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:
- 9a4b6a74ab1315aebde3b3234bd3eb8f607cf371cf3fdb6daa6d49bd0551202e
- Size of remote file:
- 1.41 MB
- SHA256:
- da501593742857c0b34fee8243afc386ae7cf117a228be11fa117e48fb9dc872
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