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
File size: 536 Bytes
9798390 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"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"
} |