Instructions to use manateelazycat/MiniMax-H3-Image-AIPod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use manateelazycat/MiniMax-H3-Image-AIPod with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("manateelazycat/MiniMax-H3-Image-AIPod", 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
MiniMax-H3-Image for AI Pod
Powered by MiniMax-H3-Image. Read the complete upstream license, territorial and use restrictions, and NOTICE before use.
This release uses SGLang Diffusion's native MiniMax-H3-Image pipeline, explicitly selecting --backend sglang.
It does not use a standalone PyTorch/Diffusers model implementation or automatic backend fallback.
The model directory retains the original immutable upstream checkpoint layout and separate H3 BF16 text encoder.
Only completed fixed-revision release manifests and their final LPK are installation inputs.
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