Instructions to use tangjs/uv-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tangjs/uv-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("SG161222/Realistic_Vision_V5.1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tangjs/uv-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- checkpoint-15500/optimizer.bin +1 -1
- checkpoint-15500/pytorch_model.bin +1 -1
- checkpoint-15500/scheduler.bin +1 -1
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- image_3.png +0 -0
- pytorch_lora_weights.bin +1 -1
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