Instructions to use Silan10/flux-quantized-bitsandbytes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Silan10/flux-quantized-bitsandbytes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Silan10/flux-quantized-bitsandbytes", 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
- Draw Things
- DiffusionBee
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@@ -22,7 +22,7 @@ text-to-image model. In this version, the **`transformer`**, **`text_encoder`**
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**`text_encoder_2`** components have been quantized to 8-bit precision using
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[bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
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Bitsandbytes quantization uses **
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representation** with dynamic scaling factors. This provides substantial memory savings while maintaining high image quality through
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mixed-precision computation.
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**`text_encoder_2`** components have been quantized to 8-bit precision using
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[bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
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Bitsandbytes quantization uses **8-bit integer
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representation** with dynamic scaling factors. This provides substantial memory savings while maintaining high image quality through
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mixed-precision computation.
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