Image-Text-to-Video
Diffusers
Safetensors
text-to-video
image-to-video
audio-video-generation
runpod
Instructions to use ovedrive/MiniMax-H3-generator-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ovedrive/MiniMax-H3-generator-bf16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ovedrive/MiniMax-H3-generator-bf16", 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
File size: 1,047 Bytes
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pipeline_tag: image-text-to-video
library_name: diffusers
license: other
license_name: minimax-h3-community-license-agreement
license_link: LICENSE
base_model: MiniMaxAI/MiniMax-H3
tags:
- text-to-video
- image-to-video
- audio-video-generation
- runpod
---
# MiniMax H3 BF16 generator subset
This is a slim, unquantized BF16 generator cache derived from MiniMaxAI/MiniMax-H3 for a split RunPod worker. The model-card text has been modified to describe this subset.
It contains transformer, vae, audio_vae, scheduler, audio_scheduler, and modular_model_index.json. The external H3 conditioner is intentionally not included and is called through its existing Hugging Face Space API.
The weight and configuration files were copied server-side from the official repository without conversion or quantization. Use ovedrive/MiniMax-H3-generator-bf16 in the RunPod endpoint Model field; no Hugging Face model-cache token is required.
See the official model repository for full documentation: https://huggingface.co/MiniMaxAI/MiniMax-H3
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