Instructions to use BiliSakura/NiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/NiT-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("BiliSakura/NiT-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
Delete model_index.json
Browse files- model_index.json +0 -16
model_index.json
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"_class_name": "NiTPipeline",
|
| 3 |
-
"_diffusers_version": "0.30.1",
|
| 4 |
-
"scheduler": [
|
| 5 |
-
"diffusers",
|
| 6 |
-
"NiTFlowMatchScheduler"
|
| 7 |
-
],
|
| 8 |
-
"transformer": [
|
| 9 |
-
"diffusers",
|
| 10 |
-
"NiTTransformer2DModel"
|
| 11 |
-
],
|
| 12 |
-
"vae": [
|
| 13 |
-
"diffusers",
|
| 14 |
-
"AutoencoderDC"
|
| 15 |
-
]
|
| 16 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|