Image-to-3D
Diffusers
Safetensors
LGMFullPipeline
text-to-3d
3d-generation
3d-gaussian-splatting
gaussian-splatting
multi-view-diffusion
lgm
objaverse
research
computer-graphics
Instructions to use WasabiOctopus/LGM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WasabiOctopus/LGM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WasabiOctopus/LGM", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2918df6c565e5f834baea51ba7e271ab388fe23123d42e5f317a95fec14a228c
- Size of remote file:
- 167 MB
- SHA256:
- 3e4c08995484ee61270175e9e7a072b66a6e4eeb5f0c266667fe1f45b90daf9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.