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import torch
import torch.nn as nn
import math
class FlowLoss(nn.Module):
"""Flow Loss"""
def __init__(self, target_channels, z_channels, depth, width, num_sampling_steps):
super(FlowLoss, self).__init__()
self.in_channels = target_channels
self.net = SimpleMLPAdaLN(
in_channels=target_channels,
model_channels=width,
out_channels=target_channels,
z_channels=z_channels,
num_res_blocks=depth
)
self.num_sampling_steps = num_sampling_steps
def forward(self, target, z, mask=None, mask_y=None):
noise = torch.randn_like(target)
t = torch.rand(target.shape[0], device=target.device)
noised_target = t[:, None] * target + (1 - t[:, None]) * noise
predict_v = self.net(noised_target, t * 1000, z)
weights = 1.0 / \
torch.arange(1, self.in_channels + 1, dtype=torch.float32, device=target.device)
if mask_y is not None:
loss = (mask_y * weights * (predict_v - target) ** 2).sum(dim=-1)
else:
loss = (weights * (predict_v - target) ** 2).sum(dim=-1)
if mask is not None:
loss = (loss * mask).sum() / mask.sum()
return loss.mean()
def sample(self, z, num_samples=1):
z = z.repeat(num_samples, 1)
noise = torch.randn(z.shape[0], self.in_channels).to(z.device)
x = noise
dt = 1.0 / self.num_sampling_steps
for i in range(self.num_sampling_steps):
t = (torch.ones((x.shape[0])) * i /
self.num_sampling_steps).to(x.device)
pred = self.net(x, t * 1000, z)
x = x + (pred - noise) * dt
x = x.reshape(num_samples, -1, self.in_channels).transpose(0, 1)
return x
def modulate(x, shift, scale):
return x * (1 + scale) + shift
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0,
end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat(
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class ResBlock(nn.Module):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
"""
def __init__(
self,
channels
):
super().__init__()
self.channels = channels
self.in_ln = nn.LayerNorm(channels, eps=1e-6)
self.mlp = nn.Sequential(
nn.Linear(channels, channels, bias=True),
nn.SiLU(),
nn.Linear(channels, channels, bias=True),
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(channels, 3 * channels, bias=True)
)
def forward(self, x, y):
shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
y).chunk(3, dim=-1)
h = modulate(self.in_ln(x), shift_mlp, scale_mlp)
h = self.mlp(h)
return x + gate_mlp * h
class FinalLayer(nn.Module):
"""
The final layer adopted from DiT.
"""
def __init__(self, model_channels, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(
model_channels, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(model_channels, out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(model_channels, 2 * model_channels, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class SimpleMLPAdaLN(nn.Module):
"""
The MLP for Diffusion Loss.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param z_channels: channels in the condition.
:param num_res_blocks: number of residual blocks per downsample.
"""
def __init__(
self,
in_channels,
model_channels,
out_channels,
z_channels,
num_res_blocks,
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.time_embed = TimestepEmbedder(model_channels)
self.cond_embed = nn.Linear(z_channels, model_channels)
self.input_proj = nn.Linear(in_channels, model_channels)
res_blocks = []
for i in range(num_res_blocks):
res_blocks.append(ResBlock(
model_channels,
))
self.res_blocks = nn.ModuleList(res_blocks)
self.final_layer = FinalLayer(model_channels, out_channels)
self.initialize_weights()
def initialize_weights(self):
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize timestep embedding MLP
nn.init.normal_(self.time_embed.mlp[0].weight, std=0.02)
nn.init.normal_(self.time_embed.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers
for block in self.res_blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def forward(self, x, t, c):
"""
Apply the model to an input batch.
:param x: an [N x C] Tensor of inputs.
:param t: a 1-D batch of timesteps.
:param c: conditioning from AR transformer.
:return: an [N x C] Tensor of outputs.
"""
x = self.input_proj(x)
t = self.time_embed(t)
c = self.cond_embed(c)
y = t + c
for block in self.res_blocks:
x = block(x, y)
return self.final_layer(x, y)
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