PixArt-alpha Alternatives

PixArt-alpha reviews and mentions

• Open-source PixArt-δ image generator spits out high-res AI images in 0.5 seconds
  1 project | news.ycombinator.com | 28 Jan 2024

  Yes, it's mostly a new training technique (that is impressive: "PIXART-α only takes 10.8% of Stable Diffusion v1.5's training time"). I'm not really sure if it really improves the image quality over SDXL, but it may a bit: https://pixart-alpha.github.io

• Pixart-α: Fast Training of Diffusion Transformer for Text-to-Image Synthesis
  1 project | news.ycombinator.com | 17 Dec 2023
• It's sad how much hate AI is receiving just because of some people using it in a bad way.
  1 project | /r/StableDiffusion | 8 Dec 2023

  I know she does not want to listen, but if you ever get into an argument with her again, point out to her that there are models that are build on photos and artwork that have given permission explicitly to allow training for A.I., and that these models are very capable: https://github.com/PixArt-alpha/PixArt-alpha

• DiffiT: Diffusion Vision Transformers for Image Generation
  1 project | /r/singularity | 5 Dec 2023

  Isn't this the same technique that PIXART-α is already using? Pixart has already achieved state of the art in image generation with a fraction of the training cost and data using transformers

• PixArt-α:A New Open-Source Text-to-Image Model Challenging SDXL and Dalle·3
  2 projects | news.ycombinator.com | 13 Nov 2023

  The source code license is AGPL-3.0 license. Perfect for these kinds of models: https://github.com/PixArt-alpha/PixArt-alpha

• 50% smaller and 60% faster distilled Stable Diffusion XL
  3 projects | news.ycombinator.com | 25 Oct 2023

  > But it's all been built on top of a base model trained by Stability AI at a cost of $600k (or at least, it would have cost that at AWS GPU prices).

  The LAION dataset was notoriously bad, and the training process wasn't optimal by any measure, though. The costs of training are rapidly falling due to various optimizations.

  Take a look at Pixart-alpha [0]. They claim SDXL-comparable performance for just $26k in training from scratch, with just 600M parameters in the unet and 25M pictures in the training set. Supposedly they achieved this due to the high quality training set tagged by a third-party model. The weights got leaked recently and the claim looks beleivable.

  [0] https://pixart-alpha.github.io/

• [R] Set-of-Mark (SoM) Unleashes Extraordinary Visual Grounding in GPT-4V
  1 project | /r/MachineLearning | 21 Oct 2023

  I wonder if you could use this to auto-label training data as well - similar to how PIXART-α got better results from less training data by auto-labeling it with an image captioning model.

• Another transformer + diffusion model
  1 project | /r/narrative_ai_art | 19 Oct 2023

  Unfortunately, the GitHub link they provide returns a 404 Not Found. I tried going to the organization's GitHub page, then the repo's, but there wasn't anything there other than some HTML. The link to their Hugging Face page doesn't have the model, either. So I guess they aren't ready to share either yet, if they ever will. The pictures they posted on this page https://pixart-alpha.github.io/ look quite good, though.

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