Instructions to use MiniMaxAI/MiniMax-H3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MiniMaxAI/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("MiniMaxAI/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] - Inference
- Notebooks
- Google Colab
- Kaggle
MiniMax H3: 715 Sampler & Scheduler Combinations Tested | Interactive Video Benchmark
I recently completed a large-scale MiniMax H3 sampler and scheduler benchmark using 65 samplers × 11 schedulers, covering 715 combinations.
The goal was to provide a practical, controlled comparison of video quality, rendering speed, and reliability.
Testing methodology:
- Base MiniMax H3 model
- 20 sampling steps
- No Turbo acceleration or LoRAs
- Consistent settings across all combinations
- Total cumulative successful render time: 49 hours, 39 minutes, 47 seconds
The results are available through a free interactive comparison website, where you can inspect individual video outputs, compare rendering times, and see which combinations failed.
Full benchmark and interactive comparison:
This is intended as a reference resource rather than a claim that one sampler or scheduler is universally best. Different settings can produce different results depending on the workflow and desired output.
I'd welcome feedback from other MiniMax H3 users, especially anyone who has tested different configurations or noticed interesting sampler/scheduler behavior.
KRuins | KineticRuins.com