Instructions to use liming518/FluxBodyTypeDamageStng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use liming518/FluxBodyTypeDamageStng 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("liming518/FluxBodyTypeDamageStng") prompt = "cropped original img from lora creator" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: cropped original img from lora creator
output:
url: images/Img0.jpg
- text: clothed asian prompt
output:
url: images/img1.png
- text: clothed latina prompt
output:
url: images/img2.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
Civitai lora for Flux1dev
Original image for this lora and images i made:

- Prompt
- cropped original img from lora creator

- Prompt
- clothed asian prompt

- Prompt
- clothed latina prompt