Initial files

requirements.txt

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+diffusers
+xformers
+accelerate
+opencv-python

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

app.py

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+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+from diffusers.utils import load_image, make_image_grid
+from PIL import Image
+import numpy as np
+import torch
+import cv2
+import random
+
+def launch(image_input, prompt, negative_prompt, sd_models_dropdown):
+    
+    return predict(image_input, prompt, negative_prompt, sd_models_dropdown)
+
+def predict(image_input, prompt, negative_prompt, sd_models_dropdown):
+    
+    image = np.array(image_input)
+
+    # Get canny image
+    image = cv2.Canny(image, 100, 200)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    canny_image = Image.fromarray(image)
+    
+    selected_model = f"""{sd_models_dropdown}"""
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        selected_model, 
+        torch_dtype=torch.float16
+    )
+    pipe = pipe.to("cuda")
+
+    prompt = "a photo of an astronaut riding a horse on mars"
+    negative_promt=""
+    
+    generator = torch.manual_seed(random.randint(0, 1000))
+    
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=65,
+        generator=generator,
+        image=canny_image,
+        strength=0.15,
+        guidance_scale=15.5
+        
+    ).images[0]  
+
+    return image
+
+# SYSTEM INPUTS
+
+image_input = gr.Image(label="Upload iamge candidate", type="filepath")
+prompt = gr.Textbox(type="text", promt="Prompt")
+negative_prompt = gr.Textbox(type="text", promt="Negative prompt")
+
+sd_models_dropdown = gr.Dropdown(
+    [
+        "runwayml/stable-diffusion-v1-5",
+    ], 
+    label="Stable Diffusion Models")
+
+# SYSTEM OUTPUTS
+output_image = gr.outputs.Image()
+
+demo = gr.Interface(
+    launch,
+    inputs=[image_input, prompt, negative_prompt, sd_models_dropdown],
+    outputs=output_image,
+    title="Stable Diffusion with ControlNet",
+)
+
+if __name__ == "__main__":
+    demo.launch()