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@@ -31,7 +31,7 @@ Here are some "Fakemons" generated by the model: (64x64 Resolution)
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/68df176c403a7bf9e8ae85a8/pkP0-pWDjxDZkvXrBnJYQ.png)
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- ## 🧠 Overview
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  TinyPokemonDiffusion is a lightweight DDPM-based generative model trained on Pokémon images.
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@@ -42,7 +42,7 @@ Despite its small size and CPU-only training, the model learns:
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  ---
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- ## ⚙️ Specifications
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  | Component | Value |
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  |------------------|------|
@@ -56,7 +56,7 @@ Despite its small size and CPU-only training, the model learns:
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  ---
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- ## 🧪 Features
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  - Full DDPM implementation from scratch
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  - Custom UNet with attention blocks
@@ -66,7 +66,7 @@ Despite its small size and CPU-only training, the model learns:
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  ---
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- ## 🖼️ Results
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  The model generates:
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@@ -81,9 +81,8 @@ Limitations:
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  ---
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- ## THE INITIAL IDEA WAS A STUDENT U-NET FROM A TEACHER U-NET, BUT THIS WAS DISCONTINUED BECAUSE THE TEACHER WAS INITIALIZATED WITH RANDOM WEIGHTS, THAT WOULD KILL THE STUDENT LEARNING
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- ## 🚀 Usage
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  ### Generate images
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@@ -174,13 +173,13 @@ python generate.py \
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  --seed 42
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  ```
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- 📁 Output
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  Generated images are saved as a horizontal grid:
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  outputs/generated.png
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- >> ⚠️ Limitations
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  Unconditional model (no prompts)
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@@ -188,7 +187,7 @@ Limited dataset diversity
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  Early training stage
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  No DDIM (yet)
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- >> 🔬 Research Direction
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  This project demonstrates that:
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@@ -200,7 +199,7 @@ Conditional generation (class-based)
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  Text-to-image (v2.0)
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  DDIM sampling
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  Larger model variants
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- 💡 Motivation
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  Most diffusion research focuses on scaling up.
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@@ -208,16 +207,19 @@ This project explores the opposite direction:
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  What is the minimum viable diffusion model?
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- 📜 License
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  MIT
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- 🙌 Acknowledgments
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  Hugging Face datasets
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  PyTorch
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  The open-source AI community
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- If you like this project:
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- Give it a star and follow the evolution to v2.0(conditional) 🚀
 
 
 
 
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/68df176c403a7bf9e8ae85a8/pkP0-pWDjxDZkvXrBnJYQ.png)
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+ ## Overview
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  TinyPokemonDiffusion is a lightweight DDPM-based generative model trained on Pokémon images.
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  ---
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+ ## Specifications
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  | Component | Value |
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  |------------------|------|
 
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  ---
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+ ## Features
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  - Full DDPM implementation from scratch
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  - Custom UNet with attention blocks
 
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  ---
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+ ## Results
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  The model generates:
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  ---
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+ ## Usage
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  ### Generate images
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  --seed 42
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  ```
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+ Output
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  Generated images are saved as a horizontal grid:
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  outputs/generated.png
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+ >> Limitations
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  Unconditional model (no prompts)
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  Early training stage
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  No DDIM (yet)
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+ >> Research Direction
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  This project demonstrates that:
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  Text-to-image (v2.0)
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  DDIM sampling
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  Larger model variants
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+ Motivation
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  Most diffusion research focuses on scaling up.
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  What is the minimum viable diffusion model?
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+ License
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  MIT
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+ Acknowledgments
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  Hugging Face datasets
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  PyTorch
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  The open-source AI community
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+ If you like this project:
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+ Give it a star and follow the evolution to v2.0(conditional)
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+
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+ ## Other
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+ THE INITIAL IDEA WAS A STUDENT U-NET FROM A TEACHER U-NET, BUT THIS WAS DISCONTINUED BECAUSE THE TEACHER WAS INITIALIZATED WITH RANDOM WEIGHTS, THAT WOULD KILL THE STUDENT LEARNING