Unconditional Image Generation
Diffusers
Safetensors
English
bitdance
imagenet
class-conditional
custom-pipeline
Instructions to use BiliSakura/BitDance-ImageNet-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/BitDance-ImageNet-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("BiliSakura/BitDance-ImageNet-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: 510 Bytes
11d1151 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"_class_name": "BitDanceImageNetTransformer",
"_diffusers_version": "0.36.0",
"architecture": "BitDance-B",
"parallel_num": 16,
"resolution": 256,
"down_size": 16,
"latent_dim": 32,
"num_classes": 1000,
"source_checkpoint": "/data/projects/BitDance/models/shallowdream204/BitDance-ImageNet/BitDance_B_16x.pt",
"source_key": "ema",
"runtime_impl": "model_parallel.py",
"parallel_mode": "patch",
"time_schedule": "logit_normal",
"time_shift": 1.0,
"p_std": 1.0,
"p_mean": 0.0
}
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