Added training info

README.md

@@ -18,9 +18,9 @@ language:
 library_name: diffusers
 ---
     
-# controlnet- mfidabel/controlnet-segment-anything
+# ControlNet - mfidabel/controlnet-segment-anything
 
-These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following. 
+These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with a new type of conditioning. You can find some example images in the following. 
 
 **prompt**: contemporary living room of a house
 
@@ -35,4 +35,34 @@ These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new
 **prompt**: contemporary living room,  high quality, 4k, realistic
 
 **negative prompt**: low quality, monochrome, low res
-![images_2)](./images_2.png)
+![images_2)](./images_2.png)
+
+## Training
+
+**Training Data** This model was trained using a Segmented dataset based on the [COYO-700M Dataset](https://huggingface.co/datasets/kakaobrain/coyo-700m). 
+[Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint was used as the base model for the controlnet.
+
+
+The model was trained as follows:
+
+- 25k steps with the [SAM-COYO-2k](https://huggingface.co/datasets/mfidabel/sam-coyo-2k) dataset
+- 28k steps with the [SAM-COYO-2.5k](https://huggingface.co/datasets/mfidabel/sam-coyo-2.5k) dataset
+- 38k steps with the [SAM-COYO-3k](https://huggingface.co/datasets/mfidabel/sam-coyo-3k) dataset
+
+In that particular order.
+
+- **Hardware**: Google Cloud TPUv4-8 VM
+
+- **Optimizer**: AdamW
+
+- **Train Batch Size**: 2 x 4 = 8
+
+- **Learning rate**: 0.00001 constant
+
+- **Gradient Accumulation Steps**: 1
+
+- **Resolution**: 512
+
+Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant
+
+