Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
Optimize and train foundation models using IBM's FMS
View and compare language model evaluations
Evaluate LLM over-refusal rates with OR-Bench
Open Persian LLM Leaderboard
Evaluate model predictions with TruLens
Browse and filter machine learning models by category and modality
Display LLM benchmark leaderboard and info
Merge machine learning models using a YAML configuration file
Retrain models for new data at edge devices
Measure execution times of BERT models using WebGPU and WASM
Text-To-Speech (TTS) Evaluation using objective metrics.
SD-XL To Diffusers (fp16) is a tool designed to convert Stable Diffusion XL checkpoints into a format compatible with the Diffusers library, leveraging fp16 precision for optimized performance. This tool simplifies the process of adapting pre-trained models for use in diffusion-based generative tasks while maintaining efficiency and accuracy.
What does SD-XL To Diffusers (fp16) do?
It converts Stable Diffusion XL checkpoints into Diffusers-compatible models with fp16 precision.
Why is fp16 used instead of fp32?
fp16 reduces memory usage and accelerates inference while maintaining sufficient precision for most generative tasks.
Where can I find pre-converted models?
Converted models are typically shared through pull requests in the Diffusers repository or published by the community.
How do I handle errors during conversion?
Check the logs for specific error messages and ensure all dependencies are up-to-date. If issues persist, consider reaching out to the community or reviewing the documentation.
Can I use this tool for other models?
Currently, it is optimized for Stable Diffusion XL checkpoints. Support for other models may be added in future updates.