update_name

graph_dit/models/conditions.py

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+import torch
+import torch.nn as nn
+import math
+
+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 = t.view(-1)
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+class CategoricalEmbedder(nn.Module):
+    """
+    Embeds categorical conditions such as data sources into vector representations. 
+    Also handles label dropout for classifier-free guidance.
+    """
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None, t=None):
+        labels = labels.long().view(-1)
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        if True and train:
+            noise = torch.randn_like(embeddings)
+            embeddings = embeddings + noise
+        return embeddings
+    
+class ClusterContinuousEmbedder(nn.Module):
+    def __init__(self, input_size, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+
+        if use_cfg_embedding:
+            self.embedding_drop = nn.Embedding(1, hidden_size)
+
+        self.mlp = nn.Sequential(
+            nn.Linear(input_size, hidden_size, bias=True),
+            nn.Softmax(dim=1),
+            nn.Linear(hidden_size, hidden_size, bias=False)
+        )
+        self.hidden_size = hidden_size
+        self.dropout_prob = dropout_prob
+
+    def forward(self, labels, train, force_drop_ids=None, timestep=None):
+        use_dropout = self.dropout_prob > 0
+        if force_drop_ids is not None:
+            drop_ids = force_drop_ids == 1
+        else:
+            drop_ids = None
+
+        if (train and use_dropout):
+            drop_ids_rand = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
+            if force_drop_ids is not None:
+                drop_ids = torch.logical_or(drop_ids, drop_ids_rand)
+            else:
+                drop_ids = drop_ids_rand
+        
+        if drop_ids is not None:
+            embeddings = torch.zeros((labels.shape[0], self.hidden_size), device=labels.device)
+            embeddings[~drop_ids] = self.mlp(labels[~drop_ids])
+            embeddings[drop_ids] += self.embedding_drop.weight[0]
+        else:
+            embeddings = self.mlp(labels)
+
+        if train:
+            noise = torch.randn_like(embeddings)
+            embeddings = embeddings + noise
+        return embeddings