fix compatability issue between PAG and IP-adapter (#8379)

* fix compatability issue between PAG and IP-adapter

* fix compatibility issue between PAG and IP-adapter plus

Author: sunovivid

src/diffusers/loaders/unet.py

@@ -928,9 +928,7 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
                 hidden_size = self.config.block_out_channels[block_id]
 
             if cross_attention_dim is None or "motion_modules" in name:
-                attn_processor_class = (
-                    AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
-                )
+                attn_processor_class = self.attn_processors[name].__class__
                 attn_procs[name] = attn_processor_class()
 
             else:

src/diffusers/pipelines/pag_utils.py

@@ -45,7 +45,7 @@ def enable_pag(
         self._pag_applied_layers = pag_applied_layers
         self._pag_applied_layers_index = pag_applied_layers_index
         self._pag_cfg = pag_cfg
-
+        self._is_pag_enabled = True
         self._set_pag_attn_processor()
 
     def _get_self_attn_layers(self):
@@ -180,6 +180,7 @@ def disable_pag(self):
         self._pag_applied_layers = None
         self._pag_applied_layers_index = None
         self._pag_cfg = None
+        self._is_pag_enabled = False
 
     @property
     def pag_adaptive_scaling(self):
@@ -191,4 +192,4 @@ def do_pag_adaptive_scaling(self):
 
     @property
     def do_perturbed_attention_guidance(self):
-        return hasattr(self, "_pag_scale") and self._pag_scale is not None and self._pag_scale > 0
+        return self._is_pag_enabled

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -536,7 +536,7 @@ def encode_image(self, image, device, num_images_per_prompt, output_hidden_state
 
     # 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, do_classifier_free_guidance
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance, do_perturbed_attention_guidance
     ):
         if ip_adapter_image_embeds is None:
             if not isinstance(ip_adapter_image, list):
@@ -560,6 +560,10 @@ def prepare_ip_adapter_image_embeds(
                     [single_negative_image_embeds] * num_images_per_prompt, dim=0
                 )
 
+                if do_perturbed_attention_guidance:
+                    single_image_embeds = torch.cat([single_image_embeds, single_image_embeds], dim=0)
+                    single_image_embeds = single_image_embeds.to(device)
+
                 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)
@@ -577,11 +581,16 @@ def prepare_ip_adapter_image_embeds(
                     single_negative_image_embeds = single_negative_image_embeds.repeat(
                         num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
                     )
-                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    if do_perturbed_attention_guidance:
+                        single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds, single_image_embeds], dim=0)
+                    else:
+                        single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
                 else:
                     single_image_embeds = single_image_embeds.repeat(
                         num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
                     )
+                    if do_perturbed_attention_guidance:
+                        single_image_embeds = torch.cat([single_image_embeds, single_image_embeds], dim=0)
                 image_embeds.append(single_image_embeds)
 
         return image_embeds
@@ -1170,6 +1179,7 @@ def __call__(
                 device,
                 batch_size * num_images_per_prompt,
                 self.do_classifier_free_guidance,
+                self.do_perturbed_attention_guidance,
             )
 
         # 8. Denoising loop
@@ -1205,7 +1215,7 @@ def __call__(
                 if self.interrupt:
                     continue
 
-                # expand the latents if we are doing classifier free guidance
+                # expand the latents if we are doing classifier free guidance, perturbed-attention guidance, or both
                 latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
 
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)