Instructions to use neta-art/Neta-Lumina-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neta-art/Neta-Lumina-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neta-art/Neta-Lumina-diffusers", 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
| { | |
| "model_type": "lumina2", | |
| "name": "neta-lumina-v1.0", | |
| "description": "Neta-Lumina v1.0 - 基于Lumina2的动漫风格图像生成模型", | |
| "author": "Neta-Lumina Team", | |
| "license": "CreativeML Open RAIL-M", | |
| "tags": [ | |
| "text-to-image", | |
| "anime", | |
| "lumina2", | |
| "diffusers" | |
| ], | |
| "pipeline_tag": "text-to-image", | |
| "library_name": "diffusers", | |
| "framework": "pytorch", | |
| "base_model": "Lumina-Image-2.0", | |
| "model_format": "diffusers", | |
| "scheduler": "FlowMatchEulerDiscreteScheduler", | |
| "torch_dtype": "bfloat16" | |
| } |