P66

Conditional Generation and Guidance

P67

✅ 通常需要的是特定的生成，而不是随意的生成。因此需要通过control引入特定的需求。

以下是文生图的例子：

Ramesh et al., “Hierarchical Text-Conditional Image Generation with CLIP Latents”, arXiv 2022.
Saharia et al., “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”, arXiv 2022.

P68

Conditioning and Guidance Techniques

Explicit Conditions
Classifier Guidance
Classifier-free Guidance

P69

Explicit Conditions

P70
Conditional sampling can be considered as training $p(\mathbf{x} |\mathbf{y} )$ where $\mathbf{y}$ is the input conditioning (e.g., text) and $\mathbf{x}$ is generated output (e.g., image)

Train the score model for $\mathbf{x}$ conditioned on $\mathbf{y}$ using:

$$ \mathbb{E} _ {(\mathbf{x,y} )\sim P\mathrm{data} (\mathbf{x,y} )}\mathbb{E} _ {\epsilon \sim \mathcal{N}(\mathbf{0,I} ) }\mathbb{E} _{t\sim u[0,T]}||\epsilon _ \theta (\mathbf{x} _ t,t;\mathbf{y} )- \epsilon ||^2_2 $$

The conditional score is simply a U-Net with $\mathbf{x}_t$ and $\mathbf{y}$ together in the input.