Instructions to use 2bidoubi/SPARK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 2bidoubi/SPARK with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("2bidoubi/SPARK", 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
metadata
license: mit
pipeline_tag: image-to-3d
tags:
- partcrafter
- 3d-generation
- rectified-flow
PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers
PartCrafter is a structured 3D generative model that jointly generates multiple parts and objects from a single RGB image in one shot. This HuggingFace🤗 repo stores pretrained model weights for PartCrafter on object level.
For more details about usage, please refer to the official GitHub repo.
- Project page: https://wgsxm.github.io/projects/partcrafter/
- Code: https://github.com/wgsxm/PartCrafter
- Paper: https://arxiv.org/abs/2506.05573