Instructions to use rockerBOO/flux-bpo-po-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rockerBOO/flux-bpo-po-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("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rockerBOO/flux-bpo-po-lora") prompt = "a cyberpunk cat holding a neon sign that says \"BPO Preference Optimization\"" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Flux-PO-BPO-LoRA
note Not indicative of performance but an example of the current file.

- Prompt
- a cyberpunk cat holding a neon sign that says "BPO Preference Optimization"

- Prompt
- a cyberpunk cat holding a neon sign that says "BPO Preference Optimization"
Model description
Preference optimization example (for testing) using BPO
Download model
Download them in the Files & versions tab.
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Model tree for rockerBOO/flux-bpo-po-lora
Base model
black-forest-labs/FLUX.1-dev