metadata
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: zzyskpq lash kit
widget:
- text: zzyskpq lash kit
output:
url: image_0.png
- text: zzyskpq lash kit
output:
url: image_1.png
- text: zzyskpq lash kit
output:
url: image_2.png
- text: zzyskpq lash kit
output:
url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
Flux DreamBooth LoRA - 0xZeno/flux1-dev-LashGlow-LoRAV6
- Prompt
- zzyskpq lash kit
- Prompt
- zzyskpq lash kit
- Prompt
- zzyskpq lash kit
- Prompt
- zzyskpq lash kit
Model description
These are 0xZeno/flux1-dev-LashGlow-LoRAV6 DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using DreamBooth with the Flux diffusers trainer.
Was LoRA for the text encoder enabled? False.
Trigger words
You should use zzyskpq lash kit 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 AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('0xZeno/flux1-dev-LashGlow-LoRAV6', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('zzyskpq lash kit').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]