Instructions to use VHKE/uzuri-nwflps-flip-flops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VHKE/uzuri-nwflps-flip-flops 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("VHKE/uzuri-nwflps-flip-flops") prompt = "A young woman wearing uzuri nwflps flip flops --d 45" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
uzuri nwflps flip flops
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- A young woman wearing uzuri nwflps flip flops --d 45
Trigger words
You should use uzuri nwflps flip flops to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
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Model tree for VHKE/uzuri-nwflps-flip-flops
Base model
black-forest-labs/FLUX.1-dev