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Visual ChatGPT: The next frontier of conversational AI

Visual chatGPT
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As AI continues to evolve and improve, it has become a crucial area of interest for individuals and businesses alike. AI models are gradually taking over certain tasks previously only possible for humans to perform. As per Grand View Research, the global chatbot market was estimated at USD 5,132.8 million in 2022 and is anticipated to increase at a CAGR of 23.3% between 2023 and 2030. According to Market.US, the global chatbot market was valued at USD 4.92 billion in 2022 and is forecasted to exhibit the highest CAGR of 23.91% from 2023 to 2032, with an expected market size of USD 42 billion by the end of the forecast period. The rising demand for customer service will likely be the major driver behind this projected growth.

The way ChatGPT has transformed human-machine interaction blurring the barriers between the two is a powerful demonstration of AI’s immense potential and a clear sign of its promising future. However, ChatGPT has certain limitations; it can neither create images nor process visual prompts. Microsoft has made significant progress by developing Visual ChatGPT, a language model that generates coherent and contextually relevant responses to image-based prompts. The model uses a combination of natural language processing techniques and computer vision algorithms to understand the content and context of images and generate textual responses accordingly.

Visual ChatGPT combines ChatGPT with Visual Foundation Models (VFMs) like Transformers, ControlNet, and Stable Diffusion. Its sophisticated algorithms and advanced deep learning techniques allow it to interact with users in natural language, offering them the information they seek. With the visual foundation models, ChatGPT can also evaluate pictures or videos that users upload to comprehend the input and offer a more customized solution.

Let’s delve deeper into Visual ChatGPT to understand and explore the potential of this recently developed technology.

What is Visual ChatGPT?

Visual ChatGPT is a conversational AI model that combines computer vision and natural language processing to create a more enhanced and engaging chatbot experience. There are many potential applications for Visual ChatGPT, such as creating and editing photographs, which may not be available online. It can remove objects from pictures, change the background color, and provide more accurate AI descriptions of uploaded pictures.

Visual foundation models play an important role in the functioning of Visual ChatGPT, allowing computer vision to decipher visual data. VFM models typically consist of deep-learning neural networks trained on massive datasets of labeled photos or videos and can identify objects, faces, emotions, and other visual aspects of images.

Visual ChatGPT, also known as Image-Chat, is an AI model that combines natural language processing with computer vision to generate responses based on text and image prompts. The model is based on the GPT (Generative Pre-trained Transformer) architecture and has been trained on a large dataset of images and text.

Visual ChatGPT uses computer vision algorithms to extract visual features from the image and encode them into a vector representation when presented with an image. This vector is then concatenated with the textual input and fed into the model’s transformer architecture, which generates a response based on the combined visual and textual input.

For example, if presented with an image of a cat and a prompt such as “Change the cat color from black to white?” Visual ChatGPT may generate an image of the white cat. The model is designed to generate relevant responses to the image and the prompt and produce coherent responses.

Applications of Visual ChatGPT range from social networking and marketing to customer care and support.

Features of Visual ChatGPT

The key features of Visual ChatGPT are as follows:

Multi-modal input: One of the key features of the Visual ChatGPT is multi-modal input. It enables the model to handle both textual and visual data, which can be incredibly helpful in generating responses considering both input types. For instance, if you provide Visual ChatGPT an image of a woman wearing a green dress and use the prompt, “Can you change the color of her dress to red?” it can use both the image and the text to produce an image of a woman wearing a red dress. This can be extremely useful in tasks like labeling pictures and responding to visual questions.

Image embedding: A key component of Visual ChatGPT is image embedding. When Visual ChatGPT gets an input image, it creates an embedding, a compact and dense representation of the image. With the help of this embedding, the model can use the image’s visual characteristics to generate responses that consider the prompt’s visual context. Through the use of this picture embedding, Visual ChatGPT can comprehend the input’s visual content in a better way and can produce responses that are highly precise and relevant. Essentially, Visual ChatGPT incorporates image embedding to detect visual elements and objects within an image. This information is utilized in constructing a response to a prompt that involves an image. This can result in more accurate and contextually relevant replies, especially in scenarios that require understanding text and visual information.

Object recognition: The model has been trained on a large image dataset, enabling it to develop the ability to identify a range of items in pictures. When given a prompt that includes a picture, Visual ChatGPT can use its object recognition abilities to recognize particular elements in the image and provide responses. For instance, if given a picture of a beach, Visual ChatGPT might be able to identify elements like water, sand, and palm trees and use that information to respond to the prompt. This can result in more thorough and precise responses, particularly against queries requiring a deep understanding of visual data.

Contextual understanding: The model is intended to comprehend the connections between a prompt’s text and visual content and use this information to provide more accurate and pertinent responses. Visual ChatGPT can produce highly complex and contextually appropriate responses by considering a prompt’s text and visual context. For instance, if given an image of a person standing in front of a car and the prompt, “What is the person doing?” Visual ChatGPT can use its visual understanding to determine that the person is standing in front of a car and then use this textual understanding to produce an answer that makes sense in this situation. A plausible reaction from the model might be “The individual is admiring the car” or “The person is taking a picture of the car,” both of which fit with the image’s overall theme.

Large-scale training: A critical feature of Visual ChatGPT is large-scale training, which contributes to the model’s ability to produce high-quality responses to various prompts. A sizable dataset of text and images that covers a wide range of themes, styles, and genres was used to train the model. This has helped Visual ChatGPT develop the ability to provide responses that are instructive, engaging, and relevant to the context in addition to being grammatically correct. With extensive training, Visual ChatGPT has learned to recognize and produce responses that align with the patterns and styles of human language. This indicates that the model can produce answers comparable to those a human might give, making the responses seem more natural and compelling.

What is the role of visual foundation models in Visual ChatGPT?

Visual Foundation Models are computer vision models created to mimic the early visual processing that occurs in the human visual system. Convolutional neural networks (CNNs) are generally used in their working. They are trained on a massive image dataset to learn a hierarchical collection of attributes that may be applied to tasks like object recognition, detection, and segmentation.

VFMs try to follow how the human visual system processes information by extracting low-level features of objects like edges, corners, and texture and then combining them to form more complex features like shapes. This hierarchical approach is similar to how the visual cortex processes information, with lower-level neurons responding to simple features and higher-level neurons responding to more complex stimuli.

The initial stage of the working of VFMs in Visual ChatGPT is to train a CNN on a large image dataset, often using a supervised learning method. By using a series of convolutional filters on the input picture during training, the network learns to extract features from the images. Each filter creates a response map focusing on a certain image aspect, such as a shade, texture, or shape. A pooling layer is applied after the first layer of filters, which lowers the spatial resolution of the response maps and aids in extracting more robust features. Each layer learns to extract more abstract and complicated information than the one before it, as the process is repeated for numerous levels. A fully connected layer serves as the VFM’s last layer and often transfers the high-level characteristics retrieved from the image to a collection of output layers, such as object categories or segmentation labels. The VFM uses the learned set of filters to extract features from an input image during inference and uses those features to generate a prediction about an object or scene in the image.

Visual Foundation Models (VFMs) enable advanced performance on various applications in computer vision. VFMs can effectively identify and comprehend the visual world by extracting a massive set of information from images.

How does Visual ChatGPT work?

Visual ChatGPT is a neural network model that combines text and image information to generate contextually relevant responses in a conversational setting. Here are the steps of how Visual ChatGPT works:

Input processing

The image prompt provides a meaningful context of the input, while the textual input consists of a collection of words that constitute the user’s message. Using the image input is not always necessary, but it can offer additional details that can aid the model in producing more contextually appropriate responses. The model can produce more detailed and precise responses in a conversational situation when text and visual inputs are integrated.

Textual encoding

A transformer-based neural network called the text encoder processes the textual input by compiling a list of contextually relevant word embeddings. The transformer model assigns a vector representation, or embedding, to each word in the input sequence. Based on each word’s context within the sequence, the embeddings capture the semantic meaning of each individual word. The transformer-based text encoder is often pre-trained utilizing unsupervised learning approaches like the self-attention mechanism on large text datasets, enabling it to recognize intricate word relationships and patterns in the input text. The generated embeddings are fed into the model’s subsequent stage as input.

Image encoding

Deep learning neural networks, such as convolutional neural networks (CNNs), are very effective for image recognition applications. A pre-trained model like VGG, ResNet, or Inception trained on sizable image datasets like ImageNet often serves as the CNN-based image encoder. CNN uses convolutional and pooling layers to extract high-level features from a picture after receiving it as input. The image is then represented as a fixed-length vector by flattening these features and passing them through one or more completely linked layers.

Multimodal fusion

The image and text encodings are typically concatenated or summed together to create a joint representation of the input. This joint representation is then passed through one or more fusion layers that combine the information from the two modalities. The fusion layer can take many forms, such as:

  • Simple concatenation: This method combines the image and text embeddings along the feature dimension to produce a single joint representation. The final output can be produced by passing this combined representation via one or more completely connected layers.
  • Bilinear transformation: With this technique, a set of linear transformations are used to first translate the image and text embeddings to a common feature space. After that, a bilinear pooling process is performed by multiplying the two embeddings element by element. The pooling process captures the interactions between the picture and text characteristics, which creates a combined representation that may be fed through subsequent layers to produce the final output.
  • Attention mechanism: This technique generates context vectors for each modality by first passing the picture and text embeddings via independent attention mechanisms. These context vectors are then integrated using an attention process that creates a joint representation by learning how important each modality is based on the input. Thanks to this attention mechanism, the model can concentrate on the image’s and text’s most important areas when producing the output.

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The transformer-based neural network that comprises a stack of decoder blocks serves as the decoder in the multimodal model. Each decoder block resembles its counterpart in the transformer-based language models, with a few adjustments to account for the image data. Each decoder block specifically focuses on the preceding output tokens and the combined image-text representation to produce the subsequent token in the sequence. The most typical method is to sample from this distribution after the decoder creates a probability distribution over the vocabulary of potential output tokens to produce the final output sequence. It is possible to accomplish this using a greedy method, in which the token with the highest probability is chosen at each time step, or a more complex method like beam search decoding and teacher forcing is used.

Output generation

After processing and encoding the input, the model produces a series of output tokens representing the response. A beam search technique looks through every possible combination of tokens to identify the one that most closely matches the input context. It operates by keeping track of a collection of potential sequences, known as beams, which are expanded at each stage of the decoding procedure. The search is carried out until the algorithm identifies the sequences with the highest probability, maintaining a predetermined number of beams. In contrast, sampling tokens are selected at random from the probability distribution of the model at each stage, leading to a wider range of original responses. Regardless of the method employed, the final output tokens are transformed into a word sequence to provide the answer from the model. This response must deliver pertinent information appropriate for the input context continuously and coherently.

Architectural components of Visual ChatGPT and its role

visual chatgpt

Architectural components of Visual ChatGPT

The architectural components of Visual ChatGPT and their roles are discussed below:

User query

A query is the user’s initial input, typically visual representations like photographs or videos. Visual ChatGPT then processes this input to produce a response or output pertinent to the user’s inquiry. The user question is a crucial part of the system since it establishes the nature of the task that Visual ChatGPT must perform and directs the subsequent processing and reasoning phases. The user enters the query, and then Visual ChatGPT processes the input to produce relevant output.

Prompt manager

After the user generates a query, it goes to the prompt manager. The visual data is transformed into a textual representation and fed into the Visual ChatGPT model using an image recognition program or prompt manager. Computer vision techniques are generally used to evaluate the visual input and extract relevant information, such as text detection, object detection, and facial recognition. The information is then transformed into a natural language format to be utilized as input for additional analysis and the creation of responses.

Prompt manager assists Visual ChatGPT by iteratively providing data from VFMs to ChatGPT. To simplify the user’s requested task, the prompt manager integrates 22 separate VFMs and specifies their internal communication. The three key functions of a prompt manager are as follows:

  • Demonstrating the capabilities of each VFM and the appropriate input-output format for ChatGPT.
  • Translates several visual information formats, such as PNG images, depth images, and mask matrices, into text that ChatGPT can read.
  • Handles the varying VFMs’ histories, priorities, and conflicts.

The prompt manager uses an effective technique to coordinate with VFMs for particular tasks because Visual ChatGPT interacts with various VFMs. This is because various VFMs have features in common, such as generating new images by replacing certain components in an image. Conversely, the VQA task (visual image question answering) might respond according to the image presented.

Computer vision techniques are generally used to evaluate the visual input and extract pertinent information, such as text detection, object recognition, and facial recognition. The information is then transformed into a natural language format to be utilized as input for additional analysis and the creation of responses.

Computer vision

The prompt manager performs its task using computer vision, a branch of artificial intelligence (AI) that enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and execute actions or make recommendations based on that information. If AI allows computers to think, computer vision allows them to see, observe, and comprehend. Computer vision aims to program computers to analyze and comprehend images down to the pixel level. Technically, machines try to retrieve, interpret, and analyze visual data using specialized software algorithms.

In his Image Processing and Computer Vision article, Golan Levin provides technical information regarding machines’ procedures to understand images. In essence, computers perceive images as a collection of pixels, each with a unique set of color values. Think of a picture of a red flower as an example. The image’s brightness is encoded as a single 8-bit value that ranges from 0 to 255 (0 being black and 255 being white). When you enter an image of a red flower into the software, it sees these numbers. The Visual ChatGPT employs a computer vision algorithm that assesses an image, analyzes it further, and makes decisions based on its findings.

We must explore the algorithms this technique is based on to comprehend the latest developments in computer vision technology. Contemporary computer vision relies on deep learning, a branch of machine learning that uses algorithms to extract information from data. Visual ChatGPT incorporates all these technologies to produce reliable outcomes.

Deep learning employs a neural network algorithm and is a more efficient method of performing computer vision. Using specified data samples, neural networks are used to extract patterns. The human understanding of how brains work, particularly the connections between the neurons in the cerebral cortex, inspired these algorithms. The perceptron, a mathematical model of a biological neuron, lies at the fundamental level of a neural network. There may be numerous layers of interconnected perceptrons, similar to the biological neurons in the cerebral cortex. The output layer of the perceptron-created network receives input values (raw data), which are then transformed into predictions about a specific object.

Visual Foundation Model (VFM)

The Visual Foundation Model (VFM) is a deep learning model for visual identification tasks like object detection, image classification, natural language processing, and question-answering. It is built on a “visual vocabulary,” a collection of image attributes learned from a massive sample of images. This visual vocabulary trains the VFM model to identify items and scenes in photographs. Overfitting is less likely to occur using the VFM model, and this is because it was trained using a predetermined set of image features rather than creating brand-new features from scratch. The VFM model is more interpretable than earlier deep learning models since it enables researchers to explore the learned visual language and understand how the network generates predictions.

A “visual vocabulary” is used by the Visual Foundation Model (VFM) to represent images. Images or graphics that represent words and their meanings are called visual vocabulary. In the same way, individual words enable written language, and individual images provide visual language. A picture can be the basis for a search in a Visual Vocabulary rather than a text. A search will produce a list of images and videos that can be sorted based on how closely they resemble the original image. Visual vocabulary is a collection of visuals and their attributes learned from a massive dataset of images using unsupervised learning methods like clustering. Edges, corners, and textures are examples of lower-level visual information from which the features are often retrieved.

The VFM model first extracts the histogram of information from a picture to classify an image. The closest match is then determined by comparing this histogram to others from a collection of training images. This is accomplished by utilizing a similarity metric, such as cosine similarity or Euclidean distance.

To identify objects in the image, the VFM model initially divides the image into small sections and extracts the histogram of information for each region. Then, it contrasts these histograms with histograms derived from a collection of training images that include relevant items. Regions similar to the training images are considered to contain objects of interest, and the model outputs the position and class of the objects. Blip, Clip, and Stable Diffusion are examples of the VFM models used in Visual ChatGPT.

History of dialogue

The history of dialogue makes the outcome more meaningful as per the input. When a user inserts an inquiry, the history of dialogue aids the system in responding to questions based on previously asked queries while considering the context of the dialogue. It follows the patterns and influences the training data’s design to create a response. With the history of dialogue, Visual ChatGPT can pinpoint common patterns and structures of communication, including turn-taking, topic-switching, and conversational coherence, by studying enormous databases of human-to-human interactions.

History of reasoning

The history of reasoning is the ability to use contextual information, including visual cues, to produce responses that are pertinent and meaningful in the context of Visual ChatGPT. Additionally, the system can learn to identify and resolve conflicts between various information sources by analyzing the reasoning procedures of various VFMs. When the provided information is confusing or conflicting, the system must utilize its reasoning skills to determine the most probable interpretation of the data. When the user inquires, the system responds to user inquiries with greater accuracy and relevance thanks to the history of reasoning.

It examines the reasoning processes of multiple VFMs and develops the ability to recognize and resolve conflicts across various sources of information. When the provided information is confusing or conflicting, the system utilizes its reasoning skills to determine the most probable interpretation of the data.

Intermediate response

Finally, the system produces several intermediate responses that make sense to user queries. The system can evaluate many interpretations of the input data by generating multiple intermediate responses, and then it can decide which interpretation is most likely to be relevant to the user. When the available information is ambiguous or uncertain, this technique helps the system to identify and resolve contradictory or incomplete information.

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Use cases of Visual ChatGPT

Businesses can use Visual ChatGPT to access a variety of effective use cases. Here are only a few examples:

Customer service: Visual ChatGPT serves as a chatbot that can interact with clients in natural language and help them find the required information. The chatbot can scan customer-provided photographs or videos to understand their problems better and offer a more customized solution, thanks to computer vision. Businesses can provide 24/7 customer service with Visual ChatGPT, ensuring clients get support around the clock. Businesses having an international clientele may find this function to be of great use.

E-commerce: Visual ChatGPT has the potential to significantly impact the e-commerce industry by boosting the customer experience, streamlining business operations, and improving marketing strategies. Customers can view products before purchasing by using Visual ChatGPT, which can produce visuals of products based on written descriptions. Businesses may improve the online shopping experience and increase sales by producing high-quality images in a matter of seconds utilizing powerful hardware and customized software. Visual ChatGPT can be used as a virtual shopping assistant that interacts with customers, responds to their product inquiries, and provides tailored recommendations based on their preferences. The assistant can also provide suggestions for products likely to interest the customer by analyzing user behavior using computer vision.

Social media: Based on their content and interaction metrics, social media influencers can also be found and assessed using Visual ChatGPT. The model can analyze the influencers’ visual content to see if their aesthetic and sense of style match the goals and principles of the company. This can assist companies in finding appropriate influencers to work with for sponsored content and collaborations. It is possible to use visual ChatGPT to analyze social media discussions and find patterns, emotions, and insights that can help companies enhance their marketing plans. The approach can aid businesses in determining their customers’ interests, preferences, and behaviors by analyzing photographs and videos.

Healthcare: A virtual assistant can be created using Visual ChatGPT to interact with patients, respond to their inquiries, and offer individualized health advice by analyzing medical images like X-rays, CT scans, and MRIs. Visual ChatGPT can help doctors and other health professionals make more precise diagnoses. Using computer vision, the model can highlight potential irregularities or areas of concern for the doctor’s attention. Visual ChatGPT can also be used to track and monitor patients. For instance, patients undertaking physical therapy or rehabilitation exercises could record their motions with a camera or a wearable device, and Visual ChatGPT could analyze the visual data to give feedback on their form, posture, or progress. When patients cannot visit their doctor in person, this can be extremely helpful for remote patient monitoring their symptoms.

Education: It is possible to create a virtual instructor using Visual ChatGPT to interact with students, respond to their inquiries, and offer tailored feedback on their work. The tutor can also pinpoint places where students need extra help by employing computer vision to monitor student behavior. Interactive educational resources can be produced using Visual ChatGPT. For example, the model can produce pictures or videos that clarify difficult concepts or show how scientific procedures work. These resources can be altered to meet the requirements of certain students or groups of students. Furthermore, Visual ChatGPT can help with language acquisition by producing visuals or videos that clarify the meaning of unfamiliar words or expressions. Also, the program can give students comments on their usage of grammar or pronunciation, assisting them in developing their language abilities.

Implementation of Visual ChatGPT on your local machine

You can implement Visual ChatGPT on your local machine using Python. Let’s start with the process:

Step-1: Download Python (use version above 3.9).

Here is the download link – Download Python

Step-2: Download Anaconda. During installation, ensure adding the path to the system environment variable. The download link is provided below:

Download Anaconda

Step-3: Download the Cuba tool kit (version 11.6). Find the download link below:

CUDA Toolkit 11.6 Downloads

Step-4: Download Pytorch (version 12.1). The download link is given below:


Run the following command after installing the cuda tool kit:

1 # CUDA 11.6 conda

2 conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge

Step-5: Clone the repository

You have to clone the repository in your system and use the following command:

git clone

Step-6: Navigate to the directory

After cloning the repository, you can move to the directory using the following command:

cd visual-chatgpt

Your current working directory will be changed to the cloned repository. You can start working with the code and exploring the repository’s contents.

Step-7: Create a new environment

Create a new Anaconda environment called “visgpt” with Python version 3.8 using the command:

conda create -n visgpt python=3.8

Step-8: Activate the new environment

You can activate a new environment using the following command:

conda activate visgpt

Step-9: Prepare and install the basic environment using the command given below:

pip install -r requirements.txt

Step-10: Prepare your private Open AI key

To authenticate your requests to the OpenAI API, set your private OpenAI API key as an environment variable. Your operating system will determine the precise command you should use.

The command for Linux:

export OPENAI_API_KEY={Your_Private_Openai_Key}

The command for Windows:

set OPENAI_API_KEY={Your_Private_Openai_Key}

Step-11: Start your Visual ChatGPT

Start your Visual ChatGPT using the following commands:

 You can specify the GPU/CPU assignment by "--load", the parameter indicates which 
# Visual Foundation Model to use and where it will be loaded to
# The model and device are separated by underline '_', the different models are separated by comma ','
# The available Visual Foundation Models can be found in the following table
# For example, if you want to load ImageCaptioning to cpu and Text2Image to cuda:0
# You can use: "ImageCaptioning_cpu,Text2Image_cuda:0"

Step-12: Run Visual ChatGPT script

These commands are used to run the Visual ChatGPT script with different settings depending on the available hardware resources. Run the following command:

For CPU users:

python --load ImageCaptioning_cpu,Text2Image_cpu

For Google Colab (1 Tesla T4 15GB):

If you are running the script on Google Colab with a single Tesla T4 GPU with 15GB memory, you can use the following command:

python --load "ImageCaptioning_cuda:0,Text2Image_cuda:0"

For 4 Tesla V100 32GB:

python --load "ImageCaptioning_cuda:0,ImageEditing_cuda:0,

Step-13: See the result

Visual ChatGPT


Visual ChatGPT, an open system, integrates several VFMs to allow users to interact with ChatGPT. It understands the user’s questions, creates or edits images accordingly, and makes changes based on user feedback. Advanced editing features in Visual ChatGPT include deleting or replacing an object in a picture, and it can also describe the image’s contents in simple English. Visual ChatGPT excels at processing natural language tasks and provides outputs that match human quality standards. It can comprehend text-based and visual inputs by fusing natural language processing with computer vision, giving users accurate and individualized responses in real time. Businesses can use Visual ChatGPT to increase customer engagement, improve customer service, cut costs, and operate more effectively. Visual ChatGPT can assist organizations in fostering closer relationships with their customers and achieving success by responding to client inquiries in a personalized manner, thereby driving growth. As technology develops and advances, we anticipate seeing more companies use Visual ChatGPT as a crucial tool for internal workflows and ensuring client and customer satisfaction.

Unleash the power of natural language processing and computer vision with Visual ChatGPT. LeewayHertz offers consultancy and development expertise for Visual ChatGPT. Contact now!

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Author’s Bio


Akash Takyar

Akash Takyar LinkedIn
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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