Image-Text-to-Video
Diffusers
Safetensors
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] - Notebooks
- Google Colab
- Kaggle
Please collaborate with PrismML on a Bonsai version
#17
by mindplay - opened
At 33B, a 1-bit version would likely be usable on a 16 GB GPU, and a 1.5-bit version for 24 GB GPUs.
Their approach is not as simple as just quantizing - post training is required to recover precision, so you would probably need to collaborate with them.
The community will likely quantize this model to 4-bit, but it's going to be so much worse than what PrismML can do with 1-bit.
A Bonsai version of this model would be light-years ahead of anything else that runs locally today.