Instructions to use AX1Y2JP/Krea-2-Turbo-INT8-ConvRot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AX1Y2JP/Krea-2-Turbo-INT8-ConvRot with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AX1Y2JP/Krea-2-Turbo-INT8-ConvRot", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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⚠️ComfyUI's native int8 support
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A Krea 2 model quantized to INT8 with ConvRot using a conservative quantization policy.
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<img src="ComparisonA.webp" width="1000">
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inference: false
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⚠️ComfyUI's native int8 support prioritizes the old format, so this model does not work. Please continue using int8-fast.
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A Krea 2 model quantized to INT8 with ConvRot using a conservative quantization policy.
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<img src="ComparisonA.webp" width="1000">
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