added test qwenimage multicontrolnet

Author: akiseakusa

tests/pipelines/qwenimage/test_qwenimage_controlnet.py

@@ -23,8 +23,10 @@
     FlowMatchEulerDiscreteScheduler,
     QwenImageControlNetPipeline,
     QwenImageTransformer2DModel,
+    QwenImageControlNetModel, 
+    QwenImageMultiControlNetModel
 )
-from diffusers.models.controlnets.controlnet_qwenimage import QwenImageControlNetModel
+
 from diffusers.utils.testing_utils import enable_full_determinism, torch_device
 from diffusers.utils.torch_utils import randn_tensor
 
@@ -213,6 +215,53 @@ def test_qwen_controlnet(self):
         generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
         self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
 
+    def test_qwen_controlnet_multicondition(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+
+        components["controlnet"] = QwenImageMultiControlNetModel(
+            [components["controlnet"]]
+        )
+
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        control_image = inputs["control_image"] 
+        inputs["control_image"] = [control_image, control_image]  
+        inputs["controlnet_conditioning_scale"] = [0.5, 0.5]  
+
+        image = pipe(**inputs).images
+        generated_image = image[0]
+        self.assertEqual(generated_image.shape, (3, 32, 32))
+        # Expected slice from the generated image
+        expected_slice = torch.tensor(
+            [
+                0.6239, 
+                0.6642, 
+                0.5768, 
+                0.6039, 
+                0.5270, 
+                0.5070, 
+                0.5006, 
+                0.5271, 
+                0.4506,
+                0.3085, 
+                0.3435, 
+                0.5152, 
+                0.5096, 
+                0.5422, 
+                0.4286, 
+                0.5752
+            ]
+        )
+
+        generated_slice = generated_image.flatten()
+        generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
+        self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
+    
+
     def test_attention_slicing_forward_pass(
         self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
     ):