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 advanced, high-dimensional outputs. You might know them for his or her potential to supply beautiful AI artwork and hyper-realistic artificial pictures, however they’ve additionally discovered success in different purposes akin to drug design and steady management. The important thing thought behind diffusion fashions is to iteratively remodel random noise right into a pattern, akin to a picture or protein construction. That is sometimes motivated as a most probability estimation drawback, the place the mannequin is educated to generate samples that match the coaching knowledge as carefully as attainable.

Nonetheless, most use instances of diffusion fashions are usually 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 pictures, however one which has a particular kind of look; we don’t simply need a drug molecule that’s bodily believable, however one that’s as efficient as attainable. On this publish, we present how diffusion fashions will be educated on these downstream targets immediately utilizing reinforcement studying (RL). To do that, we finetune Steady Diffusion on a wide range 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.

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 pictures.

Denoising Diffusion Coverage Optimization

When turning diffusion into an RL drawback, we make solely probably the most fundamental assumption: given a pattern (e.g. a picture), we’ve got entry to a reward operate that we will 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 sometimes educated utilizing a loss operate derived from most probability estimation (MLE), which means they’re inspired to generate samples that make the coaching knowledge look extra probably. Within the RL setting, we not have coaching knowledge, solely samples from the diffusion mannequin and their related rewards. A method we will 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 offers us an algorithm that we name reward-weighted regression (RWR), after current algorithms from RL literature.

Nonetheless, there are a couple 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 that RWR maximizes the reward via 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 most effective 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 the complete sequence of denoising steps that received 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 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 attributable to 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 operate estimator of the coverage gradient also referred to as REINFORCE; and DDPOIS, which makes use of a extra highly effective significance sampled estimator. DDPOIS is our best-performing algorithm and its implementation carefully follows that of proximal coverage optimization (PPO).

Finetuning Steady Diffusion Utilizing DDPO

For our principal outcomes, we finetune Steady Diffusion v1-4 utilizing DDPOIS. Now we have 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 dimension 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 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 nicely does the picture signify 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 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 “driving a motorcycle”. We discovered that Steady Diffusion usually struggled to supply 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 easy rewards (compressibility, incompressibility, and aesthetic high quality). The entire pictures are generated with the identical random seed. Within the high left quadrant, we illustrate what “vanilla” Steady Diffusion generates for 9 totally different animals; the entire RL-finetuned fashions present a transparent qualitative distinction. Curiously, the aesthetic high quality mannequin (high proper) tends in direction of minimalist black-and-white line drawings, revealing the sorts of pictures that the LAION aesthetic predictor considers “extra aesthetic”.

results on aesthetic, compressibility, and incompressibility

Subsequent, we show DDPO on the extra advanced prompt-image alignment activity. Right here, we present a number of snapshots from the coaching course of: every collection of three pictures exhibits samples for a similar immediate and random seed over time, with the primary pattern coming from vanilla Steady Diffusion. Curiously, 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

Sudden 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 usually 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 widespread animals. We discover that it generalizes not solely to unseen animals but additionally to on a regular basis objects.

aesthetic quality generalization

Our prompt-image alignment mannequin used the identical listing of 45 widespread 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.

prompt-image alignment generalization


It’s well-known that finetuning on a reward operate, particularly a realized one, can result in reward overoptimization the place the mannequin exploits the reward operate to realize a excessive reward in a non-useful manner. Our setting is not 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 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 vital space for future work.


Diffusion fashions are onerous to beat in relation to producing advanced, high-dimensional outputs. Nonetheless, up to now they’ve largely been profitable in purposes the place the aim is to be taught patterns from heaps and plenty of knowledge (for instance, image-caption pairs). What we’ve discovered is a approach to successfully prepare diffusion fashions in a manner 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 way in which we used DDPO on this work is impressed by the current successes of language mannequin finetuning. OpenAI’s GPT fashions, like Steady Diffusion, are first educated on big quantities of Web knowledge; they’re then finetuned with RL to supply helpful instruments like ChatGPT. Sometimes, their reward operate is realized from human preferences, however others have extra not too long ago found out easy methods 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 definitely looks like 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 technology, however for a lot of thrilling purposes akin to video technology, music technology,  picture modifying, protein synthesis, robotics, and extra.

Moreover, the “pretrain + finetune” paradigm isn’t the one manner to make use of DDPO. So long as you’ve got 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 may 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 purposes of RL to the following stage — and even to find new ones.

This publish 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 evokes your work, please cite it with:

      title={Coaching Diffusion Fashions with Reinforcement Studying}, 
      creator={Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},


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