Update README.md

README.md

@@ -9,12 +9,15 @@ base_model: jimmycarter/LibreFLUX
 # LibreFLUX-ControlNet
 ![Example: Control image vs result](examples/side_by_side.png)
 
-This model/pipeline is the product of my [LibreFlux ControlNet training repo](https://github.com/NeuralVFX/LibreFLUX-ControlNet). For the dataset I auto labeled 165K images from the SA1B dataset, and trained for that many iterations. I've tested using this ControlNet model as a base for transfer learning to less generic datasets, the results are good! 
+This model/pipeline is the product of my [LibreFlux ControlNet training repo](https://github.com/NeuralVFX/LibreFLUX-ControlNet),[LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX) is used as the base model for the ControlNet. For the dataset I auto labeled 165K images from the SA1B dataset, and trained for that many iterations. I've tested using this ControlNet model as a base for transfer learning to less generic datasets, the results are good! 
+
+# How does this relate to LibreFLUX?
+- Base model is [LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX)
+- Trained in same non-distilled fashion 
+- Uses Attention Masking
+- Uses CFG during Inference
 
 # Fun Facts
-- Trained in non-distilled fashion in style of: [https://huggingface.co/jimmycarter/LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX)
-- Uses Attention Masking (LibreFLUX style)
-- Inference runs with CFG (LibreFLUX style)
 - Trained on 165K segmented images from Meta's [SA1B Dataset](https://ai.meta.com/datasets/segment-anything/)
 - Trained using this repo: [https://github.com/NeuralVFX/LibreFLUX-ControlNet](https://github.com/NeuralVFX/LibreFLUX-ControlNet)
 - Transformer model used: [https://huggingface.co/jimmycarter/LibreFlux-SimpleTuner](https://huggingface.co/jimmycarter/LibreFlux-SimpleTuner)