Add Simple Scheduler
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@ -76,6 +76,14 @@ def kl_optimal(n, sigma_min, sigma_max, device):
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sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
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sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
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return sigmas
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return sigmas
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def simple_scheduler(n, sigma_min, sigma_max, inner_model, device):
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sigs = []
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ss = len(inner_model.sigmas) / n
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for x in range(n):
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sigs += [float(inner_model.sigmas[-(1 + int(x * ss))])]
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sigs += [0.0]
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return torch.FloatTensor(sigs).to(device)
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schedulers = [
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schedulers = [
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Scheduler('automatic', 'Automatic', None),
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Scheduler('automatic', 'Automatic', None),
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@ -86,6 +94,7 @@ schedulers = [
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Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
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Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
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Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
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Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
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Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
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Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
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Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
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]
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]
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schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
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schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
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