two input

app.py

@@ -14,8 +14,8 @@ if torch.cuda.is_available():
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+# pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -38,17 +38,17 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
+    # image = pipe(
+    #     prompt=prompt,
+    #     negative_prompt=negative_prompt,
+    #     guidance_scale=guidance_scale,
+    #     num_inference_steps=num_inference_steps,
+    #     width=width,
+    #     height=height,
+    #     generator=generator,
+    # ).images[0]
 
-    return image, seed
+    # return image, seed
 
 
 examples = [
@@ -70,10 +70,18 @@ with gr.Blocks(css=css) as demo:
 
         with gr.Row():
             prompt = gr.Text(
-                label="Prompt",
+                label="Prompt_1",
                 show_label=False,
                 max_lines=1,
-                placeholder="Enter your prompt",
+                placeholder="Enter your prompt for the first image",
+                container=False,
+            )
+
+            prompt = gr.Text(
+                label="Prompt_2",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt for the second",
                 container=False,
             )