Diffusion fashions have just lately emerged because the de facto normal for producing advanced, high-dimensional outputs. It’s possible you’ll know them for his or her potential to supply gorgeous AI artwork and hyper-realistic artificial pictures, however they’ve additionally discovered success in different functions reminiscent of drug design and steady management. The important thing thought behind diffusion fashions is to iteratively rework random noise right into a pattern, reminiscent of a picture or protein construction. That is usually motivated as a most chance estimation downside, the place the mannequin is skilled to generate samples that match the coaching knowledge as carefully as potential.
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 selected 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 submit, we present how diffusion fashions will be skilled on these downstream goals immediately utilizing reinforcement studying (RL). To do that, we finetune Secure Diffusion on quite a lot of goals, together with picture compressibility, human-perceived aesthetic high quality, and prompt-image alignment. The final of those goals 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 downside, we make solely essentially the most fundamental assumption: given a pattern (e.g. a picture), now we have 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 usually skilled utilizing a loss operate derived from most chance 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 number of issues with this strategy. One is that RWR just 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 also be not precise and is as a substitute derived utilizing a variational certain on the true chance of every pattern. Which means RWR maximizes the reward by means 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 persistently 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 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 might be 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 simple to compute.
We selected to use coverage gradient algorithms because 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 carefully follows that of proximal coverage optimization (PPO).
Finetuning Secure Diffusion Utilizing DDPO
For our important outcomes, we finetune Secure Diffusion v1-4 utilizing DDPOIS. Now we have 4 duties, every outlined by a special reward operate:
- Compressibility: How simple is the picture to compress utilizing the JPEG algorithm? The reward is the unfavourable file measurement 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 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 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 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 “driving a motorcycle”. We discovered that Secure 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 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; 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 pictures that the LAION aesthetic predictor considers “extra aesthetic”.
Subsequent, we exhibit DDPO on the extra advanced prompt-image alignment activity. Right here, we present a number of snapshots from the coaching course of: every sequence of three pictures reveals samples for a similar immediate and random seed over time, with the primary pattern coming from vanilla Secure 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 knowledge, so the mannequin shifts in direction of this model 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 giant 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 had been chosen from a listing 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 checklist 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 manner. Our setting is not 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 vulnerable 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 proper quantity.
There’s presently no general-purpose technique for stopping overoptimization, and we spotlight this downside as an necessary space for future work.
Conclusion
Diffusion fashions are onerous to beat in terms of producing advanced, high-dimensional outputs. Nonetheless, thus far they’ve largely been profitable in functions the place the objective is to study patterns from heaps and plenty of knowledge (for instance, image-caption pairs). What we’ve discovered is a strategy 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 latest successes of language mannequin finetuning. OpenAI’s GPT fashions, like Secure Diffusion, are first skilled on large 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 just lately discovered tips on how to produce highly effective chatbots utilizing reward features 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 as if it’s price pursuing additional on this planet of diffusion fashions. We hope that others can construct on our work to enhance giant diffusion fashions, not only for text-to-image era, however for a lot of thrilling functions reminiscent of video era, music era, picture modifying, protein synthesis, robotics, and extra.
Moreover, the “pretrain + finetune” paradigm just isn’t the one manner to make use of DDPO. So long as you will 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 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 submit is predicated on the next paper:
If you wish to study 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 mission, take a look at my PyTorch + LoRA implementation the place you may 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},
12 months={2023},
eprint={2305.13301},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
