Instructions to use adsfda/NiRNE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adsfda/NiRNE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("adsfda/NiRNE", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download model_index.json from adsfda/NiRNE: direct link, hf CLI and curl.
- Browser
- Download file 260 Bytes
-
https://huggingface.co/adsfda/NiRNE/resolve/main/model_index.json
- Command line
-
hf download hf://adsfda/NiRNE/model_index.json
-
curl -L -o model_index.json https://huggingface.co/adsfda/NiRNE/resolve/main/model_index.json
260 Bytes
| { | |
| "_class_name": "YOSONormalsPipeline", | |
| "_diffusers_version": "0.32.2", | |
| "controlnet": [ | |
| "controlnetvae", | |
| "ControlNetVAEModel" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |