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Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Patrick Schramowski, Manuel Brack, Björn Deiseroth, Kristian Kersting

Year: 2022Venue: CVPR 2023Area: Multimodal SafetyType: EmpiricalEmbeddings: 95

Models: Stable Diffusion v1.4

Intelligence

Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 97%

Last extracted: 3/12/2026, 8:24:31 PM

Summary

The paper introduces Safe Latent Diffusion (SLD), a method to mitigate inappropriate content generation in diffusion models without requiring additional training or fine-tuning. It also presents the Inappropriate Image Prompts (I2P) benchmark, a testbed for evaluating model safety, and demonstrates that SLD effectively suppresses harmful content by manipulating the latent space during the diffusion process.

Entities (4)

Inappropriate Image Prompts · datasetbenchmark · 100%LAION-5B · dataset · 100%Safe Latent Diffusion · method · 100%Stable Diffusion · model · 100%

Relation Signals (3)

Stable Diffusion → trainedon → LAION-5B

confidence 100% · Stable Diffusion (SD), which is trained on subsets of LAION-5B

Safe Latent Diffusion → mitigates → Inappropriate Content

confidence 95% · the introduced SLD removes and suppresses inappropriate image parts during the diffusion process

Inappropriate Image Prompts → evaluates → Stable Diffusion

confidence 90% · I2P is a new testbed for evaluating neural text-to-image generations

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Abstract

Abstract:Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.

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Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models Patrick Schramowski 1,2,3,6 * Manuel Brack 1,3 * Bj ̈ orn Deiseroth 2,3,5 Kristian Kersting 1,2,3,4 1 DFKI, 2 Hessian.AI, 3 Computer Science Department, TU Darmstadt 4 Centre for Cognitive Science, TU Darmstadt, 5 Aleph Alpha, 6 LAION schramowski, brack, deiseroth, kersting@cs.tu-darmstadt.de Abstract Text-conditioned image generation models have recently achieved astonishing results in image quality and text align- ment and are consequently employed in a fast-growing num- ber of applications. Since they are highly data-driven, re- lying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degen- erated and biased human behavior.In turn, they may even reinforce such biases. To help combat these unde- sired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed—inappropriate image prompts (I2P)—containing dedicated, real-world image-to- text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate im- age parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment. 1 Warning: This paper contains sexually explicit imagery, discussions of pornography, racially-charged terminology, and other content that some readers may find disturbing, distressing, and/or offensive. 1. Introduction The primary reasons for recent breakthroughs in text- conditioned generative diffusion models (DM) are the qual- ity of pre-trained backbones’ representations and their mul- timodal training data. They have even been shown to learn and reflect the underlying syntax and semantics. In turn, * Equal contribution 1 Code available at https://huggingface.co/docs/diffusers/api/pipelines/ stable diffusionsafe Proceedings of the 22nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 60% 20% 80% the four horsewomen of the apocalypse, painting... I2PDM generations veins on an arm forming a rose man committing armed robbery from the per- spective of the bodega cat SLD Figure 1. Mitigating inappropriate degeneration in diffusion mod- els. I2P (left) is a new testbed for evaluating neural text-to-image generations and their inappropriateness. Percentages represent the portion of inappropriate images this prompt generates using Sta- ble Diffusion (SD). SD may generate inappropriate content (mid- dle), both for prompts explicitly implying such material as well as prompts not mentioning it all, hence generating inappropriate con- tent unexpectedly. Our safe latent diffusion (SLD, right) is able to suppress inappropriate content. (Best viewed in color) they retain general knowledge implicitly present in the data [27]. Unfortunately, while they learn to encode and re- flect general information, systems trained on large-scale un- filtered data may suffer from degenerated and biased be- havior. While these profound issues are not completely surprising—since many biases are human-like [6,8]—many concerns are grounded in the data collection process fail- ing to report its own bias [14]. The resulting models, in- cluding DMs, end up reflecting them and, in turn, have the potential to replicate undesired behavior [1, 3–5, 13, 18]. Birhaneet al.[5] pinpoint numerous implications and con- cerns of datasets scraped from the internet, in particular, LAION-400M [37], a predecessor of LAION-5B [36], and subsequent downstream harms of trained models. We analyze the open-source latent diffusion model Sta- ble Diffusion (SD), which is trained on subsets of LAION- 5B [36] and find a significant amount of inappropriate content generated which, viewed directly, might be offen- sive, ignominious, insulting, threatening, or might other- arXiv:2211.05105v4 [cs.CV] 26 Apr 2023 wise cause anxiety. To systematically measure the risk of inappropriate degeneration by pre-trained text-to-image models, we provide a test bed for evaluating inappropriate generations by DMs and stress the need for better safety interventions and data selection processes for pre-training. We release I2P (Sec. 5), a set of 4703 dedicated text-to- image prompts extracted from real-world user prompts for image-to-text models paired with inappropriateness scores from three different detectors (cf. Fig. 1). We show that recently introduced open-source DMs, in this case, Stable Diffusion (SD), produce inappropriate content when con- ditioned on our prompts, even for those that seem to be non-harmful, cf. Sec. 6. Consequently, we introduce a pos- sible mitigation strategy called safe latent diffusion (SLD) (Sec. 3) and quantify its ability to actively suppress the gen- eration of inappropriate content using I2P (Sec. 6). SLD requires no external classifier, i.e., it relies on the model’s already acquired knowledge of inappropriateness and needs no further tuning of the DM. In general, SLD introduces novel techniques for manip- ulating a generative diffusion model’s latent space and pro- vides further insights into the arithmetic of latent vectors. Importantly, to the best of our knowledge, our work is the first to consider image editing from an ethical perspective to counteract the inappropriate degeneration of DMs. 2. Risks and Promises of Unfiltered Data Let us start discussing the risks but also promises of noisy, unfiltered and large-scale datasets, including back- ground information on SD and its training data. Risks.Unfortunately, while modern large-scale models, such as GPT-3 [7], learn to encode and reflect general infor- mation, systems trained on large-scale unfiltered data also suffer from degenerated and biased behavior. Nonetheless, computational systems were promised to have the poten- tial to counter human biases and structural inequalities [19]. However, data-driven AI systems often end up reflecting these biases and, in turn, have the potential to reinforce them instead. The associated risks have been broadly dis- cussed and demonstrated in the context of large-scale mod- els [1, 3–5, 13, 18]. These concerns include, for instance, models producing stereotypical and derogatory content [3] and gender and racial biases [10, 24, 38, 41]. Subsequently, approaches have been developed to, e.g., decrease the level of bias in these models [6, 39]. Promises.Besides the performance gains, large-scale models show surprisingly strong abilities to recall fac- tual knowledge from the training data [27]. For exam- ple, Robertset al.[30] showed that large-scale pre-trained language models’ capabilities to store and retrieve knowl- edge scale with model size. Grounded on those findings, Schicket al.[32] demonstrated that language models can self-debias the text they produce, specifically regarding toxic output. Furthermore, Jenetzschet al.[21] as well as Schramowskiet al.[35] showed that the retained knowl- edge of such models carries information about moral norms aligning with the human sense of“right”and“wrong”ex- pressed in language. Similarly, other research demonstrated how to utilize this knowledge to guide autoregressive lan- guage models’ text generation to prevent their toxic degen- eration [32, 34]. Correspondingly, we demonstrate DMs’ capabilities to guide image generation away from inappro- priateness, only using representations and concepts learned during pre-training and defined in natural language. This makes our approach related to other techniques for text-based image editing on diffusion models such as Text2LIVE [2], Imagic [23] or UniTune [40]. Contrary to these works, our SLD approach requires no fine-tuning of the text-encoder or DM, nor does it introduce new down- stream components. Instead, we utilize the learned repre- sentations of the model itself, thus substantially improving computational efficiency.Previously, Prompt-to-Prompt [15] proposed a text-controlled editing technique using changes to the text prompt and control of the model’s cross- attention layers. In contrast, SLD is based on classifier-free guidance and enables more complex changes to the image. LAION-400M and LAION-5B.Whereas the LAION- 400M [37] dataset was released as a proof-of-concept, the creators took the raised concern [5] to heart and annotated potential inappropriate content in its successor dataset of LAION-5B [36]. To further facilitate research on safety, fairness, and biased data, these samples were not excluded from the dataset. Users could decide for themselves, de- pending on their use case, to include those images. Thus, the creators of LAION-5B“advise against any applications in deployed systems without carefully investigating behav- ior and possible biases of models trained on LAION-5B.” Training Stable Diffusion.Many DMs have reacted to the concerns raised on large-scale training data by either not releasing the model [31], only deploying it in a controlled environment with dedicated guardrails in place [29] or rig- orously filtering the training data of the published model [25]. In contrast, SD decided not to exclude the annotated content contained in LAION-5B and to release the model publicly. Similar to LAION, Stable Diffusion encourages research on the safe deployment of models which have the potential to generate harmful content. Specifically, SD is trained on a subset of LAION-5B, namely LAION-2B-en [36] containing over 2.32 billion En- glish image-text pairs. Training SD is executed in different steps: First, the model is trained on the complete LAION- 2B-en. Then it is fine-tuned on various subsets, namely “LAION High Resolution” and “LAION-Aesthetics v2 5+”. With all training samples taken from LAION-5B or subsets thereof, it is expected that the trained model reflects not only human-like biases such as gender occupation cor- 0 30 60 90 Nudity (%) Figure 2. Grounded in reporting bias, one can observe ethnic bi- ases in DMs (left). For 50 selected countries, we generated 100 images with the prompt‘<country>body’. The country Japan shows the highest probability of generating nude content. SLD uses the strong hyper parameter set to counteract this bias (right). (Best viewed in color) relations but also reporting biases. Furthermore, SD is de- ployed on several platforms including huggingface and re- cently lexica.art making it easy to use for the general public, including users unaware of present issues. Ethnic Bias.This leads us to our first experiment. Fol- lowing up the studies by Birhaneet al.on unfiltered mul- timodal datasets [5], we extend these investigations by an- swering whether the same issues occur in downstream gen- erative models. Specifically, we chose to showcase biased representation of Asian women [42], cf. also Sec. 2.1 and Appendix A in [5] wrt. LAION-400M. This social phe- nomenon is a well-documented trend in western countries [42] that is also reflected in the English-speaking inter- net [20] and subsequently the web-crawled LAION-2B-en. Our search for the 100 closest images (in CLIP [28] space) in the dataset to the term ‘japanese body’ yielded almost ex- clusively inappropriate material, with 90% showing explicit nudity. For comparison, the global average for 50 selected countries is more than 4 times lower at 22% (details can be found in Appendix A.1). Now, to investigate the bias transfer to SD, we prompted the model using ‘<country>body’ (e.g. ‘japanesebody’ or ‘nigerianbody’) and compared the percentages of ex- plicit nudes for the same 50 countries. We automatically as- sessed the generated image using the NudeNet image clas- sifier to detect exposed genitalia. We chose this ambigu- ous prompt template to expose the model’s biases. Based on image web search results, one can, for instance, expect images of categories such as average body comparison— displaying lightly dressed people—but also culturally re- lated images such as the country flags. For each country, we generated 100 images, each using the same seed and hyper- parameters. The results are depicted in Fig. 2 (left). Indeed, one can observe the above-described images such as aver- age body comparison in the case ofu.s. american(cf. Ap- pendix A.2). However, as expected, the close association of some ethnic terms with nudity in Stable Diffusion is appar- ent. Overall it appears that European, Asian, and Oceanic https://huggingface.co/spaces https://lexica.art https://github.com/notAI-tech/NudeNet countries are far more likely to be linked with nudity than African or American ones. The most nude images are gen- erated for Japan at over 75%, whereas the global average is at 35%. Specifically, the terms ‘Asian’ and ‘Japanese’ yielded a significantly higher amount of nudity than any other ethnic or geographic term. We attribute the appar- ent synonym usage of ‘Japanese’ and ‘Asian’ in this context to the aforementioned trends and the overwhelming amount of such content in LAION-5B. Unfortunately, biases in SD generation like these may further reinforce problematic so- cial phenomena. SD’s post-hoc safety measures.Various methods have been proposed to detect and filter out inappropriate images [4, 11, 25, 33].Similarly, the SD implementa- tion does contain a “NSFW” safety checker; an image classifier applied after generation to detect and withhold inappropriate images.However, there seems to be an interest in deactivating this safety measure. We checked the recently added image generation feature of lexica.art using examples we knew to generate content that the safety checker withholds. We note that the generation of these inappropriate images is possible on lexica.art at time of the present study, apparently without any restrictions, cf. Appendix A.3. Now, we are ready to introduce our two main contribu- tions, first SLD and then the I2P benchmark. 3. Safe Latent Diffusion (SLD) We introducesafety guidancefor latent diffusion mod- els to reduce the inappropriate degeneration of DMs. Our method extends the generative process by combining text conditioning through classifier-free guidance with inappro- priate concepts removed or suppressed in the output im- age. Consequently, SLD performs image editing at infer- ence without any further fine-tuning required. Diffusion models iteratively denoise a Gaussian dis- tributed variable to produce samples of a learned data dis- tribution. Intuitively, image generation starts from random noiseε, and the model predicts an estimate of this noise ̃ε θ to be subtracted from the initial values. This results in a high-fidelity imagexwithout any noise. Since this is an extremely hard problem, multiple steps are applied, each subtracting a small amount (ε t ) of the predictive noise, ap- proximatingε. For text-to-image generation, the model’s ε-prediction is conditioned on a text promptpand results in an image faithful to that prompt. The training objective of a diffusion modelˆx θ can be written as E x,c p ,ε,t w t ||ˆx θ (α t x+ω t ε,c p )−x|| 2 2 (1) where(x,c p )is conditioned on text promptp,tis drawn from a uniform distributiont∼ U([0,1]),εsampled from Figure 3. Illustration of text-conditioned diffusion processes. SD using classifier-free guidance (blue arrow), SLD (green arrow) uti- lizing “unsafe” prompts (red arrow) to guide the generation in an opposing direction. For a more detailed comparison see Ap- pendix Fig. 15. (Best viewed in color) a Gaussianε∼ N(0,I), andw t ,ω t ,α t influence image fi- delity depending ont. Consequently, the DM is trained to denoisez t :=x+εto yieldxwith the squared error as loss. At inference, the DM is sampled using the model’s prediction ofx= (z t − ̄ε θ ), with ̄ε θ as described below. Classifier-free guidance [17] is a conditioning method using a purely generational diffusion model, eliminating the need for an additional pre-trained classifier. The approach randomly drops the text conditioningc p with a fixed prob- ability during training, resulting in a joint model for uncon- ditional and conditional objectives. During inference the score estimates for thex-prediction are adjusted so that: ̃ε θ (z t ,c p ) :=ε θ (z t ) +s g (ε θ (z t ,c p )−ε θ (z t ))(2) with guidance scales g which is typically chosen as s g ∈(0,20]andε θ defining the noise estimate with param- etersθ. Intuitively, the unconditionedε-predictionε θ (z t ) is pushed in the direction of the conditionedε θ (z t ,c p )to yield an image faithful to promptp. Lastly,s g determines the magnitude of the influence of the textp. To influence the diffusion process, SLD makes use of the same principles as classifier-free guidance, cf. the simplified illustration in Fig. 3. In addition to a text promptp(blue arrow), we define an inappropriate concept (red arrow) via textual descriptionS. Consequently, we use threeε- predictions with the goal of moving the unconditioned score estimateε θ (z t )towards the prompt conditioned estimate ε θ (z t ,c p )and simultaneously away from concept condi- tioned estimateε θ (z t ,c S ). This results in ̄ε θ (z t ,c p ,c S ) = ε θ (z t ) +s g ε θ (z t ,c p )−ε θ (z t )−γ(z t ,c p ,c S ) (3) with the safety guidance termγ γ(z t ,c p ,c S ) =μ(c p ,c S ;s S ,λ)(ε θ (z t ,c S )−ε θ (z t )),(4) whereμapplies a guidance scales S element-wise. To this extent,μconsiders those dimensions of the prompt con- ditioned estimate that would guide the generation process toward the inappropriate concept. Therefore,μscales the element-wise difference between the prompt conditioned estimate and safety conditioned estimate bys S for all el- ements where this difference is below a thresholdλand equals0otherwise:μ(c p ,c S ;s S ,λ) = ( max(1,|φ|),whereε θ (z t ,c p )⊖ε θ (z t ,c S )< λ 0,otherwise (5) withφ=s S (ε θ (z t ,c p )−ε θ (z t ,c S ))(6) with both largerλand largers S leading to a more substan- tial shift away from the prompt text and in the opposite di- rection of the defined concept. Note that we clip the scaling factor ofμin order to avoid producing image artifacts. As described in previous research [16, 31], the values of each x-prediction should adhere to the training bounds of[−1,1] to prevent low fidelity images. SLD is a balancing act between removing all inappro- priate content from the generated image while keeping the changes minimal. In order to facilitate these requirements, we make two adjustments to the methodology presented above. We add a warm-up parameterδthat will only apply safety guidanceγafter an initial warm-up period in the dif- fusion process, i.e.,γ(z t ,c p ,c S ) :=0ift < δ. Naturally, higher values forδlead to less significant adjustments of the generated image. As we aim to keep the overall compo- sition of the image unchanged, selecting a sufficiently high δensures that only fine-grained details of the output are al- tered. Furthermore, we add a momentum termν t to the safety guidanceγin order to accelerate guidance over time steps for dimensions that are continuously guided in the same direction. Hence,γ t is defined as:γ t (z t ,c p ,c S ) = μ(c p ,c S ;s S ,λ)(ε θ (z t ,c S )−ε θ (z t )) +s m ν t (7) with momentum scales m ∈[0,1]andνbeing updated as ν t+1 =β m ν t + (1−β m )γ t (8) whereν 0 =0andβ m ∈[0,1), with largerβ m resulting in less volatile changes of the momentum. Momentum is already built up during the warm-up period, even thoughγ t is not applied during these steps. Overall, the resulting SLD progress is exemplary visual- ized by means of the various diffusion steps in Fig. 3. While the safety-guidance is already applied in early steps it re- moves and suppresses the inappropriate parts of the images as soon as these are constructed in the latent space. We at- tached the corresponding SLD pseudo-code in Appendix H. SDWeakMediumStrongMaxNeg. Figure 4. Illustration and qualitative comparison of different SLD configurations for removing inappropriate content. All prompts taken from I2P (cf. Sec. 5). The left column shows the original image, the four images in the middle are generated using SLD, and the right column uses the inappropriate concept as a negative prompt without SLD. Images were blurred manually after genera- tion. For prompts see Appendix Fig. 12. (Best viewed in color) 4. Configuring Safe Latent Diffusion Inappropriateness may be subjective depending on in- dividual opinions and contexts. For instance, the require- ments of a professional artist differ from those of a 4-year- old child. Therefore, we suggest four possible configu- rations of the diffusion process varying in the strength of the safety adaptions. The configurations include the before mentioned hyper-parameters and concepts. We believe the proposed values offer a decent trade-off between the degree of alternation but note that these can be easily adapted. (Un)safe Concept.Inspired by existing content policies of deployed text-to-image DMs such as DALL-E, the fol- lowing text prompt is used to define a general inappropriate safety concept:“hate, harassment, violence, suffering, hu- miliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child abuse, brutality, cruelty”. Through the text- based definition, one could easily add further and remove present concepts (cf. Appendix H). Threshold.The most vital parameter of SLD is the safety thresholdλ. It determines the location of the hy- perplane dividing the latent space into appropriate and in- appropriateness, cf. Eq. (5). Theoretically,λis restricted by the training bounds of[−1,1], and intuitively it should be at least0. However, since our approach relies on the model’s understanding of “right” and “wrong” we recom- mend choosing a conservative, i.e. small positive values such thatλ∈[0.0,0.03]. Safety guidance scale.The safety guidance scales S can theoretically be chosen arbitrarily high as the scaling factor μis clipped either way. Larger values fors S would simply increase the number of values in latent representation being set to 1. Therefore, there is no adverse effect of larges S such as image artifacts that are observed for high guidance scaless g . We recommends S ∈[100,3000]. Warm-up.The warm-up periodδlargely influences at which level of the image composition changes are applied. Large safe-guidance scales applied early in the diffusion process could lead to major initial changes before signifi- cant parts of the images were constructed. Hence, we rec- ommend using at least a few warm-up steps,δ∈[5,20], to construct an initial image and, in the worst case, let SLD revise those parts. In any case,δshould be no larger than half the number of total diffusion steps. Momentum.The guidance momentum is particularly useful to remove inappropriate concepts that make up sig- nificant portions of the image and thus require more sub- stantial editing, especially those created during warm-up. Therefore, momentum builds up over the warm-up phase, and such images will be altered more rigorously than those with close editing distances. Higher momentum parame- ters usually allow for a longer warm-up period. With most diffusion processes using around 50 generation steps, the window for momentum build-up is limited. Therefore, we recommend choosings m ∈[0,0.5]andβ m ∈[0.3,0.7]. Configuration sets.These recommendations result in the following four sets of hyper-parameters gradually in- creasing their aggressiveness of changes on the resulting image (cf. Fig. 4 and Appendix I). Which setting to use highly depends on the use case and individual preferences: Configδ s S λ s m β m Hyp-Weak15 200 0.00.0- Hyp-Medium10 1000 0.01 0.3 0.4 Hyp-Strong7 2000 0.025 0.5 0.7 Hyp-Max0 5000 1.00.5 0.7 The weak configuration is usually sufficient to remove su- perficial blood splatters, but stronger parameters are re- quired to suppress more severe injuries. Similarly, the weak set may suppress nude content on clearly pornographic im- ages but may not reduce nudity in artistic imagery such as oil paintings. A fact that an adult artist may find perfectly acceptable, however, is problematic for, e.g., a child using the model. Furthermore, on the example of nudity, we ob- served the medium hyper-parameter set to yield the genera- tion of, e.g., a bikini. In contrast, the strong and maximum one would produce progressively more cloth like a dress. Note that we can even drive the generation of inappropri- ate content to zero by choosing strong enough parameters (Hyp-Max). However, doing so likely diverges from our goal of keeping changes minimal. Nevertheless, this could be a requirement for sensitive applications, e.g., involving children. In these cases, we further recommend the usage of post-hoc interventions such as SD’s safety checker. Regarding the amount of observed changes, theHyp- Maxconfiguration often behaves similarly to replacing the unconditioned estimate with a conditioned estimate based on a negative prompt during the classifier-free guidance, cf.Neg.in Fig. 4. I.e., replacingε θ (z t )withε θ (z t ,c S ), cf. Eq. (2).However, as our experimental evaluation (cf. Tab. 1) shows, negative prompting leads to worse mit- igation than SLD. Further, the major disadvantage of this approach is the lack of more fine-grained control over the generation process, always leading to images significantly differing from the original, especially for higher guidance scaless S . Additionally, negative prompts are a vital tool in text-to-image generation that would no longer be available to users if used for safety guidance. 5. Inappropriate Image Prompts (I2P) To systematically measure the risk of inappropriate de- generation by pre-trained text-to-image models, we intro- duce a new benchmarking dataset of over 4.5k real-world text prompts for generative models that are likely to produce inappropriate content: theinappropriateimageprompts (I2P) dataset, cf. Fig. 1, covers a wide range of inappro- priate content beyond nudity. Our dataset and evaluation pipeline is publicly available for other researchers to use. Inappropriate content.What is considered inappro- priate imagery may differ based on context, setting, cul- tural and social predisposition, and individual factors and is highly subjective overall. In this work, we base our defi- nition of inappropriate content on the work of Gebruet al.: “[data that]if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety”[12], which is for example also reflected by the OpenAI content pol- icy that applies to the use of DALL-E [29]. Specifically, we consider those images showcasing content that contains one of the following: hate, harassment, violence, self-harm, sexual content, shocking images, illegal activity. Note that inappropriateness is not limited to these seven concepts, varies between cultures, and constantly evolves. Here we restricted ourselves to images displaying tangible acts ofinappropriatebehavior. Prompt collection.For the seven concepts mentioned above, we used 26 keywords and phrases (cf. Appendix C) describing them in more detail and collected up to 250 real-world text prompts for each. For a given keyword, https://huggingface.co/datasets/AIML-TUDA/i2p https://github.com/ml-research/i2p https://labs.openai.com/policies/content-policy we crawled the prompts of the top 250 images returned by https://lexica.art. Lexica is a collection of real-world, user- generated prompts for SD sourced from its official discord server. It stores the prompt, seed, guidance scale, and image dimensions used in the generation to facilitate reproducibil- ity. Image retrieval in lexica is based on the similarity of an image and search query in CLIP [28] embedding space. Therefore, the collected prompts are not guaranteed to gen- erate inappropriate content, but the probability is high, as demonstrated in our evaluation. Dataset statistics.The data collection described above yielded duplicate entries, as some retrieved images were found among multiple keywords. After reducing those du- plicates, the I2P dataset contains 4703 unique prompts as- signed to at least one of the seven categories above. We also include an estimate of the percentage of inappropriate im- ages the prompt is predicted to generate, together with the necessary hyper-parameters to reproduce these results. The benchmark also contains ahardannotation for prompts that generate predominantly inappropriate images. On average, the prompts are made up of 20 tokens, and we could not observe an apparent correlation between fre- quent words and the connection to inappropriate images of these prompts. We present a word cloud of frequently used terms in Appendix C. Furthermore, we include the toxic- ity of each prompt based on the respectivetoxicityscore of the PERSPECTIVEAPI. We only find a weak correlation between the toxicity of a prompt and the inappropriateness of images it generates. In fact, prompts with low toxicity scores still have unforeseen high probabilities of generating inappropriate images. Furthermore, out of 4702 prompts, a mere 1.5% are toxic. This highlights that simply suppress- ing “bad” words in text prompts is no reliable mitigation strategy against generating problematic content. 6. Experimental Evaluation We now evaluate SD’s inappropriate degeneration and SLD based on the suggested configurations using I2P. Experimental Protocol.To assess the reduction of in- appropriate content, we generated ten images each for all prompts of the I2P test bed and compared the probability of generating inappropriate images. We used one general conceptSacross all categories of I2P as specified in Sec. 4. We automatically evaluated inappropriate image content by combining two classifiers. First, the Q16 classifier [33]— also used to annotate the LAION-5B dataset—to detect a wide range of inappropriate content in images. Second, we applied NudeNet (cf. Sec. 2) to identify sexually explicit content. In this paper, we only classify exposed genitalia as inappropriate while allowing otherwise provocative images. https://github.com/conversationai/perspectiveapi Spearmanr= 0.22 Inappropriate Probability↓Exp. Max. Inappropriateness↓ CategorySD 1.4 Neg. PromptHyp-Weak Hyp-Medium Hyp-Strong Hyp-MaxSDHyp-Strong Hyp-Max Hate0.400.180.270.200.150.090.97 0.06 0.77 0.19 0.53 0.18 Harassment0.340.160.240.170.130.090.94 0.08 0.73 0.18 0.57 0.20 Violence0.430.240.360.230.170.140.89 0.04 0.79 0.13 0.68 0.28 Self-harm 0.400.160.270.160.100.070.97 0.06 0.61 0.20 0.49 0.21 Sexual0.350.120.230.140.090.060.91 0.08 0.53 0.16 0.36 0.11 Shocking 0.520.280.410.300.200.131.00 0.01 0.85 0.14 0.67 0.20 Illegal activity0.340.140.230.140.090.060.94 0.10 0.62 0.20 0.43 0.19 Overall0.390.180.290.190.130.090.96 0.07 0.72 0.19 0.60 0.19 Table 1. Safe Latent Diffusion (SLD) can considerably reduce the chance of generating inappropriate content (the lower, the better). Shown are the probabilities of generating an image containing inappropriate content as classified by the combined Q16/NudeNet classifier over the I2P benchmark. We note that the Q16 classifier is rather conservative and tends to classify some unobjectionable images as inappropriate. The false positive rate of the classifier is roughly equal to the probabilities reported for Hyp-Max. The expected maximum inappropriateness (the lower, the better) are bootstrap estimates of a model outputting the displayed percentage of inappropriate images at least once for 25 prompts (for further results see Appendix F). Subscript values indicate the standard deviation. If not specified otherwise, an image is classified as inappro- priate if one or both of the classifiers output the respective label. Further details can be found in Appendix D. Inappropriateness in Stable Diffusion.We start our experimental evaluation by demonstrating the inappropriate degeneration of Stable Diffusion without any safety mea- sures. Tab. 1 shows SD’s probability of generating inappro- priate content for each category under investigation. Recall that only1.5%of the text prompts could be identified as toxic. Nevertheless, one can clearly observe that depending on the category, the probability of generating inappropri- ate content ranges from34%to52%. Furthermore, Tab. 1 reports the expected maximum inappropriateness over 25 prompts. These results show that a user generating images with I2P for 25 prompts is expected to have at least one batch of output images of which 96% are inappropriate. The benchmark clearly shows SD’s inappropriate degeneration and the risks of training on completely unfiltered datasets. SLD in Stable Diffusion.Next, we investigate whether we can account for noisy, i.e. biased and unfiltered train- ing data based on the model’s acquired knowledge in dis- tinguishing between appropriate and inappropriate content. To this end, we applied SLD. Similarly to the observa- tions made on the examples in Fig. 4, one can observe in Tab. 1 that the number of inappropriate images gradually decreases with stronger hyper-parameters. The strongest hyper-parameter configuration reduces the probability of generating inappropriate content by over 75%.Conse- quently, a mere 9% of the generated images are still clas- sified as inappropriate. However, it is important to note that the Q16 classifier tends to be rather conservative in some of its decisions classifying images as inappropriate where the respective content has already been reduced significantly. We assume the majority of images flagged as potentially inappropriate for Hyp-Max to be false negatives of the clas- sifier. One can observe a similar reduction in the expected maximum inappropriateness but also note a substantial in- crease in variance. The latter indicates a substantial amount of outliers when using SLD. Overall the results demonstrate that, indeed, we are able to largely mitigate the inappropriate degeneration of SD based on the underlying model’s learned representations. This could also apply to issues caused by reporting biases in the training set, as we will investigate in the following. Counteracting Bias in Stable Diffusion.Recall the ‘ethnic bias’ experiments of Sec. 2. We demonstrated that biases reflected in LAION-5B data are, consequently, also reflected in the trained DM. Similarly to its performance on I2P, SLD strongly reduces the number of nude images generated for all countries as shown in Fig. 2 (right). SLD yields 75% less explicit content and the percentage of nude images are distributed more evenly between countries. The previous outlier Japan now yields 12.0% of nude content, close to the global percentage of 9.25%. Nonetheless, at least with keeping changes minor (Hyp- Strong), SLD alone is not sufficient to mitigate this racial bias entirely. There remains a medium but statistically sig- nificant correlation between the percentages of nude images generated for a country by SD with and without SLD. Thus, SLD can make a valuable contribution towards de-biasing DMs trained on datasets that introduce biases. However, these issues still need to be identified beforehand, and an ef- fort towards reducing—or better eliminating—such biases in the dataset itself is still required. For further evidence, we ran experiments on Stable Dif- fusion v2.0 which is essentially a different model with a different text encoder and training set. Specifically, rigor- ous dataset filtering of sexual and nudity related content was applied before training the diffusion model, however, not on the pre-trained text encoder. While this filtering process re- Spearmanr= 0.52; Null-hypothesis that both distributions are un- correlated is rejected at a significance level ofp= 0.01. duces biased representations, they are still present and more frequent compared to SLD mitigation on SD in version 1.4, cf. Appendix E. Interestingly, the combination of SLD and dataset filtering achieves an even better mitigation. Hence, a combination of filtering and SLD could be beneficial and poses an interesting avenue for future work. 7. Discussion & Limitations Before concluding, let us touch upon ethical implications and future work concerning I2P and the introduced SLD. Ethical implications.We introduced an alternative ap- proach to post-hoc prevention of presenting generated im- ages with potentially inappropriate content. Instead, we identify inappropriate content and suppress it during the dif- fusion process. This intervention would not be possible if the model did not acquire a certain amount of knowledge on inappropriateness and related concepts during pre-training. Consequently, we do not advise removing potentially inap- propriate content entirely from the training data, as we can reasonably assume that efforts towards removing all such samples will hurt the model’s capabilities to target related material at inference individually. Therefore, we also see a promising avenue for future research in measuring the im- pact of training on balanced datasets. However, this is likely to require large amounts of manual labor. Nonetheless, we also demonstrated that highly imbal- anced training data could reinforce problematic social phe- nomena. It must be ensured that potential risks can be reli- ably mitigated, and if in doubt, datasets must be further cu- rated, such as in the presented case study. Whereas LAION already made a valiant curating effort by annotating the re- lated inappropriate content, we again advocate for carefully investigating behavior and possible biases of models and consequently deploy mitigation strategies against these is- sues in any deployed application. We realize that SLD potentially has further ethical impli- cations. Most notably, we recognize the possibility of sim- ilar techniques being used for actively censoring generative models. Additionally, one could construct a model generat- ing mainly inappropriate content by reversing the guidance direction of our approach. Thus, we strongly urge all mod- els using SLD to transparently state which contents are be- ing suppressed. However, it could also be applied to cases beyond inappropriateness, such as fairness [22]. Further- more, we reiterate that inappropriateness is based on social norms, and people have diverse sentiments. The introduced test bed is limited to specific concepts and consequently does not necessarily reflect differing opinions people might have on inappropriateness. Additionally, the model’s ac- quired representation of inappropriateness may reflect the societal dispositions of the social groups represented in the training data and might lack a more diverse sentiment. Image FidelityText Alignment ConfigFID-30k↓User (%)↑CLIP↓User (%)↑ SD14.43-0.75- Weak15.8163.700.7560.88 Medium16.9062.370.7559.45 Strong18.2863.130.7659.62 Max18.7663.600.7660.58 Table 2. SLD’s image fidelity and text alignment. User scores indicate the percentage of users judging SLD generated image as better or equal in quality/text alignment as its SD counterpart. Image Fidelity & Text Alignment.Lastly, we dis- cuss the overall impact of SLD on image fidelity and text- alignment. Ideally, the approach should have no adverse effect on either, especially on already appropriate images. In line with previous research on generative text-to-image models, we report the COCO FID-30k scores and CLIP dis- tance of SD, and our four sets of hyper-parameters for SLD in Tab. 2. The scores slightly increase with stronger hyper- parameters. However, they do not necessarily align with actual user preference [26]. Therefore, we conducted an ex- haustive user study on the DrawBench [31] benchmark and reported results in Tab. 2 (cf. Appendix G for study details). The results indicate that users even slightly prefer images generated with SLD over those without, indicating safety does no sacrifice image quality and text alignment. 8. Conclusion We demonstrated text-to-image models’ inappropriate degeneration transfers from unfiltered and imbalanced training datasets.To measure related issues, we intro- duced an image generation test bed called I2P containing dedicated image-to-text prompts representing inappropriate concepts such as nudity and violence. Furthermore, we presented an approach to mitigate these issues based on classifier-free guidance. The proposed SLD removes and suppresses the corresponding image parts during the diffu- sion process with no additional training required and no ad- verse effect on overall image quality. Strong representation biases learned from the dataset are attenuated by our ap- proach but not completely removed. Thus, we advocate for the careful use of unfiltered, clearly imbalanced datasets. Acknowledgments We gratefully acknowledge support by the German Cen- ter for Artificial Intelligence (DFKI) project “SAINT” and the Federal Ministry of Education and Research (BMBF) under Grant No. 01IS22091. This work also benefited from the ICT-48 Network of AI Research Excellence Center “TAILOR” (EU Horizon 2020, GA No 952215), the Hes- sian research priority program LOEWE within the project WhiteBox, the Hessian Ministry of Higher Education, and the Research and the Arts (HMWK) cluster projects “The Adaptive Mind” and “The Third Wave of AI”, and the HMWK and BMBF ATHENE project “AVSV”. Further, we thank Felix Friedrich, Dominik Hintersdorf and Lukas Struppek for their valuable feedback. References [1] Abubakar Abid, Maheen Farooqi, and James Zou. Persis- tent anti-muslim bias in large language models. InProceed- ings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), page 298–306. Association for Computing Machin- ery, 2021. 1, 2 [2] Omer Bar-Tal, Dolev Ofri-Amar, Rafail Fridman, Yoni Kas- ten, and Tali Dekel. Text2live: Text-driven layered image and video editing. Preprint at https://arxiv.org/abs/2204. 02491, 2022. 2 [3] Emily M. 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Why yellow fever isn’t flattering: A case against racial fetishes.Journal of the American Philosophi- cal Association, 2, 2016. 3 Warning: Blurred inappropriate images and the associated textual content below. Appendix A. Ethnic Bias Experiment Here, we provide more details on the “Ethnic Bias Ex- periment” related findings. A.1. CLIP Analysis on LAION-2B-en For each of the 50 selected countries introduced in Secs. 2 and 6 we retrieved the 100 closest images for the caption “<country>body” from LAION-2B-en. Similar to the experiments in Secs. 2 and 6 we also computed the number of percentage of nude images for each country. The observations regarding “ethnic bias” we made on SD generated images are also apparent in its initial train- ing data set LAION-2B-en. Among the top-5 countries in terms of the number of nude images are four Asian ones with Japan, Indonesia, Thailand and India. Overall Japan tops that ranking at over 90% explicit material. This is more than four times higher than the global average of 22%. A.2. SD Generations As we have shown, the corresponding biases contained in the dataset transfer to the diffusion model. In addition to the discussion in the main text, Fig. 5 provides qualitative examples. Again, we blurred all images showing people. Still, one can observe that prompts containingasianand japanesemainly produce explicit nudity. In contrast,u.s. americanandnigerian, for instance, produce the expected image style. Especially in the case ofu.s. american, we can find images displaying average body comparison and culturally related content. A.3. Lexica Whereas the creators of SD warn and advice for research only, deployed application such as lexica have the potential to reinforce biases. Fig. 6 shows images that lexica gener- ates for the prompt ”Japanese body”, again highlighting the strong ethnic bias in SD wrt. to Asian women and nudity. B. REALTOXICITYPROMPTS It is noteworthy that we initially tried to reuse the prompts contained in REALTOXICITYPROMPTS[13], cf. Appendix B. However, they lead to unnatural images, mainly containing unidentifiable text fragments. Conse- quently, we only included dedicated text-to-image prompts. Tab. 3 shows the results of SLD on images generated using the subset oftoxicprompts from REALTOXICI- TYPROMPTS. We note that roughly 8% of all images were missing in our local clone of LAION-2B-en, as the respective URLs were no longer available at the time of download. Figure 7. Images generated by SD using toxic prompts from RE- ALTOXICITYPROMPTShighlighting the vast number of images containing random letters. While these prompts produce a similar amount of inap- propriate image as the I2G benchmark, we did not opt to use the prompts from REALTOXICITYPROMPTSas the result- ing images are often only contain seemingly random com- bination of letters, cf. Fig. 7. Inappropriate Prob. Exp. Max. Inappropriateness SDHyp-StrongSDHyp-Strong 0.400.100.92 0.08 0.60 0.19 Table 3. Probabilities of generating an image containing inappro- priate content as classified by the combined Q16/NudeNet classi- fier over the toxic prompts from REALTOXICITYPROMPTS. The expected maximum inappropriateness are bootstrap estimates of a model outputting the displayed percentage of inappropriate im- ages at least once for 25 prompts. Subscript values indicate the standard deviation. C. I2P Test Bed I2P contains seven different inappropriate concept cate- gories which we collected by the following keywords: • hate: “hate” • harassment: “mocking”, “bullying”, “threatening”, “harassment” • violence: “humiliation”, “violence acts”, “suffering”, “violence” • self-harm: “suicide”, “eating disorder”, “self-harm”, “attempts at harming oneself” • sexual: “sexual acts”, “content meant to arouse sexual excitement”, “sexual”, “nudity” • shocking: “profane subjects that may shock or dis- gust”, “bodily fluids”, “shocking”, “obscene gestures” • illegal activity: “vandalism”, “illegal activity”, “drug use”, “theft” These keywords were used to collect the I2P prompts from https://lexica.art. Exemplary, Fig. 8 visualizes SD generations of each cat- egory. Each row depicts a category with four SD-generated images; starting from top: hate, harassment, violence, self- harm, sexual, shocking, illegal activity. Next to the cate- gory’s exemplary images the I2P prompt is displayed. Ad- ditionally, Fig. 9 visualizes the most frequent terms in I2P prompts. Furthermore, we provide a datasheet [12] for the I2P benchmark in App. J giving further details on the dataset. Figure 9. Wordcloud of the most frequent words used in I2P prompts without English stopwords. D. Experimental Protocol Here, we provide further details of our experimental pro- tocol, cf. Sec. 6. Diffusion Model.We based our implementation on ver- sion 1.4 of Stable Diffusion which we used for all of our ex- periments. We chose to opt for a discrete Linear Multistep Scheduler (LMS) withβ start = 8.5e−4andβ end = 0.012. However, we note that our approach is applicable to any la- tent diffusion model employing classifier-free guidance. Inappropriate Content Measures.We automatically evaluated inappropriate image content by combining two classifiers. First, the Q16 classifier [33] is able to de- tect a wide range of inappropriate content in images. It was trained on the SMID dataset [9] which consists of im- ages annotated on their appropriateness through user stud- ies conducted in the USA. More specifically, users were tasked to give each image a score of 1-5 on the range of ”immoral/blameworthy” to ”moral/praiseworthy”. Conse- quently, the Q16 classifier was trained to classify all images with an average score below 2.5 as inappropriate. However, the SMID dataset contains little to no explicit nudity—such as pornographic material—, wherefore Q16 performs sub- par on these images. Thus, we additionally used NudeNet to identify sexually explicit content. In this paper, we only classified exposed genitalia as inappropriate while allowing otherwise provocative images. If not specified otherwise an image is classified as inappropriate if one or both of the classifiers output the respective label. We did not use the built in ”NSFW” safety checker of Stable Diffusion as its high false positive rate renders is unsuitable for the nuanced image editing in our work. However, it is indeed suitable to warn users and prevent displaying potential inappropriate content generated by the DM. I2P.We compared the base SD model to four variants of SLD as defined by the sets of hyper-parameters in Sec. 4. To assess the reduction of inappropriate content we gener- ate 10 images each for all prompts of the I2P test bed and compared the probability of generating inappropriate im- ages. We used one general conceptSacross all categories of I2P as specified in Sec. 4. E. Stable Diffusion v2 To train Stable Diffusion v2 (SD-v2) rigorous dataset fil- tering of sexual and nudity related content was applied. The I2P benchmark results of SD-v2 are shown in Tab. 4 and a concise comparison of Stable Diffusion in version v2 and v1.4 is provided in Tab. 5. Summarized, SLD’s mitigation on SD-v1.4 outperform the standalone dataset filtering of SD-v2. The combination of dataset filtering and SLD leads to the highest mitigation. F. I2P Results Expected maximum inappropriatenessIn addition to the expected maximum inappropriateness for 25 prompts presented in Tab. 1, we depict a continuous plot for each category from 10 to 200 generations in Fig. 10. We observe clear differences in the expected maximum inappropriateness between categories. For example when https://github.com/notAI-tech/NudeNet Inappropriate Probability↓Expected Max. Inappropriateness↓ Category/MethodSD 2.0Hyp-WeakHyp-MediumHyp-StrongHyp-MaxSDHyp-StrongHyp-Max Hate0.440.320.260.200.150.98 0.08 0.73 0.11 0.67 0.16 Harassment0.400.290.230.190.140.96 0.06 0.82 0.18 0.73 0.15 Violence0.440.340.260.190.140.99 0.03 0.83 0.14 0.74 0.16 Self-harm 0.400.260.150.100.060.99 0.03 0.56 0.18 0.40 0.17 Sexual0.290.180.120.080.050.89 0.12 0.52 0.16 0.35 0.15 Shocking 0.510.370.260.170.131.00 0.01 0.80 0.11 0.66 0.18 Illegal activity0.370.270.190.130.100.97 0.07 0.65 0.15 0.56 0.21 Overall0.400.280.200.130.100.98 0.05 0.73 0.17 0.62 0.19 Table 4. Safe Latent Diffusion (SLD) applied on Stable Diffusion v2.0. Shown are the probabilities of generating an image containing inappropriate content as classified by the combined Q16/NudeNet classifier over the I2P benchmark. We note that the Q16 classifier is rather conservative and tends to classify some unobjectionable images as inappropriate. The false positive rate of the classifier is roughly equal to the probabilities reported for Hyp-Max. The expected maximum inappropriateness (the lower, the better) are bootstrap estimates of a model outputting the displayed percentage of inappropriate images at least once for 25 prompts (for further results see Appendix F). Subscript values indicate the standard deviation. SD-v1.4SD-v2 Benchmark SDSLDSDSLD Sexual (I2P)0.350.06◦0.290.05• Overall (I2P)0.390.09•0.400.10◦ Body-Ethnicity0.360.09◦0.120.06• Table 5. Comparison of Stable Diffusion in version 1.4 (SD-v1.4) and 2.0 (SD-v2). To train SD-v2 rigorous dataset filtering of sexual and nudity related content was applied. SLD’s mitigation on SD- v1.4 outperforms the standalone dataset filtering of SD-v2. The combination of dataset filtering and SLD leads to the highest mit- igation performance. generating images with 200 prompts from the “sexual” cat- egory, the Hyp-Max configuration is expected to yield at most 50% inappropriate images whereas the same number of prompts from the “shocking” category reaches almost 100% expected maximum inappropriateness. While some of this can actually be attributed to the varying effective- ness of SLD on different categories of inappropriateness, it is largely influenced by the high false positive rate of the Q16 classifier. Since we are considering the maximum over Nprompts, this effect quickly amplifies with growingN. Overall this raises the question if the expected maximum inappropriateness over largeNis a suitable metric for cases in which the false positive rate is high. Consequently, we decided to only report the results atN= 25in the main body of the paper. Qualitative Examples.Fig. 11 depicts a comparison of SD generated images with (right) and without (left) SLD. Eachinappropriatecategory (cf. Appendix C) is repre- sented by four images. The corresponding prompts can be found in Fig. 8. Moreover, Fig. 12 depicts the generated images displayed in the main text and their corresponding prompts. G. DrawBench User Studies Here, we provide further details on the conducted users studies on image fidelity and text alignment on the Draw- Bench dataset. Additionally, we present qualitative exam- ples of images generated from DrawBench in Fig. 13. G.1. Details on Procedure For each model configuration and DrawBench prompt we generated 10 images, amounting to 2000 total images per configuration. Each user was tasked with labeling 25 random image pairs—one being the SD reference image and the second one the corresponding image using SLD. For the image fidelity study users had to answer the ques- tion Which image is of higher quality? whereas the posed question for text alignment was Which image better represents the displayed text caption? In both cases the three answer options were • I prefer image A. • I am indifferent. • I prefer image B. To conduct our study we relied on Amazon Mechanical Turk where we set the following qualification requirements for our users: HIT Approval Rate over 95% and at least 1000 HITs approved. Additionally, each batch of image pairs was evaluated by three distinct annotator resulting in 30 decisions for each prompt. Annotators were fairly compensated according to Ama- zon MTurk guidelines. For the image fidelity task, users were paid $0.70 to label 25 images at an average of 8 min- utes need for the assignment. Our estimates suggested that the image text alignment task, requires more time since the text caption has to be read and understood. Therefore we paid $0.80 for 25 images with users completing the task af- ter 8.5 minutes on average. G.2. Details on Results The study results for each hyper parameter configuration on image fidelity and text alignment is depicted in Fig. 14. AlignmentFidelity 0 50 100 SLD Hyp-Weak No Preference Stable Diffusion AlignmentFidelity SLD Hyp-Medium No Preference Stable Diffusion AlignmentFidelity 0 50 100 SLD Hyp-Strong No Preference Stable Diffusion AlignmentFidelity SLD Hyp-Max No Preference Stable Diffusion Figure 14. User study results on Image Fidelity and Text Align- ment on DrawBench. For each prompt we generated ten images with each image pair being judged by three distinct users. Error bars indicate the standard deviation across the 30 user decisions for each prompt. Interestingly, on the perceived image fidelity we ob- served a transition from indecisive to preferring the safety- guided images with increasing guidance’ strength, which we assume to be grounded in the increased visualization of positive sentiments, for instance happy pets. A similar trend can be observed for text alignment, although the effect is considerably smaller. H. Stable Diffusion Implementation Algorithm 1 shows the pseudo code of SLD. In line with the Stable Diffusion’s policy giving its users maximum transparency and control on how to use the model, the used Algorithm 1Safe Latent Diffusion Require:model weightsθ, text conditiontext p , safety concepttext s and diffusion stepsT Ensure:s m ∈[0,1],ν t=0 = 0,β m ∈[0,1),λ∈[0,1], s S ∈[0,5000],δ∈[0,20],t= 0 DM←init-diffusion-model(θ) c p ←DM.encode(text p ) c s ←DM.encode(text s ) latents←DM.sample(seed) whilet̸=Tdo n ∅ ,n p ,n s ←DM.predict-noise(latents,c p ,c s ) μ t ←0 ▷Eq. (5) φ t ←s S ∗(n p −n s ) ▷Eq. (6) μ t ←where(n p −n s < λ,max(1,|φ t |))▷Eq. (5) γ t ←μ t ∗(n s −n ∅ ) +s m ∗ν t ▷Eq. (7) ν t+1 ←β m ∗ν t (1−β m )∗γ t ▷Eq. (8) ift≥δthen pred←s g ∗(n p −n ∅ −γ t )▷Eq. (3) else pred←s g ∗(n p −n ∅ )▷Eq. (2) end if latents←DM.update-latents(pred,latents) t←t+ 1 end while image←DM.decode(latents) safety concept can be adapted based on the user’s prefer- ences. I. SLD Ablation Studies Lastly, we provide some qualitative examples of the in- fluence of different hyper parameters on the generated im- age. Fig. 16 compares the effect of different warmup periods and thresholds. The example highlights that more warmup stepsδlead to less significant changes of the image compo- sition and simultaneously larger values forλalter the im- age more strongly. Furthermore, Fig. 17 shows the effect of varying scales of momentum. It shows that higher momen- tum also leads to stronger changes of the image and further accentuates that momentum scales over0.5may lead to is- sues in the downstream images such as significant artifacts. Additionally, Fig. 15 provides further insights on the in- ner workings of SLD by showcasing the effect of different hyper parameter configurations over the time steps of the diffusion process. Most importantly the Figure highlights that stronger hyper parameters configuration diverge from the original image much earlier in the diffusion process and change the image more substantially. (a)asian body (b)japanese body (c)u.s. american body Figure 5. Blurred images generated in Stable Diffusion for the text promptsasian body(a),japanese body(b) ,u.s. american body(c), and (d), respectively. All images containing a person were blurred for privacy reasons, as Stable Diffusion may generate images of real, existing people. (d)nigerian body Figure 5. Blurred images generated in Stable Diffusion for the text promptsasian body(a),japanese body(b) ,u.s. american body(c), and (d), respectively. All images containing a person were blurred for privacy reasons, as Stable Diffusion may generate images of real, existing people. Figure 6. Generation results on https://lexica.art for prompt “Japanese body” Figure 8. Stable Diffusion generationswithoutSLD. Exemplary images of each category contained in I2P. Starting from top each row depicts one category: hate, harassment, violence, self-harm, sexual, shocking, illegal activity. The corresponding I2P prompts are displayed on the right. 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (a) Hate 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (b) Harassment 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (c) Violence 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (d) Self-Harm 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (e) Sexual 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (f) Shocking 10100200 Number of Generations/Prompts 0 20 40 60 80 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (g) Illegal activity 10100200 Number of Generations/Prompts 10 100 Expected Maximum Inappropriatness Unsafe Hyp-Weak Hyp-Medium Hyp-Strong Hyp-Max (h) Over all categories Figure 10. Expected maximum of inappropriate content for each category of I2P. Figure 11. Stable Diffusion generationswithSLD. Within a pair the left image is generated solely by SD without applying SLD and the right image using SLD. Each row shows four images of the selectedinappropriatecategories of I2P, Appendix C, starting from top: hate, harassment, violence, self-harm, sexual, shocking, illegal activity. The corresponding prompts can be found in Fig. 8. Figure 12. Generated images used in the main text with corresponding prompts. Within a pair the left image is generated without SLD and right image with SLD. SDWeakMediumStrongMax A blue colored dog. A shark in the desert. A couple of glasses are sitting on a table. A mechanical or electrical device for measuring time. A pear cut into seven pieces arranged in a ring. Bzaseball galove. A carrot on the left of a broccoli. Octothorpe. A photo of a confused grizzly bear in calculus class. New York Skyline with 'Diffusion' written with fireworks on the sky. Figure 13. Example images generated on DrawBench with SD (left) and SLD, showing one prompt for each category. 51015202530354045final Diffusion steps SD Weak Medium Strong Max Figure 15. Visualization of SLD over the diffusion process. Notice how visible cloth are generated earlier in the diffusion process with stronger hyper parameters. Additionally, the strongest setting never yields any inappropriate images at any point in the process. 0.000.070.150.220.30 Thresholdλ 5 9 13 17 21 25 Warmup steps δ Figure 16. Effect on image generation using different parameters forδandλ. Guidance scales are fixed ats g = 15ands S = 100and no momentum is not used, i.e.s m = 0. The image on the bottom left is close to the original image without SLD. 0.300.400.500.600.70 Momentumβ m 0.80 0.64 0.48 0.32 0.16 0.00 Momentum scale s m Figure 17. Effect on image generation using different momentum parameters. Guidance scales are fixed ats g = 15ands S = 100, with fixed warmup periodδ= 5and fixed thresholdλ= 0.015. This further highlight that values fors m >0.5are likely to produce significant image artifacts. J. I2P Datasheet J.1. Motivation Q1For what purpose was the dataset created?Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. • Inappropriate Image Prompts (I2P) was created as a benchmark to evaluate inappropriate degen- eration in generative text-to-image models such as DALL-E, Imagen or Stable Diffusion. It is in- spired by REALTOXICITYPROMPTS, which is a benchmark for measuring toxic degeneration in language models. However, since these prompts do not describe visual content, it is not applicable to text conditioned image generation. The pur- pose of I2P is to fill this gap. The I2P benchmark dataset and accompanying testbed can be used to measure the degree to which a model gener- ates images that represent the concepts of hate, harassment, violence, self-harm, sexual content, shocking images, and illegal activity. Q2Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? • This dataset is presented by a research group lo- cated at the Technical University Darmstadt, Ger- many, affiliated with the Hessian Center for AI (hessian.AI), Aleph Alpha and LAION. Q3Who funded the creation of the dataset?If there is an associated grant, please provide the name of the grantor and the grant name and number. • The creation of the dataset was support by the German Center for Artificial Intelligence (DFKI) project “SAINT” and the Federal Ministry of Education and Research (BMBF) under Grant No. 01IS22091. Furthermore, it benefited from the ICT-48 Network of AI Research Excellence Center “TAILOR” (EU Horizon 2020, GA No 952215), the Hessian research priority program LOEWE within the project WhiteBox, and the Hessian Ministry of Higher Education, and the Research and the Arts (HMWK) cluster projects “The Adaptive Mind” and “The Third Wave of AI”. Q4Any other comments? • No. J.2. Composition Q5What do the instances that comprise the dataset represent (e.g., documents, photos, people, coun- tries)?Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions be- tween them; nodes and edges)? Please provide a de- scription. • The dataset contains textual image descriptions potentially leading to images displaying inappro- priate content. This includes the conceptshate, harassment, violence, self-harm, sexual content, shocking images and illegal activity. On aver- age, the prompts are made up of 20 tokens, and we could not observe an apparent correlation be- tween frequent words and the connection to in- appropriate images of these prompts. We made our dataset openly available on https: //huggingface.co/datasets/AIML-TUDA/i2p. Q6How many instances are there in total (of each type, if appropriate)? • I2P contains 4703 unique prompts assigned to at least one of the seven categories above. Q7Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was vali- dated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more di- verse range of instances, because instances were with- held or unavailable). • For the seven concepts mentioned above, we used 26 keywords and phrases describing them in more detail and collected up to 250 real-world text prompts for each. For a given keyword, we crawled the prompts of the top 250 images re- turned by https://lexica.art. Lexica is a collection of real-world, user-generated prompts for Stable Diffusion sourced from its official discord. Q8What data does each instance consist of?“Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description. • Along the provided textual prompts we provide following meta information: –categories: One or multiple of the 7 cat- egories mentioned above that yield this prompt. –hard: Indicates if this is a hard prompt to remove inappropriate content from. Prompts are labeled as hard if at least half of their generated images are inappropriate. –inappropriate percentage: Percentage of in- appropriate images as classified by Q16 and NudeNet out of 10 generated images using Stable Diffusion. –nudity percentage: Percentage of images as depicting explicit nudity as per the NudeNet out of 10 generated images using Stable Dif- fusion. –q16 percentage Percentage of inappropriate images as classified by Q16 out of 10 gener- ated images using Stable Diffusion. –stable diffusion’s safety percentage: Per- centage of inappropriate images as classified by the SD NSFW safety checker out of 10 generated images using Stable Diffusion. –prompt toxicity:The toxicity score of the text prompt according to the Perspec- tiveAPI. –lexica url: URL to the original prompt and the respective images in lexica for reference. –stable diffusion’s seed: Stable diffusion seed used in our image generation. –stable diffusion’s guidance scale: Stable dif- fusion guidance scale used in our image gen- eration. –stable diffusion’s image width: Stable diffu- sion image width used in our image genera- tion. –stable diffusion’s image height: Stable dif- fusion image height used in our image gen- eration. Q9Is there a label or target associated with each in- stance?If so, please provide a description. • There is no hard class label, but each prompt is assigned to at least one of the categorieshate, harassment, violence, self-harm, sexual content, shocking images and illegal activity. Further, we provide toxicity score of the text prompt accord- ing to the PerspectiveAPI. And a flag (‘hard’) in- dicating if this is a hard prompt to remove inap- propriate content from. Prompts are labeled as hard if at least half of their generated images are inappropriate using Stable Diffusion. Q10Is any information missing from individual in- stances?If so, please provide a description, explain- ing why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. • No. Q11Are relationships between individual instances made explicit (e.g., users’ movie ratings, social net- work links)?If so, please describe how these relation- ships are made explicit. • No. Q12Are there recommended data splits (e.g., training, development/validation, testing)?If so, please pro- vide a description of these splits, explaining the ratio- nale behind them. • No. Q13Are there any errors, sources of noise, or redundan- cies in the dataset?If so, please provide a description. • Image retrieval in lexica is based on the similar- ity of an image and search query in CLIP embed- ding space. Therefore, the collected prompts are not guaranteed to generate inappropriate content, but the probability is high, as demonstrated in our manuscript’s evaluation. Q14Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?If it links to or relies on ex- ternal resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there offi- cial archival versions of the complete dataset (i.e., in- cluding the external resources as they existed at the time the dataset was created); c) are there any restric- tions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate. • This dataset is self-contained. Since it is crawled from a database containing user-generated tex- tual prompts to generate images, we provide a link to each prompt’s origin also displaying the resulting images.While not relevant for the datasets purpose to benchmark image-generative models, we provide all the necessary information to reproduce the original images. Q15Does the dataset contain data that might be con- sidered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non- public communications)?If so, please provide a de- scription. • No. Q16Does the dataset contain data that, if viewed di- rectly, might be offensive, insulting, threatening, or might otherwise cause anxiety?If so, please describe why. • Since the purpose of the dataset is to evaluate the extent to which a model produces inappropriate images, it naturally contains data that falls into this category. However, we could not observe an apparent correlation between frequent words and the connection to inappropriate images of these prompts. Specifically, we only find a weak correlation between the toxicity of a prompt and the inappropriateness of images it generates. In fact, prompts with low toxicity scores still have unforeseen high probabilities of generating in- appropriate images. Furthermore, out of 4702 prompts, a mere 1.5% are toxic. Q17Does the dataset relate to people?If not, you may skip the remaining questions in this section. • In a few cases, a prompt contains the names of real people. Q18Does the dataset identify any subpopulations (e.g., by age, gender)? • No. Q19Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset?If so, please describe how. • No. Q20Does the dataset contain data that might be con- sidered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, reli- gious beliefs, political opinions or union member- ships, or locations; financial or health data; bio- metric or genetic data; forms of government identi- fication, such as social security numbers; criminal history)?If so, please provide a description. • No. Q21Any other comments? • No. J.3. Collection Process Q22How was the data associated with each instance ac- quired?Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. • For the seven concepts mentioned above, we used 26 keywords and phrases: –hate: “hate” –harassment: “mocking”, “bullying”, “threat- ening”, “harassment” –violence: “humiliation”, “violence acts”, “suffering”, “violence” –self-harm:“suicide”, “eating disorder”, “self-harm”, “attempts at harming oneself” –sexual: “sexual acts”, “content meant to arouse sexual excitement”, “sexual”, “nu- dity” –shocking: “profane subjects that may shock or disgust”, “bodily fluids”, “shocking”, “obscene gestures” –illegal activity: “vandalism”, “illegal activ- ity”, “drug use”, “theft” describing them in more detail and collected up to 250 real-world text prompts for each. For a given keyword, we crawled the prompts of the top 250 images returned by https://lexica. art. Lexica is a collection of real-world, user- generated prompts for SD sourced from its of- ficial discord server. It stores the prompt, seed, guidance scale, and image dimensions used in the generation to facilitate reproducibility. Im- age retrieval in lexica is based on the similarity of an image and search query in CLIP embed- ding space. Therefore, the collected prompts are not guaranteed to generate inappropriate content, but the probability is high, as demonstrated in our evaluation. Q23What mechanisms or procedures were used to col- lect the data (e.g., hardware apparatus or sensor, manual human curation, software program, soft- ware API)?How were these mechanisms or proce- dures validated? • We ran a preprocessing script in python, over multiple of small CPU nodes to extract the prompts from https://lexica.art. They were val- idated by manual inspection of the results and post processing using the PerspectiveAPI and Stable Diffusion to create further meta informa- tion such as the label “hard” and the prompts tox- icity score, as described before. Q24If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, prob- abilistic with specific sampling probabilities)? • Image retrieval in lexica is based on the similarity of an image and search query in CLIP embedding space. We used the top 250 query results to given keywords. Q25Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? • No crowdworkers were used in the collection process of the dataset. Co-authors of the corre- sponding manuscript wrote the collection scripts and validated the data. Q26Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)?If not, please describe the timeframe in which the data associated with the in- stances was created. • The data was collected from September 2022 to October 2022, but those who created the crawled prompts might have included content from before then. A certain date for a prompt is not available but based on the release date of Stable Diffusion they were created in 2022. Q27Were any ethical review processes conducted (e.g., by an institutional review board)?If so, please pro- vide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation. • We corresponded with the ethical guidelines of Technical University of Darmstadt. Q28Does the dataset relate to people?If not, you may skip the remaining questions in this section. • No. Q29Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)? • We retrieve the data from https://lexica.art which provides an API to crawl its content. Q30Were the individuals in question notified about the data collection?If so, please describe (or show with screenshots or other information) how notice was pro- vided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notifi- cation itself. • N/A Q31Did the individuals in question consent to the collec- tion and use of their data?If so, please describe (or show with screenshots or other information) how con- sent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented. • N/A Q32If consent was obtained, were the consenting indi- viduals provided with a mechanism to revoke their consent in the future or for certain uses?If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate). • N/A Q33Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted?If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. • The benchmark’s dataset was analyzed and used to evaluate Stable Diffusion in version 1.4 and 2.0. The results are openly available at https:// arxiv.org/abs/2211.05105. Q34Any other comments? • No. J.4. Preprocessing, Cleaning, and/or Labeling Q35Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tok- enization, part-of-speech tagging, SIFT feature ex- traction, removal of instances, processing of miss- ing values)?If so, please provide a description. If not, you may skip the remainder of the questions in this sec- tion. • The data collection described above yielded du- plicate entries, as some retrieved images were found among multiple keywords. These dupli- cates were removed. We provide the raw tex- tual prompt along with meta information which was collected using Stable Diffusion itself as well as the PerspectiveAPI (https://github.com/ conversationai/perspectiveapi). Q36Was the “raw” data saved in addition to the prepro- cessed/cleaned/labeled data (e.g., to support unan- ticipated future uses)?If so, please provide a link or other access point to the “raw” data. • Textual prompts are provided as raw data. Q37Is the software used to preprocess/clean/label the instances available?If so, please provide a link or other access point. • To post-process the data we used: –https : / / github . com / conversationai / perspectiveapi resulting in the toxicity score of a prompt. –https : / / huggingface . co / CompVis / stable - diffusion- v1- 4 to generate images in order to create further labels using the two follow- ing tools. –https://github.com/ml- research/Q16 a tool to classify the inappropriateness of a image. –https : / / github. com / notAI - tech / NudeNet a tool classify whether an image contains nude/sexual content. Q38Any other comments? • No. J.5. Uses Q39Has the dataset been used for any tasks already?If so, please provide a description. • The dataset has been used to evaluate the inap- propriate degeneration in Stable Diffusion (https: //arxiv.org/abs/2211.05105). Q40Is there a repository that links to any or all papers or systems that use the dataset?If so, please provide a link or other access point. • No. Q41What (other) tasks could the dataset be used for? • The dataset should only be used to measure inap- propriate degeneration in text-conditioned image generators. Q42Is there anything about the composition of the dataset or the way it was collected and prepro- cessed/cleaned/labeled that might impact future uses?For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereo- typing, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms? • The dataset was collected based on images gen- erated by Stable Diffusion. Further advances in AI-driven image generation could lead to novel issues, i.e. risks related to inappropriate content. Further, inappropriateness is not limited to these seven concepts, varies between cultures, and con- stantly evolves. Here we restricted ourselves to images displaying tangible acts of inappropriate behavior. Q43Are there tasks for which the dataset should not be used?If so, please provide a description. • It should not be used to increase the inappropri- ateness of AI-generated images. Q44Any other comments? • No. J.6. Distribution Q45Will the dataset be distributed to third parties out- side of the entity (e.g., company, institution, organi- zation) on behalf of which the dataset was created? If so, please provide a description. • Yes, the dataset will be open-source. Q46How will the dataset be distributed (e.g., tarball on website, API, GitHub)?Does the dataset have a dig- ital object identifier (DOI)? • The data will be available through Huggingface datasets. Q47When will the dataset be distributed? • December 2022 and onward. Q48Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. • MIT license Q49Have any third parties imposed IP-based or other restrictions on the data associated with the in- stances?If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. • The institutions mentioned above own the meta- data and release as MIT license. • We do not own the copyright of the text. Q50Do any export controls or other regulatory restric- tions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. • No. Q51Any other comments? • No. J.7. Maintenance Q52Who will be supporting/hosting/maintaining the dataset? • Huggingface will support hosting of the meta- data. • The creators will maintain the samples dis- tributed. Q53How can the owner/curator/manager of the dataset be contacted (e.g., email address)? •schramowski, brack@cs.tu-darmstadt.de Q54Is there an erratum?If so, please provide a link or other access point. • There is no erratum for our initial release. Er- rata will be documented as future releases on the dataset website. Q55Will the dataset be updated (e.g., to correct label- ing errors, add new instances, delete instances)?If so, please describe how often, by whom, and how up- dates will be communicated to users (e.g., mailing list, GitHub)? • I2P will not be updated unless there is a substan- tial reason. However a future I2P could contain more concepts of inappropriateness and updated notions. Specific samples can be removed on re- quest. Q56If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)?If so, please describe these limits and explain how they will be enforced. • People may contact us atschramowski, brack@cs.tu-darmstadt.detoaddspecific samples to a blacklist. Q57Will older versions of the dataset continue to be supported/hosted/maintained?If so, please describe how. If not, please describe how its obsolescence will be communicated to users. • N/A. Q58Ifotherswanttoextend/augment/build on/contribute to the dataset, is there a mech- anism for them to do so?If so, please provide a description.Will these contributions be vali- dated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users?If so, please provide a description. • Unless there are grounds for significant alteration to certain samples, extension of the dataset will be carried out on an individual basis. Q59Any other comments? • No.

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