Update

.pre-commit-config.yaml

@@ -1,36 +1,60 @@
 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

@@ -6,76 +6,65 @@ import gradio as gr
 
 from model import Model
 
-DESCRIPTION = '# [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human)'
+DESCRIPTION = "# [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human)"
 
 model = Model()
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
             with gr.Row():
-                seed1 = gr.Number(label='Seed 1', value=6876)
-                psi1 = gr.Slider(label='Truncation psi 1',
-                                 minimum=0,
-                                 maximum=2,
-                                 step=0.05,
-                                 value=0.7)
+                seed1 = gr.Number(label="Seed 1", value=6876)
+                psi1 = gr.Slider(label="Truncation psi 1", minimum=0, maximum=2, step=0.05, value=0.7)
             with gr.Row():
-                generate_button1 = gr.Button('Generate')
+                generate_button1 = gr.Button("Generate")
             with gr.Row():
-                generated_image1 = gr.Image(label='Generated Image 1',
-                                            type='numpy',
-                                            height=600)
+                generated_image1 = gr.Image(label="Generated Image 1", type="numpy", height=600)
 
         with gr.Column():
             with gr.Row():
-                seed2 = gr.Number(label='Seed 2', value=6886)
-                psi2 = gr.Slider(label='Truncation psi 2',
-                                 minimum=0,
-                                 maximum=2,
-                                 step=0.05,
-                                 value=0.7)
+                seed2 = gr.Number(label="Seed 2", value=6886)
+                psi2 = gr.Slider(label="Truncation psi 2", minimum=0, maximum=2, step=0.05, value=0.7)
             with gr.Row():
-                generate_button2 = gr.Button('Generate')
+                generate_button2 = gr.Button("Generate")
             with gr.Row():
-                generated_image2 = gr.Image(label='Generated Image 2',
-                                            type='numpy',
-                                            height=600)
+                generated_image2 = gr.Image(label="Generated Image 2", type="numpy", height=600)
 
     with gr.Row():
         with gr.Column():
             with gr.Row():
-                num_frames = gr.Slider(label='Number of Intermediate Frames',
-                                       minimum=0,
-                                       maximum=41,
-                                       step=1,
-                                       value=7)
+                num_frames = gr.Slider(label="Number of Intermediate Frames", minimum=0, maximum=41, step=1, value=7)
             with gr.Row():
-                interpolate_button = gr.Button('Interpolate')
+                interpolate_button = gr.Button("Interpolate")
             with gr.Row():
-                interpolated_images = gr.Gallery(label='Output Images',
-                                                 object_fit='scale-down')
+                interpolated_images = gr.Gallery(label="Output Images", object_fit="scale-down")
 
-    generate_button1.click(model.generate_single_image,
-                           inputs=[
-                               seed1,
-                               psi1,
-                           ],
-                           outputs=generated_image1)
-    generate_button2.click(model.generate_single_image,
-                           inputs=[
-                               seed2,
-                               psi2,
-                           ],
-                           outputs=generated_image2)
-    interpolate_button.click(model.generate_interpolated_images,
-                             inputs=[
-                                 seed1,
-                                 psi1,
-                                 seed2,
-                                 psi2,
-                                 num_frames,
-                             ],
-                             outputs=interpolated_images)
+    generate_button1.click(
+        model.generate_single_image,
+        inputs=[
+            seed1,
+            psi1,
+        ],
+        outputs=generated_image1,
+    )
+    generate_button2.click(
+        model.generate_single_image,
+        inputs=[
+            seed2,
+            psi2,
+        ],
+        outputs=generated_image2,
+    )
+    interpolate_button.click(
+        model.generate_interpolated_images,
+        inputs=[
+            seed1,
+            psi1,
+            seed2,
+            psi2,
+            num_frames,
+        ],
+        outputs=interpolated_images,
+    )
 demo.queue(max_size=10).launch()

model.py

@@ -10,21 +10,19 @@ import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
 app_dir = pathlib.Path(__file__).parent
-submodule_dir = app_dir / 'StyleGAN-Human'
+submodule_dir = app_dir / "StyleGAN-Human"
 sys.path.insert(0, submodule_dir.as_posix())
 
 
 class Model:
     def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        self.model = self.load_model('stylegan_human_v2_1024.pkl')
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = self.load_model("stylegan_human_v2_1024.pkl")
 
     def load_model(self, file_name: str) -> nn.Module:
-        path = hf_hub_download('public-data/StyleGAN-Human',
-                               f'models/{file_name}')
-        with open(path, 'rb') as f:
-            model = pickle.load(f)['G_ema']
+        path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}")
+        with open(path, "rb") as f:
+            model = pickle.load(f)["G_ema"]
         model.eval()
         model.to(self.device)
         with torch.inference_mode():
@@ -34,29 +32,23 @@ class Model:
         return model
 
     def generate_z(self, z_dim: int, seed: int) -> torch.Tensor:
-        return torch.from_numpy(np.random.RandomState(seed).randn(
-            1, z_dim)).to(self.device).float()
+        return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).to(self.device).float()
 
     @torch.inference_mode()
-    def generate_single_image(self, seed: int,
-                              truncation_psi: float) -> np.ndarray:
+    def generate_single_image(self, seed: int, truncation_psi: float) -> np.ndarray:
         seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
 
         z = self.generate_z(self.model.z_dim, seed)
         label = torch.zeros([1, self.model.c_dim], device=self.device)
 
-        out = self.model(z,
-                         label,
-                         truncation_psi=truncation_psi,
-                         force_fp32=True)
-        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
-            torch.uint8)
+        out = self.model(z, label, truncation_psi=truncation_psi, force_fp32=True)
+        out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
         return out[0].cpu().numpy()
 
     @torch.inference_mode()
     def generate_interpolated_images(
-            self, seed0: int, psi0: float, seed1: int, psi1: float,
-            num_intermediate: int) -> list[np.ndarray]:
+        self, seed0: int, psi0: float, seed1: int, psi1: float, num_intermediate: int
+    ) -> list[np.ndarray]:
         seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
         seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
 
@@ -73,8 +65,7 @@ class Model:
         res = []
         for z, psi in zip(zs, psis):
             out = self.model(z, label, truncation_psi=psi, force_fp32=True)
-            out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
-                torch.uint8)
+            out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
             out = out[0].cpu().numpy()
             res.append(out)
         return res

style.css

@@ -1,7 +1,11 @@
 h1 {
   text-align: center;
-}
-img#visitor-badge {
   display: block;
+}
+
+#duplicate-button {
   margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }