parameterize inpaint size

app.py

@@ -40,7 +40,6 @@ inpainting_pipeline = StableDiffusionInpaintPipeline.from_pretrained(
 #seg_model = torch.compile(seg_model, backend=preferred_backend)
 
 seg_working_size = (seg_model_img_size, seg_model_img_size)
-repaint_working_size = (768, 768)
 
 default_inpainting_prompt = "award-winning photo of a leafy pedestrian mall full of people, with multiracial genderqueer joggers and bicyclists and wheelchair users talking and laughing"
 
@@ -63,7 +62,7 @@ def get_seg_mask(img):
     return blurred_widened_mask
 
 
-def app(img, prompt, num_inference_steps, seed):
+def app(img, prompt, num_inference_steps, seed, inpaint_size):
     start_time = datetime.now().timestamp()
     old_size = Image.fromarray(img).size
     img = np.array(Image.fromarray(img).resize(seg_working_size))
@@ -77,8 +76,8 @@ def app(img, prompt, num_inference_steps, seed):
         mask_image=(mask).resize(repaint_working_size),
         strength=1,
         num_inference_steps=num_inference_steps,
-        height=repaint_working_size[0],
-        width=repaint_working_size[1],
+        height=inpaint_size,
+        width=inpaint_size,
         generator=torch.manual_seed(int(seed)),
     ).images[0]
     #overlay_img.save("overlay_raw.jpg")
@@ -95,9 +94,16 @@ def app(img, prompt, num_inference_steps, seed):
 
 for i in range(2):
     for j in tqdm(range(3 ** i)):
-        app(np.array(Image.fromarray(np.zeros((1024,1024,3), dtype=np.uint8))), default_inpainting_prompt, 4, 42).save("zeros_inpainting_oneshot.jpg")
+        app(np.array(Image.fromarray(np.zeros((1024,1024,3), dtype=np.uint8))), default_inpainting_prompt, 4, 42, 512).save("zeros_inpainting_oneshot.jpg")
 
 #ideally:
 #iface = gr.Interface(app, gr.Image(sources=["webcam"], streaming=True), "image", live=True)
-iface = gr.Interface(app, [gr.Image(), gr.Textbox(value=default_inpainting_prompt), gr.Number(minimum=1, maximum=8, value=4), gr.Number(value=42)], "image")
+iface = gr.Interface(app, [
+        gr.Image(),
+        gr.Textbox(value=default_inpainting_prompt),
+        gr.Number(minimum=1, maximum=8, value=4),
+        gr.Number(value=42),
+        gr.Number(value=512, maximum=seg_model_img_size,)
+    ],
+    "image")
 iface.launch()