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
+ )