update

Author: DN6

examples/community/mod_controlnet_tile_sr_sdxl.py

@@ -701,7 +701,7 @@ def check_inputs(
             raise ValueError("`max_tile_size` cannot be None.")
         elif not isinstance(max_tile_size, int) or max_tile_size not in (1024, 1280):
             raise ValueError(
-                f"`max_tile_size` has to be in 1024 or 1280 but is {max_tile_size} of type" f" {type(max_tile_size)}."
+                f"`max_tile_size` has to be in 1024 or 1280 but is {max_tile_size} of type {type(max_tile_size)}."
             )
         if tile_gaussian_sigma is None:
             raise ValueError("`tile_gaussian_sigma` cannot be None.")

setup.py

@@ -122,7 +122,7 @@
     "pytest-timeout",
     "pytest-xdist",
     "python>=3.8.0",
-    "ruff==0.9.7",
+    "ruff==0.9.10",
     "safetensors>=0.3.1",
     "sentencepiece>=0.1.91,!=0.1.92",
     "GitPython<3.1.19",

src/diffusers/dependency_versions_table.py

@@ -29,7 +29,7 @@
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",
     "python": "python>=3.8.0",
-    "ruff": "ruff==0.9.7",
+    "ruff": "ruff==0.9.10",
     "safetensors": "safetensors>=0.3.1",
     "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
     "GitPython": "GitPython<3.1.19",

src/diffusers/pipelines/easyanimate/pipeline_easyanimate_inpaint.py

@@ -1130,7 +1130,7 @@ def __call__(
                     f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects"
                     f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                     f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
                     " `pipeline.transformer` or your `mask_image` or `image` input."
                 )
 

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -318,7 +318,7 @@ def check_inputs(
         callback_on_step_end_tensor_inputs=None,
     ):
         if not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):
-            raise ValueError("`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is" f" {type(image)}")
+            raise ValueError(f"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is {type(image)}")
         if height % 16 != 0 or width % 16 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
 

tests/pipelines/hunyuandit/test_hunyuan_dit.py

@@ -21,19 +21,23 @@
 import torch
 from transformers import AutoTokenizer, BertModel, T5EncoderModel
 
-from diffusers import (AutoencoderKL, DDPMScheduler, HunyuanDiT2DModel,
-                       HunyuanDiTPipeline)
-from diffusers.utils.testing_utils import (enable_full_determinism,
-                                           numpy_cosine_similarity_distance,
-                                           require_torch_accelerator, slow,
-                                           torch_device)
-
-from ..pipeline_params import (TEXT_TO_IMAGE_BATCH_PARAMS,
-                               TEXT_TO_IMAGE_IMAGE_PARAMS,
-                               TEXT_TO_IMAGE_PARAMS)
+from diffusers import AutoencoderKL, DDPMScheduler, HunyuanDiT2DModel, HunyuanDiTPipeline
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    numpy_cosine_similarity_distance,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import (
-    PipelineTesterMixin, check_qkv_fusion_matches_attn_procs_length,
-    check_qkv_fusion_processors_exist, to_np)
+    PipelineTesterMixin,
+    check_qkv_fusion_matches_attn_procs_length,
+    check_qkv_fusion_processors_exist,
+    to_np,
+)
+
 
 enable_full_determinism()
 
@@ -170,9 +174,9 @@ def test_fused_qkv_projections(self):
         # TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
         # to the pipeline level.
         pipe.transformer.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -188,15 +192,15 @@ def test_fused_qkv_projections(self):
         image_disabled = pipe(**inputs)[0]
         image_slice_disabled = image_disabled[0, -3:, -3:, -1]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
     @unittest.skip(
         "Test not supported as `encode_prompt` is called two times separately which deivates from about 99% of the pipelines we have."

tests/single_file/test_model_wan_autoencoder_single_file.py

@@ -56,6 +56,6 @@ def test_single_file_components(self):
         for param_name, param_value in model_single_file.config.items():
             if param_name in PARAMS_TO_IGNORE:
                 continue
-            assert (
-                model.config[param_name] == param_value
-            ), f"{param_name} differs between single file loading and pretrained loading"
+            assert model.config[param_name] == param_value, (
+                f"{param_name} differs between single file loading and pretrained loading"
+            )

tests/single_file/test_model_wan_transformer3d_single_file.py

@@ -57,9 +57,9 @@ def test_single_file_components(self):
         for param_name, param_value in model_single_file.config.items():
             if param_name in PARAMS_TO_IGNORE:
                 continue
-            assert (
-                model.config[param_name] == param_value
-            ), f"{param_name} differs between single file loading and pretrained loading"
+            assert model.config[param_name] == param_value, (
+                f"{param_name} differs between single file loading and pretrained loading"
+            )
 
 
 @require_big_gpu_with_torch_cuda
@@ -88,6 +88,6 @@ def test_single_file_components(self):
         for param_name, param_value in model_single_file.config.items():
             if param_name in PARAMS_TO_IGNORE:
                 continue
-            assert (
-                model.config[param_name] == param_value
-            ), f"{param_name} differs between single file loading and pretrained loading"
+            assert model.config[param_name] == param_value, (
+                f"{param_name} differs between single file loading and pretrained loading"
+            )