Marketing picture generation method and system based on AI automatic pipeline, and medium

Abstract

The embodiment of the invention discloses a marketing picture generation method and system based on an AI automatic pipeline and a medium, which are applied to a server, and a text pipeline, a graph pipeline and a large model Agent are deployed in the server. The method comprises the following steps: acquiring prompt information input by a user; responding to the prompt information to start the text pipeline for processing, and realizing automatic generation of the marketing copywriting; responding to the prompt information to start the image generation pipeline for processing, and generating an image highly matched with the prompt information; and combining the generated marketing copywriting with the picture, and carrying out layout on the copywriting and the picture through a preset typesetting algorithm to finally generate a complete marketing picture. The method has the beneficial effects that the high-quality marketing copywriting and the matching picture can be automatically generated according to the prompt information input by the user, and the two are synthesized into the marketing picture, so that the diversified and personalized marketing requirements of enterprises are met.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence technology and marketing, and in particular to a marketing image generation method, system and medium based on an AI automatic pipeline. Background Art

[0002] In today's highly competitive market, marketing and promotion are crucial to business development. Traditional marketing image production requires professional copywriters and designers, as well as significant time and labor costs. Copywriters must diligently research factors such as product features, brand positioning, and the festive atmosphere to create appropriate copy. Designers, on the other hand, must conceive images, select materials, and synthesize them based on the content of the copy. This approach is not only inefficient, but also skewed understanding between different personnel, leading to a poor match between copy and image, making it difficult to achieve ideal marketing results. With the development of artificial intelligence (AI), the use of AI to automate marketing content generation has become a trend. However, existing AI marketing content generation technologies are mostly single-purpose and cannot efficiently and effectively generate and synthesize both copy and image, making them unable to meet the diverse and personalized marketing needs of businesses. Summary of the Invention

[0003] In response to the technical defects of the existing technology, the purpose of the embodiments of the present invention is to provide a marketing image generation method, system and medium based on AI automatic pipeline, so as to overcome the defects of the existing technology that it is impossible to simultaneously and efficiently complete the generation and synthesis of copy and images, and it is difficult to meet the diversified and personalized marketing needs of enterprises.

[0004] To achieve the above objectives, in a first aspect, an embodiment of the present invention provides a marketing image generation method based on an AI automatic pipeline, which is applied to a server in which a text generation pipeline, an image generation pipeline, and a large model agent are deployed; the method comprises:

[0005] Get the prompt information entered by the user;

[0006] In response to the prompt information, the copy generation pipeline is started to perform processing to realize automatic generation of marketing copy;

[0007] In response to the prompt information, the image generation pipeline is started to perform processing to generate an image that highly matches the prompt information;

[0008] The generated marketing copy is then merged with the picture, and the text and picture are laid out through a preset typesetting algorithm to finally generate a complete marketing picture.

[0009] As a specific implementation of the present application, the processing of the data generation pipeline specifically includes:

[0010] Based on the prompt information, automatically matching brand information, product information, applicable population information and related holiday information in the database;

[0011] Then use the large model agent to perform task allocation and induction;

[0012] After each large model agent completes the summary, the processing results will be summarized and the final output will be marketing copy that conforms to the brand style, highlights the product selling points, and fits the marketing festival atmosphere.

[0013] As a specific implementation of the present application, starting the image generation pipeline for processing specifically includes:

[0014] Vector matching;

[0015] Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

[0016] As a specific implementation method of this application, when making the layout, we also follow the aesthetic principles and visual communication principles to ensure that the text and pictures are coordinated with each other, highlight the marketing focus, so as to enhance the attractiveness of the pictures and the information communication effect.

[0017] As an optimized implementation of the present application, the method further includes:

[0018] When the vectors are matched, they are also matched based on reverse prompt words.

[0019] In a second aspect, an embodiment of the present invention further provides a marketing image generation system based on an AI automatic pipeline, which is applied to a server, wherein a text generation pipeline, an image generation pipeline, and a large model agent are deployed on the server; the system includes:

[0020] Input module, used to obtain prompt information input by the user;

[0021] Processing module for:

[0022] In response to the prompt information, the copy generation pipeline is started to perform processing to realize automatic generation of marketing copy;

[0023] In response to the prompt information, the image generation pipeline is started to perform processing to generate an image that highly matches the prompt information;

[0024] The merging module is used to merge the generated marketing copy with the picture, layout the text and picture through a preset typesetting algorithm, and finally generate a complete marketing picture.

[0025] In a third aspect, the present invention provides a storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the first aspect.

[0026] The technical solution provided by the embodiment of the present invention, by utilizing the text generation pipeline, image generation pipeline and large model agent, can automatically generate high-quality marketing copy and matching images based on the product name, brand name and marketing holiday information input by the user, and synthesize the two into marketing images. It can be widely used in scenarios such as advertising, e-commerce promotion, and social media marketing; thereby overcoming the defects of the existing technology that cannot simultaneously and efficiently complete the generation and synthesis of copy and images, and is difficult to meet the diversified and personalized marketing needs of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation or the description of the prior art.

[0028] Figure 1 This is a flowchart of a method for generating marketing images based on an AI automatic pipeline provided by an embodiment of the present invention;

[0029] Figure 2 This is a structural block diagram of a marketing picture generation system based on AI automatic pipeline provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0032] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples.

[0033] Agent, intelligent body; in the application of artificial intelligence and large models (LLM), Agent refers to a module or system with autonomous decision-making capabilities, which has become a key link in AI applications; it can coordinate multiple tools, knowledge bases or models based on external environment input or task objectives to complete complex tasks.

[0034] It should be noted that, unless otherwise specified, the technical terms in this embodiment have the common meanings understood in the relevant technical field.

[0035] Please refer to Figure 1 An embodiment of the present invention provides a marketing image generation method based on an AI automatic pipeline, which is applied to a server in which a text generation pipeline, an image generation pipeline, and a large model agent are deployed. The method includes:

[0036] S101, obtaining prompt information input by the user; wherein the prompt information includes the input product name, brand name, marketing holiday information, etc., which serves as basic data for the entire generation process;

[0037] S102, in response to the prompt information, starting the copy generation pipeline to perform processing to realize automatic generation of marketing copy;

[0038] S103, in response to the prompt information, starting the image generation pipeline to perform processing to generate an image that highly matches the prompt information;

[0039] S104, merging the generated marketing copy with the picture, laying out the text and picture through a preset typesetting algorithm, and finally generating a complete marketing picture.

[0040] In this embodiment, for step S101, the data generation pipeline performs processing, specifically including:

[0041] Based on the prompt information, brand information, product information, applicable population information and related holiday information are automatically matched in the database; that is, the raw data pipeline is started, and the system automatically matches information in the database according to the information input by the user, matching brand information, product information, applicable population information and related holiday information; the database stores rich brand information, product feature descriptions, portraits of different populations and various holiday theme characteristics and other data.

[0042] Large-model agents are then used to assign and summarize tasks. Each match is considered a matching task, and each matching task is assigned to a large-model agent. These large-model agents leverage advanced natural language processing technology and deep learning algorithms to analyze, understand, and summarize the information they are responsible for. For example, the agent responsible for brand information will extract key content such as the brand's core values ​​and brand style; the agent responsible for product information will sort out the product's features, advantages, and other characteristics.

[0043] After each large model agent completes the induction, the processing results will be summarized and the final output is a marketing copy that conforms to the brand style, highlights the product selling points, and fits the marketing festival atmosphere; that is, after each large model agent completes the induction, the processing results will be summarized and the main model will integrate and optimize them, and the final output is a marketing copy that conforms to the brand style, highlights the product selling points, and fits the marketing festival atmosphere.

[0044] In this embodiment, in step S102, the image generation pipeline is started for processing, which specifically includes:

[0045] Vector matching;

[0046] Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

[0047] During implementation, vector matching: The image generation pipeline matches the tags of the base model, LoRa (low-rank adaptive), the image population, and the image environment based on the user input. The base model provides the infrastructure and common features for image generation; LoRa technology fine-tunes the base model to improve the match between the generated image and the input information; the tags of the image population and image environment are used to accurately locate the image subject and scene elements. For example, if information about Valentine's Day, chocolate products, and a high-end brand is entered, vector information corresponding to the Valentine's Day atmosphere, chocolate-related information, the brand style, and the appropriate consumer group will be matched.

[0048] Image Generation: Through the image generation pipeline, the matching vector information drives the image generation algorithm to generate images that closely match the input information. The generated images reflect the product characteristics, brand image, and festive atmosphere in terms of content, style, and color.

[0049] In this embodiment, when making the layout, the aesthetic principles and visual communication principles are also followed to ensure that the text and pictures are coordinated with each other, highlight the marketing focus, and enhance the attractiveness of the pictures and the information communication effect.

[0050] Specifically, the typesetting algorithm will rationally arrange the position, size, color and other elements of text and pictures, and finally generate a complete marketing picture; the typesetting algorithm will ensure the coordination of text and pictures based on aesthetic principles and visual communication principles, highlight marketing focus, and enhance the attractiveness of the picture and the effectiveness of information communication.

[0051] The layout algorithm follows pre-set rules. It inputs the location information of items in advance, and then uses layout rules to layout the images. These rules include text length, the size and relative positioning of logos and text, and the information about objects and people in the image to prevent the text from obscuring important information.

[0052] The aesthetic principles include symmetry and balance, proportion and scale, harmony and contrast, and analogy and association;

[0053] The principles of visual communication refer to conveying specific information, emotions, and meaning through the effective combination of visual elements. This includes the comprehensive use of elements such as shape, color, material, and typography, as well as in-depth research into visual psychology, visual habits, and aesthetic principles. The goal is to make design works highly recognizable, attractive, and persuasive, thereby achieving effective communication.

[0054] On the basis of the above technical solution, in order to improve the accuracy of image generation, the method further includes:

[0055] When the vectors are matched, they are also matched based on reverse prompt words.

[0056] That is, when AIGC draws pictures, it not only needs positive prompt words, but also needs to provide reverse prompt words to indicate undesirable features; for example, draw a girl: a girl; by providing reverse prompt words, it indicates undesirable features; for example: six fingers.

[0057] It should be noted that when generating the copy, you can also select the font, determine the layout of the main title and subtitle, and determine the font color before merging and outputting.

[0058] The above solution, by utilizing the text generation pipeline, image generation pipeline, and large model agent, can automatically generate high-quality marketing copy and matching images based on the product name, brand name, and marketing holiday information entered by the user, and synthesize the two into marketing images. It can be widely used in scenarios such as advertising, e-commerce promotion, and social media marketing; thus overcoming the shortcomings of existing technologies that cannot simultaneously and efficiently complete the generation and synthesis of copy and images, making it difficult to meet the diverse and personalized marketing needs of enterprises.

[0059] Based on the same inventive concept, Figure 2 The embodiment of the present invention further provides a marketing image generation system based on an AI automatic pipeline, which is applied to a server. The server is deployed with a text generation pipeline, an image generation pipeline, and a large model agent. The system includes:

[0060] Input module, used to obtain prompt information input by the user;

[0061] Processing module for:

[0062] In response to the prompt information, the copy generation pipeline is started to perform processing to realize automatic generation of marketing copy;

[0063] In response to the prompt information, the image generation pipeline is started to perform processing to generate an image that highly matches the prompt information;

[0064] The merging module is used to merge the generated marketing copy with the picture, layout the text and picture through a preset typesetting algorithm, and finally generate a complete marketing picture.

[0065] During implementation, the image generation pipeline is started for processing, specifically including:

[0066] Vector matching;

[0067] Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

[0068] In this embodiment, starting the image generation pipeline for processing specifically includes:

[0069] Vector matching;

[0070] Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

[0071] Furthermore, to improve the accuracy of image generation, the processing module is further configured to:

[0072] When matching vectors, it is also based on reverse prompt word matching.

[0073] It should be noted that for a more specific description of the workflow of the system embodiment, please refer to the aforementioned method embodiment part, which will not be repeated here.

[0074] The entire solution, by building a text generation pipeline and an image generation pipeline, implements an automated process from user input to the generation of complete marketing images. It automatically generates high-quality marketing copy and matching images, and then combines the two into marketing images. It is widely used in scenarios such as advertising, e-commerce promotion, and social media marketing.

[0075] An embodiment of the present invention further provides a storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the method embodiment.

[0076] It should be understood that in the embodiments of the present invention, the processor is used to run or execute the operating system, various software programs, and its own instruction set stored in the internal memory. The processor may include, but is not limited to, one or more of a central processing unit (CPU), a general-purpose graphics processing unit (GPU), a microprocessor (MCU), a digital signal processor (DSP), a field programmable gate array (FPGA), and an application-specific integrated circuit (ASIC).

[0077] The computer-readable storage medium may include a cache, a high-speed random access memory (RAM), such as the common double data rate synchronous dynamic random access memory (DDR SDRAM), and may also include a non-volatile memory (NVRAM), such as one or more read-only memories (ROMs), disk storage devices, flash memory devices, or other non-volatile solid-state memory devices such as optical disks (CD-ROMs, DVD-ROMs), floppy disks or data tapes.

[0078] Those skilled in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0079] In the several embodiments provided in this application, it should be understood that the described systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple modules or components into another system, or ignoring or not implementing certain features.

[0080] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional modules.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A marketing picture generation method based on AI automatic pipeline, characterized in that: Applied to a server, wherein a text generation pipeline, an image generation pipeline, and a large model agent are deployed in the server; the method includes: Get the prompt information entered by the user; In response to the prompt information, the copy generation pipeline is started to perform processing to realize automatic generation of marketing copy; In response to the prompt information, the image generation pipeline is started to perform processing to generate an image that highly matches the prompt information; The generated marketing copy is then merged with the picture, and the text and picture are laid out through a preset typesetting algorithm to finally generate a complete marketing picture.

2. The method according to claim 1, wherein The bioprocessing pipeline is processed, specifically including: Based on the prompt information, automatically matching brand information, product information, applicable population information and related holiday information in the database; Then use the large model agent to perform task allocation and induction; After each large model agent completes the summary, the processing results will be summarized and the final output will be marketing copy that conforms to the brand style, highlights the product selling points, and fits the marketing festival atmosphere.

3. The method according to claim 1 or 2, wherein: Starting the image generation pipeline for processing, specifically including: Vector matching; Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

4. The method according to claim 3, wherein When making the layout, we also follow the principles of aesthetics and visual communication to ensure that the text and pictures are coordinated with each other, highlight the marketing focus, and enhance the attractiveness of the pictures and the effectiveness of information communication.

5. The method according to claim 4, wherein The method further comprises: When the vectors are matched, they are also matched based on reverse prompt words.

6. A marketing picture generation system based on AI automatic pipeline, characterized by: Applied to a server, the server is deployed with a text generation pipeline, an image generation pipeline, and a large model agent; the system includes: Input module, used to obtain prompt information input by the user; Processing module for: In response to the prompt information, the copy generation pipeline is started to perform processing to realize automatic generation of marketing copy; In response to the prompt information, the image generation pipeline is started to perform processing to generate an image that highly matches the prompt information; The merging module is used to merge the generated marketing copy with the picture, layout the text and picture through a preset typesetting algorithm, and finally generate a complete marketing picture.

7. The system according to claim 6, wherein: Starting the image generation pipeline for processing, specifically including: Vector matching; Through the image generation pipeline, the matching vector information is used to drive the image generation algorithm to generate an image that highly matches the prompt information.

8. The system according to claim 7, wherein: The processing module is further configured to: When matching vectors, it is also based on reverse prompt word matching.

9. A storage medium storing a computer program, wherein the computer program includes program instructions, characterized in that: When the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5 .

Images