Fixed euler_a generator argument

5 files changed, 10 insertions, 23 deletions

diff --git a/data/dreambooth/csv.py b/data/dreambooth/csv.py
index 4087226..08ed49c 100644
--- a/data/dreambooth/csv.py
+++ b/data/dreambooth/csv.py
@@ -22,6 +22,7 @@ class CSVDataModule(pl.LightningDataModule):
                  identifier="*",
                  center_crop=False,
                  valid_set_size=None,
+                 generator=None,
                  collate_fn=None):
         super().__init__()
 
@@ -41,6 +42,7 @@ class CSVDataModule(pl.LightningDataModule):
         self.center_crop = center_crop
         self.interpolation = interpolation
         self.valid_set_size = valid_set_size
+        self.generator = generator
         self.collate_fn = collate_fn
         self.batch_size = batch_size
 
@@ -54,10 +56,10 @@ class CSVDataModule(pl.LightningDataModule):
     def setup(self, stage=None):
         valid_set_size = int(len(self.data_full) * 0.2)
         if self.valid_set_size:
-            valid_set_size = math.min(valid_set_size, self.valid_set_size)
+            valid_set_size = min(valid_set_size, self.valid_set_size)
         train_set_size = len(self.data_full) - valid_set_size
 
-        self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size])
+        self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size], self.generator)
 
         train_dataset = CSVDataset(self.data_train, self.tokenizer, instance_prompt=self.instance_prompt,
                                    class_data_root=self.class_data_root, class_prompt=self.class_prompt,
diff --git a/data/textual_inversion/csv.py b/data/textual_inversion/csv.py
index e082511..3ac57df 100644
--- a/data/textual_inversion/csv.py
+++ b/data/textual_inversion/csv.py
@@ -19,7 +19,8 @@ class CSVDataModule(pl.LightningDataModule):
                  interpolation="bicubic",
                  placeholder_token="*",
                  center_crop=False,
-                 valid_set_size=None):
+                 valid_set_size=None,
+                 generator=None):
         super().__init__()
 
         self.data_file = Path(data_file)
@@ -35,6 +36,7 @@ class CSVDataModule(pl.LightningDataModule):
         self.center_crop = center_crop
         self.interpolation = interpolation
         self.valid_set_size = valid_set_size
+        self.generator = generator
 
         self.batch_size = batch_size
 
@@ -48,10 +50,10 @@ class CSVDataModule(pl.LightningDataModule):
     def setup(self, stage=None):
         valid_set_size = int(len(self.data_full) * 0.2)
         if self.valid_set_size:
-            valid_set_size = math.min(valid_set_size, self.valid_set_size)
+            valid_set_size = min(valid_set_size, self.valid_set_size)
         train_set_size = len(self.data_full) - valid_set_size
 
-        self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size])
+        self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size], self.generator)
 
         train_dataset = CSVDataset(self.data_train, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation,
                                    placeholder_token=self.placeholder_token, center_crop=self.center_crop)
diff --git a/dreambooth.py b/dreambooth.py
index 88cd0da..75602dc 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -414,8 +414,6 @@ class Checkpointer:
                 prompt = [prompt for i, batch in data_enum for j, prompt in enumerate(
                     batch["prompts"]) if i * val_data.batch_size + j < self.sample_batch_size]
 
-                generator = torch.Generator(device="cuda").manual_seed(self.seed + i)
-
                 with self.accelerator.autocast():
                     samples = pipeline(
                         prompt=prompt,
@@ -425,13 +423,11 @@ class Checkpointer:
                         guidance_scale=guidance_scale,
                         eta=eta,
                         num_inference_steps=num_inference_steps,
-                        generator=generator,
                         output_type='pil'
                     )["sample"]
 
                 all_samples += samples
 
-                del generator
                 del samples
 
             image_grid = make_grid(all_samples, self.stable_sample_batches, self.sample_batch_size)
@@ -452,8 +448,6 @@ class Checkpointer:
                 prompt = [prompt for i, batch in data_enum for j, prompt in enumerate(
                     batch["prompts"]) if i * data.batch_size + j < self.sample_batch_size]
 
-                generator = torch.Generator(device="cuda").manual_seed(self.seed + i)
-
                 with self.accelerator.autocast():
                     samples = pipeline(
                         prompt=prompt,
@@ -462,13 +456,11 @@ class Checkpointer:
                         guidance_scale=guidance_scale,
                         eta=eta,
                         num_inference_steps=num_inference_steps,
-                        generator=generator,
                         output_type='pil'
                     )["sample"]
 
                 all_samples += samples
 
-                del generator
                 del samples
 
             image_grid = make_grid(all_samples, self.random_sample_batches, self.sample_batch_size)
diff --git a/schedulers/scheduling_euler_a.py b/schedulers/scheduling_euler_a.py
index d7fea85..c6436d8 100644
--- a/schedulers/scheduling_euler_a.py
+++ b/schedulers/scheduling_euler_a.py
@@ -198,7 +198,7 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         timestep: int,
         timestep_prev: int,
         sample: torch.FloatTensor,
-        generator: None,
+        generator: torch.Generator = None,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
         """
@@ -240,7 +240,6 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         sample_hat: torch.FloatTensor,
         sample_prev: torch.FloatTensor,
         derivative: torch.FloatTensor,
-        generator: None,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
         """
diff --git a/textual_inversion.py b/textual_inversion.py
index fa6214e..285aa0a 100644
--- a/textual_inversion.py
+++ b/textual_inversion.py
@@ -403,8 +403,6 @@ class Checkpointer:
                 prompt = [prompt for i, batch in data_enum for j, prompt in enumerate(
                     batch["prompts"]) if i * val_data.batch_size + j < self.sample_batch_size]
 
-                generator = torch.Generator(device="cuda").manual_seed(self.seed + i)
-
                 with self.accelerator.autocast():
                     samples = pipeline(
                         prompt=prompt,
@@ -414,13 +412,11 @@ class Checkpointer:
                         guidance_scale=guidance_scale,
                         eta=eta,
                         num_inference_steps=num_inference_steps,
-                        generator=generator,
                         output_type='pil'
                     )["sample"]
 
                 all_samples += samples
 
-                del generator
                 del samples
 
             image_grid = make_grid(all_samples, self.stable_sample_batches, self.sample_batch_size)
@@ -441,8 +437,6 @@ class Checkpointer:
                 prompt = [prompt for i, batch in data_enum for j, prompt in enumerate(
                     batch["prompts"]) if i * data.batch_size + j < self.sample_batch_size]
 
-                generator = torch.Generator(device="cuda").manual_seed(self.seed + i)
-
                 with self.accelerator.autocast():
                     samples = pipeline(
                         prompt=prompt,
@@ -451,13 +445,11 @@ class Checkpointer:
                         guidance_scale=guidance_scale,
                         eta=eta,
                         num_inference_steps=num_inference_steps,
-                        generator=generator,
                         output_type='pil'
                     )["sample"]
 
                 all_samples += samples
 
-                del generator
                 del samples
 
             image_grid = make_grid(all_samples, self.random_sample_batches, self.sample_batch_size)