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README.md

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 ---
 license: apache-2.0
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+  - controlnet
+  - lora
+  - stable-diffusion-xl
+  - text-to-image
+  - image-to-image
+  - remote-sensing
+  - satellite-imagery
+  - synthetic-data
+  - geospatial
+pipeline_tag: text-to-image
+language:
+  - en
 ---
+
+# ConLoRA-Sat
+
+**Parameter-Efficient Synthetic Satellite Imagery Generation for Data-Scarce Humanitarian Contexts**
+
+ConLoRA-Sat combines **LoRA** (domain-style adaptation) with **ControlNet** (spatial structure) on a **Stable Diffusion XL 1.0** backbone: one text prompt plus a 5-class land-cover segmentation map generates a realistic 0.5 m satellite image. It targets data-scarce humanitarian use, e.g. UN peacekeeping training, producing fictitious yet geographically plausible terrain rather than identifiable real locations.
+
+```
+Sam-Roblex/ConLoRA-Sat/
+├── lora/
+│   └── satellite_lora.safetensors          ← one file: the LoRA weights
+└── controlnet/
+    ├── config.json                         ← SDXL ControlNet architecture (required by diffusers)
+    └── diffusion_pytorch_model.safetensors ← the ControlNet weights
+```
+
+- Trained on **322 curated images** (LoRA) and **4,333 image/segmentation pairs** (ControlNet) from Mogadishu (Somalia) and Addis Ababa (Ethiopia). About **24 A6000-equivalent GPU-hours** total. Runs in 16-20 GB inference VRAM.
+
+## Usage
+
+Both components load from this repo via subfolders:
+
+```python
+from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
+import torch
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=ControlNetModel.from_pretrained(
+        "Sam-Roblex/ConLoRA-Sat", subfolder="controlnet", torch_dtype=torch.float16),
+    torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("cuda")
+
+pipe.load_lora_weights("Sam-Roblex/ConLoRA-Sat",
+                       weight_name="lora/satellite_lora.safetensors")
+
+image = pipe(
+    "satellite imagery, aerial view, Mixed-use urban settlement with moderate building density",
+    image="seg_map.png",      # 5-class map, 1024×1024
+    num_inference_steps=50,
+    guidance_scale=7.5,
+    controlnet_conditioning_scale=1.0,
+).images[0]
+```
+
+Notes:
+- LoRA trigger phrase is **"satellite imagery, aerial view"**, prepended to a descriptive caption.
+- On water-dominated maps lower `controlnet_conditioning_scale` toward **0.8** for smoother coastlines.
+- The two components are trained separately and composed at inference, so either can be reused independently.
+
+## Land-cover class legend
+
+| Class      | RGB      |
+|------------|----------|
+| Bareland   | `#D2B48C` |
+| Vegetation | `#228B22` |
+| Roads      | `#404040` |
+| Buildings  | `#B22222` |
+| Water      | `#1E90FF` |
+
+## Model details
+
+- **Backbone**: SDXL 1.0 Base, frozen throughout.
+- **LoRA** (`lora/`): cross-attention query/value projections, rank 32, α 16. Prodigy optimizer, Min-SNR weighting (γ 5). ~4,000 steps, ~2 GPU-hours.
+- **ControlNet** (`controlnet/`): trainable copy of SDXL's U-Net encoder path, injected via zero-initialised 1×1 convolutions. AdamW (lr 5e-6, 500-step warm-up), DDPM schedule. All 4,333 pairs, ~22 GPU-hours.
+
+## Results
+
+| Config | FID | LPIPS | CMMD | CLIPScore |
+|--------|-----|-------|------|-----------|
+| LoRA only | 110.98 | 0.640 | 0.034 | 0.249 |
+| ControlNet only | 106.44 | 0.490 | 0.063 | 0.246 |
+| **ConLoRA-Sat** | **100.88** | **0.474** | 0.061 | 0.248 |
+
+Downstream (DeepLabV3+/ResNet-50): a segmenter trained only on synthetic imagery retains **90.1 %** of a real-data baseline; 200 real + 600 synthetic recovers **92.5 %**. In a 53-expert perceptual study, synthetic images were misclassified as real in **20.8 %** of trials.
+
+## Train / evaluate / cite
+Training scripts, evaluation code, and dataset documentation live in the companion GitHub repository. Dataset: [Zenodo: 10.5281/zenodo.22747581](https://doi.org/10.5281/zenodo.22747581) (CC BY-NC 4.0). Paper: *ConLoRA-Sat: Parameter-Efficient Synthetic Satellite Imagery Generation for Data-Scarce Humanitarian Contexts*, EarthArXiv preprint (under review).
+
+```bibtex
+@misc{qureshi2026conlorasat,
+  title  = {ConLoRA-Sat: Parameter-Efficient Synthetic Satellite Imagery
+            Generation for Data-Scarce Humanitarian Contexts},
+  author = {Qureshi, Seyed Amir Muhammad and Granell, Carlos and Casteleyn, Sven},
+  year   = {2026},
+  note   = {EarthArXiv preprint; under review}
+}
+```
+
+**License**: weights Apache-2.0; base model CreativeML OpenRAIL++-M. Training dataset CC BY-NC 4.0. Generates fictitious terrain, not identifiable real locations.

controlnet/config.json

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+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.27.0",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}

controlnet/diffusion_pytorch_model.safetensors

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lora/satellite_lora.safetensors

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