Instructions to use codemanCheng/lora-trained-xl_demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codemanCheng/lora-trained-xl_demo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("codemanCheng/lora-trained-xl_demo") prompt = "a photo of sks cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 87588e7b77c257efe5780698ef723909d1b61cddcd96c44533be2d97f83aa189
- Size of remote file:
- 1.51 MB
- SHA256:
- 63ab4b1f1c55933d8db138574f0623826978e7c101fbd0cf91c7bb931f9898b4
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