Instructions to use juandelperal/jpdp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use juandelperal/jpdp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("juandelperal/jpdp") prompt = "jpdp" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ca2b1e97a895c0aac59e9f411c8514698fd68fc17348d48f749d8d5c8ada0da7
- Size of remote file:
- 172 MB
- SHA256:
- 5aadefa50989cb24d731974dc8fb80c15dcda91dfd301603fde7ff4b3ef10fd7
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