Update

.pre-commit-config.yaml

@@ -1,37 +1,60 @@
-exclude: ^stylegan3
 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: v4.6.0
+    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: v1.10.0
+    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: 24.4.2
+    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: 1.8.5
+    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"
+        }
+}

app.py

@@ -9,101 +9,86 @@ import numpy as np
 
 from model import Model
 
-DESCRIPTION = '# [Self-Distilled StyleGAN](https://github.com/self-distilled-stylegan/self-distilled-internet-photos)'
+DESCRIPTION = "# [Self-Distilled StyleGAN](https://github.com/self-distilled-stylegan/self-distilled-internet-photos)"
 
 
 def get_sample_image_url(name: str) -> str:
-    sample_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples'
-    return f'{sample_image_dir}/{name}.jpg'
+    sample_image_dir = "https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples"
+    return f"{sample_image_dir}/{name}.jpg"
 
 
 def get_sample_image_markdown(name: str) -> str:
     url = get_sample_image_url(name)
-    size = name.split('_')[1]
-    truncation_type = '_'.join(name.split('_')[2:])
-    return f'''
+    size = name.split("_")[1]
+    truncation_type = "_".join(name.split("_")[2:])
+    return f"""
     - size: {size}x{size}
     - seed: 0-99
     - truncation: 0.7
     - truncation type: {truncation_type}
-    ![sample images]({url})'''
+    ![sample images]({url})"""
 
 
 def get_cluster_center_image_url(model_name: str) -> str:
-    cluster_center_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/cluster_center_images'
-    return f'{cluster_center_image_dir}/{model_name}.jpg'
+    cluster_center_image_dir = (
+        "https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/cluster_center_images"
+    )
+    return f"{cluster_center_image_dir}/{model_name}.jpg"
 
 
 def get_cluster_center_image_markdown(model_name: str) -> str:
     url = get_cluster_center_image_url(model_name)
-    return f'![cluster center images]({url})'
+    return f"![cluster center images]({url})"
 
 
 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(label='Model',
-                                                 choices=model.MODEL_NAMES,
-                                                 value=model.MODEL_NAMES[0])
-                        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)
+                        model_name = gr.Dropdown(label="Model", choices=model.MODEL_NAMES, value=model.MODEL_NAMES[0])
+                        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)
                         truncation_type = gr.Dropdown(
-                            label='Truncation Type',
-                            choices=model.TRUNCATION_TYPES,
-                            value=model.TRUNCATION_TYPES[0])
-                        run_button = gr.Button('Run')
+                            label="Truncation Type", choices=model.TRUNCATION_TYPES, value=model.TRUNCATION_TYPES[0]
+                        )
+                        run_button = gr.Button("Run")
                 with gr.Column():
-                    result = gr.Image(label='Result', elem_id='result')
+                    result = gr.Image(label="Result", elem_id="result")
 
-        with gr.TabItem('Sample Images'):
+        with gr.TabItem("Sample Images"):
             with gr.Row():
-                paths = sorted(pathlib.Path('samples').glob('*'))
+                paths = sorted(pathlib.Path("samples").glob("*"))
                 names = [path.stem for path in paths]
-                model_name2 = gr.Dropdown(label='Type',
-                                          choices=names,
-                                          value='dogs_1024_multimodal_lpips')
+                model_name2 = gr.Dropdown(label="Type", choices=names, value="dogs_1024_multimodal_lpips")
             with gr.Row():
                 text = get_sample_image_markdown(model_name2.value)
                 sample_images = gr.Markdown(text)
 
-        with gr.TabItem('Cluster Center Images'):
+        with gr.TabItem("Cluster Center Images"):
             with gr.Row():
-                model_name3 = gr.Dropdown(label='Model',
-                                          choices=model.MODEL_NAMES,
-                                          value=model.MODEL_NAMES[0])
+                model_name3 = gr.Dropdown(label="Model", choices=model.MODEL_NAMES, value=model.MODEL_NAMES[0])
             with gr.Row():
                 text = get_cluster_center_image_markdown(model_name3.value)
                 cluster_center_images = gr.Markdown(value=text)
 
     model_name.change(fn=model.set_model, inputs=model_name)
-    run_button.click(fn=model.set_model_and_generate_image,
-                     inputs=[
-                         model_name,
-                         seed,
-                         psi,
-                         truncation_type,
-                     ],
-                     outputs=result)
-    model_name2.change(fn=get_sample_image_markdown,
-                       inputs=model_name2,
-                       outputs=sample_images)
-    model_name3.change(fn=get_cluster_center_image_markdown,
-                       inputs=model_name3,
-                       outputs=cluster_center_images)
+    run_button.click(
+        fn=model.set_model_and_generate_image,
+        inputs=[
+            model_name,
+            seed,
+            psi,
+            truncation_type,
+        ],
+        outputs=result,
+    )
+    model_name2.change(fn=get_sample_image_markdown, inputs=model_name2, outputs=sample_images)
+    model_name3.change(fn=get_cluster_center_image_markdown, inputs=model_name3, outputs=cluster_center_images)
 
 demo.queue(max_size=10).launch()

model.py

@@ -11,7 +11,7 @@ import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
 current_dir = pathlib.Path(__file__).parent
-submodule_dir = current_dir / 'stylegan3'
+submodule_dir = current_dir / "stylegan3"
 sys.path.insert(0, submodule_dir.as_posix())
 
 
@@ -29,11 +29,10 @@ class LPIPS(lpips.LPIPS):
         return [lpips.normalize_tensor(x) for x in data]
 
     @torch.inference_mode()
-    def compute_distance(self, features0: list[torch.Tensor],
-                         features1: list[torch.Tensor]) -> float:
+    def compute_distance(self, features0: list[torch.Tensor], features1: list[torch.Tensor]) -> float:
         res = 0
         for lin, x0, x1 in zip(self.lins, features0, features1):
-            d = (x0 - x1)**2
+            d = (x0 - x1) ** 2
             y = lin(d)
             y = lpips.lpips.spatial_average(y)
             res += y.item()
@@ -43,23 +42,22 @@ class LPIPS(lpips.LPIPS):
 class Model:
 
     MODEL_NAMES = [
-        'dogs_1024',
-        'elephants_512',
-        'horses_256',
-        'bicycles_256',
-        'lions_512',
-        'giraffes_512',
-        'parrots_512',
+        "dogs_1024",
+        "elephants_512",
+        "horses_256",
+        "bicycles_256",
+        "lions_512",
+        "giraffes_512",
+        "parrots_512",
     ]
     TRUNCATION_TYPES = [
-        'Multimodal (LPIPS)',
-        'Multimodal (L2)',
-        'Global',
+        "Multimodal (LPIPS)",
+        "Multimodal (L2)",
+        "Global",
     ]
 
     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._download_all_cluster_centers()
         self._download_all_cluster_center_images()
@@ -67,32 +65,27 @@ class Model:
         self.model_name = self.MODEL_NAMES[0]
         self.model = self._load_model(self.model_name)
         self.cluster_centers = self._load_cluster_centers(self.model_name)
-        self.cluster_center_images = self._load_cluster_center_images(
-            self.model_name)
+        self.cluster_center_images = self._load_cluster_center_images(self.model_name)
 
         self.lpips = LPIPS()
-        self.cluster_center_lpips_feature_dict = self._compute_cluster_center_lpips_features(
-        )
+        self.cluster_center_lpips_feature_dict = self._compute_cluster_center_lpips_features()
 
     def _load_model(self, model_name: str) -> nn.Module:
-        path = hf_hub_download('public-data/Self-Distilled-StyleGAN',
-                               f'models/{model_name}_pytorch.pkl')
-        with open(path, 'rb') as f:
-            model = pickle.load(f)['G_ema']
+        path = hf_hub_download("public-data/Self-Distilled-StyleGAN", f"models/{model_name}_pytorch.pkl")
+        with open(path, "rb") as f:
+            model = pickle.load(f)["G_ema"]
         model.eval()
         model.to(self.device)
         return model
 
     def _load_cluster_centers(self, model_name: str) -> torch.Tensor:
-        path = hf_hub_download('public-data/Self-Distilled-StyleGAN',
-                               f'cluster_centers/{model_name}.npy')
+        path = hf_hub_download("public-data/Self-Distilled-StyleGAN", f"cluster_centers/{model_name}.npy")
         centers = np.load(path)
         centers = torch.from_numpy(centers).float().to(self.device)
         return centers
 
     def _load_cluster_center_images(self, model_name: str) -> np.ndarray:
-        path = hf_hub_download('public-data/Self-Distilled-StyleGAN',
-                               f'cluster_center_images/{model_name}.npy')
+        path = hf_hub_download("public-data/Self-Distilled-StyleGAN", f"cluster_center_images/{model_name}.npy")
         return np.load(path)
 
     def set_model(self, model_name: str) -> None:
@@ -101,8 +94,7 @@ class Model:
         self.model_name = model_name
         self.model = self._load_model(model_name)
         self.cluster_centers = self._load_cluster_centers(model_name)
-        self.cluster_center_images = self._load_cluster_center_images(
-            model_name)
+        self.cluster_center_images = self._load_cluster_center_images(model_name)
 
     def _download_all_models(self):
         for name in self.MODEL_NAMES:
@@ -118,9 +110,7 @@ class Model:
 
     def generate_z(self, seed: int) -> torch.Tensor:
         seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
-        return torch.from_numpy(
-            np.random.RandomState(seed).randn(1, self.model.z_dim)).float().to(
-                self.device)
+        return torch.from_numpy(np.random.RandomState(seed).randn(1, self.model.z_dim)).float().to(self.device)
 
     def compute_w(self, z: torch.Tensor) -> torch.Tensor:
         label = torch.zeros((1, self.model.c_dim), device=self.device)
@@ -128,8 +118,7 @@ class Model:
         return w
 
     @staticmethod
-    def truncate_w(w_center: torch.Tensor, w: torch.Tensor,
-                   psi: float) -> torch.Tensor:
+    def truncate_w(w_center: torch.Tensor, w: torch.Tensor, psi: float) -> torch.Tensor:
         if psi == 1:
             return w
         return w_center.lerp(w, psi)
@@ -139,67 +128,54 @@ class Model:
         return self.model.synthesis(w)
 
     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 compute_lpips_features(self, image: np.ndarray) -> list[torch.Tensor]:
         data = self.lpips.preprocess(image)
         return self.lpips.compute_features(data)
 
-    def _compute_cluster_center_lpips_features(
-            self) -> dict[str, list[list[torch.Tensor]]]:
+    def _compute_cluster_center_lpips_features(self) -> dict[str, list[list[torch.Tensor]]]:
         res = dict()
         for name in self.MODEL_NAMES:
             images = self._load_cluster_center_images(name)
-            res[name] = [
-                self.compute_lpips_features(image) for image in images
-            ]
+            res[name] = [self.compute_lpips_features(image) for image in images]
         return res
 
-    def compute_distance_to_cluster_centers(
-            self, ws: torch.Tensor, distance_type: str) -> list[torch.Tensor]:
-        if distance_type == 'l2':
+    def compute_distance_to_cluster_centers(self, ws: torch.Tensor, distance_type: str) -> list[torch.Tensor]:
+        if distance_type == "l2":
             return self._compute_l2_distance_to_cluster_centers(ws)
-        elif distance_type == 'lpips':
+        elif distance_type == "lpips":
             return self._compute_lpips_distance_to_cluster_centers(ws)
         else:
             raise ValueError
 
-    def _compute_l2_distance_to_cluster_centers(
-            self, ws: torch.Tensor) -> np.ndarray:
-        dist2 = ((self.cluster_centers - ws[0, 0])**2).sum(dim=1)
+    def _compute_l2_distance_to_cluster_centers(self, ws: torch.Tensor) -> np.ndarray:
+        dist2 = ((self.cluster_centers - ws[0, 0]) ** 2).sum(dim=1)
         return dist2.cpu().numpy()
 
-    def _compute_lpips_distance_to_cluster_centers(
-            self, ws: torch.Tensor) -> np.ndarray:
+    def _compute_lpips_distance_to_cluster_centers(self, ws: torch.Tensor) -> np.ndarray:
         x = self.synthesize(ws)
         x = self.postprocess(x)[0]
         feat0 = self.compute_lpips_features(x)
-        cluster_center_features = self.cluster_center_lpips_feature_dict[
-            self.model_name]
-        distances = [
-            self.lpips.compute_distance(feat0, feat1)
-            for feat1 in cluster_center_features
-        ]
+        cluster_center_features = self.cluster_center_lpips_feature_dict[self.model_name]
+        distances = [self.lpips.compute_distance(feat0, feat1) for feat1 in cluster_center_features]
         return np.asarray(distances)
 
-    def find_nearest_cluster_center(self, ws: torch.Tensor,
-                                    distance_type: str) -> int:
+    def find_nearest_cluster_center(self, ws: torch.Tensor, distance_type: str) -> int:
         distances = self.compute_distance_to_cluster_centers(ws, distance_type)
         return int(np.argmin(distances))
 
-    def generate_image(self, seed: int, truncation_psi: float,
-                       truncation_type: str) -> np.ndarray:
+    def generate_image(self, seed: int, truncation_psi: float, truncation_type: str) -> np.ndarray:
         z = self.generate_z(seed)
         ws = self.compute_w(z)
         if truncation_type == self.TRUNCATION_TYPES[2]:
             w0 = self.model.mapping.w_avg
         else:
             if truncation_type == self.TRUNCATION_TYPES[0]:
-                distance_type = 'lpips'
+                distance_type = "lpips"
             elif truncation_type == self.TRUNCATION_TYPES[1]:
-                distance_type = 'l2'
+                distance_type = "l2"
             else:
                 raise ValueError
             cluster_index = self.find_nearest_cluster_center(ws, distance_type)
@@ -209,8 +185,8 @@ class Model:
         out = self.postprocess(out)
         return out[0]
 
-    def set_model_and_generate_image(self, model_name: str, seed: int,
-                                     truncation_psi: float,
-                                     truncation_type: str) -> np.ndarray:
+    def set_model_and_generate_image(
+        self, model_name: str, seed: int, truncation_psi: float, truncation_type: str
+    ) -> np.ndarray:
         self.set_model(model_name)
         return self.generate_image(seed, truncation_psi, truncation_type)