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
| 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_offload` for 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). |