Update README.md

README.md

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----
-license: creativeml-openrail-m
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+---
+license: creativeml-openrail-m
+datasets:
+- Voxel51/rico
+pipeline_tag: unconditional-image-generation
+tags:
+- diffusion
+- unet
+- image-generation
+- ui-design
+- tensorflow
+- mobile-ui
+---
+# Forma-1
+
+`Forma-1` is a diffusion model trained on 36,536 mobile UI screenshots from the 
+RICO dataset. Give it random noise and it will denoise it into something that 
+looks like a mobile app screen.
+
+It's the model behind DiffuseUI — a project I'm building to explore generative AI 
+applied to interface design.
+
+---
+
+## Details
+
+| | |
+|---|---|
+| **Architecture** | U-Net |
+| **Framework** | Tensorflow |
+| **Image Size** | 64x64 |
+| **Timesteps** | 1000 |
+| **Noise Schedule** | Linear |
+| **Epochs** | 200 |
+| **Batch Size** | 64 |
+| **Learning Rate** | 1e-4 |
+| **Loss** | MSE |
+
+---
+
+## Training Data
+
+Trained on the `RICO` dataset — 36,536 UI screenshots across 27 app 
+categories. Images were resized to 64x64 and normalized to [-1, 1] before 
+training.
+
+---
+
+## How It Works
+
+Standard DDPM setup. Forward process adds Gaussian noise to real UI screenshots 
+across 1000 steps until they're pure static. The U-Net learns to predict that 
+noise at each step. At generation time you start from pure static and denoise 
+1000 times — a new UI screen comes out the other end.
+
+---
+
+## Limitations
+
+- 64x64 resolution — outputs are small
+- Unconditional — no control over what category of UI gets generated
+- Android only — trained exclusively on Android screenshots
+- 200 epochs on this dataset size produces recognizable but rough outputs
+
+---
+
+## About
+
+Built by Ricardo Flores as part of DiffuseUI.
+
+[GitHub](https://github.com/imrichie) · [DiffuseUI](#)