Instructions to use SD-Next/Ming-Image-0.1-sdnq-uint4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SD-Next/Ming-Image-0.1-sdnq-uint4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SD-Next/Ming-Image-0.1-sdnq-uint4", 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
Download text_encoder/quantization_config.json from SD-Next/Ming-Image-0.1-sdnq-uint4: direct link, hf CLI and curl.
- Browser
- Download file 747 kB
-
https://huggingface.co/SD-Next/Ming-Image-0.1-sdnq-uint4/resolve/main/text_encoder/quantization_config.json
- Command line
-
hf download hf://SD-Next/Ming-Image-0.1-sdnq-uint4/text_encoder/quantization_config.json
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curl -L -o quantization_config.json https://huggingface.co/SD-Next/Ming-Image-0.1-sdnq-uint4/resolve/main/text_encoder/quantization_config.json
747 kB
File too large to display, you can check the raw version instead.