MNIST Diffusion Baseline
Status: Complete
Type: Baseline
Objective
Establish a working baseline for image diffusion using the standard DDPM approach on MNIST digits. This serves as a validation of our diffusion implementation before moving to text modalities.
Configuration
- Model: SimpleUNet with residual blocks and temporal embeddings
- Training: Standard DDPM on MNIST dataset
- Architecture:
- UNet backbone with down/up sampling
- Temporal embedding for timestep conditioning
- Residual connections throughout
- Dataset: MNIST handwritten digits (28x28 grayscale)
- Hardware: NVIDIA T4
- Git Commit: 4422ce927fbf61e226157e4a3f2ac8de91b583bb
Hypothesis
Standard DDPM should work well for MNIST generation, providing a solid foundation for understanding diffusion mechanics before tackling text generation challenges.
Results
Quantitative
- Training converged successfully
Qualitative
- Generated samples are recognizable MNIST digits
- Clear progression from noise to structured digits during reverse process
The training progression shows the model learning to generate increasingly coherent MNIST digits:
Early Training (Epochs 1-3)
Epoch 1
Initial noise, barely recognizable patterns
Epoch 2
Some digit-like shapes emerging
Epoch 3
More defined structures appearing
Training Progression (Epochs 100-1000)
Epoch 100
Clear digit shapes, some noise remaining
Epoch 500
Well-formed digits, improved clarity
Epoch 1000
High-quality, recognizable MNIST digits
Key Observations: - Epochs 1-3: Model learns basic structure and shape concepts - Epoch 100: Recognizable digits with some artifacts - Epochs 500-1000: Converged to high-quality digit generation
Next Steps
- Move to text diffusion experiments using similar architecture principles
- Investigate embedding space approaches for text generation
Sample Generation: