Instructions to use bombman/Nucleus-Image-FP8-Native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bombman/Nucleus-Image-FP8-Native with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bombman/Nucleus-Image-FP8-Native", 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
- Local Apps
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
base_model:
- NucleusAI/Nucleus-Image
tags:
- text-to-image
- fp8
Nucleus-Image-FP8-Native
This is a native FP8 (float8_e4m3fn) quantization of the 17B Nucleus-Image model.
⚠️ VRAM Warning
This model is EXTREMELY HEAVY. Even in FP8, the weights alone take up ~13-14GB of VRAM.
- 16GB VRAM (RTX 4060 Ti / 4070 Ti Super / 4080): Recommended to use with
sequential_cpu_offloadfor stability. Pure GPU inference might OOM at 1024x1024. - 24GB VRAM (RTX 3090 / 4090): Best experience. Can run Pure GPU without offloading.
- 12GB VRAM or less: NOT RECOMMENDED unless using heavy CPU offloading (will be slow).