Instructions to use nunchaku-ai/nunchaku-flux.1-schnell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nunchaku-ai/nunchaku-flux.1-schnell with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nunchaku-ai/nunchaku-flux.1-schnell", 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
Upload ./README.md with huggingface_hub
Browse files
README.md
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- [`svdq-int4_r32-flux.1-schnell.safetensors`](./svdq-int4_r32-flux.1-schnell.safetensors): SVDQuant quantized INT4 FLUX.1-schnell model. For users with non-Blackwell GPUs (pre-50-series).
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- [`svdq-fp4_r32-flux.1-schnell.safetensors`](./svdq-fp4_r32-flux.1-schnell.safetensors): SVDQuant quantized NVFP4 FLUX.1-schnell model. For users with Blackwell GPUs (50-series).
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### Model Sources
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- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
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- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
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- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
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- **Demo:** [
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## Usage
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- [`svdq-int4_r32-flux.1-schnell.safetensors`](./svdq-int4_r32-flux.1-schnell.safetensors): SVDQuant quantized INT4 FLUX.1-schnell model. For users with non-Blackwell GPUs (pre-50-series).
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- [`svdq-fp4_r32-flux.1-schnell.safetensors`](./svdq-fp4_r32-flux.1-schnell.safetensors): SVDQuant quantized NVFP4 FLUX.1-schnell model. For users with Blackwell GPUs (50-series).
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### Model Sources
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- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
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- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
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- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
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- **Demo:** [demo.nunchaku.tech](https://demo.nunchaku.tech)
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## Usage
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