change the behavior of discard_next_to_last_sigma for sgm_uniform to match other schedulers

modules/sd_samplers_kdiffusion.py

@@ -113,10 +113,6 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             if scheduler.need_inner_model:
                 sigmas_kwargs['inner_model'] = self.model_wrap
 
-            if scheduler.name == "sgm_uniform":  # XXX check this
-                # Ensure the "step" will be target step + 1
-                steps += 1 if not discard_next_to_last_sigma else 0
-
             sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
 
         if discard_next_to_last_sigma:

modules/sd_schedulers.py

@@ -25,7 +25,7 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
     end = inner_model.sigma_to_t(torch.tensor(sigma_min))
     sigs = [
         inner_model.t_to_sigma(ts)
-        for ts in torch.linspace(start, end, n)[:-1]
+        for ts in torch.linspace(start, end, n + 1)[:-1]
     ]
     sigs += [0.0]
     return torch.FloatTensor(sigs).to(device)