There is also a large area of risk as documented in [4] where marginalized groups are associated with harmful connotations reinforcing societal hateful stereotypes. For example, representation of demographic groups that conflates humans with animals or mythological creatures (such as black people as monkeys or other primates), conflating humans with food or objects (like associating people with disabilities and vegetables) or associating demographic groups with negative semantic concepts (such as terrorism with muslim people).
Problematic associations like these between groups of people and concepts reflect long-standing negative narratives about the group. If a generative AI model learns problematic associations from existing data, it may reproduce them in content that is generates [4].
There are several ways to fine-tune the LLMs. According to [6], one common approach is called Supervised Fine-Tuning (SFT). This involves taking a pre-trained model and further training it with a dataset that includes pairs of inputs and desired outputs. The model adjusts it’s parameters by learning to better match these expected responses.
Typically, fine-tuning involves two phases: SFT to establish a base model, followed by RLHF for enhanced performance. SFT involves imitating high-quality demonstration data, while RLHF refines LLMs through preference feedback.
RLHF can be done in two ways, reward-based or reward-free methods. In reward-based method, we first train a reward model using preference data. This model then guides online Reinforcement Learning algorithms like PPO. Reward-free methods are simpler, directly training the models on preference or ranking data to understand what humans prefer. Among these reward-free methods, DPO has demonstrated strong performances and become popular in the community. Diffusion DPO can be used to steer the model away from problematic depictions towards more desirable alternatives. The tricky part of this process is not training itself, but data curation. For each risk, we need a collection of hundreds or thousands of prompts, and for each prompt, a desirable and undesirable image pair. The desirable example should ideally be a perfect depiction for that prompt, and the undesirable example should be identical to the desirable image, except it should include the risk that we want to unlearn.
These mitigations are applied after the model is finalized and deployed in the production stack. These cover all the mitigations applied on the user input prompt and the final image output.
Prompt filtering
When users input a text prompt to generate an image, or upload an image to modify it using inpainting technique, filters can be applied to block requests asking for harmful content explicitly. At this stage, we address issues where users explicitly provide harmful prompts like “show an image of a person killing another person” or upload an image and ask “remove this person’s clothing” and so on.
For detecting harmful requests and blocking, we can use a simple blocklist based approached with keyword matching, and block all prompts that have a matching harmful keyword (say “suicide”). However, this approach is brittle, and can produce large number of false positives and false negatives. Any obfuscating mechanisms (say, users querying for “suicid3” instead of “suicide”) will fall through with this approach. Instead, an embedding-based CNN filter can be used for harmful pattern recognition by converting the user prompts into embeddings that capture the semantic meaning of the text, and then using a classifier to detect harmful patterns within these embeddings. However, LLMs have been proved to be better for harmful pattern recognition in prompts because they excel at understanding context, nuance, and intent in a way that simpler models like CNNs may struggle with. They provide a more context-aware filtering solution and can adapt to evolving language patterns, slang, obfuscating techniques and emerging harmful content more effectively than models trained on fixed embeddings. The LLMs can be trained to block any defined policy guideline by your organization. Aside from harmful content like sexual imagery, violence, self-injury etc., it can also be trained to identify and block requests to generate public figures or election misinformation related images. To use an LLM based solution at production scale, you’d have to optimize for latency and incur the inference cost.
Prompt manipulations
Before passing in the raw user prompt to model for image generation, there are several prompt manipulations that can be done for enhancing the safety of the prompt. Several case studies are presented below:
Prompt augmentation to reduce stereotypes: LDMs amplify dangerous and complex stereotypes [5] . A broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects. For example, prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, or prompting for occupations resulting in amplification of racial and gender disparities. Prompt engineering to add gender and racial diversity to the user prompt is an effective solution. For example, “image of a ceo” -> “image of a ceo, asian woman” or “image of a ceo, black man” to produce more diverse results. This can also help reduce harmful stereotypes by transforming prompts like “image of a criminal” -> “image of a criminal, olive-skin-tone” since the original prompt would have most likely produced a black man.
Prompt anonymization for privacy: Additional mitigation can be applied at this stage to anonymize or filter out the content in the prompts that ask for specific private individuals information. For example “Image of John Doe from
Prompt rewriting and grounding to convert harmful prompt to benign: Prompts can be rewritten or grounded (usually with a fine-tuned LLM) to reframe problematic scenarios in a positive or neutral way. For example, “Show a lazy [ethnic group] person taking a nap” -> “Show a person relaxing in the afternoon”. Defining a well-specified prompt, or commonly referred to as grounding the generation, enables models to adhere more closely to instructions when generating scenes, thereby mitigating certain latent and ungrounded biases. “Show two people having fun” (This could lead to inappropriate or risky interpretations) -> “Show two people dining at a restaurant”.
Output image classifiers
Image classifiers can be deployed that detect images produced by the model as harmful or not, and may block them before being sent back to the users. Stand alone image classifiers like this are effective for blocking images that are visibly harmful (showing graphic violence or a sexual content, nudity, etc), However, for inpainting based applications where users will upload an input image (e.g., image of a white person) and give a harmful prompt (“give them blackface”) to transform it in an unsafe manner, the classifiers that only look at output image in isolation will not be effective as they lose context of the “transformation” itself. For such applications, multimodal classifiers that can consider the input image, prompt, and output image together to make a decision of whether a transformation of the input to output is safe or not are very effective. Such classifiers can also be trained to identify “unintended transformation” e.g., uploading an image of a woman and prompting to “make them beautiful” leading to an image of a thin, blonde white woman.
Regeneration instead of refusals
Instead of refusing the output image, models like DALL·E 3 uses classifier guidance to improve unsolicited content. A bespoke algorithm based on classifier guidance is deployed, and the working is described in [3]—
When an image output classifier detects a harmful image, the prompt is re-submitted to DALL·E 3 with a special flag set. This flag triggers the diffusion sampling process to use the harmful content classifier to sample away from images that might have triggered it.
Basically this algorithm can “nudge” the diffusion model towards more appropriate generations. This can be done at both prompt level and image classifier level.
