GPT Image 2.5 作品展示
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GPT Image-2 - Text to Image
Traditionally, FL involves $L$ numbers of global training rounds, indexed by $\ell \in \mathcal{L}=\{1, 2, \cdots, L\}$, and within each $\ell$, each device trains local model with its local dataset. However, by considering devices' heterogeneities, such as communication, computation, and mobility, a DT--enabled model pruning FL framework is proposed to enhance the training performance. As shown in Fig.~1, in this paper, we group every $T_{\ell}$ numbers of global training rounds as one time slot, denoted as $t$. Suppose that there exist $T$ numbers of time slots, and within each time slot $t \in \mathcal{T}=\{1, 2, \cdots, T\}$, we perform pruning global model to preserve the structured sparsity and model performance. In a global training round $\ell$, we further prune parameters of the pruned global model.In our framework, at the beginning of each federated learning round $\ell$, the edge server makes initial pruning rate and bandwidth allocation decisions for each device based on the computing frequency and location reported by the device, and sends these decisions back to the devices. Suppose that there exist $\ell$ numbers of $\Delta {\ell}$ slots in a global training round $\ell$, devices periodically report their current computing frequency and location to the digital twin layer and the edge server at intervals of $\Delta {\ell}$. Upon receiving these updates, the digital twin layer immediately refreshes the virtual replicas and adjusts the edge server's pruning rate and bandwidth allocation decisions, thereby achieving rapid local policy updates. Simultaneously, within each $\Delta {\ell}$ interval, the digital twin layer validates and optimizes these decisions based on the constructed global virtual environment. Ultimately, the optimized global decisions are returned to the devices within the same global federated learning round}. The procedures of proposed DT--enabled model pruning FL can be described as follows. 1) \textbf{Determinations of Model Pruning Ratios}: At the beginning of time slot $\ell$, each device uploads its up--to--date states, such as computing capacity, to its DT. The DT will determine $\Psi_{k}^{t,\ell}$ and $\Phi_{k}^{t,\ell}$, which will be detailed later. Then, based on $\Psi_{k}^{t,\ell}$, the DT will prune the global model $\textbf{w}$ to obtain ${\textbf{w}}_{k}^{t,\ell}$, and train ${\textbf{w}}_{k}^{t,\ell}$ on its synthetic dataset $\hat{\mathcal{D}}_k$.2) \textbf{Local Training}: At time slot $\ell$, device $k$ trains the received model ${\textbf{w}}_{k}^{t,\ell}$ on $\mathcal{D}_k$ for $\varsigma_k$ numbers of local iterations to obtain $\tilde{{\textbf{w}}}_{k}^{t,\ell}$.3) \textbf{Locally Model Pruning}: Based on received $\Phi_{k}^{t,\ell}$, device $k$ prunes the model $\tilde{{\textbf{w}}}_{k}^{t,\ell}$.4) \textbf{Model Upload}: Once completion of local model training, devices should transmit their models to DTs.5) \textbf{Global Aggregation}: Upon the receipt of all local models, we implement the following aggregation scheme at each slot $\ell$.6) \textbf{Model Distribution}: At the next global training round, the edge server transfers the aggregated model to DTs, and a next learning process is launched.
GPT-Image-2 Image Generation
将图片中产品的颜色换成银色
GPT-Image-2 Image Generation
生成参考图片类似风格的图片
GPT-Image-2 Image Generation
为 [CITY] 创作一张 3:2 比例的高级 3D 字体旅行海报，风格融合奢华编辑式目的地广告与真实雕塑感字母建筑。 城市名称 “[CITY]” 必须是画面中的绝对主体，占据大部分画布。将字母塑造成大型、真实、三维的雕塑形态，材质可以是高光漆面、抛光陶瓷、柔和石膏、雕刻石材、阳光照射下的建筑表面，或根据城市气质定制的材质。每个字母都应自然转化为这座城市的身份象征：地标、天际线轮廓、拱门、塔楼、穹顶、桥梁、窗户、阳台、文化纹样、海岸形态或街道细节，都必须直接从字母结构中生长出来。 地标应与字母在建筑结构上融为一体，而不是被粘贴在文字后方或周围。例如，塔楼可以从竖直笔画中升起，桥梁可以连接两个字母，穹顶可以形成圆形字母的弧线，屋顶可以塑造字母顶部边缘，窗户或装饰细节可以嵌入字母表面。 使用低角度三分之四视角，让字体看起来宏伟、电影感、高级且亲和。将 3D 城市名称雕塑略微放置在画面偏下的位置，填满画面的中央和下部区域，并在上方保留充足留白。 在顶部标题区域，添加一排精致横向排列的淡化城市地标符号：可以是小型矢量图标，或半透明建筑图形。它们应非常柔和、优雅、次要，像高级杂志页眉中的细节。每个图标下方添加对应地标名称。整体保持淡雅和高级。 移除左上角任何可见太阳。使用明亮自然日光，光源从左上方柔和照射，搭配轻微补光、干净高光、细腻环境光遮蔽，以及字母下方柔和的接触阴影。整体光感应清新、愉悦、编辑感强，而不是昏暗或过度电影化。 根据 [CITY] 采用明亮且适配城市气质的配色方案：海滨城市使用水蓝、珊瑚色、奶油色和阳光黄；历史城市使用暖石色、陶土色、橄榄绿和柔和天空蓝；热带城市使用青绿、芒果黄、棕榈绿和白色；山地城市使用高山蓝、草甸绿、雪白和温暖金色；夜生活城市使用紫罗兰、青蓝、蜜桃色和琥珀色。色彩应保持干净、乐观、高级且克制。 添加一些细腻的编辑式文字元素来增强海报质感：例如小号大写标题 “DESTINATION SERIES”、极小的位置编号 “CITY / 01”、精致的竖向分隔线、极简页脚文字 “VISIT [CITY]”，以及靠近底部边缘的小型坐标或期号信息。这些文字元素必须安静且次要，确保大型 3D 城市名称始终是视觉主角。 背景应干净、宽敞，可以使用柔和天空渐变、细腻云层或抽象色块。不要在背景中放置额外地标；所有主要城市身份都必须来自字体本身，
GPT-Image-2 Image Generation
为一个名为"链Q必达"的快递聚合平台设计品牌 Logo，整合以下三个设计要素： 1. 链环结构（Task #5）：Q的圆弧左侧断口处嵌入2-3节白色链扣，链扣为规整椭圆形，两两相扣，方向沿圆弧弧度排列，形成"链环断口"的视觉特征，暗示聚合平台的链接属性。 2. Q字母形态（Task #2）：整体轮廓明确读作字母"Q"——圆弧部分为粗实线闭合环（仅在左侧链扣处有断口），Q的尾巴从圆弧右下方延伸为向右上方45°的箭头，箭头尾部附带2条平行细速度线，增强动感。圆弧内部嵌入一个蓝色对勾符号，线条尖锐利落，象征"必达"的完成保证。 3. 整体结构（Task #1）：Logo由上方图标+下方品牌文字构成完整品牌标识。图标居上，下方第一行是英文品牌名"QuickLink"——"Quick"用深蓝色粗体无衬线字体，"Link"用橙色粗体无衬线字体；第二行是灰色中文标语"快链·聚合平台"，字号小于英文，标语两侧配短横线装饰。整体上下居中对称构图，层级分明。 色彩与风格：主色深钴蓝（#0D47FF）用于圆弧和对勾，信号橙（#FF6B2B）仅用于箭头尖端和"Link"文字，灰色用于中文标语。纯白底，扁平矢量风格，无渐变、无3D、无投影。1:1正方形构图，四周留足呼吸空间。
GPT-Image-2 Image Generation
为一个名为"链Q必达"的快递聚合平台设计品牌 Logo。 视觉主体：以字母"Q"为核心图形，Q的圆弧部分采用几何正圆结构，体现聚合平台的完整性与包容感；Q的尾巴向右上方45°延伸，末端收为锐角箭头，象征"必达"的送达承诺与速度感。箭头与圆弧的连接处做断口处理，暗示"链环扣接"的结构，而非简单的一笔画。 辅助符号：在Q的圆弧笔画中段，融入一组（2-3节）链条环节的负空间细节——不是外加链条图案，而是在笔画的粗细变化与间隙中自然呈现链环的扣合形态。链环方向与圆弧弧度一致，视觉上既是笔画的一部分，又暗含"链接、聚合"的品牌含义。链环细节保持克制，远看是Q，近看才发现链的结构。 负空间巧思：Q的内圈空白区隐约构成一个对勾（✓）的轮廓，用负空间而非实线表达，强化"必达"的保证感，同时不破坏Q形的整体识别度。 笔画节奏：从链环节点（最粗）到箭头尖端（最细），笔画宽度呈加速递减，视觉上产生"从聚合到送达"的动势，就像一个包裹从多网汇聚到精准投递。 色彩系统：主色为深钴蓝（#0D47FF），传达专业信任与效率；箭头尖端点缀信号橙（#FF6B2B），仅在尾端一瞬亮色，制造"最后一公里送达"的视觉高潮。两色不渐变、不混色，各自占独立区域。 风格约束：纯白底，矢量平涂风格，无渐变、无3D立体、无投影、无描边。线条干净利落，边缘锐利，专业级品牌标志品质。1:1正方形构图，标志居中，四周留充足呼吸空间。Logo下方不放任何文字。
GPT-Image-2 Image Generation
A logo mark for "链Q必达", a courier aggregation platform. The icon is an abstract speed symbol composed of two overlapping geometric elements: a perfect circle (representing the aggregated network) sliced by a diagonal chevron-arrow (representing velocity and delivery direction). Where the arrow cuts through the circle, a small interlocking chain-link gap appears at the intersection — the only hint at the "链" concept. The overall shape loosely evokes a "Q" without literally being one. Ultra-minimal, Swiss design influence, near-pictogram clarity. Colors: single ink black (#111111) primary shape, one small accent dot in rocket red (#E53935) at the arrow tip. Solid white background. Flat vector, absolutely no gradients, no 3D, no shadows. Maximum reduction, iconic and timeless. 1:1 square with wide breathing room.
GPT-Image-2 Image Generation
A logo mark for "链Q必达", a courier aggregation platform. The icon is a single continuous calligraphic stroke forming the letter "Q" — the stroke begins as a chunky chain-link node at the top-left, loops smoothly through the bowl, and the tail accelerates into a razor-sharp arrow pointing upper-right, evoking a package in flight. Inside the counter space, a clever negative-space checkmark silently reinforces the "必达" guarantee. The stroke weight transitions dramatically from heavy to light, mimicking acceleration. Clean geometric Bézier curves, no decorative noise. Colors: deep cobalt blue (#0D47FF) for the main stroke, a vivid tangerine accent (#FF6B2B) only on the arrow tip. Solid white background. Flat vector, zero gradients, zero 3D, zero shadows. Crisp edges, professional-grade logomark. 1:1 square with generous margins.
GPT-Image-2 Image Generation
修改这张海报，将小孩穿的衣服换成我提供的款式，左下角的“梦想起航，未来可期”改成“黔風起，龍必至”，其他保持不变
GPT-Image-2 Image Generation
根据我提供的小孩照片，生成一份贵州猛龙篮球俱乐部的儿童节祝福海报，要求上面有儿童节和篮球的元素，以及我提供给你的贵州猛龙logo，用漫画的风格进行设计，可以出现比较多的漫画小孩。整体风格红色为主，设计简洁美观。
GPT-Image-2 Image Generation
根据我提供的小孩照片，生成一份贵州猛龙篮球俱乐部的儿童节祝福海报，要求上面有儿童节和篮球的元素，以及我提供给你的贵州猛龙logo，不要出现个人特写，产生群像的效果。图片中的小孩都身穿男人怀抱的小男孩的同款球衣。设计简洁美观。
GPT-Image-2 Image Generation
根据我提供的小孩照片，生成一份贵州猛龙篮球俱乐部的儿童节祝福海报，要求上面有儿童节和篮球的元素，以及我提供给你的贵州猛龙logo
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