Instructions to use kingjones777/Ming-Image-0.1-Design-ROCm-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kingjones777/Ming-Image-0.1-Design-ROCm-INT8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kingjones777/Ming-Image-0.1-Design-ROCm-INT8", 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
Download code/quant/__init__.py from kingjones777/Ming-Image-0.1-Design-ROCm-INT8: direct link, hf CLI and curl.
- Browser
- Download file 101 Bytes
-
https://huggingface.co/kingjones777/Ming-Image-0.1-Design-ROCm-INT8/resolve/main/code/quant/__init__.py
- Command line
-
hf download hf://kingjones777/Ming-Image-0.1-Design-ROCm-INT8/code/quant/__init__.py
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curl -L -o __init__.py https://huggingface.co/kingjones777/Ming-Image-0.1-Design-ROCm-INT8/resolve/main/code/quant/__init__.py
101 Bytes
| """Weight-only INT8 for the Ming-Image MLLM: quantize_stream.py writes it, load_int8.py loads it.""" | |