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 filesThis view is limited to 50 files because it contains too many changes. See raw diff
- checkpoint-1000/optimizer.bin +1 -1
- checkpoint-1000/pytorch_model.bin +1 -1
- checkpoint-1000/random_states_0.pkl +1 -1
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- checkpoint-10000/random_states_0.pkl +1 -1
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- checkpoint-13500/optimizer.bin +1 -1
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- checkpoint-13500/random_states_0.pkl +1 -1
- checkpoint-14000/optimizer.bin +1 -1
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- checkpoint-1500/optimizer.bin +1 -1
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