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The AI tools for Art Newsletter - Issue 1
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The AI tools for Art Newsletter - Issue 1

First issue 🎉

The AI space is moving so fast it’s hard to believe that a year ago we still struggled to generate people with the correct amount of fingers 😂.

The last couple of years have been pivotal for open source models and tools for artistic usage. AI tools for creative expression have never been more accessible, and we’re only scratching the surface. Join us as we look back at the key milestones, tools, and breakthroughs in AI & Arts from 2024, and forward for what’s to come in 2025 (spoiler 👀: we’re starting a new monthly roundup 👇).

Table of Contents

  • Major Releases of 2024
  • Image Generation
    • Text-to-image generation
    • Personalization & stylization
  • Video Generation
  • Creative Tools that Shined in 2024
  • What should we expect for AI & Art in 2025?
  • Starting off strong - Open source releases of January 25

Major Releases of 2024

What were the standout releases of creative AI tools in 2024? We'll highlight the major releases across creative and artistic fields, with a particular focus on open-source developments in popular tasks like image and video generation.

Image Generation

Over 2 years since the OG stable diffusion was released and made waves in image generation with open source models, it’s now safe to say that when it comes to image generation from text, image editing and controlled image generation - open source models are giving closed source models a run for their money.

Text-to-image generation

2024 was the year we shifted paradigms of diffusion models - from the traditional Unet based architecture to Diffusion Transformer (DiT), as well as an objective switch to flow matching.

TD;LR - diffusion models and Gaussian flow matching are equivalent. Flow matching proposes a vector field parametrization of the network output that is different compared to the ones commonly used in diffusion models previously.

  • We recommend this great blog by Google DeepMind if you’re interested in learning more about flow matching and the connection with diffusion models

Original source

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