2023 Diffusion 模型简介
上次更新时间：2024-10-29
课程售价: 2.9 元
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课程内容
1 - Introduction
2 - Initial paper on Diffusion Models
- 2 - Forward Diffusion process (免费)
- 3 - Forward Diffusion process implementation
- 4 - Diffusion process tricks
- 5 - Diffusion process incorporation of the tricks in the implementation
- 6 - Diffusion process visualization
- 7 - Reverse process
- 8 - Reverse process implementation
- 9 - Architecture of the model
- 10 - Reverse process sampling
- 11 - Reverse process visualization
- 12 - Training equations part 1
- 13 - Training equations part 2
- 14 - Training equations implementation part 1
- 15 - Training equations implementation part 2
- 16 - Implementation of the training loop
- 17 - Training on GPU
- 18 - Correct typo
- 19 - Reproduction of a Figure from the paper Analysis of the results
3 - Denoising Diffusion Probabilistic Models
- 20 - Review of the paper
- 21 - Time embedding
- 22 - Pseudocode
- 23 - UNet Implementation time embedding
- 24 - UNet Implementation downsampling
- 25 - UNet Implementation upsampling
- 26 - UNet Implementation ResNet part1
- 27 - UNet Implementation ResNet part2
- 28 - UNet Implementation ResNet part3
- 29 - UNet Implementation Attention Mechanism part1
- 30 - UNet Implementation Attention Mechanism part2
- 31 - Finishing the UNet Implementation part1
- 32 - Finishing the UNet Implementation part2
- 33 - Finishing the UNet Implementation part3
- 34 - Finishing the UNet Implementation part4
- 35 - Finishing the UNet Implementation part5
- 36 - Denoising Diffusion Probabilistic Models implementation
- 37 - Denoising Diffusion Probabilistic Models training
- 38 - Denoising Diffusion Probabilistic Models sampling
- 39 - Denoising Diffusion Probabilistic Models utils
- 40 - Denoising Diffusion Probabilistic Models training loop
- 41 - Denoising Diffusion Probabilistic Models visualization
- 42 - Denoising Diffusion Probabilistic Models training on GPU
- 43 - Analysis of the results
4 - Inpainting
5 - Animating Diffusion Models
课程内容
5个章节 , 48个讲座
1 - Introduction
2 - Initial paper on Diffusion Models
- 2 - Forward Diffusion process (免费)
- 3 - Forward Diffusion process implementation
- 4 - Diffusion process tricks
- 5 - Diffusion process incorporation of the tricks in the implementation
- 6 - Diffusion process visualization
- 7 - Reverse process
- 8 - Reverse process implementation
- 9 - Architecture of the model
- 10 - Reverse process sampling
- 11 - Reverse process visualization
- 12 - Training equations part 1
- 13 - Training equations part 2
- 14 - Training equations implementation part 1
- 15 - Training equations implementation part 2
- 16 - Implementation of the training loop
- 17 - Training on GPU
- 18 - Correct typo
- 19 - Reproduction of a Figure from the paper Analysis of the results
3 - Denoising Diffusion Probabilistic Models
- 20 - Review of the paper
- 21 - Time embedding
- 22 - Pseudocode
- 23 - UNet Implementation time embedding
- 24 - UNet Implementation downsampling
- 25 - UNet Implementation upsampling
- 26 - UNet Implementation ResNet part1
- 27 - UNet Implementation ResNet part2
- 28 - UNet Implementation ResNet part3
- 29 - UNet Implementation Attention Mechanism part1
- 30 - UNet Implementation Attention Mechanism part2
- 31 - Finishing the UNet Implementation part1
- 32 - Finishing the UNet Implementation part2
- 33 - Finishing the UNet Implementation part3
- 34 - Finishing the UNet Implementation part4
- 35 - Finishing the UNet Implementation part5
- 36 - Denoising Diffusion Probabilistic Models implementation
- 37 - Denoising Diffusion Probabilistic Models training
- 38 - Denoising Diffusion Probabilistic Models sampling
- 39 - Denoising Diffusion Probabilistic Models utils
- 40 - Denoising Diffusion Probabilistic Models training loop
- 41 - Denoising Diffusion Probabilistic Models visualization
- 42 - Denoising Diffusion Probabilistic Models training on GPU
- 43 - Analysis of the results