Diffusion fashions have just lately emerged because the de facto customary for producing advanced, high-dimensional outputs. You might know them for his or her capacity to supply gorgeous AI artwork and hyper-realistic artificial photographs, however they’ve additionally discovered success in different functions corresponding to drug design and steady management. The important thing concept 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 chance estimation downside, the place the mannequin is skilled to generate samples that match the coaching information as intently as doable.
Nevertheless, most use circumstances of diffusion fashions will not be 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 present photographs, however one which has a selected kind of look; we don’t simply need a drug molecule that’s bodily believable, however one that’s as efficient as doable. On this submit, we present how diffusion fashions may be skilled on these downstream targets straight 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.
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 downside, we make solely essentially the most primary assumption: given a pattern (e.g. a picture), we’ve entry to a reward operate 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 operate.
Diffusion fashions are sometimes skilled utilizing a loss operate derived from most chance estimation (MLE), which means they’re inspired to generate samples that make the coaching information look extra possible. Within the RL setting, we now not have coaching information, solely samples from the diffusion mannequin and their related rewards. A technique we will 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.
Nevertheless, there are just a few issues with this method. One is that RWR shouldn’t be 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 sure on the true chance of every pattern. Which means RWR maximizes the reward via 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 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 will higher maximize the reward of the ultimate pattern if we take note of the complete sequence of denoising steps that bought us there. To do that, we reframe the diffusion course of as a multi-step Markov choice 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 as a result of 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 intently follows that of proximal coverage optimization (PPO).
Finetuning Steady Diffusion Utilizing DDPO
For our essential outcomes, we finetune Steady Diffusion v1-4 utilizing DDPOIS. Now we 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 damaging file measurement of the picture (in kB) when saved as a JPEG.
- Incompressibility: How exhausting is the picture to compress utilizing the JPEG algorithm? The reward is the constructive 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 characterize 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 Steady Diffusion is a text-to-image mannequin, we additionally want to select a set of prompts to offer 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 usually struggled to supply photographs 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 photographs are generated with the identical random seed. Within the high left quadrant, we illustrate what “vanilla” Steady Diffusion generates for 9 completely different animals; all the 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”.
Subsequent, we reveal 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 photographs exhibits samples for a similar immediate and random seed over time, with the primary pattern coming from vanilla Steady Diffusion. Apparently, the mannequin shifts in 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 direction of this fashion to extra simply align with the immediate by leveraging what it already is aware of.
Surprising Generalization
Shocking 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 in addition to on a regular basis objects.
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 in addition to unseen actions, and even novel combos of the 2.
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 realize a excessive reward in a non-useful approach. 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 capable of efficiently idiot LLaVA by as a substitute producing textual content loosely resembling the right quantity.
There’s at present no general-purpose methodology for stopping overoptimization, and we spotlight this downside as an vital space for future work.
Conclusion
Diffusion fashions are exhausting to beat relating to producing advanced, high-dimensional outputs. Nevertheless, up to now they’ve largely been profitable in functions the place the objective is to be taught patterns from tons and many information (for instance, image-caption pairs). What we’ve discovered is a technique to successfully practice diffusion fashions in a approach 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 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 skilled on large quantities of Web information; they’re then finetuned with RL to supply helpful instruments like ChatGPT. Sometimes, their reward operate is discovered from human preferences, however others have extra just lately found out the right way to 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 big success of this “pretrain + finetune” paradigm in language modeling, it definitely looks as if 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 functions corresponding to video technology, music technology, picture modifying, protein synthesis, robotics, and extra.
Moreover, the “pretrain + finetune” paradigm shouldn’t be the one approach to make use of DDPO. So long as you could have an excellent 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 may 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 present functions of RL to the subsequent degree — and even to find new ones.
This submit relies on the next paper:
If you wish to be taught extra about DDPO, you may 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 personal challenge, take a look at my PyTorch + LoRA implementation the place you may finetune Steady 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},
writer={Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},
yr={2023},
eprint={2305.13301},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
