使用 U-Net 预测噪声
扩散模型核心中的去噪器
使用 U-Net 预测噪声 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
The Denoiser's Job
At every reverse step a single network does the heavy lifting. Its job is simple to state: look at a noisy image and predict the noise inside it.
Why a U-Net
Diffusion almost always uses a U-Net, because its output is the same size as its input: a noisy image in, a noise map out.
The Shrinking Path
The encoder downsamples the image, capturing big-picture structure like shapes and layout at a small resolution.
The Growing Path
The decoder then upsamples back to full size, rebuilding fine detail so the predicted noise lines up pixel for pixel.
Skip Connections
The U shape comes from skip connections that pass detail from encoder to decoder, so sharp edges survive the squeeze.
Feeding the Timestep
The U-Net also needs the timestep t, so it knows whether to remove a lot of noise or just a little at this stage.
Time Embeddings
That timestep is turned into a vector called a time embedding and mixed into the network's layers as a conditioning signal.
t_emb = embed(timestep) # added inside each blockAttention in the Middle
Many U-Nets add attention at the lowest resolution, letting distant regions of the image coordinate their content.
One Loss, One Target
Training compares the predicted noise to the real noise with simple MSE loss. That single objective is all it takes.
loss = mse(unet(x_t, t), true_noise)Same Net, Every Step
One U-Net handles all timesteps. The t input is what tells it how aggressively to denoise right now.
Subtract and Repeat
Predict noise, subtract a slice of it, feed the result back in. Repeating this is how the U-Net walks toward a clean image. 🖼️
Quick Check
What role do the U-Net's skip connections play?
Recap
A U-Net predicts noise at every step, using skip connections for detail and a time embedding to gauge the timestep. One MSE loss trains it all. 🎉
用 AI 导师学习 Python — 免费
在浏览器中编写并运行真实代码，获得全天候 AI 导师的即时帮助，并在网页或应用中继续学习。
- 课程
- 30
- 课程
- 120
常见问题解答
「使用 U-Net 预测噪声」课时是免费的吗？
是的 — 「使用 U-Net 预测噪声」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。
「使用 U-Net 预测噪声」这节课中我会学到什么？
扩散模型核心中的去噪器 你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Deep Learning Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「使用 U-Net 预测噪声」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Deep Learning Academy 课中编写并运行代码吗？
能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 前向加噪与反向去噪
- 使用 U-Net 预测噪声
- 采样调度与引导
- 使用 Diffusers 运行推理流程