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Align simple scheduling closer to official stable cascade scheduler.
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@ -150,10 +150,10 @@ class StableCascadeSampling(ModelSamplingDiscrete):
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self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
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self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
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#This part is just for compatibility with some schedulers in the codebase
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#This part is just for compatibility with some schedulers in the codebase
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self.num_timesteps = 1000
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self.num_timesteps = 10000
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sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
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sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
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for x in range(self.num_timesteps):
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for x in range(self.num_timesteps):
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t = x / self.num_timesteps
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t = (x + 1) / self.num_timesteps
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sigmas[x] = self.sigma(t)
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sigmas[x] = self.sigma(t)
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self.set_sigmas(sigmas)
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self.set_sigmas(sigmas)
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