这是indexloc提供的服务，不要输入任何密码
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Update diffusion_utils.py #27

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Update diffusion_utils.py #27

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58 changes: 57 additions & 1 deletion diffusion_tf/diffusion_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,15 @@ def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_time
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == 'jsd': # 1/T, 1/(T-1), 1/(T-2), ..., 1
betas = 1. / np.linspace(num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == 'cosine':
s = 0.008
steps = num_diffusion_timesteps + 1
x = np.linspace(0, num_diffusion_timesteps, steps, dtype=np.float64)
alphas_cumprod = np.cos(((x / num_diffusion_timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
betas = np.clip(betas, 0, 0.999)

else:
raise NotImplementedError(beta_schedule)
assert betas.shape == (num_diffusion_timesteps,)
Expand Down Expand Up @@ -84,7 +93,25 @@ def __init__(self, *, betas, loss_type, tf_dtype=tf.float32):
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod), dtype=tf_dtype)
self.posterior_mean_coef2 = tf.constant(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod), dtype=tf_dtype)


self.snr = tf.constant(alphas_cumprod / (1.0 - alphas_cumprod), dtype=tf_dtype)


def get_timestep_embedding(self, timesteps, embedding_dim, max_positions=10000):
half_dim = embedding_dim // 2
emb = tf.math.log(tf.cast(max_positions, tf.float32)) / (half_dim - 1)
emb = tf.exp(tf.range(half_dim, dtype=tf.float32) * -emb)
emb = tf.cast(timesteps, tf.float32)[:, None] * emb[None, :]
emb = tf.concat([tf.sin(emb), tf.cos(emb)], axis=1)
if embedding_dim % 2 == 1:
emb = tf.pad(emb, [[0, 0], [0, 1]])
return emb

def _scale_timesteps(self, t):
if self.rescale_timesteps:
return tf.cast(t, tf.float32) * (1000.0 / self.num_timesteps)
return tf.cast(t, tf.float32)

@staticmethod
def _extract(a, t, x_shape):
"""
Expand Down Expand Up @@ -156,6 +183,35 @@ def p_losses(self, denoise_fn, x_start, t, noise=None):
# predict the noise instead of x_start. seems to be weighted naturally like SNR
assert x_recon.shape == x_start.shape
losses = nn.meanflat(tf.squared_difference(noise, x_recon))
elif self.loss_type == 'mse':
# predict x_start directly
if model_output.shape.as_list()[-1] == x_start.shape.as_list()[-1] * 2 and self.learned_variance:
model_output, _ = tf.split(model_output, 2, axis=-1)
assert model_output.shape == x_start.shape
x_recon = model_output
if clip_denoised:
x_recon = tf.clip_by_value(x_recon, -1., 1.)
losses = nn.meanflat(tf.squared_difference(x_start, x_recon))
elif self.loss_type == 'hybrid':
# Combination of noise prediction and data prediction
if model_output.shape.as_list()[-1] == x_start.shape.as_list()[-1] * 2 and self.learned_variance:
model_output, _ = tf.split(model_output, 2, axis=-1)
assert model_output.shape == x_start.shape

# Reconstruct x_start from predicted noise
x_recon = self.predict_start_from_noise(x_noisy, t=t, noise=model_output)
if clip_denoised:
x_recon = tf.clip_by_value(x_recon, -1., 1.)

# Compute both losses
noise_loss = nn.meanflat(tf.squared_difference(noise, model_output))
data_loss = nn.meanflat(tf.squared_difference(x_start, x_recon))

# Weight losses by SNR at timestep t
snr = self._extract(self.snr, t, x_start.shape)
weights = 1.0 / (1.0 + snr)
losses = weights * noise_loss + (1.0 - weights) * data_loss

else:
raise NotImplementedError(self.loss_type)

Expand Down