GPT Image Hub
提示詞庫
生成
影片
畫布
模型
工具
我的收藏
定價
繁體中文
登入
註冊
新上線 GPT Image 2.5 Flare 和 Sunburst:註冊即可免費生成 3 張,之後每張 8 點數。
試用 GPT Image 2.5
圖像工作台
模型
GPT IMAGE 2
提示詞
4,138 / 1,000
{"type":"並排式AI模型架構對比資訊圖","style":"整潔的技術架構圖、白底、細黑色輪廓線、圓角矩形、虛線標註框、顏色高亮、簡報幻燈片美學、向量資訊圖","canvas":{"aspect_ratio":"2:1","resolution":"wide horizontal"},"title_row":{"left_title":"[DeepSeek V3/R1 (671 billion)]","right_title":"[DeepSeek V4 (1.2 trillion)]","left_title_color":"亮橙紅色","right_title_color":"鮮藍色"},"layout":{"columns":2,"sections":[{"title":"[DeepSeek V3/R1 (671 billion)]","position":"左半部","count":9,"labels":["Vocabulary size of 129k","FeedForward (SwiGLU) module","Intermediate hidden layer dimension of 2,048","MoE layer","Supported context length of 128k tokens","First 3 blocks use dense FFN with hidden size 18,432 instead of MoE","Sample input text","Embedding dimension of 7,168","128 heads"]},{"title":"[DeepSeek V4 (1.2 trillion)]","position":"右半部","count":9,"labels":["Vocabulary size of 160k","FeedForward (SwiGLU) module","Intermediate hidden layer dimension of 3,072","MoE layer","Supported context length of 256k tokens","First 3 blocks use dense FFN with hidden size 24,576 instead of MoE","Sample input text","Embedding dimension of 8,192","128 heads"]},{"title":"底部對比表格","position":"底部全寬","count":10,"labels":["Total parameters","Active parameters per token","Hidden size","Intermediate (FF)","Attention heads","Context length","Embedding dimension","Vocabulary size"]}]},"left_panel":{"background":"極淺灰色圓角矩形","main_stack":{"count":8,"blocks":["Tokenized text","Token embedding layer","RMSNorm 1","Multi-head Latent Attention","RMSNorm 2","MoE","Final RMSNorm","Linear output layer"]},"side_module":"左側注意力模組附帶RoPE","attention_block":{"label":"Multi-head Latent Attention","accent":"Latent字樣使用橙紅色"},"feedforward_inset":{"title":"FeedForward (SwiGLU) module","count":4,"blocks":["Linear layer","SiLU activation","Linear layer","Linear layer"],"diagram":"雙分支相乘後投影"},"moe_inset":{"title":"MoE layer","count":5,"blocks":["top combine node","Feed forward","Feed forward","Router","expert count badge 256"],"details":"代表1個選中專家的黑色小方塊、指向專家層的向上箭頭、虛線分隔線"},"annotations":{"vocab":"Vocabulary size of 129k","ff_dim":"Intermediate hidden layer dimension of 2,048","context":"Supported context length of 128k tokens","dense_first_blocks":"First 3 blocks use dense FFN with hidden size 18,432 instead of MoE","resource_savings":"Resource savings: Model size is 671B but only 1 (shared) + 8 experts active per token; only 37B parameters are active per inference step"},"bottom_stats":{"count":8,"items":["Total parameters: 671B","Active parameters per token: 37B (1 + 8 experts)","Hidden size: 7,168","Intermediate (FF): 2,048","Attention heads: 128","Context length: 128k","Embedding dimension: 7,168","Vocabulary size: 129k"]}},"right_panel":{"background":"極淺藍色圓角矩形","main_stack":{"count":8,"blocks":["Tokenized text","Token embedding layer","RMSNorm 1","Multi-head Latent Attention","RMSNorm 2","MoE","Final RMSNorm","Linear output layer"]},"side_module":"左側注意力模組附帶RoPE","attention_block":{"label":"Multi-head Latent Attention","accent":"Latent字樣使用藍色"},"feedforward_inset":{"title":"FeedForward (SwiGLU) module","count":4,"blocks":["Linear layer","SiLU activation","Linear layer","Linear layer"],"diagram":"與左側結構相同"},"moe_inset":{"title":"MoE layer","count":5,"blocks":["top combine node","Feed forward","Feed forward","Router","expert count badge 384"],"details":"代表1個選中專家的黑色小方塊、指向專家層的向上箭頭、虛線分隔線、藍色邊框強調"},"annotations":{"vocab":"Vocabulary size of 160k","ff_dim":"Intermediate hidden layer dimension of 3,072","context":"Supported context length of 256k tokens","dense_first_blocks":"First 3 blocks use dense FFN with hidden size 24,576 instead of MoE","resource_savings":"Resource savings: Model size is 1.2T but only 1 (shared) + 8 experts active per token; only 52B parameters are active per inference step"},"bottom_stats":{"count":8,"items":["Total parameters: 1.2T","Active parameters per token: 52B (1 + 8 experts)","Hidden size: 8,192","Intermediate (FF): 3,072","Attention heads: 128","Context length: 256k","Embedding dimension: 8,192","Vocabulary size: 160k"]}},"global_notes":"生成高度對稱的超詳細Transformer模型架構對比圖。左右各包含一個完整模型堆疊以及2個內嵌結構圖(前饋模組與MoE專家層)。模組間附帶連線箭頭、細小技術文字與指引線。保持密集專業的幻燈片排版風格,左側使用橙紅色強調,右側使用藍色強調,底部附帶橫跨全寬的資料對比表。"}
Ctrl / ⌘ + Enter
參考
0 / 16
將參考圖像拖放到此處
PNG, JPG, WebP · 50 MB
畫幅
自動
品質
中
解析度
1K
圖像數量
1 / 4
結果
從一句提示詞開始
生成的圖像將出現在此。
獲得靈感
商品攝影
室內設計
社群媒體廣告