SD.Next is a web interface for AI image and video generation, captioning, and processing.
Open source
24 alternativesapple/ml-no-token-left-behind
PyTorch Implementation of No Token Left Behind: Explainability-Aided Image Classification and Generation.
About apple/ml-no-token-left-behind
This repository provides a PyTorch implementation of No Token Left Behind for explainability-aided image classification and generation.
It includes notebooks for image generation, image editing, and spatially conditioned image generation, plus command-line instructions for prompt engineering that require dataset installation, a conda environment with specified PyTorch versions, and setup of the Dassl component under the external directory.
Key features
- Notebook for spatially conditioned image generation
- Notebook for image editing
- Notebook for image generation
- Command-line instructions for prompt engineering training
- PyTorch implementation for explainability-aided classification and generation
Best for: Researchers and Python users working on explainability-aided image classification and generation.
Good for
- Provides separate notebooks for generation, editing, and spatially conditioned generation
- Documents conda environment, PyTorch versions, and Dassl setup steps
Watch out for
- Requires manual dataset installation before running
- Requires specific conda environment and PyTorch versions
- Requires additional Dassl installation and command-line configuration
apple/ml-no-token-left-behind at a glance
- Category
- AI & Machine Learning › AI Image Generators
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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apple/ml-no-token-left-behind FAQ
What setup is needed before running?
Install the datasets per DATASETS.md, create the conda environment, install the specified PyTorch versions, and install Dassl under the external directory.
How are prompt engineering experiments run?
Use the train.py script in external/CoOp with dataset, config, model, output paths and training options.
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