data-is-better-together/open-image-preferences-v1

Dataset

31

stars

244

commits

1

linked in READMEs

Dec 9, 2024

updated

distilabel
flux
preference
stable-diffusion
synthetic
vlm

README

Open Image Preferences

.row { display: flex; justify-content: space-between; width: 100%; } #container { display: flex; flex-direction: column; font-family: Arial, sans-serif; width: 98% } .prompt { margin-bottom: 10px; font-size: 16px; line-height: 1.4; color: #333; background-color: #f8f8f8; padding: 10px; border-radius: 5px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); } .image-container { display: flex; gap: 10px; } .column { flex: 1; position: relative; } img { max-width: 100%; height: auto; display: block; } .image-label { position: absolute; top: 10px; right: 10px; background-color: rgba(255, 255, 255, 0.7); color: black; padding: 5px 10px; border-radius: 5px; font-weight: bold; }
Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.
Image 1
Image 2
Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.
Image 1
Image 2
  • Goal: This project aims to create 10K text-to-image preference pairs. These pairs can be used to evaluate the performance of image generation models across a wide variety of common image categories, based on prompt with varying levels of difficulty.
  • How: We use the prompts from fal/imgsys-results, these prompts are evolved based on complexity and quality for various image categories. We then asked the community to annotate the preference between two generated images for each prompt.
  • Result: We achieved to annotate 10K preference pairs. You can take a look at the resulting dataset here.

Contributors

data-is-better-together/open-image-preferences-v1

Dataset

31

stars

244

commits

1

linked in READMEs

Dec 9, 2024

updated

distilabel
flux
preference
stable-diffusion
synthetic
vlm

README

Open Image Preferences

.row { display: flex; justify-content: space-between; width: 100%; } #container { display: flex; flex-direction: column; font-family: Arial, sans-serif; width: 98% } .prompt { margin-bottom: 10px; font-size: 16px; line-height: 1.4; color: #333; background-color: #f8f8f8; padding: 10px; border-radius: 5px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); } .image-container { display: flex; gap: 10px; } .column { flex: 1; position: relative; } img { max-width: 100%; height: auto; display: block; } .image-label { position: absolute; top: 10px; right: 10px; background-color: rgba(255, 255, 255, 0.7); color: black; padding: 5px 10px; border-radius: 5px; font-weight: bold; }
Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.
Image 1
Image 2
Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.
Image 1
Image 2
  • Goal: This project aims to create 10K text-to-image preference pairs. These pairs can be used to evaluate the performance of image generation models across a wide variety of common image categories, based on prompt with varying levels of difficulty.
  • How: We use the prompts from fal/imgsys-results, these prompts are evolved based on complexity and quality for various image categories. We then asked the community to annotate the preference between two generated images for each prompt.
  • Result: We achieved to annotate 10K preference pairs. You can take a look at the resulting dataset here.

Contributors