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
| { | |
| "_class_name": "DDIMScheduler", | |
| "_diffusers_version": "0.8.0", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "num_train_timesteps": 1000, | |
| "set_alpha_to_one": false, | |
| "skip_prk_steps": true, | |
| "steps_offset": 1, | |
| "trained_betas": null | |
| } | |