[HN Gopher] Image Generation with Electrostatics
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       Image Generation with Electrostatics
        
       Author : SleekEagle
       Score  : 36 points
       Date   : 2022-11-30 18:10 UTC (4 hours ago)
        
 (HTM) web link (www.assemblyai.com)
 (TXT) w3m dump (www.assemblyai.com)
        
       | agumonkey wrote:
       | Kinda reminds me of the recent morphogenesis studies shown on
       | youtube (Levin and others).
        
       | fxtentacle wrote:
       | All the novelty appears to boil down to "this is a new sampler
       | for diffusion-style models".
       | 
       | Apparently, they are inspired by a different physical process.
       | But that doesn't change the fact that they start with noise and
       | then iteratively solve a differential equation to get to its end
       | point. 2nd-order samplers like Heun already massively increased
       | sampling speed over the original diffusion models, but sadly the
       | article doesn't compare to that but merely to the original 1st-
       | order samplers. So it might be that this one doesn't even create
       | a speedup in practice.
       | 
       | Does anyone else have more info on how this is different from
       | "traditional" numerical differential-equation-based de-noising?
        
         | thatcherc wrote:
         | The big this seems to be that their noising/denoising process
         | is invertible (to some extent) so it can be used to generate
         | uncertainties, which is something the article says diffusion
         | models can't do. The intuition is that the "flow" process used
         | here is reversible (imagine going backwards in time and
         | watching a cloud of charged particles coalescing into a bunch),
         | which gives some nice properties that random blurring used in
         | more common diffusion models does not.
         | 
         | I think it's the reversible flow part that's important for the
         | results, not the connection to the physical electrostatics
         | system. It just happens to be that electrostatics have this
         | nice flow behavior too and are a pretty approachable analogy
         | for what the model's doing.
        
           | fxtentacle wrote:
           | I also noticed that they present it as if other diffusion
           | models were not invertible, but they are. Also, they appear
           | to be using DDPM++, the exact same neural network
           | architecture as Stable Diffusion?
           | 
           | Article says: "to train the neural network, which in this
           | case is a U-Net (DDPM++ backbone)."
           | 
           | Also it's kinda weird that they show videos generating images
           | from noise (like Imagen / Stable Diffusion) but don't cite
           | any recent diffusion paper.
           | 
           | EDIT: In fact they don't cite ANY 2022 paper. So my guess
           | would be that they submitted this last year and now it's been
           | made public because now the NeurIPS 2022 conference is taking
           | place. But most likely, the actual research here predates the
           | Stable Diffusion release.
        
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