[Styling] stylify using ruff (#5841)

* ruff format

* not need to use doc-builder's black styling as the doc is styled in ruff

* make fix-copies

* comment

* use run_ruff

.github/workflows/pr_quality.yml

@@ -27,9 +27,8 @@ jobs:
           pip install .[quality]
       - name: Check quality
         run: |
-          black --check examples tests src utils scripts
-          ruff examples tests src utils scripts
-          doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+          ruff check examples tests src utils scripts
+          ruff format examples tests src utils scripts --check
 
   check_repository_consistency:
     runs-on: ubuntu-latest

CONTRIBUTING.md

@@ -410,7 +410,7 @@ Diffusers has grown a lot. Here is the command for it:
  $ make test
  ```
 
-🧨 Diffusers relies on `black` and `isort` to format its source code
+🧨 Diffusers relies on `ruff` and `isort` to format its source code
 consistently. After you make changes, apply automatic style corrections and code verifications
 that can't be automated in one go with:
 

Makefile

@@ -9,8 +9,8 @@ modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
 	@if test -n "$(modified_py_files)"; then \
 		echo "Checking/fixing $(modified_py_files)"; \
-		black $(modified_py_files); \
-		ruff $(modified_py_files); \
+		ruff check $(modified_py_files) --fix; \
+		ruff format $(modified_py_files);\
 	else \
 		echo "No library .py files were modified"; \
 	fi
@@ -40,23 +40,21 @@ repo-consistency:
 # this target runs checks on all files
 
 quality:
-	black --check $(check_dirs)
-	ruff $(check_dirs)
-	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+	ruff check $(check_dirs) setup.py
+	ruff format --check $(check_dirs) setup.py 
 	python utils/check_doc_toc.py
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
 extra_style_checks:
 	python utils/custom_init_isort.py
-	doc-builder style src/diffusers docs/source --max_len 119 --path_to_docs docs/source
 	python utils/check_doc_toc.py --fix_and_overwrite
 
 # this target runs checks on all files and potentially modifies some of them
 
 style:
-	black $(check_dirs)
-	ruff $(check_dirs) --fix
+	ruff check $(check_dirs) setup.py --fix
+	ruff format $(check_dirs) setup.py
 	${MAKE} autogenerate_code
 	${MAKE} extra_style_checks
 

examples/community/composable_stable_diffusion.py

@@ -65,6 +65,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(

examples/community/latent_consistency_img2img.py

@@ -564,9 +564,7 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)

examples/community/latent_consistency_txt2img.py

@@ -469,9 +469,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)

examples/community/lpw_stable_diffusion.py

@@ -56,10 +56,10 @@ def parse_prompt_attention(text):
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
       [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
+      \\( - literal character '('
+      \\[ - literal character '['
+      \\) - literal character ')'
+      \\] - literal character ']'
       \\ - literal character '\'
       anything else - just text
     >>> parse_prompt_attention('normal text')
@@ -68,7 +68,7 @@ def parse_prompt_attention(text):
     [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
     >>> parse_prompt_attention('(unbalanced')
     [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
+    >>> parse_prompt_attention('\\(literal\\]')
     [['(literal]', 1.0]]
     >>> parse_prompt_attention('(unnecessary)(parens)')
     [['unnecessaryparens', 1.1]]

examples/community/lpw_stable_diffusion_onnx.py

@@ -82,10 +82,10 @@ def parse_prompt_attention(text):
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
       [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
+      \\( - literal character '('
+      \\[ - literal character '['
+      \\) - literal character ')'
+      \\] - literal character ']'
       \\ - literal character '\'
       anything else - just text
     >>> parse_prompt_attention('normal text')
@@ -94,7 +94,7 @@ def parse_prompt_attention(text):
     [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
     >>> parse_prompt_attention('(unbalanced')
     [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
+    >>> parse_prompt_attention('\\(literal\\]')
     [['(literal]', 1.0]]
     >>> parse_prompt_attention('(unnecessary)(parens)')
     [['unnecessaryparens', 1.1]]
@@ -433,6 +433,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
     """
+
     if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
 
         def __init__(

examples/community/lpw_stable_diffusion_xl.py

@@ -46,10 +46,10 @@ def parse_prompt_attention(text):
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
       [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
+      \\( - literal character '('
+      \\[ - literal character '['
+      \\) - literal character ')'
+      \\] - literal character ']'
       \\ - literal character '\'
       anything else - just text
 
@@ -59,7 +59,7 @@ def parse_prompt_attention(text):
     [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
     >>> parse_prompt_attention('(unbalanced')
     [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
+    >>> parse_prompt_attention('\\(literal\\]')
     [['(literal]', 1.0]]
     >>> parse_prompt_attention('(unnecessary)(parens)')
     [['unnecessaryparens', 1.1]]

examples/community/magic_mix.py

@@ -127,9 +127,9 @@ class MagicMixPipeline(DiffusionPipeline):
                         timesteps=t,
                     )
 
-                    input = (mix_factor * latents) + (
-                        1 - mix_factor
-                    ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
+                    input = (
+                        (mix_factor * latents) + (1 - mix_factor) * orig_latents
+                    )  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                     input = torch.cat([input] * 2)
 
                 else:  # content generation phase

examples/community/mixture_canvas.py

@@ -453,9 +453,7 @@ class StableDiffusionCanvasPipeline(DiffusionPipeline):
                     :,
                     region.latent_row_init : region.latent_row_end,
                     region.latent_col_init : region.latent_col_end,
-                ] += (
-                    noise_pred_region * mask_weights_region
-                )
+                ] += noise_pred_region * mask_weights_region
                 contributors[
                     :,
                     :,

examples/community/pipeline_prompt2prompt.py

@@ -65,6 +65,7 @@ class Prompt2PromptPipeline(StableDiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     @torch.no_grad()

examples/community/pipeline_zero1to3.py

@@ -94,6 +94,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline):
         cc_projection ([`CCProjection`]):
             Projection layer to project the concated CLIP features and pose embeddings to the original CLIP feature size.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(
@@ -658,7 +659,8 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline):
 
         if isinstance(generator, list):
             init_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.mode(generator[i]) for i in range(batch_size)  # sample
+                self.vae.encode(image[i : i + 1]).latent_dist.mode(generator[i])
+                for i in range(batch_size)  # sample
             ]
             init_latents = torch.cat(init_latents, dim=0)
         else:

examples/community/run_onnx_controlnet.py

@@ -651,9 +651,10 @@ class OnnxStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = num_controlnet
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

examples/community/run_tensorrt_controlnet.py

@@ -755,9 +755,10 @@ class TensorRTStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = num_controlnet
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

examples/community/sd_text2img_k_diffusion.py

@@ -68,6 +68,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(

examples/community/stable_diffusion_ipex.py

@@ -89,6 +89,7 @@ class StableDiffusionIPEXPipeline(DiffusionPipeline, TextualInversionLoaderMixin
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(

examples/community/stable_diffusion_mega.py

@@ -50,6 +50,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(

examples/community/stable_diffusion_repaint.py

@@ -170,6 +170,7 @@ class StableDiffusionRepaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(

examples/research_projects/colossalai/train_dreambooth_colossalai.py

@@ -464,9 +464,7 @@ def main(args):
     unet = gemini_zero_dpp(unet, args.placement)
 
     # config optimizer for colossalai zero
-    optimizer = GeminiAdamOptimizer(
-        unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm
-    )
+    optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
 
     # load noise_scheduler
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")