add image

.gitattributes

@@ -41,3 +41,4 @@ content/example_0.png filter=lfs diff=lfs merge=lfs -text
 content/example_1.jpg filter=lfs diff=lfs merge=lfs -text
 content/output_0.png filter=lfs diff=lfs merge=lfs -text
 content/output_1.png filter=lfs diff=lfs merge=lfs -text
+content/Schermafbeelding[[:space:]]2023-05-05[[:space:]]om[[:space:]]14.29.39.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -315,7 +315,7 @@ def main():
             #         "data-centric framework that allows you to prepare large scale multimodal datasets with ease. We have implemented the components "
             #         "that we used to train this controlnet model in Fondant as an example pipeline, and we are excited to see what you can do with it! In the future we will add a whole library of plug-and-play data preparation components, such as different ML models and filtering steps, in addition to dataset scraping components that connect to LAION5B."
             #         )
-            # st.write("The framework is build on top of kubeflow pipelines and abstracts all the complexity of efficient storing and moving of large datasets, so you can focus on implemented just that piece of code that you need without worrying about the rest. We also build it to run on each Cloud provider or VM. You can find the code on our github page: https://github.com/ml6team/fondant.")
+            # st.write("The framework is built on top of kubeflow pipelines and abstracts all the complexity of efficient storing and moving of large datasets, so you can focus on implemented just that piece of code that you need without worrying about the rest. We also build it to run on each Cloud provider or VM. You can find the code on our github page: https://github.com/ml6team/fondant.")
 
         st.write("### Testing images")
         st.write("If you don't have any pictures close, you can use one of these images to test the model:")

models.py

@@ -52,7 +52,7 @@ def make_image_controlnet(image: np.ndarray,
     generated_image = pipe(
         prompt=positive_prompt,
         negative_prompt=negative_prompt,
-        num_inference_steps=30,
+        num_inference_steps=50,
         strength=1.00,
         guidance_scale=7.0,
         generator=[torch.Generator(device="cuda").manual_seed(seed)],
@@ -89,7 +89,7 @@ def make_inpainting(positive_prompt: str,
                     mask_image=mask_image,
                     prompt=positive_prompt,
                     negative_prompt=negative_prompt,
-                    num_inference_steps=30,
+                    num_inference_steps=50,
                     height=HEIGHT,
                     width=WIDTH,
                     ).images[0]