The Stability-AI/generative-models project is a Python-based software project focused on generative models. It is under active development, with the last push made on November 22, 2023.
The project has a healthy number of forks (1362) and stars (11454), indicating a high level of interest and engagement from the community. However, there are 98 open issues, suggesting that there are ongoing challenges and areas for improvement.
The project is actively maintained, with regular updates and new features being added. The most recent update was the release of Stable Video Diffusion, an image-to-video model, for research purposes. This update includes two versions of the model, a streamlit demo, a standalone python script for inference, and a technical report.
Several issues stand out in the project:
The project's README is comprehensive and includes detailed installation instructions, usage examples, and explanations of the codebase. However, there are some inconsistencies in the training and inference configs as reported in Issue #101.
The Stability-AI/generative-models project is a popular and actively maintained project in the field of generative models. While there are ongoing issues and areas for improvement, the maintainers are actively addressing these issues and regularly adding new features.
The project is in an active state with a high volume of recent issues, indicating a vibrant and engaged community. The project recently released the Stable Video Diffusion (SVD) model, which has sparked a lot of discussions and questions.
Key issues include:
The project maintainers are actively responding to issues, which is a positive sign. However, the high volume of open issues suggests that the project could benefit from more detailed documentation, especially around the use of SVD, training configurations, and multi-GPU support.
The project is actively maintained with 23 open pull requests. Recent pull requests are focused on code quality improvements, such as replacing print() with logging calls and removing deprecated functions. There is also an effort to remove star imports for better static analysis and to avoid duplicate function definitions. A new feature, a local SVD demo using gradio, is also being added.
Notable pull requests include:
Summary
Detailed Reports
print() calls with logging calls across multiple files.Logger.warn function.get_interactive_image function.Long-standing pull requests like PR #102 and PR #90 indicate some issues with code quality and project management. PR #102, for example, has extensive discussions about type annotations, code structure, and naming conventions. PR #90 was criticized for adding unnecessary visual outputs to tests.
Overall, the project is making steady progress, but could benefit from stricter code review and better management of pull requests.
The Stability-AI/generative-models project is a Python-based software project focused on generative models for AI research. The project is under active development, with the latest push made on November 22, 2023. The repository has a significant size of 41520 kB, indicating a substantial codebase. The project has gained considerable attention with 11454 stars, 141 watchers, and 1362 forks, suggesting a high level of interest and engagement from the community.
The project has released several models over time, including the Stable Video Diffusion (SVD) model and the SDXL models. The SVD model generates video frames from a context frame, while the SDXL models are diffusion models for research purposes. The project also provides a demo for inference of these models.
The project is organized around a philosophy of modularity, with a config-driven approach to building and combining submodules. It uses PyTorch Lightning for training and has adopted the "denoiser framework" for both training and inference. The project also provides a script for invisible watermark detection in generated images.
The project has 98 open issues, indicating active engagement from the community but also potential areas for improvement or ongoing development challenges. The project has made 46 commits across 4 branches, suggesting a moderate level of development activity.
The project's README provides detailed instructions for installation, packaging, inference, and training, indicating a focus on usability and accessibility for users. The project is licensed under the MIT License, allowing for broad use, modification, and distribution.