JRPG Pixel Art Background Generator

Model Description

JRPG Background Generator is a Diffusion Model trained from scratch to generate pixel art backgrounds in the style of classic JRPG games (Final Fantasy, Chrono Trigger, Secret of Mana).

The model uses a UNet2D architecture with DDPM (Denoising Diffusion Probabilistic Models) scheduler to generate 128x128 pixel art landscapes.

Model Details

• Model Type: Diffusion Model (DDPM)
• Architecture: UNet2D with Attention Blocks
• Resolution: 128x128 pixels
• Training Epochs: 600
• Training Time: ~200 hours on NVIDIA GPU
• Framework: PyTorch + Hugging Face Diffusers
• Parameters: ~45M

Supported Styles

The model can generate various scene types:

• 🏜️ Desert ruins and ancient temples
• 🏰 Castles and fortresses
• 🏔️ Canyons and mountain passes
• ☁️ Floating islands
• 🌅 Sunset/sunrise landscapes
• 🌊 Coastal scenes
• • and lot of more

Intended Use

Primary Use Cases

1. Indie Game Development: Generate background assets for retro-style RPG games
2. Concept Art: Quick ideation for game environments
3. NFT Collections: Create unique pixel art landscape collections
4. Game Prototyping: Rapid background generation for mockups
5. AI Art Research: Study diffusion models on pixel art domain

Out-of-Scope Use

• Photorealistic image generation
• Character/sprite generation
• High-resolution outputs (>256x256)
• Real-time generation (inference takes ~30-60 seconds)

How to Use

Installation

pip install torch diffusers pillow matplotlib transformers

Quick Start

Kullanım

from diffusers import DDPMPipeline

pipe = DDPMPipeline.from_pretrained("ayhant/Pixel-Art-JRPG-Background-Generator")
image = pipe(batch_size=1, num_inference_steps=256).images[0]
image.save("background.png")

Training Details

Training Data

• Dataset Size: Custom curated dataset of ~500 JRPG background images
• Data Sources: Open Art, Final Fantasy series, Chrono Trigger, Secret of Mana, etc.
• Preprocessing:
  • Resized to 128x128
  • Random horizontal flip augmentation
  • Normalized to [-1, 1]

Training Hyperparameters

- Training epochs: 100
- Batch size: 32
- Learning rate: 1e-4
- Optimizer: Adam
- Scheduler: DDPM (512 timesteps)
- Loss function: MSE Loss
- GPU: NVIDIA (CUDA)

Training Procedure

for epoch in range(100):
    for batch in dataloader:
        # Random timestep
        timesteps = torch.randint(0, 512, (batch_size,))
        
        # Add noise
        noise = torch.randn_like(batch)
        noisy_images = scheduler.add_noise(batch, noise, timesteps)
        
        # Predict noise
        noise_pred = model(noisy_images, timesteps).sample
        
        # MSE loss
        loss = F.mse_loss(noise_pred, noise)
        
        # Backprop
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

Evaluation

Qualitative Results

The model successfully generates:

• ✅ Diverse scene compositions
• ✅ Appropriate color palettes (warm/cool contrasts)
• ✅ Volumetric lighting effects
• ✅ Multi-layered depth perception
• ✅ JRPG aesthetic consistency

Limitations

• 🔸 Limited to 128x128 resolution
• 🔸 Occasional artifacts in complex scenes
• 🔸 Inference time: ~10-20 seconds per image
• 🔸 Cannot generate characters or sprites
• 🔸 Style limited to training dataset

🚀 Want to try it yourself? • Interactive Demo: https://huggingface.co/spaces/ayhant/Pixel-Art-JRPG-Assets-Background-Generator • Technical Details: https://huggingface.co/ayhant/Pixel-Art-JRPG-Background-Generator

Sample Outputs

License

MIT License - Free for commercial and non-commercial use.

Citation

@misc{jrpg-background-generator-2024,
  author = {[Your Name]},
  title = {JRPG Pixel Art Background Generator},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/[your-username]/jrpg-background-generator}}
}

Contact

• LinkedIn: www.linkedin.com/in/ayhan-taşyurt-5a01591a4

Interested in citing this work? Please contact us for proper citation format.

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🤝 Collaboration Opportunities

I am open to:

• 🎓 Academic Partnerships - Research collaborations
• 🏢 Enterprises - Commercial licensing
• 👥 Community Projects - Open-source initiatives

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Average Response Time: 24-48 hours

Acknowledgments

• Inspired by classic JRPG games (Final Fantasy, Chrono Trigger)
• Built with Hugging Face Diffusers
• Thanks to the open pixel art community

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Tags: #GenerativeAI #DiffusionModels #PixelArt #GameDev #JRPG #PyTorch

🔒 Licensing

Current Status: Private / Gated Access

Available Licenses:

1. Academic License - Free for research
2. Indie Developer License - Affordable for small studios
3. Commercial License - Full commercial rights
4. Enterprise License - Custom terms

🎮 Ready to create amazing JRPG backgrounds?

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Made with ❤️ for the retro gaming community