From 1cf571ea8198af2019a10bd02acf1e3598ff1728 Mon Sep 17 00:00:00 2001
From: Mr-Neutr0n <64578610+Mr-Neutr0n@users.noreply.github.com>
Date: Tue, 10 Feb 2026 00:00:47 +0530
Subject: [PATCH] Fix DDIMScheduler.step() prev_timestep for non-leading
 spacing

When timestep_spacing is set to 'linspace' or 'trailing', the formula
timestep - num_train_timesteps // num_inference_steps does not produce
the correct previous timestep. Use a lookup into self.timesteps instead,
matching the approach used in DDPMScheduler.previous_timestep().
---
 src/diffusers/schedulers/scheduling_ddim.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 92c3e20013dd..a58f416fd4a2 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -442,7 +442,7 @@ def step(
         # - pred_prev_sample -> "x_t-1"
 
         # 1. get previous step value (=t-1)
-        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
+        prev_timestep = self.previous_timestep(timestep)
 
         # 2. compute alphas, betas
         alpha_prod_t = self.alphas_cumprod[timestep]
@@ -591,5 +591,13 @@ def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: tor
         velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
         return velocity
 
+    def previous_timestep(self, timestep: int):
+        index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
+        if index == self.timesteps.shape[0] - 1:
+            prev_t = torch.tensor(-1)
+        else:
+            prev_t = self.timesteps[index + 1]
+        return prev_t
+
     def __len__(self) -> int:
         return self.config.num_train_timesteps