SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation
Authors: Jaehong Yoon
Shoubin Yu
Vaidehi Patil
Huaxiu Yao
Mohit Bansal
Venue: ICLR 2025 Poster (2025)
Keywords: Safe generation
Text-to-image generation
Text-to-video generation
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe generation remove harmful concepts from models but face several challenges: (1) They cannot instantly remove harmful or undesirable concepts (e.g., artist styles) without additional training. (2) Their safe generation capabilities depend on collected training data. (3) They alter model weights, risking degradation in quality for content unrelated to the targeted toxic concepts. To address these challenges, we propose SAFREE, a novel, training-free approach for safe text-to-image and video generation, that does not alter the model's weights. Specifically, we detect a subspace corresponding to a set of toxic concepts in the text embedding space and steer prompt token embeddings away from this subspace, thereby filtering out harmful content while preserving intended semantics. To balance the trade-off between filtering toxicity and preserving safe concepts, SAFREE incorporates a novel self-validating filtering mechanism that dynamically adjusts the denoising steps when applying the filtered embeddings. Additionally, we incorporate adaptive re-attention mechanisms within the diffusion latent space to selectively diminish the influence of features related to toxic concepts at the pixel level. By integrating filtering across both textual embedding and visual latent spaces, SAFREE ensures coherent safety checking, preserving the fidelity, quality, and safety of the generated outputs. Empirically, SAFREE achieves state-of-the-art performance in suppressing unsafe content in T2I generation (reducing it by 22% across 5 datasets) compared to other training-free methods and effectively filters targeted concepts, e.g., specific artist styles, while maintaining high-quality output. It also shows competitive results against training-based methods. We further extend SAFREE to various T2I backbones and T2V tasks, showcasing its flexibility and generalization. As generative AI rapidly evolves, SAFREE provides a robust and adaptable safeguard for ensuring safe visual generation.

Published as a conference paper at ICLR 2025
SAFREE: TRAINING-FREE AND ADAPTIVE GUARD FOR SAFE TEXI-TO-IMAGE AND VIDEO GENERATION
Jaehong Yoon
Shoubin Yu*
Vaidehi Patil
Huaxiu Yao
Mohit Bansal
UNC Chapel Hill
htt.ttt...tttbiiioott io/
SAFREE: A Strong, Adaptive, and Training-Free Concept Guard For Safe T21
(Ours) SAFREE
SD1.4
RECE A
CA
UCE A
SLD
Remove Nudity
Prompt:
Swammp princess, art station, full body image...
Gogh
Prompt: life vase sunflowers, ... Gogh's iconic paintings Apply SAFREE to Diffusion Transformer and T2V SD-v3
SD-v3 SAFREE (Ours)
Nudity
Prompts: [adversarial taxic prompts] CogVideoX
CogVideoX SAFREE (Ours)
Animal
Prompt: pig *** by buttcher, ..... ARA is everywhere.
Figure 1: We present SAFREF, an adaptive, training-free method for T2T that filters variety of defined concepts. SAFREE enables safe faithful generation that can remove toxic concepts create a safer version of inappropriate prompts without requiring any model updates. SAFREE is also versatile and adaptable. enabling its application to other backbones (such as Diffusion Transformer) and across different applications (like T2V) for enhanced safe generation. Fire icon: training/editing- based methods that alter model weights. Snowflake sensitive text prompts and generated results for display purposes.
training-fre free methods with no weights updating. We manually masked/blurred
Content warning: this paper contains content that may inappropriate or offensive, such as vio- lence, sexually explicit content, and negative stereotypes and actions.
ABSTRACT
Recent advances in diffusion models have significantly enhanced their ability generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unleaming/editing generation remove harmful concepts from models but face several challenges; (1) They cannot instantly remove harmful or undesirable concepts (e.g., artist styles) without additional training. (2) Their safe generation capabilities depend on col- lected training data. (3) They alter model weights, risking degradation in quality for content unrelated to the targeted toxic concepts. To address these challenges,
methods for safe
"Equal contribution.

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Published as a conference paper at ICLR 2025
we propose SAFREE, novel, training-free approach for safe text-to-image and video generation. that does not alter the model's weights. Specifically, we de- tect subspace corresponding to set of toxic concepts in the text embedding space and steer prompt token embeddings away from this subspace, thereby fil- tering out harmful content while preserving intended semantics. To balance the trade-off' between filtering toxicity and preserving safe concepts, SAFREE incor- porates a novel self-validating filtering mechanism that dynamically adjusts the denoising steps when applying the filtered embeddings. Additionally, we incor- porate adaptive a--ateeetion mechanisms within the diffusion latent space to selec- tively diminish the influence of features related to toxic concepts at the pixel level. By integrating filtering across both textual embedding and visual latent spaces, SAFREE ensures coherent safery checking, preserving the fidelity, quality, and safety of the generated outputs. Empirically, SAFREE achieves state-of-the-art performance in suppressing unsafe content in T2I generation (reducing it by 22% % across 5 datasets) compared to other training-free methods and effectively filters targeted concepts, e.g., specific artist styles, while maintaining high-quality out- put. It also shows competitive results against training-based methods. We further extend SAFREE to various T21 backbones and T2V tasks, showcasing its flexi- bility and generalization. As generative AI rapidly evolves, SAFREE provides a robust and adaptable safeguard for ensuring safe visual generation.
INTRODUCTION
Recent advancements in Generative Al have significantly impacted various modalities, including text (Brown, 2020; Dubey et al., 2024), code (Chen et al., 2021; Liu et al., 2024a; Zhong et al., 2024), audio (Kreuk et al., 2022; Copet et al., 2024), image (Podell et al., 2023; Tian et al., 2024), and video generation (Ho et al., 2022; Kondratyuk et al., 2023; Yoon et 2024; Yang et al., 2024c). Generation tools such as DALL-E 3 (OpenAI, 2023), Midjourney (Midjourney, 2024), Sora (ope- nai, 2024), and KLING (Kuaishou, 2024) have seen significant growth, enabling wide range of applications in digital art, AR/VR, and educational content creation. However, these tools/models also carry the risk of generating content with unsafe concepts such as bias, discrimination, sex, or violence. Moreover, the definition of "unsafe content" varies according to societal perceptions. For example, individuals with Post- Trat-mrauattic Stress Disorder (PTSD) might find specific images (e.g., skyscrapers, deep-sea scenes) distressing. This underscores the need for an adaptable, flexible solution to enhance the safety of generative Al while considering individual sensitivities.
To tackle these challenges, recent research has incorporated safety mechanisms in diffusion models. Unlearning methods (Zhang et al., 2023a; Huang et al., 2023; Park et al., 2024; Wu et al., 2024) fine- tune models to remove harmful concepts, but they lack adaptability and are less practical due to the significant training resources they require. Model editing methods (Orgad et al., 2023; Gandikota et al., 2024; Xiong et al., 2024) modify model weights to enhance safety, but they often degrade out- put quality and make it challenging to maintain consistent model behavior. A promising alternative is training-free, filtering- based methods that exclude unsafe concepts from input prompts without altering the model's original capabilities. However, prior training-frea filtering- methods en- counter two significant challenges: (1) they may not effectively guard against implicit or indirect triggers of unsafe content, as highlighted in earlier studies (Deng & Chen, 2023), and (2) our find- ings indicate that prompts subjected to hard filtering can result in distribution shifts, leading to quality degradation even without modifying the model weights. Thus, there is an urgent need for an efficient and adaptable mechanism to ensure safe visual generation across diverse contexts.
This paper presents SAFREE, a training-free adaptive alug-and-play mechanism for any diffusion- based generative model to ensure safe generation without altering well-trained model weights. SAFREE employs unsafe concept filtering in both textual prompt embedding and visual latent space, thereby enhancing the fidelity, quality, and efficiency for safe visual content generation. Specifically, SAFREE first identifies the unsafe concept subspace, i.e., the subspace within the in- put text embedding space that corresponds to undesirable concepts, by concatenating the column vectors of unsafe keywords. Then, to measure the proximity of each input prompt token to the unsafe/toxic subspace, we mask each token in the prompt and calculate the projected distance of the masked prompt embedding the subspace. Given the proximity, SAFREE aims to filter out
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tokens that drive the prompt embedding closer to the unsafe subspace. Rather than directly moving or replacing unsafe tokens which can compromise the coherence of the input prompt and degrade generation
Soallty-SAFREE efficiently projects the identified unsafe tokens into space orthogonal to the unsafe concept subspace while maintaining them on the original input embedding space. Such orthogonal projection design aims to preserve the overall integrity and the safe content within the original prompt while filtering out harmful content in the embedding space. To balance the trade-off between filtering out toxicity and preserving safe concepts (examples shown in Fig. 1), SAFREE incorporates novel self-validating filtering scheme, which dynamically adjusts the num- ber of denoising steps for applying filtered embeddings, enhancing the suppression of undesirable prompts only when needed. Additionally, since we observe that unsafe content usually emerges at the regional pixel level, SAFREE extends filtering to the pixel space using novel adaptive latent re-attention mechanism within the diffusion latent space. It selectively reduces the influence of fea- tures in the frequency domain tied to the detected unsafe prompt, ensuring desirable outputs without drawing attention to those contents. In the end, SAFREE filters out unsafe content simultancously in both the embedding and diffusion latent spaces. This approach ensures flexible and adaptive safe T2I/T2V generation that efficiently handles a broad range of unsafe concepts without extra training or modifications to model weights, while preserving the quality of safe outputs.
Empirically, SAFREE achieves the state-of-the-art performance on five popular T2I benchmarks (12P (Schramowski et al., 2023), P4D (Chin et al., 2024), Ring-A-Bell (Tsai et al., 2024), MMA- Diffusion (Yang et al., 2024a), and UnlearnDiff Zhang al. (2023b)) outperforming other training- free safeguard methods with superior efficiency, lower resource use, and better inference-time adaptability. We further apply SAFREE to various T2I diffusion backbones (e.g., SDXL (Podell et al., 2023), SD-v3 (stabilityai, 2024)) and T2V models (ZeroScope (zeroscope, 2024), VogVideoX (Yang et al., 2024c)), showcasing SAFREE's strong generalization and flexibility by effectively managing unsafe concept outputs across different models and tasks. As generative Al advances, SAFREE establishes a strong baseline for safety, promoting ethical practices across ap- plications like image and video synthesis meet the Al community's needs.
Our contributions are summarized as:
We propose SAFREE, a strong, adaptive, and training-free safeguard for T21 and T2V generation. This ensures more reliable and responsible visual content creation by jointly filtering out unsafe concepts in textual embeddings and visual latent spaces with conceptual proximity analysis.
SAFREE achieves state-of-the-ar performance among training-free methods for concept removal in visual generation while maintaining high-quality outputs for desirable concepts, and it exhibits competitive results compared to training-based methods while maintaining better visual quality.
SAFREE effectively operates across various visual diffusion model architectures and applica- tions, demonstrating strong generalization and flexibility,
2 RELATED WORK
2.1 T2I ATTACKS
Recent works address vulnerabilities in generative models, including LLMS (Zou al., 2023; Patil et al., 2023; Wei et al., 2023; Liu et al., 2024c). VLMs (Zhao et al., 2023; Zong et al., 2024; Patil et al., 2024), and T2I models (Yang et al., 2024b; Wang et al., 2024; Li et al., 2024c). Cross- modality jailbreaks, like Shayegani et al. (2023), pair adversarial images with prompts to disrupt VLMs without accessing the language model. Tools like Ring-A-Bell (Tsai et al., 2024) and auto- mated frameworks by Kim et al. (2024b) and Li et al. (2024a) focus on model-agnostic red-teaming and adversarial prompt generation, revealing safety flaws. Methods by Ma al. (2024), Yang et al. (2024a), and Mehrabi et al. (2023) exploit text embeddings and multimodal inputs to bypass safe- guards, using strategics like adversarial prompt optimization and in-context learning (Chin et al., 2024; Liu et al., 2024d). These works highlight vulnerabilities in T2I models.
2.2 SAFE T21 GENERATION
bonining-bssed Training-base based approaches (Lyu et al., 2024; Pham et al., 2024; Zhang et al., 2024) ensure safe T21 generation by removing unsafe elements, in Li et al. (2024c) and Gandikota
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Masked Token
P
Embbrddings
Psafe
Prequinny Danoei
Text Prompt]
PV
Self- Validating
Adaptive
Seeomar aeteentor brief
Encoder
SM
Latent Re-attn
Filtering
Telietroooggh L. ofter coercceeecennn comcurt
Houggh saconrtin
Pproj +> p
Platme deenaaking stops
Paife
Remove Nodiy
Image Diffusion
Safe Images
purpusaling soctia
Poro
PA
dy
Concepts to
Narth taller (at))
Text Prompt]
Paufe orm Potre Denobing stups Video
Crge Vildence
Boperss and CERIAUEE onihe
Toxic Taken Detection
Diffusion (wit) UNer DVT)
Darmows Phoderccw
Selective Orthogonal Projection
Safe Videos
Figure 2: SAFREE framework. Based on proximity analysis between the masked token embed- dings and the toxic subspace C, we detect unsafe tokens and project them into orthogonal the toxic concept (in red), but still be in the input space I (in green). SAFREE adaptively controls the filtering strength in an ueput-dependent manner, which also regulates latent-level re-attention mechanism. Note that our approach can be broadly applied to various image and video diffusion backbones.
et al. (2023), or using negative guidance. Adversarial training frameworks like Kim et al. (2024a) neutralize harmful text embeddings, while works like Das al. (2024) and Park et al. (2024) filter harmful representations through concept removal and preference optimization. Fine-tuning methods such as Lu et (2024)'s cross-attention refinement and Heng & Soh (2023)'s continual learning remove inappropriate content. Latent space manipulation, explored by Liu et al. (2024b) and Li et al. (2024b), enhances safety using self-supervised learning. While effective, they require extensive fine-tuning, degrade image quality, and lack inference-time adaptation. SAFREE is training-frree, dynamically adapts to concepts, and controls filtering strength without modifying weights, offering efficient safety across T2I and T2V models.
Training-fre free: Training-free free methods for safe T2I generation adjust model behavior without retrain- ing. These include (1) Closed-form weight editing, like Gandikota et al. (2024)'s model projection editing and Gong et al. (2024)'s target embedding methods, which remove harmful content while preserving generative capacity, and Orgad et al. (2023)'s minimal parameter updates diffusion models. (2) Non-weight editing, such as Schramowski et al. (2023)'s Safe Latent Diffusion using classifier-free guidance and Cai et al. (2024)'s prompt refinement framework. However, these meth- ods lack robustness and test-time adaptation. Our training -ree method dynamically adjusts filtering based on prompts, and extends other architectures and video tasks without weight edits, offering improved scalability and efficiency.
SAFREE: TRAINING-FREE AND ADAPTIVE GUARD FOR SAFE TEXT-TO-IMAGE AND VIDEO GENERATION
We propose SAFREE, training-free yet adaptive remedy for safe T2I and T2V generation (Fig. 2). We first determine the trigger tokens that can potentially induce toxicity based on the proximity be- tween masked imput prompt embeddings and toxic concept subspace (Sec. 3.1). The detected trigger token embeddings are projected onto a subspace orthogonal to the toxic concept subspace, while maintaining them within the input space (Sec. 3.2). SAFREE automatically adjusts the number of denoising timesteps conditioned on the text inputs through self-validating filtering mechanism (Sec. 3.3). Additionally, we introduce an adaptive re-attention strategy in the latent space during the de-noising process, facilitating robust joint filtering mechanism actoss both text and visual embed- ding spaces (Sec. 3.4). Finally, we extend our approach to high-resolution models like SDXL (Podell al., 2023), DiT-based image diffusion (SD-v3) (Peebles & Xie, 2023), and representative text-to- video generative models such as ZeroScopeTTv (zeroscope, 2024) and CogVideoX (Yang et al., 2024c) (Sec. 3.5).
3.1 ADAPTIVE TOKEN SELECTION BASED ON TOXIC CONCEPT SUBSPACE PROXIMITY
Random noise 6o ~ (O, I) sampled from a Gaussian distribution can lead to the generation of un- safe or undesirable images in diffusion models, primarily due inappropriate semantics embedded
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in the text, which conditions the iterative denoising process (Rombach et al., 2022; Ho & Salimans, 2022). To mitigate this risk, recent studies (Miyake et al., 2023; Schramowski et al., 2023; Ban et al., 2024a) have demonstrated the effectiveness of using negative prompts. In this approach, the model aims to predict the refined noise from én over several autoregressive denoising steps, synthesizing an image conditioned on the input (i.e., the input text prompt). The denoising process of diffusion models, parameterized by 0, at timestep follows the classifier-free guidance approach:
E, (1 (1+ w) Eo (Zc, wep (zz, 0),
where is a hyperparameter controlling the guidance scale. p and 0 denote the embedding of the input prompt and null text, respectively. The negative prompt is applied by replacing @ with the embedding of the negative prompt. We note that even if the input prompts are adversarial and unreadable to humans, such as those generated by adversarial attack methods through prompt opti- mization (Tsai al., 2024; Yang al., 2024a; Chin al,, 2024), they are still encoded within the same text embedding space, like CLIP (Radford al., 2021), highlighting the importance and ne- cessity of the feature embedding level unsafe concepts identification for safeguard. To this end, we propose detect token embeddings that trigger inappropriate image generation and transform them to be distant from the toxic concept subspace: C = [co: C1; ci; ...; CK-1l E R K which represents the embedding matrix that denotes the toxic subspace, where each column vector C2 corresponds to the embedding of the relevant text associated with the k-th user-defined toxic concept, such as Sexual Acts Pornography for Nudity concept.
Detecting Trigger Tokens Driving Toxic Outputs. To assess the relevance of specific tokens in the input prompt to the toxic concept subspace, we design pooled input embedding P E R that averages the token embeddings in p while masking out the i-th token. Suppose ZE IR K be the vector of coefficients (i.e., the projection coordinates) of p,, onto C, it satisfies,
C ( (D, =C)= (C=== =0,
z == -
We estimate the conceptual proximity of a token in the input prompt with C by computing the distance between the pooled text embedding obtained after masking out (i.e., removing) the corre- sponding token and C. The residual vector di which is the component of P orthogonal to the subspace C is then formulated as follows:
di = P CC -C CCTCC) "CT) Die
=II-
where Pc CC
and I E IRDDD denotes the identity matrix (See Fig. 2 middle left). A longer residual vector distance indicates that the removed token in the prompt is more strongly associated with the concept we aim to eliminate. In the end, derive masked vector E R (where N denotes the token length) to identify tokens related to the target concept, allowing us to subtly project them within the input token subspace while keeping them distant from the toxie concept subspace. We obtain a set of distances of masked token embeddings P i E [0, N 1] to the concept subspace, D(p(C), and select tokens for masking by evaluating the disparity between each token's distance and the average distance of the set, excluding the token itself:
D(p(C) = [l]d,oll2, ||db, 1l2,,,,,.., [12)]
1 if |d,ill2 > (1 + a)) mean (D(plC). (i)),
(4)
m, o otherwise,
where a is non-negative hyperparameter that controls the sensitivity of detecting concept-relevant tokens. X. (idelete(i denotes an operation that produces list X removing the i-th item. We set = 0.01 for all experiments in this paper, demonstrating the robustness of our approach to across T2I generation tasks with varying concepts. We project the detected token embedding (i.e., m, 1)
to safer embedding space (See Sec. 3.2).
3.2 SAFE GENERATION VIA CONCEPT ORTHOGONAL TOKEN PROJECTION
We aim to project toxic concept tokens into a safer space to encourage the model to generate ap- propriate images. However, directly removing or replacing these tokens with irrelevant ones, such
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as random tokens replacing the token embeddings with null embeddings, disrupts the coherence between words and sentences, compromising the quality of the generated image to the safe input prompt, particularly when the prompt is unrelated to the toxic concepts. To address this, we pro- pose projecting the detected token embeddings into a space orthogonal to the toxic concept subspace while keeping them within the input space ensure that the integrity of the original prompt is pre- served as much as possible. We begin by formalizing the input space I using pooled embeddings
from masked prompts as described in See. 3.1, such that I = P PA.ii...PPN E RRD
Given the projection matrix into input space I formulated by Pt I [ (derived by Eq. (3)), we perform selective detoxification of input token embeddings based on the obtained token masks that project assigned tokens into PI and to be orthogonal to Pe:
Pyroj = P1 Pc) P.
(5)
Paofe m O Pyroj + (1 m) Op,
where @ indicates an element-wise multiplication operator. That is, for the i-th token, we use the projected safe embeddings Pproj.: only if it is detected as toxic token (m, C Pproj. m, 1); otherwise, we retain the original (safe) token embeddings pi, since (1 m2) O Pi, 0.
3.3 ADAPTIVE CONTROL OF SAFEGUARD STRENGTHIS WITH SELF-VALIDATING FILTERING
While our approach so far adaptively controls the number of token embeddings to be updated, sometimes lacks flexibility in preserving the original generation capabilities for content outside the target concept. Recent observations (Kim et al., 2024a; Ban et al., 2024a) suggest that different denoising timesteps in T2I models contribute unevenly to generating toxic or undesirable content. Based on this insight, we propose a self- validating filtering mechanism during the denoising steps of the diffusion model that automatically adjusts the number of denoising timesteps conditioned on the obtained embedding (middle in Fig. 2). This mechanism amplifies the model's filtering capability when the input prompt is deemed undesirable, while approximating the original backbone model's generation for safe content. In the end, our updated input text embedding Psafree at different denoising step is determined follows:
Psafe if t L round(l'),
sigmoid - cos(p. (p. Ppros)), Peafree
(6)
(p
otherwise,
where y is hyperparameter (y 10 throughout the paper) and cos represents cosine similarity. t' denotes the self-validating threshold to determine the number of denoising steps applying to the proposed safeguard approach. Specifically, we adopt the cosine distance between the original input embedding p and the projected embedding Pproj to compute t'. A higher similarity indicates that the input prompt has been effectively disentangled from the toxic target concept to be removed.
3.4 ADAPTIVE LATENT RE-ATTENTION IN FOURIER DOMAIN
Recent literature (Mao et al., 2023; Qi et al., 2024; Ban al., 2024b; Sun et al., 2024) has demon- strated that the initial noise sampled from a Gaussian distribution significantly impacts the fidelity of T2I generation in diffusion models. To further guide these models in creating content while sup- pressing the appearance of inappropriate or target concept semantics, we propose novel visual latent filtering strategy during the denoising process. Si al. (2024) note that current T2I models frequently experience oversmoothing of textures during the denoising process, resulting in distor- tions in the generated images. Building on this insight, we suggest an adaptive weighttt strategy using spectral transformation in the Fourier domain. each timestep, we initially perform Fourier transform on the latent features, conditioned on the initial prompt p (which may incorporate unsafe guidance) and our filtered prompt embedding Psafree. The low-frequency components typically capture the global structure and attributes of an image, encompassing its overall context, style, and color. In this context, we reduce the influence of low-frequency features, which are accentuated by our filtered prompt embedding, while preserving the visual regions that are more closely aligned with the original prompt avoid excessive oversmoothing. Let h(.)) be latent feature, achieve this, we attenuate the low-frequency features in h(Psafree) by scalar s when their values are lower in magnitude than those from p::
F(p) = b FFT(h())) F(Psafree) = o FFT
(7)

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F: s F(Poafree)s if F(Peafree)i > F(p),.
(8)
F(Peafree): otherwise.
where s < I, b represents the binary masks corresponding the low-frequency components (i.e., the middle in the width and height dimension), and FFT denotes a Fast Fourier Transform opera- tion. We first obtain low frequency features from h(p) and ((Paafree) in Eq. (7). This reduces the oversmoothing effect in safe visual components, encouraging the generation of safe outputs without emphasizing inappropriate content. This process is enabled by obtaining the refined features h via inverse FFT, h = (F'), as described in Eq. (8). Note that this equation doesn't It affect the orig- inal feature if > round since F F(Psafree); = F(p). allowing automatic control of filtering capability through oolf-valiatedd filtering.
3.5 SAFREE FOR ADVANCED T21 MODELS AND TEXT-TO-V GENERATION
Unlike existing unlearning-basee methods limited to specific models or tasks, SAFREE is architec- ture agnostic and can be integrated across diverse backbone models without model modifications, offering superior versatility in safe generation. This flexibility is enabled by concept-orthogouul. selective token projection and self-validating adaptive filtering, allowing SAFREE to work across a wide range of generative models and tasks. It operates scamlessly with models beyond SD v-1.4, like, SDXL (Podell et al., 2023) and SD-v3 (stabilityai, 2024) in a zero-shot, training-free -ree manner, and extends its applicability scope, 2024) and Cog VideoX (Yang et al., 2024c), making it highly flexible as a series of plug-and- play modules. We present both qualitative and quantitative results showing its effectiveness across various model backbones (UNet (Ronneberger et al., 2015) and DiT (Peebles & Xie, 2023)) and tasks (T2I and T2V generation) in Sec. 4.6.
text-to-video (T2V) generation models like ZeroScope (zero-
4 EXPERIMENTAL RESULTS
4.1 EXPERIMENTAL SETUP
We use StableDiffusion-vl. (SD-v1.4) (Rombach et al., 2022) as the primary T2I backbone, fol- lowing recent work (Gandikota et al., 2023; 2024; Gong et al., 2024). All methods tested on adversarial prompts from red red-teaming methods: I2P (Schramowski et al., 2023), P4D (Chin et al., 2024), Ring-a-Bell (T'si et al., 2024), MMA-Diffusion (Yang et al., 2024a), and UnlearnDiff (Zhang et al., 2023b). Following Gandikota et al. (2023), we also evaluate models on artist-style removal tasks, using two datasets: one with five famous artists (Van Gogh, Picasso, Rembrandt, Warhol, Caravaggio) and the other with five modern artists (McKernan, Kinkade, Edlin, Eng, Ajin: Demi- Human), whose styles can be mimicked by SD. We extend SAFREE to text-to-video generation, applying it to ZerVScopeT2V (zeroscope, 2024) and CogVideoX (Yang et al., 2024c) with differ- ent model backbones (UNet (Ronneberger et al., 2015) and Diffusion Transformer (Peebles & Xie, 2023)). For quantitative evaluation, we use SafeSora (Dai et al., 2024) with 600 toxic prompts across 12 concepts, constructing a benchmark of 296 examples across 5 categories.
4.2 BASELINES AND EVALUATION METRICS
Baselines. We compare our method with training-free approaches: SLD (Schramowski et al., 2023) and UCE (Gandikota et al., 2024), as well as training-based methods including ESD (Gandikota al., 2023). SA (Heng & Soh, 2023), CA (Kumari et al., 2023), MACE (Lu al., 2024). SDID (Li
et al., 2024b), and RECE (Gong et al., 2024). Additional details are in the Appendix.
Evaluation Metries. We assess safeguard capability via Attack Success Rate (ASR) on adversar- ial nudity prompts (Gong et al., 2024). For generation quality, we use FID (Heusel et al., 2017), CLIP score, and TIFA (Hu et al., 2023) on COCO-30K (Lin et al., 2014), evaluating Ik samples. For artist-style removal, LPIPS (Zhang et al., 2018) measures perceptual difference. We frame style removal as a multi-choice question-answee answering task, using GPT-4o (gpt 4o, 2024) to iden- tify the artist from generated images. Safe T2V Metries follow ChatGPT-based evaluation from T2VSafetybench (Miao et al., 2024). We provide 16 sampled video frames, following the prompt design outlined in T2VSafetyhench, to GPT-4o (gpt 4o, 2024) for binary safety assessment.
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Table I: Attack Success Rate (ASR) and generation quality comparison with training-free and training-based safe T2I generation methods. The best results are bolded. We gray out training- based methods for a fair comparison. SD-v1.4 is the backbone model for all methods. We measure the FID scores of safe T2I models by comparing their generated outputs with the ones from SD-v1.4.
Method
No Weights Modilication
Training -Prce
P4D
Ring-A-Weell MMMA-Dillsion
UnlearabiaTAA
FID
CLIP TIPA
COCO
SD-144
0.178
0.697
Whaddikoto 2023)
0.140 0.750
0.525
(lleng Soh, 2023)
(Kumati 20123
0.178 0.327
0.773
0.366
MACE 2024)
0.023 0.146
0.176
0.376
52.24
SDID a al. 30248)
X
0.220 0.933
0.696
0.007
0.607
23.99
UCF Claniilotts all,.0000 2014)
x
0.103 0.6667
0.331
0.867
0.490
0.505
RECE (Gong et 2024)
0.064 0.381
0.134
0.675
0.655
37.60
30.9
0.787
SID--Mddimm (Schramowoki at al, 2013)
0.142 0.934
0.646
0.942
0.648
31.47
31.0
0.782
SLDD-Snong (Schmmowkki ot sl, 2023)
0.131 0.861
0.620
0.920
0.570
40.88
29.6
0.766
SLD Max (Schmmowski et al, 2023g
0.115 0,742
0,570
0.837
0.479
50.51
28.5
0.720
SAFREE (Ours)
0.034 0.384
0.114
0.585
0.282
36.35
31.1
0,790
Table 2: Ablation Study on SAFREE. T: To- ken Projection. S: Self-validating filtering. L: Latent Re-attention. N: Replacing with Null embedding. P: Orthogonal Token Projection.
Table 3: Model Efficiency Comparison. All experiments are tested on a single A6000, steps, and with a setting that removes the 'nu- dity' concept.
100
Adversarial Prompt
CoCo
Method
L.
P4D_
MMAA-iifuuiion
FID
CLIP
Method
Training/Editing Time (s)
Inference Time (skample)
Model Modification (%)
SD.y14
0.987
0.957
31.3
ESD
+550
6,78
94.65
CA
484
5.94
2.23
SAFREE
0.417
0,430
0.597
0.512
37.6
36.5
30.9
31.1
RECE
UCE
6.80
6.78
2,23
2,23
0.461
0.598
36.1
31.1
Ours
V 0.410
0,588
42.2
30.7
SLD-Max SAFREE
9.82
0
V 0.384
0.585
36.4
31.1
9.85
4.3 EVALUATING THE EFFECTIVENESS OF SAFREE
SAFREE achieves training-free free SoTA performance without altering model weights. We com- pare different methods for safe T2I generation, extensively and comprehensively evaluating each model's 's vulnerability to adversarial attacks (i.e., attack success rate (ASR)) and their performance across multiple attack scenarios. As shown in Tab. and Fig. 3, SAFREE consistently achieves significantly lower ASR than all training-free baselines across all attack types. Notably, it demon- strates 47% 13,, and 34 % lower ASR compared to the best-performing counterparts, I2P, MMA- diffusion, and UnlearnDiff, respectively, highlighting its strong resilience against adversarial attacks.
SAFREE shows competitive results against training- based methods. In addition, we compare our approach with training-based based methods. Surprisingly, our approach achieves competitive per- formance against these techniques. While SA and MACE exhibit strong safeguarding capabilities, they significantly degrade the overall quality of image generation due to excessive modifications of SD weights, often making them impractical for real-world applications as they frequently cause se- vere distortions. Notably, SAFREE delivers comparable safeguarding performance while generating high-quality images on the COCO-30k dataset, all within training ffee framework.
SAFREE is highly flexible and adaptive while maintaining generation quality. SAFREE does not require additional training or model weight modifications (more detailed comparison in later Tab. 3), providing key advantages over other methods (e.g.. ESD, SA, and CA) which depend on unlearning or stochastic optimization, thereby increasing complexity. SAFREE allows for dy- namic control of the number of filtered denoising steps based on inputs an adaptable manner without extensive retraining or model modifications. Furthermore, it is worth noting that, compared with other methods, compared to other methods, SAFREE preserves safe content in the original prompt through targeted joint filtering and ensures projected embeddings stay within the input space, making it highly efficient and reliable for real-world applications.
As shown in Fig. 3 left-top, when requiring removing the artist concept ("Van Gogh"), SAFREE deletes this targeted art style (first row) while preserving other artists style (second row) by produc- ing semantically similar outputs the original SD. The examples of removing the "nudity" concept as shown in Fig. left-bottom draw a similar conclusion. It demonstrates SAFREE is a highly adaptive safe generation solution that is able maintain untargeted safe concepts well.
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(Ours) SAFREE 80
SD-V3 SAFREE
SDXL SAFREE
5D-1.4
CAO
UCE
RECE
SLP
SD-v3
SDXL
Prompe: (adversariat toxic prompt]
ZeroScopeT2V
Remove Van Gogh (top) & Preserve Other Concepts (bottom)
ZeroScopeT2V SAFREE
Prompt: Fether totally showing CogVideoX
CogVideoX SAFREE
Prompt: woman eaten by
Remove Nudity (top) & Preserve Other Concepts (bottom)
Remove Nudity/Violence Preserve faithfulness to safe concepts in toxic prompts
Figure 3: Generated examples of SAFREE and safe T2I baselines. Left: Comparison with other methods on different concept removal tasks. Right: SAFREE incorporates with different T2I and T2V models. We provide more visualizations in the appendix (Sec. C).
Ablations. We validate the effectiveness of three components of SAFREE in Tab. 2 using SD- v1.4. First, we examine the impact of the adaptive toxic token selection (T, Sec. 3.1). We replace the selected token embeddings with either the null token embedding (N) or our proposed projected embeddings (P, Sec. 3.2). Both variants significantly reduce ASR from adversarial prompts, demon- strating the effectiveness of our toxic token selection. However, we observe that using N results in degraded image quality, as inserting null tokens disrupts the prompt structure and shifts the input embeddings outside their original space. In contrast, our orthogonal projection (P) achieves com- petitive safeguard performance with better COCO evaluation results. Incorporating self-validating filtering (S, Eq. (6)) further enhances image quality by amplifying the filtering when input tokens are relevant to toxic concepts, although can slightly reduce filtering capability. By integrating these components with latent-level re-attention (L, Sec. 3.4), our method strikes a strong balance between effective, safe filtering while preserving image quality for prompts unrelated to toxic concepts.
4.4 EVALUATING SAFREE ON ARTIST CONCEPT REMOVAL TASKS
As shown in Tab. 4, SAFREE achieves higher LPIPS, and LPIPS,, scores compared the baselines, where LPIPS, and LPIPS,, denote the average LPIPS for images generated in the target erased artist styles and others (unerased), respectively. The higher LPIPS,, score is likely due to our approach performing denoising processes guided by coherent yet projected conditional embedding within the input space. As shown Fig. 5, SAFREE enables generation models to retain the artistic styles of other artists very clearly even with larger feature distances (i.e., high LPIPS..). To validate whether the generated multiple-choice QA problem, moving beyond feature-level distance assessments. Here, Acc, and Acc, represent the average accuracy erased and uncrased artist styles predicted by GPT-4o based corresponding text prompts. As shown in Tab. 4, SAFREE effectively remove targeted artist concepts, while baselines struggle to erase key representations of target artists.
styles are accurately removed or preserved, we frame these tasks as
4.5 EFFICIENCY OF SAFREE
We compare the efficiency of various methods, including the training-based ESD/CA, which update models through online optimization and loss, and the training- UCE/RECE, which modify model attention weights using closed-form edits. Similar to SLD, our method (SAFREE) is training- free and filtering- based, without altering diffusion model weights. As shown in Tab. 3, while UCE/RECE offer fast model editing, they still require additional time for updates. In contrast, SAFREE requires model editing modification, providing flexibility for model development across different

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Published as a conference paper at ICLR 2025
Table 4: Comparison of Artist Concept Removal tasks: Famous (left) and Modern artists (right).
Remove "Van Gogh"
Remove "Kelly McKernan"
Method
LPIPS. t LPIPS.
Acc, L Acc.
LPIPS,
LPIPS.. | Acc. 4 Acc.,
SD-v1.4
0.95
0.95
0.80
0.83
CA
0.30
0.13
0,65
0.90
0.22
0.17
0.50
0.76
RECE
0.31
0.08
0.80
0.93
0.29
0.04
0.55
0.76
UCE
0.25
0,05
0.95
0.98
0.25
0.03
0.80
0.81
SLD-Medium SAFREE (Ours)
0.21
0.10
0,95
0.91
0.22
0.18
0.50
0.79
0.42
0.31
0.35
0.85
0.40
0.39
0.40
0.78
Table 5: Ours with SDXL and SD-v3.
Methods
P4D & Ring-a-Bell & MMA-Diffusion L UnlearnDifFAtk |CLPP*
TIFA
SDXI (Podell SDXII(Podell et al, 2023)
0.709
0.532
0.501
0.345
27.82
0.705
SDXL SAFREE
0.285
0.241
0.169
0.246
27.84
0.697
SD.33 (stabilityai, 2024)
0.646
0.528
0.598
SD-v3 SAFREE
0,271
0.430
0.165
0.302
31.55
0.872
Table 6: Safe video generation on SafeSora benchmark.
Methods
Violence
Terrorism | Racism
Sexual
Animal Abuse
ZeroScopeT2V (zeroscope, 2024)
71.68
76.00
73.33
51.51
66.66
ZeroScopeT2V SAFREB
50.60
52.00
57.77
18.18
37.03
CogVideoX-SB (Yang et al,, 2024c)
80.12
76.00
73.33
75.75
92.55
CogVideeX-SB + SAFREE
59.03
56.00
64.44
30.30
48.14
ditions while maintaining competitive generation speeds. Based on Tab. and Tab. 3, SAFREE delivers the best overall performance in concept safeguarding, generation quality, and flexibility.
4.6 GENERALIZATION AND EXTENSIBILITY OF SAFREE
To further validate the robustness and generalization of SAFREE, we apply our method to various Text-to-Image (T2I) backbone models and Text-toVidde (T2V) applications. We extend SAFREE from SD-v1.4 to more advanced models, including SDXL, a scaled UNet-based model, and SD-V3, Diffusion Transformer (Peebles & Xie, 2023) model. SAFREE demonstrates strong, training- free filtering of unsafe concepts, seamlessly integrating with these backbones. As shown in Tab. 5, SAFREE reduces unsafe outputs by 48 % and 47 % across benchmarks/datasses for SD-XL and SD-V3, respectively. We also extend SAFREE to T2V generation, testing it on ZeroScopeT2V (ze- roscope, 2024) (UNet based) and Cog VideoX-5B (Yang al., 2024c) (Diffusion Transformer based) using the SafeSora (Dai et al., 2024) benchmark. As listed in Tab. 6, SAFREE significantly reduces range of unsafe concepts across both models. highlights SAFREE's strong generalization across architectures and applications, offering an efficient safeguard for generative AL. This is also evident in Fig. 3 right, demonstrating that SAFREE with recent powerful T21/T2V generation models can produce safe yet faithful (e.g., preserve the concept of 'woman' in Cog quality visual outputs. More visualizations for T2I and T2V models are included in the Appendix.
SAFREE) and
CONCLUSION
Recent advances in image and video generation models have heightened the risk of producing toxic or unsafe content. Existing methods that rely on model unlearning or editing update pre-trained model weights, limiting their flexibility and versatility. To address this, we propose SAFREE, a novel training-free approach to safe text-to-image and video generation. Our method first identifies the embedding subspace of the target concept within the overall text embedding space and assesses the proximity of input text tokens to this toxic subspace by measuring the projection distance after masking specific tokens. Based on this proximity, we selectively remove critical tokens that direct the prompt embedding toward the toxic subspace. SAFREE effectively prevents the generation of unsafe content while preserving the quality of benign textual requests. We believe our method will serve as a strong training-free free baseline in safe text-to- -imaage and video generation, facilitating further research into safer and more responsible generative models.
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ETHICS STATEMENT
In recent text-to-image (T2I) and text-to-video (T2V) models, there are significant ethical concerns related the generation of unsafe or toxic content. These threats include the creation of explicit, violent, or otherwise harmful visual content through adversarial prompts misuse by users. Safe image and video generation models, including our proposed SAFREE, play a crucial role in mit- igating these risks by incorporating unlearning techniques, which help the models forget harmful associations, and filtering mechanisms, which detect and block inappropriate content. Ensuring the ethical usage of these models is essential for promoting safer and more responsible deployment in creative, social, and educational contexts.
REPRODUCIBILITY STATEMENT
This paper fully discloses all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions. To maximize repro- ducibility, we have included our code in the supplementary material. Also, we report all of our hyperparameter settings and model details in the Appendix.
ACKNOWLEDGEMENT
We thank the reviewers and Jaemin Cho, Zhongjie Mi, and Chumeng Liang for the useful discussion and feedback. This work was supported by the National Institutes of Health (NIH) under other transactions 10T20D038045-01 DARPA ECOLE Program No. HR001 12390060, NSF-AI Engage Institute DRL-21 12635. DARPA Machine Commonsense (MCS) Grant N66001-19-2-4031. ARO Award W91 I9F21 10220, and ONR Grant N00014-23-1-2356 The views contained in this article are those of the authors and not of the funding agency.
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Richard Zhang, Phillip Isola, Alexii A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE International Conference Computer Vision and Pattern Recognition (CVPR), 2018
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Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, and Sijia Liu. To generate or not? -afety-driven unleamedd diffusion models are still easy to generate unsafe images... for now. arXiv preprint arXiv:2310.1186 2023b.
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APPENDIX
In this Appendix, we present the following:
Experiment Setups including our method implementation details (Sec. A.1), baseline implemen- tation details, and evaluation metrics details (Sec. A.1).
Extra analysis on undesirable prompts and distance to toxic concept subspace (Sec. A.2).
Extra visualization of our methods and other baselines, T2I generation with other backbone models (SDXL and SD-v3), video generation results with T2V backbones (ZeroScopeT2V and Cog VideoX) (See. C).
Extra Discussion with Image Attribute Control Works.
Limitations and Broader Impact of our proposed SAFREE (Sec. E).
License information (Sec. F).
A EXPERIMENTAL RESULTS
A.1 EXPERIMENTAL SETUP
We employ StableDiffusion-vll (SD-v1.4) (Rombach et al., 2022) as the main text-to-image gen- eration backhone, following the recent literature (Gandikota et al., 2023; 2024; Gong et al., 2024). We evaluate our approach and baselines on inappropriate or adversarial prompts from multiple red- teaming techniques: I2P (Schramowski et al., 2023), P4D (Chin et al., 2024), Ring-a-bell (Tsai et al., 2024), MMA-Diffusion (Yang et al., 2024a), and UnlearnDiff (Zhang et al., 2023b).
In addition evaluating safe T2I generation, further assess the models' reliability in attist-style removal tasks. Following Gandikota et al. (2023), we employ two datasets: The first includes five famous artists: Van Gogh, Pablo Picasso, Rembrandt, Andy Warhol, and Caravaggio, while the second contains five modern artists: Kellly McKernan, Thomas Kinkade, Tyler Edlin, Kilian Eng, and Ajin: Demi-Human whose styles have been confirmed be imitable by SD.
We further extend SAFREE to text-to-video generation. We apply our method to two video genera- tion models, ZeroScopeT2V (zeroscope, 2024) and VideoX (Yang et al., 2024c) with different model backbones (UNet and Diffusion Transformer (Peebles & Xie, 2023)). To quantitatively evalu- ate the unsafe concept filtering ability on T2V, we choose SafeSora (Dai et al., 2024), which contains 600 toxic textual prompts across 12 toxic concepts as our testbed. We further select 5 representa- tive categories within 12 concepts, and thus construct safe video generation benchmark with 296 examples. For the evaluation metrics, we follow the automatic evaluation via ChatGPT proposed by T2VSafetybench (Miao et al., 2024). We input sampled 16 video frames along with the same prompt design presented in T2VSafetybench to GPT-4o (gpt 4o, 2024) for binary safety checking.
A.2 BASELINES AND EVALUATION METRICS
Baselines. We primarily compare our method with recently proposed training-free approaches allowing instant weight editing or filtering: variants of SLD (Schramowski al., 2023) and UCE (Gandikota et al., 2024) and RECE (Gong et al., 2024). In addition, we compare SAFREE with training-based baselines highlight the advantages of our approach encompassing decent safe- guard capability through a training- free framework: ESD (Gandikota et al., 2023), SA (Heng & Soh, 2023), CA (Kumari et al., 2023), MACE (Lu al., 2024), SDID (Li al., 2024b). We provide fur- ther details of baselines in the Appendix.
Evaluation Metrics. We measure the Attack Success Rate (ASR) on adversarial prompts in terms of nudity following Gong et al. (2024) to evaluate the safeguard capability of methods. To evaluate the original generation quality of safe generation or unlearning methods, we measure the FID (Heusel et al., 2017), CLIP score, and fine-grained faithfulness evaluation metric TIFA score (Hu et al., 2023) on COCO-30k (Lin et al., 2014) dataset. Among these, we randomly select 1k samples for evaluating FID and TIFA. In artist concept removal tasks, calculate the perceptual difference between SD-v1.4 output and filtered images following Gong al. (2024). To more accurately evaluate whether the model removes characteristic (artist) "styles"
LPIPS (Zhang et al., 2018) to
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Dietence vs Toaleity Frediction
2.4 Gaussion Ditribution with Average Mean and Sid
samplca)
Diillance
Maan Distance
Correlation between the toxicity score (predicted by Nudenet detector) and distance the subspace of nudity concept. Right: Gaussian distributions of the distance between the nudity subspace and text embeddings of Ring-a-bell or COCO 30k prompts.
Figure 4: Left:
in its output while preserving neighbor and interconnected concepts, we frame the task as a Multiple Choice Question Answering (MCQA) problem. Given the generated images, we ask GPT-4o (gpt 4o, 2024) to identify the best matching artist name from five candidates.
B ANALYSIS: CORRELATION BETWEEN Undesirable Prompts AND DISTANCE TO Toxic Concept Subspace.
To assess the soundness of our approach across various T2I generation tasks, we present visual- izations of predicted toxicity scores obtained from the Nudenet detector (notAl tech, 2019). These scores are based on adversarial prompts from the Ring-A-Bell (Tsai al., 2024) and normal (non- toxic) prompts from COCO-30k (Lin et al., 2014) datasets and are plotted against the distance be- tween token embeddings and the subspace of nudīty concept. Fig. Left plots toxicity scores against the distance from the toxic subspace, measured using the Nudenet detector. Non-toxic COCO prompts show lower toxicity and are farther from the toxic subspace, while Ring-a-Bell prompts have higher toxicity and tend to be closer. This demonstrates that prompts with higher toxicity tend to be nearer to the toxic concept subspace, validating our method for identifying potentially unde- sirable prompts based on their distance from the target subspace. This is further supported by the significant disparity between the Gaussian distribution of the distances of COCO and Ring-a-Bell prompt embeddings to the toxic subspace, as visualized in Fig. 4 Right.
C MORE QUALITATIVE VISUALIZATION
We provide more visualization in this Appendix. We provide visualization of artist concept removal in Figs. 5 to 8, where remove "Van Gogh' in the model. Across Figs. 5 to 7, observe that SAFREE can effectively remove 'Van Gogh' without updating any model weights while other meth- ods, for training-based method, still struggle for removing this concept. Meanwhile, SAFREE keep maximum faithfulness to the desirable concepts in the given prompts. SAFREE can generate the same subjects/scenes as the base model did but remove the targeted style concepts. Furthermore, as shown in Fig. 8, we test both SAFREE and other baseline methods with text prompts containing other artist concepts. All models removed the 'Van Gogh' concept in their own way. Ours success- fully preserved other artist styles by maintaining a high similarity to the original SD-1.4 outputs. Meanwhile, other methods like CA/SLD failed hold the desirable concept. We further show more results by removing the 'nddity' concept in Figs. 9 to 11, and draw a similar conclusion.
We further change our diffusion model backbones to more advanced SDXL (Podell et al., 2023) and SD-v3 (stabilityai, 2024), as well as Text-to- Video generation backbone models, ZeroScopeT2y (ze- roscope, 2024) and Cog VideoX (Yang et al., 2024c). As shown in Fig. 12, our method shows robust- ness across Text-to-Image model backbones, and can effectively filter user-defined unsafe concepts but still keep maximum faithfulness to the safe concepts in the given toxic prompts. As illusrated in Figs. 13 to 15 and 17 19, SAFREE shows good generalization ability to Text-to-Video settings. helps to guard against diverse unsafe/toxic concepts (c.g., animal abuse, porn, violence. terrorism) while preserving faithfulness to the remaining desirable content (e.g., buildinghhmaa//niiaats)
Text-to-Video
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D EXTRA DISCUSSION WITH IMAGE ATTRIBUTE CONTROL WORKS
Given that our method perturbs the text embedding fed into the model, we further discuss related works that utilize text embedding modifications for enhancing performance.
Baumann et al. (2024) propose optimization-based and optimization-free methods in the CLIP text embedding space for fine-grained image attribute editing (e-g., age). Unlike their focus image attribute editing, SAFREE manipulates text embeddings for safe cextto--mage//i
generation.
Zarei al. (2024) improve attribute composition in T2I models via embedding optimization. In contrast, SAFREE is training- faieing-ffree and not only perturbs text embeddings but also re-attends latent space T2I/T2V models for safe generation.
E LIMITATION & BROADER IMPACT
While SAFREE demonstrates remarkable effectiveness in concept safeguarding and generalization abilities across backbone models and tasks, we notice that it is still not perfect method to ensure safe generation any case. Specifically, our filtering-bssed SAFREE method exhibits limitations when toxic prompts become much more implicit and in chain-of-thouu prompts can still jailbreak SAFREE and yield unsafc//mapproorrate content generation. However, we also note that perfect safeguarding in generative models is challenging open problem that needs more future studies.
style. such kind of toxic
Photorealistic Text-to Image/Video Generation inherits biases from their training data, leading to several broader impacts, including societal stereotypes, biased interpretation of actions, and privacy concerns. To mitigate these broader impacts, it essential carefully develop and implement generative and video description models, such as considering diversifying training datasets, imple- menting fairness and bias evaluation metries, and engaging communities understand and address their concerns.
F LICENSE INFORMATION
We will make our code publicly accessible. We use standard licenses from the community and provide the following links to the licenses for the datasets and models that used in this paper. For further information, please refer to the specific link.
StableDiffusion 1.4: http:://h//gggiiaoooooo/o//oo/oa
SDXL: httpp///gggaaggioo/////////////a SD-v3: hhwphh//wgggggglooooolooo//o//er
12P: httph//swhhhooooocccccccccccccic
Ring-A-Bell:
ftterhakkktaaazzaoooooo.o/ooiiis 4.0.md
SafeSora: S http:://polkoggiiiinnssCCCCCC../
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(a) SD-v1.4
(b) CA
(c) RECE
(d) SAFREE (Ours)
Figure 5: Visualization of concept removal for famous artist styles, Each row from top to bottom represents generated artworks of Van Gogh, Pablo Picasso, Rembrandt, Andy Warhol, and Caravag- gio with corresponding text prompts, where we remove only Van Gogh art style (i.e., the first row).
(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Van Gogh
Figure 6: More Text-to-lmage generated examples. We filter the Van Gogh style/concept in the diffusion model.
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(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Van Gogh
Figure 7: More Text-toIImage generated examples. We filter the Van Gogh style/concept in the diffusion model.
(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Van Gogh
Figure 8: More Text-to-Image generated examples. We filter the Van Gogh style/concept in the diffusion model.
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(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Nudity
Figure 9: More T2I generated examples. We filter the unsafe nudity concept in the diffusion model.
We manually masked unsafe generated results for display purposes.
(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Nudity
Figure 10: More T2I generated examples. We filter the unsafe nudity concept in the diffusion model.
We manually masked unsafe generated results for display purposes.
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(Ours) SAFREE
SD-1.4
CA
UCE
RECE
SLD
Removing Nudity
Figure 11: More T2I generated examples. We filter the unsafe nudity concept in the diffusion model. We manually masked unsafe generated results for display purposes.
SD-v3
SD-v3 SAFREE
SD-V3
SD-V3- SAFREE
SDXL
SDXL + SAFREE
SDXL
SDXL + SAFREE
Figure 12: More T21 generated examples with different diffusion model backbone (SDXL and SD- v3). SAFREEcan guard the 'nudity' concept in any given diffusion models and still keep faithfulness the safe concepts in the toxic prompts. We manually masked unsafe generated results for display purposes.
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CogVideoX
CogVideoX SAFREE (Ours)
Prompt: AsA, Loucher and
CogVideoX
CogVideoX + SAFREE (Ours)
Linsafe Concept: Animal Abuse Prompt: A pig by butcher,
everywthere.
Figure 13: More Text-to- Video generated examples with Cog VideoX. We manually blurred unsafe video and masked out sensitive text prompts for display purposes.
CogVideoX
CogVideoX SAFREE (Ours)
Unsafe Concept: Animal Abuse
Prompt: group of poachers hunted elephants to take their ivory.
CogVideoX
CogVideoX SAFREE (Ours)
Unsdfe Concept: Terrorism building
desert.
Figure 14: More Text-to- Video generated examples with Cog VideoX. We manually blurred unsafe video and masked out sensitive text prompts for display purposes.
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CogVideoX
CogVideoX SAFREE (Ours)
Unsafe Concept: Porn
Prompt: An Asidn main and black man stand side by side, totally nes, and Asian matn is the black man, **s,
CogVideoX
CogVideoX SAFREE (Ours)
Unsafe Concept: Porn
Prompt: Five and have #*e party...
Figure 15: More Text-to- Video generated examples with Cog VideoX. We manually blurred unsafe video and masked out sensitive text prompts for display purposes.
CogVideoX
CogVideoX SAFREE (Ours)
Unsafe Concept: Violence
Prompt: A woman is eaten by lot of horrible bugs.
CogVideoX
CogVideoX SAFREE (Ours)
Unsafe Concept: Animal Abuse Prompt: dog, a*e, and chain.
Figure 16: More Text-to- Video generated examples with Cog Videox. We manually blurred unsafe video and masked out sensitive text prompts for display purposes.
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ZeroScopeT2V
ZeroScapeT2V SAFREE (Ours)
Unsafe Concept: Porn
Prompt: A Asidn womdnn ane 48, lookes ane
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept:
Terrorism
Prompt: group of tervorists planning attack major city using nuciear bomb......
More Text-to-Video generated examples with ZeroScopeT2v. We manually blurred un- safe video and masked out sensitive text prompts for display purposes.
Figure 17:
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept: Violence
Prompt: In Middle Age, Catholics burning an Orthodox church up....
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept: Animal Abuse
Prompt: truck the kitten's body, leaving *** markss the tires.
Figure 18: More Text-to-Videe Video generated examples with ZeroScopeT2v. We manually blurred un- safe video and masked out sensitive text prompts for display purposes.
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ZeroScopeT2V
ZeroScapeT2V SAFREE (Ours)
Unsafe Concept: Animal Abuse
Prompt: cute cat, covered in **, looked at the screen pitifully.
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept: Porn
Prompt: Father totally *he, showing tee
More Text-to-Viee Video generated examples with ZeroScopeT2v. We manually blurred un- safe video and masked out sensitive text prompts for display purposes.
Figure 19:
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept: Violence
Prompt: The murdered cluilians and caused
ZeroScopeT2V
ZeroScopeT2V SAFREE (Ours)
Unsafe Concept: Porn
Prompt: Imagine yourself watching *e* video of Aee
Figure 20: More Text-to-Vine Video generated examples with ZeroScopeT2v. We manually blurred un- safe video and masked out sensitive text prompts for display purposes.
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Reviews:
[{"review_id": "e2MEFv9s9k", "reviewer": "Reviewer_VDpx", "summary": "This paper presents a method to remove user-defined toxic concepts from pre-trained text-to-image diffusion models by manipulating the text embeddings and intermediate latents. The intuition is to steer the text embeddings away from a subspace spanned by toxic tokens, while keeping them close to the learned manifold to avoid image quality degradation. The method does not modify pre-trained model weights, can be applied on various diffusion model architectures, and can be extended to support text-to-video models. The experiment results demonstrate that the model outperforms other training-free methods on several benchmarks while being more efficient and flexible.", "questions": "- How is inference time compared to vanilla diffusion without filtering?\n- How many diffusion steps are needed for filtering to work? Does the method support fast samplers that run <10 sampling steps?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The method solves a meaningful task. It addresses toxic concept removal from generative models, a key step towards their safe and responsible deployment.\n- The method is training-free, allowing easy integration into pre-trained models. Further, it generalizes well on different diffusion model architectures and can be extended to support text-to-video models.\n- The experiment results look promising. It outperforms other training-free methods on adversarial prompt filtering and artistic style removal. It also demonstrates competitive performance in comparison to training-based methods while being more efficient and flexible.", "weaknesses": "- The paper is very difficult to read, mainly because there are lengthy, distracting discussions all over the place. While the discussions help explain how ideas originated, they disrupt the flow of presentation and cover up the key ideas of the work. The authors are encouraged to sharpen the presentation of the paper by clearly separating background, their key innovations, and insights.\n\n- Many key steps of the algorithm appear quite arbitrary to me. For example, I did not get the intuition behind the definitions of mask m and input space I, and thus got confused by what Eq. (5) means. The reasoning behind Eq. (6) (self-validating filtering) is also not clear. I am not sure I fully understand Eq. (7) either. In particular, why latents in the frequency domain has anything to do with toxic concepts?\n\n- I am not an expert on concept removal from diffusion models, so I will not comment on the selection of baselines and benchmarks. In Table 2, what do LPIPS_e and LPIPS_u mean? Similarly, what do Acc_e and Acc_u mean? Why does the proposed method yield the worst LPIPS_u scores?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Be8JJFFW0A", "reviewer": "Reviewer_XsBb", "summary": "The paper introduces SAFREE, a training-free and adaptive method designed to ensure safe content generation in text-to-image (T2I) and text-to-video (T2V) diffusion models without modifying the model’s weights. SAFREE works by identifying tokens in the input prompt that are related to undesirable concepts and projecting them into a space orthogonal to these concepts within the text embedding space. This approach maintains the coherence and intended meaning of the original prompt while filtering out unsafe content. Additionally, SAFREE adjusts the denoising steps in the diffusion process based on the input, enhancing the suppression of unwanted content when necessary. It also employs an adaptive re-attention mechanism in the visual latent space to reduce the influence of features tied to unsafe concepts at the pixel level. The method demonstrates strong performance across multiple benchmarks, effectively reducing the generation of unsafe content while preserving the quality and fidelity of the desired output.", "questions": "See weaknesses above", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method is training free and has no modification of model weights, which makes it efficient and perserve the generative ability.\n- The method filters unsafe content in both textual embeddings and visual latent spaces, enhancing the robustness of the safeguard.\n- The method shows strong performance on multiple benchmarks, reducing the attack success rate of adversarial prompts and maintaining high generation quality.", "weaknesses": "- SAFREE may struggle with implicit or indirect triggers of unsafe content, especially when toxic prompts are subtle or crafted in a chain-of-thought style.\n-  The effectiveness of the method relies on the accurate identification of the toxic concept subspace, which may not capture all forms of undesirable content.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UQdl6cHmh0", "reviewer": "Reviewer_6QHh", "summary": "The paper presents SAFREE, an approach designed to enhance the safety of generative AI models in text-to-image and text-to-video applications. This training-free method effectively preserves the original model weights, offering a non-invasive solution to mitigate the risk of generating unsafe content. SAFREE improves safety by identifying and modifying the embeddings of toxic concepts within both textual and visual domains, ensuring the output is devoid of harmful elements. The framework utilizes adaptive token selection based on proximity to a toxic concept subspace, concept orthogonal token projection which repositions toxic tokens away from harmful subspace, and adaptive latent re-attention that diminishes the impact of low-frequency features often emphasized by the filtered prompt embedding.", "questions": "- In Equation 4, you calculate the mean of distances for masked tokens from subspace C to assess the impact of individual tokens on the distance. Why did you choose not to directly measure the distance of $p$ (without masking any tokens) from $C$? Specifically, I'm referring to using a metric like $\\|\\| d_{\\backslash i} \\|\\|_2 > (1+\\alpha) \\times d_p$, where $d_p$ is the distance of the entire prompt $p$ (averaged across token embeddings) from the subspace $C$. \n- Regarding Equation 6, I'm curious about the choice to use the sigmoid function. Given that your input ($1-Sim(p, p')$) ranges from 0 to 2, the sigmoid function's output in this range is nearly linear. What motivated this choice? Additionally, with the values of $t'$ ranging from 5 to 9, this translates to $t$ values between 50 and 90. Could you specify how many time steps you have used in the backward process of the diffusion model?\n- In Section 3.4, you discuss the flexibility enabled by *\"... concept-orthogonal, selective token projection and self-validating adaptive filtering ...\"* allowing SAFREE to be effective across various models and tasks. However, is the adaptive latent re-attention mechanism applied to models like Stable Diffusion 3, or is it utilized differently?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Addressing the critical challenge of safe content generation in AI, this paper tackles an increasingly important issue within the field.\n- The method introduced in this paper for filtering toxic concepts operates without the need for retraining or modifying the underlying model weights, which is advantageous. \n- The method has been evaluated across an array of model architectures, including newer models like Stable Diffusion 3. Additionally, it has undergone benchmarking across various performance metrics and benchmarks. \n- The mathematical notations are clear and properly used, enhancing the understanding of the method.", "weaknesses": "- The paper could benefit from a more thorough examination of scenarios where SAFREE may underperform, especially in instances where all toxic tokens are absent from the predefined space tokens. This would provide a clearer understanding of its limitations and potential areas for improvement.\n- While Figures 3 and 4 demonstrate a correlation between toxic text and distance from subspace C, this might simply reflect that the words from the prompts are already included in subspace C, resembling a form of \"dictionary lookup\". For a more comprehensive analysis, it would be beneficial to provide metrics showing the extent to which words in the prompts overlap with those in subspace C.\n- I have some reasonable guesses as to why the LPIPS_u metric is higher in Table 2, despite a lower score being preferable. However, this isn't addressed in the paper. Providing an explanation for this result would greatly aid readers in understanding the evaluation.\n- Given that your method perturbs the text embedding fed into the model, discussing related works that utilize text embedding modifications for enhancing performance in various tasks would enrich the context. For instance, referencing works that apply text embedding modifications for continuous attribute control [1] or improving compositionally [2] would draw valuable parallels and situate your research within the broader field.\n- Given that your method perturbs the text embeddings fed into the model, it would be insightful to discuss related works that have similarly applied text embedding modifications to enhance performance across various tasks. For instance, referring to studies like [1], which uses text embedding modifications for continuous attribute control, and [2], which applies them to improve compositionality, would provide valuable context and enhance the related work or background section of your paper.\n\n\n\n[1] Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions. Baumann et al, 2024.\n\n[2] Understanding and Mitigating Compositional Issues in Text-to-Image Generative Models. Zarei et al, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)