Image-to-Image
Diffusers
Safetensors
TransNormalPipeline
normal-estimation
surface-normal-estimation
transparent-objects
diffusion
dinov3
computer-vision
robotics
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
- Xet hash:
- 91bc1dcc5e3b904ec97a6f6820714e50c9ae1d15909c83e9dce93eec1b1105fb
- Size of remote file:
- 681 MB
- SHA256:
- 681c555376658c81dc273f2d737a2aeb23ddb6d1d8e5b3a7064636d359a22668
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