Update

.pre-commit-config.yaml

@@ -1,36 +1,61 @@
-exclude: ^(Text2Human|patch)
+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: purple
 colorTo: gray
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 suggested_hardware: t4-small

app.py

@@ -8,26 +8,30 @@ import random
 import shlex
 import subprocess
 
-import gradio as gr
-import numpy as np
+if os.getenv("SYSTEM") == "spaces":
+    subprocess.run(shlex.split("pip install click==7.1.2"))
+    subprocess.run(shlex.split("pip install typer==0.9.4"))
 
-if os.getenv('SYSTEM') == 'spaces':
     import mim
 
-    mim.uninstall('mmcv-full', confirm_yes=True)
-    mim.install('mmcv-full==1.5.2', is_yes=True)
+    mim.uninstall("mmcv-full", confirm_yes=True)
+    mim.install("mmcv-full==1.5.2", is_yes=True)
 
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'), cwd='Text2Human', stdin=f)
+    with open("patch") as f:
+        subprocess.run(shlex.split("patch -p1"), cwd="Text2Human", stdin=f)
+
+
+import gradio as gr
+import numpy as np
 
 from model import Model
 
-DESCRIPTION = '''# [Text2Human](https://github.com/yumingj/Text2Human)
+DESCRIPTION = """# [Text2Human](https://github.com/yumingj/Text2Human)
 
 You can modify sample steps and seeds. By varying seeds, you can sample different human images under the same pose, shape description, and texture description. The larger the sample steps, the better quality of the generated images. (The default value of sample steps is 256 in the original repo.)
 
 Label image generation step can be skipped. However, in that case, the input label image must be 512x256 in size and must contain only the specified colors.
-'''
+"""
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -40,76 +44,61 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 
 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():
-                input_image = gr.Image(label='Input Pose Image',
-                                       type='pil',
-                                       elem_id='input-image')
+                input_image = gr.Image(label="Input Pose Image", type="pil", elem_id="input-image")
                 pose_data = gr.State()
             with gr.Row():
-                paths = sorted(pathlib.Path('pose_images').glob('*.png'))
-                gr.Examples(examples=[[path.as_posix()] for path in paths],
-                            inputs=input_image)
+                paths = sorted(pathlib.Path("pose_images").glob("*.png"))
+                gr.Examples(examples=[[path.as_posix()] for path in paths], inputs=input_image)
 
             with gr.Row():
                 shape_text = gr.Textbox(
-                    label='Shape Description',
-                    placeholder=
-                    '''<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
-Note: The outer clothing type and accessories can be omitted.''')
+                    label="Shape Description",
+                    placeholder="""<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
+Note: The outer clothing type and accessories can be omitted.""",
+                )
             with gr.Row():
                 gr.Examples(
-                    examples=[['man, sleeveless T-shirt, long pants'],
-                              ['woman, short-sleeve T-shirt, short jeans']],
-                    inputs=shape_text)
+                    examples=[["man, sleeveless T-shirt, long pants"], ["woman, short-sleeve T-shirt, short jeans"]],
+                    inputs=shape_text,
+                )
             with gr.Row():
-                generate_label_button = gr.Button('Generate Label Image')
+                generate_label_button = gr.Button("Generate Label Image")
 
         with gr.Column():
             with gr.Row():
-                label_image = gr.Image(label='Label Image',
-                                       type='numpy',
-                                       elem_id='label-image')
+                label_image = gr.Image(label="Label Image", type="numpy", elem_id="label-image")
 
             with gr.Row():
                 texture_text = gr.Textbox(
-                    label='Texture Description',
-                    placeholder=
-                    '''<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
-Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.'''
+                    label="Texture Description",
+                    placeholder="""<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
+Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.""",
                 )
             with gr.Row():
-                gr.Examples(examples=[
-                    ['pure color, denim'],
-                    ['floral, stripe'],
-                ],
-                            inputs=texture_text)
+                gr.Examples(
+                    examples=[
+                        ["pure color, denim"],
+                        ["floral, stripe"],
+                    ],
+                    inputs=texture_text,
+                )
             with gr.Row():
-                sample_steps = gr.Slider(label='Sample Steps',
-                                         minimum=10,
-                                         maximum=300,
-                                         step=1,
-                                         value=256)
+                sample_steps = gr.Slider(label="Sample Steps", minimum=10, maximum=300, step=1, value=256)
             with gr.Row():
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=MAX_SEED,
-                                 step=1,
-                                 value=0)
-                randomize_seed = gr.Checkbox(label='Randomize seed',
-                                             value=True)
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             with gr.Row():
-                generate_human_button = gr.Button('Generate Human')
+                generate_human_button = gr.Button("Generate Human")
 
         with gr.Column():
             with gr.Row():
-                result = gr.Image(label='Result',
-                                  type='numpy',
-                                  elem_id='result-image')
+                result = gr.Image(label="Result", type="numpy", elem_id="result-image")
 
     input_image.change(
         fn=model.process_pose_image,
@@ -124,17 +113,21 @@ Note: Currently, only 5 types of textures are supported, i.e., pure color, strip
         ],
         outputs=label_image,
     )
-    generate_human_button.click(fn=randomize_seed_fn,
-                                inputs=[seed, randomize_seed],
-                                outputs=seed,
-                                queue=False).then(
-                                    fn=model.generate_human,
-                                    inputs=[
-                                        label_image,
-                                        texture_text,
-                                        sample_steps,
-                                        seed,
-                                    ],
-                                    outputs=result,
-                                )
-demo.queue(max_size=10).launch()
+    generate_human_button.click(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+    ).then(
+        fn=model.generate_human,
+        inputs=[
+            label_image,
+            texture_text,
+            sample_steps,
+            seed,
+        ],
+        outputs=result,
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()

model.py

@@ -9,11 +9,10 @@ import numpy as np
 import PIL.Image
 import torch
 
-sys.path.insert(0, 'Text2Human')
+sys.path.insert(0, "Text2Human")
 
 from models.sample_model import SampleFromPoseModel
-from utils.language_utils import (generate_shape_attributes,
-                                  generate_texture_attributes)
+from utils.language_utils import generate_shape_attributes, generate_texture_attributes
 from utils.options import dict_to_nonedict, parse
 from utils.util import set_random_seed
 
@@ -47,37 +46,36 @@ COLOR_LIST = [
 
 class Model:
     def __init__(self):
-        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.config = self._load_config()
-        self.config['device'] = device.type
+        self.config["device"] = device.type
         self._download_models()
         self.model = SampleFromPoseModel(self.config)
         self.model.batch_size = 1
 
     def _load_config(self) -> dict:
-        path = 'Text2Human/configs/sample_from_pose.yml'
+        path = "Text2Human/configs/sample_from_pose.yml"
         config = parse(path, is_train=False)
         config = dict_to_nonedict(config)
         return config
 
     def _download_models(self) -> None:
-        model_dir = pathlib.Path('pretrained_models')
+        model_dir = pathlib.Path("pretrained_models")
         if model_dir.exists():
             return
-        path = huggingface_hub.hf_hub_download('yumingj/Text2Human_SSHQ',
-                                               'pretrained_models.zip')
+        path = huggingface_hub.hf_hub_download("yumingj/Text2Human_SSHQ", "pretrained_models.zip")
         model_dir.mkdir()
         with zipfile.ZipFile(path) as f:
             f.extractall(model_dir)
 
     @staticmethod
     def preprocess_pose_image(image: PIL.Image.Image) -> torch.Tensor:
-        image = np.array(
-            image.resize(
-                size=(256, 512),
-                resample=PIL.Image.Resampling.LANCZOS))[:, :, 2:].transpose(
-                    2, 0, 1).astype(np.float32)
-        image = image / 12. - 1
+        image = (
+            np.array(image.resize(size=(256, 512), resample=PIL.Image.Resampling.LANCZOS))[:, :, 2:]
+            .transpose(2, 0, 1)
+            .astype(np.float32)
+        )
+        image = image / 12.0 - 1
         data = torch.from_numpy(image).unsqueeze(1)
         return data
 
@@ -107,8 +105,7 @@ class Model:
         self.model.feed_pose_data(data)
         return data
 
-    def generate_label_image(self, pose_data: torch.Tensor,
-                             shape_text: str) -> np.ndarray:
+    def generate_label_image(self, pose_data: torch.Tensor, shape_text: str) -> np.ndarray:
         if pose_data is None:
             return
         self.model.feed_pose_data(pose_data)
@@ -120,16 +117,14 @@ class Model:
         colored_segm = self.model.palette_result(self.model.segm[0].cpu())
         return colored_segm
 
-    def generate_human(self, label_image: np.ndarray, texture_text: str,
-                       sample_steps: int, seed: int) -> np.ndarray:
+    def generate_human(self, label_image: np.ndarray, texture_text: str, sample_steps: int, seed: int) -> np.ndarray:
         if label_image is None:
             return
         mask = label_image.copy()
         seg_map = self.process_mask(mask)
         if seg_map is None:
             return
-        self.model.segm = torch.from_numpy(seg_map).unsqueeze(0).unsqueeze(
-            0).to(self.model.device)
+        self.model.segm = torch.from_numpy(seg_map).unsqueeze(0).unsqueeze(0).to(self.model.device)
         self.model.generate_quantized_segm()
 
         set_random_seed(seed)

style.css

@@ -1,5 +1,6 @@
 h1 {
   text-align: center;
+  display: block;
 }
 #input-image {
   max-height: 300px;
@@ -10,7 +11,3 @@ h1 {
 #result-image {
   height: 300px;
 }
-img#visitor-badge {
-  display: block;
-  margin: auto;
-}