Instructions to use amd-shark/sdxl-quant-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amd-shark/sdxl-quant-int8 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amd-shark/sdxl-quant-int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update quant params structure (#2)
Verified signature- Feat (math model/tests): Updated math model and tests to match use format (1b07a9de121e37d97d97e8c920bbb4731875b756)6b62ce4
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Remove potential overflow / saturation error.
161df88 -
Added comments - highlight possible overflow situation
3f5851c -
Updated math model to target int8 x int8 kernels.
4024f9d
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Updated QOp model to fuse SmoothQuant scales with input quantization
dca9b6e
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Fix: set `keepdim=True`
9ab1060 -
[math_model/test] Added "QOp" implementation and basic tests.
eb5a5f6
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Upload math_model.py with huggingface_hub
Verified signature049c65f -
Upload math_model.py with huggingface_hub
Verified signatured5dfd96