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
| { | |
| "_class_name": "Lumina2Transformer2DModel", | |
| "_diffusers_version": "0.35.0.dev0", | |
| "axes_dim_rope": [ | |
| 32, | |
| 32, | |
| 32 | |
| ], | |
| "axes_lens": [ | |
| 300, | |
| 512, | |
| 512 | |
| ], | |
| "cap_feat_dim": 2304, | |
| "ffn_dim_multiplier": null, | |
| "hidden_size": 2304, | |
| "in_channels": 16, | |
| "multiple_of": 256, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 24, | |
| "num_kv_heads": 8, | |
| "num_layers": 26, | |
| "num_refiner_layers": 2, | |
| "out_channels": null, | |
| "patch_size": 2, | |
| "sample_size": 128, | |
| "scaling_factor": 1.0 | |
| } | |