Instructions to use corechan/MiniMax-H3-LightVAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use corechan/MiniMax-H3-LightVAE with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Diffusers
How to use corechan/MiniMax-H3-LightVAE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("corechan/MiniMax-H3-LightVAE", 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 lynnreal_light_vae_decoder.onnx.data from corechan/MiniMax-H3-LightVAE: direct link, hf CLI and curl.
- Browser
- Download file 3.5 GB
-
https://huggingface.co/corechan/MiniMax-H3-LightVAE/resolve/main/lynnreal_light_vae_decoder.onnx.data
- Command line
-
hf download hf://corechan/MiniMax-H3-LightVAE/lynnreal_light_vae_decoder.onnx.data
-
curl -L -o lynnreal_light_vae_decoder.onnx.data https://huggingface.co/corechan/MiniMax-H3-LightVAE/resolve/main/lynnreal_light_vae_decoder.onnx.data
3.5 GB
- Xet hash:
- 0313a11de55e875a0db618eb31686fd3c0949d2d4cfa8c6ed6f664b11f8c3cbf
- Size of remote file:
- 3.5 GB
- SHA256:
- 636ef79d26d327d9943122b480384f8d0ac58112ecd65f415382900a498aeff3
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