Instructions to use RenderFormer/renderformer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RenderFormer/renderformer-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RenderFormer/renderformer-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,058 Bytes
2a5582b | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"schema_version": 1,
"components": [
{
"name": "transformer_512",
"class": "RenderFormerModel",
"pipeline_class": "RenderFormerV2Pipeline",
"path": "transformer_512",
"resolution": 512
},
{
"name": "transformer_2048",
"class": "RenderFormerModel",
"pipeline_class": "RenderFormerV2Pipeline",
"path": "transformer_2048",
"resolution": 2048
},
{
"name": "material_autoencoder",
"class": "MaterialAutoencoder",
"path": "material_autoencoder",
"resolution": 256
},
{
"name": "diffspec_mapper",
"class": "DiffuseSpecularToLatent",
"path": "diffspec_mapper",
"resolution": null
},
{
"name": "metallic_mapper",
"class": "PrincipledBRDFToLatent",
"path": "metallic_mapper",
"resolution": null
},
{
"name": "metallic_transmission_mapper",
"class": "PrincipledBRDFToLatentWithTransmission",
"path": "metallic_transmission_mapper",
"resolution": null
}
]
}
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