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

Coaching Diffusion Fashions with Reinforcement Studying

Diffusion fashions have just lately emerged because the de facto customary for producing complicated, high-dimensional outputs. You could know them for his or her means to supply gorgeous AI artwork and hyper-realistic artificial photos, however they’ve additionally discovered success in different functions akin to drug design and steady management. The important thing concept behind diffusion fashions is to iteratively rework 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 skilled to generate samples that match the coaching information as carefully as attainable.

Nonetheless, most use circumstances of diffusion fashions usually are not straight 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 photos, however one which has a selected kind of look; we don’t simply desire a drug molecule that’s bodily believable, however one that’s as efficient as attainable. On this publish, we present how diffusion fashions might be skilled on these downstream targets straight utilizing reinforcement studying (RL). To do that, we finetune Steady 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.

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 photos.

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 now have entry to a reward perform that we are able to 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 skilled 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 provides us an algorithm that we name reward-weighted regression (RWR), after current algorithms from RL literature.

Nonetheless, 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 be not actual and is as a substitute derived utilizing a variational certain on the true probability of every pattern. Which means that RWR maximizes the reward by 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 the whole 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 can be designed particularly for multi-step MDPs. As a substitute 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 straightforward rating perform 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 important outcomes, we finetune Steady Diffusion v1-4 utilizing DDPOIS. We’ve 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 destructive 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 skilled on human preferences.
  • Immediate-Picture Alignment: How effectively does the picture characterize 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 provide 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 motorbike”. We discovered that Steady Diffusion typically struggled to supply photos 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 photos 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. Curiously, the aesthetic high quality mannequin (prime proper) tends in direction of minimalist black-and-white line drawings, revealing the sorts of photos that the LAION aesthetic predictor considers “extra aesthetic”.

results on aesthetic, compressibility, and incompressibility

Subsequent, we show 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 photos 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 model, which was not intentional. We hypothesize that it’s because animals doing human-like actions usually tend to seem in a cartoon-like model within the pretraining information, so the mannequin shifts in direction of this model 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 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 combos of the 2.

prompt-image alignment generalization


It’s well-known that finetuning on a reward perform, particularly a realized one, can result in reward overoptimization the place the mannequin exploits the reward perform 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 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 a substitute producing textual content loosely resembling the right quantity.

RL exploiting LLaVA on the counting task

There’s at present 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 the case of producing complicated, high-dimensional outputs. Nonetheless, up to now they’ve principally been profitable in functions the place the objective is to study patterns from tons and many information (for instance, image-caption pairs). What we’ve discovered is a solution to successfully practice diffusion fashions in a manner that goes past pattern-matching — and with out essentially requiring any coaching information. The probabilities 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 current successes of language mannequin finetuning. OpenAI’s GPT fashions, like Steady Diffusion, are first skilled on enormous quantities of Web information; they’re then finetuned with RL to supply helpful instruments like ChatGPT. Usually, their reward perform is realized from human preferences, however others have extra just lately discovered learn how 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 big success of this “pretrain + finetune” paradigm in language modeling, it actually looks as if it’s value 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 technology, however for a lot of thrilling functions akin to video technology, music technology,  picture enhancing, protein synthesis, robotics, and extra.

Moreover, the “pretrain + finetune” paradigm shouldn’t be the one manner to make use of DDPO. So long as you’ve got a very good 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 combination has the potential to take current functions of RL to the following stage — and even to find new ones.

This publish is predicated on the next paper:

If you wish to study extra about DDPO, you may 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 mission, take a look at my PyTorch + LoRA implementation the place you may finetune Steady Diffusion with lower than 10GB of GPU reminiscence!

If DDPO conjures up 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},
      12 months={2023},

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