| import os
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|
|
| import torch
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| from torch import nn
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| from modules import devices, paths
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|
|
| sd_vae_approx_model = None
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|
|
|
|
| class VAEApprox(nn.Module):
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| def __init__(self):
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| super(VAEApprox, self).__init__()
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| self.conv1 = nn.Conv2d(4, 8, (7, 7))
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| self.conv2 = nn.Conv2d(8, 16, (5, 5))
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| self.conv3 = nn.Conv2d(16, 32, (3, 3))
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| self.conv4 = nn.Conv2d(32, 64, (3, 3))
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| self.conv5 = nn.Conv2d(64, 32, (3, 3))
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| self.conv6 = nn.Conv2d(32, 16, (3, 3))
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| self.conv7 = nn.Conv2d(16, 8, (3, 3))
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| self.conv8 = nn.Conv2d(8, 3, (3, 3))
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|
|
| def forward(self, x):
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| extra = 11
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| x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
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| x = nn.functional.pad(x, (extra, extra, extra, extra))
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|
|
| for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
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| x = layer(x)
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| x = nn.functional.leaky_relu(x, 0.1)
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|
|
| return x
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|
|
|
|
| def model():
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| global sd_vae_approx_model
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|
|
| if sd_vae_approx_model is None:
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| sd_vae_approx_model = VAEApprox()
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| sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt")))
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| sd_vae_approx_model.eval()
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| sd_vae_approx_model.to(devices.device, devices.dtype)
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|
|
| return sd_vae_approx_model
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|
|
|
|
| def cheap_approximation(sample):
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|
|
|
|
| coefs = torch.tensor([
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| [0.298, 0.207, 0.208],
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| [0.187, 0.286, 0.173],
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| [-0.158, 0.189, 0.264],
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| [-0.184, -0.271, -0.473],
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| ]).to(sample.device)
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|
|
| x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
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|
|
| return x_sample
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|
|
