Programming Generative AI

File Name

Size

Lesson 2 PyTorch for the Impatient/016. 2.15 Linear Regression with PyTorch.mp4

130 MB

Introduction/001. Programming Generative AI Introduction.mp4

25 MB

Lesson 1 The What, Why, and How of Generative AI/001. Topics.en.srt

1.1 kB

Lesson 1 The What, Why, and How of Generative AI/001. Topics.mp4

3.8 MB

Lesson 1 The What, Why, and How of Generative AI/002. 1.1 Generative AI in the Wild.en.srt

12 kB

Lesson 1 The What, Why, and How of Generative AI/002. 1.1 Generative AI in the Wild.mp4

68 MB

Lesson 1 The What, Why, and How of Generative AI/003. 1.2 Defining Generative AI.en.srt

7.3 kB

Lesson 1 The What, Why, and How of Generative AI/003. 1.2 Defining Generative AI.mp4

24 MB

Lesson 1 The What, Why, and How of Generative AI/004. 1.3 Multitudes of Media.en.srt

15 kB

Lesson 1 The What, Why, and How of Generative AI/004. 1.3 Multitudes of Media.mp4

41 MB

Lesson 1 The What, Why, and How of Generative AI/005. 1.4 How Machines Create.en.srt

14 kB

Lesson 1 The What, Why, and How of Generative AI/005. 1.4 How Machines Create.mp4

49 MB

Lesson 1 The What, Why, and How of Generative AI/006. 1.5 Formalizing Generative Models.en.srt

17 kB

Lesson 1 The What, Why, and How of Generative AI/006. 1.5 Formalizing Generative Models.mp4

57 MB

Lesson 1 The What, Why, and How of Generative AI/007. 1.6 Generative versus Discriminative Models.en.srt

12 kB

Lesson 1 The What, Why, and How of Generative AI/007. 1.6 Generative versus Discriminative Models.mp4

42 MB

Lesson 1 The What, Why, and How of Generative AI/008. 1.7 The Generative Modeling Trilemma.en.srt

9.2 kB

Lesson 1 The What, Why, and How of Generative AI/008. 1.7 The Generative Modeling Trilemma.mp4

31 MB

Lesson 1 The What, Why, and How of Generative AI/009. 1.8 Introduction to Google Colab.en.srt

24 kB

Lesson 1 The What, Why, and How of Generative AI/009. 1.8 Introduction to Google Colab.mp4

115 MB

Lesson 2 PyTorch for the Impatient/001. Topics.en.srt

1.2 kB

Lesson 2 PyTorch for the Impatient/001. Topics.mp4

4.3 MB

Lesson 2 PyTorch for the Impatient/002. 2.1 What Is PyTorch.en.srt

5.2 kB

Lesson 2 PyTorch for the Impatient/002. 2.1 What Is PyTorch.mp4

18 MB

Lesson 2 PyTorch for the Impatient/003. 2.2 The PyTorch Layer Cake.en.srt

12 kB

Lesson 2 PyTorch for the Impatient/003. 2.2 The PyTorch Layer Cake.mp4

37 MB

Lesson 2 PyTorch for the Impatient/004. 2.3 The Deep Learning Software Trilemma.en.srt

8.1 kB

Lesson 2 PyTorch for the Impatient/004. 2.3 The Deep Learning Software Trilemma.mp4

24 MB

Lesson 2 PyTorch for the Impatient/005. 2.4 What Are Tensors, Really.en.srt

6.4 kB

Lesson 2 PyTorch for the Impatient/005. 2.4 What Are Tensors, Really.mp4

22 MB

Lesson 2 PyTorch for the Impatient/006. 2.5 Tensors in PyTorch.en.srt

11 kB

Lesson 2 PyTorch for the Impatient/006. 2.5 Tensors in PyTorch.mp4

39 MB

Lesson 2 PyTorch for the Impatient/007. 2.6 Introduction to Computational Graphs.en.srt

16 kB

Lesson 2 PyTorch for the Impatient/007. 2.6 Introduction to Computational Graphs.mp4

25 MB

Lesson 2 PyTorch for the Impatient/008. 2.7 Backpropagation Is Just the Chain Rule.en.srt

21 kB

Lesson 2 PyTorch for the Impatient/008. 2.7 Backpropagation Is Just the Chain Rule.mp4

35 MB

Lesson 2 PyTorch for the Impatient/009. 2.8 Effortless Backpropagation with torch.autograd.en.srt

15 kB

Lesson 2 PyTorch for the Impatient/009. 2.8 Effortless Backpropagation with torch.autograd.mp4

56 MB

Lesson 2 PyTorch for the Impatient/010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).en.srt

5.0 kB

Lesson 2 PyTorch for the Impatient/010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).mp4

12 MB

Lesson 2 PyTorch for the Impatient/011. 2.10 Working with Devices.en.srt

12 kB

Lesson 2 PyTorch for the Impatient/011. 2.10 Working with Devices.mp4

54 MB

Lesson 2 PyTorch for the Impatient/012. 2.11 Components of a Learning Algorithm.en.srt

8.9 kB

Lesson 2 PyTorch for the Impatient/012. 2.11 Components of a Learning Algorithm.mp4

23 MB

Lesson 2 PyTorch for the Impatient/013. 2.12 Introduction to Gradient Descent.en.srt

7.2 kB

Lesson 2 PyTorch for the Impatient/013. 2.12 Introduction to Gradient Descent.mp4

24 MB

Lesson 2 PyTorch for the Impatient/014. 2.13 Getting to Stochastic Gradient Descent (SGD).en.srt

5.4 kB

Lesson 2 PyTorch for the Impatient/014. 2.13 Getting to Stochastic Gradient Descent (SGD).mp4

15 MB

Lesson 2 PyTorch for the Impatient/015. 2.14 Comparing Gradient Descent and SGD.en.srt

7.2 kB

Lesson 2 PyTorch for the Impatient/015. 2.14 Comparing Gradient Descent and SGD.mp4

29 MB

Lesson 2 PyTorch for the Impatient/016. 2.15 Linear Regression with PyTorch.en.srt

29 kB

Introduction/001. Programming Generative AI Introduction.en.srt

8.0 kB

Lesson 2 PyTorch for the Impatient/017. 2.16 Perceptrons and Neurons.en.srt

9.0 kB

Lesson 2 PyTorch for the Impatient/017. 2.16 Perceptrons and Neurons.mp4

31 MB

Lesson 2 PyTorch for the Impatient/018. 2.17 Layers and Activations with torch.nn.en.srt

14 kB

Lesson 2 PyTorch for the Impatient/018. 2.17 Layers and Activations with torch.nn.mp4

62 MB

Lesson 2 PyTorch for the Impatient/019. 2.18 Multi-layer Feedforward Neural Networks (MLP).en.srt

9.9 kB

Lesson 2 PyTorch for the Impatient/019. 2.18 Multi-layer Feedforward Neural Networks (MLP).mp4

47 MB

Lesson 3 Latent Space Rules Everything Around Me/001. Topics.en.srt

1.1 kB

Lesson 3 Latent Space Rules Everything Around Me/001. Topics.mp4

4.5 MB

Lesson 3 Latent Space Rules Everything Around Me/002. 3.1 Representing Images as Tensors.en.srt

10 kB

Lesson 3 Latent Space Rules Everything Around Me/002. 3.1 Representing Images as Tensors.mp4

35 MB

Lesson 3 Latent Space Rules Everything Around Me/003. 3.2 Desiderata for Computer Vision.en.srt

6.2 kB

Lesson 3 Latent Space Rules Everything Around Me/003. 3.2 Desiderata for Computer Vision.mp4

22 MB

Lesson 3 Latent Space Rules Everything Around Me/004. 3.3 Features of Convolutional Neural Networks.en.srt

9.5 kB

Lesson 3 Latent Space Rules Everything Around Me/004. 3.3 Features of Convolutional Neural Networks.mp4

30 MB

Lesson 3 Latent Space Rules Everything Around Me/005. 3.4 Working with Images in Python.en.srt

13 kB

Lesson 3 Latent Space Rules Everything Around Me/005. 3.4 Working with Images in Python.mp4

51 MB

Lesson 3 Latent Space Rules Everything Around Me/006. 3.5 The FashionMNIST Dataset.en.srt

5.7 kB

Lesson 3 Latent Space Rules Everything Around Me/006. 3.5 The FashionMNIST Dataset.mp4

17 MB

Lesson 3 Latent Space Rules Everything Around Me/007. 3.6 Convolutional Neural Networks in PyTorch.en.srt

14 kB

Lesson 3 Latent Space Rules Everything Around Me/007. 3.6 Convolutional Neural Networks in PyTorch.mp4

40 MB

Lesson 3 Latent Space Rules Everything Around Me/008. 3.7 Components of a Latent Variable Model (LVM).en.srt

11 kB

Lesson 3 Latent Space Rules Everything Around Me/008. 3.7 Components of a Latent Variable Model (LVM).mp4

36 MB

Lesson 3 Latent Space Rules Everything Around Me/009. 3.8 The Humble Autoencoder.en.srt

6.6 kB

Lesson 3 Latent Space Rules Everything Around Me/009. 3.8 The Humble Autoencoder.mp4

20 MB

Lesson 3 Latent Space Rules Everything Around Me/010. 3.9 Defining an Autoencoder with PyTorch.en.srt

7.1 kB

Lesson 3 Latent Space Rules Everything Around Me/010. 3.9 Defining an Autoencoder with PyTorch.mp4

20 MB

Lesson 3 Latent Space Rules Everything Around Me/011. 3.10 Setting up a Training Loop.en.srt

12 kB

Lesson 3 Latent Space Rules Everything Around Me/011. 3.10 Setting up a Training Loop.mp4

34 MB

Lesson 3 Latent Space Rules Everything Around Me/012. 3.11 Inference with an Autoencoder.en.srt

5.7 kB

Lesson 3 Latent Space Rules Everything Around Me/012. 3.11 Inference with an Autoencoder.mp4

18 MB

Lesson 3 Latent Space Rules Everything Around Me/013. 3.12 Look Ma, No Features!.en.srt

11 kB

Lesson 3 Latent Space Rules Everything Around Me/013. 3.12 Look Ma, No Features!.mp4

33 MB

Lesson 3 Latent Space Rules Everything Around Me/014. 3.13 Adding Probability to Autoencoders (VAE).en.srt

6.2 kB

Lesson 3 Latent Space Rules Everything Around Me/014. 3.13 Adding Probability to Autoencoders (VAE).mp4

18 MB

Lesson 3 Latent Space Rules Everything Around Me/015. 3.14 Variational Inference Not Just for Autoencoders.en.srt

9.4 kB

Lesson 3 Latent Space Rules Everything Around Me/015. 3.14 Variational Inference Not Just for Autoencoders.mp4

29 MB

Lesson 3 Latent Space Rules Everything Around Me/016. 3.15 Transforming an Autoencoder into a VAE.en.srt

18 kB

Lesson 3 Latent Space Rules Everything Around Me/016. 3.15 Transforming an Autoencoder into a VAE.mp4

35 MB

Lesson 3 Latent Space Rules Everything Around Me/017. 3.16 Training a VAE with PyTorch.en.srt

19 kB

Lesson 3 Latent Space Rules Everything Around Me/017. 3.16 Training a VAE with PyTorch.mp4

36 MB

Lesson 3 Latent Space Rules Everything Around Me/018. 3.17 Exploring Latent Space.en.srt

17 kB

Lesson 3 Latent Space Rules Everything Around Me/018. 3.17 Exploring Latent Space.mp4

41 MB

Lesson 3 Latent Space Rules Everything Around Me/019. 3.18 Latent Space Interpolation and Attribute Vectors.en.srt

18 kB

Lesson 3 Latent Space Rules Everything Around Me/019. 3.18 Latent Space Interpolation and Attribute Vectors.mp4

38 MB

Lesson 4 Demystifying Diffusion/001. Topics.en.srt

1.2 kB

Lesson 4 Demystifying Diffusion/001. Topics.mp4

4.5 MB

Lesson 4 Demystifying Diffusion/002. 4.1 Generation as a Reversible Process.en.srt

6.3 kB

Lesson 4 Demystifying Diffusion/002. 4.1 Generation as a Reversible Process.mp4

17 MB

Lesson 4 Demystifying Diffusion/003. 4.2 Sampling as Iterative Denoising.en.srt

5.4 kB

Lesson 4 Demystifying Diffusion/003. 4.2 Sampling as Iterative Denoising.mp4

20 MB

Lesson 4 Demystifying Diffusion/004. 4.3 Diffusers and the Hugging Face Ecosystem.en.srt

8.6 kB

Lesson 4 Demystifying Diffusion/004. 4.3 Diffusers and the Hugging Face Ecosystem.mp4

35 MB

Lesson 4 Demystifying Diffusion/005. 4.4 Generating Images with Diffusers Pipelines.en.srt

36 kB

Lesson 4 Demystifying Diffusion/005. 4.4 Generating Images with Diffusers Pipelines.mp4

98 MB

Lesson 4 Demystifying Diffusion/006. 4.5 Deconstructing the Diffusion Process.en.srt

25 kB

Lesson 4 Demystifying Diffusion/006. 4.5 Deconstructing the Diffusion Process.mp4

81 MB

Lesson 4 Demystifying Diffusion/007. 4.6 Forward Process as Encoder.en.srt

22 kB

Lesson 4 Demystifying Diffusion/007. 4.6 Forward Process as Encoder.mp4

68 MB

Lesson 4 Demystifying Diffusion/008. 4.7 Reverse Process as Decoder.en.srt

9.6 kB

Lesson 4 Demystifying Diffusion/008. 4.7 Reverse Process as Decoder.mp4

28 MB

Lesson 4 Demystifying Diffusion/009. 4.8 Interpolating Diffusion Models.en.srt

12 kB

Lesson 4 Demystifying Diffusion/009. 4.8 Interpolating Diffusion Models.mp4

49 MB

Lesson 4 Demystifying Diffusion/010. 4.9 Image-to-Image Translation with SDEdit.en.srt

9.8 kB

Lesson 4 Demystifying Diffusion/010. 4.9 Image-to-Image Translation with SDEdit.mp4

28 MB

Lesson 4 Demystifying Diffusion/011. 4.10 Image Restoration and Enhancement.en.srt

14 kB

Lesson 4 Demystifying Diffusion/011. 4.10 Image Restoration and Enhancement.mp4

38 MB

Lesson 5 Generating and Encoding Text with Transformers/001. Topics.en.srt

1.2 kB

Lesson 5 Generating and Encoding Text with Transformers/001. Topics.mp4

4.0 MB

Lesson 5 Generating and Encoding Text with Transformers/002. 5.1 The Natural Language Processing Pipeline.en.srt

17 kB

Lesson 5 Generating and Encoding Text with Transformers/002. 5.1 The Natural Language Processing Pipeline.mp4

44 MB

Lesson 5 Generating and Encoding Text with Transformers/003. 5.2 Generative Models of Language.en.srt

12 kB

Lesson 5 Generating and Encoding Text with Transformers/003. 5.2 Generative Models of Language.mp4

40 MB

Lesson 5 Generating and Encoding Text with Transformers/004. 5.3 Generating Text with Transformers Pipelines.en.srt

20 kB

Lesson 5 Generating and Encoding Text with Transformers/004. 5.3 Generating Text with Transformers Pipelines.mp4

48 MB

Lesson 5 Generating and Encoding Text with Transformers/005. 5.4 Deconstructing Transformers Pipelines.en.srt

10 kB

Lesson 5 Generating and Encoding Text with Transformers/005. 5.4 Deconstructing Transformers Pipelines.mp4

30 MB

Lesson 5 Generating and Encoding Text with Transformers/006. 5.5 Decoding Strategies.en.srt

17 kB

Lesson 5 Generating and Encoding Text with Transformers/006. 5.5 Decoding Strategies.mp4

38 MB

Lesson 5 Generating and Encoding Text with Transformers/007. 5.6 Transformers are Just Latent Variable Models for Sequences.en.srt

16 kB

Lesson 5 Generating and Encoding Text with Transformers/007. 5.6 Transformers are Just Latent Variable Models for Sequences.mp4

43 MB

Lesson 5 Generating and Encoding Text with Transformers/008. 5.7 Visualizing and Understanding Attention.en.srt

32 kB

Lesson 5 Generating and Encoding Text with Transformers/008. 5.7 Visualizing and Understanding Attention.mp4

56 MB

Lesson 5 Generating and Encoding Text with Transformers/009. 5.8 Turning Words into Vectors.en.srt

13 kB

Lesson 5 Generating and Encoding Text with Transformers/009. 5.8 Turning Words into Vectors.mp4

52 MB

Lesson 5 Generating and Encoding Text with Transformers/010. 5.9 The Vector Space Model.en.srt

9.4 kB

Lesson 5 Generating and Encoding Text with Transformers/010. 5.9 The Vector Space Model.mp4

24 MB

Lesson 5 Generating and Encoding Text with Transformers/011. 5.10 Embedding Sequences with Transformers.en.srt

13 kB

Lesson 5 Generating and Encoding Text with Transformers/011. 5.10 Embedding Sequences with Transformers.mp4

30 MB

Lesson 5 Generating and Encoding Text with Transformers/012. 5.11 Computing the Similarity Between Embeddings.en.srt

9.3 kB

Lesson 5 Generating and Encoding Text with Transformers/012. 5.11 Computing the Similarity Between Embeddings.mp4

24 MB

Lesson 5 Generating and Encoding Text with Transformers/013. 5.12 Semantic Search with Embeddings.en.srt

8.2 kB

Lesson 5 Generating and Encoding Text with Transformers/013. 5.12 Semantic Search with Embeddings.mp4

23 MB

Lesson 5 Generating and Encoding Text with Transformers/014. 5.13 Contrastive Embeddings with Sentence Transformers.en.srt

8.7 kB

Lesson 5 Generating and Encoding Text with Transformers/014. 5.13 Contrastive Embeddings with Sentence Transformers.mp4

20 MB

Lesson 6 Connecting Text and Images/001. Topics.en.srt

1.1 kB

Lesson 6 Connecting Text and Images/001. Topics.mp4

4.2 MB

Lesson 6 Connecting Text and Images/002. 6.1 Components of a Multimodal Model.en.srt

6.9 kB

Lesson 6 Connecting Text and Images/002. 6.1 Components of a Multimodal Model.mp4

16 MB

Lesson 6 Connecting Text and Images/003. 6.2 Vision-Language Understanding.en.srt

12 kB

Lesson 6 Connecting Text and Images/003. 6.2 Vision-Language Understanding.mp4

38 MB

Lesson 6 Connecting Text and Images/004. 6.3 Contrastive Language-Image Pretraining.en.srt

7.5 kB

Lesson 6 Connecting Text and Images/004. 6.3 Contrastive Language-Image Pretraining.mp4

21 MB

Lesson 6 Connecting Text and Images/005. 6.4 Embedding Text and Images with CLIP.en.srt

19 kB

Lesson 6 Connecting Text and Images/005. 6.4 Embedding Text and Images with CLIP.mp4

41 MB

Lesson 6 Connecting Text and Images/006. 6.5 Zero-Shot Image Classification with CLIP.en.srt

4.7 kB

Lesson 6 Connecting Text and Images/006. 6.5 Zero-Shot Image Classification with CLIP.mp4

12 MB

Lesson 6 Connecting Text and Images/007. 6.6 Semantic Image Search with CLIP.en.srt

14 kB

Lesson 6 Connecting Text and Images/007. 6.6 Semantic Image Search with CLIP.mp4

41 MB

Lesson 6 Connecting Text and Images/008. 6.7 Conditional Generative Models.en.srt

6.7 kB

Lesson 6 Connecting Text and Images/008. 6.7 Conditional Generative Models.mp4

25 MB

Lesson 6 Connecting Text and Images/009. 6.8 Introduction to Latent Diffusion Models.en.srt

11 kB

Lesson 6 Connecting Text and Images/009. 6.8 Introduction to Latent Diffusion Models.mp4

33 MB

Lesson 6 Connecting Text and Images/010. 6.9 The Latent Diffusion Model Architecture.en.srt

7.1 kB

Lesson 6 Connecting Text and Images/010. 6.9 The Latent Diffusion Model Architecture.mp4

23 MB

Lesson 6 Connecting Text and Images/011. 6.10 Failure Modes and Additional Tools.en.srt

8.7 kB

Lesson 6 Connecting Text and Images/011. 6.10 Failure Modes and Additional Tools.mp4

29 MB

Lesson 6 Connecting Text and Images/012. 6.11 Stable Diffusion Deconstructed.en.srt

16 kB

Lesson 6 Connecting Text and Images/012. 6.11 Stable Diffusion Deconstructed.mp4

38 MB

Lesson 6 Connecting Text and Images/013. 6.12 Writing Our Own Stable Diffusion Pipeline.en.srt

15 kB

Lesson 6 Connecting Text and Images/013. 6.12 Writing Our Own Stable Diffusion Pipeline.mp4

32 MB

Lesson 6 Connecting Text and Images/014. 6.13 Decoding Images from the Stable Diffusion Latent Space.en.srt

5.5 kB

Lesson 6 Connecting Text and Images/014. 6.13 Decoding Images from the Stable Diffusion Latent Space.mp4

14 MB

Lesson 6 Connecting Text and Images/015. 6.14 Improving Generation with Guidance.en.srt

12 kB

Lesson 6 Connecting Text and Images/015. 6.14 Improving Generation with Guidance.mp4

26 MB

Lesson 6 Connecting Text and Images/016. 6.15 Playing with Prompts.en.srt

40 kB

Lesson 6 Connecting Text and Images/016. 6.15 Playing with Prompts.mp4

121 MB

Lesson 7 Post-Training Procedures for Diffusion Models/001. Topics.en.srt

1.0 kB

Lesson 7 Post-Training Procedures for Diffusion Models/001. Topics.mp4

4.2 MB

Lesson 7 Post-Training Procedures for Diffusion Models/002. 7.1 Methods and Metrics for Evaluating Generative AI.en.srt

8.3 kB

Lesson 7 Post-Training Procedures for Diffusion Models/002. 7.1 Methods and Metrics for Evaluating Generative AI.mp4

22 MB

Lesson 7 Post-Training Procedures for Diffusion Models/003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.en.srt

19 kB

Lesson 7 Post-Training Procedures for Diffusion Models/003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.mp4

54 MB

Lesson 7 Post-Training Procedures for Diffusion Models/004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.en.srt

25 kB

Lesson 7 Post-Training Procedures for Diffusion Models/004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.mp4

64 MB

Lesson 7 Post-Training Procedures for Diffusion Models/005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.en.srt

12 kB

Lesson 7 Post-Training Procedures for Diffusion Models/005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.mp4

23 MB

Lesson 7 Post-Training Procedures for Diffusion Models/006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.en.srt

10 kB

Lesson 7 Post-Training Procedures for Diffusion Models/006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.mp4

24 MB

Lesson 7 Post-Training Procedures for Diffusion Models/007. 7.6 Generating Automatic Captions with BLIP-2.en.srt

10 kB

Lesson 7 Post-Training Procedures for Diffusion Models/007. 7.6 Generating Automatic Captions with BLIP-2.mp4

22 MB

Lesson 7 Post-Training Procedures for Diffusion Models/008. 7.7 Parameter Efficient Fine-Tuning with LoRA.en.srt

15 kB

Lesson 7 Post-Training Procedures for Diffusion Models/008. 7.7 Parameter Efficient Fine-Tuning with LoRA.mp4

45 MB

Lesson 7 Post-Training Procedures for Diffusion Models/009. 7.8 Inspecting the Results of Fine-Tuning.en.srt

6.4 kB

Lesson 7 Post-Training Procedures for Diffusion Models/009. 7.8 Inspecting the Results of Fine-Tuning.mp4

16 MB

Lesson 7 Post-Training Procedures for Diffusion Models/010. 7.9 Inference with LoRAs for Style-Specific Generation.en.srt

16 kB

Lesson 7 Post-Training Procedures for Diffusion Models/010. 7.9 Inference with LoRAs for Style-Specific Generation.mp4

42 MB

Lesson 7 Post-Training Procedures for Diffusion Models/011. 7.10 Conceptual Overview of Textual Inversion.en.srt

9.6 kB

Lesson 7 Post-Training Procedures for Diffusion Models/011. 7.10 Conceptual Overview of Textual Inversion.mp4

33 MB

Lesson 7 Post-Training Procedures for Diffusion Models/012. 7.11 Subject-Specific Personalization with Dreambooth.en.srt

9.5 kB

Lesson 7 Post-Training Procedures for Diffusion Models/012. 7.11 Subject-Specific Personalization with Dreambooth.mp4

33 MB

Lesson 7 Post-Training Procedures for Diffusion Models/013. 7.12 Dreambooth versus LoRA Fine-Tuning.en.srt

7.9 kB

Lesson 7 Post-Training Procedures for Diffusion Models/013. 7.12 Dreambooth versus LoRA Fine-Tuning.mp4

23 MB

Lesson 7 Post-Training Procedures for Diffusion Models/014. 7.13 Dreambooth Fine-Tuning with Hugging Face.en.srt

18 kB

Lesson 7 Post-Training Procedures for Diffusion Models/014. 7.13 Dreambooth Fine-Tuning with Hugging Face.mp4

48 MB

Lesson 7 Post-Training Procedures for Diffusion Models/015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.en.srt

18 kB

Lesson 7 Post-Training Procedures for Diffusion Models/015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.mp4

51 MB

Lesson 7 Post-Training Procedures for Diffusion Models/016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.en.srt

5.5 kB

Lesson 7 Post-Training Procedures for Diffusion Models/016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.mp4

16 MB

Lesson 7 Post-Training Procedures for Diffusion Models/017. 7.16 Creating Edge and Depth Maps for Conditioning.en.srt

20 kB

Lesson 7 Post-Training Procedures for Diffusion Models/017. 7.16 Creating Edge and Depth Maps for Conditioning.mp4

58 MB

Lesson 7 Post-Training Procedures for Diffusion Models/018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.en.srt

22 kB

Lesson 7 Post-Training Procedures for Diffusion Models/018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.mp4

69 MB

Lesson 7 Post-Training Procedures for Diffusion Models/019. 7.18 Understanding and Experimenting with ControlNet Parameters.en.srt

11 kB

Lesson 7 Post-Training Procedures for Diffusion Models/019. 7.18 Understanding and Experimenting with ControlNet Parameters.mp4

36 MB

Lesson 7 Post-Training Procedures for Diffusion Models/020. 7.19 Generative Text Effects with Font Depth Maps.en.srt

3.6 kB

Lesson 7 Post-Training Procedures for Diffusion Models/020. 7.19 Generative Text Effects with Font Depth Maps.mp4

7.1 MB

Lesson 7 Post-Training Procedures for Diffusion Models/021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).en.srt

8.5 kB

Lesson 7 Post-Training Procedures for Diffusion Models/021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).mp4

34 MB

Lesson 7 Post-Training Procedures for Diffusion Models/022. 7.21 Reasons to Distill.en.srt

7.4 kB

Lesson 7 Post-Training Procedures for Diffusion Models/022. 7.21 Reasons to Distill.mp4

18 MB

Lesson 7 Post-Training Procedures for Diffusion Models/023. 7.22 Comparing SDXL and SDXL Turbo.en.srt

16 kB

Lesson 7 Post-Training Procedures for Diffusion Models/023. 7.22 Comparing SDXL and SDXL Turbo.mp4

38 MB

Lesson 7 Post-Training Procedures for Diffusion Models/024. 7.23 Text-Guided Image-to-Image Translation.en.srt

22 kB

Lesson 7 Post-Training Procedures for Diffusion Models/024. 7.23 Text-Guided Image-to-Image Translation.mp4

73 MB

Lesson 7 Post-Training Procedures for Diffusion Models/025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.en.srt

16 kB

Lesson 7 Post-Training Procedures for Diffusion Models/025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.mp4

79 MB

Lesson 7 Post-Training Procedures for Diffusion Models/026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.en.srt

14 kB

Lesson 7 Post-Training Procedures for Diffusion Models/026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.mp4

32 MB

Summary/001. Programming Generative AI Summary.en.srt

1.4 kB

Summary/001. Programming Generative AI Summary.mp4

4.8 MB