Looking for something like JoePenna/Dreambooth-Stable-Diffusion? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 23 are open source.
This repository implements DreamBooth subject fine-tuning for Stable Diffusion using textual inversion.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | examples/stable-diffusion-finetuning at main · LambdaLabsML/examples | — | Yes | — |
| 2 | DreamBooth | — | Not stated | Web |
| 3 | cl-random-forest | — | Yes | — |
| 4 | finetuning-scheduler | — | Yes | — |
| 5 | cl-online-learning | — | Yes | — |
| 6 | metric-learn | — | Yes | — |
| 7 | PyTorch Lightning | — | Yes | — |
| 8 | autoai | — | Yes | — |
| 9 | Katib | — | Yes | — |
| 10 | Determined | — | Yes | — |
This repository includes examples and a guide for fine-tuning Stable Diffusion models.
DreamBooth describes fine-tuning text-to-image diffusion models to generate images of specific subjects.
This repository implements random forest models in Common Lisp for classification tasks.
PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.
This Common Lisp repository collects algorithms for online linear classification.
This metric-learn repository contains Python implementations of metric learning algorithms.
PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.
autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.
Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.
Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.
Learnergy is a Python library for energy-based machine learning models.
TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.
Karate Club offers algorithms for unsupervised learning on graph data in Python.
doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.
AutoGluon provides tools for automated machine learning and model development.
FEDOT automates machine learning model development through its modeling framework.
Enables single machine or distributed training and evaluation of deep learning models.
Detecto supports building and training computer vision models with the PyTorch machine learning framework.
neural-fortran provides a parallel framework for deep learning in Fortran.
Bender helps developers build neural networks for iOS using TensorFlow models and Metal.
metric-learn provides Python implementations of metric learning algorithms.
ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.
PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.
mgl provides a machine learning library in Common Lisp, including neural networks and Gaussian processes.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
