config_sdxl.yml

# the name of the brands (short human readable name), corresponds to the
# zipfiles the script will download
brands:
- for-sale-signs
# - coke
# - docker
# - kubernetes
# - nike
# - vision-pro

# the prompts for each brand, adjust these to generate different images
# note with SDXL you should reference words and phrases you used in the captions for best effect
prompts:
  img1: "cj hole for sale sign in front of a posh house with a tesla in winter with snow"
  img2: "cj hole sold sign in front of a council house with a vw beetle in spring with daffodills"
  img3: "cj hole for sale sign in front of a detached house in summer with a bbq in the front garden"
  img4: "cj hole for sale sign in front of a posh house, with spider webs and halloween decorations"
  img5: "cj hole for sale sign in front of a detached house with christmas decorations"

# how many images to generate for each prompt for each brand
num_images: 1

# how much emphasis to place on the finetune set
finetune_weighting: 0.8

# the script expects zip files to download from "{url_prefix}/sdxl_{brand}.zip", e.g. https://storage.googleapis.com/dagger-assets/sdxl_coke.zip
# the zip file must just contain the images to fine tune the model on, and for each image file foo.jpg a foo.txt file containing a descriptive caption of the image
# you will then be able to reuse the language in the captions in the trained model when prompting it for inference
# **must have trailing slash**
url_prefix: https://storage.googleapis.com/dagger-assets/