Update app.py

app.py

@@ -33,6 +33,11 @@ def infer(
 
     conditioning_image_raw = Image.fromarray(conditioning_image)
     conditioning_image = conditioning_image_raw.convert('L')
+    w = conditioning_image.width
+    h = conditioning_image.height
+    ratio = 768 / max(W, H)
+    w = int(W * ratio)
+    h = int(H * ratio)
     g_cpu = torch.Generator()
 
     if seed == -1:
@@ -43,8 +48,8 @@ def infer(
     output_image = pipe(
         prompt,
         conditioning_image,
-        height=size,
-        width=size,
+        height=h,
+        width=w,
         num_inference_steps=num_inference_steps,
         generator=generator,
         negative_prompt=negative_prompt,
@@ -131,6 +136,19 @@ with gr.Blocks() as demo:
         ],
         outputs=output
     )
+    gr.Examples(
+        examples=[
+            ["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty1.jpg","./conditioning_images/tyt1.jpg"],
+                        ["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty2.jpg","./conditioning_images/tyt2.jpg"],
+
+                    ],
+        inputs=[
+            prompt, negative_prompt, conditioning_image
+        ],
+        outputs=output,
+        fn=infer,
+        cache_examples=True,
+    )
     gr.Markdown(
         """
     * [Dataset](https://huggingface.co/datasets/ioclab/grayscale_image_aesthetic_3M) Note that this was handled extra, and a preview version of the processing is here