Instructions to use To-Hitori/GeoMVD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use To-Hitori/GeoMVD 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("To-Hitori/GeoMVD", 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
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Download README.md from To-Hitori/GeoMVD: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
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https://huggingface.co/To-Hitori/GeoMVD/resolve/main/README.md
- Command line
-
hf download hf://To-Hitori/GeoMVD/README.md
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curl -L -o README.md https://huggingface.co/To-Hitori/GeoMVD/resolve/main/README.md
255 Bytes
metadata
license: apache-2.0
datasets:
- allenai/objaverse
base_model:
- sudo-ai/zero123plus-v1.2
pipeline_tag: image-to-image
base_model: GeoMVD is a modified version of the zero123++ weight files.
LoRA_ckpt: The LoRA weights that we trained.