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BiliSakura/
controlearth

StableDiffusionPipeline

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[!WARNING] we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn

ControlEarth

ControlNet model conditioned on OpenStreetMaps (OSM) to generate the corresponding satellite images.

Trained on the region of the Central Belt.

Repo structure

This repo is self-contained and includes:

controlnet/ — ControlNet weights (OSM → satellite)
text_encoder/, unet/, vae/, scheduler/, tokenizer/ — Stable Diffusion v1-5 base
demo_images/ — Placeholder for input OSM images
inference_demo.py — Full diffusers inference script (uses only this repo, no external downloads)

Dataset used for training

The dataset used for the training procedure is the WorldImagery Clarity dataset.

The code for the dataset construction can be accessed in https://github.com/miquel-espinosa/map-sat.

Usage

bash

# From the repo root
python inference_demo.py

Or load programmatically:

python

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch

repo = "/path/to/controlearth"  # or "." when run from repo root
controlnet = ControlNetModel.from_pretrained(f"{repo}/controlnet", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    repo, controlnet=controlnet, torch_dtype=torch.float16,
    safety_checker=None, requires_safety_checker=False
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

image = pipe("convert this openstreetmap into its satellite view", num_inference_steps=50, image=control_image).images[0]

BiliSakura/controlearth

ControlEarth

Repo structure

Dataset used for training

Usage

BiliSakura/
controlearth