In this blog, I’m going to share how I deployed LLMs (1~3b) and ASR models (whisper tiny & base) on my old smart phone, Xiaomi 8 (dipper). It was released in 2018, with a broken screen and depleted battery after several years of heavy usage, it still offers acceptable speed and fluency running daily light-weight apps. So I wonder, if I deploy modern machine models, such as LLMs, on the phone, and can I run the large models smoothly. Let’s see. The hardware configuration: 8-cores CPU with 4 big cores (Cortex-A75) and 4 small cores (Cortex-A55), 64 GB storage and 6 GB RAM. It has a integrated GPU, which I didn’t find a way to utilize during model inference, so the computation is totally counted on CPU in this trial. To better leverage the power of this CPU, I uninstall the android system (MIUI 12) and port a Ubuntu touch on the phone. Basically it’s a linux OS but using the underlying andriod framework to control hardwares, and it gives me a much longer battery life compared to android, also more convenience since I am a rookie in android development. Models are quantized versions from llamacpp and whispercpp. To get rid of the cumbersome work build an app with GUI, I run models in command line using the terminal app originally from Ubuntu touch, which can be regarded as the same terminal on Ubuntu desktop in this case. All I need to do is to compile the c++ code into an executable file that runs on my phone. Since the architecture of my laptop cpu is x86, the version of glibc, libstdc++ are different from the libs on the phone, I could either compile on the phone directly or cross compile on my PC with a specific toolchain. I kept all the heavy work on PC and built my own toolchain using crosstool-NG, which is targeted at building toolchains.
Following the offical tutorial, I set the version of glibc and libstdc++ according to my phone configuration, while the version of gcc is chosen based on the target code to build, in my case I use gcc 8.5. The version of libstdc++.so.6.0.25 comes with gcc8.5 is not aligned with the libstdc++.so.6.0.21 on my phone, a simple way to address this problem is upgrading old libstdc++ (just replace libstdc++.so could work). I also tried other methods such as building the project in a docker container from clickable (commonly used for deploying applications on ubuntu touch) with a old version of gcc (gcc5.4), I have to define the data types such as ‘vld1q_s16_x2’ manually since these are new types introduced in arm_neon.h in later gcc versions. Anyway, solving problems poping up during cross compiling helps me understand the code and computer system better, and finally I managed to get executable files from both llamacpp and whispercpp. After playing models of different sizes on the phone, I found that the model under 3b (quantized gguf) could achieve balance between speed and performance. I have tried Phi series models from Microsoft and stablelm (1.6b, 3b) from stabilityai, among which phi3 mini (4bit) is the largest one and gives best responses, but the speed is quite slow (1 token/s for prompt evaluation and 3 token/s for generation). The smallest one, stablelm 1.6b (4bit) yeilds 12 token/s and 7 token/s for prompt evaluation and generation respectively, while maintains a good generation quality. For daily use, I prefer this version of stablelm 1.6b, which gives better response after finetuning, with pleasent speed (10 token/s and 6 token/s) at the same time. I also built piper from source, in which case piper-phonemize must be built in advance. There are built binary releases avaliable for aarch64 or arm, but none of them were built under the same old version of linux kernel and glibc as I have on my phone. Another thread related bug arose when I was naively combining piper with llamacpp (run piper everytime the LLM completed a response), I set the number of thread to 1 when loading piper mode (SetInterOpNumThreads) to avoid the error. Then a voice assistant is ready to serve. This project proves that my a 6-year old phone is sufficient to run modern machine learning models after optimization, I will try to test image processing on my phone by running some famous models in computer vision.
Facilitated by the development of deep learning in subfields of computer vision including single object tracking (SOT), video object detection (VOD), and re-identification (Re-ID), many methods of multi-object tracking (MOT) have emerged, such as various embedding models designed for object locations and track identities [1]. While tracking-by-detection is still an effective paradigm for MOT, since we can always combine off-the-shelf state-of-the-art detectors and Re-ID models to improve the MOT system. The idea is: detect the objects in the current frame, then try to associate the detected boxes with tracked boxes in the previous frames. In practice, we need a more sophisticated design (e.g. choose similarity indices for data association, use Kalman Filter to predict new locations of tracks etc.) and proper strategies (e.g. to delete or initialize tracks). Hunarian algorithm is commonly used to matching objects after we get the similarity matrix, in this blog, I will try to explain how and why Hunarian algorithm works from the perspective of linear programming.
Problem Description
Hunarian algorithm or Kuhn-Munkres algorithm is well known for solving assignment problem, which involves assigning tasks to agents (or jobs to workers) such that the total cost is minimized (or the total profit is maximized), subject to the constraint that each agent is assigned exactly one task, and each task is assigned to exactly one agent.
Primal Form of the Assignment Problem
We are given an $n \times n$ cost matrix $C$, where $c_{ij}$ is the cost of assigning agent $i$ to task $j$. \(\begin{aligned} \text{min} & \sum_{i \in I} \sum_{j \in J} c_{ij}x_{ij} \\ s.t. & \sum_{j \in J}x_{ij} = 1 \text{ for all } i \in I \\ &\sum_{i \in I}x_{ij} = 1 \text{ for all } j \in J \\ & x_{ij} \geq 0 \end{aligned}\)
Note that $x_{ij} = 1$ if i is assigned to j and 0 otherwise.
Assignment Problem is a classic linear programming problem, since both the objective function and constraints are linear sums, even though it involves binary decision variables. We can model the problem as a bipartite graph and interpret the algorithm from the perspective of graph (find the perfect matching by finding augmenting path), but this blog will not cover this topic. Let’s try to understand Hungarian algorithm under the framework of linear programming. Since the primal problem is tricky to deal with, let’s go for the dual form of the problem.
Primal to Dual To convert the primal Assignment Problem to its dual form, we need to use Lagrange multipliers to relax the equality constraints in the primal problem. Introduce $u_i$ as the Lagrange multiplier associated with the constraint that agent $i$ is assigned exactly one task ($\sum_j x_{ij} = 1$), $v_j$ as the Lagrange multiplier associated with the constraint that task $j$ is assigned to exactly one agent ($\sum_i x_{ij} = 1$), then formulate the Lagrangian: $$ L(x_{ij},u_i,v_j)=\sum_{i=1}^n \sum_{j=1}^n c_{ij}x_{ij}+\sum_{i=1}^n u_i (1-\sum_{j=1}^n x_{ij})+\sum_{j=1}^n v_j (1-\sum_{i=1}^n x_{ij}) $$ Lagrange dual function is defined as the minimum value of the Lagrangian over primal variables: $$ g(u,v)=\text{inf}_x L(x,u,v) $$ where $\text{inf}_x$ means the infimum (the lower bound) of the Lagrangian over $x$. Since the dual function is the pointwise infimum of a family of affine functions of (u, ν), it is concave, even when the problem is not convex [2]. To minimize this Lagrangian $L(x_{ij},u_i,v_j)$ with respect to $x_{ij}$, rewrite the Lagrangian as: $$ L(x_{ij},u_i,v_j)=\sum_{i=1}^n \sum_{j=1}^n [(c_{ij}-u_i-v_j)x_{ij}] + \sum_{i=1}^n u_i + \sum_{j=1}^n v_j $$ The minimization occurs in the condition: $$ x_{ij}=1 \ \text{ if }\ c_{ij} \leq u_i+v_j \\ x_{ij}=0 \ \text{ if }\ c_{ij} > u_i+v_j $$ Now we want to find the tightest lower bound or minimum duality gap, so we need to maximize the dual function w.r.t $u$ and $v$. In the case of $x_{ij}=1$ If $c_{ij} \leq u_i+v_j$, the maximum occurs when $c_{ij} = u_i+v_j$, so $c_{ij}-u_i-v_j$ in Lagrangian is non-negative.
Dual Form of the Assignment Problem
The dual form of the Assignment Problem can be written as: \(\begin{aligned} \text{max} &\sum_{i=1}^n u_i + \sum_{j=1}^n v_j \\ s.t. & u_i+v_j \leq c_{ij}, \ \forall i, j \end{aligned}\)
where $u_i$ is the dual variable associated with agent $i$’s assignment constraint, $v_j$ is the dual variable associated with task $j$’s assignment constraint. The strong duality theorem of linear programming guarantees that if a feasible and bounded solution exists for the primal linear problem, the dual linear problem is feasible (and bounded as well, by weak duality), with the same optimum value as the primal [3]. The primal problem has constraints that ensure every agent is assigned exactly one task, and every task is assigned exactly one agent, a feasible primal solution exists (since the problem is well-posed with the same number of agents and tasks).
Hungarian Algorithm
Given a $n \times n$ cost matrix (Adjacent Matrix in Graph theory) $C$, the algorithm is summarized as 5 steps below:
Find and subtract the row minimum, namely find the minimum element in each row of the cost matrix and subtract the minimum element in each row from the element in that row.
Find and subtract the colume minimum, namely find the minimum element of each column in the remaining matrix and subtract the minimum element in each column from the elements in that column.
Use the minimum number of horizontal or vertical lines to cover all zero elements in the matrix. If the minimum number of lines is equal to $n$, stop running; if the minimum number of lines is less than $n$, continue to the next step.
Find the minimum value among the uncovered elements, subtract this minimum value from the uncovered elements, and add this minimum value to the elements at the intersection of different lines.
Go back to step 3, that is, continue to cover all zero elements in the matrix with the minimum number of horizontal or vertical lines. If the minimum number of lines is equal to $n$, stop running; if the minimum number of lines is less than $n$, execute step 4.
For details of $O(n^3)$ algorithm implementation, refer to this blog from cp algorithms, where the dual variables $u,v$ are denoted as potential. Hungarian algorithm updates the search of optimal value by continuesly increase the sum of $u,v$: \(f= \sum_{i=1}^n u[i] + \sum_{j=1}^n v[j]\)
In the context of MOT, cost matrix is filled with IOU or similarity score obtained from ReID features between objects in two consecutive frames. Sometimes the matrix is non-square, we can add dummy rows or columes with zeros and run more iterations.
Reference: [1] Wang, G., Song, M., & Hwang, J. N. (2022). Recent advances in embedding methods for multi-object tracking: a survey. arXiv preprint arXiv:2205.10766. [2] Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. [3] Matoušek, Jiří, and Bernd Gärtner. Understanding and using linear programming. Vol. 1. Berlin: Springer, 2007.
Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling significant advancements in tasks such as language translation, text summarization, and sentiment analysis. However, despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges facing LLMs today is the problem of inference speed. Due to the sheer size and complexity of these models, the process of making predictions or extracting information from them can be computationally expensive and time-consuming. Several ways to speed up the LLMs without updating hardware:
Large Language Models (LLMs) are the brains of various chatbots, and luckily we all have access to some brilliant cheap or even free LLMs now, thanks to open source community. It is possible to run LLMs on a PC and keep everything local. This blog presents my solution to building a chatbot running on my PC, with a totally local file storage system and a costumized graphical user interface.
Diffusion probabilistic models, or diffusion models for brevity are first proposed in [1]. The modified version called Denoising diffusion probabilistic models (DDPM) [2] were popularized due to the excellent high quality samples they generated, and Latent Diffusion Models (LDM) [3] make diffusion models practical. As a generative model, A DM models complex datasets in a computational tractable way [1]. Specificly, inspired by non-equilibrium statistical physics, a diffusion model first uses a parameterized Markov chain to gradually convert one distribution to another, usually gradually adds noise to the data to convert a very complex distribution to a simple one, e.g. normal distribution. Then it uses trainable Markov chain to reconstruct the target distribution from a simple known distribution. Here an image $x_0 \sim q(x_0)$ is transitioned to an noisy image $x_t$ over $t$ timesteps by applying the Markov diffusion kernel $q(x_t|x_{t-1})$, or forward diffusion kernel. In the reverse process, $x_t$ is converted back to the target image $x_0$ by using the reverse diffusion kernel $p_\theta (x_t|x_{t-1})$, where $\theta$ represents trainable parameters.
Forward & Reverse Process
Forward Process $$ q(x_1,x_2,...,x_T|x_0):=\prod \limits_{t=1}^T q(x_t|x_{t-1}) \\ q(x_t|x_{t-1}):= \mathcal N (x_t;\sqrt{1-\beta_t}x_{t-1},\beta_t I) $$ where $q(x_t|x_{t-1})$ is known as the forward diffusion kernel, $\beta _1,..., \beta _T$ is a variance schedule, which can be learned by reparameterization (refer to VAE) or held constant as hyperparameters. In the forward process or diffusion process, DDPM destroys the signal by gradually adding Gaussian noise to the data via a Markov chain and obtains the posterior $q(x_1,x_2,...,x_T|x_0)$ or $q(x_{1:T}|x_0)$ for simplicity, where the sampling chain transitions are set to conditional when the diffusion consists of small amounts of Gaussian noise. Thanks to the good traits of Gaussian distribution: Given N independent variables $r_1,...r_N$ that follow Gaussian distributions, the sum $\sum^N_{i=1} r_i$ still obeys Gaussian distribution. $$ r_i \sim \mathcal N(\mu_i,\sigma_i^2), i = 1,2,...,N \\ \sum^N_{i=1} r_i = \mathcal N(\sum^N_{i=1} \mu_i, \sum^N_{i=1} \sigma_i^2) $$ we can easily parameterize the neural network as shown below. The sample at timestep $t$: $$ x_t=\sqrt{1-\beta_t}x_{t-1} + \sqrt{\beta_t}\epsilon_{t-1} $$ where $\epsilon_{t-1} \sim \mathcal N(0,I)$, then let $\alpha_t = 1-\beta_t, \bar \alpha_t = \prod ^T _{i=1} \alpha_i$, substitute $\beta_t$: $$ \begin{aligned} x_t &= \sqrt{\alpha_t}x_{t-1}+\sqrt{1-\alpha_t}\epsilon_{t-1} \\ &= \sqrt{\alpha_t}(\sqrt{\alpha_{t-1}}x_{t-2}+\sqrt{1-\alpha_{t-1}}\epsilon_{t-2})+\sqrt{1-\alpha_t}\epsilon_{t-1} \ \ \ \ (\text{Expanding} \space x_{t-1})\\ &= \sqrt{\alpha_t \alpha_{t-1}}x_{t-2} + \sqrt{\alpha_t(1-\alpha_{t-1})}\epsilon_{t-2} + \sqrt{1-\alpha_t}\epsilon_{t-1} \\ &= \sqrt{\alpha_t \alpha_{t-1}}x_{t-2} + \sqrt{\alpha_t(1-\alpha_{t-1})+(1-\alpha_t)}\hat\epsilon \ \ \ \ (\text{Sum of Gaussian distributions})\\ &= \sqrt{\alpha_t \alpha_{t-1}}x_{t-2} + \sqrt{1-\alpha_t \alpha_{t-1}}\hat\epsilon\\ &= ...\\ &= \sqrt{\bar{\alpha_t}}x_0+\sqrt{1-\bar{\alpha_t}}\epsilon \end{aligned} $$ where $\epsilon \sim \mathcal N (0, I)$. Note that $\sqrt{\alpha_t(1-\alpha_{t-1})}\epsilon_{t-2} \sim \mathcal N (0, \alpha_t(1-\alpha_{t-1})), \sqrt{1-\alpha_t}\epsilon_{t-1} \sim \mathcal N (0, \sqrt{1-\alpha_t})$, the sum of these two Gaussians follows $\mathcal N (0,\sqrt{\alpha_t(1-\alpha_{t-1})+(1-\alpha_t)})$. The above derivations enable us to get samples from arbitary timestep directly without simulating the entire Markov chain, that is the charm of Gaussian distribution.
Reverse Process The reverse process starts from a standard Gaussian $p(x_T)=\mathcal N(x_T;0,I)$: $$ p_\theta (x_0,x_1,...,x_T):=p(x_T) \prod ^T_{t=1}p_\theta (x_{t-1}|x_t) \\ p_\theta (x_{t-1}|x_t) := \mathcal N(x_{t-1};\mu_\theta(x_t,t),\Sigma_\theta(x_t,t)) $$ The joint distribution $p(x_0,...,x_T)$ is called the reverse process and $p_\theta (x_{t-1}|x_t)$ is known as the reverse diffusion kernel. $\mu_\theta(x_t,t)$ and $\Sigma_\theta(x,t)$ are parameters learned by a neural network. The reverse process aims to remove the noise gradually using Markov chain to recover the original data.
Objective Function
\(p_\theta (x_0) = \int p_\theta(x_0,x_1,...,x_T)dx_1dx_2...dx_T\) As a generative model, diffusion models are supposed to maximize the likelihood $p(x)$. However, integrating over all latent variables is intractable, we maximize Evidence Lower Bound (ELBO) instead.
Evidence Lower Bound $$ E_{q_\phi (z|x)} [log \frac{p(x,z)}{q_\phi (z|x)}] $$ Here, $q_\phi(z|x)$ is a probabilistic model to estimate the true distribution over latent variables for given observations $x$. Two ways to derive ELBO, we can use Jensen's inequality: $$ \begin{aligned} \text{log }p(x)&=\text{log }\int p(x,z)dz \\ &=\text{log }\int \frac{p(x,z){q_\phi (z|x)}}dz\\ &=\text{log }E_{q_\phi (z|x)}[\frac{p(x,z)}{q_\phi (z|x)}]\\ &\geq E_{q_\phi (z|x)}[\text{log }\frac{p(x,z)}{q_\phi (z|x)}] \ \ \ \ \text{(Jensen's inequality)} \end{aligned} $$ or we can do it another way: $$ \begin{aligned} \text{log }p(x)&=\text{log }p(x) \underbrace{\int q_\phi(z|x) dz}_1 \\ &= \int q_\phi (z|x)(\text{log }p(x))dz \\ &= E_{q_\phi (z|x)}[\text{log }p(x)] \\ &= E_{q_\phi (z|x)}[\text{log } \frac{p(x,z)}{p(z|x)}] \\ &= E_{q_\phi (z|x)}[\text{log } \frac{p(x,z)q_\phi(z|x)}{p(z|x)q_\phi(z|x)}] \\ &= E_{q_\phi (z|x)}[\text{log } \frac{p(x,z)}{q_\phi (z|x)}]+\underbrace{E_{q_\phi (z|x)}[\text{log } \frac{q_\phi (z|x)}{p(z|x)}]}_{D_{KL}(q_\phi (z|x)||p(z|x))} \\ &\geq E_{q_\phi (z|x)}[\text{log } \frac{p(x,z)}{q_\phi (z|x)}] \ \ \ \ \ \ \text{(KL Divergence is non negative)} \end{aligned} $$ From the second derivation, we now clearly see that ELBO is indeed a lower bound. Moreover, maximizing ELBO is equavalent to minizing $D_{KL}(q_\phi (z|x)||p(z|x))$ considering that $p(x)$ is a constant w.r.t $\phi$, thus we are approaching the true latent posterior distribution as we optimize ELBO.
In diffusion models, $x_0$ is observed data denoted by $x$, $x_1, x_2,…,x_T$ are hidden state denoted by $z$ in above ELBO derivations, and $q_\phi(z|x)$ is a Markov chain modeled by Gaussian distributions, so we can remove $\phi$ in later derivations. We can simply rewrite the ELBO as: \(\text{log } p(x) \geq E_{q(x_{1:T}|x_0)} [\text{log } \frac{p(x_{0:T})}{q(x_{1:T}|x_0)}]\) Take Markov property into consideration $q(x_t|x_{t-1})=q(x_t|x_{t-1},x_0)$, we continue this derivation: \(\begin{aligned} \text{log } p(x) &\geq E_{q(x_{1:T}|x_0)} [\text{log } \frac{p(x_{0:T})}{q(x_{1:T}|x_0)}]\\ &=E_{q(x_{1:T}|x_0)} [\text{log } \frac{p(x_T)\prod^T_{t=1}p_\theta(x_{t-1}|x_t)}{\prod^T_{t=1}q(x_t|x_{t-1})}]\\ &=E_{q(x_{1:T}|x_0)} [\text{log } \frac{p(x_T)p_\theta(x_0|x_1)\prod^T_{t=2}p_\theta(x_{t-1}|x_t)}{q(x_1|x_0)\prod^T_{t=2}q(x_t|x_{t-1},x_0)}]\\ &=E_{q(x_{1:T}|x_0)} [\text{log }\frac{p(x_T)p_\theta(x_0|x_1)}{q(x_1|x_0)}+\text{log } \prod^T_{t=2} \frac{p_\theta(x_{t-1}|x_t)}{\frac{q(x_{t-1}|x_t,x_0)q(x_t|x_0)}{q(x_{t-1}|x_0)}}] \ \ \ \ \ \text{(Bayes rule)}\\ &=E_{q(x_{1:T}|x_0)} [\text{log }\frac{p(x_T)p_\theta(x_0|x_1)}{q(x_1|x_0)}+\text{log }\frac{q(x_1|x_0)}{q(x_T|x_0)} +\text{log } \prod^T_{t=2} \frac{p_\theta(x_{t-1}|x_t)}{q(x_{t-1}|x_t,x_0)}] \\ &=E_{q(x_{1:T}|x_0)} [\text{log }\frac{p(x_T)p_\theta(x_0|x_1)}{q(x_T|x_0)}+\sum^T_{t=2}\text{log } \frac{p_\theta(x_{t-1}|x_t)}{q(x_{t-1}|x_t,x_0)}]\\ &=E_{q(x_{1:T}|x_0)}[\text{log }p_\theta(x_0|x_1)]+E_{q(x_{1:T}|x_0)}[\text{log }\frac{p(x_T)}{q(x_T|x_0)}]+\sum^T_{t=2}E_{q(x_{1:T}|x_0)}[\text{log } \frac{p_\theta(x_{t-1}|x_t)}{q(x_{t-1}|x_t,x_0)}] \\ &=E_{q(x_1|x_0)}[\text{log }p_\theta(x_0|x_1)]+E_{q(x_{T}|x_0)}[\text{log }\frac{p(x_T)}{q(x_T|x_0)}]+\sum^T_{t=2} \underbrace{E_{q(x_t,x_{t-1}|x_0)}[\text{log }\frac{p_\theta(x_{t-1}|x_t)}{q(x_{t-1}|x_t,x_0)}]}_{E_{q(x_t|x_0)}[E_{q(x_{t-1}|x_t,x_0)}\text{log }[\frac{p_\theta(x_{t-1}|x_t)}{q(x_{t-1}|x_t,x_0)}]]}\\ &=\underbrace{E_{q(x_1|x_0)}[\text{log }p_\theta(x_0|x_1)]}_{\text{RV1}}-\underbrace{D_{\text{KL}}(q(x_T|x_0)||p(x_T))}_{\text{RV2}}-\sum^T_{t=2} \underbrace{E_{q(x_t|x_0)}[D_{\text{KL}}(q(x_{t-1}|x_t,x_0)||p_\theta(x_{t-1}|x_t))]}_{\text{RV3}} \end{aligned}\)
The essence of this derivation is to use Bayes rule and Markov property, so we have the diffusion kernel written as: \(q(x_t|x_{t-1},x_0)=\frac{q(x_{t-1}|x_t,x_0)q(x_t|x_0)}{q(x_{t-1}|x_0)}\)
Now look at the interpretation of ELBO, which consists of 3 terms: a. RV1 is a reconstruction term, which can be optimized using a Monte Carlo estimate. b. RV2 measures the distance between the distribution of the output of the forward process and the prior distribution of the reverse process. Since we assume that both are stardard Gaussian, RV2 is 0. c. RV3 represents how close the distribution of learned denoising transition $ p_\theta(x_{t-1}|x_t) $ and the distribution of ground-truth denoising transition $ q(x_{t-1}|x_t,x_0) $ are. RV3 is called a denoising matching term, which we try to minimize so the learned denoising transition step is approaching the true distribution.
The optimization cost lies in the sum of RV3. Recall that for any timestep t, we have $x_t \sim \mathcal N(x_t;\sqrt{\bar \alpha_t}x_0,(1-\bar \alpha_t)I)$, and according to Bayes rule we have: \(\begin{aligned} q(x_{t-1}|x_t,x_0)&=\frac{q(x_t|x_{t-1},x_0)q(x_{t-1}|x_0)}{q(x_t|x_0)}\\ &=\frac{\mathcal N(x_t;\sqrt{\alpha_t}x_{t-1},(1- \alpha_t)I) \ \mathcal N(x_{t-1};\sqrt{\bar \alpha_{t-1}}x_0,(1-\bar \alpha_{t-1})I)}{\mathcal N(x_t;\sqrt{\bar \alpha_t}x_0,(1-\bar \alpha_t)I)}\\ &\propto\text{exp} \left\{-[\frac{(x_t-\sqrt{\alpha_t}x_{t-1})^2}{2(1-\alpha_t)}+\frac{(x_{t-1}-\sqrt{\bar \alpha_{t-1}}x_0)^2}{2(1-\bar \alpha_{t-1})}-\frac{(x_t-\sqrt{\bar \alpha_t}x_0)^2}{2(1-\bar \alpha_t)}]\right\} \\ &=\text{exp} \left\{-\frac{1}{2}[\frac{-2\sqrt{\alpha_t}x_tx_{t-1}+\alpha_t x_{t-1}^2}{1-\alpha_t}+\frac{x_{t-1}^2-2\sqrt{\bar \alpha_{t-1}}x_{t-1}x_0}{1-\bar \alpha_{t-1}}+\underbrace{\frac{x_t^2}{1-\alpha_t}+\frac{\bar \alpha_{t-1}x_0^2}{1-\bar \alpha_{t-1}}-\frac{(x_t-\sqrt{\bar \alpha_t}x_0)^2}{1-\bar \alpha_t}}_{C(x_t,x_0)}]\right\} \\ &=\text{exp} \left\{-\frac{1}{2}[(\frac{\alpha_t}{1-\alpha_t}+\frac{1}{1-\bar \alpha_{t-1}})x_{t-1}^2-2(\frac{\sqrt{\alpha_t}x_t}{1-\alpha_t}+\frac{\sqrt{\bar \alpha_{t-1}}x_0}{1-\bar \alpha_{t-1}})x_{t-1}+C(x_t,x_0)]\right\}\\ &=\text{exp} \left\{- \frac{1-\bar \alpha_t}{2(1- \alpha_t)(1-\bar \alpha_{t-1})}[x_{t-1}^2-2\frac{\sqrt{\alpha_t}(1-\bar \alpha_{t-1})x_t+\sqrt{\bar \alpha_{t-1}}(1-\alpha_t)x_0}{1-\bar \alpha_t}x_{t-1}+\frac{\alpha_t(1-\bar\alpha_{t-1})^2x_t^2+\bar\alpha_{t-1}(1-\alpha_t)^2x_0^2+2\sqrt{\bar \alpha_t}(1-\alpha_t)(1-\bar \alpha_{t-1})x_tx_0}{(1-\bar \alpha_t)^2}]\right\}\\ &=\text{exp} \left\{ -\frac{1}{2 \cdot \frac{(1- \alpha_t)(1-\bar \alpha_{t-1})}{1-\bar \alpha_t}}(x_{t-1}-\frac{\sqrt{\alpha_t}(1-\bar \alpha_{t-1})x_t+\sqrt{\bar \alpha_{t-1}}(1-\alpha_t)x_0}{1-\bar \alpha_t})^2 \right\}\\ &=\mathcal N(x_{t-1};\underbrace{\frac{\sqrt{\alpha_t}(1-\bar \alpha_{t-1})x_t+\sqrt{\bar \alpha_{t-1}}(1-\alpha_t)x_0}{1-\bar \alpha_t}}_{\mu_q(x_t,x_0)},\underbrace{\frac{(1- \alpha_t)(1-\bar \alpha_{t-1})}{1-\bar \alpha_t}I}_{\Sigma_q(t)}) \end{aligned}\) Note that $\alpha$ coefficients are known and frozen at each timestep, in order for $p_\theta(x_{t-1}|x_t)$ to approach $q(x_{t-1}|x_t,x_0)$, we can construct its varience $\Sigma_\theta=\Sigma_q$ and mean $\mu_\theta$ as a function of $x_t$. The mean of $q(x_{t-1}|x_t,x_0)$: \(\mu_q(x_t,x_0)=\frac{\sqrt{\alpha_t}(1-\bar \alpha_{t-1})x_t+\sqrt{\bar \alpha_{t-1}}(1-\alpha_t)x_0}{1-\bar \alpha_t}\)
Follow the form of $\mu_q$, we have: \(\mu_\theta(x_t,x_0)=\frac{\sqrt{\alpha_t}(1-\bar \alpha_{t-1})x_t+\sqrt{\bar \alpha_{t-1}}(1-\alpha_t) \hat x_\theta(x_t,t)}{1-\bar \alpha_t}\)
where $\hat x_\theta(x_t,t)$ is the prediction of original data $x_0$ from noisy data $x_t$ at timestep $t$.
KL Divergence between two Gaussian distributions The $D_\text{KL}$ between two Gaussians is: $$ D_{\text{KL}}(\mathcal N(x;\mu_x,\Sigma_x)||\mathcal N(y;\mu_y,\Sigma_y))=\frac{1}{2}[\text{log}\frac{|\Sigma_y|}{|\Sigma_x|}-d+\text{tr}(\Sigma_y^{-1}\Sigma_x)+(\mu_y-\mu_x)^T\Sigma_y^{-1}(\mu_y-\mu_x)] $$
Our goal is to minimize the KL Divergence: \(\begin{aligned} L &=\underset{\theta}{\text{arg min }} D_{\text{KL}}(q(x_{t-1}|x_t,x_0)||p_\theta(x_{t-1}|x_t))\\ &=\underset{\theta}{\text{arg min }} D_{\text{KL}}(\mathcal N(x_{t-1};\mu_q,\Sigma_q(t))||\mathcal N(x_{t-1};\mu_\theta,\Sigma_q(t)))\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2}[(\mu_\theta-\mu_q)^T\Sigma_q(t)^{-1}(\mu_\theta-\mu_q)]\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}[||\mu_\theta-\mu_q||_2^2] \end{aligned}\)
Here we rewrite $\Sigma_q(t)=\sigma_q^2(t)$. Substitute $\mu_q$, $\mu_\theta$: \(\begin{aligned} L &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}[||\frac{\sqrt{\bar \alpha_{t-1}}(1-\alpha_t)}{1-\bar \alpha_t}(\hat x_\theta(x_t,t)-x_0)||^2_2]\\ &=\underset{\theta}{\text{arg min }}\frac{\bar \alpha_{t-1}(1-\alpha_t)^2}{2\sigma_q^2(t)(1-\bar \alpha_t)^2}[||\hat x_\theta(x_t,t)-x_0||^2_2] \end{aligned}\)
In this way, a neural network is trained to predict the original data $x_0$ from a noisy data $x_t$ at any timestep $t$. While DDPM in [2] take a step further, write $x_0$ using reparameterization as: \(x_0 = \frac{x_t-\sqrt{1-\bar \alpha_t}\epsilon_0}{\sqrt{\bar \alpha_t}}\)
Accordingly we set $\mu_\theta$ as: \(\mu_\theta (x_t,t)=\frac{1}{\sqrt{\alpha_t}}x_t - \frac{1-\alpha_t}{\sqrt{1-\bar \alpha_t}\sqrt{\alpha_t}}\hat \epsilon_\theta(x_t,t)\)
and plug this into our optimization problem: \(\begin{aligned} L&=\underset{\theta}{\text{arg min }} D_{\text{KL}}(q(x_{t-1}|x_t,x_0)||p_\theta(x_{t-1}|x_t))\\ &=\underset{\theta}{\text{arg min }} D_{\text{KL}}(\mathcal N(x_{t-1};\mu_q,\Sigma_q(t))||\mathcal N(x_{t-1};\mu_\theta,\Sigma_q(t)))\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}[||\mu_\theta-\mu_q||_2^2]\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}[||\frac{1}{\sqrt{\alpha_t}}x_t - \frac{1-\alpha_t}{\sqrt{1-\bar \alpha_t}\sqrt{\alpha_t}}\hat \epsilon_\theta(x_t,t)-\frac{1}{\sqrt{\alpha_t}}x_t + \frac{1-\alpha_t}{\sqrt{1-\bar \alpha_t}\sqrt{\alpha_t}}\epsilon_0||_2^2]\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}[||\frac{1-\alpha_t}{\sqrt{1-\bar \alpha_t}\sqrt{\alpha_t}}(\epsilon_0-\hat \epsilon_\theta(x_t,t))||_2^2]\\ &=\underset{\theta}{\text{arg min }} \frac{1}{2\sigma_q^2(t)}\frac{(1-\alpha_t)^2}{(1-\bar \alpha_t)\alpha_t}[||\epsilon_0-\hat \epsilon_\theta(x_t,t)||_2^2] \end{aligned}\)
Here a neural network $\hat \epsilon_\theta(x_t,t)$ is learned to predict $\epsilon_0 \sim \mathcal N(\epsilon;0,1)$. Mathematically learning noise is equivalent to learning the original data, but in practice, DDPM [2], which learns to predict noise, achieves better performance. There’s another equivalent interpretations related to score-based generative models, refers to [5] for more info.
From VAE to DM
This section gives some background knowledge for readers to better understand VAE and diffusion models, and relationship between the two popular types of generative models. Let’s start from VAE, for more detailed illustration please read VAE. A Variational Autoencoder follows a simple encoder-decoder structure, encoder $q(z|x)$ learns the distribution over latent variables $z$ given observations $x$, decoder $p(x|z)$ generates data in observation space from the latent variables:
A generalization of a VAE called Hierarchical Variational Autoencoder (HVAE)extends latent variables to multiple hierarchies. If each transition down the hierarchy is Markovian, we call it a Markovian HVAE (MHVAE), which seems to be a stack of VAEs:
A Diffusion Model is also a special MHVAE, but its encoder is pre-defined as a linear Gaussian model and at final timestep the distribution of the latent is a standard Gaussian. The latent dimension is equal to the data dimension, while some improvements are made using Perceptual Compression to speed up both training and inference process [3].
Reference: [1] Sohl-Dickstein, Jascha, et al. “Deep unsupervised learning using nonequilibrium thermodynamics.” International conference on machine learning. PMLR, 2015. [2] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. “Denoising diffusion probabilistic models.” Advances in neural information processing systems 33 (2020): 6840-6851. [3] Rombach, Robin, et al. “High-resolution image synthesis with latent diffusion models.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. [4] Nichol, Alexander Quinn, and Prafulla Dhariwal. “Improved denoising diffusion probabilistic models.” International conference on machine learning. PMLR, 2021. [5] Luo, Calvin. “Understanding diffusion models: A unified perspective.” arXiv preprint arXiv:2208.11970 (2022).
