RandomArt DDPM
My first real Model, following the HF diffusion model course.
RandomArt is an unconditional diffusion model trained from scratch on 7k Images of huggan/wikiart on Apple Silicon. It generates 128×128 painterly and abstract compositions without text prompts.
Examples
Usage
Install PyTorch and Diffusers, then run the model as follows:
import torch
from diffusers import DDPMPipeline
if torch.backends.mps.is_available():
device = "mps"
elif torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
pipe = DDPMPipeline.from_pretrained("DD-65/randomart").to(device)
image = pipe(num_inference_steps=250).images[0]
image.save("randomart.png")
For faster exploration, use 100 inference steps. Around 250 steps provides a useful speed/quality balance, while 500 steps gives more time to final renders.
Model details
- Architecture:
UNet2DModelwith aDDPMScheduler - Resolution: 128×128 RGB
- Conditioning: none
- Training objective: epsilon/noise prediction
- Scheduler training timesteps: 1,000
Limitations
The model is designed for experimental image generation. It tends to learn local painterly texture more strongly than coherent object anatomy or global geometry. Outputs may resemble characteristics present in the training collection. The training images and their associated rights are not included in this repository; users are responsible for evaluating generated outputs for their intended use.
License
This model was trained on huggan/wikiart, whose license limits use to non-commercial research and requires compliance with WikiArt's terms and conditions.
The model weights are therefore provided for non-commercial research and experimental use only. No rights are granted to the underlying artworks, and users are responsible for ensuring their use complies with applicable copyright law and WikiArt's terms.