style

Author: yiyixuxu

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
 import torch
 

src/diffusers/guiders/adaptive_projected_guidance_mix.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
 import torch
 
@@ -88,9 +88,7 @@ def __init__(
         self.use_original_formulation = use_original_formulation
         self.momentum_buffer = None
 
-    def prepare_inputs(
-        self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
-
+    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
         if self._step == 0:
             if self.adaptive_projected_guidance_momentum is not None:
                 self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)

src/diffusers/guiders/classifier_free_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
 import torch
 
@@ -92,7 +92,6 @@ def __init__(
         self.use_original_formulation = use_original_formulation
 
     def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
-
         tuple_indices = [0] if self.num_conditions == 1 or not self._is_cfg_enabled() else [0, 1]
         data_batches = []
         for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
 import torch
 

src/diffusers/guiders/guider_utils.py

@@ -216,7 +216,6 @@ def _prepare_batch(
         """
         from ..modular_pipelines.modular_pipeline import BlockState
 
-
         data_batch = {}
         for key, value in data.items():
             try:

src/diffusers/guiders/tangential_classifier_free_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
 import torch
 

src/diffusers/pipelines/hunyuan_image/pipeline_hunyuanimage.py

@@ -531,8 +531,7 @@ def __call__(
                 instead.
             negative_prompt (`str` or `List[str]`, *optional*):
                 The prompt or prompts not to guide the image generation. If not defined and negative_prompt_embeds is
-                not provided, will use an empty negative prompt. Ignored when not using guidance.
-                ).
+                not provided, will use an empty negative prompt. Ignored when not using guidance. ).
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
@@ -548,10 +547,10 @@ def __call__(
                 A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance
                 where the guidance scale is applied during inference through noise prediction rescaling, guidance
                 distilled models take the guidance scale directly as an input parameter during forward pass. Guidance
-                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate images that
-                are closely linked to the text `prompt`, usually at the expense of lower image quality. 
-                For guidance distilled models, this parameter is required. 
-                For non-distilled models, this parameter will be ignored.
+                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate
+                images that are closely linked to the text `prompt`, usually at the expense of lower image quality. For
+                guidance distilled models, this parameter is required. For non-distilled models, this parameter will be
+                ignored.
             num_images_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -782,7 +781,9 @@ def __call__(
                     guider.prepare_models(self.transformer)
 
                     # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states)
-                    cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
+                    cond_kwargs = {
+                        input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()
+                    }
 
                     # e.g. "pred_cond"/"pred_uncond"
                     context_name = getattr(guider_state_batch, guider._identifier_key)

src/diffusers/pipelines/hunyuan_image/pipeline_hunyuanimage_refiner.py

@@ -467,10 +467,10 @@ def __call__(
                 A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance
                 where the guidance scale is applied during inference through noise prediction rescaling, guidance
                 distilled models take the guidance scale directly as an input parameter during forward pass. Guidance
-                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate images that
-                are closely linked to the text `prompt`, usually at the expense of lower image quality. 
-                For guidance distilled models, this parameter is required. 
-                For non-distilled models, this parameter will be ignored.
+                is enabled by setting `distilled_guidance_scale > 1`. Higher guidance scale encourages to generate
+                images that are closely linked to the text `prompt`, usually at the expense of lower image quality. For
+                guidance distilled models, this parameter is required. For non-distilled models, this parameter will be
+                ignored.
             num_images_per_prompt (`int`, *optional*, defaults to 1):
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
@@ -621,9 +621,7 @@ def __call__(
         if distilled_guidance_scale is None:
             raise ValueError("`distilled_guidance_scale` is required for guidance-distilled model.")
         guidance = (
-            torch.tensor(
-                [distilled_guidance_scale] * latents.shape[0], dtype=self.transformer.dtype, device=device
-            )
+            torch.tensor([distilled_guidance_scale] * latents.shape[0], dtype=self.transformer.dtype, device=device)
             * 1000.0
         )
 
@@ -670,7 +668,9 @@ def __call__(
                     guider.prepare_models(self.transformer)
 
                     # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states)
-                    cond_kwargs = {input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()}
+                    cond_kwargs = {
+                        input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()
+                    }
 
                     # e.g. "pred_cond"/"pred_uncond"
                     context_name = getattr(guider_state_batch, guider._identifier_key)