Update all files for DiffusionSat-Single-512

README.md

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+---
+language: en
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+  - satellite
+  - controlnet
+  - diffusers
+  - text-to-image
+---
+
+# DiffusionSat Custom Pipelines
+
+Custom community pipelines for loading DiffusionSat checkpoints directly with `diffusers.DiffusionPipeline.from_pretrained()`.
+
+> See [Diffusers Community Pipeline Documentation](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)
+
+## Model Index
+
+`model_index.json` is set to the default text-to-image pipeline (`DiffusionSatPipeline`) so `DiffusionPipeline.from_pretrained()` works out of the box. The ControlNet variant is loaded via `custom_pipeline` plus the `controlnet` subfolder, as shown below.
+
+## Available Pipelines
+
+This directory contains two custom pipelines:
+
+1. **`pipeline_diffusionsat.py`**: Standard text-to-image pipeline with DiffusionSat metadata support.
+2. **`pipeline_diffusionsat_controlnet.py`**: ControlNet pipeline with DiffusionSat metadata and conditional metadata support.
+
+## Setup
+
+The checkpoint folder (`ckpt/diffusionsat/`) should contain the standard diffusers components (unet, vae, scheduler, etc.). You can reference these pipeline files directly from this directory or copy them to your checkpoint folder.
+
+## Usage
+
+### 1. Text-to-Image Pipeline
+
+Use `pipeline_diffusionsat.py` for standard generation.
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+# Load pipeline
+pipe = DiffusionPipeline.from_pretrained(
+    "path/to/ckpt/diffusionsat",
+    custom_pipeline="./custom_pipelines/pipeline_diffusionsat.py",  # Path to this file
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+pipe = pipe.to("cuda")
+
+# Optional: Metadata (normalized lat, lon, timestamp, GSD, etc.)
+# metadata = [0.5, -0.3, 0.7, 0.2, 0.1, 0.0, 0.5] 
+
+# Generate
+image = pipe(
+    "satellite image of farmland",
+    metadata=None,  # Optional
+    num_inference_steps=30,
+).images[0]
+```
+
+### 2. ControlNet Pipeline
+
+Use `pipeline_diffusionsat_controlnet.py` for ControlNet generation.
+
+```python
+import torch
+from diffusers import DiffusionPipeline, ControlNetModel
+from diffusers.utils import load_image
+
+# 1. Load ControlNet
+controlnet = ControlNetModel.from_pretrained(
+    "path/to/ckpt/diffusionsat/controlnet",
+    torch_dtype=torch.float16
+)
+
+# 2. Load Pipeline with ControlNet
+pipe = DiffusionPipeline.from_pretrained(
+    "path/to/ckpt/diffusionsat",
+    controlnet=controlnet,
+    custom_pipeline="./custom_pipelines/pipeline_diffusionsat_controlnet.py", # Path to this file
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+pipe = pipe.to("cuda")
+
+# 3. Prepare Control Image
+control_image = load_image("path/to/conditioning_image.png")
+
+# 4. Generate
+# metadata: Target image metadata (optional)
+# cond_metadata: Conditioning image metadata (optional)
+
+image = pipe(
+    "satellite image of farmland",
+    image=control_image,
+    metadata=None,       
+    cond_metadata=None,
+    num_inference_steps=30,
+).images[0]
+```