t2i Adapter community member fix (#5090)

* convert tensorrt controlnet

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix code quality

* Fix number controlnet condition

* Add convert SD XL to onnx

* Add convert SD XL to tensorrt

* Add convert SD XL to tensorrt

* Add examples in comments

* Add examples in comments

* Add test onnx controlnet

* Add tensorrt test

* Remove copied

* Move file test to examples/community

* Remove script

* Remove script

* Remove text

* Fix import

* Fix T2I MultiAdapter

* fix tests

---------

Co-authored-by: dotieuthien <[email protected]>
Co-authored-by: dotieuthien <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: dotieuthien <[email protected]>

Author: 5 people

src/diffusers/models/adapter.py

@@ -90,6 +90,8 @@ def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = Non
             features = adapter(x)
             if accume_state is None:
                 accume_state = features
+                for i in range(len(accume_state)):
+                    accume_state[i] = w * accume_state[i]
             else:
                 for i in range(len(features)):
                     accume_state[i] += w * features[i]

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py

@@ -727,9 +727,14 @@ def __call__(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Denoising loop
-        adapter_state = self.adapter(adapter_input)
-        for k, v in enumerate(adapter_state):
-            adapter_state[k] = v * adapter_conditioning_scale
+        if isinstance(self.adapter, MultiAdapter):
+            adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)
+            for k, v in enumerate(adapter_state):
+                adapter_state[k] = v
+        else:
+            adapter_state = self.adapter(adapter_input)
+            for k, v in enumerate(adapter_state):
+                adapter_state[k] = v * adapter_conditioning_scale
         if num_images_per_prompt > 1:
             for k, v in enumerate(adapter_state):
                 adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)

tests/pipelines/stable_diffusion/test_stable_diffusion_adapter.py

@@ -216,7 +216,9 @@ def get_dummy_components(self):
         return super().get_dummy_components("multi_adapter")
 
     def get_dummy_inputs(self, device, seed=0):
-        return super().get_dummy_inputs(device, seed, num_images=2)
+        inputs = super().get_dummy_inputs(device, seed, num_images=2)
+        inputs["adapter_conditioning_scale"] = [0.5, 0.5]
+        return inputs
 
     def test_stable_diffusion_adapter_default_case(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator