Instructions to use 24aittl/control_inpaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 24aittl/control_inpaint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("24aittl/control_inpaint", 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
- Xet hash:
- 811e537f3431d9ae3eec25ed3d8b38c96fcb77b5f1ba06f6e314eae58fa819c6
- Size of remote file:
- 5.39 GB
- SHA256:
- f37095d0ba0b523f87feeca210af9f17a0efb69c605201fea27627183fc16ac5
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