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