Key updates:
Added AddFramesToVideoFolder node to add frames to a video folder
Introduced VideoFolderProvider and VideoDefinitionProvider nodes to manage video folders and video definitions
Added FadeToInVideoFolder node for fading between frames in a video folder
Implemented ListedSVDConditioning node for applying SVD conditioning to a list of parameters
Added VideoFolderFFMPEGVideoInterpolate node to interpolate frames in a video folder using FFmpeg
Introduced AudioFolderProvider and AudioDefinitionProvider nodes for managing audio folders and definitions
Added AddAudioToAudioFolder and AddSilenceToAudioFolder nodes for adding audio files and silence to audio folders
Implemented GetAudioFilesLengthsFromAudioFolder node to retrieve lengths of audio files in a folder
Added AudioFolderRenderAudio node to join audio files in an audio folder
Introduced GenerateMusic node for generating music from descriptions using the AudioCraft library
Added XttsNode for text-to-speech conversion using the XTTS library
Implemented AdjustAudioVolumeForFiles node to adjust volume for a list of audio files
The changes also include the addition of LiteLLM nodes for text generation:
Added FuncAnysToText node that runs Python code with multiple input types and outputs a string
Introduced FuncAnysToLatent node for running code that outputs a latent representation
Added FuncAnysToString node for executing code with various inputs and producing a string output
Implemented FuncAnysToList node to run code that takes different input types and returns a list
Added FuncAnysToCond node for running code with multiple inputs and generating a conditioning output
These new nodes expand the capabilities for video and audio processing, allowing more advanced manipulations and generation of video and audio content within the graph.
This commit introduces a series of refactoring and enhancements across multiple modules, improving code readability, consistency, and adding new functionalities.
Highlights:
1. `tests/test_hf_diffusers_nodes.py`:
- Removed unnecessary whitespace.
- Improved variable assignment consistency (e.g., `ret=ret[0]` to `ret = ret[0]`).
- Added a new test case `test_create_unet2d_model` for Unet2d model creation.
2. `server_endpoints.py`:
- Cleaned up commented-out code and added more descriptive comments.
- Refactored the construction of `real_git_path` for better readability and future maintenance.
- Implemented minor formatting improvements for better code structure.
3. `basic.py`:
- Introduced a new class `TupleNode` to handle tuple data types.
- Modified `StrBreakout` to include dynamic typing for list conversion.
- Enhanced code readability with better variable naming and added comments for clarity.
4. `pytorch_nodes.py`:
- Added import statements within functions for `torch`.
- New classes `SaveTorchTensor` and `LoadTorchTensor` added for saving and loading torch tensors.
- `GridSearchTraining` class updated with minor formatting changes.
5. `image.py`:
- In `FloodFillNode`, removed a redundant category assignment.
- Added two new classes: `DecorateHeightMap` and `RandomImageFromFolder`. These classes provide functionalities for decorating height maps with images and selecting random images from a folder, respectively.
6. `hf_diffusers_nodes.py`:
- Fixed formatting in tensor format options and added a new line for clarity.
- New class `CreateUnet2dModel` added for creating a UNet2D model with customizable parameters.
- Introduced `TrainUnet2dModel` for training a UNet2D model with a dataset, including detailed setup for the training process.
7. `config.yaml`:
- Added a new base path for `gitio` to centralize the configuration.
These changes aim to improve the maintainability of the codebase, streamline testing processes, and expand the functionalities within the modules, particularly related to image processing and model training in PyTorch and diffusers.
This commit introduces significant enhancements and new features across multiple files in the project. Major changes include:
1. `pytorch_nodes.py`: Expanded PyTorch node functionalities with additional imports, device compatibility checks, optimizer state updates, and training enhancements. Introduced new methods like `optimizer_to` for transferring optimizer state to a specified device, `reset_optimizer_weights` to clear optimizer states, and `make_image_tensor` for converting outputs to image tensors. Also, improved the training process with device compatibility checks, optimizer updates, and the ability to save and load models during training for efficiency.
2. `server_endpoints.py`: Modified server endpoints for improved git integration and user management. Adjustments include dynamic handling of different git repositories, refined branch and user management in GitIO class, and the integration of user-specific git settings in user management functions.
3. `config.yaml`: Added new configurations for temporary directory and git repositories, providing more flexibility and control over file management and version control integration.
4. `torchvision_nodes.py`: Introduced a new file to integrate torchvision functionalities. It includes classes for transform composition, application of torchvision transforms, and handling of torchvision-specific data structures.
5. `hf_diffusers_nodes.py`: Integrated Hugging Face's diffusers library with new nodes for loading and utilizing DDPMPipelines, handling tensor to image conversions, and managing datasets from the Hugging Face ecosystem.
6. `image.py`, `video.py`, `functional.py`: Refactored and enhanced existing functionalities. Included minor tweaks and bug fixes to improve the overall efficiency and robustness of image and video processing capabilities.
7. `requirements.txt`: Updated dependencies, replacing `fastnoisesimd` with `diffusers` to align with the newly integrated Hugging Face diffusers functionalities.
8. `config.py`: Improved the configuration file loading mechanism with dynamic path handling and the ability to replace placeholders with actual paths, enhancing the flexibility of configuration management.
This comprehensive update significantly enhances the project's capabilities in machine learning, especially in PyTorch and torchvision, and improves server endpoint functionalities for better user and version control management.
Tasha Upchurch