Instructions to use thebluser/lightswitch-multi-fov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thebluser/lightswitch-multi-fov 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("thebluser/lightswitch-multi-fov", 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
Enhance model card for LightSwitch with metadata, links, usage, and abstract
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for LightSwitch by adding crucial information for better discoverability and usability.
It includes:
- Metadata:
pipeline_tag: image-to-image, making the model discoverable on the Hub via https://huggingface.co/models?pipeline_tag=image-to-image.library_name: diffusers, enabling the "Use in Diffusers" code snippet for easy access.
- Content:
- A link to the paper: LightSwitch: Multi-view Relighting with Material-guided Diffusion.
- The full abstract of the paper for a comprehensive overview.
- A link to the project page: https://yehonathanlitman.github.io/light_switch/.
- A link to the GitHub repository: https://github.com/yehonathanlitman/LightSwitch.
- A clear "Usage" section, demonstrating how to load the model with
diffusersand explicitly directing users to the GitHub repository for detailed operational instructions due to the model's complex input requirements. - The teaser image from the project to visually represent the model's output.
- The BibTeX citation for the paper.
Please review and merge if everything looks good.
thebluser changed pull request status to merged