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73 lines
3.0 KiB
Markdown
73 lines
3.0 KiB
Markdown
---
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base_model: Qwen/Qwen-Image
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base_model_relation: quantized
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datasets:
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- mit-han-lab/svdquant-datasets
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frameworks: PyTorch
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language:
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- en
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license: Apache License 2.0
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tags:
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- text-to-image
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- SVDQuant
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- Qwen-Image
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- Diffusion
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- Quantization
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- ICLR2025
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tasks:
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- text-to-image-synthesis
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---
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<p align="center" style="border-radius: 10px">
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<img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
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</p>
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# Model Card for nunchaku-qwen-image
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This repository contains Nunchaku-quantized versions of [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), designed to generate high-quality images from text prompts, advances in complex text rendering. It is optimized for efficient inference while maintaining minimal loss in performance.
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## Model Details
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### Model Description
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- **Developed by:** Nunchaku Team
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- **Model type:** text-to-image
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- **License:** apache-2.0
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- **Quantized from model:** [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image)
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### Model Files
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- [`svdq-int4_r32-qwen-image.safetensors`](./svdq-int4_r32-qwen-image.safetensors): SVDQuant quantized INT4 Qwen-Image model with rank 32. For users with non-Blackwell GPUs (pre-50-series).
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- [`svdq-int4_r128-qwen-image.safetensors`](./svdq-int4_r128-qwen-image.safetensors): SVDQuant quantized INT4 Qwen-Image model with rank 128. For users with non-Blackwell GPUs (pre-50-series). It offers better quality than the rank 32 model, but it is slower.
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- [`svdq-fp4_r32-qwen-image.safetensors`](./svdq-fp4_r32-qwen-image.safetensors): SVDQuant quantized NVFP4 Qwen-Image model with rank 32. For users with Blackwell GPUs (50-series).
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- [`svdq-fp4_r128-qwen-image.safetensors`](./svdq-fp4_r128-qwen-image.safetensors): SVDQuant quantized NVFP4 Qwen-Image model with rank 128. For users with Blackwell GPUs (50-series). It offers better quality than the rank 32 model, but it is slower.
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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:** [svdquant.mit.edu](https://svdquant.mit.edu)
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## Usage
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- Diffusers Usage: See [qwen-image.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/v1/qwen-image.py).
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- ComfyUI Usage: Coming soon!
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## Performance
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## Citation
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```bibtex
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@inproceedings{
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li2024svdquant,
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title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
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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},
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booktitle={The Thirteenth International Conference on Learning Representations},
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year={2025}
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}
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``` |