Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+### 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
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+
+## Training Details
+
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### 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
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+