An ai image generation method and system based on image analysis and language description

Abstract

The application provides an AI image generation method based on image analysis and language description, comprising the following steps: S1: calling an image analysis module, and performing multi-scale edge detection and refined semantic region segmentation on an input reference image through a contour extraction unit subordinate to the module. The AI image generation method based on image analysis and language description provided by the application does not need to perform secondary generation on the whole image, guarantees the overall generation efficiency, greatly improves the accuracy of detail performance, can significantly improve the directional controllability and result accuracy of AI image generation, and the generation result has visual structure consistency with the reference image and semantic matching degree with the language description, effectively reduces the workload and time cost of manual retouching in the later stage, can adapt to the use scene of high-precision directional generation, and meets the higher use requirements for generation accuracy and controllability in the fields of design creation, scheme visualization, customized material production and the like.

Description

Technical Field

[0001] This invention relates to the field of AI image generation technology, and in particular to an AI image generation method and system based on image analysis and language description. Background Technology

[0002] AI image generation relies on large deep learning models trained on massive amounts of text and image data. After receiving input instructions such as text descriptions, sketches, or reference images, it autonomously infers pixel colors, composition, and details to generate entirely new images. This technology constructs the logic of the image through algorithms such as diffusion models and generative adversarial networks. It can produce various image content such as illustrations, real-life images, and design renderings without manual hand-drawing. It can quickly iterate and modify image elements, significantly shortening the image creation cycle. It is widely used in design, cultural and creative industries, film and television, and publicity, lowering the threshold for visual content creation.

[0003] AI-powered image generation, based on image analysis and language description, leverages a multimodal large model to integrate image parsing and text understanding capabilities. It can simultaneously receive reference images and text instructions. The model first extracts visual features such as composition, lighting, and texture from the original image, then analyzes the style, elements, and color requirements in the language description. It integrates and processes these two types of information, and uses a diffusion algorithm to reconstruct the image. This process preserves the basic structure of the original image while modifying details according to the text, generating a new image that meets both needs. It is commonly used in product design, scene redrawing, and creative image retouching, effectively improving the accuracy of image creation.

[0004] In existing technologies, most generation methods operate independently, and the fusion of image analysis and language description is limited to simple feature stitching. It cannot achieve bidirectional verification and dynamic correction of feature dimensions. When there is semantic deviation between the text description and the reference image, the generated results are prone to problems such as subject misalignment, attribute conflict, and detail distortion. At the same time, it is impossible to perform targeted fine-tuning of local features during the generation process, resulting in insufficient generation accuracy and controllability, which makes it difficult to meet the needs of high-precision targeted generation.

[0005] Therefore, it is necessary to provide an AI image generation method and system based on image analysis and language description to solve the above-mentioned technical problems. Summary of the Invention

[0006] This invention provides an AI image generation method and system based on image analysis and language description, which solves the problems of existing image generation methods that only involve simple feature stitching for image-text fusion, lack bidirectional verification and dynamic correction, and have insufficient generation accuracy and controllability.

[0007] To address the aforementioned technical problems, the present invention provides an AI image generation method and system based on image analysis and language description, comprising the following steps:

[0008] S1: Call the image parsing module. First, the contour extraction unit under the module performs multi-scale edge detection and fine semantic region segmentation on the input reference image to accurately extract the main contour, spatial layout and scene hierarchy information in the image. Then, the texture encoding unit performs layered encoding on the color distribution, material texture and light and shadow features of each segmented region to finally obtain a structured image feature set and complete the full-dimensional visual information decomposition of the reference image.

[0009] S2: Call the semantic encoding module, perform fine-grained word segmentation and semantic entity annotation on the input language description text through the subordinate word segmentation mapping unit, extract the core semantic elements, and then map each element to a unified feature space through the semantic alignment unit to generate a structured semantic feature vector group, thus completing the standardized encoding of text information.

[0010] S3: Call the feature fusion module, and use the cross-validation unit under the module to perform dimension-by-dimensional matching and verification of the image feature set and the semantic feature vector group to accurately identify the specific feature dimensions and corresponding degree of semantic deviation between the two. Then, the weight allocation unit assigns fusion weights to each dimension feature according to the verification results, corrects the weights of the deviation dimensions, and generates the fused directional feature vector.

[0011] S4: Call the generation iteration module, and through the subordinate step-by-step sampling unit, use the fused directional generation feature vector as the core constraint to perform multi-step progressive diffusion sampling, gradually generate image contours, basic colors and preliminary details, and obtain the initial generated image. At the same time, record the sampling parameters and intermediate states of each step to provide data support for subsequent corrections.

[0012] S5: Call the local correction unit under the generation iteration module, accurately locate the local region corresponding to the deviation in the initial generated image based on the deviation feature dimension obtained by cross-validation, perform feature resampling and detail optimization adjustment on the region, correct the main attributes, texture performance and lighting effects of the local region, and obtain the generated image to be verified after correction, eliminating the generation error caused by semantic deviation of input information.

[0013] S6: Call the quality verification module, and use the subordinate semantic matching unit to perform full-dimensional semantic consistency verification between the generated image to be verified and the original language description. Then, use the visual consistency unit to perform visual feature matching degree verification between the generated image and the original reference image. If both verifications meet the preset threshold, the image is deemed qualified; otherwise, return to the corresponding step for readjustment.

[0014] S7: Call the result output module to perform format standardization and detail clarity optimization on the generated image that has passed the verification. Simultaneously output the feature parameters, verification data and adjustment records generated this time to form a complete and traceable result data package and complete the entire generation process.

[0015] An AI image generation method based on image analysis and language description further includes the following steps:

[0016] S8: Call the image basis construction module, and use the subordinate basis feature extraction unit to perform full-image semantic parsing and core framework extraction on the input reference image, separate the three types of core basis features: main structure, scene composition and basic tone, and then independently standardize and encode them through the basis encoding unit to generate a unique basis feature constraint set as the underlying visual constraint basis for the entire generation process.

[0017] S9: Call the semantic instruction parsing module, decompose the language description text and mark the semantic entities through the instruction word segmentation unit, extract various modification instructions, and then divide them into three levels according to the stage adaptability through the instruction hierarchical unit to generate a hierarchical semantic instruction set.

[0018] S10: Call the phased generation module, and use the base feature constraint set as the core constraint through the subordinate frame generation unit. Combined with the frame-level semantic instructions, execute the first-stage generation operation to generate an initial frame image containing the main outline, scene layout and basic color tone, and strictly lock the base structure to prevent it from shifting.

[0019] S11: Call the stage feedback calibration module, compare the structural consistency between the initial frame image and the basis constraint set through the feature comparison unit, calculate the deviation value, and then generate the calibrated attribute generation parameter group through the parameter adjustment unit, and pass it to the next stage;

[0020] S12: Call the attribute generation unit under the phased generation module, based on the calibrated attribute generation parameter group, and combine attribute-level semantic instructions to perform the second-stage generation operation, fill the main attributes, replace scene elements and adjust the tone style of the initial frame image, and generate the intermediate attribute image.

[0021] S13: Call the stage feedback calibration module, compare the attribute matching degree between the intermediate attribute image and the hierarchical semantic instruction set through the subordinate feature comparison unit, obtain the attribute deviation data, and then generate the calibrated detail generation parameter group through the parameter adjustment unit.

[0022] S14: Call the detail generation unit under the phased generation module, and based on the calibrated detail generation parameter group, combine the detail-level semantic instructions to perform the third-stage operation, perform texture refinement, lighting optimization and edge smoothing on the intermediate attribute image, and generate the image to be verified.

[0023] S15: Call the hierarchical verification module, and use the subordinate basis verification unit to perform full-dimensional basis consistency verification between the image to be verified and the basis feature constraint set. Then, use the instruction verification unit to perform instruction matching degree verification. If both meet the preset threshold, it is deemed qualified. If it does not meet the standard, return to the corresponding stage for recalibration.

[0024] An AI image generation system based on image analysis and language description includes: an image parsing module, a semantic encoding module, a feature fusion module, a generation iteration module, a quality verification module, and a result output module.

[0025] Preferably, the image parsing module includes a contour extraction unit and a texture encoding unit, and the semantic encoding module includes a word segmentation mapping unit and a semantic alignment unit.

[0026] Preferably, the feature fusion module includes a cross-validation unit and a weight allocation unit, and the generation iteration module includes a step-by-step sampling unit and a local correction unit;

[0027] The cross-validation unit compares the two types of features dimension by dimension to identify the degree of deviation. The weight allocation unit adjusts the feature weights accordingly and outputs the generated fused features. The step-by-step sampling unit builds the image framework, and the local correction unit corrects the deviation areas in a targeted manner to ensure the generation accuracy and controllability.

[0028] Preferably, the quality verification module includes a semantic matching unit and a visual consistency unit, and the result output module includes a format optimization unit and a data archiving unit;

[0029] The semantic matching unit verifies the semantic fit between the generated image and the language description, the visual consistency unit compares the visual feature matching between the generated image and the reference image, thus ensuring double quality control of the output. The format optimization unit unifies the image format and optimizes the clarity, and the data archiving unit retains the generation parameters and verification records, ensuring that the results are standardized and the generation process is traceable.

[0030] An AI image generation system based on image analysis and language description further includes: an image substrate construction module, a semantic instruction parsing module, a phased generation module, a phased feedback calibration module, and a hierarchical verification module.

[0031] Preferably, the image basis construction module includes a basis feature extraction unit and a basis encoding unit, and the semantic instruction parsing module includes an instruction word segmentation unit and an instruction hierarchical unit.

[0032] Preferably, the phased generation module includes a framework generation unit, an attribute generation unit, and a detail generation unit;

[0033] The frame generation unit is responsible for the first stage of generation operations, generating an initial frame image that includes the main outline, scene layout, and basic color tone. The attribute generation unit is responsible for the second stage of generation operations, completing the main attribute filling, scene element replacement, and color tone style adjustment. The detail generation unit is responsible for the third stage of generation operations, completing texture refinement, lighting and shadow optimization, and edge smoothing.

[0034] Preferably, the stage feedback calibration module includes a feature comparison unit and a parameter adjustment unit, and the hierarchical verification module includes a base verification unit and an instruction verification unit.

[0035] Compared with related technologies, the AI ​​image generation method and system based on image analysis and language description provided by this invention have the following beneficial effects:

[0036] This invention provides an AI image generation method based on image analysis and language description. This method, through a complete technical architecture of parallel dual-path feature processing and cross-validation fusion, effectively solves the problems of shallow fusion of image analysis and language description, lack of bidirectional verification mechanism, semantic conflicts in generated results, and inability to directionally adjust local features in existing technologies. Existing technologies often use simple splicing to fuse the two types of features, lacking corresponding verification mechanisms for feature dimensions. This makes it difficult to simultaneously consider the structural inheritance of the reference image and the attribute customization of the text description. The generation process is prone to problems such as misalignment of subject categories, inconsistent attribute descriptions, and deviations in detail representation, often requiring multiple adjustments to input parameters for regeneration. Overall, the generation efficiency and usability of the results are low. This invention establishes a dimension-wise bidirectional fusion of visual and semantic features. The verification mechanism actively identifies and corrects semantic deviations between the two types of input information during the feature fusion stage, reducing semantic conflicts from the generation source and avoiding directional deviations in the generated results. Simultaneously, a local directional correction mechanism allows for precise adjustments to deviation areas without requiring secondary generation of the entire image. This significantly improves the accuracy of detail representation while maintaining overall generation efficiency. It substantially enhances the directional controllability and accuracy of AI image generation, ensuring that the generated results possess both visual structural consistency with the reference image and semantic matching of the language description. This effectively reduces the workload and time cost of manual image retouching in the later stages, making it suitable for high-precision directional generation scenarios and meeting the high demands for generation accuracy and controllability in fields such as design creation, scheme visualization, and customized material production. Attached Figure Description

[0037] Figure 1 A schematic diagram of a first embodiment of the AI ​​image generation system based on image analysis and language description provided by the present invention;

[0038] Figure 2 A schematic diagram of the image parsing module provided for this invention;

[0039] Figure 3 A schematic diagram of the semantic encoding module is provided for this invention;

[0040] Figure 4 A schematic diagram of the feature fusion module is provided for this invention;

[0041] Figure 5 A schematic diagram of the generation iteration module is provided for this invention;

[0042] Figure 6 A schematic diagram of the quality verification module provided for this invention;

[0043] Figure 7 A schematic diagram of the result output module provided for this invention;

[0044] Figure 8 A schematic diagram of a second embodiment of the AI ​​image generation system based on image analysis and language description provided by the present invention;

[0045] Figure 9 A schematic diagram of the image substrate construction module is provided for this invention;

[0046] Figure 10 A schematic diagram of the semantic instruction parsing module provided for this invention;

[0047] Figure 11 A schematic diagram of the phased generation module is provided for this invention;

[0048] Figure 12 A schematic diagram of the stage feedback calibration module provided for this invention;

[0049] Figure 13 A schematic diagram of the layered verification module provided for this invention. Detailed Implementation

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] An AI image generation method based on image analysis and language description includes the following steps:

[0052] S1: Call the image parsing module. First, the contour extraction unit under the module performs multi-scale edge detection and fine semantic region segmentation on the input reference image to accurately extract the main contour, spatial layout and scene hierarchy information in the image. Then, the texture encoding unit performs layered encoding on the color distribution, material texture and light and shadow features of each segmented region to finally obtain a structured image feature set and complete the full-dimensional visual information decomposition of the reference image.

[0053] S2: Call the semantic encoding module, perform fine-grained word segmentation and semantic entity annotation on the input language description text through the subordinate word segmentation mapping unit, extract the core semantic elements, and then map each element to a unified feature space through the semantic alignment unit to generate a structured semantic feature vector group, thus completing the standardized encoding of text information.

[0054] S3: Call the feature fusion module, and use the cross-validation unit under the module to perform dimension-by-dimensional matching and verification of the image feature set and the semantic feature vector group to accurately identify the specific feature dimensions and corresponding degree of semantic deviation between the two. Then, the weight allocation unit assigns fusion weights to each dimension feature according to the verification results, corrects the weights of the deviation dimensions, and generates the fused directional feature vector.

[0055] S4: Call the generation iteration module, and through the subordinate step-by-step sampling unit, use the fused directional generation feature vector as the core constraint to perform multi-step progressive diffusion sampling, gradually generate image contours, basic colors and preliminary details, and obtain the initial generated image. At the same time, record the sampling parameters and intermediate states of each step to provide data support for subsequent corrections.

[0056] S5: Call the local correction unit under the generation iteration module, accurately locate the local region corresponding to the deviation in the initial generated image based on the deviation feature dimension obtained by cross-validation, perform feature resampling and detail optimization adjustment on the region, correct the main attributes, texture performance and lighting effects of the local region, and obtain the generated image to be verified after correction, eliminating the generation error caused by semantic deviation of input information.

[0057] S6: Call the quality verification module, and use the subordinate semantic matching unit to perform full-dimensional semantic consistency verification between the generated image to be verified and the original language description. Then, use the visual consistency unit to perform visual feature matching degree verification between the generated image and the original reference image. If both verifications meet the preset threshold, the image is deemed qualified; otherwise, return to the corresponding step for readjustment.

[0058] S7: Call the result output module to perform format standardization and detail clarity optimization on the generated image that has passed the verification. Simultaneously output the feature parameters, verification data and adjustment records of this generation to form a complete and traceable result data package and complete the entire generation process.

[0059] During the process of extracting structured image feature sets by the image parsing module, unique spatial coordinate labels and visual attribute labels are generated simultaneously for each segmented semantic region, establishing a one-to-one mapping relationship between image regions and labels. This mapping relationship is synchronously transmitted to the feature fusion module along with the structured image feature set. When the cross-validation unit of the feature fusion module performs matching verification, a hierarchical matching mechanism is adopted. Priority is given to matching the category and spatial location of the main entity, followed by matching the features of the auxiliary attributes and the environmental scene. The deviation degree data of each dimension is output according to the matching priority. When the local correction unit of the generation iteration module performs region correction, only the feature vectors corresponding to the local regions obtained by deviation localization are resampled and adjusted. The generated feature parameters of non-deviation regions remain unchanged in their original sampling state and do not participate in the secondary correction calculation. When the quality verification module determines that the verification fails, deviation type identifier and parameter adjustment magnitude data are generated synchronously. The corresponding execution step is automatically matched according to the deviation type and execution is jumped, without the need for manual intervention to determine the adjustment node.

[0060] The working principle of the AI ​​image generation method based on image analysis and language description provided by this invention is as follows:

[0061] This method uses both image and language description as dual input conditions and relies on a dual-path parallel feature processing architecture to construct a collaborative generation logic. It aims to overcome the limitations of insufficient controllability and limited customization in single-input mode. On the visual information processing side, the image parsing module first performs hierarchical information decomposition and structured encoding on the reference image. The processing follows a progressive logic from macro to micro, first sorting out the spatial layout, subject boundaries, and other framework information of the image, then decomposing the detailed attributes such as color distribution, material texture, and light and shadow transitions in each region layer by layer. Finally, the discrete visual information is transformed into image feature data with unified dimensions and comparability, fully preserving the core visual characteristics of the reference image. Simultaneously on the semantic information processing side, the semantic encoding module performs semantic extraction and feature mapping on the input language description. Addressing the flexibility and ambiguity of natural language expression, it first completes the identification and attribute classification of semantic entities, then transforms the qualitative description into standardized feature vectors, enabling precise mapping of text semantics. The system enters the visual feature space, providing aligned semantic data support for subsequent feature matching and fusion. After the two feature data are generated, the feature fusion module performs cross-validation and dynamic weight allocation dimensionally. First, it establishes the dimensional correspondence between visual and semantic features, comparing the matching degree of the two types of features in dimensions such as subject category, spatial location, and attribute performance. Then, it dynamically adjusts the feature weights of each dimension according to the deviation, actively eliminating semantic deviations between input information. Finally, it forms a unified directional generated feature that takes into account both visual basis and semantic instructions. On this basis, the generation iteration module completes the image generation operation, following the generation path of first the whole and then the parts. First, it uses step-by-step sampling to build the overall framework and basic content of the image, and then performs local correction and detail optimization for specific areas of deviation location. After generation, the quality verification module performs double verification, judging whether the result meets the standards from the two dimensions of semantic consistency and visual matching degree. Finally, it outputs a qualified image, fully realizing the deep collaborative generation of image analysis and language description.

[0062] Compared with related technologies, the AI ​​image generation method based on image analysis and language description provided by this invention has the following beneficial effects:

[0063] This method effectively solves the problems of shallow fusion between image analysis and language description, lack of bidirectional verification mechanism, semantic conflicts in generated results, and inability to directionally adjust local features in existing technologies through a complete technical architecture of dual-path feature parallel processing and cross-validation fusion. Existing technologies often use simple concatenation to fuse the two types of features, lacking corresponding verification mechanisms for feature dimensions. This makes it difficult to simultaneously consider the structural inheritance of the reference image and the attribute customization of the text description. The generation process is prone to problems such as misalignment of subject categories, inconsistent attribute descriptions, and deviations in detail representation, often requiring multiple adjustments to input parameters for regeneration. Overall generation efficiency and result usability are low. This method establishes a dimension-wise bidirectional verification mechanism for visual and semantic features, actively... It identifies and corrects semantic discrepancies between two types of input information, reducing semantic conflicts from the generation source and avoiding directional deviations in the generated results. Combined with a local directional correction mechanism, it can precisely adjust the deviation areas without performing secondary generation on the entire image. While ensuring overall generation efficiency, it significantly improves the accuracy of detail representation, substantially enhancing the directional controllability and result accuracy of AI image generation. The generated results simultaneously possess visual structural consistency with the reference image and semantic matching of the language description, effectively reducing the workload and time cost of post-processing manual image editing. It is suitable for high-precision directional generation scenarios, meeting the high requirements for generation accuracy and controllability in fields such as design creation, scheme visualization, and customized material production.

[0064] An AI image generation method based on image analysis and language description also includes the following steps:

[0065] S8: Call the image basis construction module, and use the subordinate basis feature extraction unit to perform full-image semantic parsing and core framework extraction on the input reference image, separate the three types of core basis features: main structure, scene composition and basic tone, and then independently standardize and encode them through the basis encoding unit to generate a unique basis feature constraint set as the underlying visual constraint basis for the entire generation process.

[0066] S9: Call the semantic instruction parsing module, decompose the language description text and mark the semantic entities through the instruction word segmentation unit, extract various modification instructions, and then divide them into three levels according to the stage adaptability through the instruction hierarchical unit to generate a hierarchical semantic instruction set.

[0067] S10: Call the phased generation module, and use the base feature constraint set as the core constraint through the subordinate frame generation unit. Combined with the frame-level semantic instructions, execute the first-stage generation operation to generate an initial frame image containing the main outline, scene layout and basic color tone, and strictly lock the base structure to prevent it from shifting.

[0068] S11: Call the stage feedback calibration module, compare the structural consistency between the initial frame image and the basis constraint set through the feature comparison unit, calculate the deviation value, and then generate the calibrated attribute generation parameter group through the parameter adjustment unit, and pass it to the next stage;

[0069] S12: Call the attribute generation unit under the phased generation module, based on the calibrated attribute generation parameter group, and combine attribute-level semantic instructions to perform the second-stage generation operation, fill the main attributes, replace scene elements and adjust the tone style of the initial frame image, and generate the intermediate attribute image.

[0070] S13: Call the stage feedback calibration module, compare the attribute matching degree between the intermediate attribute image and the hierarchical semantic instruction set through the subordinate feature comparison unit, obtain the attribute deviation data, and then generate the calibrated detail generation parameter group through the parameter adjustment unit.

[0071] S14: Call the detail generation unit under the phased generation module, and based on the calibrated detail generation parameter group, combine the detail-level semantic instructions to perform the third-stage operation, perform texture refinement, lighting optimization and edge smoothing on the intermediate attribute image, and generate the image to be verified.

[0072] S15: Call the hierarchical verification module, and use the subordinate basis verification unit to perform full-dimensional basis consistency verification between the image to be verified and the basis feature constraint set. Then, use the instruction verification unit to perform instruction matching degree verification. If both meet the preset threshold, it is judged as qualified. If it does not meet the standard, it returns to the corresponding stage for recalibration.

[0073] When inputting reference images, it is necessary to first confirm that the main subject of the image is not heavily obscured and that the compositional boundaries are complete. Before extracting base features, the extraction and differentiation thresholds for three types of features—main structure, scene composition, and basic color tone—must be set in advance to avoid cross-contamination of different feature categories. When processing descriptive text, standardized written expressions must be used consistently, and ambiguous expressions within the text must be checked in advance. The instruction hierarchy must strictly match the operational scope of the three generation stages, and cross-level instruction calls are prohibited. Once the three generation stages have started, they cannot be forcibly interrupted midway. Intermediate images produced by each stage must be automatically cached locally. Both parameter calibration stages must follow the rule that the degree of deviation is positively correlated with the adjustment weight. When parameters are passed, a copy of the corresponding deviation data must be included simultaneously. In the final verification stage, independent pass thresholds must be set for the two types of verification. When the verification fails and returns, the valid operational parameters of the previous stage must be retained to avoid repeated execution of invalid operations.

[0074] The AI ​​image generation method based on image analysis and language description provided by this invention has the following beneficial effects:

[0075] The method uses a reference image as the generation base and language descriptions as hierarchical editing instructions. It employs a phased, progressive approach combined with inter-phase feedback calibration, differing from the conventional single-phase, one-time generation method. First, the image base construction module extracts and standardizes the core framework of the reference image, separating three core base features: main structure, scene composition, and basic color tone, forming a dedicated constraint set. This constraint set remains effective throughout the entire generation process, serving as the underlying visual constraint to ensure the core framework of the generated result does not undergo structural shifts. Simultaneously, the semantic instruction parsing module performs semantic decomposition and hierarchical classification of the input language description, identifying three types of instructions in the text: framework adjustment, attribute modification, and detail optimization. These are then divided into corresponding instruction sets according to the adaptability of the generation stage, avoiding feature conflicts and instruction failures caused by injecting all instructions at once. Subsequently, the phased generation module sequentially executes framework construction, ... The generation process involves three stages: attribute customization, detail optimization, and more. After each stage, a stage feedback calibration module compares the generation result with the corresponding constraints, calculating the deviation value and specific deviation dimension. Based on this, the weight, constraint strength, and scope of the next stage's instructions are dynamically adjusted, forming a closed-loop feedback adjustment mechanism between stages. After the entire generation process is completed, a layered verification module performs dual verification from two dimensions: base consistency and instruction matching degree. Once all indicators are confirmed to meet the standards, the final image is output, fully realizing a progressive and controllable layered generation process. This method effectively solves the problems of insufficient flexibility in the single-stage, one-time generation mode of existing technologies, inability to match constraint strength according to the generation stage, lack of inter-stage feedback linkage, and weak layered detail optimization capabilities. It significantly improves the adjustability and controllability of the generation process and the effect of layered detail optimization, adapting to the usage requirements of progressive customized generation.

[0076] First Embodiment

[0077] Please refer to the following: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 ,in, Figure 1 A schematic diagram of a first embodiment of the AI ​​image generation system based on image analysis and language description provided by the present invention; Figure 2 A schematic diagram of the image parsing module provided for this invention; Figure 3 A schematic diagram of the semantic encoding module is provided for this invention; Figure 4 A schematic diagram of the feature fusion module is provided for this invention; Figure 5 A schematic diagram of the generation iteration module is provided for this invention; Figure 6 A schematic diagram of the quality verification module provided for this invention; Figure 7This diagram illustrates the result output module for this invention. An AI image generation system based on image analysis and language description includes: an image parsing module, a semantic encoding module, a feature fusion module, a generation iteration module, a quality verification module, and a result output module.

[0078] Six modules work together to complete the entire AI image generation process under dual input conditions. The image parsing module is responsible for decomposing the visual information such as the contour layout, texture and color of the reference image and completing the structured encoding, providing a stable visual reference for the generation process. The semantic encoding module performs semantic extraction and feature space mapping on the input language description, transforming natural language into standardized feature instructions that the generation system can recognize. The feature fusion module performs cross-validation and dynamic weight allocation on the two types of features, eliminating semantic bias of the input information from the feature level. The generation iteration module completes image generation and local correction step by step, taking into account both overall efficiency and detail restoration accuracy. The quality verification module controls the output quality from both semantic and visual dimensions. The result output module completes image optimization and process data archiving. The entire set of modules works together to ensure the accuracy and controllability of the generated results.

[0079] Please refer to Figure 2 and Figure 3 The image parsing module includes a contour extraction unit and a texture encoding unit, and the semantic encoding module includes a word segmentation mapping unit and a semantic alignment unit;

[0080] The contour extraction unit extracts the main contour layout of the image through edge detection and semantic segmentation, and anchors the generation benchmark. The texture encoding unit encodes the color, material and light and shadow of each region in layers, and converts visual details into matching feature data. The word segmentation and mapping unit completes text segmentation and entity annotation, and extracts core semantic elements. The semantic alignment unit maps the semantic elements to a unified feature space and completes the vectorization conversion.

[0081] Please refer to Figure 4 and Figure 5 The feature fusion module includes a cross-validation unit and a weight allocation unit, and the generation iteration module includes a step-by-step sampling unit and a local correction unit.

[0082] The cross-validation unit compares the two types of features dimension by dimension to identify the degree of deviation. The weight allocation unit adjusts the feature weights accordingly and outputs the generated fused features. The step-by-step sampling unit builds the image framework, and the local correction unit corrects the deviation areas in a targeted manner to ensure the generation accuracy and controllability.

[0083] Please refer to Figure 6 and Figure 7 The quality verification module includes a semantic matching unit and a visual consistency unit, and the result output module includes a format optimization unit and a data archiving unit.

[0084] The semantic matching unit verifies the semantic fit between the generated image and the language description, the visual consistency unit compares the visual feature matching between the generated image and the reference image, thus ensuring double quality control of the output. The format optimization unit unifies the image format and optimizes the clarity, and the data archiving unit retains the generation parameters and verification records, ensuring that the results are standardized and the generation process is traceable.

[0085] Compared with related technologies, the AI ​​image generation system based on image analysis and language description provided by this invention has the following beneficial effects:

[0086] This system, with feature pre-fusion, bias pre-verification, and single-round generation correction as its core logic, comprises six functional modules to build a complete closed-loop generation system. During actual operation, the image parsing module decomposes the visual information of the reference image and completes structured encoding; the semantic encoding module simultaneously processes the language description, completing semantic extraction and feature space mapping; and outputs two types of feature data with mutually matching dimensions in parallel. The feature fusion module performs dimension-wise cross-verification and dynamic weight allocation on the two types of features, eliminating semantic biases in the input information before generation and forming unified directional generation features. The generation iteration module builds the image foundation through step-by-step sampling and optimizes bias details through local correction to complete the core generation. The quality verification module verifies the results from both semantic and visual dimensions. Finally, the result output module completes the standardized image output and process data archiving. This solution places bias verification before feature fusion, effectively reducing large-scale rework after generation and ensuring both generation accuracy and controllability within a single generation cycle, making it adaptable to various high-precision directional generation application scenarios.

[0087] Second Embodiment

[0088] Please refer to the following: Figures 8-9 - Figures 10-11 - Figures 12-13 , Figure 8 A schematic diagram of a second embodiment of the AI ​​image generation system based on image analysis and language description provided by the present invention; Figure 9 A schematic diagram of the image substrate construction module is provided for this invention; Figure 10 A schematic diagram of the semantic instruction parsing module provided for this invention; Figure 11 A schematic diagram of the phased generation module is provided for this invention; Figure 12 A schematic diagram of the stage feedback calibration module provided for this invention; Figure 13 This invention provides a schematic diagram of a hierarchical verification module. Based on the AI ​​image generation system based on image analysis and language description provided in the first embodiment of this application, the second embodiment of this application proposes another AI image generation system based on image analysis and language description. The second embodiment is merely a preferred embodiment of the first embodiment, and the implementation of the second embodiment will not affect the separate implementation of the first embodiment.

[0089] Specifically, the difference between the AI ​​image generation system based on image analysis and language description provided in the second embodiment of this application and the AI ​​image generation system based on image analysis and language description further includes: an image substrate construction module, a semantic instruction parsing module, a phased generation module, a phased feedback calibration module, and a hierarchical verification module.

[0090] The image basis construction module extracts the core basis features of the reference image and encodes them into a constraint set that is effective throughout the process, ensuring that the generated structure baseline does not shift. The semantic instruction parsing module decomposes the language description and completes the instruction classification according to the adaptability of the generation stage, matching instructions for each stage. The phased generation module performs generation operations in three progressive layers: framework, attributes, and details, gradually improving the image content. The phase feedback calibration module compares the generation deviation after each stage and dynamically adjusts the parameters for the next stage, forming a closed-loop mechanism. The layered verification module verifies the results from two dimensions: basis consistency and instruction matching degree, controlling the final image output quality.

[0091] Please refer to Figure 9 and Figure 10 The image basis construction module includes a basis feature extraction unit and a basis encoding unit, and the semantic instruction parsing module includes an instruction word segmentation unit and an instruction hierarchical unit;

[0092] The basic feature extraction unit is responsible for the semantic parsing of the whole image, separating the three core basic features: main structure, scene composition, and basic color tone. The basic encoding unit is responsible for independently standardizing and encoding the three basic features to generate a unique basic feature constraint set. The instruction segmentation unit is responsible for text semantic decomposition and entity annotation, extracting instruction content such as attribute modification, scene adjustment, and detail optimization. The instruction hierarchical unit is responsible for dividing the instructions into three levels: frame level, attribute level, and detail level according to the adaptability of the generation stage.

[0093] Please refer to Figure 11 The phased generation module includes a framework generation unit, an attribute generation unit, and a detail generation unit;

[0094] The frame generation unit is responsible for the first stage of generation operations, generating an initial frame image that includes the main outline, scene layout, and basic color tone. The attribute generation unit is responsible for the second stage of generation operations, completing the main attribute filling, scene element replacement, and color tone style adjustment. The detail generation unit is responsible for the third stage of generation operations, completing texture refinement, lighting and shadow optimization, and edge smoothing.

[0095] Please refer to Figure 12 and Figure 13 The stage feedback calibration module includes a feature comparison unit and a parameter adjustment unit, and the hierarchical verification module includes a base verification unit and an instruction verification unit.

[0096] The feature comparison unit is responsible for comparing the generated results of the current stage with the corresponding constraints and calculating the deviation value or deviation data. The parameter adjustment unit is responsible for dynamically adjusting the weight, constraint strength and scope of the next stage instruction based on the deviation results. The basis verification unit is responsible for verifying the basis consistency between the generated image and the basis feature constraint set. The instruction verification unit is responsible for verifying the instruction matching degree between the generated image and the hierarchical semantic instruction set.

[0097] Compared with related technologies, the AI ​​image generation system based on image analysis and language description provided by this invention has the following beneficial effects:

[0098] This system is based on the core logic of full-process base locking, hierarchical instruction delivery, and stage feedback calibration. It relies on five functional modules to build a hierarchical and progressive generation system. During operation, the image base construction module extracts core base features from the reference image and encodes them to form a constraint set, maintaining the structural baseline without deviation throughout the entire generation process. The semantic instruction parsing module simultaneously decomposes the language description into hierarchical instructions adapted to different generation stages and delivers them layer by layer according to the generation rhythm. The stage generation module completes the three-stage operation of framework building, attribute customization, and detail optimization in sequence. After each stage is completed, the stage feedback calibration module compares the deviation between the generation result and the corresponding constraint and dynamically adjusts the instruction parameters of the next stage. Finally, the result is output after double verification by the hierarchical verification module. This scheme achieves dynamic control of the generation process through closed-loop feedback between stages, effectively making up for the lack of flexibility in single-stage generation, adapting to progressive customized generation scenarios, and is logically independent and complementary to the feature pre-fusion verification scheme.

[0099] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An AI image generation method based on image analysis and language description, characterized in that, Includes the following steps: S1: Call the image parsing module. First, the contour extraction unit under the module performs multi-scale edge detection and fine semantic region segmentation on the input reference image to accurately extract the main contour, spatial layout and scene hierarchy information in the image. Then, the texture encoding unit performs layered encoding on the color distribution, material texture and light and shadow features of each segmented region to finally obtain a structured image feature set and complete the full-dimensional visual information decomposition of the reference image. S2: Call the semantic encoding module, perform fine-grained word segmentation and semantic entity annotation on the input language description text through the subordinate word segmentation mapping unit, extract the core semantic elements, and then map each element to a unified feature space through the semantic alignment unit to generate a structured semantic feature vector group, thus completing the standardized encoding of text information. S3: Call the feature fusion module, and use the cross-validation unit under the module to perform dimension-by-dimensional matching and verification of the image feature set and the semantic feature vector group to accurately identify the specific feature dimensions and corresponding degree of semantic deviation between the two. Then, the weight allocation unit assigns fusion weights to each dimension feature according to the verification results, corrects the weights of the deviation dimensions, and generates the fused directional feature vector. S4: Call the generation iteration module, and through the subordinate step-by-step sampling unit, use the fused directional generation feature vector as the core constraint to perform multi-step progressive diffusion sampling, gradually generate image contours, basic colors and preliminary details, and obtain the initial generated image. At the same time, record the sampling parameters and intermediate states of each step to provide data support for subsequent corrections. S5: Call the local correction unit under the generation iteration module, accurately locate the local region corresponding to the deviation in the initial generated image based on the deviation feature dimension obtained by cross-validation, perform feature resampling and detail optimization adjustment on the region, correct the main attributes, texture performance and lighting effects of the local region, and obtain the generated image to be verified after correction, eliminating the generation error caused by semantic deviation of input information. S6: Call the quality verification module, and use the subordinate semantic matching unit to perform full-dimensional semantic consistency verification between the generated image to be verified and the original language description. Then, use the visual consistency unit to perform visual feature matching degree verification between the generated image and the original reference image. If both verifications meet the preset threshold, the image is deemed qualified; otherwise, return to the corresponding step for readjustment. S7: Call the result output module to perform format standardization and detail clarity optimization on the generated image that has passed the verification. Simultaneously output the feature parameters, verification data and adjustment records generated this time to form a complete and traceable result data package and complete the entire generation process.

2. The AI ​​image generation method based on image analysis and language description according to claim 1, characterized in that, It also includes the following steps: S8: Call the image basis construction module, and use the subordinate basis feature extraction unit to perform full-image semantic parsing and core framework extraction on the input reference image, separate the three types of core basis features: main structure, scene composition and basic tone, and then independently standardize and encode them through the basis encoding unit to generate a unique basis feature constraint set as the underlying visual constraint basis for the entire generation process. S9: Call the semantic instruction parsing module, decompose the language description text and mark the semantic entities through the instruction word segmentation unit, extract various modification instructions, and then divide them into three levels according to the stage adaptability through the instruction hierarchical unit to generate a hierarchical semantic instruction set. S10: Call the phased generation module, and use the base feature constraint set as the core constraint through the subordinate frame generation unit. Combined with the frame-level semantic instructions, execute the first-stage generation operation to generate an initial frame image containing the main outline, scene layout and basic color tone, and strictly lock the base structure to prevent it from shifting. S11: Call the stage feedback calibration module, compare the structural consistency between the initial frame image and the basis constraint set through the feature comparison unit, calculate the deviation value, and then generate the calibrated attribute generation parameter group through the parameter adjustment unit, and pass it to the next stage; S12: Call the attribute generation unit under the phased generation module, based on the calibrated attribute generation parameter group, and combine attribute-level semantic instructions to perform the second-stage generation operation, fill the main attributes, replace scene elements and adjust the tone style of the initial frame image, and generate the intermediate attribute image. S13: Call the stage feedback calibration module, compare the attribute matching degree between the intermediate attribute image and the hierarchical semantic instruction set through the subordinate feature comparison unit, obtain the attribute deviation data, and then generate the calibrated detail generation parameter group through the parameter adjustment unit. S14: Call the detail generation unit under the phased generation module, and based on the calibrated detail generation parameter group, combine the detail-level semantic instructions to perform the third-stage operation, perform texture refinement, lighting optimization and edge smoothing on the intermediate attribute image, and generate the image to be verified. S15: Call the hierarchical verification module, and use the subordinate basis verification unit to perform full-dimensional basis consistency verification between the image to be verified and the basis feature constraint set. Then, use the instruction verification unit to perform instruction matching degree verification. If both meet the preset threshold, it is deemed qualified. If it does not meet the standard, return to the corresponding stage for recalibration.

3. An AI image generation system based on image analysis and language description, characterized in that, include: The system includes an image parsing module, a semantic encoding module, a feature fusion module, a generation iteration module, a quality verification module, and a result output module.

4. The AI ​​image generation system based on image analysis and language description according to claim 3, characterized in that, The image parsing module includes a contour extraction unit and a texture encoding unit, and the semantic encoding module includes a word segmentation mapping unit and a semantic alignment unit.

5. The AI ​​image generation system based on image analysis and language description according to claim 3, characterized in that, The feature fusion module includes a cross-validation unit and a weight allocation unit, and the generation iteration module includes a step-by-step sampling unit and a local correction unit.

6. The AI ​​image generation system based on image analysis and language description according to claim 3, characterized in that, The quality verification module includes a semantic matching unit and a visual consistency unit, and the result output module includes a format optimization unit and a data archiving unit.

7. The AI ​​image generation system based on image analysis and language description according to claim 3, characterized in that, Also includes: The system includes an image substrate construction module, a semantic instruction parsing module, a phased generation module, a phased feedback calibration module, and a layered verification module.

8. The AI ​​image generation system based on image analysis and language description according to claim 7, characterized in that, The image basis construction module includes a basis feature extraction unit and a basis encoding unit, and the semantic instruction parsing module includes an instruction word segmentation unit and an instruction hierarchical unit.

9. The AI ​​image generation system based on image analysis and language description according to claim 7, characterized in that, The phased generation module includes a framework generation unit, an attribute generation unit, and a detail generation unit.

10. The AI ​​image generation system based on image analysis and language description according to claim 7, characterized in that, The stage feedback calibration module includes a feature comparison unit and a parameter adjustment unit, and the hierarchical verification module includes a base verification unit and an instruction verification unit.

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