Which is your favorite text to image model overall?

• artroom-stable-diffusion

  8 219 0.0 Python

• dalle-mini

  3,446 14,641 5.2 Python

  DALL·E Mini - Generate images from a text prompt

Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• VQGAN-CLIP

  67 2,563 0.0 Python

  Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

I've screwed with many text-to-image models over the past couple of years, and I found that while I currently enjoy Stable Diffusion's coherency, I have a soft spot for the ImageNet model used by default for VQGAN+CLIP. It easily approaches the uncanny valley when generating people or animals, but makes for great abstract backgrounds and wallpapers. I already have nostalgia for generating images with it on my CPU overnight.

• disco-diffusion

  22 7,454 0.0 Jupyter Notebook
  

Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.

• mindall-e

  8 631 0.0 Python

  PyTorch implementation of a 1.3B text-to-image generation model trained on 14 million image-text pairs

  

Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.

• CLIP-Guided-Diffusion

  4 377 0.0 Python

  Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. (by nerdyrodent)

Runner-ups are Craiyon (for being more "creative" than SD), Disco Diffusion, minDALL-E, and CLIP Guided Diffusion.

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