Sampler - Stable Diffusion

1. DDPM: Denoising Diffusion Probabilistic Model
1. basic sampler -> Stable Diffusion's basic sampler 
2. 1000 steps 
2. DDIM: Denoising Diffusion Implicit Model
1. DDPM -> DDIM 
2. 10 ~ 50 steps 
3. Euler Discrete
1. Ordinary Differential Equation (OED) 
2. simple & fast 
3. same of DDIM or more steps
4. OLSS: Optimal Linear Subspace Search
1. fast, but using the old model 
2. 5 ~ 10 steps 
5. CM: Consistency Model
1. 1 step
2. pixel space
3. complex calculation
4. limited high resolution image 
6. LCM: Latent Consistency Model
1. 1 ~ 4 steps 
2. latent space
3. low dimension calculation
4. support high resolution image 
5. need LoRA or need re-training model (cannot use old model) 

Image quality metric (Generative AI)

Image Quality Metrics for Generative AI 

 

1. PSNR (Peak Signal to Noise Ratio)
1.  Image Quality Metrics: PSNR vs. SSIM
2. SSIM (Structural SIMilarity)
1.  Image quality assessment: from error visibility to structural similarity
2.  Understanding SSIM
3. The SSIM Index for Image Quality Assessment 
3. FID (Frechet Inception Distance)
1.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
2. Frechet distance 
1. How long should your dog leash be? 
4. LPIPS
1.  The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

 

Code Review - C++

- Papers

• Challenges of Refactoring C Programs 
• https://refactoring.guru/design-patterns/cpp 
• Discipline Matters: Refactoring of Preprocessor Directives in the #ifdef Hell 
• Embracing the C preprocessor during refactoring 

 

- Articles

• Why Google Stores Billions of Lines of Code in a single Repository
• Software Engineering at Google  
• C to C++: 5 Tips for Refactoring C Code into C++
• Tips for C++ Refactoring 
• Refactoring for C/C++ and C#: Let the Machine do the Work
• Refactoring in C++: Top Techniques and Best Practices

 

- Books

• Clean Code
• Clean Coder
• Software Engineering at Google 
  

LVM - DDPM, DDIM

1. Evaluation
1. Inception Score
2. FID (Freche Inception Distance)
2. LVM - Large Visual Model
1. GAN
2. DDPM: denoising diffusion probabilistic models (https://arxiv.org/abs/2006.11239)
3. DDIM: denoising diffusion models (https://arxiv.org/abs/2010.02502)

LLM - Sampling Temperature, Top K, and Top P, What & How?

In the LLM, using sampling

When we set the temperature, top K, and top P in the LLM, do you know what they are and what the order for applying them is?

• LLM Samplers Explained
• How to sample from the language model
• What is the actual order of execution when Top-K, Top-P, and Temperature are used together for NLP decoding?
• Token selection strategies: Top-K, Top-P, and Temperature
• Can someone explain what Top K and Top P are and what they do and how to use them?
• Mastering LLM Parameters: A Deep Dive into Temperature, Top-K, and Top-P

actual order: Temperature -> Top K -> Top P

arXiv

• The Curious Case of Neural Text Degeneration (Nucleus Sampling)

Repetition penalties are terribly implemented - A short explanation and solution

https://www.reddit.com/r/LocalLLaMA/comments/1g383mq/repetition_penalties_are_terribly_implemented_a/?rdt=41955

 

PyTorch - get the total number of model parameter

Total number of model parameters

• https://stackoverflow.com/questions/49201236/check-the-total-number-of-parameters-in-a-pytorch-model
• https://discuss.pytorch.org/t/how-do-i-check-the-number-of-parameters-of-a-model/4325

1. simple version

pytorch_total_params = sum(p.numel() for p in model.parameters())

2. listed version

def count_parameters(model):

  str_name = "name"

  str_parameter = "parameter"

  print(f"{str_name:50s}: {str_parameter:10s}")

  total_params = 0

  for name, parameter in model.named_parameters():

    if not parameter.requires_grad:

      continue

    params = parameter.numel()

    print(f"{name:50s}: {params:10s}")

    total_params += params

  print(f"Total Trainable Params: {total_params}")

  return total_params

PIL - image resize() from the basic code & idea

PIL: Python Image Library: https://github.com/python-pillow/

  Written by Fredrik Lundh(https://groups.google.com/g/dev-python/c/fbv5gWgaGpM?pli=1)

image resize algorithm

• https://en.wikipedia.org/wiki/Image_scaling
• https://en.wikipedia.org/wiki/Affine_transformation#Examples
• affine transformation scaling
• Please refer to "scale about image" image
• https://en.wikipedia.org/wiki/Bicubic_interpolation
• https://en.wikipedia.org/wiki/Bicubic_interpolation#/media/File:Comparison_of_1D_and_2D_interpolation.svg
• Pillow(PIL) Image resize NEAREST behavior differs depending on the version
• Resizing Images With Bicubic Interpolation