Update

.pre-commit-config.yaml

@@ -1,37 +1,60 @@
-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']
-      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

@@ -13,29 +13,30 @@ import torch
 from app_generated_image import create_prompt_demo
 from app_real_image import create_real_image_demo
 
-DESCRIPTION = '# [Plug-and-Play diffusion features](https://github.com/MichalGeyer/plug-and-play)'
+DESCRIPTION = "# [Plug-and-Play diffusion features](https://github.com/MichalGeyer/plug-and-play)"
 
-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():
-    weight_dir = pathlib.Path('plug-and-play/models/ldm/stable-diffusion-v1')
+    weight_dir = pathlib.Path("plug-and-play/models/ldm/stable-diffusion-v1")
     if not weight_dir.exists():
+        subprocess.run(shlex.split("mkdir -p plug-and-play/models/ldm/stable-diffusion-v1/"))
         subprocess.run(
             shlex.split(
-                'mkdir -p plug-and-play/models/ldm/stable-diffusion-v1/'))
-        subprocess.run(
-            shlex.split(
-                'wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O plug-and-play/models/ldm/stable-diffusion-v1/model.ckpt'
-            ))
+                "wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O plug-and-play/models/ldm/stable-diffusion-v1/model.ckpt"
+            )
+        )
 
-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('Use real image as input'):
+        with gr.TabItem("Use real image as input"):
             create_real_image_demo()
-        with gr.TabItem('Use prompt as input'):
+        with gr.TabItem("Use prompt as input"):
             create_prompt_demo()
-demo.queue(max_size=10).launch()
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()

app_generated_image.py

@@ -19,15 +19,14 @@ def gen_feature_extraction_config(
     guidance_scale: float,
     ddim_steps: int,
 ) -> str:
-    config = OmegaConf.load(
-        'plug-and-play/configs/pnp/feature-extraction-generated.yaml')
+    config = OmegaConf.load("plug-and-play/configs/pnp/feature-extraction-generated.yaml")
     config.config.experiment_name = exp_name
     config.config.prompt = prompt
     config.config.seed = seed
     config.config.scale = guidance_scale
     config.config.ddim_steps = ddim_steps
-    temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
-    with open(temp_file.name, 'w') as f:
+    temp_file = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
+    with open(temp_file.name, "w") as f:
         f.write(OmegaConf.to_yaml(config))
     return temp_file.name
 
@@ -39,7 +38,7 @@ def run_feature_extraction_command(
     ddim_steps: int,
 ) -> tuple[str, str]:
     exp_name = f'{prompt.replace(" ", "_")}_{seed}_{guidance_scale:.1f}_{ddim_steps}'
-    if not pathlib.Path(f'plug-and-play/experiments/{exp_name}').exists():
+    if not pathlib.Path(f"plug-and-play/experiments/{exp_name}").exists():
         config_path = gen_feature_extraction_config(
             exp_name,
             prompt,
@@ -47,10 +46,8 @@ def run_feature_extraction_command(
             guidance_scale,
             ddim_steps,
         )
-        subprocess.run(shlex.split(
-            f'python run_features_extraction.py --config {config_path}'),
-                       cwd='plug-and-play')
-    return f'plug-and-play/experiments/{exp_name}/samples/0.png', exp_name
+        subprocess.run(shlex.split(f"python run_features_extraction.py --config {config_path}"), cwd="plug-and-play")
+    return f"plug-and-play/experiments/{exp_name}/samples/0.png", exp_name
 
 
 def gen_pnp_config(
@@ -63,7 +60,7 @@ def gen_pnp_config(
     negative_prompt_alpha: float,
     negative_prompt_schedule: str,
 ) -> str:
-    config = OmegaConf.load('plug-and-play/configs/pnp/pnp-generated.yaml')
+    config = OmegaConf.load("plug-and-play/configs/pnp/pnp-generated.yaml")
     config.source_experiment_name = exp_name
     config.prompts = [prompt]
     config.scale = guidance_scale
@@ -72,8 +69,8 @@ def gen_pnp_config(
     config.negative_prompt = negative_prompt
     config.negative_prompt_alpha = negative_prompt_alpha
     config.negative_prompt_schedule = negative_prompt_schedule
-    temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
-    with open(temp_file.name, 'w') as f:
+    temp_file = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
+    with open(temp_file.name, "w") as f:
         f.write(OmegaConf.to_yaml(config))
     return temp_file.name
 
@@ -98,107 +95,76 @@ def run_pnp_command(
         negative_prompt_alpha,
         negative_prompt_schedule,
     )
-    subprocess.run(shlex.split(f'python run_pnp.py --config {config_path}'),
-                   cwd='plug-and-play')
+    subprocess.run(shlex.split(f"python run_pnp.py --config {config_path}"), cwd="plug-and-play")
 
     out_dir = pathlib.Path(
         f'plug-and-play/experiments/{exp_name}/translations/{guidance_scale}_{prompt.replace(" ", "_")}'
     )
     out_label = f'INJECTION_T_{feature_injection_threshold}_STEPS_{ddim_steps}_NP-ALPHA_{negative_prompt_alpha}_SCHEDULE_{negative_prompt_schedule}_NP_{negative_prompt.replace(" ", "_")}'
-    out_path = out_dir / f'{out_label}_sample_0.png'
+    out_path = out_dir / f"{out_label}_sample_0.png"
     return out_path.as_posix()
 
 
-def process_example(source_prompt: str, seed: int,
-                    translation_prompt: str) -> tuple[str, str, str]:
-    generated_image, exp_name = run_feature_extraction_command(
-        source_prompt, seed, guidance_scale=5, ddim_steps=50)
-    result = run_pnp_command(exp_name,
-                             translation_prompt,
-                             negative_prompt='',
-                             guidance_scale=7.5,
-                             ddim_steps=50,
-                             feature_injection_threshold=40,
-                             negative_prompt_alpha=0.75,
-                             negative_prompt_schedule='linear')
+def process_example(source_prompt: str, seed: int, translation_prompt: str) -> tuple[str, str, str]:
+    generated_image, exp_name = run_feature_extraction_command(source_prompt, seed, guidance_scale=5, ddim_steps=50)
+    result = run_pnp_command(
+        exp_name,
+        translation_prompt,
+        negative_prompt="",
+        guidance_scale=7.5,
+        ddim_steps=50,
+        feature_injection_threshold=40,
+        negative_prompt_alpha=0.75,
+        negative_prompt_schedule="linear",
+    )
     return generated_image, exp_name, result
 
 
 def create_prompt_demo() -> gr.Blocks:
     with gr.Blocks() as demo:
         with gr.Box():
-            gr.Markdown(
-                'Step 1 (This step will take about 1.5 minutes on A10G.)')
+            gr.Markdown("Step 1 (This step will take about 1.5 minutes on A10G.)")
             with gr.Row():
                 with gr.Column():
-                    source_prompt = gr.Text(label='Source prompt')
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=100000,
-                                     step=1,
-                                     value=0)
-                    with gr.Accordion(label='Advanced settings', open=False):
+                    source_prompt = gr.Text(label="Source prompt")
+                    seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=0)
+                    with gr.Accordion(label="Advanced settings", open=False):
                         source_guidance_scale = gr.Slider(
-                            label='Guidance scale',
-                            minimum=0,
-                            maximum=50,
-                            step=0.1,
-                            value=5)
-                        source_ddim_steps = gr.Slider(label='DDIM steps',
-                                                      minimum=1,
-                                                      maximum=100,
-                                                      step=1,
-                                                      value=50)
-                    extract_feature_button = gr.Button(
-                        'Generate and extract features')
+                            label="Guidance scale", minimum=0, maximum=50, step=0.1, value=5
+                        )
+                        source_ddim_steps = gr.Slider(label="DDIM steps", minimum=1, maximum=100, step=1, value=50)
+                    extract_feature_button = gr.Button("Generate and extract features")
                 with gr.Column():
-                    generated_image = gr.Image(label='Generated image',
-                                               type='filepath')
+                    generated_image = gr.Image(label="Generated image", type="filepath")
                     exp_name = gr.Text(visible=False)
         with gr.Box():
-            gr.Markdown(
-                'Step 2 (This step will take about 1.5 minutes on A10G.)')
+            gr.Markdown("Step 2 (This step will take about 1.5 minutes on A10G.)")
             with gr.Row():
                 with gr.Column():
-                    translation_prompt = gr.Text(
-                        label='Prompt for translation')
-                    negative_prompt = gr.Text(label='Negative prompt')
-                    with gr.Accordion(label='Advanced settings', open=False):
-                        guidance_scale = gr.Slider(label='Guidance scale',
-                                                   minimum=0,
-                                                   maximum=50,
-                                                   step=0.1,
-                                                   value=7.5)
+                    translation_prompt = gr.Text(label="Prompt for translation")
+                    negative_prompt = gr.Text(label="Negative prompt")
+                    with gr.Accordion(label="Advanced settings", open=False):
+                        guidance_scale = gr.Slider(label="Guidance scale", minimum=0, maximum=50, step=0.1, value=7.5)
                         ddim_steps = gr.Slider(
-                            label='Number of inference steps',
-                            minimum=1,
-                            maximum=100,
-                            step=1,
-                            value=50)
+                            label="Number of inference steps", minimum=1, maximum=100, step=1, value=50
+                        )
                         feature_injection_threshold = gr.Slider(
-                            label='Feature injection threshold',
-                            minimum=0,
-                            maximum=100,
-                            step=1,
-                            value=40)
+                            label="Feature injection threshold", minimum=0, maximum=100, step=1, value=40
+                        )
                         negative_prompt_alpha = gr.Slider(
-                            label='Negative prompt alpha',
-                            minimum=0,
-                            maximum=1,
-                            step=0.01,
-                            value=0.75)
+                            label="Negative prompt alpha", minimum=0, maximum=1, step=0.01, value=0.75
+                        )
                         negative_prompt_schedule = gr.Dropdown(
-                            label='Negative prompt schedule',
-                            choices=['linear', 'constant', 'exp'],
-                            value='linear')
-                    generate_button = gr.Button('Generate')
+                            label="Negative prompt schedule", choices=["linear", "constant", "exp"], value="linear"
+                        )
+                    generate_button = gr.Button("Generate")
                 with gr.Column():
-                    result = gr.Image(label='Result', type='filepath')
+                    result = gr.Image(label="Result", type="filepath")
         with gr.Row():
             gr.Examples(
                 examples=[
-                    ['horse in mud', 50, 'a photo of a zebra in the snow'],
-                    ['horse in mud', 50, 'a photo of a husky in the grass'],
+                    ["horse in mud", 50, "a photo of a zebra in the snow"],
+                    ["horse in mud", 50, "a photo of a husky in the grass"],
                 ],
                 inputs=[
                     source_prompt,
@@ -211,7 +177,7 @@ def create_prompt_demo() -> gr.Blocks:
                     result,
                 ],
                 fn=process_example,
-                cache_examples=os.getenv('CACHE_EXAMPLES'),
+                cache_examples=os.getenv("CACHE_EXAMPLES"),
             )
 
         extract_feature_button.click(
@@ -244,6 +210,6 @@ def create_prompt_demo() -> gr.Blocks:
     return demo
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     demo = create_prompt_demo()
     demo.queue().launch()

app_real_image.py

@@ -14,30 +14,27 @@ from omegaconf import OmegaConf
 
 
 def get_exp_name(path: str) -> str:
-    with open(path, 'rb') as f:
+    with open(path, "rb") as f:
         res = hashlib.md5(f.read()).hexdigest()
     return res
 
 
 def gen_feature_extraction_config(exp_name: str, init_image_path: str) -> str:
-    config = OmegaConf.load(
-        'plug-and-play/configs/pnp/feature-extraction-real.yaml')
+    config = OmegaConf.load("plug-and-play/configs/pnp/feature-extraction-real.yaml")
     config.config.experiment_name = exp_name
     config.config.init_img = init_image_path
-    temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
-    with open(temp_file.name, 'w') as f:
+    temp_file = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
+    with open(temp_file.name, "w") as f:
         f.write(OmegaConf.to_yaml(config))
     return temp_file.name
 
 
 def run_feature_extraction_command(init_image_path: str) -> tuple[str, str]:
     exp_name = get_exp_name(init_image_path)
-    if not pathlib.Path(f'plug-and-play/experiments/{exp_name}').exists():
+    if not pathlib.Path(f"plug-and-play/experiments/{exp_name}").exists():
         config_path = gen_feature_extraction_config(exp_name, init_image_path)
-        subprocess.run(shlex.split(
-            f'python run_features_extraction.py --config {config_path}'),
-                       cwd='plug-and-play')
-    return f'plug-and-play/experiments/{exp_name}/samples/0.png', exp_name
+        subprocess.run(shlex.split(f"python run_features_extraction.py --config {config_path}"), cwd="plug-and-play")
+    return f"plug-and-play/experiments/{exp_name}/samples/0.png", exp_name
 
 
 def gen_pnp_config(
@@ -50,7 +47,7 @@ def gen_pnp_config(
     negative_prompt_alpha: float,
     negative_prompt_schedule: str,
 ) -> str:
-    config = OmegaConf.load('plug-and-play/configs/pnp/pnp-real.yaml')
+    config = OmegaConf.load("plug-and-play/configs/pnp/pnp-real.yaml")
     config.source_experiment_name = exp_name
     config.prompts = [prompt]
     config.scale = guidance_scale
@@ -59,8 +56,8 @@ def gen_pnp_config(
     config.negative_prompt = negative_prompt
     config.negative_prompt_alpha = negative_prompt_alpha
     config.negative_prompt_schedule = negative_prompt_schedule
-    temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
-    with open(temp_file.name, 'w') as f:
+    temp_file = tempfile.NamedTemporaryFile(suffix=".yaml", delete=False)
+    with open(temp_file.name, "w") as f:
         f.write(OmegaConf.to_yaml(config))
     return temp_file.name
 
@@ -85,102 +82,83 @@ def run_pnp_command(
         negative_prompt_alpha,
         negative_prompt_schedule,
     )
-    subprocess.run(shlex.split(f'python run_pnp.py --config {config_path}'),
-                   cwd='plug-and-play')
+    subprocess.run(shlex.split(f"python run_pnp.py --config {config_path}"), cwd="plug-and-play")
 
     out_dir = pathlib.Path(
         f'plug-and-play/experiments/{exp_name}/translations/{guidance_scale}_{prompt.replace(" ", "_")}'
     )
     out_label = f'INJECTION_T_{feature_injection_threshold}_STEPS_{ddim_steps}_NP-ALPHA_{negative_prompt_alpha}_SCHEDULE_{negative_prompt_schedule}_NP_{negative_prompt.replace(" ", "_")}'
-    out_path = out_dir / f'{out_label}_sample_0.png'
+    out_path = out_dir / f"{out_label}_sample_0.png"
     return out_path.as_posix()
 
 
-def process_example(image: str, translation_prompt: str,
-                    negative_prompt: str) -> tuple[str, str, str]:
+def process_example(image: str, translation_prompt: str, negative_prompt: str) -> tuple[str, str, str]:
     reconstructed_image, exp_name = run_feature_extraction_command(image)
-    result = run_pnp_command(exp_name,
-                             translation_prompt,
-                             negative_prompt,
-                             guidance_scale=10,
-                             ddim_steps=50,
-                             feature_injection_threshold=40,
-                             negative_prompt_alpha=1,
-                             negative_prompt_schedule='linear')
+    result = run_pnp_command(
+        exp_name,
+        translation_prompt,
+        negative_prompt,
+        guidance_scale=10,
+        ddim_steps=50,
+        feature_injection_threshold=40,
+        negative_prompt_alpha=1,
+        negative_prompt_schedule="linear",
+    )
     return reconstructed_image, exp_name, result
 
 
 def create_real_image_demo():
     with gr.Blocks() as demo:
         with gr.Box():
-            gr.Markdown(
-                'Step 1 (This step will take about 5 minutes on A10G.)')
+            gr.Markdown("Step 1 (This step will take about 5 minutes on A10G.)")
             with gr.Row():
                 with gr.Column():
-                    image = gr.Image(label='Input image', type='filepath')
-                    extract_feature_button = gr.Button(
-                        'Reconstruct and extract features')
+                    image = gr.Image(label="Input image", type="filepath")
+                    extract_feature_button = gr.Button("Reconstruct and extract features")
                 with gr.Column():
-                    reconstructed_image = gr.Image(label='Reconstructed image',
-                                                   type='filepath')
+                    reconstructed_image = gr.Image(label="Reconstructed image", type="filepath")
                     exp_name = gr.Text(visible=False)
         with gr.Box():
-            gr.Markdown(
-                'Step 2 (This step will take about 1.5 minutes on A10G.)')
+            gr.Markdown("Step 2 (This step will take about 1.5 minutes on A10G.)")
             with gr.Row():
                 with gr.Column():
-                    translation_prompt = gr.Text(
-                        label='Prompt for translation')
-                    negative_prompt = gr.Text(label='Negative prompt')
-                    with gr.Accordion(label='Advanced settings', open=False):
-                        guidance_scale = gr.Slider(label='Guidance scale',
-                                                   minimum=0,
-                                                   maximum=50,
-                                                   step=0.1,
-                                                   value=10)
+                    translation_prompt = gr.Text(label="Prompt for translation")
+                    negative_prompt = gr.Text(label="Negative prompt")
+                    with gr.Accordion(label="Advanced settings", open=False):
+                        guidance_scale = gr.Slider(label="Guidance scale", minimum=0, maximum=50, step=0.1, value=10)
                         ddim_steps = gr.Slider(
-                            label='Number of inference steps',
-                            minimum=1,
-                            maximum=100,
-                            step=1,
-                            value=50)
+                            label="Number of inference steps", minimum=1, maximum=100, step=1, value=50
+                        )
                         feature_injection_threshold = gr.Slider(
-                            label='Feature injection threshold',
-                            minimum=0,
-                            maximum=100,
-                            step=1,
-                            value=40)
+                            label="Feature injection threshold", minimum=0, maximum=100, step=1, value=40
+                        )
                         negative_prompt_alpha = gr.Slider(
-                            label='Negative prompt alpha',
-                            minimum=0,
-                            maximum=1,
-                            step=0.01,
-                            value=1)
+                            label="Negative prompt alpha", minimum=0, maximum=1, step=0.01, value=1
+                        )
                         negative_prompt_scheduler = gr.Dropdown(
-                            label='Negative prompt schedule',
-                            choices=['linear', 'constant', 'exp'],
-                            value='linear')
-                    generate_button = gr.Button('Generate')
+                            label="Negative prompt schedule", choices=["linear", "constant", "exp"], value="linear"
+                        )
+                    generate_button = gr.Button("Generate")
                 with gr.Column():
-                    result = gr.Image(label='Result', type='filepath')
+                    result = gr.Image(label="Result", type="filepath")
 
         with gr.Row():
             gr.Examples(
                 examples=[
                     [
-                        'plug-and-play/data/horse.png',
-                        'a photo of a robot horse',
-                        'a photo of a white horse',
+                        "plug-and-play/data/horse.png",
+                        "a photo of a robot horse",
+                        "a photo of a white horse",
                     ],
                     [
-                        'plug-and-play/data/horse.png',
-                        'a photo of a bronze horse in a museum',
-                        'a photo of a white horse',
+                        "plug-and-play/data/horse.png",
+                        "a photo of a bronze horse in a museum",
+                        "a photo of a white horse",
                     ],
                     [
-                        'plug-and-play/data/horse.png',
-                        'a photo of a pink horse on the beach',
-                        'a photo of a white horse',
+                        "plug-and-play/data/horse.png",
+                        "a photo of a pink horse on the beach",
+                        "a photo of a white horse",
                     ],
                 ],
                 inputs=[
@@ -194,7 +172,7 @@ def create_real_image_demo():
                     result,
                 ],
                 fn=process_example,
-                cache_examples=os.getenv('CACHE_EXAMPLES'),
+                cache_examples=os.getenv("CACHE_EXAMPLES"),
             )
 
         extract_feature_button.click(
@@ -223,6 +201,6 @@ def create_real_image_demo():
     return demo
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     demo = create_real_image_demo()
     demo.queue().launch()

style.css

@@ -1,3 +1,11 @@
 h1 {
   text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }