Update README with finetuning details and credits

README.md

@@ -4,13 +4,14 @@ tags:
   - diffusers
   - ddpm
   - unconditional-image-generation
+  - landscape
 library_name: diffusers
 pipeline_tag: unconditional-image-generation
 ---
 
 # ddpm-landscape
 
-A DDPM model fine-tuned to generate 256x256 landscape images.
+A 256x256 unconditional DDPM that generates natural landscape images. Full fine-tune of [`google/ddpm-church-256`](https://huggingface.co/google/ddpm-church-256) on the **Landscapes HQ (LHQ)** dataset.
 
 ## Usage
 
@@ -29,3 +30,40 @@ image = ddpm().images[0]
 # save image
 image.save("ddpm_generated_image.png")
 ```
+
+## Base model
+
+- **`google/ddpm-church-256`** — original 256x256 DDPM by Ho et al. All credit for the base architecture and pretrained weights goes to the original authors.
+
+## Dataset
+
+- **Landscapes HQ (LHQ)**, 256x256 split, from *ALIS — Aligning Latent and Image Spaces to Connect the Unconnectable* (Skorokhodov et al., 2021).
+- Project / data: https://github.com/universome/alis
+
+```bibtex
+@article{ALIS,
+  title   = {Aligning Latent and Image Spaces to Connect the Unconnectable},
+  author  = {Skorokhodov, Ivan and Sotnikov, Grigorii and Elhoseiny, Mohamed},
+  journal = {arXiv preprint arXiv:2104.06954},
+  year    = {2021}
+}
+```
+
+## Fine-tuning
+
+| | |
+|---|---|
+| Base model | `google/ddpm-church-256` |
+| Dataset | LHQ (256x256) |
+| Epochs | 50 |
+| Batch size | 32 |
+| Optimizer | AdamW |
+| Learning rate | 1e-5 (cosine schedule, 500 warmup steps) |
+| Loss | MSE on predicted noise |
+| Augmentation | Random horizontal flip |
+
+## Acknowledgements
+
+- Base weights: Google / the original DDPM authors (Ho, Jain, Abbeel, 2020).
+- Dataset: Skorokhodov et al., authors of ALIS / LHQ — https://github.com/universome/alis
+- Built with [`diffusers`](https://github.com/huggingface/diffusers).