Generative AI : Create an impressive AI Art 2025
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Welcome to this in-depth and comprehensive course where you will explore the fascinating world of artificial intelligence and learn how to generate realistic images using cutting-edge techniques. With the rapid development of deep learning and neural networks, the potential of AI-generated images is enormous. In this course, you will learn how to build your own machine that can generate images that look strikingly real.
You will start with an introduction to diffusion models, which are a powerful class of models that can be used for image generation. You will explore how they work, their underlying principles, and how to use them in different tasks like inpainting and image-to-image generation. You will also delve into the techniques that are used to train these models and how they can be optimized to produce the best possible results.
Throughout the course, you will gain hands-on experience with practical applications of AI-generated images. You will learn how to use diffusion models to create stunning, high-quality images for a variety of applications. You will also gain an understanding of the ethical implications of AI-generated images and how to navigate these issues in your work.
By the end of the course, you will have the knowledge and skills to build your own machine that can generate realistic images using AI. You will have a deep understanding of the underlying principles of diffusion models and how to apply them to create images that are not only realistic but also aesthetically pleasing. Whether you are a beginner or an experienced AI developer, this course will equip you with the tools you need to take your work to the next level.
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1What you will learn in this course ?Video lesson
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2Where to find the codes ?Video lesson
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3what is stable diffusion ?Video lesson
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4what is gaussian distribution?Video lesson
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5How does stable diffusion model work?Video lesson
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6What is Markove chain ?Video lesson
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7What is Forward diffusion ?Video lesson
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8What is Reparameterization Trick ?Video lesson
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9What is variance schedule ?Video lesson
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10Linear variance schedule Vs cosine-based variance scheduleVideo lesson
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11What is Reverse diffusion ?Video lesson
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12How can we train our network part1 ?Video lesson
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13How can we train our network part2 ?Video lesson
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14How can we train our network part3 ?Video lesson
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15How can we train our network part4 ?Video lesson
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16Stable Diffusion InferenceVideo lesson
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17What is U network ?Video lesson
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18How to create Unconditional diffusion model ?Video lesson
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19What is positional embedding ?Video lesson
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20How to create Conditional diffusion model ?Video lesson
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21How to build and train unconditional diffusion model from scratch ?Video lesson
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22How to use any pretrained diffusion model from Hugging Face Hub ?Video lesson
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23How can we do Inpaint using our diffusion model?Video lesson
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24How to implement Repaint algorithm? python codeVideo lesson
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25How to speed up your diffusion model ?Video lesson
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26DDIM vs DDPM python codeVideo lesson
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27DDIM and out of domain imagesVideo lesson
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28DDIM and out of domain images python codeVideo lesson
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29What is style transfer ?Video lesson
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30style transfer python codeVideo lesson
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31What is stable diffusion ?Video lesson
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32stable diffusion part 1 python codeVideo lesson
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33stable diffusion part 2 python codeVideo lesson
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34Fine-tuning your stable diffusion model using DreamBooth notebook part1Video lesson
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35What is Mixed Precision ?Video lesson
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36Fine-tuning your stable diffusion model using DreamBooth notebook part2Video lesson
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37What is ControlNet ?Video lesson
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38ControlNet with stable diffusion model using diffusersVideo lesson
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39Inpainting with stable diffusion modelVideo lesson
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40Optical flow using FlowNetVideo lesson
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41Optical flow using GMFlowVideo lesson
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42Optical flow using GMFlow ?python codeVideo lesson
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43How to convert your video into frames ?python codeVideo lesson
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44How to extract the latents ? python codeVideo lesson
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45How to find the canny inputs and the latents for each key-frame ? python codeVideo lesson
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46How to generate new key-frames using sd model with control net ? python codeVideo lesson
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47What is cross frame-attention ?Video lesson
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48How to do cross-frame attention part1 ? python codeVideo lesson
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49How to do cross-frame attention part2 ? python codeVideo lesson
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50How to make your generated key-frames sharing the same textures ?Video lesson
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51Shape-aware module python codeVideo lesson
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52Pixel-Aware moduleVideo lesson
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53Pixel-Aware module python codeVideo lesson
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54How to do keyframes to animation?Video lesson
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63what is diffusers library ?Video lesson
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64how to create a pipeline object and how to use it ?Video lesson
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65how these pipelines work ?Video lesson
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66How to use the same diffusion model to do different tasks ?Video lesson
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67How to select the best scheduler ?Video lesson
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68How to use lora models ?Video lesson
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69How to use lora models ? part2Video lesson
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70How to use AnimateDiff pipeline?Video lesson
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71How to use AudioDML pipeline?Video lesson
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72How to use depthmap controlnet with your sd model ?Video lesson
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73How to install AUTOMATIC1111 on Windows ?Video lesson
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74How to add your stable diffusion models & control nets in AUTOMATIC1111 ?Video lesson
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75txt2img in AUTOMATIC1111Video lesson
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76What are LoRA models ?Video lesson
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77How to train stable difusion models using LoRA method?Video lesson
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78img2img + inpainting + outpaintingVideo lesson
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79sketch + inpaint sketchVideo lesson
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80How to create good prompt ?Video lesson
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81What is Regional Prompter ?Video lesson
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82Dynamic promptsVideo lesson
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83open pose ControlnetVideo lesson
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84How to upscale your image using controlnet ?Video lesson
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85How to use multi controlNets ?Video lesson
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86How to do perfect fingers ?Video lesson
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87How to use photopea editor in AUTOMATIC1111 ?Video lesson
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88control lighting with controlnetVideo lesson
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89How to do zoom in and zoom out in automatic 1111 ?Video lesson
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90How can you convert your video into animation ??Video lesson

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