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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.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.weights = torch.load(model_path) if torch.cuda.is_available() else torch.load(model_path, map_location=self.device)
self.model.load_state_dict(self.weights)
if use_float16:
self.model = self.model.half()
self.model.to(self.device)
if __name__ == "__main__":
unet = UNet() |