Instructions to use fal/Bernini-R-Aux-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Bernini-R-Aux-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Bernini-R-Aux-FlashPack", 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
Download config.json from fal/Bernini-R-Aux-FlashPack: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/fal/Bernini-R-Aux-FlashPack/resolve/main/config.json
- Command line
-
hf download hf://fal/Bernini-R-Aux-FlashPack/config.json
-
curl -L -o config.json https://huggingface.co/fal/Bernini-R-Aux-FlashPack/resolve/main/config.json
347 Bytes
| { | |
| "model_type": "bernini_renderer", | |
| "architectures": ["BerniniRendererModel"], | |
| "wan22_base": "ByteDance/Bernini-R-Diffusers", | |
| "skip_transformer_1": false, | |
| "skip_transformer_2": false, | |
| "switch_dit_boundary": 0.875, | |
| "max_sequence_length": 512, | |
| "shift": 3.0, | |
| "use_unipc": true, | |
| "use_src_id_rotary_emb": true | |
| } | |