init

app.py

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+import torch
+import kornia
+import gradio as gr
+
+
+def image_to_grayscale(image, version, step_no):
+    image = kornia.utils.image_to_tensor(image, keepdim=False) / 255.
+    image = kornia.geometry.transform.resize(
+        image, 384, interpolation='bilinear', align_corners=None, side='short', antialias=False)
+    dissolved = kornia.filters.StableDiffusionDissolving(version)(image, int(step_no))
+    output = kornia.utils.tensor_to_image(dissolved)
+    return output
+
+
+iface = gr.Interface(
+    fn=image_to_grayscale,
+    inputs=[
+        gr.Image(type="numpy"),
+        gr.Dropdown(
+            ["2.1", "1.5", "1.4"], value="2.1", multiselect=False, label="Stable Diffusion Version"
+        ),
+        gr.Number(value=500, minimum=0, precision=0, maximum=1000, label="Timestep No."),
+    ],
+    outputs="image",
+    title="Dissolving Transformations with Stable Diffusion",
+    description="Dissolving transformation based on `Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection`. Original rep is https://github.com/shijianjian/DIA. It has been included in Kornia lib (https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation.RandomDissolving). You may try by `kornia.filters.StableDiffusionDissolving(version)(image, step_no)`, or `kornia.augmentation.RandomDissolving()(image)`. In this demo, the images are downsampled for fast computation."
+)
+
+# Launch the Gradio app
+iface.launch()

requirements.txt

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+torch
+kornia @ git+https://github.com/kornia/kornia@main