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![[Pasted image 20250612202606.png]] 是是否直接用 flow + diffusion mixed approach 解決這個問題？

重點在於比例。可以任意決定 u(x, t) 和 D(t) 的大小！只要符合 conservation of probability (FP Equation)!

Fokker-Planck 偏微分方程

在 generative AI 的 diffusion process 或是 flow method: 守恆量是機率 (任意時間點的機率和為 1) $\Phi = p(x, t)$，沒有 source $S=0$. 不過一般物理的 diffusion 是從高濃度向低濃度，但是 probability 卻是從低機率 diffuse 到高機率。可以等價視爲負 diffusion constant: $D = -\Gamma$ ，如果假設是 isotropic diffusion, $D(t)$ 和位置無關，可以和時間有關 (noise scheduling)。一般寫成： \(\frac{\partial p(x,t)}{\partial t}=-\nabla \cdot[\mathbf{u}(x,t)\, p(x,t)]+\frac{g^2(t)}{2}\nabla \cdot \nabla p(x,t)\) 微分 SDE/ODE 形式

Forward SDE: for training. \(d \mathbf{x}_t={\mathbf{u}}(\mathbf{x}_t, t)\, d t+g(t)\, d \mathbf{w}_t,\quad \text{ with } d \mathbf{w}_t \sim N(0, d t)\)

Reverse SDE: for sampling. 下式的 $dt$ 是負無窮小 time step. \(d \mathbf{x}_t=[{\mathbf{u}}(\mathbf{x}_t, t)-g^2(t)\nabla_{\mathbf{x}_t} \log p(\mathbf{x}_t)]\, d t+g(t)\, d \mathbf{w}_t,\quad \text{ with } d \mathbf{w}_t \sim N(0, d t)\)

Equivalent Fokker-Planck ODE \(d \mathbf{x}_t=[{\mathbf{u}}(\mathbf{x}_t, t)-\frac{1}{2} g^2(t)\nabla_{\mathbf{x}_t} \log p(\mathbf{x}_t)]\, d t\)

给定正向 SDE： \(dx_t = \left( u(x_t, t) - \frac{1}{2} \left( g^2(t) - \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) \right) dt + \sigma(t) dw\) 给定逆向 SDE： \(dx_t = \left( u(x_t, t) - \frac{1}{2} \left( g^2(t) + \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) \right) dt + \sigma(t) dw\) 此方程描述了一个扩散过程，其中：

  • $u(x_t, t)$ 是确定性漂移项，
  • $\nabla_{x_t} \log p(x_t)$ 是得分函数（Score Function），
  • $\sigma(t) > 0$ 是扩散系数，
  • $dw$ 是标准布朗运动增量。

该形式符合 正向 SDE 的标准结构（漂移项含得分函数修正，扩散项为 $\sigma(t) dw$），表示数据从初始分布 $p_0(x)$ 演化到终末分布 $p_T(x)$ 的过程。

2. 逆向 SDE（式 (13)）的确认

Flow Matching 與 Score Matching 的理論基礎比較

Flow matching（FM）與 score matching（SM）是兩種生成模型方法，理論上密切相關但目標不同。在 score-based（擴散）模型 中，我們學習一個 score function（機率密度的梯度），或等效地學習一個 noise predictor，透過最小化一個去雜訊的均方誤差（MSE）損失。例如，在 DDPM 類型的模型中，目標是最小化：

\[\mathbb{E}[\|\epsilon - \epsilon_\theta(x_t,t)\|^2]\]

以預測加上去的高斯噪聲 。

相較之下，flow matching 直接對一個預先定義的傳輸路徑中的 velocity field $v(x,t)$ 進行回歸。具體而言，FM 定義一個從雜訊到資料的條件分布族 $p_t(x\mid x_1)$（如高斯內插），然後導出該條件分布對應的真實 velocity field，再訓練一個網路 $f_\theta(x,t)$ 來擬合它。FM 的損失為：

\[\mathcal{L}_{FM} = \mathbb{E}_{t,x\sim p_t}[\,\|f_\theta(x,t) - v(x,t)\|^2\,]\]

這使得 $f_\theta$ 能精確擬合真實 flow 。

總結來說，score matching 是訓練一個對 diffusion 過程中的噪聲或 score 進行預測的模型，而 flow matching 是訓練一個描述資料傳輸 ODE 的向量場模型。FM 可以被視為對 diffusion 模型的一般化形式：如果選擇 diffusion-style 的高斯分布路徑，FM 模型可以模擬 score matching 效果，但通常更穩定 。

需要注意的還有 時間方向差異：score-based 模型通常從乾淨資料（t=0）逐步加噪聲到雜訊（t=1），並在生成時反向。而 FM 模型則多為雜訊到資料的方向（0→1），透過 ODE 積分前向產生資料 。

實作差異

  • 模型結構與維度要求：score matching 模型運作於資料空間中，不要求可逆性；而 flow matching 通常實作為 continuous normalizing flow（CNF），因此要求模型是可逆的，且輸入輸出維度一致。這使得 FM 更自然地應用於連續資料上，而 SM 可以更靈活地擴展到潛空間甚至離散資料。

  • 訓練穩定性：兩者都使用簡單的 MSE 損失函數，不需要模擬 forward/reverse 流程。在實作上，FM 常被認為比 score-based diffusion 模型更穩定，尤其是當選用 diffusion path 進行 flow matching 訓練時 。

  • 最佳化與成本：SM 與 FM 每個訓練步驟通常只需一個 forward 和 backward pass，因此每次迭代的成本相當。但傳統 CNF 的最大似然訓練需要昂貴的 ODE 求解，而 FM 可免去此成本。SM 本身也不需 ODE 求解，這使得兩者在訓練時成本相近，但 FM 更具效率。

  • 生成速度：最大差異在於抽樣速度。score-based 模型（如 DDPM）需經過數百或數千步反向推理，而 FM 通常只需少數 ODE 積分步驟。Liu 等人提出的 rectified flow 僅用一個 Euler step 就能生成高品質影像 ，FM 的效率優勢明顯。

  • 高維資料的效率：在高維資料（如影像）中，diffusion 模型需大量噪聲更新步驟，而 flow matching（特別是設計良好的 optimal transport path）可透過直線式流動（shorter paths）大幅減少所需的神經網路評估次數（NFEs） 。

Flow Matching 與 Score Matching 可以合併嗎？

可以，且目前已有理論與實務上的合併方向。許多文獻指出，當 flow matching 使用高斯路徑時，它和 diffusion 模型在數學上是等價的 。FM 與 SM 其實是在不同框架下參數化相同的生成過程；因此，也可以交叉使用彼此的技巧。例如可以使用 diffusion path 進行 flow matching 訓練，並透過 deterministic ODE 或 stochastic SDE 進行抽樣。

此外，像 rectified flow 就是明確將 diffusion 轉化為 flow matching 框架的一種方式 。這些研究說明 FM 與 SM 並非競爭，而是可以互補與轉換的框架。

Flow Matching 可否應用於 VAE 的 Latent Space？

可以。由於 flow matching 需處理高維輸入，因此許多研究將其應用在低維的潛空間（latent space）中，特別是與 VAE 搭配。

例如 Dao 等人（2023） 提出 “Flow Matching in Latent Space”：先訓練一個 VAE 以將資料編碼成潛空間，再在潛空間中使用 FM 進行生成建模。這種方法能有效降低計算成本，且維持高品質的圖像生成 。

同樣地，Samaddar 等人（2025） 的 Latent-CFM 利用預訓練模型提取的潛在特徵進行 flow matching。他們指出，在潛空間中訓練 FM 不僅加快訓練速度，還能提升生成品質 。這些 latent flow 模型也可與條件資訊結合（如分類標籤、inpainting、語義標籤）來實現條件生成。

與此類似的還有 diffusion 模型中的 latent 變體，例如 NVidia 的 Latent Score-based Generative Model (LSGM)，將 score matching 訓練在 VAE 的潛空間中以加速生成 。這些工作都顯示，將 flow matching 應用於潛空間是可行且有效的策略。

應用與結合模型實例

Flow matching 與 score matching 均已應用於各種生成與下游任務，其中包括：

  • 無條件與有條件生成：兩種方法皆能實現高品質的圖像、音訊等資料生成。例如，Lipman 等人基於 FM 的模型在 ImageNet 上達到 SOTA 表現 。Dao 的 latent FM 模型也支援高解析度條件生成（如 inpainting） 。SM 模型如 latent diffusion 也應用於文本到圖像生成（如 Stable Diffusion）。

  • 表徵學習（Representation Learning）：VAE 所學習的潛空間可與 FM 結合，透過在潛空間中訓練 flow，可學得有用的資料表徵，並進行如分類、生成、資料操控等下游任務。

  • 半監督學習：雖然目前使用 flow matching 進行半監督學習的工作較少，但過去已有 flow-based 模型（如 FlowGMM）用於建模資料與標籤的聯合分布，可望結合 FM 進一步擴展。

  • 應用於物理、語言與 3D 資料：FM 已延伸應用於影片生成、分子構象預測、離散資料建模等領域。例如 Polyak 等人將 FM 用於 foundation video models ，Hassan 等人應用於分子生成 。

  • 結合 VAE 與 Flow Matching 的模型：除了上述 latent FM 外，還有如 Variational Rectified Flow Matching（Guo & Schwing, 2025）結合 latent mixture 與 rectified flow，以及 FlowLLM（Sriram 等人, 2024）用 flow matching 微調 LLM 的輸出。這些皆顯示 FM 可與潛變數模型靈活結合，形成高效生成器。

我們看 reverse SDE: 包含 flow and score terms, and random walk dw.

![[Pasted image 20250201233020.png]]

From 8-Gaussian to moons.

![[Pasted image 20250522235631.png]]

![[Pasted image 20250515212250.png]]

Fuse Method 1: t \in [0, 1] Noise_{FM} ~ N(0, I) Noise_{Diff} ~ N(0, I) * scale where scale = 0.1 Dataset

Training flow matching using Input: t, Noise_{FM}, Dataset + Noise_{Diff}, u_t(x\mid z)= Dataset + Noise_{Diff} - Noise_{FM}

Training diffusion using (denoiser): only when 0.9 < t < 1 Input: Dataset + Noise_{Diff}, output = Noise_{Diff}, neural network is used to predict score function.

Flow matching part:

Flow process:

  • **物理意義: 就是讓 noise 移動變成 image.
  • Training:
    • Input: scale input + noise, t
    • Output: vector field
    • $x_n = x_{n-1} + v_t(x_t, \theta)$
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# Flow sampling
dt = T / N
x = x0  # initial sample from prior

for i in range(N):
    t = i * dt
    v = model(x, t)  # predict vector field at (x, t)
    x = x + v * dt  # Euler integration step

Diffusion process: 物理意義: 就是 denoise 變成 image.

  • Training:
    • Input: scale input + noise, t
    • Output: noise $\epsilon_{\theta}$
  • Sampling:
    • Start from noise N(0, I)
    • $x_n = k x_{n-1} - (1-k) \epsilon_{\theta}$

For each timestep $t$, you use the reverse process:

\[x_{t-1} = \frac{1}{\sqrt{\alpha_t}} \left( x_t - \frac{1 - \alpha_t}{\sqrt{1 - \bar{\alpha}_t}} \cdot \epsilon_\theta(x_t, t) \right) + \sigma_t z\]

Where:

  • $\epsilon_\theta(x_t, t)$: predicted noise
  • $\alpha_t$, $\bar{\alpha}_t$: noise schedule values
  • $\sigma_t$: standard deviation of the noise added at step $t$
  • $z \sim \mathcal{N}(0, I)$: noise (used for stochasticity)

🧾 DDPM Sampling Algorithm (Step-by-Step)

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x = torch.randn(batch_size, channels, height, width)  # Start with Gaussian noise

for t in reversed(range(1, T + 1)):  # T to 1
    z = torch.randn_like(x) if t > 1 else 0  # no noise added at final step
    eps = model(x, t)  # Predict the noise

    alpha_t = alphas[t]
    alpha_bar_t = alpha_bars[t]
    sigma_t = compute_sigma(t)  # depends on schedule

    x = (1 / torch.sqrt(alpha_t)) * (x - (1 - alpha_t) / torch.sqrt(1 - alpha_bar_t) * eps) + sigma_t * z

Combine Flow Matching and Score Matching

考慮 g(t) = 0, 可以 degenerate 得到 ODE. 這是 1st order 的 flow?

![[Pasted image 20250530185251.png]]

再加上 2nd order score stochastic function

![[Pasted image 20250530185557.png]]

這是 objective function. 基本是兩個 neural networks? ![[Pasted image 20250530190205.png]]

Takeaway

Diffusion process:

  • 物理意義/現象是從頭到脚的 stochastic SDE
  • 經由對應的 Fokker-Planck ODE equation 可以得到每個時間的 $p_t(x)$, 以及 forward/reverse dynamics.
  • 其中 deterministic score function (vector field) $\nabla_{x} \log p_t(x)$ 扮演關鍵的角色。發動者是 forward path 加 noise; 關鍵是 reverse path score function 的引導者。
  • 訓練 neural network 近似 score function: score matching!
  • Sampling 時則是利用 random sampling from initial distribution -> Langevin dynamics + denoiser (score function) 產生 samples
  • Likelihood: 還是要經過 Fokker-Planc ODE 計算出.

Flow process:

  • 物理意義/現象是 deterministic ODE + random initial condition/distribution
  • 物理順序是: vector field $u_t$ + initial condition 產生-> trajectory $X_t$ 產生-> flow $\phi_t(x_t)$ 產生-> distribution $p_t(x_t)$
  • Flow equation 是非常簡單的 ODE: $X_0 = x_0, \frac{d X_t}{dt} = u_t(X_t)$, $X_t$ 是每一個 trajectory 對應不同的 initial condition $x_0$.
  • 把所有的 initial condition trajectories 集合起來就是 flow: $\phi_t(X_t)$，當然也滿足 $\frac{d \phi_t(X_t)}{dt} = u_t(\phi_t(X_t))$
  • 訓練 neural network 近似 vector fild $u_t$ (是 flow 的微分): flow matching!
  • Sampling 時則是利用 random sample initial distribution -> flow ODE 產生 samples
  • Likelihood: 基本經過 reversed flow 可以計算出.

Flow matching is simulation-free (不用 ODE solver) in training! but need ODE solver in sampling! Consistency mode is simulation-free in sampling!

Flow matching vs. Score Matching

我用 ChatGPT 做了一個比較：

| Aspect | Score Matching (e.g., Diffusion Models) | Flow Matching | | ————————– | ———————————————————————– | ——————————————————————————————- | | Training Objective | Match score ∇ₓ log pₜ(x) using denoising or noise-perturbed samples. | Match vector field uₜ(x) using interpolated paths between base and target. | | Training Data Pairs | Uses perturbations of single samples x ∼ q(x). | Uses sample pairs (x₀, x₁) ∼ p₀ × q. | | Stochasticity | Involves stochastic reverse-time SDE or ODE. | Can be trained and used deterministically (via ODE). | | Interpretability | Score function is less interpretable. | Vector field can be directly visualized as transport directions. | | Sample Quality | High quality, especially for complex distributions. | May suffer in multi-modal or complex geometries due to poor interpolation. | | Interpolation Issues | None — doesn’t interpolate between mismatched samples. | Yes — interpolates between randomly sampled pairs, which may be semantically unrelated. | | Manifold Respect | ✅ Yes — local noise keeps dynamics near the data manifold. | ❌ No — interpolations may go off-manifold, especially in high dimensions. | | Simulation-Free | No — often requires simulation of forward diffusion process. | Yes — training can be done without simulation of trajectories. | | Training Stability | Stable and well-understood, but computationally intensive. | Often more efficient, but may suffer from spurious vector fields. | | Mode Coverage | Good mode coverage due to noise-based training. | Can miss modes if interpolation crosses low-density areas. | | Theoretical Guarantees | Strong links to Fokker-Planck, optimal denoising, and score estimation. | Tied to optimal transport and flow ODEs. | | Applications | Denoising diffusion, image synthesis, audio generation. | Neural ODEs, flow-based generative modeling, fast training. | Add how to do sampling for both score matching and flow matching

  • Score matching - Annealed Langevin dynamics based on score function denoise
  • Flow matching - 利用 vector field (不是 flow) 可以得到 samples

Summary:

  • Score Matching is more robust in complex distributions and provides high sample quality, but can be computationally heavy.

  • Flow Matching is efficient and deterministic, but can suffer from poor interpolation unless guided or regularized carefully.

Appendix D: Flow Matching vs. Score Matching

What’s the issue?

In flow matching, you’re learning a vector field $u_t(x)$ such that the flow transforms samples from a base distribution $p_0$ to a target distribution $q$, by matching trajectories defined by conditional fields (e.g., between $x_0 \sim p_0$ and $x_1 \sim q$).

But during training:

  • You sample independent pairs $(x_0, x_1)$ from $p_0(x_0) \times q(x_1)$.
  • The vector field $u_t(x)$ is trained to point from $x_0$ to $x_1$ (linearly or via some transport plan).

This means:

You’re interpolating between randomly paired samples, not samples that lie on a meaningful trajectory between modes.

Why is this a problem?

  1. Mismatch in Semantics: If $x_0$ and $x_1$ lie in different modes (e.g., digits “1” and “8”), the interpolated vector field may not follow a realistic path — it may go through “garbage” regions.

  2. Conflicting Training Signals: In high dimensions, many training pairs induce vector directions that contradict each other in overlapping regions of space.

  3. Generalization in Between: The learned field might look good at data points but behaves poorly between them — i.e., the model has no guarantee of generating valid intermediate samples during inference.

Contrast with Score-based Models

Score-based models (e.g., diffusion models) estimate the score $\nabla_x \log p_t(x)$, which is locally defined and smooth, and avoid the “random pairing” problem — they only rely on perturbing data and denoising, not interpolating arbitrary sample pairs.

Possible Fixes or Improvements

  • Conditional Flow Matching (CFM): Instead of random pairing, use conditional transport or kernel matching to align similar samples.

  • Use trajectories from known dynamics: If you have access to optimal transport maps (e.g., displacement interpolation), training becomes much more stable.

  • Guidance during interpolation: Add constraints or learned alignment that discourage implausible transitions.

Here’s a table comparing Score Matching (used in diffusion models) and Flow Matching (used in flow-based models):

Certainly! Here’s the updated comparison table with “Manifold Respect” moved right after “Interpolation Issues”:

Aspect Score Matching (e.g., Diffusion Models) Flow Matching
Training Objective Match score ∇ₓ log pₜ(x) using denoising or noise-perturbed samples. Match vector field uₜ(x) using interpolated paths between base and target.
Training Data Pairs Uses perturbations of single samples x ∼ q(x). Uses sample pairs (x₀, x₁) ∼ p₀ × q.
Stochasticity Involves stochastic reverse-time SDE or ODE. Can be trained and used deterministically (via ODE).
Interpretability Score function is less interpretable. Vector field can be directly visualized as transport directions.
Sample Quality High quality, especially for complex distributions. May suffer in multi-modal or complex geometries due to poor interpolation.
Interpolation Issues None — doesn’t interpolate between mismatched samples. Yes — interpolates between randomly sampled pairs, which may be semantically unrelated.
Manifold Respect ✅ Yes — local noise keeps dynamics near the data manifold. ❌ No — interpolations may go off-manifold, especially in high dimensions.
Simulation-Free No — often requires simulation of forward diffusion process. Yes — training can be done without simulation of trajectories.
Training Stability Stable and well-understood, but computationally intensive. Often more efficient, but may suffer from spurious vector fields.
Mode Coverage Good mode coverage due to noise-based training. Can miss modes if interpolation crosses low-density areas.
Theoretical Guarantees Strong links to Fokker-Planck, optimal denoising, and score estimation. Tied to optimal transport and flow ODEs.
Applications Denoising diffusion, image synthesis, audio generation. Neural ODEs, flow-based generative modeling, fast training.

🌐 Manifold Assumption Recap

The manifold hypothesis states that high-dimensional data (e.g., images) actually lies on or near a low-dimensional manifold embedded in the ambient space (e.g., ℝⁿ).

🌀 Score Matching (Diffusion Models)

  • Preserves the manifold assumption — at least during inference:

    • The forward diffusion process adds noise, pushing data off the manifold.
    • But during reverse-time generation, the score function learns to bring samples back toward the data manifold.
    • The model gradually denoises toward realistic-looking images on or near the original manifold.

  • At each denoising step, small updates guided by the score field respect local structure.

✅ Bottom line: Score matching implicitly respects the data manifold by learning how to denoise back to it.

🔁 Flow Matching

  • Potentially breaks the manifold:

    • Flow matching defines vector fields between a base distribution (e.g., Gaussian) and the data distribution, often through interpolated paths.
    • These interpolations (e.g., linear interpolations in pixel space between two image samples) often leave the manifold — they can pass through unrealistic regions that are far from the true data manifold.
    • The resulting vector field pushes probability mass through low-density regions, which is unnatural for image data.

🚨 Problem: If you interpolate between two real images of, say, a cat and a car, the middle of the interpolation may not correspond to any real image-like structure — it’s off-manifold.

🧠 Summary

  Score Matching Flow Matching
Manifold Respect ✅ Mostly stays on/near the data manifold ❌ May generate off-manifold trajectories
Interpolation Implicit via small local noise steps Explicit interpolation between possibly unrelated samples
Risk Lower — local denoising follows manifold Higher — global flows may cross unrealistic regions

🧩 Final Thought

Flow matching can respect the manifold, but only if interpolation is carefully chosen. Some recent work uses manifold-aware interpolations (e.g., via encoders or geodesic paths in latent space) to mitigate this issue.

Would you like to see a visual example comparing on- vs. off-manifold interpolations?

Reference

Schrodinger bridge https://arxiv.org/pdf/2307.03672

Appendix A

根据随机微分方程（SDE）的正向与逆向关系理论，验证如下：

1. 正向 SDE（式 (12)）的确认

给定正向 SDE： \(dx_t = \left( f(x_t, t) - \frac{1}{2} \left( g^2(t) - \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) \right) dt + \sigma(t) dw\) 此方程描述了一个扩散过程，其中：

  • $f(x_t, t)$ 是确定性漂移项，
  • $\nabla_{x_t} \log p(x_t)$ 是得分函数（Score Function），
  • $\sigma(t) > 0$ 是扩散系数，
  • $dw$ 是标准布朗运动增量。

该形式符合 正向 SDE 的标准结构（漂移项含得分函数修正，扩散项为 $\sigma(t) dw$），表示数据从初始分布 $p_0(x)$ 演化到终末分布 $p_T(x)$ 的过程。

2. 逆向 SDE（式 (13)）的确认

给定逆向 SDE： \(dx_t = \left( f(x_t, t) - \frac{1}{2} \left( g^2(t) + \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) \right) dt + \sigma(t) dw\) 根据 Anderson (1982) 的逆向 SDE 理论，一个正向 SDE： \(dx_t = \mu(x_t, t) dt + \sigma(t) dw\) 对应的逆向 SDE 为： \(dx_t = \left( \mu(x_t, t) - \sigma^2(t) \nabla_{x_t} \log p_t(x_t) \right) dt + \sigma(t) d\bar{w}\) 其中 $\bar{w}$ 是反向布朗运动。

验证步骤：

  • 将正向 SDE 的漂移项记为： \(\mu(x_t, t) = f(x_t, t) - \frac{1}{2} \left( g^2(t) - \sigma^2(t) \right) \nabla_{x_t} \log p(x_t)\)
  • 代入逆向 SDE 公式： \(\begin{aligned} dx_t &= \left[ \mu(x_t, t) - \sigma^2(t) \nabla_{x_t} \log p_t(x_t) \right] dt + \sigma(t) d\bar{w} \\ &= \left[ f(x_t, t) - \frac{1}{2} \left( g^2(t) - \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) - \sigma^2(t) \nabla_{x_t} \log p_t(x_t) \right] dt + \sigma(t) d\bar{w} \end{aligned}\)
  • 合并得分函数项： \(-\frac{1}{2} \left( g^2(t) - \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) - \sigma^2(t) \nabla_{x_t} \log p(x_t) = -\frac{1}{2} \left( g^2(t) + \sigma^2(t) \right) \nabla_{x_t} \log p(x_t)\)
  • 结果与式 (13) 一致： \(dx_t = \left( f(x_t, t) - \frac{1}{2} \left( g^2(t) + \sigma^2(t) \right) \nabla_{x_t} \log p(x_t) \right) dt + \sigma(t) d\bar{w}\) 其中 $d\bar{w}$ 在时间反演下等价于 $dw$，故扩散项写为 $\sigma(t) dw$ 是合理的。

3. 结论

  • 式 (12) 是 正向 SDE，描述数据从简单分布向复杂分布的扩散过程。
  • 式 (13) 是 逆向 SDE，用于从噪声中重建数据（生成过程），其漂移项中的得分函数系数符号和大小与理论一致（正向系数为 $-\frac{1}{2}(g^2 - \sigma^2)$，逆向系数为 $-\frac{1}{2}(g^2 + \sigma^2)$）。

两者通过得分函数 $\nabla_{x_t} \log p(x_t)$ 关联，构成完整的生成模型框架（如 Score-Based Generative Models）。验证通过。