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



Coaching Diffusion Fashions with Reinforcement Studying

Diffusion fashions have lately emerged because the de facto commonplace for producing complicated, high-dimensional outputs. You might know them for his or her potential to provide 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 remodel random noise right into a pattern, resembling a picture or protein construction. That is usually motivated as a most chance estimation drawback, the place the mannequin is skilled to generate samples that match the coaching knowledge as intently as attainable.

Nevertheless, most use circumstances of diffusion fashions should not immediately involved with matching the coaching knowledge, 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 desire a drug molecule that’s bodily believable, however one that’s as efficient as attainable. On this submit, we present how diffusion fashions could be skilled on these downstream aims immediately utilizing reinforcement studying (RL). To do that, we finetune Secure Diffusion on a wide range 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 guage generated photographs.

Denoising Diffusion Coverage Optimization

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

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

Nevertheless, there are a number of issues with this strategy. 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 also be not precise and is as a substitute derived utilizing a variational certain on the true chance of every pattern. Which means that RWR maximizes the reward by way 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 features and discover that DDPO persistently achieves one of the best 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 received 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 are designed particularly for multi-step MDPs. As a substitute of utilizing the approximate chance of the ultimate pattern, these algorithms use the precise chance of every denoising step, which is extraordinarily simple to compute.

We selected to use coverage gradient algorithms resulting from their ease of implementation and previous success in language mannequin finetuning. This led to 2 variants of DDPO: DDPOSF, which makes use of the straightforward rating operate 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 Secure Diffusion Utilizing DDPO

For our fundamental outcomes, we finetune Secure Diffusion v1-4 utilizing DDPOIS. We’ve 4 duties, every outlined by a unique reward operate:

  • Compressibility: How simple is the picture to compress utilizing the JPEG algorithm? The reward is the damaging 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 optimistic 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 skilled on human preferences.
  • Immediate-Picture Alignment: How effectively does the picture signify what was requested for within the immediate? This one is a little more difficult: 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 Secure Diffusion is a text-to-image mannequin, we additionally want to choose a set of prompts to present 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”, “enjoying chess”, and “using a motorbike”. We discovered that Secure Diffusion typically struggled to provide photographs that matched the immediate for these uncommon eventualities, leaving loads of room for enchancment with RL finetuning.

First, we illustrate the efficiency of DDPO on the straightforward rewards (compressibility, incompressibility, and aesthetic high quality). All the photographs are generated with the identical random seed. Within the high left quadrant, we illustrate what “vanilla” Secure Diffusion generates for 9 totally different animals; the entire RL-finetuned fashions present a transparent qualitative distinction. Apparently, the aesthetic high quality mannequin (high 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 process. Right here, we present a number of snapshots from the coaching course of: every collection of three photographs exhibits samples for a similar immediate and random seed over time, with the primary pattern coming from vanilla Secure Diffusion. Apparently, the mannequin shifts in direction of a extra cartoon-like type, which was not intentional. We hypothesize that it’s because animals doing human-like actions usually tend to seem in a cartoon-like type within the pretraining knowledge, so the mannequin shifts in direction of this type 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 massive 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 had been chosen from a listing 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 record 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 operate, particularly a discovered one, can result in reward overoptimization the place the mannequin exploits the reward operate to attain a excessive reward in a non-useful approach. Our setting isn’t any exception: in all of the duties, the mannequin finally destroys any significant picture content material to maximise reward.

overoptimization of reward functions

We additionally found that LLaVA is prone 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 proper quantity.

RL exploiting LLaVA on the counting task

There may be at present no general-purpose methodology for stopping overoptimization, and we spotlight this drawback as an necessary space for future work.

Conclusion

Diffusion fashions are onerous to beat in the case of producing complicated, high-dimensional outputs. Nevertheless, to this point they’ve principally been profitable in functions the place the aim is to study patterns from heaps and many knowledge (for instance, image-caption pairs). What we’ve discovered is a approach to successfully prepare diffusion fashions in a approach that goes past pattern-matching — and with out essentially requiring any coaching knowledge. The probabilities are restricted solely by the standard and creativity of your reward operate.

The best way we used DDPO on this work is impressed by the latest successes of language mannequin finetuning. OpenAI’s GPT fashions, like Secure Diffusion, are first skilled on enormous quantities of Web knowledge; they’re then finetuned with RL to provide helpful instruments like ChatGPT. Sometimes, their reward operate is discovered from human preferences, however others have extra lately discovered how you can produce highly effective chatbots utilizing reward features primarily based 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 as if it’s price pursuing additional on the earth of diffusion fashions. We hope that others can construct on our work to enhance massive 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 approach to make use of DDPO. So long as you could have a great reward operate, 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 enjoying video games to robotic manipulation to nuclear fusion to chip design. Including the highly effective expressivity of diffusion fashions to the combination has the potential to take current functions of RL to the following stage — and even to find new ones.


This submit is predicated on the next paper:

If you wish to study extra about DDPO, you may try the paper, web site, unique code, or get the mannequin weights on Hugging Face. If you wish to use DDPO in your personal venture, try my PyTorch + LoRA implementation the place you may finetune Secure 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}, 
      creator={Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},
      12 months={2023},
      eprint={2305.13301},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}




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