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So, this is surprising. Apparently there’s more cause, effect, and sequencing in diffusion models than what I expected, which would be roughly ‘none’. Google he

So, this is surprising. Apparently there’s more cause, effect, and sequencing in diffusion models than what I expected, which would be roughly ‘none’. Google here uses SD 1.4, as the core of the diffusion model, which is a nice reminder that open models are useful to even giant cloud monopolies. The two main things of note I took away from the summary were: 1) they got infinite training data using agents playing doom (makes sense), and 2) they added Gaussian noise to source frames and rewarded the agent for ‘correcting’ sequential frames back, and said this was critical to get long range stable ‘rendering’ out of the model. That last is intriguing — they explain the intuition as teaching the model to do error correction / guide it to be stable. Finally, I wonder if this model would be easy to fine tune for ‘photo realistic’ / ray traced restyling — I’d be super curious to see how hard it would be to get a ‘nicer’ rendering out of this model, treating it as a doom foundation model of sorts. Anyway, a fun idea that worked! Love those.

refibrillator 2y ago
Just want to clarify a couple possible misconceptions: The diffusion model doesn’t maintain any state itself, though its weights may encode some notion of cause/effect. It just renders one frame at a time (after all it’s a text to image model, not text to video). Instead of text, the previous states and frames are provided as inputs to the model to predict the next frame. Noise is added to the previous frames before being passed into the SD model, so the RL agents were not involved with “correcting” it. De-noising objectives are widespread in ML, intuitively it forces a predictive model to leverage context, ie surrounding frames/words/etc. In this case it helps prevent auto-regressive drift due to the accumulation of small errors from the randomness inherent in generative diffusion models. Figure 4 shows such drift happening when a player is standing still.
- rvnx 2y ago
  The concept is that if you train a Diffusion model by feeding all the possible frames seen in the game. The training was over almost 1 billion frames, 20 days of full-time play-time, taking a screenshot of every single inch of the map. Now you show him N frames as input, and ask it "give me frame N+1", then it gives you the frame n. N+1 back based on how it was originally seen during training. But it is not frame N+1 from a mysterious intelligence, it's simply frame N+1 given back from past database. The drift you mentioned is actually a clear (but sad) proof that the model does not work at inventing new frames, and can only spit out an answer from the past dataset. It's a bit like if you train stable diffusion on Simpsons episodes, and that it outputs the next frame of an existing episode that was in the training set, but few frames later goes wild and buggy.
  - jetrink 2y ago
    I don't think you've understood the project completely. The model accepts player input, so frame 601 could be quite different if the player decided to turn left rather than right, or chose that moment to fire at an exploding barrel.
    - rvnx 2y ago
      1 billion frames in memory... With such dataset, you have seen practically all realistic possibilities in the short-term. If it would be able to invent action and maps and let the user play "infinite doom", then it would be very different (and impressive!).
      - OskarS 2y ago
        > 1 billion frames in memory... With such dataset, you have seen practically all realistic possibilities in the short-term. I mean... no? Not even close? Multiply the number of game states with the number of inputs at any given frame gives you a number vastly bigger than 1 billion, not even comparable. Even with 20 days of play time to train no, it's entirely likely that at no point did someone stop at a certain location and look to the left from that angle. They might have done from similar angles, but the model then has to reconstruct some sense of the geometry of the level to synthesize the frame. They might also not have arrived there from the same direction, which again the model needs some smarts to understand. I get your point, it's very overtrained on these particular levels of Doom, which means you might as well just play Doom. But this is not a hash table lookup we're talking about, it's pretty impressive work.
        rvnx 2y ago
        This was the basis for the reasoning: The map 1 has 2'518 walkable map units. There are 65536 angles. 2'518*65'536=165'019'648 If you capture 165M frames, you already cover all the possibilities in terms of camera / player view, but probably the diffusion models don't even need to have all the frames (the same way that LLMs don't).
nine_k 2y ago
But it's not a game. It's a memory of a game video, predicting the next frame based on the few previous frames, like "I can imagine what happened next". I would call it the world's least efficient video compression. What I would like to see is the actual predictive strength, aka imagination, which I did not notice mentioned in the abstract. The model is trained on a set of classic maps. What would it do, given a few frames of gameplay on an unfamiliar map as input? How well could it imagine what happens next?
- WithinReason 2y ago
  If it's trained on absolute player coordinates then it would likely just morph into the known map at those coordinates.
  - nine_k 2y ago
    But it's trained on the actual screen pixel data, AFAICT. It's literally a visual imagination model, not gameplay / geometry imagination model. They had to make special provisions to the pixel data on the HUD which by its nature different than the pictures of a 3D world.
- PoignardAzur 2y ago
  > But it's not a game. It's a memory of a game video, predicting the next frame based on the few previous frames, like "I can imagine what happened next". It's not super clear from the landing page, but I think it's an engine? Like, its input is both previous images and input for the next frame. So as a player, if you press "shoot", the diffusion engine need to output an image where the monster in front of you takes damage/dies.
  - bergen 2y ago
    How is what you think they say not clear? We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality.
- 2y ago
deleted 2y ago
[deleted]
wavemode 2y ago
> Apparently there’s more cause, effect, and sequencing in diffusion models than what I expected To temper this a bit, you may want to pay close attention to the demo videos. The player rarely backtracks, and for good reason - the few times the character does turn around and look back at something a second time, it has changed significantly (the most noticeable I think is the room with the grey wall and triangle sign). This falls in line with how we'd expect a diffusion model to behave - it's trained on many billions of frames of gameplay, so it's very good at generating a plausible -next- frame of gameplay based on some previous frames. But it doesn't deeply understand logical gameplay constraints, like remembering level geometry.
- hoosieree 2y ago
  Small objects like powerups appear and disappear as the player moves (even without backtracking), the ammo count is constantly varying, getting shot doesn't deplete health or armor, etc.
- mensetmanusman 2y ago
  That is kind of cool though, I would play like being lost in a dream. If on the backend you could record the level layouts in memory you could have exploration teams that try to find new areas to explore.
  - debo_ 2y ago
    It would be cool for dream sequences in games to feel more like dreams. This is probably an expensive way to do it, but it would be neat!
- Groxx 2y ago
  There's an example right at the beginning too - the ammo drop on the right changes to something green (I think that's a body?)
- codeflo 2y ago
  Even purely going forward, specks on wall textures morph into opponents and so on. All the diffusion-generated videos I’ve seen so far have this kind of unsettling feature.
raghavbali 2y ago
Nicely summarised. Another important thing that clearly standsout (not to undermine the efforts and work gone into this) is the fact that more and more we are now seeing larger and more complex building blocks emerging (first it was embedding models then encoder decoder layers and now whole models are being duck-taped for even powerful pipelines). AI/DL ecosystem is growing on a nice trajectory. Though I wonder if 10 years down the line folks wouldn't even care about underlying model details (no more than a current day web-developer needs to know about network packets). PS: Not great examples, but I hope you get the idea ;)
pradn 2y ago
> Google here uses SD 1.4, as the core of the diffusion model, which is a nice reminder that open models are useful to even giant cloud monopolies. A mistake people make all the time is that massive companies will put all their resources toward every project. This paper was written by four co-authors. They probably got a good amount of resources, but they still had to share in the pool allocated to their research department. Even Google only has one Gemini (in a few versions).
bubaumba 2y ago
> nice reminder that open models are useful to You didn't say open _what_ models. Was that intentional?
- Philpax 2y ago
  They did, SD 1.4
fennecfoxy 2y ago
If anybody Google would know most about that after their LLM memo all that time ago (basically "we're losing because we're trying to fight/compete with OS models"): https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...

refibrillator 2y ago

Just want to clarify a couple possible misconceptions: The diffusion model doesn’t maintain any state itself, though its weights may encode some notion of cause/effect. It just renders one frame at a time (after all it’s a text to image model, not text to video). Instead of text, the previous states and frames are provided as inputs to the model to predict the next frame. Noise is added to the previous frames before being passed into the SD model, so the RL agents were not involved with “correcting” it. De-noising objectives are widespread in ML, intuitively it forces a predictive model to leverage context, ie surrounding frames/words/etc. In this case it helps prevent auto-regressive drift due to the accumulation of small errors from the randomness inherent in generative diffusion models. Figure 4 shows such drift happening when a player is standing still.

rvnx 2y ago
The concept is that if you train a Diffusion model by feeding all the possible frames seen in the game. The training was over almost 1 billion frames, 20 days of full-time play-time, taking a screenshot of every single inch of the map. Now you show him N frames as input, and ask it "give me frame N+1", then it gives you the frame n. N+1 back based on how it was originally seen during training. But it is not frame N+1 from a mysterious intelligence, it's simply frame N+1 given back from past database. The drift you mentioned is actually a clear (but sad) proof that the model does not work at inventing new frames, and can only spit out an answer from the past dataset. It's a bit like if you train stable diffusion on Simpsons episodes, and that it outputs the next frame of an existing episode that was in the training set, but few frames later goes wild and buggy.
- jetrink 2y ago
  I don't think you've understood the project completely. The model accepts player input, so frame 601 could be quite different if the player decided to turn left rather than right, or chose that moment to fire at an exploding barrel.
  - rvnx 2y ago
    1 billion frames in memory... With such dataset, you have seen practically all realistic possibilities in the short-term. If it would be able to invent action and maps and let the user play "infinite doom", then it would be very different (and impressive!).
    - OskarS 2y ago
      > 1 billion frames in memory... With such dataset, you have seen practically all realistic possibilities in the short-term. I mean... no? Not even close? Multiply the number of game states with the number of inputs at any given frame gives you a number vastly bigger than 1 billion, not even comparable. Even with 20 days of play time to train no, it's entirely likely that at no point did someone stop at a certain location and look to the left from that angle. They might have done from similar angles, but the model then has to reconstruct some sense of the geometry of the level to synthesize the frame. They might also not have arrived there from the same direction, which again the model needs some smarts to understand. I get your point, it's very overtrained on these particular levels of Doom, which means you might as well just play Doom. But this is not a hash table lookup we're talking about, it's pretty impressive work.
      - rvnx 2y ago
        This was the basis for the reasoning: The map 1 has 2'518 walkable map units. There are 65536 angles. 2'518*65'536=165'019'648 If you capture 165M frames, you already cover all the possibilities in terms of camera / player view, but probably the diffusion models don't even need to have all the frames (the same way that LLMs don't).

nine_k 2y ago

But it's not a game. It's a memory of a game video, predicting the next frame based on the few previous frames, like "I can imagine what happened next". I would call it the world's least efficient video compression. What I would like to see is the actual predictive strength, aka imagination, which I did not notice mentioned in the abstract. The model is trained on a set of classic maps. What would it do, given a few frames of gameplay on an unfamiliar map as input? How well could it imagine what happens next?

WithinReason 2y ago
If it's trained on absolute player coordinates then it would likely just morph into the known map at those coordinates.
- nine_k 2y ago
  But it's trained on the actual screen pixel data, AFAICT. It's literally a visual imagination model, not gameplay / geometry imagination model. They had to make special provisions to the pixel data on the HUD which by its nature different than the pictures of a 3D world.
PoignardAzur 2y ago
> But it's not a game. It's a memory of a game video, predicting the next frame based on the few previous frames, like "I can imagine what happened next". It's not super clear from the landing page, but I think it's an engine? Like, its input is both previous images and input for the next frame. So as a player, if you press "shoot", the diffusion engine need to output an image where the monster in front of you takes damage/dies.
- bergen 2y ago
  How is what you think they say not clear? We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality.
2y ago

deleted 2y ago

[deleted]

wavemode 2y ago

> Apparently there’s more cause, effect, and sequencing in diffusion models than what I expected To temper this a bit, you may want to pay close attention to the demo videos. The player rarely backtracks, and for good reason - the few times the character does turn around and look back at something a second time, it has changed significantly (the most noticeable I think is the room with the grey wall and triangle sign). This falls in line with how we'd expect a diffusion model to behave - it's trained on many billions of frames of gameplay, so it's very good at generating a plausible -next- frame of gameplay based on some previous frames. But it doesn't deeply understand logical gameplay constraints, like remembering level geometry.

hoosieree 2y ago
Small objects like powerups appear and disappear as the player moves (even without backtracking), the ammo count is constantly varying, getting shot doesn't deplete health or armor, etc.
mensetmanusman 2y ago
That is kind of cool though, I would play like being lost in a dream. If on the backend you could record the level layouts in memory you could have exploration teams that try to find new areas to explore.
- debo_ 2y ago
  It would be cool for dream sequences in games to feel more like dreams. This is probably an expensive way to do it, but it would be neat!
Groxx 2y ago
There's an example right at the beginning too - the ammo drop on the right changes to something green (I think that's a body?)
codeflo 2y ago
Even purely going forward, specks on wall textures morph into opponents and so on. All the diffusion-generated videos I’ve seen so far have this kind of unsettling feature.

raghavbali 2y ago

Nicely summarised. Another important thing that clearly standsout (not to undermine the efforts and work gone into this) is the fact that more and more we are now seeing larger and more complex building blocks emerging (first it was embedding models then encoder decoder layers and now whole models are being duck-taped for even powerful pipelines). AI/DL ecosystem is growing on a nice trajectory. Though I wonder if 10 years down the line folks wouldn't even care about underlying model details (no more than a current day web-developer needs to know about network packets). PS: Not great examples, but I hope you get the idea ;)

pradn 2y ago

> Google here uses SD 1.4, as the core of the diffusion model, which is a nice reminder that open models are useful to even giant cloud monopolies. A mistake people make all the time is that massive companies will put all their resources toward every project. This paper was written by four co-authors. They probably got a good amount of resources, but they still had to share in the pool allocated to their research department. Even Google only has one Gemini (in a few versions).

bubaumba 2y ago

> nice reminder that open models are useful to You didn't say open _what_ models. Was that intentional?

Philpax 2y ago
They did, SD 1.4

fennecfoxy 2y ago

If anybody Google would know most about that after their LLM memo all that time ago (basically "we're losing because we're trying to fight/compete with OS models"): https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...