Migrate from yapf to black

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
+  rev: v4.4.0
   hooks:
   - id: check-executables-have-shebangs
   - id: check-json
@@ -8,39 +8,43 @@ repos:
   - id: check-shebang-scripts-are-executable
   - id: check-toml
   - id: check-yaml
-  - id: double-quote-string-fixer
   - id: end-of-file-fixer
   - id: mixed-line-ending
-    args: ['--fix=lf']
+    args: ["--fix=lf"]
   - id: requirements-txt-fixer
   - id: trailing-whitespace
 - repo: https://github.com/myint/docformatter
-  rev: v1.4
+  rev: v1.7.5
   hooks:
   - id: docformatter
-    args: ['--in-place']
+    args: ["--in-place"]
 - repo: https://github.com/pycqa/isort
   rev: 5.12.0
   hooks:
     - id: isort
+      args: ["--profile", "black"]
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
+  rev: v1.5.1
   hooks:
     - id: mypy
-      args: ['--ignore-missing-imports']
-      additional_dependencies: ['types-python-slugify']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
+      args: ["--ignore-missing-imports"]
+      additional_dependencies: ["types-python-slugify", "types-requests", "types-PyYAML"]
+- repo: https://github.com/psf/black
+  rev: 23.7.0
   hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+    - id: black
+      language_version: python3.10
+      args: ["--line-length", "119"]
 - repo: https://github.com/kynan/nbstripout
-  rev: 0.6.0
+  rev: 0.6.1
   hooks:
     - id: nbstripout
-      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
+      args: ["--extra-keys", "metadata.interpreter metadata.kernelspec cell.metadata.pycharm"]
 - repo: https://github.com/nbQA-dev/nbQA
   rev: 1.7.0
   hooks:
+    - id: nbqa-black
+    - id: nbqa-pyupgrade
+      args: ["--py37-plus"]
     - id: nbqa-isort
-    - id: nbqa-yapf
+      args: ["--float-to-top"]

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

.vscode/settings.json

@@ -1,18 +1,21 @@
 {
-    "python.linting.enabled": true,
-    "python.linting.flake8Enabled": true,
-    "python.linting.pylintEnabled": false,
-    "python.linting.lintOnSave": true,
-    "python.formatting.provider": "yapf",
-    "python.formatting.yapfArgs": [
-        "--style={based_on_style: pep8, indent_width: 4, blank_line_before_nested_class_or_def: false, spaces_before_comment: 2, split_before_logical_operator: true}"
-    ],
     "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
         "editor.formatOnType": true,
         "editor.codeActionsOnSave": {
             "source.organizeImports": true
         }
     },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.args": [
+        "--line-length=119"
+    ],
     "editor.formatOnSave": true,
     "files.insertFinalNewline": true
 }

app.py

@@ -11,31 +11,32 @@ import PIL.Image
 import torch
 from diffusers import DiffusionPipeline
 
-DESCRIPTION = '# SD-XL'
+DESCRIPTION = "# SD-XL"
 if not torch.cuda.is_available():
-    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv(
-    'CACHE_EXAMPLES') == '1'
-MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024'))
-USE_TORCH_COMPILE = os.getenv('USE_TORCH_COMPILE') == '1'
-ENABLE_CPU_OFFLOAD = os.getenv('ENABLE_CPU_OFFLOAD') == '1'
-ENABLE_REFINER = os.getenv('ENABLE_REFINER', '1') == '1'
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 if torch.cuda.is_available():
     pipe = DiffusionPipeline.from_pretrained(
-        'stabilityai/stable-diffusion-xl-base-1.0',
+        "stabilityai/stable-diffusion-xl-base-1.0",
         torch_dtype=torch.float16,
         use_safetensors=True,
-        variant='fp16')
+        variant="fp16",
+    )
     if ENABLE_REFINER:
         refiner = DiffusionPipeline.from_pretrained(
-            'stabilityai/stable-diffusion-xl-refiner-1.0',
+            "stabilityai/stable-diffusion-xl-refiner-1.0",
             torch_dtype=torch.float16,
             use_safetensors=True,
-            variant='fp16')
+            variant="fp16",
+        )
 
     if ENABLE_CPU_OFFLOAD:
         pipe.enable_model_cpu_offload()
@@ -47,13 +48,9 @@ if torch.cuda.is_available():
             refiner.to(device)
 
     if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet,
-                                  mode='reduce-overhead',
-                                  fullgraph=True)
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
         if ENABLE_REFINER:
-            refiner.unet = torch.compile(refiner.unet,
-                                         mode='reduce-overhead',
-                                         fullgraph=True)
+            refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
 else:
     pipe = None
     refiner = None
@@ -65,21 +62,23 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     return seed
 
 
-def generate(prompt: str,
-             negative_prompt: str = '',
-             prompt_2: str = '',
-             negative_prompt_2: str = '',
-             use_negative_prompt: bool = False,
-             use_prompt_2: bool = False,
-             use_negative_prompt_2: bool = False,
-             seed: int = 0,
-             width: int = 1024,
-             height: int = 1024,
-             guidance_scale_base: float = 5.0,
-             guidance_scale_refiner: float = 5.0,
-             num_inference_steps_base: int = 50,
-             num_inference_steps_refiner: int = 50,
-             apply_refiner: bool = False) -> PIL.Image.Image:
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    prompt_2: str = "",
+    negative_prompt_2: str = "",
+    use_negative_prompt: bool = False,
+    use_prompt_2: bool = False,
+    use_negative_prompt_2: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale_base: float = 5.0,
+    guidance_scale_refiner: float = 5.0,
+    num_inference_steps_base: int = 50,
+    num_inference_steps_refiner: int = 50,
+    apply_refiner: bool = False,
+) -> PIL.Image.Image:
     generator = torch.Generator().manual_seed(seed)
 
     if not use_negative_prompt:
@@ -90,140 +89,153 @@ def generate(prompt: str,
         negative_prompt_2 = None  # type: ignore
 
     if not apply_refiner:
-        return pipe(prompt=prompt,
-                    negative_prompt=negative_prompt,
-                    prompt_2=prompt_2,
-                    negative_prompt_2=negative_prompt_2,
-                    width=width,
-                    height=height,
-                    guidance_scale=guidance_scale_base,
-                    num_inference_steps=num_inference_steps_base,
-                    generator=generator,
-                    output_type='pil').images[0]
+        return pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale_base,
+            num_inference_steps=num_inference_steps_base,
+            generator=generator,
+            output_type="pil",
+        ).images[0]
     else:
-        latents = pipe(prompt=prompt,
-                       negative_prompt=negative_prompt,
-                       prompt_2=prompt_2,
-                       negative_prompt_2=negative_prompt_2,
-                       width=width,
-                       height=height,
-                       guidance_scale=guidance_scale_base,
-                       num_inference_steps=num_inference_steps_base,
-                       generator=generator,
-                       output_type='latent').images
-        image = refiner(prompt=prompt,
-                        negative_prompt=negative_prompt,
-                        prompt_2=prompt_2,
-                        negative_prompt_2=negative_prompt_2,
-                        guidance_scale=guidance_scale_refiner,
-                        num_inference_steps=num_inference_steps_refiner,
-                        image=latents,
-                        generator=generator).images[0]
+        latents = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale_base,
+            num_inference_steps=num_inference_steps_base,
+            generator=generator,
+            output_type="latent",
+        ).images
+        image = refiner(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            guidance_scale=guidance_scale_refiner,
+            num_inference_steps=num_inference_steps_refiner,
+            image=latents,
+            generator=generator,
+        ).images[0]
         return image
 
 
 examples = [
-    'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k',
-    'An astronaut riding a green horse',
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
 ]
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value='Duplicate Space for private use',
-                       elem_id='duplicate-button',
-                       visible=os.getenv('SHOW_DUPLICATE_BUTTON') == '1')
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
     with gr.Group():
         with gr.Row():
             prompt = gr.Text(
-                label='Prompt',
+                label="Prompt",
                 show_label=False,
                 max_lines=1,
-                placeholder='Enter your prompt',
+                placeholder="Enter your prompt",
                 container=False,
             )
-            run_button = gr.Button('Run', scale=0)
-        result = gr.Image(label='Result', show_label=False)
-    with gr.Accordion('Advanced options', open=False):
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Image(label="Result", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
         with gr.Row():
-            use_negative_prompt = gr.Checkbox(label='Use negative prompt',
-                                              value=False)
-            use_prompt_2 = gr.Checkbox(label='Use prompt 2', value=False)
-            use_negative_prompt_2 = gr.Checkbox(label='Use negative prompt 2',
-                                                value=False)
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
+            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
         negative_prompt = gr.Text(
-            label='Negative prompt',
+            label="Negative prompt",
             max_lines=1,
-            placeholder='Enter a negative prompt',
+            placeholder="Enter a negative prompt",
             visible=False,
         )
         prompt_2 = gr.Text(
-            label='Prompt 2',
+            label="Prompt 2",
             max_lines=1,
-            placeholder='Enter your prompt',
+            placeholder="Enter your prompt",
             visible=False,
         )
         negative_prompt_2 = gr.Text(
-            label='Negative prompt 2',
+            label="Negative prompt 2",
             max_lines=1,
-            placeholder='Enter a negative prompt',
+            placeholder="Enter a negative prompt",
             visible=False,
         )
 
-        seed = gr.Slider(label='Seed',
-                         minimum=0,
-                         maximum=MAX_SEED,
-                         step=1,
-                         value=0)
-        randomize_seed = gr.Checkbox(label='Randomize seed', value=True)
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         with gr.Row():
             width = gr.Slider(
-                label='Width',
+                label="Width",
                 minimum=256,
                 maximum=MAX_IMAGE_SIZE,
                 step=32,
                 value=1024,
             )
             height = gr.Slider(
-                label='Height',
+                label="Height",
                 minimum=256,
                 maximum=MAX_IMAGE_SIZE,
                 step=32,
                 value=1024,
             )
-        apply_refiner = gr.Checkbox(label='Apply refiner',
-                                    value=False,
-                                    visible=ENABLE_REFINER)
+        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
         with gr.Row():
-            guidance_scale_base = gr.Slider(label='Guidance scale for base',
-                                            minimum=1,
-                                            maximum=20,
-                                            step=0.1,
-                                            value=5.0)
+            guidance_scale_base = gr.Slider(
+                label="Guidance scale for base",
+                minimum=1,
+                maximum=20,
+                step=0.1,
+                value=5.0,
+            )
             num_inference_steps_base = gr.Slider(
-                label='Number of inference steps for base',
+                label="Number of inference steps for base",
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=50)
+                value=50,
+            )
         with gr.Row(visible=False) as refiner_params:
             guidance_scale_refiner = gr.Slider(
-                label='Guidance scale for refiner',
+                label="Guidance scale for refiner",
                 minimum=1,
                 maximum=20,
                 step=0.1,
-                value=5.0)
+                value=5.0,
+            )
             num_inference_steps_refiner = gr.Slider(
-                label='Number of inference steps for refiner',
+                label="Number of inference steps for refiner",
                 minimum=10,
                 maximum=100,
                 step=1,
-                value=50)
+                value=50,
+            )
 
-    gr.Examples(examples=examples,
-                inputs=prompt,
-                outputs=result,
-                fn=generate,
-                cache_examples=CACHE_EXAMPLES)
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
 
     use_negative_prompt.change(
         fn=lambda x: gr.update(visible=x),
@@ -281,7 +293,7 @@ with gr.Blocks(css='style.css') as demo:
         fn=generate,
         inputs=inputs,
         outputs=result,
-        api_name='run',
+        api_name="run",
     )
     negative_prompt.submit(
         fn=randomize_seed_fn,
@@ -331,4 +343,6 @@ with gr.Blocks(css='style.css') as demo:
         outputs=result,
         api_name=False,
     )
-demo.queue(max_size=20).launch()
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

notebook.ipynb

@@ -51,47 +51,9 @@
    "source": [
     "import os\n",
     "\n",
-    "os.environ['ENABLE_REFINER'] = '0'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 710,
-     "referenced_widgets": [
-      "68c1e33d84b94f009db258e278fe7068",
-      "b1b1ca6d1cc44a738c3b4b6de17f3a5b",
-      "104833166be14046873bfea2c1a2a887",
-      "32f25821a48d4c9589f58c134e3b56d7",
-      "3ed7cc7759074df58a91fd7fb28a4933",
-      "c8885bd4a35d4cdcbb6acce5c52e15e2",
-      "5d1d83dfd090460d9f948b71f95aaed8",
-      "773e06ed1d734e53a7def5305cd35131",
-      "753b336dbeb147349e4520715035d8da",
-      "c5215236213242b89a971a1095afcea5",
-      "bd0a6a0e16944533b59eaa3f5188e99f",
-      "96b1de32a367400bba75babd39bc7308",
-      "65291f8203964f4499a1b422af91f75e",
-      "0c3fad2a850b4320b47586ff4d0ac73e",
-      "69a6be1033c5424988a702c5d69590ee",
-      "b22729413d9b449a94892b91d95cf1e4",
-      "6c8f51c69f394eeea67eb515831f60b2",
-      "bb779e8367e44a939d607ace70493d94",
-      "4d3862b22c3245d8b3d8b6442e149c8d",
-      "16ef5a40c9d441aea180d1732442df97",
-      "db54ca7070cf43adbda196d44967464c",
-      "cadddb2624804c308710a219bf8cf4f3"
-     ]
-    },
-    "id": "4FTmJkt_J8j_",
-    "outputId": "850aba86-acb4-4452-bac2-28b5c815ec0f"
-   },
-   "outputs": [],
-   "source": [
-    "import app"
+    "import app\n",
+    "\n",
+    "os.environ[\"ENABLE_REFINER\"] = \"0\""
    ]
   },
   {