update

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -35,8 +35,7 @@ import torch.utils.checkpoint
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
-from accelerate.utils import (DistributedDataParallelKwargs,
-                              ProjectConfiguration, set_seed)
+from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
 from huggingface_hub import create_repo, hf_hub_download, upload_folder
 from huggingface_hub.utils import insecure_hashlib
 from packaging import version
@@ -52,24 +51,31 @@ from tqdm.auto import tqdm
 from transformers import AutoTokenizer, PretrainedConfig
 
 import diffusers
-from diffusers import (AutoencoderKL, DDPMScheduler,
-                       DPMSolverMultistepScheduler, EDMEulerScheduler,
-                       EulerDiscreteScheduler, StableDiffusionXLPipeline,
-                       UNet2DConditionModel)
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    EDMEulerScheduler,
+    EulerDiscreteScheduler,
+    StableDiffusionXLPipeline,
+    UNet2DConditionModel,
+)
 from diffusers.loaders import StableDiffusionLoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import (_set_state_dict_into_text_encoder,
-                                      cast_training_params, compute_snr)
-from diffusers.utils import (check_min_version, convert_all_state_dict_to_peft,
-                             convert_state_dict_to_diffusers,
-                             convert_state_dict_to_kohya,
-                             convert_unet_state_dict_to_peft,
-                             is_wandb_available)
-from diffusers.utils.hub_utils import (load_or_create_model_card,
-                                       populate_model_card)
+from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr
+from diffusers.utils import (
+    check_min_version,
+    convert_all_state_dict_to_peft,
+    convert_state_dict_to_diffusers,
+    convert_state_dict_to_kohya,
+    convert_unet_state_dict_to_peft,
+    is_wandb_available,
+)
+from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
 
+
 if is_wandb_available():
     import wandb
 

examples/research_projects/anytext/anytext.py

@@ -187,9 +187,9 @@ def get_clip_token_for_string(tokenizer, string):
         return_tensors="pt",
     )
     tokens = batch_encoding["input_ids"]
-    assert (
-        torch.count_nonzero(tokens - 49407) == 2
-    ), f"String '{string}' maps to more than a single token. Please use another string"
+    assert torch.count_nonzero(tokens - 49407) == 2, (
+        f"String '{string}' maps to more than a single token. Please use another string"
+    )
     return tokens[0, 1]
 
 

examples/research_projects/anytext/ocr_recog/RecSVTR.py

@@ -312,9 +312,9 @@ class PatchEmbed(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert (
-            H == self.img_size[0] and W == self.img_size[1]
-        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        assert H == self.img_size[0] and W == self.img_size[1], (
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        )
         x = self.proj(x).flatten(2).permute(0, 2, 1)
         return x
 

src/diffusers/models/__init__.py

@@ -26,6 +26,7 @@ _import_structure = {}
 
 if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
+    _import_structure["auto_model"] = ["AutoModel"]
     _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
     _import_structure["autoencoders.autoencoder_dc"] = ["AutoencoderDC"]
     _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
@@ -41,7 +42,6 @@ if is_torch_available():
     _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
     _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["autoencoders.vq_model"] = ["VQModel"]
-    _import_structure["auto_model"] = ["AutoModel"]
     _import_structure["cache_utils"] = ["CacheMixin"]
     _import_structure["controlnets.controlnet"] = ["ControlNetModel"]
     _import_structure["controlnets.controlnet_flux"] = ["FluxControlNetModel", "FluxMultiControlNetModel"]

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -19,8 +19,7 @@ import ftfy
 import PIL
 import regex as re
 import torch
-from transformers import (AutoTokenizer, CLIPImageProcessor, CLIPVisionModel,
-                          UMT5EncoderModel)
+from transformers import AutoTokenizer, CLIPImageProcessor, CLIPVisionModel, UMT5EncoderModel
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput
@@ -33,6 +32,7 @@ from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
 from .pipeline_output import WanPipelineOutput
 
+
 if is_torch_xla_available():
     import torch_xla.core.xla_model as xm
 
@@ -334,7 +334,7 @@ class WanImageToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
                 "Provide either `image` or `prompt_embeds`. Cannot leave both `image` and `image_embeds` undefined."
             )
         if image is not None and 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}.")
 

src/diffusers/utils/import_utils.py

@@ -29,6 +29,7 @@ from packaging.version import Version, parse
 
 from . import logging
 
+
 # The package importlib_metadata is in a different place, depending on the python version.
 if sys.version_info < (3, 8):
     import importlib_metadata

tests/pipelines/test_pipelines_common.py

@@ -2639,12 +2639,12 @@ class FasterCacheTesterMixin:
         output = run_forward(pipe).flatten()
         image_slice_faster_cache_disabled = np.concatenate((output[:8], output[-8:]))
 
-        assert np.allclose(
-            original_image_slice, image_slice_faster_cache_enabled, atol=expected_atol
-        ), "FasterCache outputs should not differ much in specified timestep range."
-        assert np.allclose(
-            original_image_slice, image_slice_faster_cache_disabled, atol=1e-4
-        ), "Outputs from normal inference and after disabling cache should not differ."
+        assert np.allclose(original_image_slice, image_slice_faster_cache_enabled, atol=expected_atol), (
+            "FasterCache outputs should not differ much in specified timestep range."
+        )
+        assert np.allclose(original_image_slice, image_slice_faster_cache_disabled, atol=1e-4), (
+            "Outputs from normal inference and after disabling cache should not differ."
+        )
 
     def test_faster_cache_state(self):
         from diffusers.hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK

tests/single_file/test_sana_transformer.py

@@ -47,9 +47,9 @@ class SanaTransformer2DModelSingleFileTests(unittest.TestCase):
         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"
+            )
 
     def test_checkpoint_loading(self):
         for ckpt_path in self.alternate_keys_ckpt_paths: