GptImage4 - GPT Image 2 AI 图片生成器与 API
浏览 GptImage4 的公开 GPT Image 2 图片生成案例。GptImage4 是独立产品，GPT Image 4 访问暂未开放。
GPT-Image-2 Image Generation
根据我提供的人物照片和logo，生成一份球员加盟的欢迎海报，球队姓名：孙椿棚，场上位置：中锋，球队名称为贵州猛龙，英文名“Guizhou Menglong”，口号为“黔風起，龍必至”，主色调为红色和黄色，需要有贵州的元素，表达欢迎来到贵州的意思，设计简介美观
GPT-Image-2 Image Generation
参考我提供的海报模板，生成一份球员签约官宣海报，球员姓名：孙椿棚，场上位置：中锋，
GPT-Image-2 Image Generation
修改这张照片，将人物所穿的衣服替换成我提供的红色样式，去掉球衣的号码
GPT-Image-2 Image Generation
修改这张海报，将“继续携手”修改为“续约完成”，其他内容保持不变。
GPT Image 2 - Image To Image
整张图分为左半边的FL System和右半边。将左边的FL System里面的device删掉，edge server和DT融入到右半边。
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. Devices periodically report their current computing frequency and location to the digital twin layer and the edge server. 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, 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. 注意:将edge server和DT画在一起，device不要与edge server和DT画在一起
GPT-Image-2 Image Generation
，请根据提供的人设、设定和容创作真人电影镜头。为分镜添加相应中文台词，分镜根据提供的比例进行调整。 圣女：图1，穿着修女的衣物。 面具：图2，带有表情的面具，会在依附在宿主的脸上，同化宿主。 修女形态的魔物：图3，圣女被面具同化后的形态。依旧穿着修女的衣物，但皮肤被乳胶覆盖，脸部被面具依附 迷宫魔物：图4，全身被乳胶覆盖，戴着长手套和大腿靴。被面具控制。 大分镜：地下城内部，圣女与迷宫魔物战斗中战败昏迷 大分镜：圣女上半身特写。圣女被两个迷宫魔物夹着手臂抬起，圣女前方还站着另一个迷宫魔物。 小分镜：迷宫魔物头部特写。迷宫魔物用手把脸上的面具摘掉。露出被乳胶覆盖的脸部 中分镜：圣女上半身特写。面具的边缘流出黑色的乳胶，乳胶像触手一样沿着圣女的头部延伸。 中分镜：圣女醒来时发现东西覆盖着脸部。 大分镜： 圣女全身特写。圣女被改造成修女形态的魔物。面具紧贴在脸上，圣女因害怕试图摘掉面具。
GPT-Image-2 Image Generation
，请根据提供的人设、设定和分镜内容创作真人电影镜头。为分镜添加相应中文台词 圣女：图1，穿着修女的衣物。 面具：图2，带有表情的面具，会在依附在宿主的脸上，同化宿主。 修女形态的魔物：图3，圣女被面具同化后的形态。依旧穿着修女的衣物，但皮肤被乳胶覆盖，脸部被面具依附 迷宫魔物：图4，全身被乳胶覆盖，戴着长手套和大腿靴。被面具控制。 大分镜：地下城内部，圣女与迷宫魔物战斗中战败昏迷 大分镜：圣女上半身特写。圣女被两个迷宫魔物夹着手臂抬起，圣女前方还站着另一个迷宫魔物。 小分镜：迷宫魔物头部特写。迷宫魔物用手把脸上的面具摘掉。露出被乳胶覆盖的脸部 小分镜：迷宫魔物拿着面具戴在圣女的脸上。圣女的眼神表现出恐惧。 分镜：圣女上半身特写。面具的边缘流出黑色的乳胶，乳胶像触手一样沿着圣女的头部延伸。 大分镜： 圣女全身特写。圣女被改造成修女形态的魔物。面具紧贴在脸上，圣女因害怕试图摘掉面具并试图逃离地下迷宫。
GPT-Image-2 Image Generation
将图1的豹纹裤子替换成图2的黑色裤子，其他不变
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
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