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Coaching Diffusion Fashions with Reinforcement Studying – The Berkeley Synthetic Intelligence Analysis Weblog



Coaching Diffusion Fashions with Reinforcement Studying

Diffusion fashions have not too long ago emerged because the de facto commonplace for producing complicated, high-dimensional outputs. Chances are you’ll know them for his or her potential to supply gorgeous AI artwork and hyper-realistic artificial photographs, however they’ve additionally discovered success in different functions resembling drug design and steady management. The important thing concept behind diffusion fashions is to iteratively rework random noise right into a pattern, resembling a picture or protein construction. That is usually motivated as a most probability estimation drawback, the place the mannequin is educated to generate samples that match the coaching information as intently as attainable.

Nonetheless, most use circumstances of diffusion fashions aren’t instantly involved with matching the coaching information, however as a substitute with a downstream goal. We don’t simply need a picture that appears like current photographs, however one which has a particular sort of look; we don’t simply need a drug molecule that’s bodily believable, however one that’s as efficient as attainable. On this put up, we present how diffusion fashions may be educated on these downstream aims instantly utilizing reinforcement studying (RL). To do that, we finetune Steady Diffusion on quite a lot of aims, together with picture compressibility, human-perceived aesthetic high quality, and prompt-image alignment. The final of those aims makes use of suggestions from a big vision-language mannequin to enhance the mannequin’s efficiency on uncommon prompts, demonstrating how highly effective AI fashions can be utilized to enhance one another with none people within the loop.

diagram illustrating the RLAIF objective that uses the LLaVA VLM


A diagram illustrating the prompt-image alignment goal. It makes use of LLaVA, a big vision-language mannequin, to judge generated photographs.

Denoising Diffusion Coverage Optimization

When turning diffusion into an RL drawback, we make solely essentially the most fundamental assumption: given a pattern (e.g. a picture), we now have entry to a reward perform that we are able to consider to inform us how “good” that pattern is. Our purpose is for the diffusion mannequin to generate samples that maximize this reward perform.

Diffusion fashions are usually educated utilizing a loss perform derived from most probability estimation (MLE), that means they’re inspired to generate samples that make the coaching information look extra doubtless. Within the RL setting, we not have coaching information, solely samples from the diffusion mannequin and their related rewards. A method we are able to nonetheless use the identical MLE-motivated loss perform is by treating the samples as coaching information and incorporating the rewards by weighting the loss for every pattern by its reward. This offers us an algorithm that we name reward-weighted regression (RWR), after current algorithms from RL literature.

Nonetheless, there are a number of issues with this method. One is that RWR isn’t a very precise algorithm — it maximizes the reward solely roughly (see Nair et. al., Appendix A). The MLE-inspired loss for diffusion can be not precise and is as a substitute derived utilizing a variational certain on the true probability of every pattern. Which means RWR maximizes the reward by means of two ranges of approximation, which we discover considerably hurts its efficiency.

chart comparing DDPO with RWR


We consider two variants of DDPO and two variants of RWR on three reward capabilities and discover that DDPO constantly achieves the perfect efficiency.

The important thing perception of our algorithm, which we name denoising diffusion coverage optimization (DDPO), is that we are able to higher maximize the reward of the ultimate pattern if we take note of all the sequence of denoising steps that obtained us there. To do that, we reframe the diffusion course of as a multi-step Markov determination course of (MDP). In MDP terminology: every denoising step is an motion, and the agent solely will get a reward on the ultimate step of every denoising trajectory when the ultimate pattern is produced. This framework permits us to use many highly effective algorithms from RL literature which can be designed particularly for multi-step MDPs. As an alternative of utilizing the approximate probability of the ultimate pattern, these algorithms use the precise probability of every denoising step, which is extraordinarily simple to compute.

We selected to use coverage gradient algorithms because of their ease of implementation and previous success in language mannequin finetuning. This led to 2 variants of DDPO: DDPOSF, which makes use of the easy rating perform estimator of the coverage gradient often known as REINFORCE; and DDPOIS, which makes use of a extra highly effective significance sampled estimator. DDPOIS is our best-performing algorithm and its implementation intently follows that of proximal coverage optimization (PPO).

Finetuning Steady Diffusion Utilizing DDPO

For our most important outcomes, we finetune Steady Diffusion v1-4 utilizing DDPOIS. We have now 4 duties, every outlined by a unique reward perform:

  • Compressibility: How simple is the picture to compress utilizing the JPEG algorithm? The reward is the detrimental file measurement of the picture (in kB) when saved as a JPEG.
  • Incompressibility: How onerous is the picture to compress utilizing the JPEG algorithm? The reward is the constructive file measurement of the picture (in kB) when saved as a JPEG.
  • Aesthetic High quality: How aesthetically interesting is the picture to the human eye? The reward is the output of the LAION aesthetic predictor, which is a neural community educated on human preferences.
  • Immediate-Picture Alignment: How properly does the picture symbolize what was requested for within the immediate? This one is a little more sophisticated: we feed the picture into LLaVA, ask it to explain the picture, after which compute the similarity between that description and the unique immediate utilizing BERTScore.

Since Steady Diffusion is a text-to-image mannequin, we additionally want to select a set of prompts to offer it throughout finetuning. For the primary three duties, we use easy prompts of the shape “a(n) [animal]”. For prompt-image alignment, we use prompts of the shape “a(n) [animal] [activity]”, the place the actions are “washing dishes”, “taking part in chess”, and “driving a motorcycle”. We discovered that Steady Diffusion typically struggled to supply photographs that matched the immediate for these uncommon situations, leaving loads of room for enchancment with RL finetuning.

First, we illustrate the efficiency of DDPO on the easy rewards (compressibility, incompressibility, and aesthetic high quality). All the photographs are generated with the identical random seed. Within the prime left quadrant, we illustrate what “vanilla” Steady Diffusion generates for 9 totally different animals; all the RL-finetuned fashions present a transparent qualitative distinction. Apparently, the aesthetic high quality mannequin (prime proper) tends in direction of minimalist black-and-white line drawings, revealing the sorts of photographs that the LAION aesthetic predictor considers “extra aesthetic”.

results on aesthetic, compressibility, and incompressibility

Subsequent, we display DDPO on the extra complicated prompt-image alignment job. Right here, we present a number of snapshots from the coaching course of: every sequence of three photographs exhibits samples for a similar immediate and random seed over time, with the primary pattern coming from vanilla Steady Diffusion. Apparently, the mannequin shifts in direction of a extra cartoon-like fashion, which was not intentional. We hypothesize that it is because animals doing human-like actions usually tend to seem in a cartoon-like fashion within the pretraining information, so the mannequin shifts in direction of this fashion to extra simply align with the immediate by leveraging what it already is aware of.

results on prompt-image alignment

Surprising Generalization

Stunning generalization has been discovered to come up when finetuning giant language fashions with RL: for instance, fashions finetuned on instruction-following solely in English typically enhance in different languages. We discover that the identical phenomenon happens with text-to-image diffusion fashions. For instance, our aesthetic high quality mannequin was finetuned utilizing prompts that have been chosen from an inventory of 45 frequent animals. We discover that it generalizes not solely to unseen animals but in addition to on a regular basis objects.

aesthetic quality generalization

Our prompt-image alignment mannequin used the identical checklist of 45 frequent animals throughout coaching, and solely three actions. We discover that it generalizes not solely to unseen animals but in addition to unseen actions, and even novel combos of the 2.

prompt-image alignment generalization

Overoptimization

It’s well-known that finetuning on a reward perform, particularly a discovered one, can result in reward overoptimization the place the mannequin exploits the reward perform to attain a excessive reward in a non-useful means. Our setting isn’t any exception: in all of the duties, the mannequin ultimately destroys any significant picture content material to maximise reward.

overoptimization of reward functions

We additionally found that LLaVA is inclined to typographic assaults: when optimizing for alignment with respect to prompts of the shape “[n] animals”, DDPO was capable of efficiently idiot LLaVA by as a substitute producing textual content loosely resembling the right quantity.

RL exploiting LLaVA on the counting task

There’s presently no general-purpose technique for stopping overoptimization, and we spotlight this drawback as an necessary space for future work.

Conclusion

Diffusion fashions are onerous to beat relating to producing complicated, high-dimensional outputs. Nonetheless, thus far they’ve largely been profitable in functions the place the purpose is to be taught patterns from heaps and many information (for instance, image-caption pairs). What we’ve discovered is a approach to successfully prepare diffusion fashions in a means that goes past pattern-matching — and with out essentially requiring any coaching information. The chances are restricted solely by the standard and creativity of your reward perform.

The way in which we used DDPO on this work is impressed by the latest successes of language mannequin finetuning. OpenAI’s GPT fashions, like Steady Diffusion, are first educated on large quantities of Web information; they’re then finetuned with RL to supply helpful instruments like ChatGPT. Usually, their reward perform is discovered from human preferences, however others have extra not too long ago found out the best way to produce highly effective chatbots utilizing reward capabilities based mostly on AI suggestions as a substitute. In comparison with the chatbot regime, our experiments are small-scale and restricted in scope. However contemplating the large success of this “pretrain + finetune” paradigm in language modeling, it actually looks like it’s price pursuing additional on the earth of diffusion fashions. We hope that others can construct on our work to enhance giant diffusion fashions, not only for text-to-image era, however for a lot of thrilling functions resembling video era, music era,  picture modifying, protein synthesis, robotics, and extra.

Moreover, the “pretrain + finetune” paradigm isn’t the one means to make use of DDPO. So long as you could have a superb reward perform, there’s nothing stopping you from coaching with RL from the beginning. Whereas this setting is as-yet unexplored, it is a place the place the strengths of DDPO might actually shine. Pure RL has lengthy been utilized to all kinds of domains starting from taking part in video games to robotic manipulation to nuclear fusion to chip design. Including the highly effective expressivity of diffusion fashions to the combo has the potential to take current functions of RL to the subsequent stage — and even to find new ones.


This put up relies on the next paper:

If you wish to be taught extra about DDPO, you possibly can take a look at the paper, web site, unique code, or get the mannequin weights on Hugging Face. If you wish to use DDPO in your personal undertaking, take a look at my PyTorch + LoRA implementation the place you possibly can finetune Steady Diffusion with lower than 10GB of GPU reminiscence!

If DDPO conjures up your work, please cite it with:

@misc{black2023ddpo,
      title={Coaching Diffusion Fashions with Reinforcement Studying}, 
      writer={Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},
      yr={2023},
      eprint={2305.13301},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}




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