Image-Text-to-Video
MiniMax H3
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] - Inference
- Notebooks
- Google Colab
- Kaggle
Request for pre-distillation / base H3 weights
#97
by muchov - opened
Would it be possible to release the pre-distillation / base H3 weights as well?
The currently released distilled checkpoint is useful for inference, but much less suitable for further training. Full fine-tuning or LoRA can cause behavioral drift and partial de-distillation, especially since the teacher model and exact distillation objective are unknown.
Without the original pre-distillation checkpoint, it is difficult to properly fine-tune, continue pretraining, or adapt H3 without destroying some of the behavior introduced during distillation.
Releasing the base weights alongside the distilled ones would make the model much more useful for research and downstream adaptation.