Allow saving trained betas

src/diffusers/configuration_utils.py

@@ -24,6 +24,8 @@
 from collections import OrderedDict
 from typing import Any, Dict, Tuple, Union
 
+import numpy as np
+
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
@@ -502,6 +504,12 @@ def to_json_string(self) -> str:
         config_dict["_class_name"] = self.__class__.__name__
         config_dict["_diffusers_version"] = __version__
 
+        def to_json_saveable(value):
+            if isinstance(value, np.ndarray):
+                value = value.tolist()
+            return value
+
+        config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
     def to_json_file(self, json_file_path: Union[str, os.PathLike]):

src/diffusers/schedulers/scheduling_ddim.py

@@ -17,7 +17,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -123,7 +123,7 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         clip_sample: bool = True,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
@@ -139,7 +139,7 @@ def __init__(
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_ddpm.py

@@ -16,7 +16,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -115,7 +115,7 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         variance_type: str = "fixed_small",
         clip_sample: bool = True,
         prediction_type: str = "epsilon",
@@ -130,7 +130,7 @@ def __init__(
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -127,7 +127,7 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         solver_order: int = 2,
         prediction_type: str = "epsilon",
         thresholding: bool = False,
@@ -147,7 +147,7 @@ def __init__(
             self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
 
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -77,10 +77,10 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -78,11 +78,11 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         prediction_type: str = "epsilon",
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_heun.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -53,10 +53,10 @@ def __init__(
         beta_start: float = 0.00085,  # sensible defaults
         beta_end: float = 0.012,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_ipndm.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 import math
-from typing import Tuple, Union
+from typing import List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
@@ -40,7 +41,9 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
     order = 1
 
     @register_to_config
-    def __init__(self, num_train_timesteps: int = 1000):
+    def __init__(
+        self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None
+    ):
         # set `betas`, `alphas`, `timesteps`
         self.set_timesteps(num_train_timesteps)
 
@@ -67,7 +70,11 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
         steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1]
         steps = torch.cat([steps, torch.tensor([0.0])])
 
-        self.betas = torch.sin(steps * math.pi / 2) ** 2
+        if self.config.trained_betas is not None:
+            self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32)
+        else:
+            self.betas = torch.sin(steps * math.pi / 2) ** 2
+
         self.alphas = (1.0 - self.betas**2) ** 0.5
 
         timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1]

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import warnings
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -77,10 +77,10 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

src/diffusers/schedulers/scheduling_pndm.py

@@ -15,7 +15,7 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
 
 import math
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -99,13 +99,13 @@ def __init__(
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
-        trained_betas: Optional[np.ndarray] = None,
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         skip_prk_steps: bool = False,
         set_alpha_to_one: bool = False,
         steps_offset: int = 0,
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":

tests/test_scheduler.py

@@ -584,6 +584,20 @@ def test_deprecated_kwargs(self):
                     " deprecated argument from `_deprecated_kwargs = [<deprecated_argument>]`"
                 )
 
+    def test_trained_betas(self):
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class == VQDiffusionScheduler:
+                continue
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config, trained_betas=np.array([0.0, 0.1]))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_pretrained(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            assert scheduler.betas.tolist() == new_scheduler.betas.tolist()
+
 
 class DDPMSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (DDPMScheduler,)
@@ -1407,7 +1421,6 @@ def get_scheduler_config(self, **kwargs):
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
-            "trained_betas": None,
         }
 
         config.update(**kwargs)
@@ -1489,7 +1502,6 @@ def get_scheduler_config(self, **kwargs):
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
-            "trained_betas": None,
         }
 
         config.update(**kwargs)
@@ -1580,7 +1592,6 @@ def get_scheduler_config(self, **kwargs):
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
-            "trained_betas": None,
         }
 
         config.update(**kwargs)
@@ -1889,7 +1900,6 @@ def get_scheduler_config(self, **kwargs):
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
-            "trained_betas": None,
         }
 
         config.update(**kwargs)