Initial commit

May 9, 2024: Added new audio visualization features, more information can be found [here](https://github.com/get-salt-AI/SaltAI_AudioViz)

.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
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+# 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
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+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+
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+__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
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+/site
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+.mypy_cache/
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+.pyre/
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+# pytype static type analyzer
+.pytype/
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+cython_debug/
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+# PyCharm
+#  JetBrains specific template is maintainted 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/
+*.pyc
+*.zip

__init__.py

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+import os
+
+from SaltAI_AudioViz.modules.node_importer import ModuleLoader
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+NAME = "Salt.AI AudioViz"
+PACKAGE = "SaltAI_AudioViz"
+NODES_DIR = os.path.join(ROOT, 'nodes')
+EXTENSION_WEB_DIRS = {}
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+# Load modules
+module_timings = {}
+module_loader = ModuleLoader(PACKAGE)
+module_loader.load_modules(NODES_DIR)
+
+# Mappings
+NODE_CLASS_MAPPINGS = module_loader.NODE_CLASS_MAPPINGS
+NODE_DISPLAY_NAME_MAPPINGS = module_loader.NODE_DISPLAY_NAME_MAPPINGS
+
+# Timings and such
+print("")
+module_loader.report(NAME)
+print("")
+
+# Export nodes
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

modules/blend.py

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+import math
+import torch
+import torch.nn.functional as F
+
+def slerp(strength, tensor_from, tensor_to, epsilon=1e-6):
+    low_norm = F.normalize(tensor_from, p=2, dim=-1, eps=epsilon)
+    high_norm = F.normalize(tensor_to, p=2, dim=-1, eps=epsilon)
+
+    dot_product = torch.clamp((low_norm * high_norm).sum(dim=-1), -1.0, 1.0)
+    omega = torch.acos(dot_product)
+    so = torch.sin(omega)
+    zero_so_mask = torch.isclose(so, torch.tensor([0.0], device=so.device), atol=epsilon)
+    so = torch.where(zero_so_mask, torch.tensor([1.0], device=so.device), so)
+    sin_omega_minus_strength = torch.sin((1.0 - strength) * omega) / so
+    sin_strength_omega = torch.sin(strength * omega) / so
+
+    res = sin_omega_minus_strength.unsqueeze(-1) * tensor_from + sin_strength_omega.unsqueeze(-1) * tensor_to
+    res = torch.where(zero_so_mask.unsqueeze(-1), 
+                      tensor_from if strength < 0.5 else tensor_to, 
+                      res)
+    return res
+
+# from  https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475
+def slerp_latents(val, low, high):
+    dims = low.shape
+
+    #flatten to batches
+    low = low.reshape(dims[0], -1)
+    high = high.reshape(dims[0], -1)
+
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+
+    # in case we divide by zero
+    low_norm[low_norm != low_norm] = 0.0
+    high_norm[high_norm != high_norm] = 0.0
+
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res.reshape(dims)
+
+def blend_latents(alpha, latent_1, latent_2):
+    if not isinstance(alpha, torch.Tensor):
+        alpha = torch.tensor([alpha], dtype=latent_1.dtype, device=latent_1.device)
+
+    blended_latent = (1 - alpha) * latent_1 + alpha * latent_2
+
+    return blended_latent
+
+def cosine_interp_latents(val, low, high):
+    if not isinstance(val, torch.Tensor):
+        val = torch.tensor([val], dtype=low.dtype, device=low.device)        
+    t = (1 - torch.cos(val * math.pi)) / 2
+    return (1 - t) * low + t * high