Instructions to use vcollos/VitorCollos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vcollos/VitorCollos with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("vcollos/VitorCollos") prompt = "Portrait of Vitor Collos in a office" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- adb9255ea5a2c34ae1e0056d468142a91e232dc7212b055c5de954af8432ab43
- Size of remote file:
- 176 MB
- SHA256:
- 4b45218ff3ef4d6503e9b1f0f8ce1d864d25854c63d7051b7aa2418c74532160
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