Fix None values in usage field for gpt-image-1 model responses (#11448)

* fix(convert_dict_to_response.py): handle None values in usage field for gpt-image-1

* test: add tests for handling None and partial values in usage fields for gpt-image-1 responses

litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py

@@ -294,6 +294,22 @@ class LiteLLMResponseObjectHandler:
     ) -> ImageResponse:
         response_object.update({"hidden_params": hidden_params})
 
+        # Handle gpt-image-1 usage field with None values
+        if "usage" in response_object and response_object["usage"] is not None:
+            usage = response_object["usage"]
+            # Check if usage fields are None and provide defaults
+            if usage.get("input_tokens") is None:
+                usage["input_tokens"] = 0
+            if usage.get("output_tokens") is None:
+                usage["output_tokens"] = 0
+            if usage.get("total_tokens") is None:
+                usage["total_tokens"] = usage["input_tokens"] + usage["output_tokens"]
+            if usage.get("input_tokens_details") is None:
+                usage["input_tokens_details"] = {
+                    "image_tokens": 0,
+                    "text_tokens": 0,
+                }
+
         if model_response_object is None:
             model_response_object = ImageResponse(**response_object)
             return model_response_object

tests/llm_translation/test_convert_dict_to_image.py

@@ -117,3 +117,107 @@ def test_convert_to_image_response_with_extra_fields_2():
 
     assert result.data[0].url == "http://example.com/image1.jpg"
     assert result.data[1].url == "http://example.com/image2.jpg"
+
+
+def test_convert_to_image_response_with_none_usage_fields():
+    """
+    Test handling of None values in usage fields, specifically for gpt-image-1 responses.
+    
+    This test verifies the fix for the bug where gpt-image-1 returns None values
+    for usage statistics fields, which caused Pydantic validation errors.
+    The fix should clean these None values and let ImageResponse constructor
+    handle the default values.
+    """
+    response_dict = {
+        "created": 1234567890,
+        "data": [{"b64_json": "base64encodedstring"}],
+        "usage": {
+            "input_tokens": None,  # gpt-image-1 returns None instead of integer
+            "input_tokens_details": None,  # gpt-image-1 returns None instead of object
+            "output_tokens": None,  # gpt-image-1 returns None instead of integer
+            "total_tokens": None,  # gpt-image-1 returns None instead of integer
+        }
+    }
+
+    # This should not raise a ValidationError
+    result = LiteLLMResponseObjectHandler.convert_to_image_response(response_dict)
+
+    assert isinstance(result, ImageResponse)
+    assert result.created == 1234567890
+    assert result.data[0].b64_json == "base64encodedstring"
+    
+    # Usage should be properly initialized with default values
+    assert result.usage is not None
+    assert result.usage.input_tokens == 0
+    assert result.usage.output_tokens == 0
+    assert result.usage.total_tokens == 0
+    assert result.usage.input_tokens_details is not None
+    assert result.usage.input_tokens_details.image_tokens == 0
+    assert result.usage.input_tokens_details.text_tokens == 0
+
+
+def test_convert_to_image_response_with_partial_none_usage_fields():
+    """
+    Test handling of mixed None and valid values in usage fields.
+    """
+    response_dict = {
+        "created": 1234567890,
+        "data": [{"b64_json": "base64encodedstring"}],
+        "usage": {
+            "input_tokens": 10,  # Valid value
+            "input_tokens_details": None,  # None value (should be cleaned)
+            "output_tokens": None,  # None value (should be cleaned)
+            "total_tokens": 10,  # Valid value
+        }
+    }
+
+    # This should not raise a ValidationError
+    result = LiteLLMResponseObjectHandler.convert_to_image_response(response_dict)
+
+    assert isinstance(result, ImageResponse)
+    assert result.created == 1234567890
+    assert result.data[0].b64_json == "base64encodedstring"
+    
+    # Usage should be properly initialized with defaults where needed
+    # Valid values should be preserved, None values should be cleaned and use defaults
+    assert result.usage is not None
+    assert result.usage.input_tokens == 10  # Valid value should be preserved
+    assert result.usage.output_tokens == 0  # None value should become 0
+    assert result.usage.total_tokens == 10  # Calculated as input_tokens + output_tokens (10 + 0)
+    assert result.usage.input_tokens_details is not None
+    assert result.usage.input_tokens_details.image_tokens == 0
+    assert result.usage.input_tokens_details.text_tokens == 0
+
+
+def test_convert_to_image_response_with_valid_usage_fields():
+    """
+    Test that valid usage fields are preserved correctly.
+    """
+    response_dict = {
+        "created": 1234567890,
+        "data": [{"b64_json": "base64encodedstring"}],
+        "usage": {
+            "input_tokens": 50,
+            "input_tokens_details": {
+                "image_tokens": 30,
+                "text_tokens": 20,
+            },
+            "output_tokens": 10,
+            "total_tokens": 60,
+        }
+    }
+
+    result = LiteLLMResponseObjectHandler.convert_to_image_response(response_dict)
+
+    assert isinstance(result, ImageResponse)
+    assert result.created == 1234567890
+    assert result.data[0].b64_json == "base64encodedstring"
+    
+    # Valid usage fields should be preserved
+    assert result.usage is not None
+    assert result.usage.input_tokens == 50
+    assert result.usage.output_tokens == 10
+    assert result.usage.total_tokens == 60
+    assert result.usage.input_tokens_details is not None
+    assert result.usage.input_tokens_details.image_tokens == 30
+    assert result.usage.input_tokens_details.text_tokens == 20