Instructions to use tiny-random/minimax-h3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tiny-random/minimax-h3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tiny-random/minimax-h3", 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
- Xet hash:
- 8c2c6246c7a74339ef9039094351f61d7ab6881e7efc9a7f2e7bd2e8fed858ae
- Size of remote file:
- 12.1 MB
- SHA256:
- 975ada26892c830c648bbe1322371a5aa646ad93d923720fbdef0d6023b0966e
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