Show HN: High-Res Neural Cellular Automata(cells2pixels.github.io)
152 pointsby esychology7 hours ago11 comments
  • whilenot-dev5 hours ago
    The automata just completely destroys the image if I draw too much over the stabilized image with the brush. 5 horizontal swipes are enough to destroy the kitty, is that to be expected?

    EDIT: video here: https://imgur.com/a/ItZGd5X

    • esychology4 hours ago
      The NeuralCA both generates and maintains the pattern. Because the NCA was not exposed to damage or erasure during training, its regeneration capability is a purely emergent phenomenon. However, this ability remains somewhat brittle, particularly when the central regions of the pattern are erased.
      • mackenney4 hours ago
        I would love to see two seeds competing for space in the grid
    • WhiteNoiz34 hours ago
      With the old model (and I suspect this one too) it's trained to generate from a single 'seed' pixel in the center of the image. If you erase the center of the image, that's when it completely collapses.
      • oersted2 hours ago
        It must be more general than that, otherwise the cells wouldn’t be able to repair their area if the damage came from the wrong direction (repair is not center-out).

        The model generally learns to generate each pixel from its surroundings, even if the surroundings are partially missing.

        • WhiteNoiz32 hours ago
          There's hidden state in the model which presumably it uses to communicate position, ie there's the 3 colors but then a bunch of other channels that the model can use how it wants.
      • cl3misch3 hours ago
        Have you actually tried that? If you specifically erase the center, the image does change a lot at first, but rebuilds itself eventually (albeit to a slightly different final state). It's uncanny how "biological" is feels!
        • WhiteNoiz32 hours ago
          I have yes.. You need to erase a larger amount of the center, but it almost always results in a collapse wheras erasing around the center typically regrows.
        • Mithriil2 hours ago
          If you hold the eraser for a second at the center, I find that it destroys the image more often than not.
  • zcw1002 hours ago
    I've always loved the original work and it's nice to see they're still working on it. I've always wondered if there was a way to connect this back to infrastructure rather than images. Something you could run on a cluster and if portions of it failed it would regenerate the system.

    Like a bio inspired Kubernettes.....Bionettes.

  • WhitneyLand3 hours ago
    At a glance it looks like it could be just iterative texture sampling.

    The difference is when creating each pixel, there’s no coordinate to look up, instead it’s using only a set of rules like Conway’s game of life.

    But the rules come from a neural network trained on the image, so… it’s kind of memorizing enough information to effectively do the same thing as texture sampling, but using only local information.

    I’m sure I’m missing something about how it works or what makes it interesting…

    • oersted3 hours ago
      To me, it is intriguing as a toy model for how cells are able to grow into complex tissue and organisms based only on local information, and how they are able to repair and recover harmed tissue.

      Of course, this is as close to cells, as neurons from neural networks are to real neurons. And I have no idea what it could be applied to (inpainting/outpainting?), but it’s interesting as exploratory research.

      • menno-shan hour ago
        Oooh, this made it click for me. Thank you
    • esychology2 hours ago
      [flagged]
  • jekude5 hours ago
    The abstract implies that strictly local updates are a hinderance to high res, however i would have thought there would be an interesting way to get speed up gains from neighbor-only traffic on GPUs CAM-style. am i making that up?
    • esychology4 hours ago
      I think performance is not the only issue for scaling to larger grids. CUDA Convolution implementation already utilizes coalescing to improve performance. The main bottleneck is that in larger grids, cells are further apart, and it takes more steps for them to be able to communicate.
  • hidelooktropic4 hours ago
    For the unfamiliar, could someone explain what I'm looking at? The abstract was a little too concrete (heh) for me to follow.
    • esychology4 hours ago
      If you're familiar with CAs (e.g. Conway's Game of Life), you can think of a NeuralCA as a CA where the update rule is given by a neural network. Here we optimize the neural net weights so that it behaves a certain way (e.g. grow a lizard from a single seed).
      • flir4 hours ago
        What are the inputs to the NN? The whole grid, or just nearby cells? What happens if two NNs overlap on the same grid? (Gonna go read the paper).
        • esychology4 hours ago
          The input to the NN is just the 3x3 neighborhood around a cell. We can overlap two NNs on the same grid (through interpolation). Checkout https://meshnca.github.io to see the effect. When the brush is in graft mode, it basically allows you to paint some regions that will follow a different NN rule.
          • flir4 hours ago
            > The input to the NN is just the 3x3 neighborhood around a cell.

            Well that sounds like black magic. Nice. Thanks for the reply.

    • soraki_soladead3 hours ago
      The original NCA is probably a helpful intro: https://distill.pub/2020/growing-ca/
  • embedding-shape5 hours ago
    Really interesting demo, nicely done :) Would be fun if switching the "Target Image" when using the second brush mode in the Growing Demo didn't erase/reset the existing canvas, so we could "stamp" new things on top of other images. Small thing perhaps but I got sad when it disappeared when I wanted to merge a kitten on top of the chameleon but couldn't :(
    • esychology2 hours ago
      That's not possible in the current demo but this sounds like an interesting feature to work on and add!
    • bfmalky4 hours ago
      You can, just enable the 'transition' switch.
      • embedding-shape3 hours ago
        That seems to be something else? It takes the current image and "transforms" it into the new target.
  • WithinReason6 hours ago
    You can make the centipede grow longer, which makes sense given how this works. Or grow a 2nd centipede for extra points.
    • esychology6 hours ago
      haha yes, also the same with the worm
  • amelius6 hours ago
    Why are the images always generated in the same orientation (upright)? Do the cells have awareness of what is "up"?
    • WhiteNoiz32 hours ago
      IIRC training starts with the initial state and the end state, and the end state is always oriented the same way. It would be interesting to see what would happen if the end state was rotated randomly though I suspect it wouldn't work so well.
    • esychology6 hours ago
      yeah normally NCAs have a sense of up and left. There are some isotropic variants that make the perception fully rotation-invariant.
  • mirekrusin5 hours ago
    So the goal is to evaporate it with minimum number of shots?
  • moralestapia2 hours ago
    @esychology this is phenomenal work, thank you so much for sharing it. I am working in a similar thing and might reach out about it soon.

    Also, what's going on? Why would the community flag and kill this comment[1], from the creator itself. If you're jealous of what the guy built, take it elsewhere. HN will implode with that attitude.

    1: https://news.ycombinator.com/item?id=48571171

    • esychology2 hours ago
      Thank you for the kind comment! Please reach out, I'm happy to have a chat.
    • bjournean hour ago
      It's the stupid spam filter going haywire: https://news.ycombinator.com/item?id=48321198
      • yorwba27 minutes ago
        If it says [flagged], a human pressed the "flag" button. Most likely because OP is commenting too much.
        • CamperBob25 minutes ago
          I don't believe that's the case. HN is running some kind of automated slop detector that classifies any suspected LLM output as "slop" even if it's entirely in-context for the conversation (e.g., a conversation about LLM output.) The comment gets autoflagged the moment it's posted. This has been the case for a while now, I've run afoul of it myself.

          It sucks, and I can only imagine they implemented it because they felt they had no other option to deal with the onslaught of bots that's appeared lately.

          One thing they could do to improve the situation is to weight vouching heavily by karma. I have plenty of karma but I don't think I've ever successfully pulled a comment out of the [dead] state by vouching for it.

  • bjourne2 hours ago
    I found your previous work here: https://distill.pub/2020/growing-ca/ For someone (like me) who wants to understand the basics its probably better. It's very well written.