---
license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-image
tags:
  - zoomldm
  - remote-sensing
  - naip
  - latent-diffusion
  - custom-pipeline
  - arxiv:2411.16969
---

# BiliSakura/ZoomLDM-naip

Diffusers-format **NAIP** variant of ZoomLDM with a bundled custom pipeline and local `ldm` modules.

## Model Description

- **Architecture:** ZoomLDM latent diffusion pipeline (`UNet + VAE + conditioning encoder`)
- **Domain:** Remote sensing imagery (NAIP)
- **Conditioning:** DINOv2-style SSL feature maps + magnification level (`0..4`)
- **Format:** Self-contained local folder for `DiffusionPipeline.from_pretrained(...)`

## Intended Use

Use this model for conditional multi-scale NAIP image generation when you already have pre-extracted SSL features.

## Out-of-Scope Use

- Not intended for clinical or safety-critical decisions.
- Not a general-purpose text-to-image model.
- Performance may degrade under domain shift or mismatched feature extractors.

## Files

- `unet/`, `vae/`, `conditioning_encoder/`, `scheduler/`
- `model_index.json`
- `pipeline_zoomldm.py`
- `ldm/` (bundled dependency modules)

## Usage

```python
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "BiliSakura/ZoomLDM-naip",
    custom_pipeline="pipeline_zoomldm.py",
    trust_remote_code=True,
).to("cuda")

out = pipe(
    ssl_features=ssl_feat_tensor.to("cuda"),      # (B, 1024, H, W), typically H=W=4
    magnification=torch.tensor([0]).to("cuda"),   # 0..4
    num_inference_steps=50,
    guidance_scale=2.0,
)
images = out.images
```

## Limitations

- Requires correctly precomputed NAIP conditioning features.
- Magnification conditioning must match expected integer codes.
- Outputs can contain dataset artifacts or biases inherited from training data.

## Citation

```bibtex
@InProceedings{Yellapragada_2025_CVPR,
  author = {Yellapragada, Srikar and Graikos, Alexandros and Triaridis, Kostas and Prasanna, Prateek and Gupta, Rajarsi and Saltz, Joel and Samaras, Dimitris},
  title = {ZoomLDM: Latent Diffusion Model for Multi-scale Image Generation},
  booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
  month = {June},
  year = {2025},
  pages = {23453-23463}
}
```