One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
Authors: Tao Liu
Kai Wang
Senmao Li
Joost van de Weijer
Fahad Shahbaz Khan
Shiqi Yang
Yaxing Wang
Jian Yang
Ming-Ming Cheng
Venue: ICLR 2025 Spotlight (2025)
Keywords: diffusion model; consistent T2I image generation; storytelling
Primary area: generative models
TL;DR: We propose a training-free approach named 1Prompt1Story for consistent text-to-image generations with a single concatenated prompt. Our method is built on the inherent context consistency propoerty of language models.

Abstract: Text-to-image generation models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements for storytelling. Existing approaches to this problem typically require extensive training in large datasets or additional modifications to the original model architectures. This limits their applicability across different domains and diverse diffusion model configurations. In this paper, we first observe the inherent capability of language models, coined $\textit{context consistency}$, to comprehend identity through context with a single prompt. Drawing inspiration from the inherent $\textit{context consistency}$, we propose a novel $\textit{training-free}$ method for consistent text-to-image (T2I) generation, termed "One-Prompt-One-Story" ($\textit{1Prompt1Story}$). Our approach $\textit{1Prompt1Story}$ concatenates all prompts into a single input for T2I diffusion models, initially preserving character identities. We then refine the generation process using two novel techniques: $\textit{Singular-Value
Reweighting}$ and $\textit{Identity-Preserving Cross-Attention}$, ensuring better alignment with the input description for each frame. In our experiments, we compare our method against various existing consistent T2I generation approaches to demonstrate its effectiveness, through quantitative metrics and qualitative assessments. Code is available at https://github.com/byliutao/1Prompt1Story.

Published as a conference paper at ICLR 2025
ONE-PROMPT-ONE-STORYY FREE-LUNCH CONSISTENT TEXT-TO-IMAGE GENERATION USING A SINGLE PROMPT
Tao Liu', Kai Wang"", Senmao Lit, Joost van de Weijer", Fahad Shahbaz Khan Shiqi Yang', Yaxing Wang", Jian Yang', Ming-Ming Chengè
IVCIP, CS, Nankai University, *Computer Vision Center, Universitat Autònoma de Barcelona
"Mohamed bin Zayed University of AI, "Linkoping University, $SB Intuitions, SoftBank Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University
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ABSTRACT
Text-to-image generation models can create high-quality images from input prompts. However, they struggle support the consistent generation of identity- preserving requirements for storytelling. Existing approaches to this problem typ- ically require extensive training in large datasets or additional modifications the original model architectures. This limits their applicability across different domains and diverse diffusion model configurations. In this paper, we first ob- serve the inherent capability of language models, coined context consistency, to comprehend identity through context with a single prompt. Drawing inspiration from the inherent context consistency, we propose novel training-free method for consistent text-to-image (T2I) generation, termed "One- Proteooooomto-toteeeee (rrompt/Story)) Our approach /Prompt|Story concatenates all prompts into single input for T2I diffusion models, initially preserving character identities. We then refine the generation process using two novel techniques: Singular- Value Reweighting and Identttt-Preservvig Cross-Artention, ensuring better alignment with the imput description for each frame. In our experiments, we compare our method against various existing consistent T2I generation approaches to demon- strate its effectiveness through quantitative metries and qualitative assessments. Code is available at https://githuu..com http:/uww.ooobyyllliiaae liutao/ Prompt]Story.
INTRODUCTION
Text-based image generation (T2I) (Ramesh et al., 2022; Saharia et al., 2022; Rombach et al., 2022; Ma et al., 2024;
al., 2023) aims to generate high-quality images from textual prompts, de- picting various subjects in various scenes. The ability of T2I diffusion models to maintain subject consistency across a wide range of scenes is crucial for applications such as animation (Hu, 2024; Guo et al., 2024), storytelling (Yang et al., 2024; Gong et al., 2023; Cheng et al., 2024), video gen- cration models (Khachatryan et al., 2023; Blattmann et al., 2023) and other narrative-driven visual applications. However, achieving consistent T2I generation remains challenge for existing models, as shown in Fig. (up).
Recent studies tackle the challenge of maintaining subject consistency through diverse approaches. Most methods require cimecconsuning training on large datasets for clustering identities (Avrahami al., 2023), learning large mapping encoders (Gal al., 2023b; Ruiz al., 2024), perform- ing fine-tuning (Ryu, 2023; Kopiczko et al., 2024), which carries the risk of inducing language drift (Heng Soh, 2024; Wu et al., 2024a: Huang al., 2024), etc. Several recent training-fire -free approaches (Tewel et al., 2024; Zhou et al., 2024) demonstrate remarkable results in generating images with consistent subjects by leveraging shared internal activations from the pre-trained mod- els. These methods require extensive memory resources or complex module designs to strengthen
Corresponding authors.

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Published as a conference paper at ICLR 2025
"A hyper-realistic digital painting of fairye, dressed in a cloak of spider silks, wearing garland of fireflies?."
SDXL
Juggernaut X-v10
Ip-Adapter
ConsiStory
NPR
Ours
"A photo of with slender figuret, vintage kitchen', reading a novel?, stand landscape", shop- ping market", sipping coffee at quaint café', tending to small urban garden', walking dog the park"."
Figure/ 1: Existing methods (up) encounter challenges in consistent T21 generation. T2I models such SDXL (Podell al., 2023) Juggernaut-X-vll (RunDiffusion, 2024) often exhibit noticeable identity consistency across generated images. Although recent methods including IP-Adapter and ConsiStory have improved identity consistency, they lost the alignment between the generated images and corresponding input prompts. Additional results of our IPrompt/ (down) demonstrate superior consistency without com- promising the alignment between text and images.
the T2I diffusion model to generate satisfactory consistent images. However, they all neglect the inherent property of long prompts that identity information is implicitly maintained by context un- derstanding, which we refer to as the context consistency of language models. For example, the dog object in "A dog is watching the movie. Afterward, the dog is lying the garden." can be easily un- derstood as the same without any confusion since it appears in the same paragraph and is connected by the context. We take advantage of this inherent feature eliminate the requirement of additional finetuning or complicated module design.
Observing the inherent context consistency of language models, we propose a novel approach to generate images with consistent characters using single prompt, termed Phe-Prompt-One- (/Prompt/Story). Specifically, /rrompt/ttory consolidates all desired prompts into a single longer sentence, which starts with an identity prompt that describes the corresponding identity attributes and continues with subsequent frame prompts describing the desired scenarios in each frame. We de- note this first step prompts consolidation. By reweighting the consolidated prompt embeddings, we can easily implement a basic method Naive Prompt Reweighting to adjust the T2I generation performance, and this approach inherently achieves excellent identity consistency. Fig. (up, the 6th column) illustrates two examples, each featuring an image generated with different frame de- scriptions within a single prompt by reweighting the frame prompt embeddings. These examples demonstrate that Naive Prompt Reweighting is able to maintain identity consistency with various scenario prompts. However, this basic approach does guarantee strong text-image alignment for each frame, as the semantics of each frame prompt are usually intertwined within the consolidated prompt embedding (Radford al., 2021). To further enhance text-image alignment and identity consistency of the T2I generative models, we introduce two additional techniques: Singular- Value Reweighting (SVR) and Identity-Preservinn Preserving Cross-Attention (IPCA). The Singular- Singular-Value Value Reweight- ing aims to refine the expression of the prompt of the current frame while attenuating the informa- strengthens
tion from the other frames. Mcanwhile, the strategy the consistency of the subject in the pross-attention layers. By applying our proposed techniques, | achieves more consistent T2I generation results compared to existing approaches.
In the experiments, extend existing consistent T2I generation benchmark ConsiStory+ and compare with several sfate-of-the-se methods, including ConsiStory (Tewel et al., 2024), Story- Diffusion (Zhou et al., 2024), IP-Adapter (Ye et al., 2023), etc. Both qualitative and quantitative
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performance demonstrate the effectiveness of our method Prrrmpt/ttory. In summary, the main contributions of this paper are:
To the best of our knowledge, we are the first to analyze the overlooked ability of language models to maintain inherent context consistency, where multiple frame descriptions within single prompt inherently refer to the same subject identity.
Based on the context consistency property, we propose One-Prompt- Soapp---nn---rryy as a novel training- free method for consistent T2I generation. More specifically, we further propose Singular- Value Reweighting and
Cross-Altention techniques to improve texl-image alignment and subject consistency, allowing each frame prompt be individually expressed within a single prompt while maintaining a consistent identity along with the identity prompt.
Through extensive comparisons with existing consistent T21 generation approaches, we con- firm the effectiveness of 1Pronpt/Story in generating images that consistently maintain identity throughout a lengthy narrative over extended ConsiStory+ benchmark.
RELATED WORK
T21 personalized generation. T2I personalization is also referred to T2I model adaptation. This aims to adapt given model to new concept by providing a few images and binding the new concept to unique token. As a result, the adaptation model can generate various renditions of the new concept. One of the most representative methods is DreamBooth (Ruiz et al., 2023), where the pre-trained T2I model learns to bind a modified unique identifier to a specific subject given a few images, while it also updates the T2I model parameters. Recent approaches (Kumari et al., 2023; Han et al., 2023b; Shi et al., 2023) follow this pipeline and further improve the quality of the generation. Another representative, Textual Inversion (Gal et al., 2023a), focuses on learning new concept tokens instead of fine-tuning the T2I generative models. Textual Inversion finds new pseudo-words by conducting personalization in the text embedding space. The coming works (Dong et al., 2022; Voynov et al., 2023; Han et al., 2023a; Zeng et al., 2024) follow similar techniques.
Consistent T21 generation. Despite recent advances, T2I personalization methods often require ex- tensive training to effectively learn modifier tokens. This training process can be cime-consuming, which limits their practical impact. More recently, there has been shift towards developing consis- tent T2I generation approaches (Wang et al., 2024b;a), which can be considered specialized form of T2I personalization. These methods mainly focus on generating human faces that possess semanti- cally similar attributes to the input images. Importantly, they aim to achieve this identity-preservigg T2I generation without the need for additional fine-tuning. They mainly take advantage of PEFT techniques (Ryu, 2023; Kopiczko et al., 2024) or pre- pre-training with large datasets (Ruiz et al., 2024; Xiao et al., 2023) to learn the image encoder to be customized in the semantic space. For exam- ple, PhotoMaker (Li et al., 2023b) enhances its ability to extract identity embeddings by fine-tuning part of the transformer layers in the image encoder and merging the class and image embeddings. The Chosen One (Avrahami al., 2023) utilizes an identity clustering method to iteratively identify images with similar appearance from a set of images generated by identical prompts.
However, most consistent T2I generation methods (Akdemir & Yanardag, 2024; Wang et al., 2024a) still require training the parameters of the T2I models, sacrificing compatibility with existing pre- trained community models, or fail to ensure high face fidelity. Additionally, as most of these sys- tems (Li et al., 2023b; Gal et al., 2023b; Ruiz et al., 2024) are designed specifically for human faces, they encounter limitations when applied to non-human subjects. Even for the state-of-the-an ap- proaches, including StoryDiffusion (Zhou et al., 2024), The Chosen One (Avrahami et al., 2023) and ConsiStory (Tewel et al., 2024), they either require time-consuming iterative clustering or high memory demand in generation to achieve identity consistency.
Storytelling. Story generation (Li et al., 2019; Maharana et al., 2021), also referred to as sto- rytelling, is one of the active research directions that is highly related to character consistency. Recent researches (Tao et al., 2024; Wang et al., 2023) have integrated the prominent pre-trained T2I diffusion models (Rombach et al., 2022; Ramesh et al., 2022) and the majority of these ap- proaches require intense training over storytelling datasets. For example, Make-a-Story (Rahman et al., 2023) introduces visual memory module designed to capture and leverage contextual in-
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formation throughout the storytelling process. StoryDALL-E (Maharana et al., 2022) extends the story generation paradigm to story continuation, using DALL-E capabilities to achieve substantial improvements over previous GAN-based methodologies. Note that the story continuation shares similarities with consistent Text-to-Image generation by using reference images. However, current consistent T2I generation methods prioritize preserving human face identities, whereas story contin- uation involves supporting various subjects or even multiple subjects within the generated images.
In this paper, our proposed consistent T2I framework, [Prompi/Storyy diverges significantly from previous approaches in storytelling and consistent T2I generation methods. We explore the inherent context consistency property in language models instead of finetuning large models or designing complex modules. Importantly, it is compatible with various T2I generative models, since the prop- erties of the text model are independent of the specific generation model used as the backbone.
METHOD
Consistent T2I generation aims to generate of images depicting consistent subjects in different scenarios using a set of prompts. These prompts start with an identity prompt, followed by the frame prompts for each subsequent visualization frame. In this section, we first empirically show that different frame descriptions included in a concatenated prompt can maintain identity consistency due the inherent context consistency property of language models. We examine this observa- tion through comprehensive analyses in Sec. 3.1 and propose the basic Naive Prompt Reweight- ing pipeline our method / Prompt/Storyy Following that, to ensure that each frame description within the prompt is expressed individually while diminishing the impact of other frame prompis, we introduce Singular- Value Reweighting and illustration of |Prompt|Story is shown in Fig. 4 and Algorithm in the Appendix.
Cross-Attention in Sec. 3.2. The
3.1 CONTEXT CONSISTENCY
Latent Diffusion Models. We build our approach on the SDXL (Podell et al., 2023) model, a latent diffusion model that contains two main components: autoencoder (i.e., an encoder & and a decoder D) ) and a diffusion model (i.e., co parameterized by 8). The model co is trained with the following loss function:
(.-0iio)))) [llc- [llc- co(2u, Te (P))))
LLDM :=
(1)
(I, and where co is a UNet that conditions the latent variable 2,, timestep t ~ Uniform(1 (1, T), text embedding Te (P). In text-guided diffusion models, images are generated based on a textual condition, with C = Tς (P) E MRRxD where M is the number of tokens, D is the feature dimension of each token, and Tę is the CLIP text encoder (Radford et al., 2021)'. For a given input, the model co(2), t, C) produces cross-attention map. Let f=, denote the feature map output from ca. We can obtain a query matrix Q = (f2,)) using the projection network lg. Similarly, the key matrix IC is computed from the text embedding C using another projection network l. such that K. = (c(C).
The cross- coss-atttntionn map Ar is then calculated as: Ar = softmar(Q KC") /Vd), where is the dimension of the query and key matrices. The entry [At]ij represents the attention weight of the j-th token the i-th token.
Problem Setups. In the T2I diffusion models, the text embedding C = (P) E R-MxD is with M tokens. The M tokens contain start token [SOT] followed by |P] tokens corresponding to the prompt, and M - IP|
padding end tokens [EOT] Previous consistent T2I generation works (Avrahami et al., 2023; Tewel et al., 2024; Zhou et al., 2024) generate images from a set of N prompts. This set of prompts starts with an identity prompt Po that describes the relevant attribute of the subject and continues with multiple frame prompt P., where i = 1,...,AA ,N describes each frame scenario. However, this separate generation pipeline ignores the inherent language property, i.c., the context consistency, by which identity is consistently ensured by the context information inherent in language models. This property stems from the self-attention mechanism within Transformer-base text encoders (Radford et al., 2021; Vaswani et al., 2017), which allows learning the interaction between phrases in the text embedding space.
SSXL two text encoders and concatenate the embeddings as the final input. M by default.
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In the following, we analyze the context consistency under different prompt configurations in both textual space and image space. Specifically, we refer to the conventional prompt setups as multi- prompt generation, which is commonly used in existing consistent T21 generation methods. The multi-prompt generation uses N prompts separately for each generated frame, each sharing the same identity prompt and the corresponding frame prompt as (Po; P.], i [1, Nj. In contrast, our single- prompt generation concatenates all the prompts as (Po; Pi:... Px] for each frame generation, which we refer as the Prompt Consolidation (PCon).
3.1.1 CONTEXT CONSISTENCY IN TEXT EMBEDDINGS
Empirically, we find that the frame prompt (P; i- 1i..,,, in the single-prompi generation setup have relatively small semantic distances among each other in the textual embedding space, whereas those across multi- multi-prompt generation have comparatively larger distances. For instance, set the identity frame Po = "A watercolor of cute kitten"
example. We then create N = 5 frame prompts (Pi, i E NT} as "in a garden", "dressed in a superhero cape", "wearing collar with bell", "sitting in basket", and "dressed in cute sweater", respectively. Under the multi-prompt setup, each frame is generated by the text embedding defined as C. = Te (PPo P.]) [eSOT, cP,, cEOT), c..T) ;. (i = 1,...... .N. while the text embedding of the Prompt Consolidation in the single-prompt case is C Te((Po:P::....... ;NN)) cSST
, c,, cEOT
To analyze the distances among the frame prompts, we extract c... from C. for multi- prompt setup and apply I-SNE for 2D visual- ization (Fig. 2-1eft). Similarly, we extract all c.: from C for the single-prompt setup (Fig. 2- left). As can be observed, the text embeddings of frame prompts under the multi-prompi setup are widely distributed the text representation space (red dots) with average Euclidean La distance of 71.25. In contrast, the embeddings in the single-pne prommpt case exhibit more compact distributions (blue dots), with a much smaller average L2 distance of 46.42. We also per- formed a similar distance analysis on all prompt sets in our benchmark ConsiStory+. As shown in Fig. F-igh, we can conclude a similar obser- vation that the frame prompts share more sim- ilar semantic information and identity consis- tency within the single-prompt setup.
117.04
115
31.09
65-
2f from
Froms
46.6
Figure 2: t-SNE visualization of text embeddings (Left): c.. from together compared to those from multi- puult- gener- ation. Statistical results (Right): We evaluated the average distances between the corresponding point sets of all prompt on the ConsiStory+ benchmark af- ter dimensionality reduction. The average distance tween text embeddings from single-promp generation is smaller than that from multi-prompp generation.
generation are closer
3.1.2 CONTEXT CONSISTENCY IN IMAGE GENERATION
To demonstrate that context consistency is also maintained in the image space, we further conducted image generation experiments using the prompt example above. The images generated by the SDXL model with the multi-promp! configuration, as illustrated in Fig. 3 (left, the first row), show various characters that lack identity consistency. Instead, we use our proposed concatenated prompt Pii.P; PN]. To generate the i-th frame (i = I,..., N), we reweight the cº. corresponding to the desired frame prompt P; by magnification factor while rescaling the embeddings of the other frame prompts by a reduction factor. This modified text embedding is then imported to the T2I model to generate the frame image. We refer to this simplistic reweighting approach as Naive Prompl Reweighting (NPR). By this means, the T2I model synthesizes frame images with the same subject identity. However, the backgrounds get blended among these frames, as shown in Fig. (left, the second row). By contrast, our full model (PromptlStory introduced in Sec. 3.2 generates images with better consistent identity and text-image alignment for each frame prompt, as shown in Fig. 3 (left, the last row).
To visualize identity similarity among images, we removed backgrounds using CarveKit (Selin, 2023) and extracted visual features with DINO-v2 (Oquab et al., 2023; Darcet et al., 2023). These features are then projected into the 2D space by t-SNE (Hinton & Roweis, 2002) (as shown in Fig. (mid)). Our complete approach / /Prompt Story obviously obtains better identity consistency than
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capel, weazing collr with a boll, string banket,, decaued cuts priaize??
"A wateroster illcttariinn cate kitwer gandens, dressed in auper
120.44
NPR Ours
100
S0N
94-
NPPR
92-
02.54
90-
01.25
Ours
86
Ours NPR SDXL
Figure 3: (Left): SDXL generales frame images using pulti-prompt generation, while Naive Prompt Reweight- ing (NPR) and our method utilize single prompt setup. (Mid): Image features are extracted by DINO- v2 (Oquab et al., 2023) and visualized by the t-SNE reduction. Naive Prompt Reweighting and IPromptIStory show more consistent identity generations than the SDXL model. (Right): Statistics of the average feature distances among generated images from the prompts in our extended ConsiStory+ benchmark, which further confirms that Prrompt/Stov Story produces better identity consistency.
the other two comparison methods, while Naive Prompt Reweighting shows improvements over the SDXL baseline. We also applied the analysis across our extended benchmark ConsiStory+ and calculated the average pairwise distance, as shown in Fig. 3 (right). These results further consolidate our conclusion that the frame prompts in a single- progleprroptttt setup share more identity consistency than the multi-prompl case.
3.2 -NT--PROMPT-ON---IIE
As also observed from the above section, simply concatenating the prompts as Naive Prompt Reweighting cannot guarantee that the generated images accurately reflect the frame prompt de- scriptions, for which we assume that the T2I model cannot accurately capture the correct partition of the concatenated prompt embeddings. Furthermore, the various semantics within the consoli- dated descriptions interact with each other (Chefer al., 2023; Rassin et al., 2024). To mitigate this issue, propose additional techniques based on the Prompt Consolidation (PCon), namely Singular- Sinulla-Vallue Reweighting (SVR) and Adeenttt-Prrseenve Cross Cross-Attention (IPCA).
Singular- Value Reweighting. After the Prompt Consolidation as C = Te Te((Po: :.::::....::.). SO _o,cc² ........PP'. cBBT), we require the current frame prompt to be better expressed in the T2I generation, which we denote as pexp = P,, (j = I..,..... We also expect the remaining frames to be suppressed in the generation, which we denote as psup Pk. k E NJM.). Thus, the N frame prompts the subject description can be written as {peap, Psup). As the [EOT] token contains significant semantic information (Li et al., 2023a; Wu et al., 2024b), the semantic information corresponding to pezp, in both P, and [EOT], needs to be enhanced, while the semantic information corresponding peup, in Pk. k + j and [EOT] need to be suppressed. We extract the token embeddings for both express and suppress sets as xexp = (eP,cEET) and suu icP......CC c.....,.... -1 co+t ,...,."' c.. CEOT..
Inspired by (Gu et al., 2014; Li et al., 2023a), we assume that the main singular values of Xeup correspond the fundamental information of peup, We then perform SVD decomposition as: Xeep UEV', where Σ To enhance the expression of the frame Pj, we introduce the augmentation for each singular value, termed SVR+ and formulated as:
"., ,ny)). the singular values σn 2 > on, 2.
θ Be az * G.
(2)
where the symbol is the exponential, and B3 are parameters with positive numbers. We recover the tokens reep U with the updated Σ diagioo, o.. :r embedding is defined as Lepp = [ EETT, and = (cSOT c ... c.o
The new prompt
cNx aEOT
Note that there is an updated Poup = [c [c Pi 1, c,c PN FOT
n, minmmmlee (D.|cP) R0TI). The dimension the SDXL. model is greater than c | |c BOT)

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(0) Overall Pipeline of APrrmppiSttryy
Mentty Pronet: roizer palnting stondy dmart? Frome Pronggt: "cunss stone oeliev, gall- reiing herbs foreet, oary mitling genss gittering.ww
expeca suppecss
Text Encoder
C= 16
(b) Vhilue Reweighting (SVR)
(c) Seeetity-Preeerving Cross-Attention (IPCA)
Caucat
Pair Concul
UEyT
Eqg.
UBUT
Figure 4: (a): The overall pipeline of Ptrompt combine identity prompt and frame prompis into single prompt, then we apply both Singular-Vaile Reweighting (SVR) and Srentit--Preeerviie Cross-Aitention (IPCA) to generate identity-consisten images, (b): During SVR, we first enhance the semantic information of the express set x (red arrow), then iteratively weaken the semanties for the suppress set u (blue arrow). (c): In IPCA, we concatenate K with & and V with to improve identity consistency.
Similarly, we suppress the expression of the remaining frames. Since Loup contains information related to multiple frames, the main singular values of SVD in d'o only capture a small portion of these descriptions, which may lead insufficient weakening of such semanties (as shown in the Appendix of Fig. 1-rigght. Therefore, we propose to weaken each frame prompt in suu separately. We construct the matrix as &oup [c?x, CROT), k * j to perform SVD with the singular values ão 2 ... on. Then, each singular value is weakened as follows, termed as SVR-:
ā B'e a'
where a' and 3 are parameters with positive numbers. The recovered structure is & FEOT, After reducing the expression of each suppress token, we finally obtain the new text embedding Ć = (cSOT cPo,CP: , , (BOT).
.......
(dentity-Preeerving Cross-Aitention. The use of Singular- Value Reweighting can reduce the blend- ing of frame descriptions in single-prompt generation. However, we observed that it could also im- pact context consistency within the single prompt, leading to images generated slightly less similar in identity (as shown in the ablation study of Fig. 7). Recent work (Liu et al., 2024) demonstrated that cross-attention maps capture the characteristic information of the token, while self-attention preserves the layout information and the shape details of the image. Inspired by this, propose Prenttty--rreevvinng Cross-Attention to further enhance the identity similarity between images gen- erated from the concatenated prompt of our proposed Prompt Consolidation.
For specific timestep f. after applying Singular- Value Reweighting, we have the updated text em- bedding Ć. During a denoising pass through the diffusion model, we obtain the corresponding Q, K. in the cross-attention layer. Here, we aim to strengthen the identity consistency among the images and mitigate the impact of irrelevant prompts. We set the token features in AC corresponding to i [1, to zero, resulting in K. Here, only the identity prompt remains to augment the identity semantics. Similarly, can get V. We form new version of IC by concatenating it with K. dubbed K Concat(KT RTTT. The new cross-attention map then given by:
À = softmar (ΩRT)) /Vd
(4)
where df the dimension of Q and Æ. Similarly, we update D = Concat(p", DTJT. The final out- put feature of the cross-attentin attention layer is Ax V. This output is a reweighted version that strengthens identity consistency using filtered features, which only contain the identity prompt semantics.

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"playfully splash- ing spakling lake!"
"nestled among blooming cherry blossoms
"soaring grace- fully through rainbow skys"
city park with autumn leaves?"
"building sand castlet"
"in flower garden
PhotoMaker
IP-Adapter
ConsiStory
Ours StoryDiffusion
frame "A pixar style illustration of a dragon"."
frame
frame
frame
frame
"A hyper-realistic painting of 16 years old girlo."
Figure S: Qualitative results. We compare our method with Pakto IP-Adapter, ConsiStory, and Story- Diffsion. Among them, Texture Inversion, PhotoMaker, ConsiStory, and StoryDiffsion struggled to maintain identity consistency for the dragon object while IP-Adapter produced images with relatively similar poses and backgrounds. See Comparison with the remaining methods in Fig. 22 of the Appendix.
EXPERIMENTS
4.1 EXPERIMENTAL SETUPS
Comparison Methods and Benchmark. We compare our method with the following consistent T2I generation approaches: -iff-Diffssoon (Li et al., 2024), Textual Inversion (TI) et al., 2023a), IP-Adapter(Ye et al., 2023), PhotoMaker (Li al., 2023b), The Chosen One (Avrahami al., 2023), ConsiStory (Tewel et al., 2024), and StoryDiffusion (Zhou et al., 2024). We follow the default configurations in their papers or open-source implementations.
To evaluate their performance, we introduce ConsiStory+, extension of the original ConsiStory (Tewel et al., 2024) benchmark. This new benchmark incorporates a wider range of subjects, de- scriptions, and styles. Following the evaluation protocol outlined in ConsiStory, prompt alignment and subject consistency across ConsiStory+, generating up to 1500 images on 200 prompt sets. Additional details the construction of our benchmark and the implementation of the methods are provided in Appendix B.2 and Appendix B.3.
evaluated both
Evaluation Metrics. To assess prompt alignment performance, compute the average CLIP- Score (Hessel et al., 2021) for each generated image in relation to its corresponding prompt, which denote CLIP-T. For the identity consistency evaluation, measure image similarity using
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Table 1: Quantitative comparison. The best and second best results are highlighted in bold and underlined.
respectively. Vanilla SD1.5 and Vanilla SDXL are shown as references and excluded from this comparison.
Method
Base Model
Train Free
CLIP-TY T
CLIP- 1T
DreamSim
Steps
Memary (GB).
Inference
Time
Vanilla SDL55
0.8353
0.7474
0.5873
50
4.73
2.4657
Vanilla SDXL
0.9074
0.8165
0.5292
16.04
13.0890
BLIP-Diflusion
SDL.5
0.7607
0.8863
0.2830
26
7.75
1.9284
Textual Inversion
0.8378
0.8229
0.4268
40
32.94
282.507
The Chosen One
SDXL
0.7614
0.7831
0.4929
35
10.93
11,2073
PhotoMaker
0,8651
0.8465
0.3996
50
23.79
18.0259
IP-Adapter
0.8458
0,9429
0.1462
13,4594
ConsiStory
0,8769
0,8737
0.3188
50
34,55
34,5894
Naive Prompt Reweighting (NPR)
SifluSiilusoon
SDXL
0.8411
0.8877
0.8916
0.8755
0.3212
0.2548
50
50
45.61
16.04
25 69.6
17.2413
1trrmpt (Ours)
0.8942
0.9117
0.1993
50
18.70
23.2088
both DreamSim (Fu et al., 2023), which has been shown to closely reflect human judgment in eval- uating visual similarity, and CLIP-I (Hessel et al., 2021), calculated by the cosine distance between image embeddings. remove image backgrounds using CarveKit (Selin, 2023) and replace them with random noise to ensure that similarity measurements focus solely on the identities of subjects.
line with the methodology proposed in DreamSim (Fu et al., 2023), we
4.2 EXPERIMENTAL RESULTS
Qualitative Comparison. In Fig. 5, we present the qualitative comparison results. Our method 1Prompt/Story demonstrates well-balanced performance in several key aspects, including identity preservation, accurate frame descriptions, and diversity in the pose of objects. In contrast, other methods exhibit shortcomings in one or more of these aspects. Specifically, PhotoMaker, ConsiS- tory, and StoryDiffusion all produce inconsistent identities for the subject "dragon" in the exam- ples on the left. Additionally, IP-Adapter tends to generate images with repetitive poses and similar backgrounds, often neglecting frame prompt descriptions. ConsiStory also displays duplicated back- ground generation in the consistent T2I generation.
Quantitative Comparison. In Table 1, we illustrate the quantitative comparison with other ap- proaches. In all evaluation metries, 1 PrromptSStory ranks first among the training-fir ffree methods, and second when including training-regue required methods. Furthermore, compared to other training- -ree methods, our approach demonstrates reasonable fast inference speed while achieving excellent per- formance. More specifically, our method / /Prompt/Story achieves the CLIP-T score closely aligned with the vanilla SDXL model. In terms of identity similarity, measured by CLIP-I and DreamSim, our method ranks just below IP-Adapter. However, the high identity similarity of IP-Adapter mainly stems from its tendency to generate images with characters depicted in similar poses and layouts. To further explore this potential bias, we conducted a user study to investigate human preferences. Following ConsiStory, we also visualized our quantitative results using a chart, as shown in Fig. 6. Training-basee methods, such as IP-Adapter and Textual Inversion, often overfit character identity and perform poorly on prompt alignment. contrast, among training-fres methods, our approach achieves the best balance in both prompt alignment and identity consistency.
User Study, In the user study, we compare our method with several sfate-of-the-art approaches, including IP-Adapter, ConsiStory, and StoryDiffusion. From our benchmark, we randomly selected 30 sets of prompts, each comprising four fixed-length prompts, to generate test images. Twenty participants were asked to select the image that best demonstrated overall performance in terms of identity consistency, prompt alignment, and image diversity. As shown in Table 2, the results indicated that method I /Prompt Story aligns best with human preference. More details of the user study are shown in Appendix. F.
Ablation study. We performed an ablation study to analyze each component, as illustrated both qualitatively and quantitatively in Fig. and Table 3. When using Singular- Value Reweighting exclusively with improving the express set as SVR+ (that is, Eq. 2), the generated images blend with other descriptions, as can be seen in Fig. 7 (left, first row). Similarly, when Singular- Value Reweighting is only to weaken the suppress set SVR- (i.e., Eq.3), the same issue appears in Fig.

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Published as a conference paper at ICLR 2025
0.91
Vanilla SDXL
Table 2: User study with 37 people to vote for the best consistent T21 generation method according to human preference.
Batte Blaa
0.90
SVR
Base Models
Prompttstory
0.89-
Training frree Mathods
Training Mathods
StoryDiflsiin
Svit-
Method
IP-Adapeer
ConsiStory StoryDiffasion
Ours
0.88
1PromptiStory (Ours)
Consisigry
Percent (4))
29.80
CUP-
0.07
PhotoMakker
Table 3: Ablation study. We evaluated the influence of each component in IPrompt]Sion Story, including the Singular- Value Reweighting (SVR+ and SVR-), and Preservgn Cross-Attention (IPCA).
0.80-
0.85-
IP-Mlapter
NPR
0.84
Textuull Inversiin
Vanilla SD1.5
0.83
Method
CLIP-TT
CLIP-IT
DreamSim
0.7 DreamSim
0.8
PCon;
0.8774
0.8886
0.2560
PCon; SVR-
0.8904
Figure 6: Prompt aligument vs. identity consistency. Our method /Prompt/ Story is positioned in the upper
PCon; SVR:; SVR-
0.8989
0.8849
0,2538
PCon; SVR+; SVR: IPCA (Ours)
0.8942
0,9117
0.1993
right corner.
"A photo of a dogo, chasing a frishee in a colorful park dancing to music at a vibrant street festival", jusm ing through hoop at circus performance', posing for a photoshoot in modern art gallery!."
SVR:
SVR
Oure
Figure 7: Qualitative ablation study. All ablated cases with incomplete components of (Prompt|Ston strug- gle to achieve both prompt alignment and identity consistency effectively our full method.
(left, second row). In contrast, integrating both SVR+ and SVR- in Singular-Vaiie Value Reweighting can effectively mitigate blending in generated images (Fig. 7 (right, first row)). Although Singular- Value Reweighting can effectively resolve frame prompt blending issues, without Identity-Presenc Cross-Attention, there remains weak inconsistency among the generated images. shown in Fig. 7 (right, second row), the results indicate that using Singular- Value Reweighting and Identity- Preserving Cross-Attention achieves the best performance, as also evident in Table (the last row). Additional results of ablation analysis and visualization are presented in the Appendix. C.
Additional applications. /Prompt Story can also achieve spatial controls, integrating with exist- ing control-based generative methods such as ControlNet (Zhang & Agrawala, 2023). As shown in Fig. (left), our method effectively generates consistent characters with human pose control. Furthermore, method can be combined with other approaches, such PhotoMaker (Li al., 2023b), to improve the consistency of identity with real images. By applying our method, the gen- erated images more closely resemble the real identities, as demonstrated in Fig. 8 (right).
CONCLUSION
In this paper, addressed the critical challenge of maintaining subject consistency in text-to- image (T2I) generation by leveraging the inherent property of context consistency found in natu- ral language. Our proposed method, The-Prompt-One-ee a single extended prompt to ensure consistent identity representation across diverse scenes. By in- tegrating techniques such as Singular-Valle Value Reweighting and (dentity-Preserviin Cross-Artention, our approach not only refines frame descriptions but also strengthens the consistency at the atten- tion level. The experimental results on the ConsiStory+ benchmark demonstrated the superiority of IPrompi/Ston over state-of-the-art techniques, showcasing its potential for applications in an- imation, interactive storytelling, and video generation. Ultimately, our contributions highlight the importance of understanding context in T2I diffusion models, paving the way for more coherent and marratve-consisterr visual output.
(IPromptIStory), effectively utilizes
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ACKNOWLEDGEMENTS
This work was supported by NSFC (NO. 62225604) and Youth Foundation (62202243). We ac- knowledge the support of the project 1-0-022-14327NB--000 funded by the Spanish Govermennt through MCIN/AEI/10, 1333////000000/501111 10001 1033 and FEDER. Additionally, we recognize the "Science and Technology Yongjiang 2035" key technology breakthrough plan project (2024Z120). The com- putations for this paper were facilitated by the resources provided by the Supercomputing Center of Nankai University (NKSC).
We would like to extend our gratitude to all the co-authors for their invaluable assistance and in- sightful suggestions throughout this work. In particular, we wish to thank Kai Wang, a postdoctoral researcher at the Computer Vision Center, Universitat Autònoma de Barcelona. His meticulous advice and guidance were instrumental in the completion of this project.
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APPENDIX
A BOARDER IMPACTS AND LIMITATIONS
Boarder Impacts. The application of T21 models in consistent image generation offers exten- sive potential for various downstream applications, enabling the adaptation of images to different contexts. In particular, synthesizing consistent characters has diverse applications, however, it is a challenging task for diffusion models. Our /Prompt/ Story can help the users customize their desired characters in different story scenarios, resulting in significant time and resource savings. Notably, current methods have inherent limitations, as discussed in this paper. However, our model can serve as an intermediary solution while offering valuable insights for further advancements.
Limitations. While method /Prompt/Ston can achieve figh-fidelity consistent T2I genera- tion, it is not free of limitations. Firstly, we have to know all the prompts in advance. Additionally, the length of the input prompt is constrained by the maximum capacity of the text encoder. Al- though we proposed a sliding window technique that facilitates infinite-length story generation in Appendix D.2, this approach may encounter issues where the identity of the generated images grad- ually diverges and becomes less consistent.
B IMPLEMENTATION DETAILS
B.1 MODEL CONFIGURATIONS
We generate subject- cobssct-consistent images by modifying text embeddings and cross-attention modules inference time, without any training or optimization processes. Our primary base model is the pre- trained Stable Diffusion XL (SDXL) SDXL has two text encoders: the CLIP L/14 encoder (Rad- ford et al., 2021) and the OpenCIIP bigG/14 encoder (Cherti et al., 2023). We separately update the text embeddings produced by each encoder. For Naive Prompt Reweighting, we multiply the text embedding corresponding to the frame prompt that needs to be expressed by a factor of 2, while the text embedding corresponding to the frame prompts that need to be suppressed is multiplied by factor of 0.5, keeping the eBOT unchanged.
In method, Prroppt Story, set the parameters follows: and a' = 0.01, 3' = 1.0 in Eq.3. During the generation process, we initialize all frames with the same noise and apply dropout rate of 0.5 to the token features in K corresponding implementation of IPCA, the concatenated K and V are derived from the original text embeddings prior to applying SVR. We design an attention mask where all values the column corresponding to i [1, N are set to zero, while all other positions are mask is then added to the original attention map. Our full algorithm is presented in Algorithm 1. Following (Tewel al., 2024; Alaluf al., 2024; Luo al., 2023), we use Free-U (Si et al., 2024) to enhance the generation quality. All generated images based on SDXL are produced at a resolution of 1024 1024 using a Quadro RTX 3090 GPU with 24GB VRAM.
0.01, 3 0.05 in Eq.2,
Po- In the
one. The natural logarithm of this
B.2 BENCHMARK DETAILS
To evaluate the effectiveness of our method, we developed ConsiStory+, an extended prompt bench- mark based on ConsiStory (Tewel et al., 2024). We enhanced both the diversity and size of the original benchmark, which only comprised 100 sets of prompts across 4 superclasses. Our ex- pansion resulted in 200 sets, with each set containing between 5 and 10 prompts, categorized into 8 superclasses: humans, animals, fantasy, inanimate, fairy tales, nature, technology, and foods. The extended prompt benchmark was generated using ChatGPT 4.0-turbo', involving two main steps. First, we expanded the 100 prompt sets from the original benchmark, increasing each to a length of 5 to 10 prompts, as shown in Fig. 9 (left). Then, generated new prompt sets for each of the new superclasses, as illustrated in Fig. 9 (right). The prompt sets collected through these two steps were combined to form benchmark, ConsiStory+.
http://hhhhnaaaccccciiiiiiiiaaaa hhtps///cattgt.ccm/
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w/jo ouTs
"A photo of an elderly geatlecnan', crovs-legzed on mounteiin, sarrounded ancient scrollss, sitting bench under cherry blossomns,, sitting sunny garden, playing puppy!.
photo of woman, enjoying cup of coffee al cony outdeor talking
cales, painting besutiful landsscae
Figure 8: (Left); Our method / /Prompt/Soon sistent character generation. (Right): Additionally, our method can also combine with other methods, such as PhotoMaker, to achieve real-image personalization with improved identity consistency.
integrate with ControlNet to enable spatial control for con-
Algorithm IStrr
Input : A text embedding C = [cSOT cPo, c., ,.... PN eBOT) and latent vector zt- Output: The subject consistency images I,, IN.
for j 1,.... N do
// Singular-val
Reweighting
Peop [cPs FOT + Xep = , (Eq. 2);
FOT
for k = [1, (j) do
Leup 1c. GBOT
f |cp EOOT (Eq. 3); 8Px C
end
C SOT Po. ci
cº
11 tyentity- for = T...,, 1 ...,.
do
K, C.V+C.
= Concat(K RT)T, V Concat TT
Ā θ, (Eq. 4);
2;-1 + colz,,t, t,C) with A. V:
end
Ij D(z0)
end
Return I,, .IN.
B.3 COMPARISON METHOD IMPLEMENTATIONS
We compare method with all other approaches based on Stable Diffusion XL, except for BLIP- Diffusion (Li et al., 2024), which based on Stable Diffusion vl.5². The DDIM steps set to default value in the open-source code of each method. Below are the third-pane party packages used for method implementations:
The unofficial implementation of Textual Inversion (TI) (Gal et al., 2023a) at https://
gi github. cww.cosococc/oecccoooocco..eo.e roettger /orrxXX-TTe - Inversion.
The unofficial implementation of The Chosen One (Avrahami al., 2023) at https://
gi github. com fomm/ichhengDuat SwwcchengguannTTehhheeeeee Tww.hhhehhhhnnn....
The official implementation of IP-Adapter (Ye et al., 2023) at https: https:://github.// com/
tencent-ail aennntaallab/brnn watptttaaaaaa//p-ddaaaaa Tabbb/re-ddaper.....
The official implementation of PhotoMaker (Li et al., 2023b) at https: http::///iie //tpt/hb.tttt.t//eeee com/
TencemtAACC/hhoookkker..
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"prompt"; photo of sticker of a cute corgi dog, student's laptop, amidst an action-packed skate session, showing its teeth, live music at inter national airport.",
"prompt": "A photo of A sticker of cute corgi dog, on student's laptop, amidst an action-packes pack-paakdd skate session, showing, its feeth, live music gig, inter national airport.",
"concept_tod tokeepttttttt........ "dog".
"dug",
"superclass": "inanimate", "subject": 'A sticker of cute corgi dog"; "settings":
"superclass": "inerillass"""nnnmmatt"
"inanimate",
"subject"; sticker cute corgi dog", "settings":
"on student's laptop",
"on student's laptop",
"amiidst action packed skate session", "adding character to urban signage", "at a live music gig",
"amidst an action-packed skate session", "adding character urban signage", "at live music gig", "at international airport"
"at an international airport"
"style": photo of"
"style": "A photo
According to the template above, select a random number between and 10, and extend the settings to should describe
According to the template above, replace the superclass category with 'airr tales' and choose a new random style, subject, and settings based on this superclass. Ensure that the concept_tol token represents the subject described, and the settings' length random number between 5 and 10, describing various actions of the sub ject.
that length. The extended subject. Also, update the content in the prompt accord ingly.
SF
Hore is the updated version based on the instructions:
{
'A photo of sticker on student's laptop, amidst action-packed skate session, adding character to urban signage, at live music gig, at an international airport, brightening up park bench, stuck
"prompt":
corgi dog,
Here the updated version based on the instructions:
"prompt": 'An illustration of A mischievous elf, dancing in the moonlight, weaving through the forest trees, whispering the woodland creatures, playing a flute the riverbank, hiding behind mushroom.) "concepL_okenn" "elf",
bottle hiking trail.", "concept_loker" loken": "dog",
'upprclass": "inanimate", "subject": 'A sticker of a cute corgi dog',
"superclass": "fair_tales",
"settings":
"subject": "A mischievous elf",
"on student's laptop", "amidst an action packed skate session',
"settings":
"dancing the moonlight", "weaving through the forest trees", "whispering to the woodland creatures", "playing flute by riverbank', "hiding behind mushroom'
"adding character to urban signage", "at live music gig", "at international airport", "brightening park bench", "stuck to water bottle in hiking trail"
1.
"style": 'An illustration of"
"style": 'A photo
this update, the superclass was changed Jales," and subject, style, and settings chosen fit this theme. The settings' length was randomly selected to be 5.
Textended the settings to random length of and updated the prompl accordingly.
Figure 9: (Left): We expand the length of the original prompt sets to random number between 5 and 10. (Right): We generate new prompt set within one of the superclass "fairy tales".
The official implementation of BLIP-Diflusion (Li et al., 2024) at httts:://itthui com/ www.e/forrre//iis/teeeeee
The official implementationn of StoryDiffusion (Zhou al., 2024) httpt:t///// http:///////ttuut.ee/ WWLaioon-NKK//Stocyyyfffi
Since Consistory (Tewel et al., 2024) is not open-source, we reimplemented it ourselves. During the inference time, BLIP-Diffusion (Li et al., 2024), IP- Id-Adtpeer (Ye et al., 2023), and PhotoMaker (Li et al., 2023b) all require a reference image as the additional input. To generate the reference image, we use their corresponding base models, providing the identity description as the input prompt. For example, if the full prompt is "a photo of a beautiful girl walking on the street", we use "a photo of a beautiful girl" to generate the reference image. The reference image is then used to generate all frames in the corresponding prompt set.
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Identity Prompt: "A photo of fox"."
Frame Prompt:
"wearing a scarf in meadow"" "playing in the snow." "at the edge of river
Order: 0
Order: 2
Order: 2
Order:
Order: 2 1 @
Order: 2
Figure 10: Robustness frame prompts order: With the same set of frame prompts but in different orders, our method IPromptIStonn consistently generates images with unified identity.
C ADDITIONAL ABLATION STUDY
C.I ROBUSTNESS TO DIVERSE DESCRIPTION ORDERS
To validate the robustness of our method regarding the order of frame prompts, we used the same three frame prompts: "wearing scarf in meadow", "playing in the snow", and "at the edge of a river" to create six different sequences for images generation. The identity prompl was consis- tently
"a photo of fox" and each sequence used the same seed for a generation. As shown in Fig. 10, our method IPrompt]Ston Story generates images with identity consistency across different orders. Furthermore, the content the images generated from varying sequences is closely aligned with the text descriptions, further demonstrating our method Singular- Sinuula-VValue Reweighting effective- ness in suppressing content of unrelated frame prompts.
C.2 Singular- Value Reweighting ANALYSIS
Our Singular-Vales Value Reweighting algorithm comprises successive components: SVR+ enhances the frame prompts we wish to express, while SVR- iteratively weakens the frame prompts we aim to suppress. In our experiments, first apply SVR+, followed by SVR- particular, found that performing SVR- before SVR+ also yields similar results (see Fig. 11-left)
In the process of applying SVR-, employed a strategy of iteratively suppressing each frame prompt. In fact, we could also concatenate the text embeddings corresponding to all frame prompts for suppression. To explore this, we conducted further ablation study specifically on the SVR- component. Assuming that we have frames to generate, we discovered that merging the text embeddings corresponding to the n frames we wish to suppress with &.O and subsequently performing the SVD decomposition does not effectively extract the main components of all frame prompts included in EOT Consequently, applying Eq. 3 to weaken the eigenvalues based on their magnitude fails to adequately eliminate the descriptions of all suppressed frames. refer to this as "joint suppress", as illustrated Fig. 11 (right, the first row). In contrast, if we handle each frame prompt to be suppressed individually and iteratively perform SVD and the operations from Eq. 3, which we term 'iterative suppress", we can more effectively suppress all irrelevant frame prompts, shown in Fig. 11 (right, the second row).
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"A mystical illustration of wise wizard", casting a spell under a full moun', standing before portal in the forestt, lower filled with ancient artifacts", summoning storm over mountain poak!""
"A mischievous goblint, climbing the walls of a deserted castlel, laughing llickering camplirei, running through under- ground tunnelt, scurrying through densa twilightt?
SVR Firs
Jenn STAEE
Figure (Leftt): "SVR+ First" indicates SVR+ is applied before SVR- in Singular- Value Reweighting process, while "SVR- First" means the opposite order. We found that both sequences yield similar results (same seed). (Right): Compared to "Joint Suppress", "Iterative Suppress" is more effective at minimizing the influence of other frame prompts when generating images for the current frame. "Joint Suppress" produces images with similar backgrounds (the first row, first and third columns).
"A watercolor illustration A puppy", wearing a small sweater,, digging a holes, wearing a training harness", sticking head out of the windows, swimmings, playing in the yards."
SVR w/o
SVR w/ TD+
Figure 12: SVR with identity enhancement. The first row represents the original SVR with enhancements applied only to the frame prompt. The second row builds upon the original by further enhancing the identity prompt in the SVR+ module. The results indicate that while the second method improves identity consistency, it also leads to more similar object poses and backgrounds.
In our SVR, we enhance only the current frame prompt that needs to be expressed. An alternative option is enhance the identity prompt simultaneously. We found that doing so can make the object's identity more consistent; however, it also introduces some negative effects, the background and subject's pose appearing more similar across images, as shown in Fig. 12. Furthermore, to demonstrate the role of the FOT in SVR, we conducted an ablation study on the FOT component. Specifically, we kept the EoT part of the text embedding unchanged during the SVR process and used this text embedding to generate images. As shown in Fig. 13, the results indicate that without performing SVR on the F.f the backgrounds of different frame prompts tend to blend together.
C.3 Naive Prompt Reweighting ABLATION STUDY
Similar to the Singular-Vulle Value Reweighting (SVR) experiment, we conducted an ablation study to verify the effectiveness of Naive Prompt Reweighting (NPR) in terms of identity preservation and prompt alignment compared to method 1P IPrompt/Story. We denote NPR+ as applying scal- ing factor 2 to the text embedding corresponding to the current frame prompt that needs to be expressed. Conversely, NPR- denotes applying a sealing factor of 0.5 to the text embeddings of all ota prompts that need to be suppressed. NPR represents the combination of both NPR+ and NPR- operations.
As shown in Fig. 14, images generated using the NPR+, NPR-, and NPR methods all exhibit varying degrees of interference from other frame prompts. In contrast, method effectively removes
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"A digital portrait a 14-year-old boys, rose garden', making sandcastle?, playing in snow "
SVR w/o BOT
SWR w/ EOT
Figure 13: Ablation study for FOT The left three images demonstrate the SVR process with a fixed For while the right illustrates the SVR procedure described in the main text. The results indicate that keeping e Bor unchanged leads background blending across images generated for different frame prompts, highlighting the importance updating for dynamically.
"A watercolor illustration of male child", in toy store', exploring an exhibit', in a backyard, playing with a puppy?, enjoying a carousel ride*, building forts."
NPR+
NPR-
NPR
Ours
Figure Naive Prompt Reweighting ablation study. NPR+, NPR-, NPR are ineffective at suppressing influence of other frame prompts. For example, the "puppy", which appears only in the frame prompt of the third frame, also shows up in the first and second frames using the aforementioned methods. In contrast, our method (the last row) effectively suppresses unwanted semantic information from other frame prompts.
irrelevant semantic information from other frame subject descriptions in the single-prone gegle-prompt setting, resulting in images that are more aligned with their corresponding frame prompts.
C.4 SEED VARIETY
Since our method /Prompt/Story does not modify the original parameters of the diffusion model, it preserves the inherent ability of the model to generate images with diverse identities and back- grounds using different seeds. By varying the initial noise while keeping the input prompt set stant, our method can produce a range of characters and backgrounds, while maintaining strong identity consistency and prompt alignment, shown in Fig. 15.
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"A reper-realistic digital painting of an elderly gentleman", wearing a smoking jacket', at a vin- tage car show3, wearing vineyard owner's attire', golf course', at a classical music concert', painting landscape"."
Seed 1
Seed 2
Seed 3
Seed 4
Seed 5
Figure 15: Seed variation. By using different seeds, our method 1rrompt] Story can generate images with diverse backgrounds while maintaining a consistent identity.
D ADDITIONAL RESULTS OF OUR METHOD 1PromptIStory
D.1 CONSISTENT STORY GENERATION WITH MULTIPLE SUBJECTS.
Our method is capable of generating stories involving multiple subjects. By specifying several sub- jects in the identity prompt and appending corresponding frame prompts, we can directly produce a series of images that maintain consistent identities across these subjects, as demonstrated in Fig. 16. However, this approach has a limitation: all generated images will include every character refer- enced in the identity prompt, which poses constraint on the flexibility of our method.
D.2 STORY GENERATION OF ANY LENGTH.
To generate stories of any length, we designed a "sliding window" technique to overcome the input text length limitations of diffusion models like SDXL. Suppose we aim to generate story with n images, each corresponding to n frame prompts, using a window size t, where < n. Similarly, we represent the identity prompt as Po and the frame prompts as P., where i E [1, n]. For generating the image corresponding to the i-th frame, if i I t, we use P = [Po: Pi:... PP)] as input prompt and apply method /Prompt/Story to generate the images. If > we use P P P.] to generate the images. As shown in Fig. 19, we applied an ultra-long prompt to generate 42 images with consistent identities, using window size of 10.
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"A photo of happy hedgehog with its cheese", amid blooming spring flowerst, beside sparkling stream;, peeking from cozy burrow", forest", next tiny cheese wheel", sitting on mushroom"?
"A heper-realistic digital painting of a young ginger boy with his ball", by an brick wall covered in colorful graffiti', in the middle of a street filled with cars?, near a bustling playground", next to a lake reflecting the early
morning light", set against the backdrop of sunset", standing in quiet meadow, under a cloudy sky"."
"A cinematic portrait man and woman", cozy coffee shop with large windows', walking along sandy beach at sunset?, on a bustling city street night', on a quiet park bench amidst falling leaves", under an um- brella during a soft rain', a vibrant art gallery surrounded by paintings*
Figure 16: Multi-subject story generation. By defining multiple subjects the identity prompt, our method
generates images featuring multiple characters, each maintaining good identity consistency.
DreanSim 54 BreansSia 0.63
SPOETTSS STET PEPAAASIIN 10000
w/o ours
w/o oues
w/ ours
w/ ours
pixar style illstration man, riding a bicycle through the city', walking her dog along the beach?? writing her journal at library"."
kid, flying wide open field', riding tricycle down the driveway?", painting colorful picture the kitchen table?"
Figure 17: Additional result with PhotoMaker. We compared additional results of our method combined with PhotoMaker, where lower DreamSim score indicates better ID consistency between the generated images. The results demonstrate that method has the potential enhance the performance of PhotoMaker.
D.3 COMBINE WITH DIFFERENT DIFFUSION MODELS.
Since our method exclusively modifies the text-embedding and cross-attention modules of the dif- fusion model, it can be directly adapted to other diffusion models. In this study, we implemented our approach within the SDXL framework. Other models utilizing the SDXL framework, such as playground-v2.55 ViXLL V4.0 and Juggernau-X-vv10% can apply our method without any additional modifications or fine-tuning. Our experimental results (see Fig. 20) indicate that these models can also achieve image generation with enhanced identity consistency when employing our method PromptiSStory...
http://cww.cccccc.oo///////aa/a/ V4.0
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Metric
SD1.5
SDXL
Dillusion
BLIP-
Textual Inversion
The Chosen
PhotoMaker IP PPAAaapeer ConsiStory
Story Diffusion
NPR
Ours
VQAScore
0.7157
0.8473
0.5735
0.6655
0.6990
0.8178
0.7834
0.8184
0.8335
0,8044
0.8275
DSG w/ dependencyt
0.7354
0.8524
0.6128
0,7219
0.6667
0.8108
0,7564
0.8196
0.8400
0.8477
0.8520
DSG w/o dependency1
0.8095 0.8961
0.6909
0.8081
0,7498
0,8700
0.8122
0.8696
0.8853
0,8863
0.8945
FIDJ
65.32
48.94
83.74
55.27
66.76
45.20
51.63
44,02
44.16
Table 4: Additional metires comparison. SD1.S and SDXL are shown as references and excluded from this comparison. The bold and underlined are the best and second best results respectively.
E
ADDITIONAL EXPERIMENTS
E.1 ADDITIONAL PROMPT ALIGNMENT METRICS
In addition to the primary evaluation metrics, we conduct an experiment using the recent prompt alignment metrics DSG(Cho et al., 2023) and VQAScore(Lin al., 2025). Both DSG and VQA are metrics that measure the consistency between images and text by evaluating questions and their corresponding answers. These metrics have been shown provide more reliable strengths in fine- grained diagnosis and align closely with human judgment. We present our comparison with all other methods in Table 4, results show that method /Prompt/Story outperforms other training-based methods and achieves the highest value on the DSG metric.
E.2 VISUAL QUALITY COMPARSION
To evaluate the impact of different methods on image quality under ID consistency generation, we use images generated by the base model as the real dataset and images generated by each method itself as the fake dataset. Then, we calculate the FIDeHeusel al., 2017). As shown in Table (the last row), Naive Prompt Reweighting (NPR) and our method / [Prompt/Stor achieved the best and second-best best results terms of FID. This indicates that our method has a smaller impact on the image generation quality of the base model compared to other methods.
E.3 CONTEXT CONSISTENCY IN TEXT EMBEDDINGS
Besides the separate t-SNE dimensionality reduction conducted for multi-prompt and single-prompt setups in sec. 3.1.1, we extended analysis by performing joint t-SNE reduction the com- bined text embeddings from both setups. This unified approach allows for a direct visual compari- son of the embeddings' spatial arrangements within the text representation space. As illustrated in Fig. 18 (left), the text embeddings originating from the multi-prompt setup remain widely dispersed (red dots), indicative their diverse semantic properties. Conversely, embeddings from the single- prompt setup (blue dots) exhibit noticeably tighter clustering. To substantiate these observations, we also perform statistical analysis on our benchmark dataset, as shown Fig. 18 (right).
F USER STUDY DETAILS
In the user study, compared method with three state-of-the--at approaches: IP-Adapter, Consistory, and Story Diffusion. We selected 30 prompt sets from our ConsiStory+ bench- mark to generate test images, with each prompt set producing four frames.
110
105-
103.02
100-
In the questionnaire, participants were first pro- vided with guidance on selecting images. They were instructed choose the set that exhibited the most balanced performance across three cri- teria: identity consistency, prompt alignment, and image diversity, according to their personal preferences. As illustrated in Fig. 21, we de- tailed these criteria at the beginning of the questionnaire. Additionally, we provided an example to demonstrate our recommended best choice, including justifications for both selecting and not selecting each set, thereby aiding participants in making informed decisions.
From
60.0
2007
RAD
Figure 18: Additonal t-SNE visualization text em- beddings (Left) and statistical results (Right).
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Identity Prompt: "A teenage boy with black hairo."
"wearing green Xnitted hat"
"performing street band"
denie forest
"dreased an astronsau
"building a sandcxatle on sunny beach"
"climbing a tree"
Pramel Trmmed
"attending science fair"
"exploring an abon-- doned building"
"riding bicycle through street"
"playing video games in lit room **
"sitting on bench overlockinng
"belping a friend with homework in library
Frame7 Preme12
bustling city marke!!
"surting beach with large wares"
"participating school play"
"walking through amusement pork.
snowy building souwman"
book living room the
Trome13 Prome
"wxlking dog in neighborhood"
"fying kite in open Geldoo
"at carnival, holding cotton candy
"at bookstore, brows- ing shelveston
"exploring cave with Bashllght
"riding horse in countryside
Prumet9 TrameDA
"reasting marsh- mallowol
"fishing riverbanie
"attending a music festiva
ssketching art studio"
"nikinng up steep t*ail**
reoftop garden, gazing at the stars"
Prame25 Prame30
"attending a
"studying insects with magnifying glass
"watching movie at outdoor sor
"painting mural walliun
"participating in community cleanup fow
"learning to cook kitchen tor
party
Tramedt Tromell
"at 200, ingg animals
"playing chess quiet park
*on trip, sitting in the backseat
"taking photographs botamical gurden!!
"riding al amusement park
"exploring underwater world in a suit
Prome37 Promee42
Figure Long story generation. By using "sliding window" technique, our method IP 1PromptIStory can generate stories of any length with consistent identity throughout.
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SDXL: 'A viniage style paster of vase with flowers), adding charm homely setting,, halding vibrunt arrangement of sunflow- displaying exotic orchids", containing cherry blossoms", filled with lavender and wild daisios", holding bouquet of flowerse."
w/o
PlayGround-22.5 'A photo of dogs, riding a bike on a city streets, picking flowers in mendow?, eating ice cream at park', draw- ing by poal", skateboarding skate park", blowing bubbless."
w/ w/o
RealvisXI_ J. "A heartwarming illustration of friendly troll", sitting by campfirel, carving runes into rock?, building shelter
fallen logs", fishing quiet stream', guarding ch cavel, helping travelers rivers"
w/
Juggernaut-x-v10. 'A quaint illustration of hobbit?, enjoying feast under starlit sky?, celebrating with friends in tavernt, read book meadow", walking through peaceful village', sitting by fireplace", warking in garden vibrant vegetablest."
NNON
Figure 20: Evaluation with different models. We test our method on various T2I diffusion models, and
without requiring fine-tuning approach could directly generate images with consistent identity.
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Selection Guidance
In this survey, you will evaluate four sets of images based on three criteria: "Identity Consistency" "Prompt Alignment" and "Image Diversity". Your task is to select the set that performs best across all three aspects. Identity Consistency: Refers to visual coherence of the subject's appearance the set, indicating that the same subject is depicted in all images.
Prompt Alignment: Indicates how well each image in the set matches its corresponding text description.
Image Diversity: Refers the variety of poses, object arrangements, and overall composition within
images.
Example
Each represents of the four image sets: A, B, C, and D. Each column corresponds to the same frame descriptions: ('wearing superhero cape', beach', 'wearing headscarf' 'wearing birthday hat').
DAIG
DO BIIGG
DAIUG
10 IDG
HAMAPPOOU
FiT POkKE
LIST
EISTATSIRET DREIRTTAR
ARKICSE TFLMA
In this example, the best choice is set A (the first row).
Reason for Selection
row) performs well
"Identity Consistency," "Text Alignment," "Image Diversity!
Set B (the second row) is not chosen becouse its identity consistency is poor.
Set C (the third row) is not selected despite its high identiity consistency because its text alignment and image diversity lacking. Set (the fourth row) also not chosen due its identity consistency.
Figure 21: User study questionnaire. Before tilling the questionnaire, participants provided with lection guidelines, including detailed explanations the three evaluation criteria: identity consistency, prompt alignment, and image diversity. Additionally, an example was provided, along with our recommended best choice and the reasoning behind the selection.
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"playfully splash- ing in a spakling lake!"
"nestled among blooming cherry blossoms
"soaring grace- fully through rainbow skya"
"in city park with autumn leaves?"
"building a sand castle!!
flower garden
Vanilla SDL.5
BLIP Diffusion
Vanilla SDXL.
The Chosen One Textrual Inversion
NPR
Ours
frame 1.
frame 3 "A pixar style illustration of dragon"."
frame
frame
frame
frame 3
"A seyper-realistic painting of 16 years old girl"!"
Figure 22: Additional qualitative comparison. We also compared our method with other existing approaches. The characters generated by vanilla SD1.5 and vanilla SDXL. exhibit significant variations both form and appearance. contrast, some training- based methods, such as Textual Inversion and The Chosen One, generate characters with consistent forms, but their appearance lacks similarity. While NPR can produce characters with consistent identities, the backgrounds often blend across images. In contrast, our method not only ensures identity consistency but also generates backgrounds that closely align with the corresponding text descriptions.
28


Reviews:
[{"review_id": "yWgqxYjQ2o", "reviewer": "Reviewer_Pz4Z", "summary": "The paper proposes a training-free method to enhance subject consistency in text-to-image models. This setting comprises an identity prompt describing the desired subject and a set of frame prompts describing the subject in different scenarios. The proposed method consists in concatenating the identity prompt and all frame prompts into a single, longer, consolidated prompt. Then, in order to generate images for each frame prompt, the consolidated prompt embeddings are reweighed accordingly. This improves identity preservation but harms text-image alignment, so authors propose a couple of techniques to mitigate it. First, the consolidated prompt embeddings are reweighed based on their singular values, which helps preventing information mixing from different frames. Second, the cross-attention between text embeddings and latent image features is modified to improve the subject's consistency. The proposed approach is evaluated on a proposed benchmark and compared against several existing methods, outperforming other training-free methods for identity preservation.", "questions": "* In figure 2, why doesn't P1 have the same text embedding for multi-prompt and single-prompt settings? I believe it should since the context is the same (`[[SOT]; P0]`) and CLIP's text encoder has causal attention.\n* What do the shaded regions in figure 2 represent? How are they computed?\n* Does changing the order of frame prompts affect results? For instance, always placing the frame prompt of interest at the end.\n* In figure 3, I don't see the NPR results as incorrect since the background isn't specified in frame prompts 2, 3, 4 and 5.\n* Why does the embedding of the [EOT] token belong to both the expressed and suppressed sets? Won't the effects cancel each other out?\n* For prompt alignment evaluation, I would suggest using more recent metrics that are more correlated with human judgment, such as DSG [1] or VQAScore [2].\n\n[1] Cho, Jaemin, et al. \"Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation.\" The Twelfth International Conference on Learning Representations.\n[2] Lin, Zhiqiu, et al. \"Evaluating text-to-visual generation with image-to-text generation.\" European Conference on Computer Vision. Springer, Cham, 2025.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The proposed method is training free and only increases inference-time compute moderately.\n* The proposed method outperforms the considered training-free existing methods on prompt alignment and identity preservation.\n* The ablation study confirms the effectiveness of singular-value reweighing and identity-preserving cross-attention.", "weaknesses": "* Stable diffusion models use a CLIP text encoder, which is limited to 77 tokens. This severely constrains the number of frame prompts that can be used.\n* Evaluating on a single benchmark of 200 examples seems insufficient. Additional benchmarks would be needed to verify the robustness of the results. Similarly, a human study on just 20 examples seems quite limited. I would suggest applying bootstrapping to obtain the sample variance.\n* In figure 8 (right), applying the proposed method doesn't seem to have a big impact in terms of identity preservation.", "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": "PFNXuZtfpQ", "reviewer": "Reviewer_Q3y4", "summary": "The goal of this article is pretty similar to Cosistory, but it proposed a new training-free method which focus on how to leverage prompts better. This article first proposes Naive Prompt Reweighting which rescaling the embeddings of the other frame prompts by a reduction factor. Then, it proposes  Singular-ValueReweighting+&- which perform a SVD decomposition and Identity-PreservingCross-Attention(IPCA) which enhance the identity similarity between images generated from the concatenated prompt.\n\nWork is relatively well done, simple and effective, but not very innovative(Reweight and Identity-Preserving or their similar ideas have appeared in previous articles, this artivle just use them into the enhancements of prompts)\n\nWhat's more, I wonder the quality and improvements for multi-IDs, because nowadays the consistency works  can  generally do well with multiple IDs.", "questions": "1. Prove your novelty (I know it's a little hard for training-free methods in this field)。\n2. Show results on milti-IDs.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Drawing, good.\n2.Focus on the enhancements of prompts and make it effectiveinstead of focus on images.\n3.Proposed different reweightmethods： NPR and SVR+-, make the article more solid.", "weaknesses": "1. Reweight and Identity-Preserving or their similar ideas have appeared in previous articles, this artivle just use them into the enhancements of prompts(I remember Reweight is used in video, the extent how a prompt or ref img affects different frames is reweighted. )(Identity-Preserving--like [1] which operate on the modality of images)\n2. The improvements for multi-IDs are NOT mentioned, which is always talked in recent works like StoryDiffusion(The example of a man and a dog).\n\n\n\n[1] JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation(CVPR24)", "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": "bm7RKenfTV", "reviewer": "Reviewer_uG1g", "summary": "This paper introduces a novel training-free approach for generating subject-consistent images using text-to-image models. The method concatenates prompts for different frames into a single prompt to achieve consistency, while utilizing two key innovations: singular-value reweighting and identity-preserving cross-attention techniques. These techniques effectively mitigate interference between frames and enhance consistency. Through comprehensive experiments, the authors demonstrate their method achieves superior performance in terms of text-image alignment and subject consistency, compared with both training-required and training-free competitors.", "questions": "- For the experiment in Section 3.1.1, it seems that the t-SNE visualization is conducted for only a single set of prompts. It would benefit the clarity if more details can be provided.\n- Instead of the proposed Identity-Preserving Cross-Attention technique, is it possible to improve the consistency by performing SVR on $[c^{P_0}, C^{EOT}]$?\n- Does the proposed method affect the visual quality/image realism? How is it compared with other methods?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The paper is well-written, and the results are nicely presented.\n- The proposed method is simple yet effective. The paper demonstrates its effectiveness by comparing it with multiple training-required and training-free methods, along with the user study and the ablation study.\n- Due to its simplicity, the proposed method should be able to easily be adapted to T2I models with different architectures and sampling strategies.", "weaknesses": "- Long prompts might pose a challenge to the applicability of the proposed method due to the limit of number of tokens (e.g., M=77 for SDXL by default). Nevertheless, this appears to be a solvable issue.", "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": "K8zxm9P192", "reviewer": "Reviewer_eHi5", "summary": "The paper proposes a training-free approach for consistent character generation. In particular, given a descriptive prompt of  the main character, followed by frame-specific prompts, the goal is to generate images that faithfully depict each frame, while maintaining a consistent visual appearance of the main character. \n\nThe main observation of the paper is that when combining all frame-prompts with the character description prompt, then the character embedding in each-frame prompt is more consistent. The authors introduce two additional enhancements for their final method.\n\n[1] Singular Value Reweighting (SVR): combining all frame-prompts leads to information blending from different frames. To avoid this, during the generation of frame i (i.e., based on prompt p_i), the authors use SVD to increase p_i contribution to the generation by enhancing the leading singular value of prompt p_i. The authors also suppress the contribution of other other prompts j != i in a similar manner. Here, the authors also take into consideration the end token (EOT), as it contains significant semantic information as well. \n\n[2] Identity-Preserving Cross-Attention: SVR alone handles intra- fame information blending, although the identity consistency may still be affected. The authors propose to concatenate the k/v- projections in which the token features corresponding to each prompt pi, i in [1,N] are zeroed out. \n\nMinor:\nL 353 (K, V) notations are different from those used later in the paragraph. Since this is a paper on consistency, please maintain consistent notations :-)", "questions": "Following my points raised in the weakness section, can you please consider answering the following ?\n\n[1] Can you please confirm that you simply zero-out the token features of all prompts P_i, i \\in [1,N] ,except for P_0 and EOT ?\n\n[2] What is the rationale behind zeroing out features of frame-prompts p_i rather than removing them entirely? How does this affect attention scores and normalization?\n\n[3] How do the P_0 and EOT features alone enhance identity consistency across frames? Given that P0 and EOT embeddings may differ across frames due to SVR, how does your method ensure consistent embeddings for different frame generations?\n\n[4] Can you please provide further motivation to the KV extension and why this generates more consistent characters. To me this is similar to the KV sharing used in Consistory, but here the the KV-extension only biases the queries to attended PO and EOT (which can be different for different frames due to the SVR procedure).\n\n[6] Can you provide memory complexity comparison between your method and existing approaches.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "[1] The paper is well written and the quantitative and qualitative comparisons are well carried. \n\n[2] I find the main observation of the paper to be very novel and might have broader implications to other tasks. In particular, exploiting the context consistency of language models for the purpose of character consistent generation. The paper analysis of this observation clearly motivates the rest of the paper. \n\n[3] Using SVR for prompt-specific enhancement/suppression rather than simple reweighing is interesting.\n\n[4] The visual quality of the generated outputs is good. \n\n[5] The method is fairly simple and doesn’t complicated procedures", "weaknesses": "[1] In the Identity-Preserving Cross-Attention module, the authors zero-out features of all frame-prompts p_i. It is not clear why not completely removing these features, instead of zeroing them? As zeroing out the features may affect the attention scores, because of the normalization. Also, why the remaining features (i.e, in this case P0 features and EOT features) are enough to enhance consistent identity among frames ? Effectively, this new module (IPCA) biases queries to attend to P_0 and EOT token features. Furthermore,  P_0 and EOT embeddings are different for different frames (because of the asymmetric nature of SVR), hence the additional K/V matrices that are concatenated for different prompts are different as well, hence this means that P_0 and EOT should maintain similar embeddings for different frame-generations, which is not clear from the paper. \n\n[2] The authors claim their method to be memory-efficient compared to existing methods. I think it is nice to know the memory complexity of the proposed method compared to existing ones.", "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": "LLvcmjavO7", "reviewer": "Reviewer_7cWp", "summary": "This paper introduces a novel, training-free approach called 1Prompt1Story to address the character consistency issue in storytelling. By concatenating all prompts into a single input, 1Prompt1Story preserves character identities from the start. The generation process is further refined using two innovative techniques: Singular-Value Reweighting and Identity-Preserving Cross-Attention. Comparative experiments demonstrate that 1Prompt1Story improves identity consistency and alignment with input descriptions, outperforming existing methods both quantitatively and qualitatively. Further experiments also show that 1Prompt1Story supports ControlNet.", "questions": "Please the first and second point of the weaknesses section.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The authors' observation of inherent context consistency in text, along with their proposed NPR method, is particularly insightful. It offers a direct and elegant solution for maintaining consistency in text-to-image generation, making a meaningful contribution to the field.\n- The paper is well-written, clearly structured, and easy to follow.\n- The quantitative analysis of context consistency, particularly through t-SNE visualizations and text-prompt distance metrics, is sound and convincing.\n- The authors employ a comprehensive set of metrics to demonstrate the advantages of their method, providing a robust evaluation that underscores the effectiveness of their solution.", "weaknesses": "- The Prompt Consolidation method concatenates all text prompts into a single input before feeding it to the text encoder. However, in my view, this approach faces a significant limitation for generating longer stories due to the token length restrictions of the diffusion model’s text encoder. In contrast, other training-free personalized storytelling methods do not encounter this issue, which limits the broader applicability of the proposed method. It would be helpful if the paper explicitly addressed the length limitation of the 1Prompt1Story approach. Additionally, are there any potential solutions to mitigate this constraint? Or will you compare the practical story length capabilities of your method versus other training-free approaches?\n- The handling of the EOT tokens in lines 290-322 appears somewhat unclear. Could you further clarify why the EOT needs to be initially expressed and then suppressed? Additional explanation of this process would enhance understanding. Will you provide a brief explanation of the role of EOT tokens in the model?\n- The user study sample size of 20 participants seems relatively small, which may limit the generalizability of the findings.", "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 (Spotlight)