fix sizes

app.py

@@ -8,6 +8,7 @@ from skimage import io
 from urllib import parse
 import numpy as np
 from climategan_wrapper import ClimateGAN
+from textwrap import dedent
 
 
 def predict(cg: ClimateGAN, api_key):
@@ -90,20 +91,96 @@ if __name__ == "__main__":
     )
     cg._setup_stable_diffusion()
 
-    with gr.Blocks() as blocks:
+    with gr.Blocks(
+        css=dedent(
+            """
+            a {
+                color: #0088ff;
+                text-decoration: underline;
+            }
+            strong {
+                color: #c34318;
+            }
+            """
+        )
+    ) as blocks:
         with gr.Row():
             with gr.Column():
                 gr.Markdown("# ClimateGAN: Visualize Climate Change")
                 gr.HTML(
-                    'Climate change does not impact everyone equally. This Space shows the effects of the climate emergency, "one address at a time". Visit the original experience at <a href="https://thisclimatedoesnotexist.com/">ThisClimateDoesNotExist.com</a>.<br>Enter an address or upload a Street View image, and ClimateGAN will generate images showing how the location could be impacted by flooding, wildfires, or smog if it happened there.'  # noqa: E501
-                    + "<br><br>This is <strong>not</strong> an exercise in climate prediction, rather an exercise of empathy, to put yourself in other's shoes, as if Climate Change came crushing on your doorstep."  # noqa: E501
+                    dedent(
+                        """
+                    <p>
+                        Climate change does not impact everyone equally.
+                        This Space shows the effects of the climate emergency,
+                        "one address at a time".
+                    </p>
+
+                    <p>
+                        Visit the original experience at
+                        <a href="https://thisclimatedoesnotexist.com/">
+                            ThisClimateDoesNotExist.com
+                        </a>
+                    </p>
+
+                    <br>
+
+                    <p>
+                        Enter an address or upload a Street View image, and ClimateGAN
+                        will generate images showing how the location could be impacted
+                        by flooding, wildfires, or smog if it happened there.
+                    </p>
+
+                    <br>
+
+                    <p>
+                        This is <strong>not</strong> an exercise in climate prediction,
+                        rather an exercise of empathy, to put yourself in other's shoes,
+                        as if Climate Change came crushing on your doorstep.
+                    </p>
+                        """
+                    )
                 )
             with gr.Column():
                 gr.HTML(
-                    "<p style='text-align: center'>Visit <a href='https://thisclimatedoesnotexist.com/'>ThisClimateDoesNotExist</a> for more information. | Original <a href='https://github.com/cc-ai/climategan'>ClimateGAN GitHub Repo</a></p>"  # noqa: E501
-                    + "<p>After you have selected an image and started the inference you will see all the outputs of ClimateGAN, including intermediate outputs such as the flood mask, the segmentation map and the depth maps used to produce the 3 events</p>"
-                    + "<p>This Space makes use of recent Stable Diffusion in-painting pipelines to replace ClimateGAN's original Painter. If you select 'Both' painters, you will see a comparison</p>"
-                    + "<p>Read the original <a href='https://openreview.net/forum?id=EZNOb_uNpJk', target='_blank'>ICLR 2021 ClimateGAN paper</a></p>"
+                    dedent(
+                        """
+                    <p style='text-align: center'>
+                        Visit
+                        <a href='https://thisclimatedoesnotexist.com/'>
+                            ThisClimateDoesNotExist.com
+                        </a>
+                        for more information.
+                        |
+                        Original
+                        <a href='https://github.com/cc-ai/climategan'>
+                            ClimateGAN GitHub Repo
+                        </a>
+                    </p>
+
+                    <p>
+                        After you have selected an image and started the inference you
+                        will see all the outputs of ClimateGAN, including intermediate
+                        outputs such as the flood mask, the segmentation map and the
+                        depth maps used to produce the 3 events.
+                    </p>
+
+                    <p>
+                        This Space makes use of recent Stable Diffusion in-painting
+                        pipelines to replace ClimateGAN's original Painter. If you
+                        select 'Both' painters, you will see a comparison
+                    </p>
+
+                    <p>
+                        Read the original
+                        <a
+                            href='https://openreview.net/forum?id=EZNOb_uNpJk'
+                            target='_blank'>
+                        ICLR 2021 ClimateGAN paper
+                        </a>
+                    </p>
+                    """
+                    )
                 )
         with gr.Row():
             gr.Markdown("## Inputs")

climategan_wrapper.py

@@ -334,6 +334,7 @@ class ClimateGAN:
         if pil_image is not None:
             print("Warning: `pil_image` has been provided, it will override `images`")
             images = self._preprocess_image(np.array(pil_image))[None, ...]
+            pil_image = Image.fromarray(((images[0] + 1) / 2 * 255).astype(np.uint8))
 
         # Retrieve numpy events as a dict {event: array[BxHxWxC]}
         outputs = self.trainer.infer_all(
@@ -372,8 +373,8 @@ class ClimateGAN:
                 if pil_image is None
                 else Image.fromarray(mask[0])
             )
-            print('input_mask size: ', input_mask.size)
-            print('input_images size: ', input_images.size)
+            print("input_mask size: ", input_mask.size)
+            print("input_images size: ", input_images.size)
             floods = self.sdip_pipeline(
                 prompt=[prompt] * images.shape[0],
                 image=input_images,