Update LCMScheduler Inference Timesteps to be More Evenly Spaced (#5836)

* Change LCMScheduler.set_timesteps to pick more evenly spaced inference timesteps.

* Change inference_indices implementation to better match previous behavior.

* Add num_inference_steps=26 test case to test_inference_steps.

* run CI

---------
Co-authored-by: patil-suraj <surajp815@gmail.com>

src/diffusers/schedulers/scheduling_lcm.py

@@ -371,10 +371,11 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
             )
 
         # LCM Timesteps Setting
-        # Currently, only linear spacing is supported.
-        c = self.config.num_train_timesteps // original_steps
-        # LCM Training Steps Schedule
-        lcm_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * c - 1
+        # The skipping step parameter k from the paper.
+        k = self.config.num_train_timesteps // original_steps
+        # LCM Training/Distillation Steps Schedule
+        # Currently, only a linearly-spaced schedule is supported (same as in the LCM distillation scripts).
+        lcm_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * k - 1
         skipping_step = len(lcm_origin_timesteps) // num_inference_steps
 
         if skipping_step < 1:
@@ -383,9 +384,13 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
             )
 
         # LCM Inference Steps Schedule
-        timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps]
+        lcm_origin_timesteps = lcm_origin_timesteps[::-1].copy()
+        # Select (approximately) evenly spaced indices from lcm_origin_timesteps.
+        inference_indices = np.linspace(0, len(lcm_origin_timesteps), num=num_inference_steps, endpoint=False)
+        inference_indices = np.floor(inference_indices).astype(np.int64)
+        timesteps = lcm_origin_timesteps[inference_indices]
 
-        self.timesteps = torch.from_numpy(timesteps.copy()).to(device=device, dtype=torch.long)
+        self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.long)
 
         self._step_index = None
 

tests/schedulers/test_scheduler_lcm.py

@@ -84,7 +84,7 @@ class LCMSchedulerTest(SchedulerCommonTest):
 
     def test_inference_steps(self):
         # Hardcoded for now
-        for t, num_inference_steps in zip([99, 39, 19], [10, 25, 50]):
+        for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]):
             self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
 
     # Override test_add_noise_device because the hardcoded num_inference_steps of 100 doesn't work