Instructions to use AcademiaSD/MiniMax-H3-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AcademiaSD/MiniMax-H3-NF4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AcademiaSD/MiniMax-H3-NF4", 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
MiniMax-H3 โ NF4 for LoRA training
NF4 (4-bit) version of MiniMax-H3 (transformer, Qwen3-VL-32B text encoder and VAEs, 41 GB instead of 498 GB), used by AcademiaSD LoRAlab Trainer Studio to train MiniMax-H3 LoRAs and RefMods on consumer GPUs. The trainer downloads it automatically.
These weights are a derivative of MiniMax-H3 and are distributed under the same MiniMax-H3 Community License Agreement. Read it before using the model or the LoRAs trained with it.
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