Padding Tone: A Mechanistic Analysis of Padding Tokens in T 2 I Models (2025)

Abstract: Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. Typically, these prompts are extended to a fixed length by appending padding tokens to the input. Despite being a default practice, the influence of padding tokens on the image generation process has not been investigated. In this work, we conduct the first in-depth analysis of the role padding tokens play in T2I models. We develop two causal techniques to analyze how information is encoded in the representation of tokens across different components of the T2I pipeline. Using these techniques, we investigate when and how padding tokens impact the image generation process. Our findings reveal three distinct scenarios: padding tokens may affect the model’s output during text encoding, during the diffusion process, or be effectively ignored. Moreover, we identify key relationships between these scenarios and the model’s architecture (cross or self-attention) and its training process (frozen or trained text encoder). These insights contribute to a deeper understanding of the mechanisms of padding tokens, potentially informing future model design and training practices in T2I systems.

## introduction text-to-image (t2i) models consist of two main components: a text encoder, which generates representations of the user's prompt, and a diffusion model, which generates an image based on this representation. to standardize sequence lengths for efficient batch processing in training and inference, input prompts are padded to a fixed length with a special padding token. unlike language models, where padding tokens are explicitly masked and thus ignored, the computation process of the t2i models can use these tokens as any other token. despite their ubiquity, the potential impact of padding 1 code available at padding-tone.github.io tokens on image generation has been overlooked. our goal in this work is to evaluate the influence of these tokens and determine whether the model learns to use semantically meaningless tokens. we introduce two methods to evaluate the influence of tokens on different model components: in ite we selectively perturb specific segments of the text encoder's output representations to isolate the contributions of two key elements: prompt tokens and padding tokens. next, we generate images using the modified prompt representations and analyze the results. the perturbation involves replacing selected token representations with those from a prompt that consists solely of padding tokens, referred to as clean pads. these clean pads differ from the original padding tokens, which contain contextual information from the prompt. the method is illustrated in figure 3 . if padding tokens carry meaningful information, we expect two outcomes: (a) replacing the prompt tokens with clean pads should still result in an image reflecting elements of the original prompt, while (b) replacing the padding tokens with clean pads should alter the image either semantically or stylistically. in cases where our analysis with ite indicates that padding tokens are not used by the text encoder, we further examine the role of padding tokens in the diffusion process. particularly, we investigate whether significant information is written into the padding token representations throughout the diffusion process. here we employ idp, illustrated in figure 9 , to interpret the causal effect of the padding tokens during the diffusion process. we begin with a standard prompt padded to a fixed length, as well as an "only pads" prompt. however, in idp, token replacement occurs before each attention block within the diffusion process and at every diffusion step. we repeat the procedure of selectively replacing either prompt tokens or padding tokens with clean pads, similarly to ite. we analyze six different t2i models and highlight two scenarios where padding tokens are utilized. first, when the text encoder was not frozen during training or fine-tuning, it learns to encode meaningful semantic information into these tokens. second, in architectures with multi-modal attention mechanisms-such as stable diffusion 3 @xcite and flux 2 -padding tokens carry meaningful information throughout the diffusion process, even if the text encoder itself does not directly encode it. here, the padding tokens seem to act as "registers", with information written into their representations to store and recall, similarly to findings from both language models and vision-2 blackforestlabs.ai figure 2 : the scenarios we observe: padding tokens may be effectively ignored (first row; image generated using ite), affect the model's output during text encoding (second row; image generated using ite), or be used during the diffusion process (last row; image generated using idp). left: baseline. right: our method. language models @xcite . to summarize, our main contributions are: ## analysis of padding in text encoding in the t2i pipeline, the text encoder processes the input prompt @xmath0, .., p k ], k tokens. to ensure a consistent input length, the prompt is usually padded to a fixed length, denoted as n . we denote this padded version of the prompt as p full , which is a concatenation of the k prompt tokens and the nk padding tokens: the text encoder then processes p full , producing a constant-length encoded representation, which is subsequently used by the diffusion model for conditional image generation. we denote this encoded full prompt representation as e full , which consists of the encoded prompt tokens and the encoded prompt-contextual padding tokens. foot_0 ## analysis of padding in the diffusion process padding tokens may not carry meaningful information after text encoding, yet some diffusion architectures may still utilize them during the diffusion process. to generate images from the encoded textual representation, t2i models use an attention mechanism to condition the generation process. this mechanism typically follows one of two common approaches: cross-attention and mm-dit @xcite blocks. in cross-attention, used in models like stable diffusion 2/xl, the model converts image patches into query vectors and text tokens into key and value vectors. the image patches gather information from the encoded textual representation, based on an attention map, but the text representation remains unchanged throughout the process. in contrast, mm-dit blocks, found in models like flux and stable diffusion 3, implement a multi-modal self-attention, by projecting both image patches and text token representations into query, key, and value vectors. thus, both the image and text representations update and influence each other during the attention process. we therefore expect that models implementing crossattention where the pads are not used in the text encoder would also not use them in the diffusion process. however, models implementing mm-dit blocks can potentially aggregate information into the padding tokens, even if initially they contain no information. motivation: attention maps and qualitative examples. to understand whether padding tokens are utilized during image generation, we examine the attention maps between image patches and text representations, resulting in an attention map between each text token and all the image tokens (see example in figure 8 ). while in stable diffusion xl only the prompt (and the end-of-text) tokens significantly attend to main areas in the image, in flux not only prompt tokens, but also many pad tokens contribute much attention to main image areas (figure 7 ). moreover, generating images with flux and stable diffusion xl, with and without padding (figure 12 , appendix g), reveals that flux without padding often misses key details, while stable diffusion xl remains consistent in its generations even without padding tokens. ## discussion this work addresses a design decision present in every t2i model that has remained largely unexplored: the choice to include padding tokens during both textual encoding and the diffusion process. as more studies begin integrating large language models (llms) into t2i pipelines using techniques like fine-tuning, lora, or adapters, the role of padding tokens becomes increasingly crucial. training these models with padding tokens could influence a wide range of methods that assume subject information is encoded in specific tokens @xcite @xcite , potentially altering their implementation when padding tokens carry significant semantic information. this factor should be carefully considered when deciding whether to train with or ignore padding tokens. furthermore, future research could explore how incorporating padding tokens into training might provide computational advantages in more integrated, end-to-end architectures, potentially allowing models to dynamically allocate resources by adjusting the use of padding tokens as needed.