IPAdapterTesterMixin (#6862)

* begin IPAdapterTesterMixin



---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -797,7 +797,11 @@ class AnimateDiffPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
         for free_init_iter in range(num_free_init_iters):

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -441,6 +441,41 @@ class AnimateDiffVideoToVideoPipeline(
 
             return image_embeds, uncond_image_embeds
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
+    ):
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                raise ValueError(
+                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                )
+
+            image_embeds = []
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
+                single_negative_image_embeds = torch.stack(
+                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
+                )
+
+                if self.do_classifier_free_guidance:
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    single_image_embeds = single_image_embeds.to(device)
+
+                image_embeds.append(single_image_embeds)
+        else:
+            image_embeds = ip_adapter_image_embeds
+        return image_embeds
+
     # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -735,6 +770,7 @@ class AnimateDiffVideoToVideoPipeline(
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -784,6 +820,9 @@ class AnimateDiffVideoToVideoPipeline(
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
             ip_adapter_image: (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
                 `np.array`.
@@ -870,13 +909,10 @@ class AnimateDiffVideoToVideoPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None:
-            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
-            image_embeds, negative_image_embeds = self.encode_image(
-                ip_adapter_image, device, num_videos_per_prompt, output_hidden_state
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt
             )
-            if self.do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
@@ -902,7 +938,11 @@ class AnimateDiffVideoToVideoPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
         for free_init_iter in range(num_free_init_iters):

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -1206,7 +1206,11 @@ class StableDiffusionControlNetPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1206,7 +1206,11 @@ class StableDiffusionControlNetImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -1495,7 +1495,11 @@ class StableDiffusionControlNetInpaintPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 7.2 Create tensor stating which controlnets to keep
         controlnet_keep = []

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -477,8 +477,9 @@ class LatentConsistencyModelImg2ImgPipeline(
 
             return image_embeds, uncond_image_embeds
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
     def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, do_classifier_free_guidance, device, num_images_per_prompt
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
     ):
         if ip_adapter_image_embeds is None:
             if not isinstance(ip_adapter_image, list):
@@ -502,7 +503,7 @@ class LatentConsistencyModelImg2ImgPipeline(
                     [single_negative_image_embeds] * num_images_per_prompt, dim=0
                 )
 
-                if do_classifier_free_guidance:
+                if self.do_classifier_free_guidance:
                     single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
                     single_image_embeds = single_image_embeds.to(device)
 
@@ -699,6 +700,10 @@ class LatentConsistencyModelImg2ImgPipeline(
     def clip_skip(self):
         return self._clip_skip
 
+    @property
+    def do_classifier_free_guidance(self):
+        return False
+
     @property
     def num_timesteps(self):
         return self._num_timesteps
@@ -845,7 +850,7 @@ class LatentConsistencyModelImg2ImgPipeline(
 
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
             image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, ip_adapter_image_embeds, False, device, batch_size * num_images_per_prompt
+                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
             )
 
         # 3. Encode input prompt
@@ -860,7 +865,7 @@ class LatentConsistencyModelImg2ImgPipeline(
             prompt,
             device,
             num_images_per_prompt,
-            False,
+            self.do_classifier_free_guidance,
             negative_prompt=None,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=None,
@@ -906,7 +911,11 @@ class LatentConsistencyModelImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 8. LCM Multistep Sampling Loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py

@@ -461,6 +461,41 @@ class LatentConsistencyModelPipeline(
 
             return image_embeds, uncond_image_embeds
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
+    ):
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                raise ValueError(
+                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                )
+
+            image_embeds = []
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
+                single_negative_image_embeds = torch.stack(
+                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
+                )
+
+                if self.do_classifier_free_guidance:
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    single_image_embeds = single_image_embeds.to(device)
+
+                image_embeds.append(single_image_embeds)
+        else:
+            image_embeds = ip_adapter_image_embeds
+        return image_embeds
+
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
@@ -590,6 +625,10 @@ class LatentConsistencyModelPipeline(
     def clip_skip(self):
         return self._clip_skip
 
+    @property
+    def do_classifier_free_guidance(self):
+        return False
+
     @property
     def num_timesteps(self):
         return self._num_timesteps
@@ -610,6 +649,7 @@ class LatentConsistencyModelPipeline(
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -660,6 +700,9 @@ class LatentConsistencyModelPipeline(
                 provided, text embeddings are generated from the `prompt` input argument.
             ip_adapter_image: (`PipelineImageInput`, *optional*):
                 Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -726,12 +769,10 @@ class LatentConsistencyModelPipeline(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        # do_classifier_free_guidance = guidance_scale > 1.0
 
-        if ip_adapter_image is not None:
-            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
-            image_embeds, negative_image_embeds = self.encode_image(
-                ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt
             )
 
         # 3. Encode input prompt
@@ -746,7 +787,7 @@ class LatentConsistencyModelPipeline(
             prompt,
             device,
             num_images_per_prompt,
-            False,
+            self.do_classifier_free_guidance,
             negative_prompt=None,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=None,
@@ -786,7 +827,11 @@ class LatentConsistencyModelPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 8. LCM MultiStep Sampling Loop:
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

src/diffusers/pipelines/pia/pipeline_pia.py

@@ -987,7 +987,11 @@ class PIAPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 8. Denoising loop
         num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -1111,7 +1111,11 @@ class StableDiffusionImg2ImgPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 7.2 Optionally get Guidance Scale Embedding
         timestep_cond = None

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -1397,7 +1397,11 @@ class StableDiffusionInpaintPipeline(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 9.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 9.2 Optionally get Guidance Scale Embedding
         timestep_cond = None

src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py

@@ -777,7 +777,11 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
 
         # 8. Denoising loop
         # Each denoising step also includes refinement of the latents with respect to the

tests/models/unets/test_models_unet_2d_condition.py

@@ -62,7 +62,10 @@ def create_ip_adapter_state_dict(model):
     key_id = 1
 
     for name in model.attn_processors.keys():
-        cross_attention_dim = None if name.endswith("attn1.processor") else model.config.cross_attention_dim
+        cross_attention_dim = (
+            None if name.endswith("attn1.processor") or "motion_module" in name else model.config.cross_attention_dim
+        )
+
         if name.startswith("mid_block"):
             hidden_size = model.config.block_out_channels[-1]
         elif name.startswith("up_blocks"):
@@ -71,6 +74,7 @@ def create_ip_adapter_state_dict(model):
         elif name.startswith("down_blocks"):
             block_id = int(name[len("down_blocks.")])
             hidden_size = model.config.block_out_channels[block_id]
+
         if cross_attention_dim is not None:
             sd = IPAdapterAttnProcessor(
                 hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0

tests/pipelines/animatediff/test_animatediff.py

@@ -18,7 +18,7 @@ from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import IPAdapterTesterMixin, PipelineTesterMixin
 
 
 def to_np(tensor):
@@ -28,7 +28,7 @@ def to_np(tensor):
     return tensor
 
 
-class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class AnimateDiffPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AnimateDiffPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -18,7 +18,7 @@ from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_PARAMS, VIDEO_TO_VIDEO_BATCH_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import IPAdapterTesterMixin, PipelineTesterMixin
 
 
 def to_np(tensor):
@@ -28,7 +28,7 @@ def to_np(tensor):
     return tensor
 
 
-class AnimateDiffVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class AnimateDiffVideoToVideoPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AnimateDiffVideoToVideoPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = VIDEO_TO_VIDEO_BATCH_PARAMS

tests/pipelines/controlnet/test_controlnet.py

@@ -54,6 +54,7 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_PARAMS,
 )
 from ..test_pipelines_common import (
+    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -110,7 +111,11 @@ def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
 
 
 class ControlNetPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin,
+    PipelineLatentTesterMixin,
+    PipelineKarrasSchedulerTesterMixin,
+    PipelineTesterMixin,
+    unittest.TestCase,
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -273,7 +278,7 @@ class ControlNetPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
@@ -490,7 +495,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetOneModelPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -52,6 +52,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
 from ..test_pipelines_common import (
+    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -62,7 +63,11 @@ enable_full_determinism()
 
 
 class ControlNetImg2ImgPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin,
+    PipelineLatentTesterMixin,
+    PipelineKarrasSchedulerTesterMixin,
+    PipelineTesterMixin,
+    unittest.TestCase,
 ):
     pipeline_class = StableDiffusionControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
@@ -181,7 +186,7 @@ class ControlNetImg2ImgPipelineFastTests(
 
 
 class StableDiffusionMultiControlNetPipelineFastTests(
-    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
 ):
     pipeline_class = StableDiffusionControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -51,11 +51,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
     TEXT_TO_IMAGE_IMAGE_PARAMS,
 )
-from ..test_pipelines_common import (
-    PipelineKarrasSchedulerTesterMixin,
-    PipelineLatentTesterMixin,
-    PipelineTesterMixin,
-)
+from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -48,6 +48,7 @@ from ..pipeline_params import (
     TEXT_TO_IMAGE_PARAMS,
 )
 from ..test_pipelines_common import (
+    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -59,6 +60,7 @@ enable_full_determinism()
 
 
 class StableDiffusionXLControlNetPipelineFastTests(
+    IPAdapterTesterMixin,
     PipelineLatentTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineTesterMixin,

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -36,6 +36,7 @@ from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
 from ..test_pipelines_common import (
+    IPAdapterTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
     PipelineLatentTesterMixin,
     PipelineTesterMixin,
@@ -46,7 +47,11 @@ enable_full_determinism()
 
 
 class ControlNetPipelineSDXLImg2ImgFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
+    IPAdapterTesterMixin,
+    PipelineLatentTesterMixin,
+    PipelineKarrasSchedulerTesterMixin,
+    PipelineTesterMixin,
+    unittest.TestCase,
 ):
     pipeline_class = StableDiffusionXLControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS

tests/pipelines/latent_consistency_models/test_latent_consistency_models.py

@@ -20,13 +20,15 @@ from diffusers.utils.testing_utils import (
 )
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
+from ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 enable_full_determinism()
 
 
-class LatentConsistencyModelPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class LatentConsistencyModelPipelineFastTests(
+    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
     pipeline_class = LatentConsistencyModelPipeline
     params = TEXT_TO_IMAGE_PARAMS - {"negative_prompt", "negative_prompt_embeds"}
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {"negative_prompt"}