Instructions to use dn6/RFDiffusion-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dn6/RFDiffusion-3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dn6/RFDiffusion-3", 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: 952 Bytes
4900749 | 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 | {
"_blocks_class_name": "RFDiffusionAutoBlocks",
"_class_name": "ModularPipeline",
"_diffusers_version": "0.37.0.dev0",
"default_num_residues": 100,
"default_num_timesteps": 200,
"sigma_data": 16.0,
"transformer": [
null,
null,
{
"pretrained_model_name_or_path": "dn6/RFDiffusion-3",
"subfolder": "transformer",
"type_hint": [
"diffusers",
"AutoModel"
],
"revision": null,
"variant": null
}
],
"scheduler": [
null,
null,
{
"pretrained_model_name_or_path": "dn6/RFDiffusion-3",
"subfolder": "scheduler",
"type_hint": [
"diffusers",
"AutoModel"
],
"revision": null,
"variant": null,
"default_creation_method": "from_config"
}
]
}
|