Text-to-Image
Diffusers
Safetensors
image-to-image
quantization
w4a4
svdquant
gptq
nunchaku
8-bit precision
Instructions to use ModelsLab/Qwen-Image-2.1-W4A4-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/Qwen-Image-2.1-W4A4-int4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ModelsLab/Qwen-Image-2.1-W4A4-int4", 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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"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16, transformer=transformer)
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```
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`nunchaku_io` and the build pipeline: https://github.com/ModelsLab/qwen-image-2-1-server
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## How it was built
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Smoothing factors from real activation statistics (SmoothQuant, α=0.5), a rank-128 SVD
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"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16, transformer=transformer)
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```
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## How it was built
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Smoothing factors from real activation statistics (SmoothQuant, α=0.5), a rank-128 SVD
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