Instructions to use ByteDance/Bernini-R-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Bernini-R-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/Bernini-R-Diffusers", 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: 347 Bytes
d74a14f | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
}
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