Instructions to use VAST-AI/DetailGen3D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VAST-AI/DetailGen3D with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("VAST-AI/DetailGen3D", 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
File size: 610 Bytes
e6ab9cd bb35137 0bcc736 d2d5b56 e6ab9cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_class_name": "DetailGen3DPipeline",
"_diffusers_version": "0.32.0.dev0",
"feature_extractor_1": [
"transformers",
"BitImageProcessor"
],
"image_encoder_1": [
"transformers",
"Dinov2Model"
],
"noise_scheduler": [
"diffusers",
"DDPMScheduler"
],
"transformer": [
"detailgen3d.models.transformers.detailgen3d_transformers",
"DetailGen3DDiTModel"
],
"vae": [
"detailgen3d.models.autoencoders.autoencoder_kl_triposg",
"TripoSGVAEModel"
],
"scheduler": [
"detailgen3d.schedulers.scheduling_rectified_flow",
"RectifiedFlowScheduler"
]
}
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