Instructions to use AX1Y2JP/FLUX.2-dev-INT8-ConvRot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use AX1Y2JP/FLUX.2-dev-INT8-ConvRot with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
metadata
license: other
license_name: flux-non-commercial-license
base_model:
- black-forest-labs/FLUX.2-dev
base_model_relation: quantized
tags:
- diffusion-single-file
- comfyui
- image-generation
- image-editing
- flux
A FLUX.2-dev model quantized to INT8 with ConvRot using a conservative quantization policy.
On my potato machine, in T2I without using distilled LoRA, int8-convrot-aggressive is 20 seconds faster than int8-convrot. Neither model produced images that deviated from bf16.
T2I Example:
Image Edit Example:
