Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling
Authors: Jaskirat Singh
Junshen K Chen
Jonas K Kohler
Michael F Cohen
Venue: ICLR 2025 Poster (2025)
Keywords: consistent text-to-image generation
visual storytelling
story generation
Primary area: generative models

Abstract: Consistent text-to-image generation depicting the *same* subjects across different images has gained significant recent attention due to its widespread applications in the fields of visual-storytelling and multiple-shot video generation.  While remarkable, existing methods often require costly finetuning for each subject and struggle to maintain consistency across multiple characters. In this work, we first analyse the reason for these limitations. Our exploration reveals that the primary-issue stems from *self-attention leakage*, which is exacerbated when trying to ensure consistency across multiple-characters. Motivated by these findings, we next propose a simple yet effective *training and optimization-free approach* for improving multiple-character consistency. In particular, we first leverage multi-modal *chain-of-thought* reasoning in order to *apriori* localize the different subjects across the storyboard frames. The final storyboard images are then generated using a modified diffusion model which includes  *1) a bounded cross-attention layer* for ensuring adherence to the initially predicted layout, and  *2) a bounded cross-frame self-attention layer* for reducing inter-character attention leakage. Furthermore, we also propose a novel *cross-frame token-merging layer* which allows for improved fine-grain consistency for the storyboard characters. 
 Experimental analysis reveals that proposed approach is not only $\times 30$ faster than prior training-based methods (*eg, textual inversion, dreambooth-lora*) but also surpasses the prior *state-of-the-art*, exhibiting improved multi-character consistency and text-to-image alignment performance.

Published as a conference paper at ICLR 2025
STORYBOOTH: TRAINING-FREE MULTI-SUBIECT CON- SISTENCY FOR IMPROVED VISUAL STORYTELLING
Jaskirat Singh 1.2
Junshen K. Chen'
Jonas Kohler
Michael Cohen'
Meta GenAI
AAustralian National University
ABSTRACT
Training-fre consistent text-to- text-to-image generation depicting the same subjects across different images topic of widespread recent interest. Existing works in this direction predominantly rely on cross-frame self-attention; which improves subject- consistency by allowing tokens in each frame to pay attention to tokens in other frames during self-attention computation. While useful for single subjects, we find that it struggles when scaling to multiple characters. In this work, we first analyze the reason for these limitations. Our exploration reveals that the primary- issue stems from self-attention leakage, which is exacerbated when trying to ensure consistency across multiple-characters. This happens when tokens from one subject pay attention to other characters, causing them to appear like each other (e.g., a dog appearing like a duck). Motivated by these findings, we propose Story Storh:ooh:: a training-free approach for improving multi-character consistency. In particular, we first leverage multi-moda chain-of- -fthought reasoning and region- -ased generation to apriori localize the different subjects across the desired story outputs. The final outputs are then generated using a modified diffusion model which consists two novel layers: 7) bounded cross-frame self-attention layer for reducing inter- character attention leakage, and 2) meeenn layer for improving consistency of fine-grain subject details. Through both qualitative and quantitative results we find that the proposed approach surpasses prior state-of-the-ary exhibiting improved consistency across both multiple-characters and fine-grain subject details.
INTRODUCTION
The field of consistent text-to-image generation aims at depicting the visually consistent subjects across diverse settings, and, has gained significant recent attention (Li et al., 2024a; Ye et al., 2023; Ruiz et al., 2022) with the advent of diffusion models (Rombach et al., 2021; Peebles & Xie, 2022). The generated subject-consistent images (traditionally referred to as the storyboard (Truong et al., 2006)) can be used visual inputs in order augment visual storytelling, or, serve anchor images for improving consistency for multiple-shot video generation (Zhao et al., 2024; Zhou et al., 2024).
Recent works on ensuring consistency (e.g., identity, appearance, background etc.) between different storyboard frames predominantly rely on subject personalization (Feng et al., 2023; Jeong et al., 2023; Avrahami et al., 2024). However, subject personalization methods often require per-subject training (Ruiz et al., 2022; Gal et al., 2022) and are observed to struggle when scaling to multiple characters (Ye et al., 2023). Furthermore, they can suffer from trade-offs between subject consistency and prompt-alignment. Training autoregressive models for consistent storyboard generation (Liu et al., 2024; Zhao et al., 2024; Yang et al., 2024b) has also been explored. However, this requires expensive training (often on curated movie / animation data) and the resulting methods tend to overfit on the training domain (Yang et al., 2024b) thereby limiting their practical efficacy for general storytelling.
In this paper, we explore simple yet effective training and optimization- approach towards improving storyboard consistency. Recent works in this direction (Zhou et al., 2024; Tewel et al., 2024) utilize fros- self-attention which improves cross-frame consistency by allowing each token to pay attention to tokens from all other boar frames. While good for ensuring single character consistency, experiments reveal that this tends exacerbate the self-attention leakage problem (Dahary et al., 2024) when scaling to multiple characters (refer Sec. 3). For instance, in Fig. 1, observe that when generating storyboard with two characters: dog and duck, the inter-

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Published as a conference paper at ICLR 2025
running in park
laying enroad
laughing mountains
skiing in mountains
nunning park
sitting on beach
Dreambooch Lait.
IP- IPAddaper
Consittry
StorDDiffaiio
Ours
Multiple Generation: dog
Single Generation: dog
Figure 1: Overview. We propose training and optimization- free approach for improving consistency across both multiple characters (dog and duck: left) and fine-grain subject details (face of dog: right).
character leakage results in significantly reduced prompt alignment with the duck appearing like dog. Furthermore, while the use cross-framt frame self-attention helps ensure general appearance similarity, the fine-grain features (e.g., face and ears of the dog in Fig. 1) can have observable inconsistencies.
To address these limitations, we propose Storybooth; a training- free approach for improving con- sistency across both 1) multiple-characters and 2) fine-grain subject details. The core idea of approach is to first use region-based planning and generation (Yang et al., 2024a) to apriori localize the region/ layout of different characters across the storyboard (Sec. 4.. Once the subject regions have been identified, we then propose a 1) a bounded cross-frame self-attemtion layer which reduces inter-character leakage by limiting each character to only pay attention to tokens corresponding to other regions for the same character (e.g.. dog only paying attention to other tokens for the same dog). Furthermore, we also propose novel 2) cross-frame token-merging layer which allows for improved fine-grain consistency (e.g., head of dog in Fig. 1) by merging matching low-level feature tokens across different generations. Despite its training- free nature, experimental analysis reveals (Sec. 5) that the proposed approach allows for improved character consistency and text-to-image alignment while exhibiting x3 faster inference time than optimization- based methods (Ruiz et al., 2022).
To summarize, the key contributions of this paper are: 1) we highlight the problem of calti-character consistency and exacerbated self aelf-attention leakage when sharing self-attention across the storyboard (Zhou et al., 2024; Tewel et al., 2024) 2) we propose the idea of combining region-based based generation with novel bounded self-attention layer, for reducing inter-character leakage (Fig. 5). 3) Finally, we also propose cross-frame token-merging layer which allows for improved fine-grain consistency (e.g., head of dog in Fig. 1) by merging matching low-level feature tokens across different generations.
RELATED WORK
Consistent text-to-image generation has been a topic great recent interest due to its applications in visual storytelligg (Liu et al., 2024; Zhou al., 2024; Yang al., 2024b) and multiple-shot
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cott and dog: Lan rooms, stoing ait mountttins, in è pärke
sakobariy vabbit
Lawi,, skilingg rosie.
Inter-charaeee leskagge
bird: Ljumping park, Laughing, sitting sofa
duubl:: [ [skling parks, sitting sefat]
Fine-grain Aigomentt
Figure 2: Identifying key consistency problems with prior state-of offte-oo-iaae the-art (storydiffusion; left and consistory: right). We find that naive cross- frame self-attention struggles with two main limitations: Intercharacter-leakagg causing the features of different characters mix (e.g., rabbit and cat, and dog). 2) lack of fine-grain consistency for subject details (body of bird, wings of duck).
video generation (Zhou al., 2024; Zhao et al., 2024; Yuan al., 2024). Prior works ensuring consistency typically reply subject personalization (Feng et al., 2023; Jeong et al., 2023; Avrahami al., 2024). This includes inference-time optimization methods (e.g., dreambooth) (Ruiz et al., 2022; Gal et al., 2022) which requires expensive optimization for each subject. Encoder based methods (Wei al., 2023; Li et al., 2024a) have also been explored, but they require large-scale scale training and fail to scale to multiple characters. Recent works have also explored training-autoregressii models (Liu et al., 2024; Zhao al., 2024; Yang al., 2024b) for storyboard generation. However, this again requires expensive training and the resulting methods observed to overfit the training domain.
More recently, (Zhou et al., 2024; Tewel et al., 2024) propose a training- free approach which ensures storyboard consistency through the use of consistent self-attention. As seen in Fig. 3, while good for ensuring single-subject consistensy, we observe that sharing attention across the output frames leads exacerbated self- leakage problem when scaling multiple characters (refer Sec. 3).
LLM driven region-basee generation and planning has been explored in the context of single text-to-image generation (Yang et al., 2024a; Feng et al., 2024; Li et al., 2023) in order improve the controllability of the generated image outputs. contrast, we explore the idea of controllable generation for storyboard generation. This helps us only establish the consistency requirements between different image regions across frames, but at the same time allows our approach to better control the compositionality of different storyboard frames depending on the story context and plot.
Token merging (Bolya et al., 2022) proposes to increase the throughput of existing ViT models by gradually merging redundant tokens in the transformer blocks. The key idea is to combine similar tokens to reduce the redundancy as well as the number of tokens, speeding up the computation. Recent works (Li al., 2024b; Wu al., 2024) apply the idea of token merging for video editing in order to better maintain temporal coherence of the edited video. In contrast, we explore fross-frame token merging to improve fine-gu grain attribute consistency across the storyboard frames (refer Sec. 4).
3 ANALYZING INTER-CHARACTER SELF-ATTENTIN LEAKAGE
Analyzing self-attention leakage. We first begin by analyzing the problem inter-character attention leakage when using cross-frame self-attention for consistency (Zhou et al., 2024). As shown in Fig. 3, we observe that while sharing self-attention across different output frames helps improve consistency, it exacerbates the self-attention leakage across different characters in the generation outputs. For instance, Fig. 3 we observe that tokens for face of cat in first frame, also pay attention the tokens corresponding to the dog in second frame. This leads to exacerbated chter-character leakage, thereby yielding black and white cat (similar to last frame) but with facial features of a dog.
Bounded self-attention and region-based generation. Given the above insights, wish to bound the self-attention computation to limit the tokens for each subject to only pay attention to the corresponding tokens both within the same image well as across the storyboard (Fig. 3). However, this requires apriori knowledge of the location of different subjects across the output frames. This final piece of insight thus motivates final approach. We first use region-based
Motivation:
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Story Dillisionn (Seif Attention: X
ConsiStory (Self- Atteniin:::::: X
StoryBooth Ours (Sell- Atention:
(a) Intercharucter leakage, Prior state-of-thee ahe methods use crossframe self-attention for consistensy, which leads to intercharacter leakage when scaling to multiple characters e.g., dog appearing like cat for StoryDiffusion, and, dog appearing like cat and viee-versa for ConsiStory. StoryBooth (right) helps address this problem.
Story StoryDiffusion Diffusion characters: and dog)
Self-Attention Leakage (dog:
attends to cal: frame)
(b) Visualizing self-attention leakage. We find that intercharacter leakage (a) occurs due to exacerbated self- attention Icakage where tokens from one subject (dog) pay attention to tokens from another subject (car).
Standard Sell-Antertion
Grosoramm Self- Aele-Anteiioon (Story Difliwilliiioo))
Bounded Cronsrame Self-Artencion (Ours)
(c) Motivating bounded croxsframe self-attention. (left) Standard self-attention allows any source token (yellow) to only pay attention to all other tokens (green) in the same image. (middle) Gross-frane self-attention (Zhou al., 2024) additionally allow each source token pay attention tokens from other images for consistency. However, this leads to self attention leakage among different subjects. (Right) We propose nounded-crossfrang self attentttenion which limits the attention of each token to only other tokens for the same subject (cat in above).
Figure 3: Analyzing Inter-character Leakage (a,b) and the motivation for proposed approach (c).
generation (Yang et al., 2024a) in order to apriori localize different subjects across the storyboard. Once we already know the approximate subject locations, we then use novel bounded cross- foos-frame self-attention limit charrcch leakage during the reverse diffusion process.
4
OUR METHOD
Given an input prompt (for the video / story), and the number storyboard frames B, we aim to generate a sequence of consistent storyboard images discussed in Sec. 3, the core idea of our approach is combine region- based generation and a novel bounded self-attention for reducing inter-character leakage. In particular, we first use region- based planning and generation (Yang et al., 2024a) to apriori localize different subjects for the storyboard (Sec. 4.1). Once we already know the expected positions/ / layout of different characters, we then propose a novel crossframe bounded self-attention layer in order to limit the self-attention of each token only the regions of the same subject across the storyboard. Finally, we propose a cross-framee token merging layer (refer Sec. 4.3) which helps better align fine- fine-grain details of different characters.
.I } in a training free manner. As
4.1 SPATIAL STORYBOARD PLANNING AND GENERATION
Spatial storyboard planning. Given input prompt P and number of desired storyboard frames N, we first leverage multi-modal chain-of of of-thoutht reasoning and planning (Zhang et al., 2023) in order to generate detailed textual description {T,, T2, ... TB} and character placements layout {Li, L..... LB} for each storyboard image IZ, IB }. In particular, given an input prompt P and laggellanguagemmdde M, the ssoryboard-reasoning and planning is performed as follows,
{C., T
MMxx| PP, B, D Dezemplar, C),
(1)
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Generation
CoT.
Soorbcord fremm Decrption 7/
Spocil Storyboani
y #ff boy
(e) Spariel Storytoard Planning (16., Till.
Prowed unviepici
gring sehoobbur and thin sonsing mode powe in the ovoniing soriiiiiig in paan
"raeedi ley and nut crjigging
Merging
Qapon Image Z
Cutpur Imager I2
Oueput Imager
Curput Image I
Beeckl Brech AusE Larns
Tean
Bounded Crom-AAttation
Bounded Cross- frame Self Aoft-Attetiion Layer
Merging
Gobges sptries
metric Maliipootiis
Has sarateggionnl ligitt for presorling intonceainster e-rttactionn Loinage
Roundid Condo atteaaii
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Go Serooio
sudjett
keys From
Saytossx
pet
Figure 4: Method Overview. We propose training-free approach for improving storyboard consistency across multiple subjects. The core idea of our approach is to combine region- based generation and novel bounded self-attention layer for reducing inter-character leakage. We first use region-based planning and generation to apriori localize different subjects (Sec. 4.1). The output images are then generated using a modified diffusion model which consists of 1) bounded cross-frame relf-attention layer (Sec. 4.2) to limit the self-attention of each token to only the regions of the same subject (e.g., boy in above) across the storyboard, and, 2) cross-frame token teee-nn layer which uses the attention map from the self-attention layer to align fine-grain subject details (Sec. 4.3).
where x is the model input, Deremplarr is the in-context learning dataset consisting of 4-5 human generated examples for prompt decomposition, and is the task description. The output layout L, for each storyboard- framme is predicted to contain tuples 1
reasoning based
=; [(,," m ) where (T." m represents the local object prompt and bounding- -bo mask respectively for the htth character and the 1th storyboard frame. Fig. provides an overview of the generated outputs. Please refer supplementary material for further implementation details.
Region-based generation. To constrain each output image to follow the predicted region-based constraints (refer Eq. 1), next adopt region-based generation approach from (Yang al., 2024a). In particular, instead of computing the reverse diffusion hidden states as z L0 f, T&) we use,
fRPG(= t, Tk. Lx);
where /RPG is the region-based sampling function from (Yang et al., 2024a), refers to the weights of diffusion model, t is the timestep, and (Tk, L*) represent respectively for storyboard frame Zg. Please refer supplementary material for further details.
and character-layout
4.2 REDUCING INTER-CHARACTER SELF-ATTENTION LEAKAGE
Once we have apriori localized different subject locations through region-based planning and genera- tion (Sec. 4.1, we next propose a bounded self-attention layer which limits inter-character leakage by limiting each token to pay attention to only other tokens for the same subject across the storyboard Also, to properly address this problem, we would like limit inter-character leakage both 1) within same image (Dahary et al., 2024), as well as, 2) across different storyboard images (e.g., as in Fig. 3). We next discuss the practical implementations for each these cases during reverse diffusion process.
Intra-imant intra-immgeaaaa self-attention bounding, Before addressing inter charr-charrcter self self-attention leakage across different storyboard frames, we would first like to reduce inter-character leakage within the same image. One simple strategy is to limit tokens in each subject region mf (Sec. 4.1) to only pay attention to other tokens in the same region during self-attention computation. However, as shown in Fig. 5, this naive approach leads to reduced inter-character leakage the cost of output quality, Our key insight here is to introduce an additional dropout term (see Eq. 3) which randomly allows each token to also pay attention to other global level tokens (e.g.. for background) with small B2. As shown in Fig. 5, we find that despite its simplicity this helps significantly reduce inter-character leakage while preserving image quality.
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floe andd dog
dack and pa maiee
Vanilla
LDM + Reginta based
LDM + RPG dropoat
LDM + RPG (with dispont
Generation (RPG)
horigghhgg and oat
Figure 5: Understanding role of bounded self self-attention and dropout. Naive masking of the self- attention tokens using region constraints leads to reduced intercharacter leakage cost of image quality. To address this, we show that combined naive masking with dropout-based bias term for self-attention computation allows for reduced inter-character leakage while preserving image quality.
In practice, given the intermediate query. key and value tuple {Qi E R BxNxd, Ki BB Vi IR x xxxx)xd corresponding to the generated image I,, we propose to modify the computation of the self-attention layer as follows,
O = Ar V1, where = softmax (QiK? / /V + log(MM)),
(3)
where N is the number of image tokens and the self-attention mask Mi E NxN is computed as,
Mi Σ (m)) (m²) N Ba
where 1l is the indicator function, NV, E NxN is a random uniform matrix with values between and 1, BB the dropout parameter and helps maintain output image quality after the self-attention masking operation (refer Fig. 5). Finally, mf E NR refers to the object level mask m E R HW which is flattened and reshaped the number of tokens N for the corresponding self-attention layer.
Inter-frame self-attention bounding. We next discuss the extension of our approach to ensure inter-frame character consistency while still avoiding self-attention leakage among different subjects. The key idea is simple: instead of limiting the self-attention for each subject region m (Sec. 4.1) to itself, we also allow it to pay attention to regions of the same subject {m, ... mf m } (i.e., Eth subject) other storyboard frames Z₁ Fig. 4). Additionally, as discussed above we also use the idea of dropout-attention (Fig. 5), which reduces inter-character leakage while preserving image quality.
In practice, given the batch of intermediate image features before the self-attention layer X R Bxxxxx XxC (B is batch-size, N is number of image tokens), we first reshape all tokens into a single batch get X E IIIBBNNCC The joint self-attention layer output then computed as,
Ō= A. where À softmax (QKT) // + log(M)).
(5)
where {Q, K, V) are queries, keys and values computed after linear projection from the reshaped feature X, and the cross-frame self-attention mask M E B-N is computed as,
M- Σ (m+)) (m+yT + Ba
s.l. m
[m] @ 2
my E BBXI
(6)
where N,, E BNBBN is a random uniform matrix and m E Nx1 refers to the object level mask mf E H which is flattened and reshaped to the number of tokens N for the corresponding self-attention layer. Finally, the joint self-attention layer output E R IXBCCC is reshaped back to batch format to generate O E BxCC which treated the final self-attention layer output.

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Published as a conference paper at ICLR 2025
stauding pes beack
sitting sofa
standinng hina logs
sitting living rosm.
Chardesaar dlacke
Jefo Dig peem marging
twro aangiia))
Our
Goupangg penple syp parperaaa und 1it6
(wilh mng rolor marginng
(with unken
Figure 6: Token merging. We observe that positive token merging (left) helps align fine ine-grain subject
details (mans shoes), while early negative token unmerging (right) improves output pose-vate aose-variance.
4.3 CROSS-FRAME TOKEN-MERGING FOR IMPROVING FINE-GRAIN CONSISTENCY
The region- based masked self-attention helps improve general appearance similarities. However, it may often struggle with fine-grain feature alignment between different characters. Motivated by the use of token merging for improving temporal coherence in video editing (Li et al., 2024b), we find that token merging when applied across different storyboard frames helps align fine-grain features between different characters by imposing a hard constraint for ensuring the consistency between final output appearances. The key idea is to use the cross-frame attention map A E RBN xBN from Eq. 5 in order to estimate the similarity of each token with other tokens from the same character in the other storyboard frames, before merging the corresponding tokens in order to ensure appearance similarity. In particular, given output O src. E IR B of self-attention layer, we perform token-merging as,
O Omerge (1 a:) Osre + a O Otarget: O Otarget Osre [argmax (A O H)],
(7)
where H E B.N, is calculated as H[i, 1, :I (1 dzy) Ali, J. (ō.g is the Kronecker delta function), and helps ensure that target and source tokens correspond to different storyboard images.
4.4 EARLY NEGATIVE TOKEN UNMERGING FOR INCREASING POSE VARIANCE
The use of token-merging helps align fine-grain features but we observe that it may also lead to reduced pose-variance in some cases (refer Fig. 6). Interestingly, we find that while using a positive merging parameter > 0 helps pull different storyboard images together, the reverse is also true ie., using a negative a < 0 can help push the corresponding features away (refer App. A for detailed analysis). Since early parts of the reverse diffusion process are primarily responsible for positional or layout consolidation, we use the above insight to increase pose-variance by using a negative a -0.5 during the initial timesteps / [1000, 950). A positive a 0.4 is then used for t E [950, 600] in order to improve visual consistency. As observed in App. A and Fig. 6, we find that the use negative-token unmerging is highly effective pushing the features of two images apart. Furthermore, when applied mainly during the early stages of the reverse diffusion process helps improve the output pose-variaee variance without affecting the character consistency storyboard images.
5 EXPERIMENTS
Baselines. We compare the performance of our approach with prior works on performing visually consistent storyboard generation. In particular, we report comparisons with three classes of methods: 1) Subjject-perronnaiization methods including both requiring inference-tine optimization: Textual- inversion (Gal et al., 2022), DB-LoRA (Ruiz et al., 2022), as well as encoder-based methods: IP-Adapter (Ye et al., 2023), gory-gennration methods: Storygen (Liu et al., 2024). 3) Training frree mehods: Storydiffusion (Zhou et al., 2024), Consistory (Tewel et al., 2024); which utilize crossframe atff-attention for ensuring consistency across different output generations.
(Li et al., 2024a). 2) Training-based autoregressive
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cating Laughing parte
altting ln park
sunnin
inandwly masendainn
Dreambborh LoRA
Toxtual Inversion
Diffussoo
Story Gen
ConsStoory
Stury Stury Diliniio
Story (Ours)
Chavaclerds todld wardy HADKEE and pialt Hodlelong Style ap snimattion
Single Claanetor mujjestie dhagon.
Style bright kerpuacle
Figure 7: Qualitative Results. Comparing the proposed approach with prior works (refer Sec. 5)
Qualitative Results. Results are shown in Fig. & 8. When generating multiple characters (nerdy mouse and pink hedgehog), prior works show significant inter-character leakage (e.g., mouse appearing like hedgehog in Fig. 7, cat appearing like rabbit in Fig. 8) and sometimes characters are missing entirely (Fig. 7). Furthermore, while training-based IP- IP-Adapter (Ye et al., 2023) shows high visual consistency, it exhibits reduced prompt alignment (e.g., no beach in Col2: Fig. 7). On the other hand, when generating single character (dragon) prior works exhibit good general consistency, but fail to align fine-gras mee-grain features (e.g., color of wings for DB-LoRA. color of dragon for StoryDiffusion). In contrast, the cross- -rame self-attention bounding and token- merging properties of our approach (Sec. 4) help achieve better consistency for both single and multiple character storyboard generation.
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Figure 8: Additional results for multi-character storyboard generation. Best-viewed zoom-in.
Table I: Quantitative Results. Comparing the proposed approach with prior works. We observe that while T2I scores for all models decrease when scaling to multiple characters, average approach is able to achieve improved character consistency (CC) and T21 alignment over prior works.
Single Subject
Multiple Subjects
Overall T21
Inference
Method
T2I f
CC
T2I t
CC t
CC1
Time
DB-LORA [Ruiz et al. 2022]
0.771
0.725
0.525
0.744
0.647
0.732
312 sec
TI [Gal et al. 2022]
0.753
0.677
0.476
0.717
0.613
0.698
455 sec
IP-ADAPTER [Ye et al. 2023]
0.625
0.789
0,594
0.834
0.607
0.812
12.1 sec
BLIP-DIFF [Li ct al. 2024a]
0.541
0.756
0.196
0.829
0.368
0.794
9.4 sec
STORYGEN [Liu et al. 2024]
0,569
0.631
0.309
0.752
0.439
0.691
16.8 sec
CONSISTORY [Tewel al. 2024]
0.779
0.791
0.388
0.785
0.583
0.788
44.8 sec
STORYDIFF [Zhou et al. 2024]
0.788
0.732
0.407
0.779
0.598
0.754
6.5 sec
STORYROOTH (Ours)
0.781
0.775
0.633
0.846
0.706
0.811
8.7 sec
Quantitative Results. We also evaluate the performance of the proposed approach quantitatively, by evaluating the text-to-image alignment and output character consistency across different storyboard frames. In particular, we use the recently proposed VQAScore (Lin et al., 2024) for evaluating text-to-image alignment. Similar to Tewel et al. (2024), Dreamsim (Fu et al., 2023) cosine similarity is used for evaluating character consistency. For consistency, the storyboard prompt dataset from Tewel et al. (2024) used for evaluating single-subject generation. We also construct an analogous multi-subject dataset (refer appendix) placing two randomly selected subjects in different settings. Results are shown in Tab. We observe that while T2I alignment scores for all models decrease when scaling to multiple characters, average the proposed approach is able to achieve improved character consistency as well as text-to-image alignment over prior works. In addition to quantitative results, we also evaluate the performance of our approach when generating multiple characters using a user study (refer App. C for details), wherein both text-lo-image alignment as well as character consistency are evaluated by actual human users. Results are shown in Tab. 2. Similar to the quantitative evaluation results, we observe that as compared to prior works the proposed approach is preferred by majority of human subjects for both text-to-image alignment as well as character consistency.
Inference Time Comparisons. We also analyse the performance of the our approach in terms of overall inference time for consistent storyboard generation. For fair comparison, all methods are benchmarked on a single N 00 GPU, using the same base model as (Zhou et al., 2024) for generation. Results are shown in Tab. 1. We observe that our proposed approach achieves better consistency, while on average using marginally higher inference time (8.7 sec) as opposed to recently proposed Storydiffusion (Zhou al., 2024) (6.5 sec). Furthermore, we note that our approach is x30 faster than prior subject-peen personalization methods such as Dreambooth-LoR. (Ruiz et al., 2022) and TI (Gal et al.. 2022), which require approximately min and 7.5 min respectively.
Ablation Study. Results are shown in Fig. 9, We observe that 1) self relf-attention bounding is important for avoiding inter-character leakage both within the same image as well as across storyboard frames. Without self-attention bounding the tokens from one subject (bear) pay attention to tokens from other subject (lion), which leads a storyboard outputs where a single-character (lion) contains features of both subjects. We also observe that 2) the token-merging helps align the finegrain character features. Thus, omitting meening leads to output characters showing general appearance similarity, however, their fine-feature (e.g., color of bear) have visible inconsistencies. Finally, observe that 3) early negative token unmerging helps increase vase-variance in the final storyboard images. Thus, dropping it can lead to characters with reduced pose-variance (e.g., pose and scale of lion).

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Published as a conference paper at ICLR 2025
Table Human User Study: comparing proposed approach with prior works (win implies ours is better). We observe that our approach is preferred by majority of users as compared to prior works.
T2I Alignment
(%)
Character Consistensy (%)
Method
Win f
Tie
Lose +
Win ?
Tie
Lose +
DB-LORA [Ruiz et al. 2022]
37.1
40.2
22.7
68.4
16.8
14.8
TI [Gal et al. 2022]
51.6
36.8
11.6
73.9
21.9
4.2
IP-ADAPTER (Ye et al. 2023]
30.3
55.5
14.2
45.8
41.7
12.5
BLIP-DIFF [Li
2024a]
83.8
12.1
2.1
78.7
14.9
6.4
STORYGEN [Liu ct al. 2024]
85.4
13.5
1.1
82.3
15.6
2.1
SIFRYDIFF [Zhou et al. 2024]
46.4
39.2
14.4
58.3
29.2
12.5
Metric
w/o Bounded Self-Attention
w/o Token Merging w/o Neg. Token Merging StoryBooth
T21 Alignment f
0.612
0.724
0.689
0.706
Char. Consistency f
0.775
0.793
0.817
0.811
wichin
bounding
bj w/o croa-liame token merging
b) wofa early neguttvve token unmarggnn
di Story Booth Fall Mechod)
"w hyper hyyerr-ealistio reatistio digital painting of cute bear and lion Imeeting, telkiing while sitting together, playing together] ow a beaksh"
Figure 9: Ablation Study: analyzing the role of different components (a-c) in the final method (d).
CONCLUSION
In this paper, explore simple yet effective training and optinization-free approach for improving both single and multi-subject consistency for visual storytelling. We note that prior works often struggle when scaling multiple characters and have visible inconsistencies in finegrain character details. To address this, we first analyse the reason for these limitations. Our exploration reveals that the primary-issue stems from self-attention leakage, which is exacerbated when trying to ensure consistency across multiple-characte planning and storyboard generation approach which uses bounded cross-frame self-attention for reducing inter-character leakage and cross-frame token merging for aligning fine-grain character features. Despite its simplicity and training-free nature, we observe that the proposed approach surpasses prior state-of-arl in terms of both character consistency and text-to-image alignment while exhibiting x30 faster inference speed than optimization-
Motivated by these findings, we next a propose region-basee based
methods. We hope that our research can help open new avenues for the development of consistent multi chati-character visual storytelling.
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Published as a conference paper at ICLR 2025
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Reviews:
[{"review_id": "nfWsfUt0Mr", "reviewer": "Reviewer_hkjf", "summary": "This work addresses the limitation of multi-characters consistency in the story visualization task. It first identifies the current flaw in the self-attention module in existing text-to-image diffusion model. Inspired by this, this work proposes a subject region-based text-to-image generation approach. In particular, subjects are localized with pretrained LLM. Then, a bounded cross-attention layer and a bounded cross-frame self-attention layer are designed to adhere to the localized layout and reduce character attention leakage, respectively. Finally, token merging is explored to maintain cross-frame coherence. The proposed approach is training-free and showing improved multi-character consistency and text-to-image alignment performance.", "questions": "1. Fig. 3 needs clearer elaboration for inter-character leakage, linking to (a), (b), (c) sub-figures.\n2. Fig. 7&8 why separate comparisons with SeedStory? Table 1&2 SeedStory (2024) is missing?\n3. For datasets, what are the statistics of the generated multi-subject prompts (L798 in supp.)? Better to mention datasets in the main paper.\n4. Minor typos: L200 fine-grain, L419 cross-frame.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written and provides comprehensive related work review.\n2. It addresses an important problem in visual storytelling - multi-character consistency. \n3. The proposed bounded cross-frame self-attention is effective in addressing inter character leakage from qualitative results.", "weaknesses": "1. The paper claims to address multi-character consistency, however, no impact for more than two character interactions are explored. \n2. The region-based generation with \"bounded cross-attention layer\" (Fig 4.) lacks clear explanation and impact ablation, though some discussion in the supplementary material. (1) In methodology, it refers to the cross-attention masking, which is better to be consistent in term. (2) What's the difference between the proposed CA masking approach and the one in (Tewel et al., 2024), where you mention it is infeasible in L160-161? (3) No ablation on the impact of the bounded CA (Fig 9.). Now the within frame self-attention bounding seems achieving the most obvious quality impact.  \n3. No quantitative ablation.", "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": "ouD1KIVdyT", "reviewer": "Reviewer_Qx42", "summary": "The paper presents a novel method for generating stories or sequences of textual descriptions for visual tasks. The focus is on creating prompts that follow a logical progression without the need for additional training. The method demonstrates the ability to adapt to a wide variety of inputs, providing more dynamic and versatile outputs for tasks that require multi-step understanding or generation. The authors test their method across different benchmarks, demonstrating promising results.", "questions": "1. In Figure 3, the authors discuss self-attention leakage. Could the authors provide more detailed examples or visualizations to illustrate this problem and how their method mitigates it?\n\n2. The paper mentions the use of a bounded cross-frame self-attention layer. How does this differ from traditional self-attention mechanisms, and what specific advantages does it offer?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The approach addresses a practical challenge in prompt-based methods for visual models, where sequences of actions or descriptions are needed. Strong empirical results demonstrate the method’s adaptability across various domains without further training requirements.\nThe proposed method is well-explained, and the experiments provide clear evidence of the method's applicability to multiple tasks.", "weaknesses": "In my understanding, the multi-agent story generation in the paper mainly relies on the two layers proposed by the authors: the \"Bounded Cross-Attention Layer\" and the \"Bounded Cross-frame Self-Attention Layer.\" This approach, which uses a mask, lacks innovation, and is relatively similar to the method already discussed in (Tewel et al., 2024) under the term \"cross-attention masking.\"  Also it looks  combine RPG [1] and SDSA . The former proposed a framework to group multiple objects.  And authors further align each object among the specific mask.\n\nIn line 161, the authors state that \"using cross-attention masking (Tewel et al., 2024) is also not feasible due to the correspondingly increased cross-attention leakage between the storyboard characters.\" This explanation is very vague and does not provide any images or data to support it.\n\nIt would be helpful to elaborate on edge cases where this method might underperform, especially in more complex or more objects visual tasks.\n\nSome of the experimental setups and comparisons could be elaborated further to provide more context and clarity.\n\n[1]  Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs, ICML2023", "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": "p5WQgzEg45", "reviewer": "Reviewer_fdrH", "summary": "The paper proposes an improved method for story telling, improving the consistency of multiple characters (a set-up for which current state-of-the-art fails). This is improved with a training and optimization-free method (as some other recent works Zhou&Tewel). The addresses the consistency problem of multiple objects (and reduces cross frame, and cross character leakage) with two dedicated new layers: 1) bounded cross-attention layer and 2) bounded cross-frame self-attention layer. They combine this with a cross-frame token-merging layer (and also early negative token unmerging). Results show better consistency of multiple characters compared to various other methods both quantitatively, qualitatively, and in user study.", "questions": "See weaknesses.\n\n---", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- the paper addresses a relevant problem in current story telling methods, namely consistency of multiple characters. \n- The problem is well analyzed, and the proposed solution direction seem good. It is nice that this can be addressed with a training-free optimization-free method. \n- the minor contribution of negative token unmerging is nice. \n- the results show the superiority of the proposed method (both qualitatively and quantitatively)", "weaknesses": "I have two main concerns with the paper: \n\n-   the main contribution in section 4.2 is not well explained. I had problems with the notation. What is the main idea behind the equations. Why adding log of M ? \n\n- another important weakness is missing quantitative ablation: I would like to see quantitative ablations of the several contributions (the two layers, and the token merging and unmerging)\n\nminor: \n- the method suffers from relative scale inconsistencies (e.g. the duck is much bigger in second image) is this because of the storyboard planning, or plays (the removed) cross-attention any role here  ?\n\n- unclear what methods are shown in figure 2.\n\n- would be nice to see some results with multiple characters n>2.\n\nremarks: \n- negative token unmerging seems to much either negative token merging or token unmerging. \n- the claim of 30x should be removed from abstract since it is also the case for other optimization-free methods. \n\n----\nI could not follow the notation in eq (4) , since it is about intra-image I would expect it to be KxK rather than NxN? Maybe a better explanation of Zhou et al. would be good to start with.  Some figures could be removed and section 4.2 should be much better explained.", "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": "nk9r6287Bs", "reviewer": "Reviewer_7R1c", "summary": "This work is for consistent storytelling, which aims to generate consistent characters in a sequence of image (or video frame) generation. Starting from the view of self attention leakage, the paper proposes a training-free solution which is applicable for multi-character storytelling. The experimental results show the proposed method is runtime-efficient and surpasses other related methods.", "questions": "I only have concern about the hyperparameter sensitivity, and how to tune the above-mentioned hyperparameters.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The method is capable of dealing with multi characters in storytelling task, which is not well investigated yet.\n- The method is training-free, which is efficient in both training (zero extra computation) and inference.\n- Proposed method is reasonable, supported by previous works and also analysis/visualization.\n- It is good to see that the LLM is well inserted into the framework, which reduce extra efforts for layout stuff.", "weaknesses": "- The whole pipeline is a bit complex, though the inference is still very fast. This is not a big issue.\n- I am wondering how sensitive is the method with different hyperparameters ($\\alpha, \\beta_d$, and also the timestep choice for token merging). \n- It is better to give some intuitive explanation or good motivation in Sec. 4.2.\n- Minor notation issue if I am correct, in line 205-206, \"number of desired storyboard frames **N**\" should be \"number of desired storyboard frames **B**\".", "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)