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README.md

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 # Animator2D
 
-Animator2D è un modello di intelligenza artificiale progettato per generare animazioni di sprite in pixel-art a partire da una descrizione testuale. Il modello utilizza un encoder di testo basato su BERT per estrarre caratteristiche testuali e una rete generativa convoluzionale per creare sprite animati.
+Animator2D is an artificial intelligence model designed to generate pixel-art sprite animations based on textual descriptions. The model uses a BERT-based text encoder to extract textual features and a convolutional generative network to create animated sprites.
 
-## Descrizione del Modello
+## Model Description
 
-- **Nome:** Animator2D
+- **Name:** Animator2D
 - **Input:**
-  - Descrizione del personaggio
-  - Numero di frame dell'animazione
-  - Azione del personaggio
-  - Direzione di visualizzazione
-- **Output:** Sprite animato in formato immagine
+  - Character description
+  - Number of animation frames
+  - Character action
+  - Viewing direction
+- **Output:** Animated sprite in image format
 
 ## Dataset
 
-Il modello è stato addestrato utilizzando il dataset [spraix_1024](https://huggingface.co/datasets/pawkanarek/spraix_1024), che contiene sprite animati con descrizioni testuali dettagliate.
+The model was trained using the [spraix\_1024](https://huggingface.co/datasets/pawkanarek/spraix_1024) dataset, which contains animated sprites with detailed textual descriptions.
+
+## Future Goals
+
+This is only the first version of the model. In the future, we aim to improve it with the following updates:
+
+- **Expand output formats:** Currently, the model generates a single frame sheet. We plan to implement the ability to export output in multiple formats, including folders containing separate images, animated GIFs, and videos.
+- **Optimize frame management:** The current frame count is manually defined, but we aim to improve control by introducing a more intuitive system that considers factors such as FPS and the actual animation duration.
+- **Enhance the model:** The current model is still in an early stage. Future updates will focus on making sprite generation more precise and consistent by improving architecture and training data quality.
+- **Customization of sprite dimensions:** We will implement an input that allows specifying the character's height in pixels. This will enable adaptation of the generated sprite's graphical style, ensuring greater flexibility and customization possibilities (e.g., Pokémon style vs. Metal Slug style).
+