Image-to-Image
Diffusers
Safetensors
English
MarigoldNormalsPipeline
normals estimation
latent consistency model
image analysis
computer vision
in-the-wild
zero-shot
Instructions to use prs-eth/marigold-normals-lcm-v0-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use prs-eth/marigold-normals-lcm-v0-1 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("prs-eth/marigold-normals-lcm-v0-1", 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:
- c3592859f5ffe2377cd68e8cf0c26dfe32cd8f20ddf009456e4caefbf2b16157
- Size of remote file:
- 681 MB
- SHA256:
- bc1827c465450322616f06dea41596eac7d493f4e95904dcb51f0fc745c4e13f
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