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: 495 Bytes
d74a14f | 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 | {
"_class_name": "WanTransformer3DModel",
"_diffusers_version": "0.35.0.dev0",
"added_kv_proj_dim": null,
"attention_head_dim": 128,
"cross_attn_norm": true,
"eps": 1e-06,
"ffn_dim": 13824,
"freq_dim": 256,
"image_dim": null,
"in_channels": 16,
"num_attention_heads": 40,
"num_layers": 40,
"out_channels": 16,
"patch_size": [
1,
2,
2
],
"pos_embed_seq_len": null,
"qk_norm": "rms_norm_across_heads",
"rope_max_seq_len": 1024,
"text_dim": 4096
}
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