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Mastering Generative Vision & Video: From GAN to Flow to DiT
VAEs · Diffusion · ControlNet · Flux · Sora-Style Video Generation · Audio-Visual Sync
5.030 reviews280 learners
- Intermediate
- 31h 38m video
- 316 lectures
- English
- Captions
- Updated 2026
Taught by Vinit Singh
- 31h 38m of on-demand video
- 316 lectures across 5 sections
- One-off payment — no subscription
- Lifetime access on web and mobile
- Closed captions (English)
Curriculum
5 sections316 lectures31h 38m total
- Introduction to Module 04:03
- 0.1 Autoencoders (AEs) and VAEs4:41
- 0111 Autoencoders (AEs) vs. Variational Autoencoder10:08
- 0112 Ambient Space (The Chaos), Manifold,Latent Space5:41
- 0113 The Core of Gen AI: Manifold, Latent Space & Autoencoders6:05
- 0114 The Decoder - From Blueprint Back to Image5:27
- Code Eg 0.1 part 1 Explaination3:47
- 0121 Autoencoder (AE) Architecture Overview2:11
- 0122 Autoencoder Implementation & Limitations4:37
- 0123 Autoencoder Loss function6:44
- Code Eg 0.1 part 2 Explaination4:40
- 0131 Variational Auto-encoder (VAE) Architecture Overview2:25
- 0132 Variational Autoencoders The Probabilistic Revolution4:05
- 0133 The Reparameterization Trick in Generative Computer Vision (VAE)5:52
- 0134 Understanding VAE Loss & The AE vs VAE Showdown5:02
- Code Eg 0.1 part 3 Explaination6:42
- 0141 AE vs. VAE Comparison6:53
- 0142 β-VAE Disentangled Representations5:21
- 0143 Conditional VAE (cVAE)6:04
- 0144 Real-World Applications of AEs & VAEs4:05
- Code Eg 0.1 part 4 Explaination4:51
- 0.2 Generative Adversarial Networks (GANs) - Learning Objectives5:43
- 0211 Generative Adversarial Networks (GANs)10:28
- 0212 GAN Training Loop Path to Nash Equilibrium6:24
- 0213 The Mathematical Engine - The Minimax Game5:40
- Code Eg 0.2 part 1 Explaination5:41
- 0221 DCGAN Architecture The Blueprint for Stable GANs11:10
- 0222 GAN Training Choreography 1 Discriminator Training5:06
- 0223 GAN Training Choreography Part 2 Generator Training3:23
- 0224 The Complete Choreography3:06
- Code Eg 0.2 part 2 Explaination7:56
- 0231 GAN Challenges - Mode Collapse & Vanishing Gradients3:54
- 0232 The Solution - Wasserstein GAN (WGAN)5:33
- 0233 Advantages of WGAN & Implementation Details2:16
- Code Eg 0.2 part 3 Explaination6:27
- 0241 StyleGAN Architecture The Disentangled Synthesis5:34
- 0242 Advanced GAN Architectures StyleGAN4:10
- 0243 CycleGAN Unpaired Image to Image Translation2:21
- 0244 CycleGAN Translation Without Paired Data5:17
- 0245 Evaluating the Unsupervised GAN Metrics6:58
- Code Eg 0.2 part 4 Explaination5:13
- 0.3 Introduction to Vision Transformers (ViT) - Learning Objectives5:01
- 0311 Vision Transformers (ViT) - The Paradigm Shift6:14
- 0312 The Vision Transformer ( ViT) Architecture7:58
- 0313 Why Vision Needs a Global View8:18
- 0314 CNN vs. ViT A Tale of Two Perspectives3:22
- Code Eg 0.3 part 1 Explaination4:43
- 0321 The Engine of Vision - Self-Attention Explained4:09
- 0322 Self-Attention & Multi-head attention6:44
- 0323 From Pixels to Tokens - The ViT Patching Process4:05
- 0324 Step 1 - Flattening Patches to Token Embeddings5:49
- 0325 The Complete Pipeline3:23
- 0326 Step -2 Linear Projection From Pixels to Visual Words5:02
- Code Eg 0.3 part 2 Explaination8:34
- 0331 Step 3 - Positional Encoding Vision Spatial Memor9:01
- 0332 Step 4 - Transformer Encoder The Processing Core8:00
- 0333 Layer Normalisation, MLP & Residual Connections7:34
- Code Eg 0.3 part 3 Explaination6:36
- 0341 Energy Landscape-Aware ViT (ELA-ViT)3:11
- 0342 Hypergraph Vision Transformer (HgVT)2:47
- 0343 2025 Vision Transformer Innovations4:09
- 0344 Data-efficient Image Transformer ( DeiT)9:47
- 0345 Swin Transformer Hierarchial Vision Transformer8:02
- Code Eg 0.3 part 4 Explaination3:20
- 1.1 Probabilistic Diffusion & Latent Diffusion Models (DDPM → LDM)3:49
- 1111. .Probabilistic Diffusion Mechanics – The Core Intuition6:18
- 1112. .Probabilistic Diffusion Mechanics - Mathematical Introduction6:38
- 1113. .Denoising Diffusion Probabilistic Model5:47
- 1114. .Closed form Sampling ( Reparametrization trick)6:05
- 1121. .Forward Process – The Mathematical Foundation6:10
- 1122. .Forward Process – Role & Values of Scheduler2:43
- 1123. .Reparametrization Trick2:27
- 1124. .The Reverse Process – Learning to Denoise3:47
- 1125. .Training steps & Objectives2:25
- 1126. .U-Net Architecture : The Learning Engine2:38
- 1127. .How the Model Learns – Training and Generation4:01
- Code Eg 1 part 115:08
- Code Ex 1 part 14:55
- 1131. .The Compute Bottleneck – Why Pixel Space Diffusion Is Impractical6:01
- 1132. .The Latency challenge - What makes it impractical5:56
- 1133. .Latent Diffusion Models – Solving the Compute Bottleneck4:55
- 1134. .The Spatial compression - Why makes it the right solution3:03
- 1135. .Latent Diffusion Models – Training & Generation method2:22
- 1136. .Diffusion in the Latent Space – The Practical Revolution6:27
- 1137. .LDM: Compute Advantage & Latency Gain6:46
- 1141. .Conditioning (Text-to-Image) – Steering the Denoising Process8:16
- 1142. .Cross Attention : Application & Implication2:01
- 1143. .Cross Attention : Integration in U-net architecture2:23
- 1144. .The Complete LDM Pipeline with Cross Attention Integration3:05
- Code Eg 1 part 220:53
- Code Ex 1 part 215:19
- 1.2 Flow Matching & Rectified Flow — SD3.5, Flux.1/Flux.2 architecture4:56
- 1211. .Why Diffusion's Geometry Matters ?6:17
- 1212. .Flow Matching & Rectified Flow: An introduction3:36
- 1213. .The Bottleneck – Why Curved Paths Kill Speed ?4:00
- 1214. .Latency ,Compute & Performance comparison2:55
- 1215. .What we learned about Diffusion steps count so far?1:15
- Code Eg 1 The Problem with Traditional Diffusion - The Bottleneck22:27
- Code Ex 1 The Problem with Traditional Diffusion - The Bottleneck7:37
- 1221. . Flow Matching: The Broader Framework6:43
- 1222. .Rectified Flow – The Straight Line to Faster Generation ?3:28
- 1223. .Rectified Flow – Impact on Latency2:43
- 1224. .Rectified Flow – Evolution & Industry Adoption2:09
- 1225. .The Mathematical Elegance of Rectified Flow ?1:47
- 1226. .Rectified Flow – The Velocity Prediction Objective5:15
- 1227. .Why Straight Lines Win – Euler Sampling3:45
- 1228. .Step count vs FID Values & Real-World Impact4:26
- Code Eg 2 Flow Matching & Rectified Flow20:54
- Code Ex 2 Flow Matching & Rectified Flow7:51
- 1231. .The 2025/2026 Architectures – Diffusion Transformers Take Over ?️5:16
- 1232. .SD 3.5 & Text Encoder Options4:01
- 1233. .Path to SD 3.5 & the next best things (Models)2:50
- 1234. .FLUX models– The Pinnacle of Flow Matching ?️1:57
- 1235. .FLUX 2.0– The Performance Beast2:50
- 1236. .FLUX 2.0– Inside Look5:05
- Code Eg 3 Stable Diffusion 3.5, Flux 1,213:40
- Code Ex 3 Stable Diffusion 3.5, Flux 1,28:51
- 1.3 Continuous vs Discrete Time Steps & ODE Solvers (Karras et al.)4:45
- 1311. .Continuous-Time Diffusion & ODE Solvers7:47
- 1312. .Discrete Time Steps – The Classical DDPM Markov Chain8:48
- 1313. .Continuous Time Formulation – From Discrete Steps to a Smooth Flow8:01
- 1314. .The Advantages of Continuous Time – Breaking Free from Discrete Steps4:00
- Code Eg 1 Continuous-Time Diffusion & ODE Solvers6:46
- 1321. .Deep Dive into ODE Solvers – Probability Flow ODE6:58
- 1322. .Comparing Popular ODE Solvers – Speed vs. Accuracy2:00
- 1323. .Eulers Method (1st Order) – Predictor‑Corrector for Curved Paths3:24
- 1324. .Heun's Method (2nd Order) – Predictor‑Corrector for Curved Paths3:23
- 1325. .Higher‑Order & Adaptive Solvers – DPM‑Solver, DOPRI, and Beyond6:05
- 1326. .Continuous vs Discrete – Head‑to‑Head Comparison3:05
- 1327. .Practical Implications – Why Video Models Love Rectified Flow & Flow Matc3:32
- Code Eg 2 ODE Solvers ,Video Models & Rectified Flow12:05
- 2.1 Cross-Attention Conditioning & IP-Adapters (Image Prompts)3:18
- 2111. .Conditioning & Control – Cross-Attention as the Steering Wheel8:37
- 2112. .How Cross-Attention steers De-noising2:46
- 2113. .Where Cross-Attention Lives in the U-Net2:40
- 2114. .The Mathematics of Cross-Attention – Aligning Images with Text3:04
- 2115. .The Attention Computation Step-by-Step5:11
- Code Eg Conditioning & Control using Cross-Attention22:43
- Code Ex Conditioning & Control using Cross-Attention6:55
- 2121. .IP-Adapters – From Vague Style to True Subject Identity7:04
- 2122. .The Root Cause : Different Modalities, different weights3:00
- 2123. .Decoupled Cross-Attention – The IP-Adapter Breakthrough3:28
- 2124. .What lambda does : Controling Image influence3:01
- 2125. .Where De-coupled attention lives2:54
- 2126. .The 2025/2026 Landscape – IP-Adapters Evolved3:31
- Code Eg IP-Adapters – From Vague Style to True Subject Identity20:49
- Code Ex IP-Adapters – From Vague Style to True Subject Identity14:01
- 2.2 Structural Guidance with ControlNet & T2I-Adapters5:04
- 2211. .Structure Control – ControlNet & T2I-Adapters6:29
- 2212. . Types of Spatial Control4:55
- 2213. .ControlNet for DiT (SD3, FLUX) – 2025 Evolution7:04
- 2214. .The ControlNet Architecture – Hijacking Pre-trained Models W/o Breaking T3:29
- 2215. .Zero-Convolutions: The Secret Sauce1:49
- 2216. . Information Flow During Training4:43
- 2221. .T2I-Adapters – The Lightweight Control Solution6:33
- 2222. . Stacking Multiple Adapters5:27
- 2223. .ControlNet vs T2I-Adapter: The Verdict3:17
- 2224. . FaceID : IP-Adapter’s Variant5:14
- 2225. .When to Use What4:03
- Code Eg Structure Control – ControlNet & T2I-Adapters21:37
- Code Ex Structure Control – ControlNet & T2I-Adapters9:54
- 2.3 Consistency Models & LCM Distillation (1–4 step inference)6:26
- 2311. .Consistency Models – The Mathematical Leap4:51
- 2312 The Mathematical Formulation7:16
- 2313. .The Consistency Property – All Paths Lead to the Same Destination3:51
- 2314. .Training the Consistency Property1:47
- 2315. .Training Consistency Models – Distillation vs. Isolation6:15
- 2316. .Latent Consistency Models (LCM) & End Notes3:48
- Code Eg Consistency Models & their Training23:33
- .Code Ex Consistency Models & their Training12:17
- 2321. .Latent Consistency Models – 50 Steps to 1 in Latent Space6:09
- 2322. .How LCM generates in 1 to 4 steps9:32
- 2323. .LCM-LoRA – The Universal Accelerator4:38
- 2324. .How LCM-LoRA Works2:15
- 2325. . Real-World Impact: Running on Entry-Level GPUs6:05
- Code Eg Latent Consistency Models ,LCM-LoRA – The Universal Accelerator20:07
- .Code Ex Latent Consistency Models ,LCM-LoRA – The Universal Accelerator10:15
- 2.4 Adversarial Diffusion Distillation — SDXL Turbo & Flux Schnell4:05
- 2411. .Adversarial Diffusion Distillation – Restoring Sharpness with GANs5:16
- 2412. .ADD – Adversarial Diffusion Distillation (1‑4 Step Generation)4:20
- 2413. .Comparison: Distillation Methods4:44
- 2414. .The ADD Solution – Two Masters, One Sharp Result3:30
- 2421. .Flux Schnell – The 2025/2026 Standard for Real-Time Generation5:16
- 2422. .FLUX.1 – The 12B Rectified Flow Transformer2:53
- 2423. .Dual-Stream MMDiT – Transformer Backbone7:16
- 2424. .Why Straight Lines Win for Distillation5:48
- Code Eg Adversarial Diffusion Distillation ,Flux Schnell12:54
- Code Ex Adversarial Diffusion Distillation ,Flux Schnell5:15
- 2.5 Autoregressive Image Generation as Alternative: Parti/Var, StyleGAN3 (brief)5:28
- 2511. .Autoregressive Image Generation – The LLM Path5:31
- 2512. .The Problem: 1D Sequence ≠ 2D Image3:14
- 2513. .Visual Autoregressive Modeling (VAR) – The 2025 Standard8:08
- 2514. .The Mathematical Shift – From 1D Sequences to Multi‑Scale Maps2:12
- 2515. .Scaling Laws: Power‑Law Behavior2:34
- Code Eg Visual Autoregressive Modeling (VAR)19:29
- Code Ex Visual Autoregressive Modeling (VAR)9:42
- Code Eg Parti - Raster-Scan Era12:25
- Code Ex Parti - Raster-Scan Era9:42
- 2521. .StyleGAN: The Master of Disentangled Synthesis4:42
- 2522. .StyleGAN 12:07
- 2523. .StyleGAN 22:21
- 2524. .StyleGAN 32:43
- 2525. .Modern GANs – StyleGAN3 and the Return of Speed3:27
- 2526. .StyleGAN3 – The Alias-Free GAN for True Spatial Control2:18
- 2527. .The Goal: Exact Equivariance2:04
- 2528. .Specific Real-Time Use Cases – Why GANs Still Reign in 20256:01
- Code Eg StyleGAN3 and the Return of Speed15:19
- 3.1 Diffusion Transformers (DiT): 3D Spacetime Patches, Sora/Veo 2/Gen-34:09
- 3111. .Diffusion Transformers (DiT) & 3D Video Generation – Replacing the U‑Net8:00
- 3112 3D DiT: Treating Video as a 3D Cube1:53
- 3113The DiT Solution – Scaling Transformers for Visual Data4:26
- 3114. .3D Spacetime Patches – Tokenizing Video for Transformers5:58
- 3115. .Patchification – Cutting Video into 3D Tokens8:20
- Code Eg Diffusion Transformers (DiT) ,3D Spacetime Patches30:24
- 3121. .3D Positional Encodings – RoPE for Video8:56
- 3122. .How 3D RoPE Works4:19
- 3123. .3D RoPE – Unifying Space & Time with Rotary Embeddings3:41
- 3124. .Real‑World Impact (Sora / Veo)2:04
- 3125. .The 2025/2026 Production Architectures – Video Generation Leaders4:49
- 3126. .Hardware Constraints for Lab Environments – Taming the Token Explosion6:37
- Code Eg RoPE for Video, DiT Based Video Models - Sora, Veo 2, and Gen 3 Alpha28:02
- 3.2 Temporal Consistency: Temporal Attention, Optical Flow & Motion Buckets (SVD3:30
- 3211. . Temporal Consistency & Dynamics – The "Flicker" Problem6:17
- 3212. . Temporal Attention Layers – The Standard Fix for Video Flicker3:19
- 3213. . Attention Mechanisms in Video Diffusion Transformers: Bidirectional vs.2:42
- 3214. . RoPE and Temporal Attention: Encoding the Geometry and Direction of Time4:22
- 3215. . The Mathematical Insight – Spatial vs Temporal Attention5:28
- 3216. . Hardware Constraints for Local Labs – Temporal Attention Memory Blowup6:02
- Code Eg Temporal Consistency and Temporal Attention Layers26:06
- 3221. . Controlling Motion in Video Generation: Optical Flow, Motion Buckets &3:51
- 3222. . What is Optical Flow?7:52
- 3223. . Optical Flow & Physics Priors – Enforcing Exact Motion4:15
- 3224. . Guiding the Generation – Injecting Optical Flow into Latent Space7:08
- 3225. . Motion Buckets – Controlling Motion from a Single Image (Stable Video Di6:24
- 3226. . Motion Bucket ID Computation & its DiT Injection process8:56
- 3227. . Motion Buckets – The Concept of Discrete Motion3:02
- 3228. . Steering the Generation – Motion Bucket as Global Control3:16
- 3231. . What is Camera Control & Noise Augmentation?4:09
- 3232. . How Noise Augmentation Enables Camera Control5:38
- 3233. . Camera Control – Noise Augmentation (cond_aug)3:17
- Code Eg Optical Flow - Dense Motion Vectors, Motion Buckets18:20
- 3.3 Camera Motion Controls & Plücker Coordinates4:00
- 3311. .Camera Motion Controls & Plücker Coordinates 33119:15
- 3312. .Euler angles 33121:53
- 3313. .Camera Motion Over Time — Euler Angles per Frame33132:26
- 3314. .What Are Plücker Coordinates?33145:02
- 3315. .Building Plücker Coordinates Step by Step33156:06
- 3316. .The Plücker Moment – Intrinsic to the Ray Itself 33162:32
- 3321. .The Difficulty of Representing Lines – Why We Need Plücker Coordinates 333:02
- 3322. .Introducing Plücker Coordinates – The Unique Language of Rays 33223:38
- 3323. .Application: Conditioning Diffusion / Flow Matching on Camera Motion33232:44
- Code Eg Camera Motion Controls & Plücker Coordinates8:42
- 3331. .The Plücker Relation – The Constraint That Defines a Valid Ray 33313:01
- 3332. .The Plücker Relation (Klein Quadric) & it’s role33326:17
- 3333. .Duality and Geometric Operations – The Power of Plücker Algebra 33334:13
- 3334. .Camera Motion in Generative Models – Plücker Ray Fields for Flow Matching5:03
- 3335. . Flow Matching Conditioning33353:17
- 3336. .Differentiable Trajectories – Interpolating Camera Motion in Plücker Spac4:38
- 3341. .Plücker‑Based Conditioning – Spatial Consistency & Multi‑View Awareness 33:55
- 3342. . Implementation in Flow Matching / Diffusion33422:07
- 3343. .Limitations of Plücker Coordinates 33439:12
- Code Eg Camera Motion Controls & Plücker Coordinates - 2nd7:29
- 4.1 Neural Audio Synthesis: AudioLM, MusicGen, Stable Audio 2.0 (Flow tokens)4:31
- 4111. . Neural Audio Synthesis – AudioLM, MusicGen, Stable Audio 2.09:38
- 4112. . Key Innovations & Trade-offs7:07
- 4113. . Neural Audio Synthesis – Foundational Concepts (Basics)7:26
- 4114. . Neural Audio Synthesis – Why Direct Audio Modeling Is Non‑Trivial6:58
- 4121. . Neural Audio Codec – Decoupling Structure from Fidelity6:15
- 4122. . Core Theory of Audio Representation & Flow – Residual Vector Quantizatio5:37
- 4123. . The Setup: Raw Audio to Down-sampled Continuous Vectors5:28
- 4124. . Continuous Vectors to Residual Vector Quantisation (RVQ) Codebooks5:40
- 4125. . Hierarchical Property of RVQ2:31
- 4131. . Straight Line Flow matching for Latent Audio2:52
- 4132. . Continuous Flow Matching – Generative ODEs for Latent Audio4:57
- 4133. . Advantages of Continuous over Discrete approach4:14
- 4134. . Continuous Flow Matching (CFM): Noise to Data5:47
- 4135. . Flow Matching – Straight-Line Paths Minimize Truncation Error for Long A1:45
- Code Eg Residual Vector Quantization (RVQ) &Continuous Flow Matching14:19
- 4141. . AudioLM: Hierarchical Two‑Stage Approach7:21
- 4142. . MusicGen: Single‑Stage Efficient Autoregression2:38
- 4143. . MusicGen – Efficient Interleaved Prediction of RVQ Layers2:15
- 4144. . MusicGen’s Delay Pattern (or token interleaving) explained5:55
- 4151. . Latent Flow Matching – Stable Audio 2.010:02
- 4152. . How Flow Matching Enables Desirable Properties in Stable Audio 2.03:29
- 4153. . Stable Audio 2.0 - Key Theoretical Limitation2:13
- 4154. . Current Frontiers (2025+)3:52
- 4155. . Continuous audio manifolds & geodesic paths2:21
- 4156. . Active Research Directions in Neural Audio Synthesis3:52
- Code Eg AudioLM, MusicGen, Stable Audio 2.013:04
- 4.2 Unified Audio-Visual Generation — Veo 2 native AV synthesis4:27
- 4211. .Unified Audio-Visual Generation & Lip-Sync – The Veo 2 Era6:43
- 4212. .Unified DiT : Architectures, Models & Why Native Wins3:01
- 4213. .The Joint Latent Space – Unifying Audio & Video Tokens3:59
- 4214. . Architecture: Multimodal Diffusion Transformer (MM‑DiT)2:59
- 4221. .Cross-Modal Attention – Locking Sound and Vision Together3:05
- 4222. .Joint Denoising: The Physics Lock4:44
- 4223. .The Post-Processing Alternative – Lip-Sync with SyncNet & Wav2Lip5:00
- 4224. .Lip-Sync with SyncNet & Wav2Lip – Use Cases & Pros2:06
- Code Eg Unified Audio-Visual Generation, MM DiT , Lip-Sync - SyncNet & Wav2Li12:03
- 4.3 Lip-Sync Post-Processing Architectures (Wav2Lip, SyncNet)2:20
- 4311. .SyncNet – The "Judge" That Measures Lip‑Sync Accuracy8:46
- 4312. .SyncNet – Why SyncNet Matters, Modern Variants (2025+)3:51
- 4313. .SyncNet – The Contrastive Loss4:25
- 4314. .Once Trained, SyncNet Becomes the Harsh Judge1:37
- 4321. .Wav2Lip – The "Artist"5:17
- 4322. .Wav2Lip – The "Artist" & it’s Training with SyncNet (The Judge)4:42
- 4323. .Wav2Lip – The Adversarial Training Loop3:37
- 4324. . Forcing Phoneme‑to‑Viseme Precision3:22
- 4325. .Hardware Constraints for Local Labs – Native vs. Lip‑Sync4:32
- 4.4 Latent Audio-Video Alignment Metrics (FAD, SyncNet scoring)2:08
- 4411. .Latent Audio-Video Alignment Metrics – FAD & SyncNet Scoring7:53
- 4412. .SyncNet Scoring (Temporal Alignment between Audio and Video)6:59
- 4413. .Why Latent Metrics (FAD & SyncNet) Are Non‑Trivial & Superior7:14
- 4414. .Core Theory of Latent Alignment Metrics – FAD & Wasserstein Distance5:05
- 4415. .SyncNet: A Different Alignment Metric (Temporal)1:47
- 4416. .FAD & SyncNet – Two Pillars of Audio‑Video Alignment , Summary & Quick R3:26
About this course
Mastering Generative Vision and Video: From GAN to Flow to DiT The Complete Engineering Guide to Modern Generative AI — Images, Video, and Audio-Visual Synthesis Generative AI is no longer a research curiosity. It is the engine behind billion-dollar products, production pipelines at studios and startups, and the most sought-after engineering skillset in the AI job market today. Stable Diffusion, Sora, DALL-E, Runway, Midjourney, Kling, and Veo — every one of these systems is built on the architectural foundations…
Before you start
- Essential Technical Knowledge: Completion of a foundational computer vision course covering CNNs, image classification, and basic deep learning — or equivalent practical experience. This course is explicitly designed as a continuation of the "Mastering Computer Vision: From Pixel to Detection to Gen-CV" course and assumes that level of preparation. Solid Python programming skills including comfort with object-oriented programming, working with libraries, and writing training loops from scratch. Working knowledge of PyTorch or TensorFlow — students should be able to define a model, write a training loop, load data, and run inference without step-by-step guidance. Basic understanding of neural network fundamentals — forward pass, backpropagation, loss functions, gradient descent, and activation functions. Familiarity with Convolutional Neural Networks and how they process image data — feature maps, pooling, and spatial hierarchies.
- Recommended but Not Strictly Required: Prior exposure to attention mechanisms and the Transformer architecture is helpful, as Module 0 covers Vision Transformers at an accelerated pace assuming some prior familiarity. Basic understanding of probability and statistics — particularly concepts like distributions, sampling, and KL divergence — will help with the diffusion and VAE modules. Familiarity with Jupyter Notebooks and running experiments on cloud GPU environments (Google Colab, Kaggle, or similar).
- Hardware and Software: A computer capable of running Python 3.8 or higher with standard deep learning libraries installed. Access to a GPU environment for running labs — cloud GPU platforms are acceptable and recommended for students without local GPU resources. No specialized hardware is required beyond access to a free-tier cloud GPU for practical lab sessions.
- This course is NOT suitable for: Complete beginners to deep learning or Python programming. Students with no prior exposure to convolutional neural networks or image classification. Those looking for a no-code or prompt-engineering course — this is an implementation and architecture-focused engineering course.
Who it’s for
- 1. AI engineers and developers who want to move beyond recognition tasks and build generative image, video, and audio-visual systems using diffusion models, flow matching, and transformer architectures.
- 2. Students who have completed a foundational computer vision course and are ready to advance into generative AI, learning the architectures behind Stable Diffusion, Sora, ControlNet, and Veo.
- 3. Machine learning researchers and practitioners who want hands-on implementation experience with state-of-the-art generative models including DiT, LCM, SDXL, Flux, and audio-visual synthesis systems.
- 4. Software developers and technical entrepreneurs building generative AI products who need architectural understanding beyond prompt engineering to integrate and customize foundation models.
- 5. Data scientists and deep learning engineers looking to specialize in generative vision and video, one of the fastest growing and highest paying areas in the current AI job market.
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