Simon Willison’s Weblog

9 items tagged “stablediffusion”

2022

I Resurrected “Ugly Sonic” with Stable Diffusion Textual Inversion (via) “I trained an Ugly Sonic object concept on 5 image crops from the movie trailer, with 6,000 steps [...] (on a T4 GPU, this took about 1.5 hours and cost about $0.21 on a GCP Spot instance)” # 20th September 2022, 3:35 am

Of all the parameters in SD, the seed parameter is the most important anchor for keeping the image generation the same. In SD-space, there are only 4.3 billion possible seeds. You could consider each seed a different universe, numbered as the Marvel universe does (where the main timeline is #616, and #616 Dr Strange visits #838 and a dozen other universes). Universe #42 is the best explored, because someone decided to make it the default for text2img.py (probably a Hitchhiker’s Guide reference). But you could change the seed, and get a totally different result from what is effectively a different universe.

swyx # 17th September 2022, 9:02 pm

The Changelog: Stable Diffusion breaks the internet. I’m on this week’s episode of The Changelog podcast, talking about Stable Diffusion, AI ethics and a little bit about prompt injection attacks too. # 17th September 2022, 2:14 am

Weeknotes: Exploring the training data behind Stable Diffusion

Two weeks ago, the Stable Diffusion image generation model was released to the public. I wrote about this last week, in Stable Diffusion is a really big deal—a post which has since become one of the top ten results for “stable diffusion” on Google and shown up in all sorts of different places online.

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Grokking Stable Diffusion (via) Jonathan Whitaker built this interactive Jupyter notebook that walks through how to use Stable Diffusion from Python step-by-step, and then dives deep into helping understand the different components of the implementation, including how text is encoded, how the diffusion loop works and more. This is by far the most useful tool I’ve seen yet for understanding how this model actually works. You can run Jonathan’s notebook directly on Google Colab, with a GPU. # 4th September 2022, 6:50 pm

Run Stable Diffusion on your M1 Mac’s GPU. Ben Firshman provides detailed instructions for getting Stable Diffusion running on an M1 Mac. # 1st September 2022, 5:41 pm

Exploring 12 Million of the 2.3 Billion Images Used to Train Stable Diffusion’s Image Generator. Andy Baio and I collaborated on an investigation into the training set used for Stable Diffusion. I built a Datasette instance with 12m image records sourced from the LAION-Aesthetics v2 6+ aesthetic score data used as part of the training process, and built a tool so people could run searches and explore the data. Andy did some extensive analysis of things like the domains scraped for the images and names of celebrities and artists represented in the data. His write-up here explains our project in detail and some of the patterns we’ve uncovered so far. # 31st August 2022, 2:10 am

Stable Diffusion is a really big deal

If you haven’t been paying attention to what’s going on with Stable Diffusion, you really should be.

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Stable Diffusion Public Release (via) New AI just dropped. Stable Diffusion is similar to DALL-E, but completely open source and with a CC0 license applied to everything it generates. I have a Twitter thread (the via) link of comparisons I’ve made between its output and my previous DALL-E experiments. The announcement buries the lede somewhat: to try it out, visit beta.dreamstudio.ai—which you can use for free at the moment, but it’s unclear to me how billing is supposed to work. # 22nd August 2022, 7:12 pm