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]Model Card for NiRNE
This repository contains the weights of NiRNE, the image-to-normal estimator of Hi3DGen
Usage
See the Github repository: https://github.com/lzt02/NiRNE regarding installation instructions.
The model can then be used as follows:
import torch
from PIL import Image
# Load an image
input_image = Image.open("path/to/your/image.jpg")
# Create predictor instance
predictor = torch.hub.load("lzt02/NiRNE", "NiRNE", trust_repo=True)
# Generate normal map using alpha channel for masking
normal_map = predictor(rgba_image, data_type="object") # Will mask out background, if alpha channel is avalible, else use birefnet
# Apply the model to the image
normal_image = predictor(input_image)
# Save or display the result
normal_image.save("output/normal_map.png")
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