Image-Text-to-Video
Diffusers
Safetensors
orbitquant
comfyui
w4
w4a4
native-w4a4-transformer-runtime
text-to-video
audio-video-generation
8-bit precision
Instructions to use WaveCut/MiniMax-H3-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/MiniMax-H3-OrbitQuant-W4A4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/MiniMax-H3-OrbitQuant-W4A4", 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
| from __future__ import annotations | |
| def enable_h3_cpu_offload( | |
| components_manager, *, memory_reserve_margin: str = "64GB" | |
| ) -> dict[str, str]: | |
| device = "cuda" | |
| components_manager.enable_auto_cpu_offload( | |
| device=device, | |
| memory_reserve_margin=memory_reserve_margin, | |
| ) | |
| return { | |
| "mode": "components_manager_auto_cpu_offload", | |
| "device": device, | |
| "memory_reserve_margin": memory_reserve_margin, | |
| } | |
| def component_device(module) -> str: | |
| tensor = next(module.parameters(), None) | |
| if tensor is None: | |
| tensor = next(module.buffers(), None) | |
| return "none" if tensor is None else str(tensor.device) | |