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
- Xet hash:
- dfaf4e2e31bf1af677e1e31a159ba50b78737902ca2ab5729ccdd15a11e69200
- Size of remote file:
- 335 MB
- SHA256:
- a4302e1efa25f3a47ceb7536bc335715ad9d1f203e90c2d25507600d74006e89
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