metadata
base_model: black-forest-labs/FLUX.1-Kontext-dev
library_name: diffusers
license: other
instance_prompt: in OnePiece style
widget:
- text: a person in fighting mode in OnePiece style
output:
url: image_0.png
- text: a person in fighting mode in OnePiece style
output:
url: image_1.png
- text: a person in fighting mode in OnePiece style
output:
url: image_2.png
- text: a person in fighting mode in OnePiece style
output:
url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-kontextflux-diffusers
- template:sd-lora
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-kontextflux-diffusers
- template:sd-lora
Flux Kontext DreamBooth LoRA - Kdn0110/kontext-OnePiece
- Prompt
- a person in fighting mode in OnePiece style
- Prompt
- a person in fighting mode in OnePiece style
- Prompt
- a person in fighting mode in OnePiece style
- Prompt
- a person in fighting mode in OnePiece style
Model description
These are Kdn0110/kontext-OnePiece DreamBooth LoRA weights for black-forest-labs/FLUX.1-Kontext-dev.
The weights were trained using DreamBooth with the Flux diffusers trainer.
Was LoRA for the text encoder enabled? True.
Trigger words
You should use in OnePiece style to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import FluxKontextPipeline
import torch
pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Kdn0110/kontext-OnePiece', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a person in fighting mode in OnePiece style').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
License
Please adhere to the licensing terms as described here.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]