Instructions to use ByteDance/XVerse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/XVerse 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/XVerse", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 496 Bytes
8a7378e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"_class_name": "CLIPModAdapter",
"_diffusers_version": "0.32.2",
"_name_or_path": "runs/0428-17_single-scratch_mod-no-cross_f800k-S3L-idv-6m-ys-qw-ds1.0_mod-t_oc-sblocks_multi-0.75_ccl-0.25_pos-diagonal_ts-lora128_cond192_res384_bs32_resume_train-SBqkv_projout/ckpt/220000/modulation_adapter",
"heads": 8,
"input_image_dim": 1024,
"input_text_dim": 4096,
"layers": 3,
"out_dim": 3072,
"pblock_layers": 3,
"pblock_single_blocks": 0,
"pblock_width": 3072,
"width": 3072
}
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