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