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README.md

@@ -1,9 +1,43 @@
----
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
----
-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+To load and initialize the `Generator` model from the repository, follow these steps:
+
+1. **Install Required Packages**: Ensure you have the necessary Python packages installed:
+
+    ```python
+    pip install torch omegaconf huggingface_hub
+    ```
+
+2. **Download Model Files**: Retrieve the `generator.pth`, `config.json`, and `model.py` files from the Hugging Face repository. You can use the `huggingface_hub` library for this:
+
+    ```python
+    from huggingface_hub import hf_hub_download
+
+    repo_id = "Kiwinicki/sat2map-generator"
+    generator_path = hf_hub_download(repo_id=repo_id, filename="generator.pth")
+    config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+    model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
+    ```
+
+3. **Load the Model**: Incorporate the downloaded `model.py` to define the `Generator` class, then load the model's state dictionary and configuration:
+
+    ```python
+    import torch
+    import json
+    from omegaconf import OmegaConf
+    import sys
+    from pathlib import Path
+    from model import Generator
+
+    # Load configuration
+    with open(config_path, "r") as f:
+        config_dict = json.load(f)
+    cfg = OmegaConf.create(config_dict)
+
+    # Initialize and load the generator model
+    generator = Generator(cfg)
+    generator.load_state_dict(torch.load(generator_path))
+    generator.eval()
+    x = torch.randn([1, cfg['channels'], 256, 256])
+    out = generator(x)
+    ```
+
+    Here, `generator` is the initialized model ready for inference.