Fix IP Adapter Support for SAG Pipeline (huggingface#7260)

* fix ip adapter support

* Update sag pipelines tests, adjust sag pipeline to pass tests

---------

Co-authored-by: YiYi Xu <[email protected]>

Author: stephen-iezzi

src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py

@@ -401,6 +401,40 @@ def encode_image(self, image, device, num_images_per_prompt, output_hidden_state
 
             return image_embeds, uncond_image_embeds
 
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+    ):
+        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 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:
@@ -535,6 +569,7 @@ def __call__(
         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,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -583,6 +618,9 @@ def __call__(
                 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 image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -636,13 +674,24 @@ def __call__(
         # `sag_scale = 0` means no self-attention guidance
         do_self_attention_guidance = sag_scale > 0.0
 
-        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:
+            ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                do_classifier_free_guidance,
             )
+
             if do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
+                image_embeds = []
+                negative_image_embeds = []
+                for tmp_image_embeds in ip_adapter_image_embeds:
+                    single_negative_image_embeds, single_image_embeds = tmp_image_embeds.chunk(2)
+                    image_embeds.append(single_image_embeds)
+                    negative_image_embeds.append(single_negative_image_embeds)
+            else:
+                image_embeds = ip_adapter_image_embeds
 
         # 3. Encode input prompt
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
@@ -687,8 +736,18 @@ def __call__(
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
         # 6.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
-        added_uncond_kwargs = {"image_embeds": negative_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
+        )
+
+        if do_classifier_free_guidance:
+            added_uncond_kwargs = (
+                {"image_embeds": negative_image_embeds}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+                else None
+            )
 
         # 7. Denoising loop
         store_processor = CrossAttnStoreProcessor()

tests/pipelines/stable_diffusion_sag/test_stable_diffusion_sag.py

@@ -32,13 +32,15 @@
 from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
 
 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 StableDiffusionSAGPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionSAGPipelineFastTests(
+    IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
     pipeline_class = StableDiffusionSAGPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS