Scheduler Clip Fix

monai/inferers/inferer.py

@@ -1607,7 +1607,7 @@ def __init__(
         self.autoencoder_latent_shape = autoencoder_latent_shape
         if self.ldm_latent_shape is not None and self.autoencoder_latent_shape is not None:
             self.ldm_resizer = SpatialPad(spatial_size=self.ldm_latent_shape)
-            self.autoencoder_resizer = CenterSpatialCrop(roi_size=[-1] + self.autoencoder_latent_shape)
+            self.autoencoder_resizer = CenterSpatialCrop(roi_size=self.autoencoder_latent_shape)
 
     def __call__(  # type: ignore[override]
         self,

monai/networks/schedulers/ddim.py

@@ -57,6 +57,8 @@ class DDIMScheduler(Scheduler):
             `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
             stable diffusion.
         prediction_type: member of DDPMPredictionType
+        clip_sample_min: minimum clipping value when clip_sample equals True
+        clip_sample_max: maximum clipping value when clip_sample equals True
         schedule_args: arguments to pass to the schedule function
 
     """
@@ -69,6 +71,8 @@ def __init__(
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
         prediction_type: str = DDIMPredictionType.EPSILON,
+        clip_sample_min: float = -1.0,
+        clip_sample_max: float = 1.0,
         **schedule_args,
     ) -> None:
         super().__init__(num_train_timesteps, schedule, **schedule_args)
@@ -90,6 +94,7 @@ def __init__(
         self.timesteps = torch.from_numpy(np.arange(0, self.num_train_timesteps)[::-1].astype(np.int64))
 
         self.clip_sample = clip_sample
+        self.clip_sample_values = [clip_sample_min, clip_sample_max]
         self.steps_offset = steps_offset
 
         # default the number of inference timesteps to the number of train steps
@@ -193,7 +198,9 @@ def step(
 
         # 4. Clip "predicted x_0"
         if self.clip_sample:
-            pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
+            pred_original_sample = torch.clamp(
+                pred_original_sample, self.clip_sample_values[0], self.clip_sample_values[1]
+            )
 
         # 5. compute variance: "sigma_t(η)" -> see formula (16)
         # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
@@ -266,7 +273,9 @@ def reversed_step(
 
         # 4. Clip "predicted x_0"
         if self.clip_sample:
-            pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
+            pred_original_sample = torch.clamp(
+                pred_original_sample, self.clip_sample_values[0], self.clip_sample_values[1]
+            )
 
         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * pred_epsilon

monai/networks/schedulers/ddpm.py

@@ -77,6 +77,8 @@ class DDPMScheduler(Scheduler):
         variance_type: member of DDPMVarianceType
         clip_sample: option to clip predicted sample between -1 and 1 for numerical stability.
         prediction_type: member of DDPMPredictionType
+        clip_sample_min: minimum clipping value when clip_sample equals True
+        clip_sample_max: maximum clipping value when clip_sample equals True
         schedule_args: arguments to pass to the schedule function
     """
 
@@ -87,6 +89,8 @@ def __init__(
         variance_type: str = DDPMVarianceType.FIXED_SMALL,
         clip_sample: bool = True,
         prediction_type: str = DDPMPredictionType.EPSILON,
+        clip_sample_min: float = -1.0,
+        clip_sample_max: float = 1.0,
         **schedule_args,
     ) -> None:
         super().__init__(num_train_timesteps, schedule, **schedule_args)
@@ -98,6 +102,7 @@ def __init__(
             raise ValueError("Argument `prediction_type` must be a member of `DDPMPredictionType`")
 
         self.clip_sample = clip_sample
+        self.clip_sample_values = [clip_sample_min, clip_sample_max]
         self.variance_type = variance_type
         self.prediction_type = prediction_type
 
@@ -219,7 +224,9 @@ def step(
 
         # 3. Clip "predicted x_0"
         if self.clip_sample:
-            pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
+            pred_original_sample = torch.clamp(
+                pred_original_sample, self.clip_sample_values[0], self.clip_sample_values[1]
+            )
 
         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf

requirements.txt

@@ -1,2 +1,2 @@
 torch>=1.9
-numpy>=1.20
+numpy>=1.20,<2.0