_transform(x_noisy)
return noisy_image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
original_image = Image.open(requests.get(url, stream=True).raw)
x_0 = transform(original_image)
def test_get_noisy_image():
fig = plt.figure(figsize=(8, 8))
columns = 5
rows = 4
for i in range(1, columns*rows +1):
timestep = 10*i-1
img = get_noisy_image(x_0, timestep)
fig.add_subplot(rows, columns, i).set_title(timestep+1)
plt.imshow(img)
plt.axis("off")
plt.show()
#test_get_noisy_image()
"""
## Network architecture
U-Net, originally developed for semantic segmentation, is an architecture that is
widely used for implementing diffusion models but with some slight modifications:
1. The network accepts two inputs: Image and time step
2. Self-attention between the convolution blocks once we reach a specific resolution
(16x16 in the paper)
3. Group Normalization instead of weight normalization
We implement most of the things as used in the original paper. We use the
`swish` activation function throughout the network. We use the variance scaling
kernel initializer.
The only difference here is the number of groups used for the
`GroupNormalization` layer. For the flowers dataset,
we found that a value of `groups=8` produces better results
compared to the default value of `groups=32`. Dropout is optional and should be
used where chances of over fitting is high. In the paper, the authors used dropout
only when training on CIFAR10.
"""
# Kernel initializer to use
def kernel_init(scale):
scale = max(scale, 1e-10)
return tf.keras.initializers.VarianceScaling(
scale, mode="fan_avg", distribution="uniform"
)
class AttentionBlock(layers.Layer):
"""Applies self-attention.
Args:
units: Number of units in the dense layers
groups: Number of groups to be used for GroupNormalization layer
"""
def __init__(self, units, groups=8, **kwargs):
self.units = units
self.groups = groups
super().__init__(**kwargs)
self.norm = layers.GroupNormalization(groups=groups)
self.query = layers.Dense(units, kernel_initializer=kernel_init(1.0))
self.key = layers.Dense(units, kernel_initializer=kernel_init(1.0))
self.value = layers.Dense(units, kernel_initializer=kernel_init(1.0))
self.proj = layers.Dense(units, kernel_initializer=kernel_init(0.0))
def call(self, inputs):
batch_size = tf.shape(inputs)[0]
height = tf.shape(inputs)[1]
width = tf.shape(inputs)[2]
scale = tf.cast(self.units, tf.float32) ** (-0.5)
inputs = self.norm(inputs)
q = self.query(inputs)
k = self.key(inputs)
v = self.value(inputs)
attn_score = tf.einsum("bhwc, bHWc->bhwHW", q, k) * scale
attn_score = tf.reshape(attn_score, [batch_size, height, width, height * width])
attn_score = tf.nn.softmax(attn_score, -1)
attn_score = tf.reshape(attn_score, [batch_size, height, width, height, width])
proj = tf.einsum("bhwHW,bHWc->bhwc", attn_score, v)
proj = self.proj(proj)
return inputs + proj
class TimeEmbedding(layers.Layer):
def __init__(self, dim, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.half_dim = dim // 2
self.emb = math.log(10000) / (self.half_dim - 1)
self.emb = tf.exp(tf.range(self.half_dim, dtype=tf.float32) * -self.emb)
def call(self, inputs):
inputs = tf.cast(inputs, dtype=tf.float32)
emb = inputs[:, None] * self.emb[None, :]
emb = tf.concat([tf.sin(emb), tf.cos(emb)], axis=-1)
return emb
def ResidualBlock(width, groups=8, activation_fn=tf.keras.activations.swish):
def apply(inputs):
x, t = inputs
input_width = x.shape[3]
if input_width == width:
residual = x
else:
residual = layers.Conv2D(
width, kernel_size=1, kernel_initializer=kernel_init(1.0)
)(x)
temb = activation_fn(t)
temb = layers.Dense(width, kernel_initializer=kernel_init(1.0))(temb)[
:, None, None, :
]
x = layers.GroupNormalization(groups=groups)(x)
x = activation_fn(x)
x = layers.Conv2D(
width, kernel_size=3, padding="same", kernel_initializer=kernel_init(1.0)
)(x)
x = layers.Add()([x, temb])
x = layers.GroupNormalization(groups=groups)(x)
x = activation_fn(x)
x = layers.Conv2D(
width, kernel_size=3, padding="same", kernel_initializer=kernel_init(0.0)
)(x)
x = layers.Add()([x, residual])
return x
return apply
def DownSample(width):
def apply(x):
x = layers.Conv2D(
width,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=kernel_init(1.0),
)(x)
return x
return apply
def UpSample(width, interpolation="nearest"):
def apply(x):
x = layers.UpSampling2D(size=2, interpolation=interpolation)(x)
x = layers.Conv2D(
width, kernel_size=3, padding="same", kernel_initializer=kernel_init(1.0)
)(x)
return x
return apply
def TimeMLP(units, activation_fn=tf.keras.activations.swish):
def apply(inputs):
temb = layers.Dense(
units, activation=activation_fn, kernel_initializer=kernel_init(1.0)
)(inputs)
temb = layers.Dense(units, kernel_initializer=kernel_init(1.0))(temb)
return temb
return apply
def build_model(
img_size,
img_channels,
widths,
has_attention,
num_res_blocks=2,
norm_groups=8,
interpolation="nearest",
activation_fn=tf.keras.activations.swish,
):
image_input = layers.Input(
shape=(img_size, img_size, img_channels), name="image_input"
)
time_input = tf.keras.Input(shape=(), dtype=tf.int64, name="time_input")
x = layers.Conv2D(
first_conv_channels,
kernel_size=(3, 3),
padding="same",
kernel_initializer=kernel_init(1.0),
)(image_input)
temb = TimeEmbedding(dim=first_conv_channels * 4)(time_input)
temb = TimeMLP(units=first_conv_channels * 4, activation_fn=activation_fn)(temb)
skips = [x]
# DownBlock
for i in range(len(widths)):
for _ in range(num_res_blocks):
x = ResidualBlock(
widths[i], groups=norm_groups, activation_fn=activation_fn
)([x, temb])
if has_attention[i]:
x = AttentionBlock(widths[i], groups=norm_groups)(x)
skips.append(x)
if widths[i] != widths[-1]:
x = DownSample(widths[i])(x)
skips.append(x)
# MiddleBlock
x = ResidualBlock(widths[-1], groups=norm_groups, activation_fn=activation_fn)(
[x, temb]
)
x = AttentionBlock(widths[-1], groups=norm_groups)(x)
x = ResidualBlock(widths[-1], groups=norm_groups, activation_fn=activation_fn)(
[x, temb]
)
# UpBlock
for i in reversed(range(len(widths))):
for _ in range(num_res_blocks + 1):
x = layers.Concatenate(axis=-1)([x, skips.pop()])
x = ResidualBlock(
widths[i], groups=norm_groups, activation_fn=activation_fn
)([x, temb])
if has_attention[i]:
x = AttentionBlock(widths[i], groups=norm_groups)(x)
if i != 0:
x = UpSample(widths[i], interpolation=interpolation)(x)
# End block
x = layers.GroupNormalization(groups=norm_groups)(x)
x = activation_fn(x)
x = layers.Conv2D(3, (3, 3), padding="same", kernel_initializer=kernel_init(0.0))(x)
return tf.keras.Model([image_input, time_input], x, name="unet")
ema_network = build_model(
img_size=image_size,
img_channels=img_channels,
widths=widths,
has_attention=has_attention,
num_res_blocks=num_res_blocks,
norm_groups=norm_groups,
activation_fn=tf.keras.activations.swish,
)
ema_network.load_weights("checkpoints/diffusion_model_checkpoint")