Instructions to use obvious-research/FLUX.1-dev-ControlNet-Proportion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use obvious-research/FLUX.1-dev-ControlNet-Proportion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("obvious-research/FLUX.1-dev-ControlNet-Proportion", 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:
- 738c09bce1d0f224563d1486db3ed3baa2a03e32132d9d39375996783d0faf46
- Size of remote file:
- 5.77 GB
- SHA256:
- 68baf653ffaa631f14d460dac354b3b218012d7dc4c4727a7c3e9df4d84812cc
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