[HN Gopher] From Noise to Image - interactive guide to diffusion
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       From Noise to Image - interactive guide to diffusion
        
       Author : simedw
       Score  : 85 points
       Date   : 2026-02-26 07:52 UTC (2 days ago)
        
 (HTM) web link (lighthousesoftware.co.uk)
 (TXT) w3m dump (lighthousesoftware.co.uk)
        
       | whilefalse wrote:
       | Hey, I made this, thanks for posting!
       | 
       | It's purposefully high level and non-technical for a general
       | audience - my theory was that most people who aren't into tech/AI
       | don't care too much about training, or how the system got to be
       | the way that it is.
       | 
       | But they do have some interest in how it actually operates once
       | you've typed in a prompt.
       | 
       | Happy to answer any questions or take on board feedback
        
         | BobbyTables2 wrote:
         | Loved the writeup!
         | 
         | Found the manual latent space exploration part really
         | interesting.
         | 
         | Too many LLM/diffusion explanations fall in the proverbial "how
         | to draw an owl" meme without giving a taste as to what's going
         | on.
        
         | plagiarist wrote:
         | I enjoyed this a lot.
         | 
         | The interpolations between butterfly and snail were pretty
         | horrifying. But something like Z-Image you could basically
         | concatenate the text and end up with a normal image of both. Is
         | the latent space for "butterfly and snail" just well off the
         | path between the two individually?
         | 
         | It's hard to imagine what is nearby in latent space and how
         | text contributes, so I did really like the section adding words
         | to the prompt 1-by-1.
        
         | adampunk wrote:
         | It's quite clever and thoughtful. thanks for making it!
        
         | in-silico wrote:
         | I think some of the visualizations would be much better if you
         | used a pixel-space model instead of a latent diffusion model.
         | 
         | Right now we are only seeing the denoising process after it's
         | been morphed by the latent decoder, which looks a lot less
         | intuitive than actual pixel diffusion.
         | 
         | If you can't find a suitable pixel-space model, then you can
         | just trivially generate a forward process and play it
         | backwards.
        
           | whilefalse wrote:
           | Thanks that's a great suggestion.
        
       | K2h wrote:
       | Scrolling through pics on mobile is difficult. Wanted to see all
       | 29 steps but couldnt scroll it reliably.
        
         | BobbyTables2 wrote:
         | Turning off the scroll mode worked very well for me on a
         | mobile.
        
       | khazhoux wrote:
       | Amazing explanations!! I absolutely love this. In 10 minutes it's
       | given me a huge boost in my intuition on diffusion, which I've
       | been missing for years.
        
       | ibizaman wrote:
       | Oh I particularly loved that you made the prompts themselves
       | interchangeable. Very well done!
        
       | adammarples wrote:
       | If the prompt is the compass, and represents a point in space,
       | why walk there? Why not just go to that point in image space
       | directly, what would be there? When does the random seed matter
       | if you're aiming at the same point anyway, don't you end up
       | there? Does the prompt vector not exist in the image manifold, or
       | is there some local sampling done to pick images which are more
       | represented in the training data?
        
         | whilefalse wrote:
         | So I'm not an expert, this post was just based on my
         | understanding, but as I understand it: the prompt embedding
         | space and the latent image space are different "spaces", so
         | there is no single "point" in the latent image space that
         | represents a given prompt. There are regions that are more or
         | less consistent with the prompt, and due to cross-attention
         | between the text embedding vector and the latent image vector,
         | it's able to guide the diffusion process in a suitable
         | direction.
         | 
         | So different seeds lead to slightly different end points,
         | because you're just moving closer to the "consistent region" at
         | each step, but approaching from a different angle.
        
       | rrgok wrote:
       | This is awesome. If you made a book or video-course that takes
       | this level of high level explanation and translate it to the
       | technical and then mathematical level, I would buy it in an
       | heartbeat.
       | 
       | This is what I think is missing in most AI (broad sense) learning
       | resources. They focus too much on the math that I miss the
       | intuitive process behind it.
        
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       (page generated 2026-02-28 23:00 UTC)