Complete rollback of diffusers core files

Author: FredyRivera-dev

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import inspect
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
-import copy
+from typing import Any, Callable, Dict, List, Optional, Union
+
 import numpy as np
 import torch
 from transformers import (

src/diffusers/pipelines/pipeline_utils.py

@@ -23,7 +23,6 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union, get_args, get_origin
 
-
 import numpy as np
 import PIL.Image
 import requests
@@ -179,7 +178,6 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
 
-            
 class DiffusionPipeline(ConfigMixin, PushToHubMixin):
     r"""
     Base class for all pipelines.

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
+
 import torch
 from packaging import version
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
@@ -150,7 +151,6 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-
 class StableDiffusionPipeline(
     DiffusionPipeline,
     StableDiffusionMixin,

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -14,6 +14,7 @@
 
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
+
 import torch
 from transformers import (
     CLIPTextModelWithProjection,

src/diffusers/schedulers/scheduling_amused.py

@@ -160,4 +160,3 @@ def add_noise(self, sample, timesteps, generator=None):
         masked_sample[mask_indices] = self.config.mask_token_id
 
         return masked_sample
-

src/diffusers/schedulers/scheduling_consistency_decoder.py

@@ -109,8 +109,6 @@ def set_timesteps(
         self.c_out = self.c_out.to(device)
         self.c_in = self.c_in.to(device)
 
-    
-
     @property
     def init_noise_sigma(self):
         return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -243,7 +243,6 @@ def set_timesteps(
         self._begin_index = None
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
-
     # Modified _convert_to_karras implementation that takes in ramp as argument
     def _convert_to_karras(self, ramp):
         """Constructs the noise schedule of Karras et al. (2022)."""

src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py

@@ -241,7 +241,6 @@ def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torc
         # if a noise sampler is used, reinitialise it
         self.noise_sampler = None
 
-
     # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_karras_sigmas
     def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
         """Constructs the noise schedule of Karras et al. (2022)."""

src/diffusers/schedulers/scheduling_ddim.py

@@ -339,7 +339,6 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
 
         self.timesteps = torch.from_numpy(timesteps).to(device)
 
-
     def step(
         self,
         model_output: torch.Tensor,

src/diffusers/schedulers/scheduling_ddim_cogvideox.py

@@ -302,7 +302,6 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
 
         self.timesteps = torch.from_numpy(timesteps).to(device)
 
-
     def step(
         self,
         model_output: torch.Tensor,