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"
+        }
+}

README.md

@@ -4,8 +4,7 @@ emoji: 👀
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 3.36.1
-python_version: 3.10.11
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 license: cc-by-nc-sa-4.0

app.py

@@ -13,31 +13,31 @@ import torch
 from app_colorization import create_demo as create_demo_colorization
 from app_superresolution import create_demo as create_demo_superresolution
 
-DESCRIPTION = '# [DDNM-HQ](https://github.com/wyhuai/DDNM/tree/main/hq_demo)'
+DESCRIPTION = "# [DDNM-HQ](https://github.com/wyhuai/DDNM/tree/main/hq_demo)"
 
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
+if (SPACE_ID := os.getenv("SPACE_ID")) is not None:
     DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
 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>"
 
 if torch.cuda.is_available():
-    MODEL_DIR = pathlib.Path('DDNM/hq_demo/data/pretrained')
+    MODEL_DIR = pathlib.Path("DDNM/hq_demo/data/pretrained")
     if not MODEL_DIR.exists():
         MODEL_DIR.mkdir()
-        subprocess.run(shlex.split(
-            'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt'
-        ),
-                       cwd=MODEL_DIR.as_posix())
-        subprocess.run(shlex.split(
-            'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt'
-        ),
-                       cwd=MODEL_DIR.as_posix())
-
-with gr.Blocks(css='style.css') as demo:
+        subprocess.run(
+            shlex.split("wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt"),
+            cwd=MODEL_DIR.as_posix(),
+        )
+        subprocess.run(
+            shlex.split("wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt"),
+            cwd=MODEL_DIR.as_posix(),
+        )
+
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Tabs():
-        with gr.TabItem(label='Super-resolution'):
+        with gr.TabItem(label="Super-resolution"):
             create_demo_superresolution()
-        with gr.TabItem(label='Colorization'):
+        with gr.TabItem(label="Colorization"):
             create_demo_colorization()
 demo.queue(api_open=False, max_size=5).launch()

app_colorization.py

@@ -10,43 +10,42 @@ import gradio as gr
 
 
 def run(image_path: str, class_index: int, sigma_y: float) -> str:
-    out_name = image_path.split('/')[-1].split('.')[0]
-    subprocess.run(shlex.split(
-        f'python main.py --config confs/inet256.yml --deg colorization --scale 1 --class {class_index} --path_y {image_path} --save_path {out_name} --sigma_y {sigma_y}'
-    ),
-                   cwd='DDNM/hq_demo')
-    return f'DDNM/hq_demo/results/{out_name}/final/00000.png'
+    out_name = image_path.split("/")[-1].split(".")[0]
+    subprocess.run(
+        shlex.split(
+            f"python main.py --config confs/inet256.yml --deg colorization --scale 1 --class {class_index} --path_y {image_path} --save_path {out_name} --sigma_y {sigma_y}"
+        ),
+        cwd="DDNM/hq_demo",
+    )
+    return f"DDNM/hq_demo/results/{out_name}/final/00000.png"
 
 
 def create_demo():
     examples = [
         [
-            'sample_images/monarch_gray.png',
-            'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
+            "sample_images/monarch_gray.png",
+            "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
             0,
         ],
         [
-            'sample_images/tiger_gray.png',
-            'tiger, Panthera tigris',
+            "sample_images/tiger_gray.png",
+            "tiger, Panthera tigris",
             0,
         ],
     ]
 
-    with open('imagenet_classes.json') as f:
+    with open("imagenet_classes.json") as f:
         imagenet_class_names = json.load(f)
 
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image(label='Input image', type='filepath')
-                class_index = gr.Dropdown(label='Class name',
-                                          choices=imagenet_class_names,
-                                          type='index',
-                                          value=950)
-                sigma_y = gr.Number(label='sigma_y', value=0, precision=2)
-                run_button = gr.Button('Run')
+                image = gr.Image(label="Input image", type="filepath")
+                class_index = gr.Dropdown(label="Class name", choices=imagenet_class_names, type="index", value=950)
+                sigma_y = gr.Number(label="sigma_y", value=0, precision=2)
+                run_button = gr.Button("Run")
             with gr.Column():
-                result = gr.Image(label='Result', type='filepath')
+                result = gr.Image(label="Result", type="filepath")
 
         gr.Examples(
             examples=examples,

app_superresolution.py

@@ -10,78 +10,75 @@ import gradio as gr
 
 
 def run(image_path: str, class_index: int, scale: str, sigma_y: float) -> str:
-    out_name = image_path.split('/')[-1].split('.')[0]
-    subprocess.run(shlex.split(
-        f'python main.py --config confs/inet256.yml --resize_y --deg sr_averagepooling --scale {scale} --class {class_index} --path_y {image_path} --save_path {out_name} --sigma_y {sigma_y}'
-    ),
-                   cwd='DDNM/hq_demo')
-    return f'DDNM/hq_demo/results/{out_name}/final/00000.png'
+    out_name = image_path.split("/")[-1].split(".")[0]
+    subprocess.run(
+        shlex.split(
+            f"python main.py --config confs/inet256.yml --resize_y --deg sr_averagepooling --scale {scale} --class {class_index} --path_y {image_path} --save_path {out_name} --sigma_y {sigma_y}"
+        ),
+        cwd="DDNM/hq_demo",
+    )
+    return f"DDNM/hq_demo/results/{out_name}/final/00000.png"
 
 
 def create_demo():
     examples = [
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/323.png',
-            'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
-            '4',
+            "DDNM/hq_demo/data/datasets/gts/inet256/323.png",
+            "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
+            "4",
             0,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/orange.png',
-            'orange',
-            '4',
+            "DDNM/hq_demo/data/datasets/gts/inet256/orange.png",
+            "orange",
+            "4",
             0,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/monarch.png',
-            'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
-            '4',
+            "DDNM/hq_demo/data/datasets/gts/inet256/monarch.png",
+            "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
+            "4",
             0.5,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/bear.png',
-            'brown bear, bruin, Ursus arctos',
-            '4',
+            "DDNM/hq_demo/data/datasets/gts/inet256/bear.png",
+            "brown bear, bruin, Ursus arctos",
+            "4",
             0,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/flamingo.png',
-            'flamingo',
-            '2',
+            "DDNM/hq_demo/data/datasets/gts/inet256/flamingo.png",
+            "flamingo",
+            "2",
             0,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/kimono.png',
-            'kimono',
-            '2',
+            "DDNM/hq_demo/data/datasets/gts/inet256/kimono.png",
+            "kimono",
+            "2",
             0,
         ],
         [
-            'DDNM/hq_demo/data/datasets/gts/inet256/zebra.png',
-            'zebra',
-            '4',
+            "DDNM/hq_demo/data/datasets/gts/inet256/zebra.png",
+            "zebra",
+            "4",
             0,
         ],
     ]
 
-    with open('imagenet_classes.json') as f:
+    with open("imagenet_classes.json") as f:
         imagenet_class_names = json.load(f)
 
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image(label='Input image', type='filepath')
-                class_index = gr.Dropdown(label='Class name',
-                                          choices=imagenet_class_names,
-                                          type='index',
-                                          value=950)
-                scale = gr.Dropdown(label='Scale',
-                                    choices=['2', '4', '8'],
-                                    value='4')
-                sigma_y = gr.Number(label='sigma_y', value=0, precision=2)
-                run_button = gr.Button('Run')
+                image = gr.Image(label="Input image", type="filepath")
+                class_index = gr.Dropdown(label="Class name", choices=imagenet_class_names, type="index", value=950)
+                scale = gr.Dropdown(label="Scale", choices=["2", "4", "8"], value="4")
+                sigma_y = gr.Number(label="sigma_y", value=0, precision=2)
+                run_button = gr.Button("Run")
             with gr.Column():
-                result = gr.Image(label='Result', type='filepath')
+                result = gr.Image(label="Result", type="filepath")
 
         gr.Examples(
             examples=examples,

style.css

@@ -1,3 +1,4 @@
 h1 {
   text-align: center;
+  display: block;
 }