fix multiple image return from api nodes (Comfy-Org#7772)

Author: thot-experiment

comfy_api_nodes/nodes_api.py

@@ -31,35 +31,43 @@ def downscale_input(image):
     s = s.movedim(1,-1)
     return s
 
-def validate_and_cast_response (response):
+def validate_and_cast_response(response):
     # validate raw JSON response
     data = response.data
     if not data or len(data) == 0:
         raise Exception("No images returned from API endpoint")
 
-    # Get base64 image data
-    image_url = data[0].url
-    b64_data = data[0].b64_json
-    if not image_url and not b64_data:
-        raise Exception("No image was generated in the response")
+    # Initialize list to store image tensors
+    image_tensors = []
 
-    if b64_data:
-        img_data = base64.b64decode(b64_data)
-        img = Image.open(io.BytesIO(img_data))
+    # Process each image in the data array
+    for image_data in data:
+        image_url = image_data.url
+        b64_data = image_data.b64_json
 
-    elif image_url:
-        img_response = requests.get(image_url)
-        if img_response.status_code != 200:
-            raise Exception("Failed to download the image")
-        img = Image.open(io.BytesIO(img_response.content))
+        if not image_url and not b64_data:
+            raise Exception("No image was generated in the response")
 
-    img = img.convert("RGBA")
+        if b64_data:
+            img_data = base64.b64decode(b64_data)
+            img = Image.open(io.BytesIO(img_data))
 
-    # Convert to numpy array, normalize to float32 between 0 and 1
-    img_array = np.array(img).astype(np.float32) / 255.0
+        elif image_url:
+            img_response = requests.get(image_url)
+            if img_response.status_code != 200:
+                raise Exception("Failed to download the image")
+            img = Image.open(io.BytesIO(img_response.content))
 
-    # Convert to torch tensor and add batch dimension
-    return torch.from_numpy(img_array)[None,]
+        img = img.convert("RGBA")
+
+        # Convert to numpy array, normalize to float32 between 0 and 1
+        img_array = np.array(img).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_array)
+
+        # Add to list of tensors
+        image_tensors.append(img_tensor)
+
+    return torch.stack(image_tensors, dim=0)
 
 class OpenAIDalle2(ComfyNodeABC):
     """