schedulers add glide noising schedule

src/diffusers/schedulers/scheduling_ddim.py

@@ -46,6 +46,7 @@ class DDIMSchedulerOutput(BaseOutput):
     pred_original_sample: Optional[torch.FloatTensor] = None
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -72,7 +73,7 @@ def alpha_bar(time_step):
         t1 = i / num_diffusion_timesteps
         t2 = (i + 1) / num_diffusion_timesteps
         betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas)
+    return torch.tensor(betas, dtype=torch.float32)
 
 
 class DDIMScheduler(SchedulerMixin, ConfigMixin):

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -25,6 +25,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -24,6 +24,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -24,6 +24,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
@@ -45,6 +46,36 @@ class EulerAncestralDiscreteSchedulerOutput(BaseOutput):
     pred_original_sample: Optional[torch.FloatTensor] = None
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
 class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
     Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson:
@@ -93,6 +124,9 @@ def __init__(
             self.betas = (
                 torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
             )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -214,6 +248,8 @@ def step(
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
+        elif self.config.prediction_type == "sample":
+            raise NotImplementedError("prediction_type not implemented yet: sample")
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
@@ -45,6 +46,36 @@ class EulerDiscreteSchedulerOutput(BaseOutput):
     pred_original_sample: Optional[torch.FloatTensor] = None
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
 class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
     Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original
@@ -97,6 +128,9 @@ def __init__(
             self.betas = (
                 torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
             )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -245,7 +279,9 @@ def step(
             sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
 
         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
-        if self.config.prediction_type == "original_sample":
+        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
+        # backwards compatibility
+        if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
             pred_original_sample = model_output
         elif self.config.prediction_type == "epsilon":
             pred_original_sample = sample - sigma_hat * model_output

src/diffusers/schedulers/scheduling_heun_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -21,6 +22,36 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
 class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
     Implements Algorithm 2 (Heun steps) from Karras et al. (2022). for discrete beta schedules. Based on the original
@@ -69,6 +100,9 @@ def __init__(
             self.betas = (
                 torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
             )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -197,6 +231,8 @@ def step(
             pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
+        elif self.config.prediction_type == "sample":
+            raise NotImplementedError("prediction_type not implemented yet: sample")
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"

src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -22,6 +23,36 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
 class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
     Scheduler created by @crowsonkb in [k_diffusion](https://github.com/crowsonkb/k-diffusion), see:
@@ -71,6 +102,9 @@ def __init__(
             self.betas = (
                 torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
             )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -254,6 +288,8 @@ def step(
             pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
+        elif self.config.prediction_type == "sample":
+            raise NotImplementedError("prediction_type not implemented yet: sample")
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"

src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -21,6 +22,36 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
 class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
     Scheduler created by @crowsonkb in [k_diffusion](https://github.com/crowsonkb/k-diffusion), see:
@@ -70,6 +101,9 @@ def __init__(
             self.betas = (
                 torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
             )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -237,6 +271,8 @@ def step(
             pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
+        elif self.config.prediction_type == "sample":
+            raise NotImplementedError("prediction_type not implemented yet: sample")
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"