深色研究图示库 UI
创建一个深色网页图库界面,展示用于学术视觉预设库的精选研究论文图示卡片。
- Category
- Charts & Infographics
- Model
- GPT Image 2
- Creator
- Heliocore.C6
- Source language
- en
- Source ID
- 23219
- Published
- May 29, 2026
Full prompt
Goal: Create a dark, high-end gallery index page for a curated preset library of research paper figure styles, titled {argument name="headline text" default="研究论文图示"}. The interface should look like a polished web app screenshot showcasing academic diagram thumbnails.
Canvas: Wide landscape browser-like composition, approximately 16:9, dark charcoal background, subtle grid borders, crisp UI typography, high contrast white and cyan text.
Header: Center the title at the top with a small stacked multicolor document icon to its left. Add a thin horizontal divider line below the header. In the upper-right area, place a compact rounded button labeled “↑ 图库索引”.
Section title: On the left below the divider, show the section heading {argument name="section title" default="研究论文图示网格"} in bold light text.
Layout: Display a 4-column gallery grid with thin gray borders and dark card gutters. Show exactly 12 visible gallery cards: 8 full cards in the first two rows and 4 partially visible cards in the third row at the bottom edge. Each card contains a white research-paper-style thumbnail image, a bold lettered title underneath, and three small dark rounded tag pills below the title reading exactly “landscape”, “high”, and “Curated”.
Visible card count and labels: Card A: “患者队列与多模态生物标志物流称流程”, thumbnail showing a patient cohort workflow with boxes, charts, and a survival curve. Card B: “单细胞免疫图谱”, thumbnail showing a single-cell immune atlas with UMAP clusters, dot plot, stacked bar chart, and trajectory plot. Card C: “多模态医疗 AI 方法图”, thumbnail showing a multimodal foundation model clinical decision support diagram with medical image, pathology, text, and model blocks. Card D: “治疗响应统计图”, thumbnail showing therapeutic response statistics with bar charts, forest plots, scatter plot, and a circular workflow. Card E: “Transformer 编码器-解码器架构”, thumbnail showing a transformer encoder-decoder architecture diagram with stacked module blocks. Card F: “多智能体 LLM 系统架构”, thumbnail showing an LLM multi-agent system architecture with central model block, surrounding tool icons, memory, reflection, and evaluation modules. Card G: “去噪扩散正/逆向链”, thumbnail showing a denoising diffusion forward and reverse process chain with noisy image panels and arrows. Card H: “经验缩放规律图”, thumbnail showing empirical scaling laws with multiple descending colored curves and a legend. Bottom partially visible card 1: title begins “Benchmark comparison across 10 frontier LLMs”, thumbnail with bar chart comparisons. Bottom partially visible card 2: title begins “Ablation of core reasoning components across 5 benchmarks”, thumbnail with grouped bars. Bottom partially visible card 3: title begins “LLM pretraining data mixture and downstream splits”, thumbnail with stacked area/data mixture blocks. Bottom partially visible card 4: title begins “Representative multi-head attention patterns in a 16-layer Transformer”, thumbnail with heatmaps.
Visual style: Use a modern SaaS dashboard aesthetic, dark mode, neat academic curation feel, subtle cyan accents, small but legible labels, and realistic research figure thumbnails that resemble high-quality journal diagrams. Keep thumbnails varied but consistently white-background scientific figures.
Constraints: Preserve the Chinese interface labels and card titles as written. Do not add people, photos, watermarks, browser address bars, or decorative clutter. The composition should feel like a screenshot of a curated research-figure template library named {argument name="project name" default="GPT-IMAGE-2-SKILL"}.Translations
深色研究图示库 UI
enGoal: Create a dark, high-end gallery index page for a curated preset library of research paper figure styles, titled {argument name="headline text" default="研究论文图示"}. The interface should look like a polished web app screenshot showcasing academic diagram thumbnails. Canvas: Wide landscape browser-like composition, approximately 16:9, dark charcoal background, subtle grid borders, crisp UI typography, high contrast white and cyan text. Header: Center the title at the top with a small stacked multicolor document icon to its left. Add a thin horizontal divider line below the header. In the upper-right area, place a compact rounded button labeled “↑ 图库索引”. Section title: On the left below the divider, show the section heading {argument name="section title" default="研究论文图示网格"} in bold light text. Layout: Display a 4-column gallery grid with thin gray borders and dark card gutters. Show exactly 12 visible gallery cards: 8 full cards in the first two rows and 4 partially visible cards in the third row at the bottom edge. Each card contains a white research-paper-style thumbnail image, a bold lettered title underneath, and three small dark rounded tag pills below the title reading exactly “landscape”, “high”, and “Curated”. Visible card count and labels: Card A: “患者队列与多模态生物标志物流称流程”, thumbnail showing a patient cohort workflow with boxes, charts, and a survival curve. Card B: “单细胞免疫图谱”, thumbnail showing a single-cell immune atlas with UMAP clusters, dot plot, stacked bar chart, and trajectory plot. Card C: “多模态医疗 AI 方法图”, thumbnail showing a multimodal foundation model clinical decision support diagram with medical image, pathology, text, and model blocks. Card D: “治疗响应统计图”, thumbnail showing therapeutic response statistics with bar charts, forest plots, scatter plot, and a circular workflow. Card E: “Transformer 编码器-解码器架构”, thumbnail showing a transformer encoder-decoder architecture diagram with stacked module blocks. Card F: “多智能体 LLM 系统架构”, thumbnail showing an LLM multi-agent system architecture with central model block, surrounding tool icons, memory, reflection, and evaluation modules. Card G: “去噪扩散正/逆向链”, thumbnail showing a denoising diffusion forward and reverse process chain with noisy image panels and arrows. Card H: “经验缩放规律图”, thumbnail showing empirical scaling laws with multiple descending colored curves and a legend. Bottom partially visible card 1: title begins “Benchmark comparison across 10 frontier LLMs”, thumbnail with bar chart comparisons. Bottom partially visible card 2: title begins “Ablation of core reasoning components across 5 benchmarks”, thumbnail with grouped bars. Bottom partially visible card 3: title begins “LLM pretraining data mixture and downstream splits”, thumbnail with stacked area/data mixture blocks. Bottom partially visible card 4: title begins “Representative multi-head attention patterns in a 16-layer Transformer”, thumbnail with heatmaps. Visual style: Use a modern SaaS dashboard aesthetic, dark mode, neat academic curation feel, subtle cyan accents, small but legible labels, and realistic research figure thumbnails that resemble high-quality journal diagrams. Keep thumbnails varied but consistently white-background scientific figures. Constraints: Preserve the Chinese interface labels and card titles as written. Do not add people, photos, watermarks, browser address bars, or decorative clutter. The composition should feel like a screenshot of a curated research-figure template library named {argument name="project name" default="GPT-IMAGE-2-SKILL"}.
深色研究图示库 UI
zh-CN目标:创建一个深色、高端的图库索引页面,用于展示精选的研究论文图示样式预设库,标题为 {argument name="headline text" default="研究论文图示"}。界面应呈现为展示学术图表缩略图的精致 Web 应用截图。 画布:宽屏横向浏览器风格构图,比例约为 16:9,深炭灰色背景,带有细微的网格边框,清晰的 UI 排版,高对比度的白色和青色文字。 页眉:标题居中置于顶部,左侧配有一个小型堆叠式多色文档图标。页眉下方添加一条细水平分割线。在右上角区域,放置一个圆角紧凑按钮,标签为“↑ 图库索引”。 章节标题:在分割线下方左侧,显示加粗的浅色章节标题 {argument name="section title" default="研究论文图示网格"}。 布局:展示一个 4 列的图库网格,带有细灰色边框和深色卡片间距。显示 12 张可见的图库卡片:前两行显示 8 张完整卡片,底部边缘显示 4 张部分可见的卡片。每张卡片包含一个白色研究论文风格的缩略图、下方加粗的字母标题,以及标题下方三个深色圆角标签,文字分别为“landscape”、“high”和“Curated”。 可见卡片数量与标签:卡片 A:“患者队列与多模态生物标志物流程”,缩略图显示包含方框、图表和生存曲线的患者队列工作流。卡片 B:“单细胞免疫图谱”,缩略图显示包含 UMAP 聚类、点图、堆叠柱状图和轨迹图的单细胞免疫图谱。卡片 C:“多模态医疗 AI 方法图”,缩略图显示包含医学影像、病理、文本和模型模块的多模态基础模型临床决策支持图。卡片 D:“治疗响应统计图”,缩略图显示包含柱状图、森林图、散点图和循环工作流的治疗响应统计数据。卡片 E:“Transformer 编码器-解码器架构”,缩略图显示包含堆叠模块块的 Transformer 编码器-解码器架构图。卡片 F:“多智能体 LLM 系统架构”,缩略图显示包含中央模型块、周边工具图标、记忆、反射和评估模块的 LLM 多 Agent 系统架构。卡片 G:“去噪扩散正/逆向链”,缩略图显示包含噪声图像面板和箭头的去噪扩散正向和逆向过程链。卡片 H:“经验缩放规律图”,缩略图显示包含多条下降彩色曲线和图例的经验缩放规律。底部部分可见卡片 1:标题以“Benchmark comparison across 10 frontier LLMs”开头,缩略图为柱状图对比。底部部分可见卡片 2:标题以“Ablation of core reasoning components across 5 benchmarks”开头,缩略图为分组柱状图。底部部分可见卡片 3:标题以“LLM pretraining data mixture and downstream splits”开头,缩略图为堆叠面积/数据混合块。底部部分可见卡片 4:标题以“Representative multi-head attention patterns in a 16-layer Transformer”开头,缩略图为热力图。 视觉风格:采用现代 SaaS 仪表盘美学,深色模式,整洁的学术策展感,细微的青色点缀,标签小巧但清晰易读,研究图示缩略图需逼真,类似于高质量期刊图表。保持缩略图多样化,但统一为白色背景的科学图示。 约束:保留所写的中文界面标签和卡片标题。不要添加人物、照片、水印、浏览器地址栏或装饰性杂物。构图应看起来像是一个名为 {argument name="project name" default="GPT-IMAGE-2-SKILL"} 的精选研究图示模板库的截图。












