Bootlicker - Corporate Photos and Portraits
Prompt
photo of a man bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of asian woman bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of indian man bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of of hipster man bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of of mexican woman bootlicker, ai company
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of a man bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of a woman bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Prompt
photo of japanese man bootlicker
Negative Prompt
cartoon, cgi, render, illustration, painting, drawing, bad quality, grainy, low resolution
Model description
Create corporate headshots and portraits. Great for outpainiting a face for LinkedIn profile photos. A weight of 0.5 works well in most models. Adjust as needed. Trigger word is "bootlicker".
Trigger words
You should use bootlicker
to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5' , torch_dtype=torch.float16).to('cuda' )
pipeline.load_lora_weights('ostris/bootlicker-corporate-photos-and-portraits' , weight_name='bootlicker.safetensors' )
image = pipeline('photo of japanese man bootlicker ' ).images[0 ]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers