ddpm-landscape

A 256x256 unconditional DDPM that generates natural landscape images. Full fine-tune of google/ddpm-church-256 on the Landscapes HQ (LHQ) dataset.

Usage

# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline

model_id = "crab27/ddpm-landscape"

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference

# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]

# save image
image.save("ddpm_generated_image.png")

Base model

• google/ddpm-church-256 — original 256x256 DDPM by Ho et al. All credit for the base architecture and pretrained weights goes to the original authors.

Dataset

• Landscapes HQ (LHQ), 256x256 split, from ALIS — Aligning Latent and Image Spaces to Connect the Unconnectable (Skorokhodov et al., 2021).
• Project / data: https://github.com/universome/alis

@article{ALIS,
  title   = {Aligning Latent and Image Spaces to Connect the Unconnectable},
  author  = {Skorokhodov, Ivan and Sotnikov, Grigorii and Elhoseiny, Mohamed},
  journal = {arXiv preprint arXiv:2104.06954},
  year    = {2021}
}

Fine-tuning

Base model	google/ddpm-church-256
Dataset	LHQ (256x256)
Epochs	50
Batch size	32
Optimizer	AdamW
Learning rate	1e-5 (cosine schedule, 500 warmup steps)
Loss	MSE on predicted noise
Augmentation	Random horizontal flip

Acknowledgements

• Base weights: Google / the original DDPM authors (Ho, Jain, Abbeel, 2020).
• Dataset: Skorokhodov et al., authors of ALIS / LHQ — https://github.com/universome/alis
• Built with diffusers.