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
File size: 667 Bytes
fa2d87b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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)
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