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change the behavior of discard_next_to_last_sigma for sgm_uniform to match other schedulers
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@ -113,10 +113,6 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
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if scheduler.need_inner_model:
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sigmas_kwargs['inner_model'] = self.model_wrap
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if scheduler.name == "sgm_uniform": # XXX check this
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# Ensure the "step" will be target step + 1
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steps += 1 if not discard_next_to_last_sigma else 0
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sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
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if discard_next_to_last_sigma:
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@ -25,7 +25,7 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
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end = inner_model.sigma_to_t(torch.tensor(sigma_min))
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sigs = [
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inner_model.t_to_sigma(ts)
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for ts in torch.linspace(start, end, n)[:-1]
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for ts in torch.linspace(start, end, n + 1)[:-1]
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]
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sigs += [0.0]
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return torch.FloatTensor(sigs).to(device)
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