Update

.pre-commit-config.yaml

@@ -1,37 +1,60 @@
-exclude: ^stylegan_xl
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - 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']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.12.0
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-      additional_dependencies: ['types-python-slugify']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        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

@@ -0,0 +1,30 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.lint.args": [
+        "--line-length=119"
+    ],
+    "notebook.output.scrolling": true,
+    "notebook.formatOnCellExecution": true,
+    "notebook.formatOnSave.enabled": true,
+        "notebook.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🏃
 colorFrom: red
 colorTo: purple
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.20.0
 app_file: app.py
 pinned: false
 suggested_hardware: t4-small

app.py

@@ -9,54 +9,54 @@ import numpy as np
 
 from model import Model
 
-DESCRIPTION = '# [StyleGAN-XL](https://github.com/autonomousvision/stylegan_xl)'
+DESCRIPTION = "# [StyleGAN-XL](https://github.com/autonomousvision/stylegan_xl)"
 
 
 def update_class_index(name: str) -> dict:
-    if 'imagenet' in name:
-        return gr.Slider.update(maximum=999, visible=True)
-    elif 'cifar' in name:
-        return gr.Slider.update(maximum=9, visible=True)
+    if "imagenet" in name:
+        return gr.Slider(maximum=999, visible=True)
+    elif "cifar" in name:
+        return gr.Slider(maximum=9, visible=True)
     else:
-        return gr.Slider.update(visible=False)
+        return gr.Slider(visible=False)
 
 
 def get_sample_image_url(name: str) -> str:
-    sample_image_dir = 'https://huggingface.co/spaces/hysts/StyleGAN-XL/resolve/main/samples'
-    return f'{sample_image_dir}/{name}.jpg'
+    sample_image_dir = "https://huggingface.co/spaces/hysts/StyleGAN-XL/resolve/main/samples"
+    return f"{sample_image_dir}/{name}.jpg"
 
 
 def get_sample_image_markdown(name: str) -> str:
     url = get_sample_image_url(name)
-    if name == 'imagenet':
+    if name == "imagenet":
         size = 128
-        class_index = '0-999'
-        seed = '0'
-    elif name == 'cifar10':
+        class_index = "0-999"
+        seed = "0"
+    elif name == "cifar10":
         size = 32
-        class_index = '0-9'
-        seed = '0-9'
-    elif name == 'ffhq':
+        class_index = "0-9"
+        seed = "0-9"
+    elif name == "ffhq":
         size = 256
-        class_index = 'N/A'
-        seed = '0-99'
-    elif name == 'pokemon':
+        class_index = "N/A"
+        seed = "0-99"
+    elif name == "pokemon":
         size = 256
-        class_index = 'N/A'
-        seed = '0-99'
+        class_index = "N/A"
+        seed = "0-99"
     else:
         raise ValueError
 
-    return f'''
+    return f"""
     - size: {size}x{size}
     - class_index: {class_index}
     - seed: {seed}
     - truncation: 0.7
-    ![sample images]({url})'''
+    ![sample images]({url})"""
 
 
 def load_class_names(name: str) -> list[str]:
-    with open(f'labels/{name}_classes.json') as f:
+    with open(f"labels/{name}_classes.json") as f:
         names = json.load(f)
     return names
 
@@ -66,139 +66,136 @@ def get_class_name_df(name: str) -> list:
     return list(map(list, enumerate(names)))  # type: ignore
 
 
-IMAGENET_NAMES = load_class_names('imagenet')
-CIFAR10_NAMES = load_class_names('cifar10')
+IMAGENET_NAMES = load_class_names("imagenet")
+CIFAR10_NAMES = load_class_names("cifar10")
 
 
 def update_class_name(model_name: str, index: int) -> dict:
-    if 'imagenet' in model_name:
+    if "imagenet" in model_name:
         if index < len(IMAGENET_NAMES):
             value = IMAGENET_NAMES[index]
         else:
-            value = '-'
-        return gr.Textbox.update(value=value, visible=True)
-    elif 'cifar' in model_name:
+            value = "-"
+        return gr.Textbox(value=value, visible=True)
+    elif "cifar" in model_name:
         if index < len(CIFAR10_NAMES):
             value = CIFAR10_NAMES[index]
         else:
-            value = '-'
-        return gr.Textbox.update(value=value, visible=True)
+            value = "-"
+        return gr.Textbox(value=value, visible=True)
     else:
-        return gr.Textbox.update(visible=False)
+        return gr.Textbox(visible=False)
 
 
 model = Model()
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
 
     with gr.Tabs():
-        with gr.TabItem('App'):
+        with gr.TabItem("App"):
             with gr.Row():
                 with gr.Column():
                     with gr.Group():
-                        model_name = gr.Dropdown(model.MODEL_NAMES,
-                                                 value=model.MODEL_NAMES[3],
-                                                 label='Model')
-                        seed = gr.Slider(0,
-                                         np.iinfo(np.uint32).max,
-                                         step=1,
-                                         value=0,
-                                         label='Seed')
-                        psi = gr.Slider(0,
-                                        2,
-                                        step=0.05,
-                                        value=0.7,
-                                        label='Truncation psi')
-                        class_index = gr.Slider(0,
-                                                999,
-                                                step=1,
-                                                value=83,
-                                                label='Class Index')
+                        model_name = gr.Dropdown(label="Model", choices=model.MODEL_NAMES, value=model.MODEL_NAMES[3])
+                        seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=0)
+                        psi = gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7)
+                        class_index = gr.Slider(label="Class Index", minimum=0, maximum=999, step=1, value=83)
                         class_name = gr.Textbox(
-                            value=IMAGENET_NAMES[class_index.value],
-                            label='Class Label',
-                            interactive=False)
-                        tx = gr.Slider(-1,
-                                       1,
-                                       step=0.05,
-                                       value=0,
-                                       label='Translate X')
-                        ty = gr.Slider(-1,
-                                       1,
-                                       step=0.05,
-                                       value=0,
-                                       label='Translate Y')
-                        angle = gr.Slider(-180,
-                                          180,
-                                          step=5,
-                                          value=0,
-                                          label='Angle')
-                        run_button = gr.Button('Run')
+                            label="Class Label", value=IMAGENET_NAMES[class_index.value], interactive=False
+                        )
+                        tx = gr.Slider(label="Translate X", minimum=-1, maximum=1, step=0.05, value=0)
+                        ty = gr.Slider(label="Translate Y", minimum=-1, maximum=1, step=0.05, value=0)
+                        angle = gr.Slider(label="Angle", minimum=-180, maximum=180, step=5, value=0)
+                        run_button = gr.Button()
                 with gr.Column():
-                    result = gr.Image(label='Result', elem_id='result')
+                    result = gr.Image(label="Result")
 
-        with gr.TabItem('Sample Images'):
+        with gr.TabItem("Sample Images"):
             with gr.Row():
-                model_name2 = gr.Dropdown([
-                    'imagenet',
-                    'cifar10',
-                    'ffhq',
-                    'pokemon',
-                ],
-                                          value='imagenet',
-                                          label='Model')
+                model_name2 = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "imagenet",
+                        "cifar10",
+                        "ffhq",
+                        "pokemon",
+                    ],
+                    value="imagenet",
+                )
             with gr.Row():
                 text = get_sample_image_markdown(model_name2.value)
                 sample_images = gr.Markdown(text)
 
-        with gr.TabItem('Class Names'):
+        with gr.TabItem("Class Names"):
             with gr.Row():
-                dataset_name = gr.Dropdown([
-                    'imagenet',
-                    'cifar10',
-                ],
-                                           value='imagenet',
-                                           label='Dataset')
+                dataset_name = gr.Dropdown(
+                    label="Dataset",
+                    choices=[
+                        "imagenet",
+                        "cifar10",
+                    ],
+                    value="imagenet",
+                )
             with gr.Row():
-                df = get_class_name_df('imagenet')
-                class_names = gr.Dataframe(df,
-                                           col_count=2,
-                                           headers=['Class Index', 'Label'],
-                                           interactive=False)
-
-    model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
-    model_name.change(fn=update_class_index,
-                      inputs=model_name,
-                      outputs=class_index)
-    model_name.change(fn=update_class_name,
-                      inputs=[
-                          model_name,
-                          class_index,
-                      ],
-                      outputs=class_name)
-    class_index.change(fn=update_class_name,
-                       inputs=[
-                           model_name,
-                           class_index,
-                       ],
-                       outputs=class_name)
-    run_button.click(fn=model.set_model_and_generate_image,
-                     inputs=[
-                         model_name,
-                         seed,
-                         psi,
-                         class_index,
-                         tx,
-                         ty,
-                         angle,
-                     ],
-                     outputs=result)
-    model_name2.change(fn=get_sample_image_markdown,
-                       inputs=model_name2,
-                       outputs=sample_images)
-    dataset_name.change(fn=get_class_name_df,
-                        inputs=dataset_name,
-                        outputs=class_names)
-
-demo.queue(max_size=10).launch()
+                df = get_class_name_df("imagenet")
+                class_names = gr.Dataframe(value=df, col_count=2, headers=["Class Index", "Label"], interactive=False)
+
+    model_name.change(
+        fn=update_class_index,
+        inputs=model_name,
+        outputs=class_index,
+        queue=False,
+        api_name=False,
+    )
+    model_name.change(
+        fn=update_class_name,
+        inputs=[
+            model_name,
+            class_index,
+        ],
+        outputs=class_name,
+        queue=False,
+        api_name=False,
+    )
+    class_index.change(
+        fn=update_class_name,
+        inputs=[
+            model_name,
+            class_index,
+        ],
+        outputs=class_name,
+        queue=False,
+        api_name=False,
+    )
+    run_button.click(
+        fn=model.set_model_and_generate_image,
+        inputs=[
+            model_name,
+            seed,
+            psi,
+            class_index,
+            tx,
+            ty,
+            angle,
+        ],
+        outputs=result,
+        api_name="run",
+    )
+    model_name2.change(
+        fn=get_sample_image_markdown,
+        inputs=model_name2,
+        outputs=sample_images,
+        queue=False,
+        api_name=False,
+    )
+    dataset_name.change(
+        fn=get_class_name_df,
+        inputs=dataset_name,
+        outputs=class_names,
+        queue=False,
+        api_name=False,
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()

model.py

@@ -10,34 +10,32 @@ import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
 current_dir = pathlib.Path(__file__).parent
-submodule_dir = current_dir / 'stylegan_xl'
+submodule_dir = current_dir / "stylegan_xl"
 sys.path.insert(0, submodule_dir.as_posix())
 
 
 class Model:
 
     MODEL_NAMES = [
-        'imagenet16',
-        'imagenet32',
-        'imagenet64',
-        'imagenet128',
-        'cifar10',
-        'ffhq256',
-        'pokemon256',
+        "imagenet16",
+        "imagenet32",
+        "imagenet64",
+        "imagenet128",
+        "cifar10",
+        "ffhq256",
+        "pokemon256",
     ]
 
     def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self._download_all_models()
         self.model_name = self.MODEL_NAMES[3]
         self.model = self._load_model(self.model_name)
 
     def _load_model(self, model_name: str) -> nn.Module:
-        path = hf_hub_download('public-data/StyleGAN-XL',
-                               f'models/{model_name}.pkl')
-        with open(path, 'rb') as f:
-            model = pickle.load(f)['G_ema']
+        path = hf_hub_download("public-data/StyleGAN-XL", f"models/{model_name}.pkl")
+        with open(path, "rb") as f:
+            model = pickle.load(f)["G_ema"]
         model.eval()
         model.to(self.device)
         return model
@@ -53,8 +51,7 @@ class Model:
             self._load_model(name)
 
     @staticmethod
-    def make_transform(translate: tuple[float, float],
-                       angle: float) -> np.ndarray:
+    def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray:
         mat = np.eye(3)
         sin = np.sin(angle / 360 * np.pi * 2)
         cos = np.cos(angle / 360 * np.pi * 2)
@@ -72,8 +69,7 @@ class Model:
         return torch.from_numpy(z).float().to(self.device)
 
     def postprocess(self, tensor: torch.Tensor) -> np.ndarray:
-        tensor = (tensor.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
-            torch.uint8)
+        tensor = (tensor.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
         return tensor.cpu().numpy()
 
     def make_label_tensor(self, class_index: int) -> torch.Tensor:
@@ -92,13 +88,12 @@ class Model:
         self.model.synthesis.input.transform.copy_(torch.from_numpy(mat))
 
     @torch.inference_mode()
-    def generate(self, z: torch.Tensor, label: torch.Tensor,
-                 truncation_psi: float) -> torch.Tensor:
+    def generate(self, z: torch.Tensor, label: torch.Tensor, truncation_psi: float) -> torch.Tensor:
         return self.model(z, label, truncation_psi=truncation_psi)
 
-    def generate_image(self, seed: int, truncation_psi: float,
-                       class_index: int, tx: float, ty: float,
-                       angle: float) -> np.ndarray:
+    def generate_image(
+        self, seed: int, truncation_psi: float, class_index: int, tx: float, ty: float, angle: float
+    ) -> np.ndarray:
         self.set_transform(tx, ty, angle)
 
         z = self.generate_z(seed)
@@ -108,10 +103,8 @@ class Model:
         out = self.postprocess(out)
         return out[0]
 
-    def set_model_and_generate_image(self, model_name: str, seed: int,
-                                     truncation_psi: float, class_index: int,
-                                     tx: float, ty: float,
-                                     angle: float) -> np.ndarray:
+    def set_model_and_generate_image(
+        self, model_name: str, seed: int, truncation_psi: float, class_index: int, tx: float, ty: float, angle: float
+    ) -> np.ndarray:
         self.set_model(model_name)
-        return self.generate_image(seed, truncation_psi, class_index, tx, ty,
-                                   angle)
+        return self.generate_image(seed, truncation_psi, class_index, tx, ty, angle)

requirements.txt

@@ -1,6 +1,7 @@
-ftfy==6.1.1
-numpy==1.23.5
-Pillow==10.0.0
-scipy==1.10.1
+ftfy==6.1.3
+gradio==4.20.0
+numpy==1.26.4
+Pillow==10.2.0
+scipy==1.12.0
 torch==2.0.1
 torchvision==0.15.2

style.css

@@ -1,11 +1,11 @@
 h1 {
   text-align: center;
-}
-div#result {
-  max-width: 600px;
-  max-height: 600px;
-}
-img#visitor-badge {
   display: block;
+}
+
+#duplicate-button {
   margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }