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 unet/diffusion_pytorch_model.bin from adsfda/NiRNE: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/adsfda/NiRNE/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://adsfda/NiRNE/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/adsfda/NiRNE/resolve/main/unet/diffusion_pytorch_model.bin
3.46 GB
- Xet hash:
- 0b92b66ee7a17de694814966a1d56448691622ffb94994f53679d873fd49ff4b
- Size of remote file:
- 3.46 GB
- SHA256:
- 0c116ae99ae575511ad8beb40a39d0d06386dab82d04d40893523986cda0d911
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