CTA-Flux: Integrating Chinese Cultural Semantics into High-Quality English Text-to-Image Communities

## Abstract

Abstract We proposed the Chinese Text Adapter-Flux (CTA-Flux). An adaptation method fits the Chinese text inputs to Flux—a powerful text-to-image (TTI) generative model initially trained on the English corpus. Despite the notable image generation ability conditioned on English text inputs, Flux performs poorly when processing non-English prompts, particularly due to linguistic and cultural biases inherent in predominantly English-centric training datasets. Existing approaches, such as translating non-English prompts into English or fine-tuning models for bilingual mappings, inadequately address culturally-specific semantics, compromising image authenticity and quality. To address this issue, we introduce a novel method to bridge Chinese semantic understanding with compatibility in English-centric TTI model communities. Existing approaches relying on ControlNet-like architectures typically require a massive parameter scale and lack direct control over Chinese semantics. In comparison, CTA-flux leverages MultiModal Diffusion Transformer (MMDiT) to control the Flux backbone directly, significantly reducing the number of parameters while enhancing the model’s understanding of Chinese semantics. This integration significantly improves the generation quality and cultural authenticity without extensive retraining of the entire model, thus maintaining compatibility with existing text-to-image plugins such as LoRA, IP-Adapter, and ControlNet. Empirical evaluations demonstrate that CTA-flux supports Chinese and English prompts and achieves superior image generation quality, visual realism, and faithful depiction of Chinese semantics.

## Introduction

Recent advancements in text-to-image (TTI) models have significantly enhanced the quality and diversity of generated images(Ramesh et al. 2021; Saharia et al. 2022; Yu et al. 2022). The Stable Diffusion (SD) model(Rombach et al. 2022; Esser et al. 2024) stands out among various TTI frameworks due to its remarkable capability in producing high-quality and photorealistic images from complex textual descriptions. SD’s diffusion-based generative process (Sohl-Dickstein et al. 2015; Ho, Jain, and Abbeel 2020; Song et al. 2020) gradually transports noisy input into clean images through iterative denoising steps, ensuring high fidelity and detailed visual outputs.
Concurrently, the emergence of the Flux framework(Labs 2024) and its active community(Labs et al. 2025) has further boosted the development of TTI models. Flux’s collaborative, open-source nature enables rapid evolution and community-driven improvements, leading to consistently improved image generation performance and better accessibility for diverse downstream applications.

However, despite these achievements, current TTI models, including those in the Flux community (Labs et al. 2025), still face limitations. A prominent challenge is that language-related bias originates from their training datasets, which are predominantly English-centric. Consequently, models often fail to perform well when generating images from non-English text prompts, struggling to capture the unique cultural semantics and nuanced symbols embedded in other languages (Liu et al. 2025). This bias manifests primarily in two distribution gaps: the linguistic feature distribution gap and the visual feature distribution gap.

From the linguistic perspective, significant distribution gaps exist due to inherent language ambiguities and cultural differences. For instance, the English word ”crane” can denote both a type of bird and a piece of heavy construction machinery, whereas the Chinese equivalent does not exhibit such polysemy. This results in semantic ambiguity when translating from languages that use words to express these concepts. Additionally, grammatical and structural disparities between languages further exacerbate the semantic mismatches and hinder the multilingual alignment(Yang et al. 2025).

Beyond linguistic factors, the visual feature distribution gap—driven by cultural biases and implicit symbolic meanings in non-English text input—poses additional challenges. For example, when describing a person’s appearance, Chinese prompts typically result in imagery featuring Asian characteristics and culturally specific contexts. In contrast, equivalent English descriptions predominantly yield imagery reflective of Western appearances and environments. Such implicit cultural distributions and symbolic representations are challenging to capture through direct translation or simple fine-tuning approaches.

Existing approaches attempt to address these challenges but remain inadequate under complex semantic conditioning. Straightforward approaches, such as translating non-English prompts into English before image generation(Zhang et al. 2022), are inadequate for TTI generation under complex semantic conditioning. These translation-based approaches lose culturally-specific meanings, subtle connotations, and symbolic expressions that are hard to translate, thus directly deteriorating image quality and authenticity. Another strategy involves fine-tuning existing models to learn the mappings between non-English and English textual representations(Chen et al. 2022), aiming to improve their understanding of non-English prompts. Nevertheless, such fine-tuned models often struggle with culture-specific semantics and nuanced concepts unique to the source language, as these meanings frequently lack concise and accurate equivalents in English.
More radical approaches involve training entirely new models from scratch to address multilingual contexts (Feng et al. 2023b; Gu et al. 2022). This requires massive computational resources and long-term training, making it prohibitive to most users. Furthermore, retraining often compromises compatibility with existing plugins and extensions developed by existing community-driven frameworks.

To address the challenges of multilingual TTI models, we propose a novel approach that integrates a multilingual adaptation branch into the existing model architecture, ensuring strong compatibility with the Flux community framework. Specifically, our method incorporates non-English embedding tokens into the cross-attention mechanism of the MultiModal Diffusion Transformer (MMDiT) model by a language adapter. This integration allows non-English linguistic control information to directly influence the backbone while maintaining compatibility with existing community models, enabling the model to handle non-English prompts without altering the integrity of the original architecture. Our approach introduces minimal additional parameters, maintaining a lightweight design that ensures efficiency and scalability. At the same time, the backbone model remains fixed, allowing the method to be easily combined with other Flux community plugins.

Moreover, to effectively address the semantic and cultural differences between languages, we employ a sophisticated two-stage training strategy. The model is trained using English and non-English data in the first stage. During this phase, we use a Representation Alignment Loss to align the features extracted by the non-English language encoder with the feature space of the original English language encoder. This alignment minimizes the linguistic feature domain gap and prevents the multilingual branch from needing to learn basic semantic concepts from scratch, accelerating the learning process. In the second stage, the model is fine-tuned exclusively on non-English data that contains culturally specific concepts. This allows the model to learn unique visual distributions associated with different cultures, enabling it to generate culturally accurate images and be visually faithful to the non-English prompts. Through this two-stage training, our approach accelerates convergence while enabling the model to capture both universal cross-lingual concepts and culture-specific semantics, thereby improving the quality and accuracy of generated images.

Our approach offers several key advantages: due to the lightweight design of the language adapter modules, it introduces minimal additional parameters to the backbone model. Additionally, it preserves the original Flux model backbone parameters, ensuring seamless integration with existing Flux community plugins and tools. Notably, our method also enhances the model’s ability to generate images that capture unique cultural and linguistic symbols, which helps mitigate data bias and discrimination, especially for non-English languages. This ensures more inclusive and accurate image generation, reducing the risks of biased or culturally inappropriate outputs.

To summarize, our work contributes:

- •
We propose a novel method for natively adapting the Flux framework to non-English languages, specifically focusing on Chinese language support. To the best of our knowledge, this is the first work to adapt Flux to handle Chinese language prompts effectively.
- •
Our approach not only extends the capabilities of Flux to non-English environments such as Chinese but also ensures seamless compatibility with existing plugins developed within the English-speaking Flux community. This multilingual compatibility makes our method versatile and capable of bridging the gap between English-centric and Chinese use cases.
- •
Experimental results demonstrate that our method performs consistently well across various metrics and benchmarks, including both general image generation tasks and those requiring cultural specificity, validating its effectiveness in multilingual generation settings.

## Related Work

### Diffusion Models and Flow Matching

Diffusion models (DMs) (Ho, Jain, and Abbeel 2020; Sohl-Dickstein et al. 2015) are powerful generative models that gradually generate images from a random Gaussian noise with a deep denoising model. Score-based generative models (SGMs) (Song and Ermon 2019; Song et al. 2020) learn to reverse a forward Ito process using stochastic differential equations (SDEs), training a score function $\nabla_{\boldsymbol{x}}\log p(\boldsymbol{x},t)$ via denoising score matching (DSM) to guide sampling. Techniques like DDIM (Song, Meng, and Ermon 2020), ADM (Dhariwal and Nichol 2021), CM (Song, Meng, and Ermon 2023), and latent diffusion (Rombach et al. 2022) improve efficiency and output quality. Flow matching (FM) (Lipman et al. 2023) and rectified flow (Liu, Gong, and Liu 2022) offer deterministic alternatives by learning velocity fields over ODEs, enabling few-step or even single-step generation (Yin et al. 2024). FM and SGMs are theoretically linked through Fokker-Planck equations and probability flow ODEs (Song et al. 2020; Evans 2010), inspiring hybrid models that balance diffusion’s robustness with flow’s efficiency (Esser et al. 2024; Labs 2024; Labs et al. 2025).

### On the adaptation of diffusion generative models

Recent advances in generative foundation models have facilitated the generation of task-specific outputs without the need to train large backbone networks from scratch. A variety of adaptation methods have been proposed to support this capability. IP-Adapter (Ye et al. 2023) adds lightweight cross-attention modules to inject reference images as prompts, enabling efficient style transfer and image-guided generation. ControlNet (Zhang, Rao, and Agrawala 2023) attaches task-specific control branches (e.g., depth, pose, edges) to frozen DMs for precise structural control. Other parameter-efficient methods like LoRA and textual inversion (Hu et al. 2022; Gal et al. 2022) reduce compute costs while maintaining quality. In flow matching (FM), models such as Flux. 1 and Flux Kontext (Labs 2024) use deterministic flows for fast generation and editing, integrating well with adapter-based control, suggesting unified controllability across DMs and FM frameworks.

Multilingual adaptation is important for T2I generative models as it enables global accessibility, allowing users to use the model in their native languages. While previous works have made progress in supporting multilingual input, they often treated non-English languages as auxiliary. For example, (Li et al. 2023) mitigated the language gap by translating English captions into other languages using a neural machine translation system. AltCLIP (Chen et al. 2022) took a different route by enhancing diffusion models with the multilingual text encoder XLM-R. Meanwhile, ERNIE-ViLG 2.0 (Feng et al. 2023a) trained a diffusion model using Chinese image-text pairs from scratch. However, these approaches did not effectively bridge native language and English-speaking communities. (Liu et al. 2025) proposed the bridge diffusion, but it can only be applied to Unet-based models such as Stable Diffusion 1.5. In contrast, our work integrates native language models into the pretrained English-centric DiT, aiming to achieve a state-of-the-art multilingual generation framework.

## Preliminary

We denote $\boldsymbol{x}$ as the latent image embedding obtained from the VAE encoder. Let $\boldsymbol{\tau}_{EN}$ and $\boldsymbol{\tau}_{CN}$ represent the English and Chinese text embeddings $\boldsymbol{y}_{EN},\ \boldsymbol{y}_{CN}$ after the T5 (Ni et al. 2021) and Qwen (Yang et al. 2025) text encoders, respectively. We aim to train a model that generates images conditioned on a Chinese or an English text prompt. Formally, we strive to approximate the conditional distribution $p(\boldsymbol{x}\mid\boldsymbol{y})$, where we use $\boldsymbol{y}$ as a simplified notation for the text condition.

Under given text conditions $\boldsymbol{y}$, flow models, such as flux.1 (Labs 2024) utilize a velocity field $\boldsymbol{v}(\boldsymbol{x},\boldsymbol{y},t)$ to gradually turn noise $\boldsymbol{x}_{0},\boldsymbol{\epsilon}\in\mathcal{N}(\boldsymbol{0},\boldsymbol{I})$ at $t=0$ into the data distribution $\boldsymbol{x}_{1}\in p_{data}(\boldsymbol{x}|\boldsymbol{y})$ at $t=1$. The dynamics of the marginal distribution $p(\boldsymbol{x},t|y)$ are formulated by probability flow ODE and the continuity equations:

$$ $\left\{\begin{aligned} &\frac{\partial}{\partial t}p(\boldsymbol{x},t|\boldsymbol{y})=-\nabla_{\boldsymbol{x}}\cdot(\boldsymbol{v}(\boldsymbol{x},\boldsymbol{y},t)\cdot p(\boldsymbol{x},t|\boldsymbol{y})),\\ &\boldsymbol{x}_{0}\sim p_{0}(\boldsymbol{x}|\boldsymbol{y})=\mathcal{N}(\boldsymbol{0},\boldsymbol{I}),\quad\boldsymbol{x}_{1}\sim p_{data}(\boldsymbol{x}|\boldsymbol{y}).\end{aligned}\right.$ (1) $$

In application, our adaptation of Flux to Chinese input follows a similar optimization strategy, with the primary difference being that the English embeddings $\boldsymbol{\tau}_{EN}$ are replaced by the Chinese embeddings $\boldsymbol{\tau}_{CN}$. To estimate the velocity field conditioned on a Chinese text prompt, we train a diffusion transformer to estimate the velocity field, denoted as $\boldsymbol{v_{\theta}}(\boldsymbol{x},\boldsymbol{y},t)$, using a dataset consisting of images and Chinese captions: $\{\mathcal{X}_{train},\mathcal{Y}_{train}\}$, via the flow matching loss:

$$ $\left\{\begin{aligned} \mathcal{L}_{\boldsymbol{\theta}}&=\mathbb{E}_{t,\boldsymbol{x}_{i},\boldsymbol{\tau}^{i}_{CN}}\left[\left\|\boldsymbol{v_{\theta}}(\boldsymbol{x},\boldsymbol{\tau}^{i}_{CN},t)-(\boldsymbol{x}_{i}-\boldsymbol{\epsilon})\right\|_{2}^{2}\right]\\ \boldsymbol{x}&=(1-t)\cdot\boldsymbol{x}_{i}+t\boldsymbol{\cdot}\boldsymbol{\epsilon},\end{aligned}\right.$ (2) $$

where $t\sim\mathcal{U}(0,1),\ \boldsymbol{x}_{i}\sim\mathcal{X}_{train}$, $\boldsymbol{y}^{i}_{CN}\sim\mathcal{Y}^{CN}_{train}$. The velocity field $\boldsymbol{v_{\theta}}$ is estimated with a transformer backbone such as DiT. During the training and inference, the English and Chinese text prompts are passed through the text encoder to obtain the text embeddings, denoted as $\boldsymbol{\tau}_{EN}$ and $\boldsymbol{\tau}_{CN}$.

## Method

Our overall framework, illustrated in Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3), consists of two primary components: the visual backbone and the language branch. To maintain compatibility with community-developed plugins built on base generative models, we adopt the pretrained Flux diffusion transformer as our backbone. In addition, we employ the same pretrained pixel Encoder and Decoder modules as those used in Flux to provide image latent embeddings. On top of this foundation, we design a parallel language branch dedicated to processing Chinese text prompts. This branch delivers Chinese linguistic information, functioning either in conjunction with English inputs or on its own, to enhance the model’s multilingual understanding and generation capabilities.

Figure: Figure 2: The overall pipeline of our proposed CTA-Flux. The parameters of the image/text encoder and decoder are frozen during the training, and we optimize the Chinese project layer in MMDiT blocks. The model is optimized with the flow matching generation loss (denoted as $\mathcal{L}_{GEN}$) and representation alignment loss (denoted as $\mathcal{L}_{RA}$).
Refer to caption: x2.png

### The Architecture of the Language Branch

As illustrated in Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3), the multilingual text inputs are passed through the Chinese Linguistic Attention Branch (CLAB) (Vaswani et al. 2023). Specifically, Chinese textual prompts are initially processed by a language-specific encoder capable of effectively capturing Chinese semantics. In our framework, we adopt Qwen2.5(Yang et al. 2025) as the encoder for Chinese language inputs, while retaining T5(Ni et al. 2021) as the encoder for English inputs, thereby providing language embedding tokens for conditional generation. These embeddings subsequently undergo dimensional alignment with the backbone branch’s latent dimensions via a multi-layer perceptron (MLP)(Represented as the ”English Adapter” and ”Chinese Adapter” in Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3)).

Given the intrinsic modality discrepancies between linguistic and visual domains, we employ a cross-attention mechanism rather than simple concatenation to integrate language information into the visual latents to facilitate the generation process effectively. As illustrated in Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3), the aligned language tokens are projected into queries (conditionally updated based on task requirements), keys, and values (QKV) via dedicated projection layers at each layer within the MMDiT block of the backbone branch. The modified MMDiT blocks consist of a double-stream block (Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3) (a)) and a single-stream block (Figure [3](https://arxiv.org/html/2508.14405v1#Sx4.F3) (b)). For the double-stream block, the Image and English tokens are passed through their respective frozen QKV projection layers ($\boldsymbol{M}_{img}$ and $\boldsymbol{M}_{EN}$) to generate QKV representations (denoted as $\boldsymbol{x}_{img}^{q},\boldsymbol{x}_{img}^{k},\boldsymbol{x}_{img}^{v}$ and $\boldsymbol{\tau}_{EN}^{q},\boldsymbol{\tau}_{EN}^{k},\boldsymbol{\tau}_{EN}^{v}$) for the attention processor. In the single-stream block, the Image and English input tokens are first concatenated and then passed through a frozen shared projection layer ($\boldsymbol{M}_{img+EN}$) to produce mixed QKV representations (denoted as $\boldsymbol{x}_{img+EN}^{q},\boldsymbol{x}_{img+EN}^{k},\boldsymbol{x}_{img+EN}^{v}$). For both the double-stream and single-stream blocks, the Chinese text embeddings are processed separately using a trainable Chinese projection layer $\boldsymbol{M}_{CN}$ to generate their corresponding KV embeddings: $\boldsymbol{\tau}_{CN}^{k},\boldsymbol{\tau}_{CN}^{v}$.
These projected Chinese language embeddings interact with their image/English pairs in the MMDiT blocks through cross-attention operations.

This cross-attention strategy enables explicit and dynamic integration of non-English linguistic semantics into the backbone branch. Consequently, the CLAB provides nuanced, culturally-aware control signals during image synthesis, significantly enhancing the generation quality and semantic fidelity of outputs conditioned on non-English prompts. To ensure more effective training of the Language Branch and enable the model to learn control signals from non-English inputs accurately, we feed an empty string (“”) as the English text inputs to the English language branch during training. This strategy prevents the model from relying on the pretrained Backbone’s inherent ability to understand English prompts, forcing it to attend to the information provided by the Chinese language branch.

It is important to note that the English embedding projection layers in the Language Branch are frozen and not updated across the MMDiT blocks, and the non-English tokens are not used as queries in the cross-attention computation. This design stems from our observation that when Chinese branch tokens are used as queries in cross-attention, they inevitably attend to features from the English empty-text tokens in the Backbone Branch, which disrupts the Chinese control signals and results in semantically unstable or even random generations. By discarding the Chinese query tokens to prevent such interference, we maintain the effectiveness of Chinese text prompt conditioning throughout the generation process.

### Adaptation Strategy

Our method preserves the architecture and parameters of the backbone branch entirely to ensure compatibility with existing community-developed plugins—such as ControlNet, LoRA, and custom checkpoints. Only the Chinese language branch parameters are updated during training, allowing our model to remain compatible with widely used tools and extensions within the Flux ecosystem.

Cross-lingual image generation presents two significant challenges: the linguistic feature distribution gap and visual feature distribution gap. The former arises from inherent linguistic differences across languages, and the latter stems from cultural biases embedded in the training data. To address these issues, we propose a carefully designed two-stage adaptation strategy. In the first stage, we mitigate the feature domain gap between the English and non-English language encoders by aligning their embedding spaces, enabling the Language Branch to capture shared semantics more effectively. In the second stage, we fine-tune the model exclusively on non-English data containing culturally specific concepts, thereby enabling the model to adapt to the target distribution shifts and generate images that reflect the unique cultural semantics of the source language.

In the first stage of training, we use a mixture of Chinese and English prompt texts. Each prompt—whether in Chinese or English—is passed through the Language Branch. We introduce an auxiliary supervision signal in addition to the standard image generation loss to accelerate the learning process and address the cross-lingual adaptation challenge. This auxiliary objective mitigates the “linguistic feature distribution gap” between non-English and English encoders.

Concretely, we use each non-English prompt’s semantically equivalent English counterpart solely to obtain features from the original English text encoder in the Backbone Branch. These English features are projected by the fixed MLP projection layer in the DiT backbone to match the DiT latent dimension, and are used only for computing the auxiliary alignment loss, without participating in the image generation process. Simultaneously, we obtain the projected non-English features from the Language Branch and minimize the mean squared error (MSE) between the projected non-English and corresponding English features. This auxiliary loss encourages the Language Branch to produce features compatible with the backbone’s embedding space and facilitates effective multilingual adaptation. The formulation of this loss is given by:

$$ $\left\{\begin{aligned} &\mathcal{L}_{p}=MSE\left(\text{AvgPool}(\tau_{CN}),\text{AvgP}(\tau_{EN}^{aux})\right),\\ &\mathcal{L}_{inter}=MSE\left(\tau_{CN},\text{Intp}(\tau_{EN}^{aux},\text{len}(\tau_{CN}))\right),\\ \end{aligned}\right.$ (3) $$

where $\tau_{EN}^{aux}$ represents the text embeddings derived from an English prompt semantically equivalent to the Chinese prompt employed to generate $\tau_{CN}$, obtained via English the text encoder, $MSE(\cdot)$ denotes the mean square error between two tensors, $\text{AvgP}(\cdot)$ denotes the average pooling on the sequence dimension of tokens, and Intp($\cdot$) interpolates the English feature to the length of the Chinese feature in the sequence dimension. The loss function consists of two parts: $\mathcal{L}_{p}$ and $\mathcal{L}_{inter}$. $\mathcal{L}_{p}$ is the Mean Squared Error (MSE) calculated on the feature after pooling the entire sentence, representing the overall alignment of the sentence-level representation. $\mathcal{L}_{inter}$ is the MSE between the text encoder’s English tokens, which are interpolated to match the length of the Chinese tokens in the text encoder, aiming to align the features across the different token lengths.

In the first stage of training, we found that incorporating a threshold $\mathcal{D}_{threshold}$ for the auxiliary loss is necessary. If the auxiliary loss falls below this threshold, it is set to zero. The purpose of this approach is to avoid overemphasizing alignment, allowing the model to retain the subtle differences inherent in the language itself. This ensures that there is greater optimization potential for improving the generation quality. The alignment loss is formulated as:

$$ $\mathcal{L}_{RA}=\begin{cases}\mathcal{L}_{p}+L_{inter}&\text{if }\mathcal{L}_{RA}\geq\mathcal{D}_{threshold}\\ \mathcal{L}_{p}&\text{if }\mathcal{L}_{RA}<\mathcal{D}_{threshold}.\end{cases}$ (4) $$

In the second stage of training, we use solely Chinese data and incorporate a wide range of concepts unique to Chinese culture, such as special holidays, cuisine, and traditional clothing. The training resolution is gradually increased, starting from 256, progressing to 512, and finally reaching 1024, to enable the model to capture culturally specific output targets. The overall training objective function $\mathcal{L}_{\boldsymbol{\theta}}$ is as follows:

$$ $\mathcal{L}_{\boldsymbol{\theta}}=\mathcal{L}_{GEN}+\mathcal{L}_{RA},$ (5) $$

where $\mathcal{L}_{GEN}$ follows the formulation in Eq. [2](https://arxiv.org/html/2508.14405v1#Sx3.E2), and $\mathcal{L}_{RA}$ denotes the alignment loss.

### Inference Strategy

We can maintain consistency with the training phase during the inference phase by leaving the backbone text empty. Alternatively, we can input the same meaning in English into the backbone while inputting Chinese into the branch. We found that when Chinese is input into the branch while English is input into the backbone, it achieves better results than either using only the backbone or the branch independently.

## Experiments

### Experimental Setup

The training dataset consists of approximately one billion image-text pairs, primarily drawn from high-quality internal Chinese datasets, supplemented with publicly available English datasets. Roughly 60% of the data is Chinese and 40% is English. We ensure that Chinese data constitutes the majority to enhance the model’s capacity to generate semantically rich Chinese content and reduce the dominance of English-centric concepts. Before training, rigorous filtering is applied to exclude samples with watermarks, low aesthetic quality, or weak image-text alignment
We adopt FLUX.1-dev (Labs 2024) as the base generative model. For the Chinese text encoder, we use Qwen2.5-VL-7B-Instruct (Bai et al. 2025), and for the English text encoder, we adopt T5 (Ni et al. 2021), with the framework consistent with those used in FLUX.1-dev, and the weights of the T5 text encoder are frozen during the training. During training, the hyperparameter $\mathcal{D}_{\text{threshold}}$ is set to 0.05.

The training of the diffusion transformer is built upon Flux’s latent space, which employs a Variational Autoencoder (VAE) to transform images between pixel space and latent representations. The training pipeline is implemented in PyTorch-Diffusers (von Platen et al. 2022). We use the AdamW optimizer (Kingma and Ba 2017; Loshchilov and Hutter 2019) with a learning rate of 1e-5 and a total batch size of 3200. Training is conducted over two weeks on 32 NVIDIA A800 GPUs.

### Training Datasets

In the first stage of training, we utilized a large-scale dataset consisting of mixed Chinese and English captions. The distribution of the ”English”, ”Chinese-long”, and ”Chinese short” captions is illustrated in Figure [4](https://arxiv.org/html/2508.14405v1#Sx5.F4). To ensure robust performance across captions of different lengths, we maintained a balanced proportion between short and long Chinese prompts during training. This design encourages the model to generalize well under diverse linguistic conditions, including semantic granularity and sentence complexities.

The construction of the dataset used for the first-stage training is illustrated in Figure [4](https://arxiv.org/html/2508.14405v1#Sx5.F4). We collected approximately 150 million images, each paired with both Chinese and English captions, by using existing image-text datasets and generating additional captions using a recaptioning model. Specifically, we first collected image-caption pairs from the LAION (Schuhmann et al. 2022) and GRIT (Fan et al. 2025) datasets as the visual input for training, and supplemented the captions using CogVLM2 (Hong et al. 2024) and Mantis (Jiang et al. 2024). To further supplement the training set, we included images generated by Flux-dev (Labs 2024), along with their corresponding captions produced by captioning models.

Figure: Figure 4: Caption language distribution across different datasets used in Stage 1 training. The root categories represent the caption language types: EN (English), CN-long (long-form Chinese), and CN-short (short-form Chinese). Subcategories denote the data sources or processing steps: GRIT and Laion are public datasets where the captions were originally collected; The suffix Flux indicates that the associated image was re-generated using Flux-dev; The suffix recap denotes that the caption has been rewritten or adjusted during preprocessing.
Refer to caption: x4.png

### Quantivate Evaluation

Evaluation on COCO.
Following prior work (Rombach et al. 2022; Podell et al. 2023; Labs 2024), we evaluate CTA-Flux on the COCO (Lin et al. 2015) 256×256 dataset using the zero-shot Fréchet Inception Distance (FID), a widely adopted metric for quantifying the quality and diversity of generated images.
We adopt a standardized benchmark based on the MS-COCO 30K dataset to ensure fair evaluation. Specifically, we use the Recaption-COCO (Li et al. 2024) dataset, reprocessing the original captions to produce more diverse and evenly distributed prompt lengths. Compared to the original COCO captions—typically short—the recaptioned prompts provide a more balanced length distribution, enabling more comprehensive and reliable assessment of model performance.

**Table 1: We compared CTA-FLux with other TTI models on the MS-COCO 256x256 dataset using FID-30K. The experimental setup included a classifier-free guidance scale of 3.5.**
| Model | Input Language | FID $\downarrow$ |
| --- | --- | --- |
| LDM(Rombach et al. 2022) | English | 37.45 |
| SD(Rombach et al. 2022) 1.5 | English | 22.87 |
| SDXL(Podell et al. 2023) | English | 18.64 |
| Flux(Labs 2024) | English | 16.39 |
| BDM(Liu et al. 2025) | Chinese and English | 28.34 |
| CTA-Flux(Ours) | Chinese only | 20.57 |
| CTA-FLux(Ours) | English only | 21.57 |

As shown in Table [1](https://arxiv.org/html/2508.14405v1#Sx5.T1), although our model exhibits a slight increase in FID compared to Flux, the overall performance remains comparable. The experimental results demonstrate that our method successfully retains the English generation capability while introducing effective Chinese generation ability. Under fully Chinese-guided prompts, our model achieves an FID of 20.57, which is slightly worse than Flux but still significantly outperforms Stable Diffusion 1.5. Furthermore, our model achieves substantially better performance than BDM, while requiring only Chinese input, highlighting the efficiency and effectiveness of our multilingual adaptation approach.

Chinese cultural tendency
To evaluate the cultural inclination of the model, we use GPT-4o to generate 100 culture-neutral prompts, consisting of 100 prompts in each of the four categories: people, architecture, cuisine, and festivals. We use these prompts to test the model and then compute the CLIP feature similarity between the generated images and the textual concepts ”Chinese” and ”Caucasian”. The higher similarity indicates the model’s stronger inclination toward the corresponding cultural concept.

The experimental results, as shown in Table [2](https://arxiv.org/html/2508.14405v1#Sx5.T2), indicate that our CTA-FLux model exhibits a stronger cultural inclination towards Chinese culture than Flux. The similarity of CTA-FLux with ”Caucasian” is nearly identical to Flux. Still, the similarity with ”Chinese” is significantly higher than that of Flux, suggesting that during our training process, the model progressively acquired unique concepts of Chinese culture.

**Table 2: We test the CLIP score $\uparrow$ to measure the Chinese cultural inclination.**
| Class | human | architecture |  |  |
| --- | --- | --- | --- | --- |
| Culture | Chinese | Caucasian | Chinese | Caucasian |
| Flux(Labs 2024) | 2.00 | 4.18 | 3.67 | 5.83 |
| CTA-FLux(ours) | 2.34 | 5.22 | 3.15 | 5.32 |

### Qualitative Results

Native language semantics.

Figure: Figure 5: The performance of different TTI models in generating descriptions related to Chinese culture is compared. Our CTA-Flux not only maintains the generation quality of Flux but also exhibits a strong inclination towards Chinese culture and offers excellent community compatibility.
Refer to caption: x5.png

We validated the Chinese semantics, and the experimental results are shown in Figure [5](https://arxiv.org/html/2508.14405v1#Sx5.F5). From the figure, it is evident that Flux (Labs et al. 2025) faces the issue of ambiguity between Chinese and English. Additionally, Flux lacks cultural bias, whereas our CTA-Flux exhibits a stronger inclination towards Chinese culture. While BDMv1.0 (Liu et al. 2025) supports Chinese prompt input and demonstrates a degree of cultural alignment with Chinese semantics, our CTA-Flux achieves superior performance in terms of image generation quality and aesthetic fidelity. Both our CTA-Flux and Seedream (Gao et al. 2025) demonstrate significant cultural bias, but compared to Seedream (Gao et al. 2025), CTA-Flux is more compatible with various plugins in the open-source Flux community, offering better community support.

Figure: Figure 6: By using CTA-Flux with different LoRA modules, images generated with various LoRA prompts demonstrate CTA-Flux’s strong community adaptability. This framework can generate high-quality, aesthetically pleasing, and high-resolution images.
Refer to caption: x6.png

Community Support
To assess the extent of compatibility with the Flux community, we tested our model using several popular LoRA weights released by the community. The test results, as shown in Figure [6](https://arxiv.org/html/2508.14405v1#Sx5.F6), demonstrate that our model is compatible with various styles of LoRA, exhibiting strong community.

### Ablation Study

Impact of Loss Function on Performance
To investigate the impact of adding loss and threshold on model performance, we conducted experiments under the same training conditions with 400,000 samples. We compared the model’s generation quality using COCO FID (Heusel et al. 2018) and CLIP score (Radford et al. 2021) under different configurations: no loss, no threshold; no loss with threshold; and with loss but without threshold. The experimental results demonstrate in table [3](https://arxiv.org/html/2508.14405v1#Sx5.T3) that adding loss improves the model’s semantic understanding and generation quality. Furthermore, incorporating the threshold further enhances the generation quality while maintaining strong semantic understanding.

**Table 3: We conducted tests with the same number of training steps for the ablation study on adding the loss function. The FID was evaluated using a fixed set of 3000 images selected from the COCO dataset as the test samples.**
| Loss function | Metrics |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| $\mathcal{L}_{RA}$ | $\mathcal{L}_{p}$ | $\mathcal{L}_{inter}$ | $D_{threshold}$ | FID $\downarrow$ | CLIP score $\uparrow$ |
| ✗ | - | - | - | 93.06 | 30.47 |
| ✓ | ✓ | ✗ | ✗ | 77.10 | 31.42 |
| ✓ | ✓ | ✗ | 58.86 | 31.28 |  |
| ✓ | ✓ | ✓ | 53.52 | 31.40 |  |

Avoiding the Use of Chinese Embedding as Query in Cross-Attention
In TTI generation models, text embeddings are typically used as queries in the cross-attention operation to facilitate information fusion across modalities. We conducted an ablation experiment to explore whether the Chinese text embeddings should be used as queries in the cross-attention computation and update their own features. We tested a model trained with 160,000 samples using COCO 256x256 FID and CLIP score metrics. The experimental results show that allowing the Chinese branch tokens to update during the cross-attention operation severely degrades the model’s generation quality and semantic understanding. We speculate that this may be due to the strong random conditions introduced by the empty text input in the English branch during training, which, when the Chinese tokens query the English tokens, makes them highly susceptible to these random conditions, weakening the representation capability of the Chinese tokens.

**Table 4: The ablation study on whether the Chinese embedding feature should be updated as a query was conducted.**
| Chinese embedded token | FID $\downarrow$ | CLIP score $\uparrow$ |
| --- | --- | --- |
| w/ update | 277.00 | 30.47 |
| w/o update | 81.58 | 31.42 |

## Conclusion

Our work realize the adaptation of the Flux model to Chinese text inputs by training a lightweight Chinese language branch within the MMDiT blocks, thereby avoiding the training of the entire TTI model from scratch. The English and image input branches remain frozen throughout, preserving compatibility with the base Flux model and the broader English TTI community. Through both qualitative visualizations and quantitative evaluations on multiple benchmarks, we demonstrate the effectiveness of our method in understanding Chinese text conditions and generating cultural and semantic-related contents. Moreover, our approach is not limited to Chinese-it is promising for broader multilingual TTI adaptation tasks, highlighting the generalization ability of the proposed framework.

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## Peer Reviews

confidence: 5
contribution: 1
initial_rating: 2
presentation: 3
questions: CTA-Flux is a foundational model. Could the authors provide evaluation results on public benchmarks, such as GenEval and T2I-CompBench?
soundness: 3
strengths: The paper successfully trains a text-to-image model that understands the semantic meaning of Chinese text, enabling Flux to process Chinese prompts effectively.
summary: This paper proposes CTA-Flux, which adapts an English-only text-to-image model to support both Chinese and English.
weaknesses: This work lacks novelty: understanding Chinese semantics is achieved simply by replacing the text encoder and retraining the model.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: 1. What is your rationale for choosing MMDiT? Why not opt another backbone to accomplish this task?

2. The Chinese prompts used in the experiments mostly refer to culturally salient items such as festivals, clothing, and food. Could you offer some illustration of prompts that are culturally neutral but linguistically complex?

3. How can the model ensure that it truly understands the Chinese language, rather than merely piecing together stereotypically "Chinese-looking" visual elements?
soundness: 3
strengths: 1. The problem is highly relevant to real-world applications, as the paper focuses on cross-lingual and cross-cultural adaptation, offering significant practical value.

2. The method is designed with simplicity and efficiency in mind. It introduces no changes to the original backbone, preserving full compatibility with community plug-ins such as LoRA.

3. The training strategy is well-designed, employing a two-stage approach. The first stage aligns Chinese and English features, while the second stage concentrates on capturing Chinese cultural semantics, effectively balancing generality with cultural specificity.

4. The approach can, to a certain extent, be generalized to other languages, demonstrating broad applicability.
summary: This paper presents a concise and logically coherent idea, bridging the linguistic gap between Chinese and English, which drives T2I models comprehend cultures across different regions. The overall framework leverages language models for different scripts and seamlessly integrates them with MMDiT. The experiments include several image samples generated from Chinese prompts, providing a certain level of illustration for the model’s effectiveness.
weaknesses: 1. The definitions and metrics employed for the linguistic and cultural gaps remain somewhat vague, relying heavily on manual evaluation or CLIP-based similarity scores.

2. Although the study proposes metrics to assess the quality of images generated from Chinese prompts, it lacks an evaluation of the depth of Chinese language understanding—such as how well the model handles complex linguistic phenomena like polysemy, idioms, or cultural metaphors.

---

confidence: 2
contribution: 3
initial_rating: 6
presentation: 3
questions: No
soundness: 3
strengths: 1. Cross-lingual and cross-cultural alignment in generative models is underexplored yet increasingly important.
2. The approach extends an existing large English T2I model without retraining it from scratch, which is pragmatic for deployment.
3. Maintaining plugin compatibility (LoRA, ControlNet) is an appealing engineering consideration for community adoption.
4. The authors provide both quantitative (FID, CLIP score, GenEval) and qualitative (human cultural authenticity ratings) results to support claims.
summary: This paper addresses the challenge of extending English-centric text-to-image (T2I) models to support Chinese language prompts while preserving cultural semantics. The authors propose CTA-Flux, a lightweight bilingual extension of the Flux model, which introduces a Chinese Linguistic Attention Branch (CLAB) and a representation alignment loss to bridge linguistic and visual distribution gaps between English and Chinese prompts. Quantitative and qualitative experiments suggest that CTA-Flux achieves comparable or better performance than Flux on English benchmarks, while significantly improving cultural faithfulness for Chinese prompts.
weaknesses: 1. The method is presented as generalizable to other non-English languages, but experiments focus exclusively on Chinese. It remains unclear whether the proposed architecture and training scheme would generalize effectively to languages with very different morphology or script systems.
2. While the paper aims to improve cultural authenticity, it does not discuss or measure potential cultural stereotyping or bias amplification, which are crucial ethical aspects of “cultural-aware” generation.
3. The cultural-specific prompt set appears hand-crafted and may not represent the full diversity of Chinese culture or real user prompts. A more systematic benchmark or open-sourced dataset would strengthen reproducibility.
4. The architectural idea (auxiliary language branch + feature alignment) resembles existing multimodal adaptation techniques; the contribution lies more in application and empirical validation than in theoretical innovation.


Decision: Reject