Update README.md

README.md

@@ -1,3 +1,38 @@
----
-license: mit
----
+---
+license: mit
+---
+This model has been trained to generate bike images. The dataset used is BIKED++ (found here in [GitHub](https://github.com/Lyleregenwetter/BIKED_multimodal/tree/main?utm_source=catalyzex.com))
+
+This is a conditional diffusion model which is trained to both generate bike images and perform infill on partially masked bike images from the dataset above.
+
+the baseline architecture was setup with diffusers UNet2DModel model for images:
+
+```python
+UNet2DModel(
+            sample_size=128,  # the target image resolution is set to 128 here (128x128 images)
+            in_channels=6,  # the number of input channels, 3 for RGB masked images(infill, feed all white for uncoditional) and 3 for RGB noise
+            out_channels=3,  # the number of output channels (RGB)
+            layers_per_block=2,  
+            block_out_channels=(128, 256, 512, 768),  
+            down_block_types=(
+                "DownBlock2D",
+                "AttnDownBlock2D",
+                "AttnDownBlock2D", 
+                "AttnDownBlock2D"
+            ),
+            up_block_types=(
+                "AttnUpBlock2D",
+                "AttnUpBlock2D",
+                "AttnUpBlock2D",
+                "UpBlock2D"
+            ),
+        )
+```
+
+The code for training and inference is included as well. There exists a postprocessing function which crops the white space above and below the bike images which the dataset was preprocessed to have such that the images become square shaped.
+
+With only 10 denoising steps you can get uncoditional samples that mimic the dataset well.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d516ba80d47a6b76fc1015/VDqGqLee0XvqtiYYkzpia.png)
+
+A pipeline with guidance is provided as well where you can feed your custom function for guidance.