Instructions to use rdcoder/del_bld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rdcoder/del_bld with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rdcoder/del_bld", torch_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
- Xet hash:
- 71a46959271690aba16b14c5667b609f633bd65de81a785c68cb901727d90aee
- Size of remote file:
- 3.44 GB
- SHA256:
- ffb8d3c08c0f664a3d6350aa7e3e543c12bf44d39c2afb01400ae4044edf24fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.