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metadata
license: openrail++
library_name: diffusers
tags:
  - text-to-image
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
base_model: SG161222/RealVisXL_V4.0
instance_prompt: a portrait of a sks person
widget:
  - text: >-
      a professional portrait of a sks person with black hair wearing a business
      outfit. grey background.
    output:
      url: image_0.png
  - text: >-
      a professional portrait of a sks person with black hair wearing a business
      outfit. grey background.
    output:
      url: image_1.png
  - text: >-
      a professional portrait of a sks person with black hair wearing a business
      outfit. grey background.
    output:
      url: image_2.png
  - text: >-
      a professional portrait of a sks person with black hair wearing a business
      outfit. grey background.
    output:
      url: image_3.png

SDXL LoRA DreamBooth - yehiaa/juggernaut-lora-drew-v1

Prompt
a professional portrait of a sks person with black hair wearing a business outfit. grey background.
Prompt
a professional portrait of a sks person with black hair wearing a business outfit. grey background.
Prompt
a professional portrait of a sks person with black hair wearing a business outfit. grey background.
Prompt
a professional portrait of a sks person with black hair wearing a business outfit. grey background.

Model description

These are yehiaa/juggernaut-lora-drew-v1 LoRA adaption weights for SG161222/RealVisXL_V4.0.

The weights were trained using DreamBooth.

LoRA for the text encoder was enabled: False.

Special VAE used for training: None.

Trigger words

You should use a portrait of a sks person to trigger the image generation.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

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]