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: 423 Bytes
1ca4cfb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_class_name": "BitDanceImageNetAutoencoder",
"_diffusers_version": "0.36.0",
"ddconfig": {
"double_z": false,
"z_channels": 32,
"in_channels": 3,
"out_ch": 3,
"ch": 256,
"ch_mult": [
1,
1,
2,
2,
4
],
"num_res_blocks": 4
},
"source_checkpoint": "/data/projects/BitDance/models/shallowdream204/BitDance-ImageNet/ae_d16c32.pt",
"num_codebooks": 4
}
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