Instructions to use Silan10/flux-quantized-half with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Silan10/flux-quantized-half 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-half", 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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text-to-image model.
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This repository only changes the **numerical precision of the weights** to
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`torch.float16` using PyTorch.
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## Usage
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text-to-image model.
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This repository only changes the **numerical precision of the weights** to
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`torch.float16` using PyTorch. This is not real quantization (like int8/int4). Still,
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converting the model to float16 saves the memory, reduces RAM usage and speeds up loading times.
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## Usage
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