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
Commit History
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Add diffusers format weights
Verified signatureAdds the diffusers format weights at the root of the repository, alongside the original checkpoint folders. The layout is component wise, so diffusers loading fetches exactly the subfolders a task needs and never touches FL2VA/ or Ref2VA/. transformer/ serves text to video+audio and first/last frame conditioning, transformer_ref/ serves omni reference conditioning, and text_encoder/, tokenizer/, processor/, vae/, audio_vae/, scheduler/ and audio_scheduler/ are shared by both. modular_model_index.json is the loading manifest. Requires the diffusers integration: https://github.com/huggingface/diffusers/pull/14355 (usage and per hardware guidance: https://github.com/huggingface/diffusers/blob/minimax-h3/docs/source/en/api/pipelines/minimax_h3.md) ```python import torch from diffusers import ModularPipeline pipe = ModularPipeline.from_pretrained("MiniMaxAI/MiniMax-H3") pipe.load_components(dtype=torch.bfloat16) ``` The converted transformer reproduces the reference implementation's de…4ae026f