Instructions to use comin/IterComp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use comin/IterComp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("comin/IterComp", 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:
- 33debf1e021bdbd6b9b7225f75b897dfccfcca4732f312fd6f3ab7dc00d5c15c
- Size of remote file:
- 1.79 GB
- SHA256:
- a6e43df171ee1d7234468fa5accfa4ee0be4f59160b5982699344d4b77e96d3c
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