Instructions to use NO8D/ImagingControl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NO8D/ImagingControl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NO8D/ImagingControl", dtype=torch.bfloat16, device_map="cuda") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- bcc62b7f50433e66e783dae6edbeab64d7979cd2c3855bbcdc5e95cf006bcee9
- Size of remote file:
- 41.4 MB
- SHA256:
- eb98df9c59997659b0db3b0623200ad8ff954fb63704d25c26cfb11fad7bbeda
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