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
| { | |
| "_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 | |
| } | |