initial_commit

musetalk/models/unet.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+import math
+import json
+
+from diffusers import UNet2DConditionModel
+import sys
+import time
+import numpy as np
+import os
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model=384, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        b, seq_len, d_model = x.size()
+        pe = self.pe[:, :seq_len, :]
+        x = x + pe.to(x.device)
+        return x
+    
+class UNet():
+    def __init__(self, 
+                 unet_config,
+                 model_path,
+                 use_float16=False,
+        ):
+        with open(unet_config, 'r') as f:
+            unet_config = json.load(f)
+        self.model = UNet2DConditionModel(**unet_config)
+        self.pe = PositionalEncoding(d_model=384)
+        self.weights = torch.load(model_path)
+        self.model.load_state_dict(self.weights)
+        if use_float16:
+            self.model = self.model.half()
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+    
+if __name__ == "__main__":
+    unet = UNet()