Update

.pre-commit-config.yaml

@@ -1,36 +1,61 @@
 exclude: ^patch
 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']
-- 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

README.md

@@ -4,7 +4,7 @@ emoji: 🏃
 colorFrom: green
 colorTo: red
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 suggested_hardware: t4-small

app.py

@@ -15,36 +15,32 @@ import torch
 import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
-if os.environ.get('SYSTEM') == 'spaces':
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'),
-                       cwd='stylegan2-pytorch',
-                       stdin=f)
+if os.environ.get("SYSTEM") == "spaces":
+    with open("patch") as f:
+        subprocess.run(shlex.split("patch -p1"), cwd="stylegan2-pytorch", stdin=f)
     if not torch.cuda.is_available():
-        with open('patch-cpu') as f:
-            subprocess.run(shlex.split('patch -p1'),
-                           cwd='stylegan2-pytorch',
-                           stdin=f)
+        with open("patch-cpu") as f:
+            subprocess.run(shlex.split("patch -p1"), cwd="stylegan2-pytorch", stdin=f)
 
-sys.path.insert(0, 'stylegan2-pytorch')
+sys.path.insert(0, "stylegan2-pytorch")
 
 from model import Generator
 
-DESCRIPTION = '''# [TADNE](https://thisanimedoesnotexist.ai/) (This Anime Does Not Exist)
+DESCRIPTION = """# [TADNE](https://thisanimedoesnotexist.ai/) (This Anime Does Not Exist)
 
 Related Apps:
 - [TADNE Image Viewer](https://huggingface.co/spaces/hysts/TADNE-image-viewer)
 - [TADNE Image Selector](https://huggingface.co/spaces/hysts/TADNE-image-selector)
 - [TADNE Interpolation](https://huggingface.co/spaces/hysts/TADNE-interpolation)
 - [TADNE Image Search with DeepDanbooru](https://huggingface.co/spaces/hysts/TADNE-image-search-with-DeepDanbooru)
-'''
-SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/TADNE/resolve/main/samples'
-ARTICLE = f'''## Generated images
+"""
+SAMPLE_IMAGE_DIR = "https://huggingface.co/spaces/hysts/TADNE/resolve/main/samples"
+ARTICLE = f"""## Generated images
 - size: 512x512
 - truncation: 0.7
 - seed: 0-99
 ![samples]({SAMPLE_IMAGE_DIR}/sample.jpg)
-'''
+"""
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -57,13 +53,12 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 
 def load_model(device: torch.device) -> nn.Module:
     model = Generator(512, 1024, 4, channel_multiplier=2)
-    path = hf_hub_download('public-data/TADNE',
-                           'models/aydao-anime-danbooru2019s-512-5268480.pt')
+    path = hf_hub_download("public-data/TADNE", "models/aydao-anime-danbooru2019s-512-5268480.pt")
     checkpoint = torch.load(path)
-    model.load_state_dict(checkpoint['g_ema'])
+    model.load_state_dict(checkpoint["g_ema"])
     model.eval()
     model.to(device)
-    model.latent_avg = checkpoint['latent_avg'].to(device)
+    model.latent_avg = checkpoint["latent_avg"].to(device)
     with torch.inference_mode():
         z = torch.zeros((1, model.style_dim)).to(device)
         model([z], truncation=0.7, truncation_latent=model.latent_avg)
@@ -71,47 +66,36 @@ def load_model(device: torch.device) -> nn.Module:
 
 
 def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
-    return torch.from_numpy(np.random.RandomState(seed).randn(
-        1, z_dim)).to(device).float()
+    return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).to(device).float()
 
 
 @torch.inference_mode()
-def generate_image(seed: int, truncation_psi: float, randomize_noise: bool,
-                   model: nn.Module, device: torch.device) -> np.ndarray:
+def generate_image(
+    seed: int, truncation_psi: float, randomize_noise: bool, model: nn.Module, device: torch.device
+) -> np.ndarray:
     seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
 
     z = generate_z(model.style_dim, seed, device)
-    out, _ = model([z],
-                   truncation=truncation_psi,
-                   truncation_latent=model.latent_avg,
-                   randomize_noise=randomize_noise)
+    out, _ = model([z], truncation=truncation_psi, truncation_latent=model.latent_avg, randomize_noise=randomize_noise)
     out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
     return out[0].cpu().numpy()
 
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 model = load_model(device)
 fn = functools.partial(generate_image, model=model, device=device)
 
-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():
-            seed = gr.Slider(label='Seed',
-                             minimum=0,
-                             maximum=MAX_SEED,
-                             step=1,
-                             value=0)
-            randomize_seed = gr.Checkbox(label='Randomize seed', value=True)
-            psi = gr.Slider(label='Truncation psi',
-                            minimum=0,
-                            maximum=2,
-                            step=0.05,
-                            value=0.7)
-            randomize_noise = gr.Checkbox(label='Randomize Noise', value=False)
-            run_button = gr.Button('Run')
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            psi = gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7)
+            randomize_noise = gr.Checkbox(label="Randomize Noise", value=False)
+            run_button = gr.Button("Run")
         with gr.Column():
-            result = gr.Image(label='Output')
+            result = gr.Image(label="Output")
     gr.Markdown(ARTICLE)
 
     run_button.click(
@@ -124,6 +108,6 @@ with gr.Blocks(css='style.css') as demo:
         fn=fn,
         inputs=[seed, psi, randomize_noise],
         outputs=result,
-        api_name='run',
+        api_name="run",
     )
 demo.queue(max_size=10).launch()

requirements.txt

@@ -1,4 +1,4 @@
-numpy==1.23.5
-Pillow==10.0.0
+numpy==1.26.4
+Pillow==10.3.0
 torch==2.0.1
 torchvision==0.15.2