Create README.md

README.md

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+---
+base_model: black-forest-labs/FLUX.1-schnell
+base_model_relation: quantized
+datasets:
+- mit-han-lab/svdquant-datasets
+language:
+- en
+library_name: diffusers
+license: apache-2.0
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- SVDQuant
+- FLUX.1-schnell
+- FLUX.1
+- Diffusion
+- Quantization
+- ICLR2025
+
+---
+**This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-flux.1-schnell and will be hidden in December 2025.**
+
+<p align="center" style="border-radius: 10px">
+  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
+</p>
+
+# Model Card for nunchaku-flux.1-schnell
+
+![visual](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/teaser.jpg)
+This repository contains Nunchaku-quantized versions of [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell), designed to generate high-quality images from text prompts. It is optimized for efficient inference while maintaining minimal loss in performance.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Nunchaku Team
+- **Model type:** text-to-image
+- **License:** apache-2.0
+- **Quantized from model:** [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
+
+### Model Files
+
+- [`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).
+- [`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).
+
+
+### Model Sources
+
+- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
+- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
+- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
+- **Demo:** [svdquant.mit.edu](https://svdquant.mit.edu)
+
+## Usage
+
+- Diffusers Usage: See [flux.1-schnell.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-schnell.py). Check our [tutorial](https://nunchaku.tech/docs/nunchaku/usage/basic_usage.html) for more advanced usage.
+- ComfyUI Usage: See [nunchaku-flux.1-schnell.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/t2i.html#nunchaku-flux-1-schnell-json).
+
+## Performance
+
+![performance](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/efficiency.jpg)
+
+## Citation
+
+```bibtex
+@inproceedings{
+  li2024svdquant,
+  title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
+  author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025}
+}
+```