Instructions to use flow666/GSFixer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use flow666/GSFixer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("flow666/GSFixer", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Add model card
#1
by nielsr HF Staff - opened
This PR adds a model card for GSFixer, linking it to the paper GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors and the official GitHub repository. It also adds relevant metadata for the diffusers library and the image-to-video pipeline tag based on the model's configuration.
Thank you nielsr! This is a good addition. Linking the paper and repository, along with the correct metadata tags, makes the model more easier for the community to use.
flow666 changed pull request status to merged