Update

.pre-commit-config.yaml

@@ -1,36 +1,61 @@
-exclude: 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

app.py

@@ -9,66 +9,57 @@ import torch
 
 from model import Model
 
-DESCRIPTION = '# [Multiresolution Textual Inversion](https://github.com/giannisdaras/multires_textual_inversion)'
+DESCRIPTION = "# [Multiresolution Textual Inversion](https://github.com/giannisdaras/multires_textual_inversion)"
 
-DETAILS = '''
+DETAILS = """
 - To run the Semi Resolution-Dependent sampler, use the format: `<jane(number)>`.
 - To run the Fully Resolution-Dependent sampler, use the format: `<jane[number]>`.
 - To run the Fixed Resolution sampler, use the format: `<jane|number|>`.
 
 For this demo, only `<jane>`, `<gta5-artwork>` and `<cat-toy>` are available.
 Also, `number` should be an integer in [0, 9].
-'''
+"""
 
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv(
-    'CACHE_EXAMPLES') == '1'
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
 
 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.Group():
             with gr.Row():
-                prompt = gr.Textbox(label='Prompt')
+                prompt = gr.Textbox(label="Prompt")
             with gr.Row():
                 num_images = gr.Slider(
-                    label='Number of images',
+                    label="Number of images",
                     minimum=1,
                     maximum=9,
                     step=1,
                     value=1,
                 )
             with gr.Row():
-                num_steps = gr.Slider(label='Number of inference steps',
-                                      minimum=1,
-                                      maximum=50,
-                                      step=1,
-                                      value=10)
+                num_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=10)
             with gr.Row():
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=100000,
-                                 step=1,
-                                 value=100)
+                seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=100)
             with gr.Row():
-                run_button = gr.Button('Run')
+                run_button = gr.Button("Run")
 
         with gr.Column():
-            result = gr.Gallery(label='Result', object_fit='scale-down')
+            result = gr.Gallery(label="Result", object_fit="scale-down")
 
     with gr.Row():
         with gr.Group():
             fn = lambda x: model.run(x, 2, 10, 100)
             with gr.Row():
                 gr.Examples(
-                    label='Examples 1',
+                    label="Examples 1",
                     examples=[
-                        ['an image of <gta5-artwork(0)>'],
-                        ['an image of <jane(0)>'],
-                        ['an image of <jane(3)>'],
-                        ['an image of <cat-toy(0)>'],
+                        ["an image of <gta5-artwork(0)>"],
+                        ["an image of <jane(0)>"],
+                        ["an image of <jane(3)>"],
+                        ["an image of <cat-toy(0)>"],
                     ],
                     inputs=prompt,
                     outputs=result,
@@ -77,16 +68,12 @@ with gr.Blocks(css='style.css') as demo:
                 )
             with gr.Row():
                 gr.Examples(
-                    label='Examples 2',
+                    label="Examples 2",
                     examples=[
-                        [
-                            'an image of a cat in the style of <gta5-artwork(0)>'
-                        ],
-                        ['a painting of a dog in the style of <jane(0)>'],
-                        ['a painting of a dog in the style of <jane(5)>'],
-                        [
-                            'a painting of a <cat-toy(0)> in the style of <jane(3)>'
-                        ],
+                        ["an image of a cat in the style of <gta5-artwork(0)>"],
+                        ["a painting of a dog in the style of <jane(0)>"],
+                        ["a painting of a dog in the style of <jane(5)>"],
+                        ["a painting of a <cat-toy(0)> in the style of <jane(3)>"],
                     ],
                     inputs=prompt,
                     outputs=result,
@@ -95,11 +82,11 @@ with gr.Blocks(css='style.css') as demo:
                 )
             with gr.Row():
                 gr.Examples(
-                    label='Examples 3',
+                    label="Examples 3",
                     examples=[
-                        ['an image of <jane[0]>'],
-                        ['an image of <jane|0|>'],
-                        ['an image of <jane|3|>'],
+                        ["an image of <jane[0]>"],
+                        ["an image of <jane|0|>"],
+                        ["an image of <jane|3|>"],
                     ],
                     inputs=prompt,
                     outputs=result,
@@ -123,10 +110,10 @@ with gr.Blocks(css='style.css') as demo:
             fn=model.run,
             inputs=inputs,
             outputs=result,
-            api_name='run',
+            api_name="run",
         )
 
-    with gr.Accordion('About available prompts', open=False):
+    with gr.Accordion("About available prompts", open=False):
         gr.Markdown(DETAILS)
 
 demo.queue(max_size=10).launch()

model.py

@@ -9,48 +9,40 @@ import PIL.Image
 import torch
 from diffusers import DPMSolverMultistepScheduler
 
-if os.getenv('SYSTEM') == 'spaces':
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'),
-                       cwd='multires_textual_inversion',
-                       stdin=f)
+if os.getenv("SYSTEM") == "spaces":
+    with open("patch") as f:
+        subprocess.run(shlex.split("patch -p1"), cwd="multires_textual_inversion", stdin=f)
 
-sys.path.insert(0, 'multires_textual_inversion')
+sys.path.insert(0, "multires_textual_inversion")
 
 from pipeline import MultiResPipeline, load_learned_concepts
 
 
 class Model:
     def __init__(self):
-        self.device = torch.device(
-            'cuda:0' if torch.cuda.is_available() else 'cpu')
-        model_id = 'runwayml/stable-diffusion-v1-5'
-        if self.device.type == 'cpu':
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        model_id = "runwayml/stable-diffusion-v1-5"
+        if self.device.type == "cpu":
             pipe = MultiResPipeline.from_pretrained(model_id)
         else:
-            pipe = MultiResPipeline.from_pretrained(model_id,
-                                                    torch_dtype=torch.float16,
-                                                    revision='fp16')
+            pipe = MultiResPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
         self.pipe = pipe.to(self.device)
         self.pipe.scheduler = DPMSolverMultistepScheduler(
             beta_start=0.00085,
             beta_end=0.012,
-            beta_schedule='scaled_linear',
+            beta_schedule="scaled_linear",
             num_train_timesteps=1000,
             trained_betas=None,
             predict_epsilon=True,
             thresholding=False,
-            algorithm_type='dpmsolver++',
-            solver_type='midpoint',
+            algorithm_type="dpmsolver++",
+            solver_type="midpoint",
             lower_order_final=True,
         )
-        self.string_to_param_dict = load_learned_concepts(
-            self.pipe, 'textual_inversion_outputs/')
+        self.string_to_param_dict = load_learned_concepts(self.pipe, "textual_inversion_outputs/")
 
-    def run(self, prompt: str, n_images: int, n_steps: int,
-            seed: int) -> list[PIL.Image.Image]:
+    def run(self, prompt: str, n_images: int, n_steps: int, seed: int) -> list[PIL.Image.Image]:
         generator = torch.Generator(device=self.device).manual_seed(seed)
-        return self.pipe([prompt] * n_images,
-                         self.string_to_param_dict,
-                         num_inference_steps=n_steps,
-                         generator=generator)
+        return self.pipe(
+            [prompt] * n_images, self.string_to_param_dict, num_inference_steps=n_steps, generator=generator
+        )