Fix the LMS pytorch regression

src/diffusers/dependency_versions_table.py

@@ -17,7 +17,6 @@
     "jaxlib": "jaxlib>=0.1.65,<=0.3.6",
     "modelcards": "modelcards>=0.1.4",
     "numpy": "numpy",
-    "onnxruntime": "onnxruntime",
     "onnxruntime-gpu": "onnxruntime-gpu",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -99,11 +99,14 @@ def __init__(
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
 
-        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas)
 
         # setable values
         self.num_inference_steps = None
-        self.timesteps = np.arange(0, num_train_timesteps)[::-1]  # to be consistent has to be smaller than sigmas by 1
+        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps)
         self.derivatives = []
 
     def get_lms_coefficient(self, order, t, current_order):
@@ -137,17 +140,14 @@ def set_timesteps(self, num_inference_steps: int):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
         self.num_inference_steps = num_inference_steps
-        timesteps = np.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=float)
 
-        low_idx = np.floor(timesteps).astype(int)
-        high_idx = np.ceil(timesteps).astype(int)
-        frac = np.mod(timesteps, 1.0)
+        timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
         sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        sigmas = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
+        sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
         sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
         self.sigmas = torch.from_numpy(sigmas)
+        self.timesteps = torch.from_numpy(timesteps)
 
-        self.timesteps = timesteps.astype(int)
         self.derivatives = []
 
     def step(

tests/test_scheduler.py

@@ -844,7 +844,7 @@ def test_timesteps(self):
             self.check_over_configs(num_train_timesteps=timesteps)
 
     def test_betas(self):
-        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
             self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
 
     def test_schedules(self):
@@ -876,5 +876,5 @@ def test_full_loop_no_noise(self):
         result_sum = torch.sum(torch.abs(sample))
         result_mean = torch.mean(torch.abs(sample))
 
-        assert abs(result_sum.item() - 1006.370) < 1e-2
+        assert abs(result_sum.item() - 1006.388) < 1e-2
         assert abs(result_mean.item() - 1.31) < 1e-3