Instructions to use wangkua1/train_lora_db_style_test1_rank32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wangkua1/train_lora_db_style_test1_rank32 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("wangkua1/train_lora_db_style_test1_rank32") prompt = "a portrait photo of a person. A cinematic scene inspired by Netflix TV shows, fused with a nostalgic vintage photograph aesthetic. A moody atmosphere with a spotlight casting sharp highlights." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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