生圖挑戰（7/10）🎁
嗨，我是慕約，今天的挑戰是「用魔法駕馭魔法」。
- 只要 2 分鐘就可以完成。
- 挑戰連結：https://forms.gle/S6cf7ew1T3BBX62v8
第七天挑戰：用魔法駕馭魔法
請先閱讀這篇案例：
白板改字 #018
用 AI 寫程式，讓自己早下班
修改以下參考指令，請更改白板上的文字（任何字、圖都可），但希望保留照片中的其他部分。
希望白板上的字不要破圖，建議字是英文的。但假如你要辦法做到中文字是完整的呈現，這樣也是可以的。
參考指令：<<<
A wide image taken with a phone of a glass whiteboard, in a room overlooking the Bay Bridge. The field of view shows a woman writing, sporting a tshirt with a large OpenAI logo. The handwriting looks natural and a bit messy, and we see the photographer's reflection.
The text reads:
(left)
"Transfer between Modalities:
Suppose we directly model
p(text, pixels, sound) [equation]
with one big autoregressive transformer.
Pros:
* image generation augmented with vast world knowledge
* next-level text rendering
* native in-context learning
* unified post-training stack
Cons:
* varying bit-rate across modalities
* compute not adaptive"
(Right)
"Fixes:
* model compressed representations
* compose autoregressive prior with a powerful decoder"
On the bottom right of the board, she draws a diagram:
"tokens -> [transformer] -> [diffusion] -> pixels"
>>>
A wide image taken with a phone of a glass whiteboard, in a room overlooking the Bay Bridge. The field of view shows a woman writing, sporting a tshirt with a large OpenAI logo. The handwriting looks natural and a bit messy, and we see the photographer's reflection.
The text reads:
(left)
"Transfer between Modalities:
Suppose we directly model
p(text, pixels, sound) [equation]
with one big autoregressive transformer.
Pros:
* image generation augmented with vast world knowledge
* next-level text rendering
* native in-context learning
* unified post-training stack
Cons:
* varying bit-rate across modalities
* compute not adaptive"
(Right)
"Fixes:
* model compressed representations
* compose autoregressive prior with a powerful decoder"
On the bottom right of the board, she draws a diagram:
"tokens -> [transformer] -> [diffusion] -> pixels"
>>>
範例：完整對話紀錄