make style

examples/train_unconditional.py

@@ -8,6 +8,9 @@ import PIL.Image
 from accelerate import Accelerator
 from datasets import load_dataset
 from diffusers import DDPM, DDPMScheduler, UNetModel
+from diffusers.hub_utils import init_git_repo, push_to_hub
+from diffusers.modeling_utils import unwrap_model
+from diffusers.utils import logging
 from torchvision.transforms import (
     CenterCrop,
     Compose,
@@ -19,10 +22,7 @@ from torchvision.transforms import (
 )
 from tqdm.auto import tqdm
 from transformers import get_linear_schedule_with_warmup
-from diffusers.modeling_utils import unwrap_model
-from diffusers.hub_utils import init_git_repo, push_to_hub
 
-from diffusers.utils import logging
 
 logger = logging.get_logger(__name__)
 

src/diffusers/hub_utils.py

@@ -1,11 +1,13 @@
-from typing import Optional
-from .utils import logging
-from huggingface_hub import HfFolder, Repository, whoami
-import yaml
 import os
-from pathlib import Path
 import shutil
+from pathlib import Path
+from typing import Optional
+
+import yaml
 from diffusers import DiffusionPipeline
+from huggingface_hub import HfFolder, Repository, whoami
+
+from .utils import logging
 
 
 logger = logging.get_logger(__name__)
@@ -68,17 +70,21 @@ def init_git_repo(args, at_init: bool = False):
     repo.git_pull()
 
     # By default, ignore the checkpoint folders
-    if (
-            not os.path.exists(os.path.join(args.output_dir, ".gitignore"))
-            and args.hub_strategy != "all_checkpoints"
-    ):
+    if not os.path.exists(os.path.join(args.output_dir, ".gitignore")) and args.hub_strategy != "all_checkpoints":
         with open(os.path.join(args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
             writer.writelines(["checkpoint-*/"])
 
     return repo
 
 
-def push_to_hub(args, pipeline: DiffusionPipeline, repo: Repository, commit_message: Optional[str] = "End of training", blocking: bool = True, **kwargs) -> str:
+def push_to_hub(
+    args,
+    pipeline: DiffusionPipeline,
+    repo: Repository,
+    commit_message: Optional[str] = "End of training",
+    blocking: bool = True,
+    **kwargs,
+) -> str:
     """
     Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.
     Parameters:
@@ -108,18 +114,19 @@ def push_to_hub(args, pipeline: DiffusionPipeline, repo: Repository, commit_mess
         return
 
     # Cancel any async push in progress if blocking=True. The commits will all be pushed together.
-    if blocking and len(repo.command_queue) > 0 and repo.command_queue[-1] is not None and not repo.command_queue[-1].is_done:
+    if (
+        blocking
+        and len(repo.command_queue) > 0
+        and repo.command_queue[-1] is not None
+        and not repo.command_queue[-1].is_done
+    ):
         repo.command_queue[-1]._process.kill()
 
-    git_head_commit_url = repo.push_to_hub(
-        commit_message=commit_message, blocking=blocking, auto_lfs_prune=True
-    )
+    git_head_commit_url = repo.push_to_hub(commit_message=commit_message, blocking=blocking, auto_lfs_prune=True)
     # push separately the model card to be independent from the rest of the model
     create_model_card(args, model_name=model_name)
     try:
-        repo.push_to_hub(
-            commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True
-        )
+        repo.push_to_hub(commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True)
     except EnvironmentError as exc:
         logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")
 
@@ -133,10 +140,7 @@ def create_model_card(args, model_name):
     # TODO: replace this placeholder model card generation
     model_card = ""
 
-    metadata = {
-        "license": "apache-2.0",
-        "tags": ["pytorch", "diffusers"]
-    }
+    metadata = {"license": "apache-2.0", "tags": ["pytorch", "diffusers"]}
     metadata = yaml.dump(metadata, sort_keys=False)
     if len(metadata) > 0:
         model_card = f"---\n{metadata}---\n"

src/diffusers/modeling_utils.py

@@ -585,4 +585,4 @@ def unwrap_model(model: torch.nn.Module) -> torch.nn.Module:
     if hasattr(model, "module"):
         return unwrap_model(model.module)
     else:
-        return model
+        return model

src/diffusers/models/__init__.py

@@ -20,4 +20,4 @@ from .unet import UNetModel
 from .unet_glide import GLIDESuperResUNetModel, GLIDETextToImageUNetModel, GLIDEUNetModel
 from .unet_grad_tts import UNetGradTTSModel
 from .unet_ldm import UNetLDMModel
-from .unet_rl import TemporalUNet
+from .unet_rl import TemporalUNet

src/diffusers/models/unet_rl.py

@@ -5,6 +5,7 @@ import math
 import torch
 import torch.nn as nn
 
+
 try:
     import einops
     from einops.layers.torch import Rearrange
@@ -103,7 +104,7 @@ class ResidualTemporalBlock(nn.Module):
         return out + self.residual_conv(x)
 
 
-class TemporalUNet(ModelMixin, ConfigMixin): #(nn.Module):
+class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
     def __init__(
         self,
         horizon,
@@ -118,7 +119,6 @@ class TemporalUNet(ModelMixin, ConfigMixin): #(nn.Module):
         in_out = list(zip(dims[:-1], dims[1:]))
         # print(f'[ models/temporal ] Channel dimensions: {in_out}')
 
-
         time_dim = dim
         self.time_mlp = nn.Sequential(
             SinusoidalPosEmb(dim),

src/diffusers/schedulers/scheduling_ddpm.py

@@ -137,8 +137,8 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         return pred_prev_sample
 
     def forward_step(self, original_sample, noise, t):
-        sqrt_alpha_prod = self.alpha_prod_t[t] ** 0.5
-        sqrt_one_minus_alpha_prod = (1 - self.alpha_prod_t[t]) ** 0.5
+        sqrt_alpha_prod = self.alphas_cumprod[t] ** 0.5
+        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[t]) ** 0.5
         noisy_sample = sqrt_alpha_prod * original_sample + sqrt_one_minus_alpha_prod * noise
         return noisy_sample
 

tests/test_modeling_utils.py

@@ -33,9 +33,9 @@ from diffusers import (
     GLIDESuperResUNetModel,
     LatentDiffusion,
     PNDMScheduler,
-    UNetModel,
-    UNetLDMModel,
     UNetGradTTSModel,
+    UNetLDMModel,
+    UNetModel,
 )
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
@@ -342,6 +342,7 @@ class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
+
 class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = UNetLDMModel
 
@@ -378,7 +379,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         }
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
-    
+
     def test_from_pretrained_hub(self):
         model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
@@ -446,7 +447,7 @@ class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
         }
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
-    
+
     def test_from_pretrained_hub(self):
         model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
@@ -464,7 +465,7 @@ class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
         torch.manual_seed(0)
         if torch.cuda.is_available():
             torch.cuda.manual_seed_all(0)
-        
+
         num_features = model.config.n_feats
         seq_len = 16
         noise = torch.randn((1, num_features, seq_len))