peacasso
fake-news
| peacasso | fake-news | |
|---|---|---|
| 2 | 1 | |
| 350 | 130 | |
| - | - | |
| 3.3 | 4.1 | |
| 9 months ago | over 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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peacasso
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[P] Mixing Prompts (with Weights) in Stable Diffusion Models
The concept is not entirely novel, but figuring out a good user experience is the more interesting part. It is implemented in the Peacasso library (based on huggingface diffusers).
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[P] Peacasso - A Web UI for Stable Diffusion Models
Peacasso - a UI for interacting with stable diffusion models. As text to image models become smaller, with available weights and generate competitive images (e.g. stable diffusion models), there have been efforts to build interfaces for interacting with these models. Code and instructutions can be found on Github - https://github.com/victordibia/peacasso.
fake-news
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Building an End-to-End Machine Learning Application From Idea to Deployment
Hi I had the same issue I think code for EDA part is in https://github.com/mihail911/fake-news/blob/master/notebooks/data_analysis.ipynb
What are some alternatives?
stablediffusion-infinity - Outpainting with Stable Diffusion on an infinite canvas
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
Deep-Learning - In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
stable-diffusion-howto - Run Stable Diffusion on your M1 Mac’s GPU (Intel and non-Apple PCs are also supported)
fastMONAI - Simplifying deep learning for medical imaging
YOLOv3-Cloud-Based-Fire-Detection - Custom Object detection using YOLOv3 on the cloud. It is trained to detect Fire in a given frame. It can be largely used for Wildfires, fire accidents, etc.
mlf-core - CPU and GPU deterministic and therefore fully reproducible machine learning pipelines using MLflow.