Instructions to use cuongdev/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuongdev/test_hntAnh", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cuongdev/lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- d71a46d383aedf3b0200aa7b3f400c9a0167bc17fb4c7ad6eb29baf5a1a5e894
- Size of remote file:
- 3.36 MB
- SHA256:
- 24d72af91a7eec391ff675f292a122f686d581ef5fabd0920e9624409e8bcb91
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