120 lines
4.5 KiB
Python
120 lines
4.5 KiB
Python
import torch
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import torch.nn as nn
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import math
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
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).to(device=t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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def forward(self, t):
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t = t.view(-1)
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
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t_emb = self.mlp(t_freq)
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return t_emb
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class CategoricalEmbedder(nn.Module):
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"""
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Embeds categorical conditions such as data sources into vector representations.
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Also handles label dropout for classifier-free guidance.
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"""
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def __init__(self, num_classes, hidden_size, dropout_prob):
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super().__init__()
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use_cfg_embedding = dropout_prob > 0
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self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
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self.num_classes = num_classes
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self.dropout_prob = dropout_prob
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def token_drop(self, labels, force_drop_ids=None):
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"""
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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else:
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drop_ids = force_drop_ids == 1
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labels = torch.where(drop_ids, self.num_classes, labels)
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return labels
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def forward(self, labels, train, force_drop_ids=None, t=None):
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labels = labels.long().view(-1)
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use_dropout = self.dropout_prob > 0
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if (train and use_dropout) or (force_drop_ids is not None):
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labels = self.token_drop(labels, force_drop_ids)
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embeddings = self.embedding_table(labels)
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if True and train:
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noise = torch.randn_like(embeddings)
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embeddings = embeddings + noise
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return embeddings
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class ClusterContinuousEmbedder(nn.Module):
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def __init__(self, input_size, hidden_size, dropout_prob):
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super().__init__()
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use_cfg_embedding = dropout_prob > 0
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if use_cfg_embedding:
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self.embedding_drop = nn.Embedding(1, hidden_size)
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self.mlp = nn.Sequential(
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nn.Linear(input_size, hidden_size, bias=True),
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nn.Softmax(dim=1),
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nn.Linear(hidden_size, hidden_size, bias=False)
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)
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self.hidden_size = hidden_size
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self.dropout_prob = dropout_prob
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def forward(self, labels, train, force_drop_ids=None, timestep=None):
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use_dropout = self.dropout_prob > 0
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if force_drop_ids is not None:
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drop_ids = force_drop_ids == 1
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else:
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drop_ids = None
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if (train and use_dropout):
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drop_ids_rand = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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if force_drop_ids is not None:
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drop_ids = torch.logical_or(drop_ids, drop_ids_rand)
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else:
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drop_ids = drop_ids_rand
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if drop_ids is not None:
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embeddings = torch.zeros((labels.shape[0], self.hidden_size), device=labels.device)
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embeddings[~drop_ids] = self.mlp(labels[~drop_ids])
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embeddings[drop_ids] += self.embedding_drop.weight[0]
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else:
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embeddings = self.mlp(labels)
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if train:
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noise = torch.randn_like(embeddings)
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embeddings = embeddings + noise
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return embeddings
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