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

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Diffusion fashions have lately emerged because the de facto normal for producing advanced, high-dimensional outputs. You could know them for his or her potential to provide beautiful AI artwork and hyper-realistic artificial pictures, however they’ve additionally discovered success in different functions equivalent to drug design and steady management. The important thing concept behind diffusion fashions is to iteratively remodel random noise right into a pattern, equivalent to a picture or protein construction. That is sometimes motivated as a most chance estimation downside, the place the mannequin is educated to generate samples that match the coaching knowledge as carefully as potential.

Nevertheless, most use circumstances of diffusion fashions are usually not instantly involved with matching the coaching knowledge, however as an alternative with a downstream goal. We don’t simply need a picture that appears like current pictures, however one which has a particular kind of look; we don’t simply desire a drug molecule that’s bodily believable, however one that’s as efficient as potential. On this put up, we present how diffusion fashions will be educated on these downstream targets instantly utilizing reinforcement studying (RL). To do that, we finetune Secure Diffusion on quite a lot of targets, together with picture compressibility, human-perceived aesthetic high quality, and prompt-image alignment. The final of those targets 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.

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

Denoising Diffusion Coverage Optimization

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

Diffusion fashions are sometimes educated utilizing a loss perform 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 will nonetheless use the identical MLE-motivated loss perform is by treating the samples as coaching knowledge 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.

Nevertheless, there are just a few issues with this strategy. One is that RWR shouldn’t be a very actual algorithm — it maximizes the reward solely roughly (see Nair et. al., Appendix A). The MLE-inspired loss for diffusion can also be not actual and is as an alternative derived utilizing a variational certain on the true chance of every pattern. Which means RWR maximizes the reward by way of two ranges of approximation, which we discover considerably hurts its efficiency.

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 will higher maximize the reward of the ultimate pattern if we take note of all the sequence of denoising steps that bought us there. To do that, we reframe the diffusion course of as a multi-step Markov resolution 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 straightforward 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: DDPO_SF, which makes use of the straightforward rating perform estimator of the coverage gradient often known as REINFORCE; and DDPO_IS, which makes use of a extra highly effective significance sampled estimator. DDPO_IS is our best-performing algorithm and its implementation carefully follows that of proximal coverage optimization (PPO).

Finetuning Secure Diffusion Utilizing DDPO

For our foremost outcomes, we finetune Secure Diffusion v1-4 utilizing DDPO_IS. We now have 4 duties, every outlined by a special reward perform:

• Compressibility: How straightforward is the picture to compress utilizing the JPEG algorithm? The reward is the adverse file dimension of the picture (in kB) when saved as a JPEG.
• Incompressibility: How arduous is the picture to compress utilizing the JPEG algorithm? The reward is the optimistic file dimension 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 effectively 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 Secure Diffusion is a text-to-image mannequin, we additionally want to select 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”, “taking part in chess”, and “using a motorcycle”. We discovered that Secure Diffusion typically struggled to provide pictures 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 straightforward rewards (compressibility, incompressibility, and aesthetic high quality). The entire pictures are generated with the identical random seed. Within the prime left quadrant, we illustrate what “vanilla” Secure Diffusion generates for 9 totally different animals; the entire RL-finetuned fashions present a transparent qualitative distinction. Curiously, the aesthetic high quality mannequin (prime proper) tends in the direction of minimalist black-and-white line drawings, revealing the sorts of pictures that the LAION aesthetic predictor considers “extra aesthetic”.

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

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 additionally to on a regular basis objects.

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 additionally to unseen actions, and even novel mixtures of the 2.

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 realize a excessive reward in a non-useful method. Our setting is not any exception: in all of the duties, the mannequin finally destroys any significant picture content material to maximise reward.

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 in a position to efficiently idiot LLaVA by as an alternative producing textual content loosely resembling the right quantity.

There may be presently no general-purpose methodology for stopping overoptimization, and we spotlight this downside as an essential space for future work.

Conclusion

Diffusion fashions are arduous to beat in terms of producing advanced, high-dimensional outputs. Nevertheless, to this point they’ve largely been profitable in functions the place the objective is to be taught patterns from heaps and plenty of knowledge (for instance, image-caption pairs). What we’ve discovered is a technique to successfully practice diffusion fashions in a method that goes past pattern-matching — and with out essentially requiring any coaching knowledge. 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 Secure Diffusion, are first educated on big quantities of Web knowledge; they’re then finetuned with RL to provide helpful instruments like ChatGPT. Sometimes, their reward perform is discovered from human preferences, however others have extra lately discovered the right way to produce highly effective chatbots utilizing reward capabilities based mostly on AI suggestions as an alternative. 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 definitely looks as if it’s price pursuing additional on this planet 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 equivalent to video era, music era,  picture enhancing, protein synthesis, robotics, and extra.

Moreover, the “pretrain + finetune” paradigm shouldn’t be the one method to make use of DDPO. So long as you’ve gotten reward perform, there’s nothing stopping you from coaching with RL from the beginning. Whereas this setting is as-yet unexplored, this can be 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 following degree — and even to find new ones.

This put up relies on the next paper:

If you wish to be taught extra about DDPO, you’ll be able to 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’ll be able to finetune Secure Diffusion with lower than 10GB of GPU reminiscence!

If DDPO evokes 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},
      yr={2023},
      eprint={2305.13301},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}