Image-to-Image
Diffusers
Safetensors
TransNormalPipeline
normal-estimation
depth-estimation
diffusion
transparent-objects
Instructions to use Longxiang-ai/TransNormal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Longxiang-ai/TransNormal 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("Longxiang-ai/TransNormal", 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
Update model card with paper, project, and code links
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community team.
This PR improves the model card for TransNormal by:
- Linking it to the corresponding Hugging Face paper page.
- Adding direct links to the official project page and GitHub repository.
- Including the authors for proper attribution.
- Providing a brief summary of the method based on the paper abstract.
- Updating the sample usage code to include recommended
bfloat16precision and showing how to save the output normal map. - Updating the citation information to the latest BibTeX entry.
- Adding a link to the license and the acknowledgements section.
This makes the model more discoverable and easier to use for the community. Please review and merge if it looks good!