ZYI-0.2

ZYI-0.2 — Lightweight Text-to-Image Diffusion Model

ZYI-0.2 is a lightweight text-to-image diffusion model (~67M parameters) designed to generate small images from natural language prompts.

The model is optimized for educational use, experimentation, and running on low-VRAM GPUs.

It uses:

• PyTorch
• CLIP text encoder
• UNet diffusion backbone
• DDIM fast sampling

Image resolution:

128 × 128

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Example Generations

Text-to-Image Examples

Model Details

Model name: ZYI-0.2 Parameters: ~67M Architecture: Text-conditioned UNet diffusion Text encoder: CLIP (ViT-B/32) Training dataset: COCO-style captions dataset Framework: PyTorch

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How to Use

Download the repository:

from huggingface_hub import snapshot_download
import sys

path = snapshot_download("caikybaldo999/ZYI-0.2")

sys.path.append(path)

Then generate an image:

from inference import generate
from IPython.display import display

img = generate("a group of people at the beach")
display(img)

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Installation

Install dependencies:

pip install torch transformers numpy Pillow tqdm

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Inference Speed

Using DDIM sampling:

Typical generation time:

~1–3 seconds on GPU

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Repository Structure

ZYI-0.2/
│
├── model.pt
├── model.py
├── ddim_sampler.py
├── inference.py
├── requirements.txt
└── README.md

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Limitations

• Low resolution (128x128)
• Not trained on large-scale datasets
• May produce artifacts

This model is mainly intended for learning and experimentation.

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License

This project is released for research and educational use.

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Author

Created by:

Caiky Baldo

Hugging Face https://huggingface.co/caikybaldo999