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
| 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: | |
| <img src="ComparisonA.webp" width="1000"> | |
| <img src="ComparisonB.webp" width="1000"> | |
| Image Edit Example: | |
| <img src="ComparisonC.webp" width="1000"> | |
| <img src="ComparisonD.webp" width="1000"> | |