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Runtime error
Runtime error
Initial files
Browse files- requirements.txt +4 -0
- .gitignore +160 -0
- app.py +72 -0
requirements.txt
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diffusers
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xformers
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accelerate
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opencv-python
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.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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app.py
ADDED
@@ -0,0 +1,72 @@
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
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from diffusers.utils import load_image, make_image_grid
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from PIL import Image
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import numpy as np
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import torch
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import cv2
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import random
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def launch(image_input, prompt, negative_prompt, sd_models_dropdown):
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return predict(image_input, prompt, negative_prompt, sd_models_dropdown)
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def predict(image_input, prompt, negative_prompt, sd_models_dropdown):
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image = np.array(image_input)
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# Get canny image
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image = cv2.Canny(image, 100, 200)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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canny_image = Image.fromarray(image)
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selected_model = f"""{sd_models_dropdown}"""
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pipe = StableDiffusionPipeline.from_pretrained(
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selected_model,
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torch_dtype=torch.float16
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)
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pipe = pipe.to("cuda")
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prompt = "a photo of an astronaut riding a horse on mars"
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negative_promt=""
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generator = torch.manual_seed(random.randint(0, 1000))
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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num_inference_steps=65,
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generator=generator,
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image=canny_image,
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strength=0.15,
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guidance_scale=15.5
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).images[0]
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return image
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# SYSTEM INPUTS
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image_input = gr.Image(label="Upload iamge candidate", type="filepath")
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prompt = gr.Textbox(type="text", promt="Prompt")
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negative_prompt = gr.Textbox(type="text", promt="Negative prompt")
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sd_models_dropdown = gr.Dropdown(
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[
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"runwayml/stable-diffusion-v1-5",
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],
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label="Stable Diffusion Models")
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# SYSTEM OUTPUTS
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output_image = gr.outputs.Image()
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demo = gr.Interface(
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launch,
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inputs=[image_input, prompt, negative_prompt, sd_models_dropdown],
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outputs=output_image,
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title="Stable Diffusion with ControlNet",
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)
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if __name__ == "__main__":
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demo.launch()
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