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### Model Description

This repository contains the trained checkpoints (.pt) and generated samples (.png) for two generative models trained on a subset of the STL-10 dataset.

- Pixel DDPM: Standard diffusion on 96x96 RGB images.

- Latent DDPM (LDM): Diffusion on a compressed 12x12x4 latent space (using a VAE).

Note: This repository only contains the weights. To load and use these models, 
you must have the original model definitions (DDPM.py and VAE.py) in your local Python environment.


- **Developed by:** Linoy Geva & Ron Chernoguz
- **Model type:** DDPM/LPM


## Uses

This model is designed to generate synthetic data that mimics the characteristics of the STL-10 dataset. 
It is intended for use in experimental research comparing the effectiveness of diffusion-based data augmentation 
against traditional/manual augmentation techniques for CNN classification.


### Recommendations

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## Training Details


### Training Data

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[More Information Needed]

### Training Procedure

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#### Training Hyperparameters

==========
               PIXEL DDPM (Baseline Model) ===========================================================
• Optimization Strategy:
  - Optimizer:      AdamW
  - Learning Rate:  2e-4 (with Cosine Annealing)
  - Weight Decay:   1e-3
  - Batch Size:     32
  - Total Epochs:   225

• Model Architecture:
  - Input:          96x96 RGB Images
  - Base Channels:  64
  - Channel Mults:  (1, 2, 4, 8)
  - EMA:            Disabled
  - Diffusion:      1000 Timesteps (Linear Schedule)

==========
               LATENT DDPM (LDM) ===========================================================
• Optimization Strategy:
  - Optimizer:      AdamW
  - Learning Rate:  1e-4 (with Cosine Annealing)
  - Weight Decay:   1e-3
  - Batch Size:     64
  - Total Epochs:   2500 (Target)

• Model Architecture:
  - Input:          12x12x4 Latents (via VAE)
  - Base Channels:  128
  - Channel Mults:  (2, 2)
  - EMA:            Enabled (Decay: 0.99)

• Conditioning (CFG):
  - Label Dropout:  15% (during training)
  - Guidance Scale: 5.0 (during inference)
===========================================================


#### Testing Data

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[More Information Needed]



### Results

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#### Summary



### Compute Infrastructure

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#### Hardware

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