Instructions to use pinecoresystems/Ming-Image-0.1-Design-Layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pinecoresystems/Ming-Image-0.1-Design-Layer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pinecoresystems/Ming-Image-0.1-Design-Layer", 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 assets/performance.webp from pinecoresystems/Ming-Image-0.1-Design-Layer: direct link, hf CLI and curl.
- Browser
- Download file 105 kB
-
https://huggingface.co/pinecoresystems/Ming-Image-0.1-Design-Layer/resolve/main/assets/performance.webp
- Command line
-
hf download hf://pinecoresystems/Ming-Image-0.1-Design-Layer/assets/performance.webp
-
curl -L -o performance.webp https://huggingface.co/pinecoresystems/Ming-Image-0.1-Design-Layer/resolve/main/assets/performance.webp
105 kB

- Xet hash:
- 9069a361cbddf7cd72c128d4d3e55729e04eeba2cfd4c526f744504064a469aa
- Size of remote file:
- 105 kB
- SHA256:
- b5a14d246ba3e1f3f9e22aa19a429094408e17f3f8e2b34330ef56b19b5ffa8a
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