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GPT Image 2 Relationship Graph Infographic: Network Data Visualization
image
GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 Relationship Graph Infographic: Network Data Visualization

Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not...

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Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not a regular poster or a simple illustration, but a modular popular science infographic that possesses a sense of "illustration book, encyclopedia, information structure, and collectability." The overall style should reference a combination of high-end natural history illustrations, modern encyclopedia pages, lifestyle knowledge cards, and highly shareable social media infographics. Please include in the frame: - A clear and beautiful main visual of the subject - Several magnified details of local characteristics - Multiple rounded modular information sections - Clear title hierarchies and key labels - Concise yet rich encyclopedic content - Visual ratings, key point summaries, or Top 5 modules Content columns should be automatically adapted based on the theme, prioritized from these directions: basic profile, classification information, appearance characteristics, habits/ecology, formation mechanism/structure, growth or use conditions, care or maintenance suggestions, risks and precautions, suitable audience or scenarios, pros and cons comparison, and quick rating cards. Visual requirements: Light-colored clean background, soft color palette, light shadows, exquisite small icons, rounded information boxes, neat layout, high information density but not crowded, good reading experience. The overall result must look like a real science encyclopedia card suitable for publishing, reading, collecting, and serialized production, rather than an advertisement. Please do not make it a regular commercial promotional poster. Highlight the features of "knowledge organization + modular information + illustration-style display."

GPT Image 2 Relationship Graph Infographic: Network Data Visualization
image
GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 Relationship Graph Infographic: Network Data Visualization

Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not...

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Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not a regular poster or a simple illustration, but a modular popular science infographic that possesses a sense of "illustration book, encyclopedia, information structure, and collectability." The overall style should reference a combination of high-end natural history illustrations, modern encyclopedia pages, lifestyle knowledge cards, and highly shareable social media infographics. Please include in the frame: - A clear and beautiful main visual of the subject - Several magnified details of local characteristics - Multiple rounded modular information sections - Clear title hierarchies and key labels - Concise yet rich encyclopedic content - Visual ratings, key point summaries, or Top 5 modules Content columns should be automatically adapted based on the theme, prioritized from these directions: basic profile, classification information, appearance characteristics, habits/ecology, formation mechanism/structure, growth or use conditions, care or maintenance suggestions, risks and precautions, suitable audience or scenarios, pros and cons comparison, and quick rating cards. Visual requirements: Light-colored clean background, soft color palette, light shadows, exquisite small icons, rounded information boxes, neat layout, high information density but not crowded, good reading experience. The overall result must look like a real science encyclopedia card suitable for publishing, reading, collecting, and serialized production, rather than an advertisement. Please do not make it a regular commercial promotional poster. Highlight the features of "knowledge organization + modular information + illustration-style display."

GPT Image 2 Relationship Graph Infographic: Network Data Visualization
image
GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 Relationship Graph Infographic: Network Data Visualization

Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not...

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Please generate a high-quality vertical "Popular Science Encyclopedia Image" based on {argument name="theme" default="animals"}. This image is not a regular poster or a simple illustration, but a modular popular science infographic that possesses a sense of "illustration book, encyclopedia, information structure, and collectability." The overall style should reference a combination of high-end natural history illustrations, modern encyclopedia pages, lifestyle knowledge cards, and highly shareable social media infographics. Please include in the frame: - A clear and beautiful main visual of the subject - Several magnified details of local characteristics - Multiple rounded modular information sections - Clear title hierarchies and key labels - Concise yet rich encyclopedic content - Visual ratings, key point summaries, or Top 5 modules Content columns should be automatically adapted based on the theme, prioritized from these directions: basic profile, classification information, appearance characteristics, habits/ecology, formation mechanism/structure, growth or use conditions, care or maintenance suggestions, risks and precautions, suitable audience or scenarios, pros and cons comparison, and quick rating cards. Visual requirements: Light-colored clean background, soft color palette, light shadows, exquisite small icons, rounded information boxes, neat layout, high information density but not crowded, good reading experience. The overall result must look like a real science encyclopedia card suitable for publishing, reading, collecting, and serialized production, rather than an advertisement. Please do not make it a regular commercial promotional poster. Highlight the features of "knowledge organization + modular information + illustration-style display."

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples

[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a ...

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[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a clothing sheet. This is NOT a beauty-preserving redraw. This is a white-line rough mannequin conversion. [PRIMARY GOAL] Extract and visualize only: - pose structure - body balance - camera angle - body line flow - inferred light source placement - illuminated areas and light intensity [INPUT ROLE] Use the provided image as the strict anchor for: - pose - camera angle - body tilt - weight distribution - approximate lighting situation Do NOT preserve: - face rendering - hairstyle rendering - clothing detail - accessories - weapon detail - background architecture - character identity - emotional expression [FIGURE CONVERSION] single rough mannequin-like human figure white body contour lines white internal construction lines simple mannequin head no face no eyes no mouth no eyelashes no personality no individual identity human figure should look like: - rough pose mannequin - anatomy proxy - line-based body guide - structural sketch - white-line rough dummy keep: - pose readability - silhouette flow - head tilt - torso direction - pelvis direction - limb placement [BACKGROUND] pure black background negative-style dark field no scenery no props no architecture no environmental storytelling [LINE STYLE] rough white line drawing clean but sketch-like construction-line feeling anatomy guide lines visible joint flow visible body contour emphasized no polished illustration finish [LIGHT ESTIMATION] predict the likely light source positions from the input image visualize the light sources and illuminated areas using green glow only use green light intensity with variation: - strongest green where the light directly hits - medium green for wrap light - soft green for reflected or fading light mark the estimated light sources with labels and arrows such as: - Main Light - Rim Light - Fill Light - Floor Bounce - Back Light only if appropriate IMPORTANT: do not invent random lights infer lighting from the original input image if the lighting is ambiguous, keep the annotations simple and plausible [GREEN LIGHT VISUALIZATION] show green glow on: - head / skull plane - neck - shoulders - chest plane - ribcage direction - pelvis edge - thigh planes - knee contact points - floor contact bounce if applicable use green light not as decoration, but as lighting analysis information [POSE PRIORITY] 1. preserve pose structure 2. preserve camera angle 3. preserve body balance 4. preserve head-torso relationship 5. visualize likely light direction 6. show illuminated areas with readable green intensity variation [NEGATIVE] finished person, cute girl, detailed face, hair rendering, clothing rendering, weapon emphasis, beautiful anatomy

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples

[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a ...

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[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a clothing sheet. This is NOT a beauty-preserving redraw. This is a white-line rough mannequin conversion. [PRIMARY GOAL] Extract and visualize only: - pose structure - body balance - camera angle - body line flow - inferred light source placement - illuminated areas and light intensity [INPUT ROLE] Use the provided image as the strict anchor for: - pose - camera angle - body tilt - weight distribution - approximate lighting situation Do NOT preserve: - face rendering - hairstyle rendering - clothing detail - accessories - weapon detail - background architecture - character identity - emotional expression [FIGURE CONVERSION] single rough mannequin-like human figure white body contour lines white internal construction lines simple mannequin head no face no eyes no mouth no eyelashes no personality no individual identity human figure should look like: - rough pose mannequin - anatomy proxy - line-based body guide - structural sketch - white-line rough dummy keep: - pose readability - silhouette flow - head tilt - torso direction - pelvis direction - limb placement [BACKGROUND] pure black background negative-style dark field no scenery no props no architecture no environmental storytelling [LINE STYLE] rough white line drawing clean but sketch-like construction-line feeling anatomy guide lines visible joint flow visible body contour emphasized no polished illustration finish [LIGHT ESTIMATION] predict the likely light source positions from the input image visualize the light sources and illuminated areas using green glow only use green light intensity with variation: - strongest green where the light directly hits - medium green for wrap light - soft green for reflected or fading light mark the estimated light sources with labels and arrows such as: - Main Light - Rim Light - Fill Light - Floor Bounce - Back Light only if appropriate IMPORTANT: do not invent random lights infer lighting from the original input image if the lighting is ambiguous, keep the annotations simple and plausible [GREEN LIGHT VISUALIZATION] show green glow on: - head / skull plane - neck - shoulders - chest plane - ribcage direction - pelvis edge - thigh planes - knee contact points - floor contact bounce if applicable use green light not as decoration, but as lighting analysis information [POSE PRIORITY] 1. preserve pose structure 2. preserve camera angle 3. preserve body balance 4. preserve head-torso relationship 5. visualize likely light direction 6. show illuminated areas with readable green intensity variation [NEGATIVE] finished person, cute girl, detailed face, hair rendering, clothing rendering, weapon emphasis, beautiful anatomy

GPT Image 2: Popular science encyclopedia map
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2: Popular science encyclopedia map

GPT Image 2 prompt: Generate a high-quality vertical "popular science encyclopedia map" based on the [topic]. This picture is not an ordinary poster, nor is it a simple illustration, but a modular science information map that has a sense of illustration, encyclopedia, information structure and collection. The overall style refers to advanced natural history illustrated books, modern encyclopedia pages, lifestyle knowledge cards, and the style of information graphics that are easier to spread on social media. Let the picture contain: A clear and beautiful main vision of the theme Several partial...

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根据【主题】生成一张高质量竖版「科普百科图」。 这张图不是普通海报,也不是单纯插画,而是一张兼具图鉴感、百科感、信息结构感和收藏感的模块化科普信息图。整体风格参考高级博物图鉴、现代百科书页、生活方式知识卡,以及社交媒体上更容易传播的信息图风格。 让画面包含: 一个清晰好看的主题主视觉 若干局部特征放大细节 多个圆角模块化信息分区 清楚的标题层级与重点标签 简洁但信息丰富的百科内容 可视化评分、要点总结或 Top 5 模块 内容栏目请根据主题自动适配,优先从这些方向里选择并合理组合: 基础档案、分类信息、外观特征、习性生态、形成机制或结构组成、生长或使用条件、养护或维护建议、风险与注意事项、适合人群或适用场景、优缺点对比、快速评分卡。 视觉要求: 浅色干净背景,柔和配色,轻阴影,精致小图标,圆角信息框,整体排版整洁清爽。信息密度要丰富,但不能显得拥挤,阅读体验要舒服。最终效果要像真正可以发布、阅读、收藏、批量做成系列内容的科普百科卡,而不是广告感很重的宣传海报。 不要做成普通商业宣传海报,要重点突出“知识整理”“模块信息”“图鉴式展示”这几个特征。

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 AI Image Generation Case Study: Prompt-to-Visual Examples

[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a ...

Prompt Preview

[CORE TASK] Transform the provided input image into a pose-and-light analysis sheet. This is NOT a finished character illustration. This is NOT a clothing sheet. This is NOT a beauty-preserving redraw. This is a white-line rough mannequin conversion. [PRIMARY GOAL] Extract and visualize only: - pose structure - body balance - camera angle - body line flow - inferred light source placement - illuminated areas and light intensity [INPUT ROLE] Use the provided image as the strict anchor for: - pose - camera angle - body tilt - weight distribution - approximate lighting situation Do NOT preserve: - face rendering - hairstyle rendering - clothing detail - accessories - weapon detail - background architecture - character identity - emotional expression [FIGURE CONVERSION] single rough mannequin-like human figure white body contour lines white internal construction lines simple mannequin head no face no eyes no mouth no eyelashes no personality no individual identity human figure should look like: - rough pose mannequin - anatomy proxy - line-based body guide - structural sketch - white-line rough dummy keep: - pose readability - silhouette flow - head tilt - torso direction - pelvis direction - limb placement [BACKGROUND] pure black background negative-style dark field no scenery no props no architecture no environmental storytelling [LINE STYLE] rough white line drawing clean but sketch-like construction-line feeling anatomy guide lines visible joint flow visible body contour emphasized no polished illustration finish [LIGHT ESTIMATION] predict the likely light source positions from the input image visualize the light sources and illuminated areas using green glow only use green light intensity with variation: - strongest green where the light directly hits - medium green for wrap light - soft green for reflected or fading light mark the estimated light sources with labels and arrows such as: - Main Light - Rim Light - Fill Light - Floor Bounce - Back Light only if appropriate IMPORTANT: do not invent random lights infer lighting from the original input image if the lighting is ambiguous, keep the annotations simple and plausible [GREEN LIGHT VISUALIZATION] show green glow on: - head / skull plane - neck - shoulders - chest plane - ribcage direction - pelvis edge - thigh planes - knee contact points - floor contact bounce if applicable use green light not as decoration, but as lighting analysis information [POSE PRIORITY] 1. preserve pose structure 2. preserve camera angle 3. preserve body balance 4. preserve head-torso relationship 5. visualize likely light direction 6. show illuminated areas with readable green intensity variation [NEGATIVE] finished person, cute girl, detailed face, hair rendering, clothing rendering, weapon emphasis, beautiful anatomy

GPT Image 2 Realistic Photography Style: Cinematic Photo-Real Visuals
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 Realistic Photography Style: Cinematic Photo-Real Visuals

{ "type": "scientific hardware diagram", "layout": { "main_scene": "3D render of an optical table with a red laser beam passing through 11 ...

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{ "type": "scientific hardware diagram", "layout": { "main_scene": "3D render of an optical table with a red laser beam passing through 11 aligned optical components mounted on black posts.", "top_brackets": [ {"label": "Dual Modulation", "span": "SLM1"}, {"label": "4f Relay Optics", "span": "Lens L1 to Lens L2"}, {"label": "Imaging Optics", "span": "SLM2 to Lens L4"}, {"label": "Detection", "span": "Camera"} ], "optical_components_left_to_right": [ {"name": "Laser", "labels": ["Laser", "λ = {argument name=\"laser wavelength\" default=\"632.8 nm\"}"]}, {"name": "SLM1", "labels": ["SLM1", "(Phase / Pol. Mod.)"]}, {"name": "Lens L1", "labels": ["Lens L1", "(f1)"]}, {"name": "Iris", "labels": ["Fourier Plane", "(Pupil Plane)", "Iris", "(Higher Orders Filtered)"]}, {"name": "HWP", "labels": ["HWP", "(λ/2)"]}, {"name": "Lens L2", "labels": ["Lens L2", "(f1)"]}, {"name": "SLM2", "labels": ["SLM2", "(Phase / Pol. Mod.)"]}, {"name": "Lens L3", "labels": ["Lens L3", "(f2)"]}, {"name": "Lens L4", "labels": ["Lens L4", "(f2)"]}, {"name": "Linear Polarizer", "labels": ["Linear", "-Polarizer", "(Global Analyzer)"]}, {"name": "Polarization Camera", "labels": ["POLARIZATION CAMERA"]} ], "inset_box": { "position": "bottom right", "title": "Polarization Camera Micro-Polarizer Array (Per-Pixel Analyzer)", "grid": "4x4 grid of colored squares with directional arrows", "legend_count": 4, "legend_items": [ "Red square, horizontal arrow, 0° (H)", "Green square, vertical arrow, 90° (V)", "Blue square, diagonal arrow, 45° (D)", "Yellow square, diagonal arrow, 135° (A)" ] }, "bottom_caption": { "figure_prefix": "{argument name=\"figure number\" default=\"Fig. 5.\"}", "title": "{argument name=\"system name\" default=\"Ellipsography Hardware Setup.\"}", "text": "Paragraph of scientific text explaining the dual-modulation configuration, 4f relay optics, and polarization camera." } } }

GPT Image 2 Infographic Visualization Design: Data-Driven UI Graphics
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GPT Image 2
Jul 5, 2026 awesome-gpt-image-2

GPT Image 2 Infographic Visualization Design: Data-Driven UI Graphics

{ "type": "scientific optical setup diagram", "main_setup": { "base": "optical breadboard table with grid of mounting holes", "beam": "...

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{ "type": "scientific optical setup diagram", "main_setup": { "base": "optical breadboard table with grid of mounting holes", "beam": "red laser beam passing horizontally through all components", "top_grouping_brackets": [ "{argument name=\"first component group\" default=\"Dual Modulation\"}", "4f Relay Optics", "Imaging Optics", "Detection" ], "components_left_to_right": [ { "name": "Laser", "label": "{argument name=\"laser wavelength\" default=\"λ = 632.8 nm\"}", "appearance": "black rectangular box" }, { "name": "SLM1", "label": "(Phase / Pol. Mod.)", "appearance": "black square device on post" }, { "name": "Lens L1", "label": "(f1)", "appearance": "lens in black ring mount" }, { "name": "Iris", "label": "Fourier Plane (Pupil Plane) / (Higher Orders Filtered)", "appearance": "black ring mount with dashed line above" }, { "name": "HWP", "label": "(λ/2)", "appearance": "purple-tinted optic in black ring mount" }, { "name": "Lens L2", "label": "(f1)", "appearance": "lens in black ring mount" }, { "name": "SLM2", "label": "(Phase / Pol. Mod.)", "appearance": "black square device on post" }, { "name": "Lens L3", "label": "(f2)", "appearance": "lens in black ring mount" }, { "name": "Lens L4", "label": "(f2)", "appearance": "lens in black ring mount" }, { "name": "Linear Polarizer", "label": "(Global Analyzer)", "appearance": "lens in black ring mount" }, { "name": "Polarization Camera", "label": "POLARIZATION CAMERA", "appearance": "blue and black box camera" } ] }, "inset_diagram": { "position": "bottom right, dashed border", "title": "{argument name=\"inset title\" default=\"Polarization Camera Micro-Polarizer Array\"} (Per-Pixel Analyzer)", "visuals": "4x4 grid of colored squares with white directional arrows", "legend_count": 4, "legend_labels": [ "red right-arrow 0° (H)", "green up-arrow 90° (V)", "blue diagonal-arrow 45° (D)", "yellow diagonal-arrow 135° (A)" ] }, "bottom_caption": { "figure_number": "Fig. 5.", "title": "{argument name=\"setup title\" default=\"Ellipsography Hardware Setup.\"}", "description": "{argument name=\"figure caption\" default=\"Our prototype display system employs a dual-modulation configuration to achieve simultaneous control of phase and polarization. A 4f relay optics setup transfers the modulated wavefront...\"}" } }

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