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 capacity to provide beautiful AI artwork and hyper-realistic artificial pictures, however they’ve additionally discovered success in different purposes corresponding to drug design and steady management. The important thing thought behind diffusion fashions is to iteratively remodel random noise right into a pattern, corresponding 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 potential.
Nonetheless, most use circumstances of diffusion fashions aren’t instantly involved with matching the coaching information, however as an alternative with a downstream goal. We don’t simply need a picture that appears like present pictures, however one which has a selected sort of look; we don’t simply desire a drug molecule that’s bodily believable, however one that’s as efficient as potential. On this submit, we present how diffusion fashions might be skilled on these downstream aims instantly 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.
A diagram illustrating the prompt-image alignment goal. It makes use of LLaVA, a big vision-language mannequin, to judge 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 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 sometimes skilled utilizing a loss operate derived from most probability estimation (MLE), that means they’re inspired to generate samples that make the coaching information look extra probably. Within the RL setting, we not have coaching information, 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 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 present algorithms from RL literature.
Nonetheless, there are a couple of issues with this strategy. One is that RWR just isn’t 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 an alternative derived utilizing a variational sure on the true probability of every pattern. Which means that 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 features 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 are able to higher maximize the reward of the ultimate pattern if we take note of your complete sequence of denoising steps that bought 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 might 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 straightforward 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 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 Secure Diffusion Utilizing DDPO
For our major outcomes, we finetune Secure Diffusion v1-4 utilizing DDPOIS. We now have 4 duties, every outlined by a unique reward operate:
- Compressibility: How straightforward is the picture to compress utilizing the JPEG algorithm? The reward is the unfavourable file dimension of the picture (in kB) when saved as a JPEG.
- Incompressibility: How laborious is the picture to compress utilizing the JPEG algorithm? The reward is the constructive 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 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 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 motorcycle”. We discovered that Secure Diffusion typically struggled to provide pictures 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). The entire pictures are generated with the identical random seed. Within the prime left quadrant, we illustrate what “vanilla” Secure Diffusion generates for 9 completely different animals; all the 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 exhibit DDPO on the extra advanced prompt-image alignment job. 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 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 information, 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 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.
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 mixtures of the 2.
Overoptimization
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 approach. Our setting isn’t any exception: in all of the duties, the mannequin ultimately 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 proper quantity.
There may be at present no general-purpose methodology for stopping overoptimization, and we spotlight this drawback as an essential space for future work.
Conclusion
Diffusion fashions are laborious to beat in terms of producing advanced, high-dimensional outputs. Nonetheless, to date they’ve principally been profitable in purposes the place the aim is to be taught patterns from heaps and many information (for instance, image-caption pairs). What we’ve discovered is a approach to successfully practice diffusion fashions in a approach 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 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 Secure Diffusion, are first skilled on large quantities of Web information; they’re then finetuned with RL to provide helpful instruments like ChatGPT. Sometimes, their reward operate is realized from human preferences, however others have extra not too long ago discovered easy methods to produce highly effective chatbots utilizing reward features primarily based 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 like it’s value pursuing additional on the 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 purposes corresponding to video technology, music technology, picture enhancing, protein synthesis, robotics, and extra.
Moreover, the “pretrain + finetune” paradigm just isn’t the one approach to make use of DDPO. So long as you’ve a great reward operate, 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 present purposes of RL to the subsequent stage — and even to find new ones.
This submit 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, authentic code, or get the mannequin weights on Hugging Face. If you wish to use DDPO in your individual mission, take a look at my PyTorch + LoRA implementation the place you possibly can 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}
}
