Image-Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
text-to-video
image-to-video
video-to-video
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
video-to-audio-video
audio-to-audio-video
audio-video-generation
multimodal
synchronized-audio-video
reference-to-audio-video
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
Will the MiniMax-H3 Post-Training Pipeline Be Open-Sourced?
#51
by The000FOOL000 - opened
Hi MiniMax team, thank you for releasing the model weights!
Are there any plans to open-source the post-training resources for MiniMax-H3 as well?
- Post-training / RL training code and recipes
- SFT/RL datasets or data-generation pipelines
- Reward functions or verifiers
- Training configs and hyperparameters
- Any other resources needed to reproduce the post-training process
Even a partial release or an indication of whether you plan to release these in the future would be greatly appreciated.
Thank you!
Not decided
ryanlee-dev changed discussion status to closed