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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo_images/GeoSynth-Canny/input.jpeg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,173 @@
+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-image
+tags:
+- controlnet
+- remote-sensing
+- arxiv:2404.06637
+widget:
+# GeoSynth-OSM: OSM tile -> satellite image
+- src: demo_images/GeoSynth-OSM/input.jpeg
+  prompt: Satellite image features a city neighborhood
+  output:
+    url: demo_images/GeoSynth-OSM/output.jpeg
+# GeoSynth-Canny: Canny edges -> satellite image
+- src: demo_images/GeoSynth-Canny/input.jpeg
+  prompt: Satellite image features a city neighborhood
+  output:
+    url: demo_images/GeoSynth-Canny/output.jpeg
+# GeoSynth-SAM: SAM segmentation -> satellite image
+- src: demo_images/GeoSynth-SAM/input.jpeg
+  prompt: Satellite image features a city neighborhood
+  output:
+    url: demo_images/GeoSynth-SAM/output.jpeg
+---
+
+# GeoSynth-ControlNets
+
+We maintain **two repositories**—one per base checkpoint—each with its compatible ControlNets:
+
+| Repo | Base Model | ControlNets |
+|------|------------|-------------|
+| **This repo** | GeoSynth (text encoder & UNet same as SD 2.1) | GeoSynth-OSM, GeoSynth-Canny, GeoSynth-SAM |
+| **[GeoSynth-ControlNets-Location](https://huggingface.co/BiliSakura/GeoSynth-ControlNets-Location)** | GeoSynth-Location (adds CoordNet branch) | GeoSynth-Location-OSM, GeoSynth-Location-SAM*, GeoSynth-Location-Canny |
+
+*[GeoSynth-Location-SAM](https://huggingface.co/MVRL/GeoSynth-Location-SAM) controlnet ckpt is missing from source.*
+
+### This repository
+
+1. **GeoSynth checkpoint** — A remote sensing visual generative model. The text encoder and UNet are the same as [Stable Diffusion 2.1](https://huggingface.co/sd2-community/stable-diffusion-2-1-base) (not fine-tuned).
+2. **ControlNet models** — OSM, Canny, and SAM conditioning, located under [`controlnet/`](controlnet/).
+
+### Architecture note: location-conditioned models
+
+Location-conditioned variants (GeoSynth-Location-*) use a **different base checkpoint** that adds a CoordNet branch. The branch takes `[lon, lat]` as input, passes it through a **SatCLIP** location encoder, then through a **CoordNet** (13 stacked cross-attention blocks, inner dim 256, 4 heads). ControlNet and CoordNet both condition the UNet. See the [GeoSynth paper](https://huggingface.co/papers/2404.06637) Figure 3.
+
+### ControlNet variants (this repo)
+
+| Control | Subfolder | Status |
+|---------|-----------|--------|
+| OSM     | `controlnet/GeoSynth-OSM` | ✅ Integrated |
+| Canny   | `controlnet/GeoSynth-Canny` | ✅ Integrated |
+| SAM     | `controlnet/GeoSynth-SAM` | ✅ Integrated |
+
+Use it with 🧨 [diffusers](#examples) or the [Stable Diffusion](https://github.com/Stability-AI/stablediffusion) repository.
+
+### Model Sources
+
+- **Source:** [GeoSynth](https://github.com/mvrl/GeoSynth)
+- **Paper:** [GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis](https://huggingface.co/papers/2404.06637)
+- **Base model:** [Stable Diffusion 2.1](https://huggingface.co/sd2-community/stable-diffusion-2-1-base)
+
+
+
+## Examples
+
+### Text-to-Image (base GeoSynth)
+
+```python
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("BiliSakura/GeoSynth-ControlNets")
+pipe = pipe.to("cuda")
+
+image = pipe("Satellite image features a city neighborhood").images[0]
+image.save("generated_city.jpg")
+```
+
+### ControlNet (diffusers integration)
+
+Use the 🧨 diffusers `ControlNetModel` wrapper with `StableDiffusionControlNetPipeline`:
+
+**GeoSynth-OSM** — synthesizes satellite images from OpenStreetMap tiles (RGB):
+
+```python
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from PIL import Image
+import torch
+
+controlnet = ControlNetModel.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    subfolder="controlnet/GeoSynth-OSM",
+)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    controlnet=controlnet,
+)
+pipe = pipe.to("cuda")
+img = Image.open("osm_tile.jpeg")  # OSM tile (RGB, 512x512)
+generator = torch.manual_seed(42)
+image = pipe("Satellite image features a city neighborhood", image=img, generator=generator, num_inference_steps=20).images[0]
+image.save("generated_city.jpg")
+```
+
+**GeoSynth-Canny** — synthesizes satellite images from Canny edge maps:
+
+```python
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from PIL import Image
+import torch
+
+controlnet = ControlNetModel.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    subfolder="controlnet/GeoSynth-Canny",
+)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    controlnet=controlnet,
+)
+pipe = pipe.to("cuda")
+img = Image.open("canny_edges.jpeg")  # Canny edge image (RGB, 512x512)
+generator = torch.manual_seed(42)
+image = pipe("Satellite image features a city neighborhood", image=img, generator=generator, num_inference_steps=20).images[0]
+image.save("generated_city.jpg")
+```
+
+**GeoSynth-SAM** — synthesizes satellite images from SAM (Segment Anything Model) segmentation masks:
+
+```python
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from PIL import Image
+import torch
+
+controlnet = ControlNetModel.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    subfolder="controlnet/GeoSynth-SAM",
+)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "BiliSakura/GeoSynth-ControlNets",
+    controlnet=controlnet,
+)
+pipe = pipe.to("cuda")
+img = Image.open("sam_segmentation.jpeg")  # SAM mask (RGB, 512x512)
+generator = torch.manual_seed(42)
+image = pipe("Satellite image features a city neighborhood", image=img, generator=generator, num_inference_steps=20).images[0]
+image.save("generated_city.jpg")
+```
+
+*For location-conditioned variants (GeoSynth-Location-OSM, GeoSynth-Location-SAM, GeoSynth-Location-Canny), see the separate [GeoSynth-ControlNets-Location](https://huggingface.co/BiliSakura/GeoSynth-ControlNets-Location) repo.*
+
+## Citation
+
+If you use this model, please cite the GeoSynth paper. For location-conditioned variants, also cite SatCLIP.
+
+```bibtex
+@inproceedings{sastry2024geosynth,
+  title={GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis},
+  author={Sastry, Srikumar and Khanal, Subash and Dhakal, Aayush and Jacobs, Nathan},
+  booktitle={IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION)},
+  year={2024}
+}
+
+@article{klemmer2025satclip,
+  title={{SatCLIP}: {Global}, General-Purpose Location Embeddings with Satellite Imagery},
+  author={Klemmer, Konstantin and Rolf, Esther and Robinson, Caleb and Mackey, Lester and Ru{\ss}wurm, Marc},
+  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={39},
+  number={4},
+  pages={4347--4355},
+  year={2025},
+  doi={10.1609/aaai.v39i4.32457}
+}
+```

controlnet/GeoSynth-Canny/config.json

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controlnet/GeoSynth-OSM/config.json

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controlnet/GeoSynth-SAM/config.json

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+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.27.2",
+  "_name_or_path": "/home/s.sastry/.cache/huggingface/hub/models--stabilityai--stable-diffusion-2-1-base/snapshots/5ede9e4bf3e3fd1cb0ef2f7a3fff13ee514fdf06/vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 768,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.safetensors

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