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@@ -293,8 +293,8 @@ def cosine_beta_schedule(timesteps, s = 0.008):
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as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
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"""
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steps = timesteps + 1
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x = torch.linspace(0, steps, steps)
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alphas_cumprod = torch.cos(((x / steps) + s) / (1 + s) * torch.pi * 0.5) ** 2
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x = torch.linspace(0, timesteps, steps)
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alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
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return torch.clip(betas, 0, 0.999)
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@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
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setup(
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name = 'denoising-diffusion-pytorch',
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packages = find_packages(),
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version = '0.9.1',
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version = '0.9.2',
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license='MIT',
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description = 'Denoising Diffusion Probabilistic Models - Pytorch',
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author = 'Phil Wang',
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