Bugfix in IPAdapterFaceID (#6835)

examples/community/ip_adapter_face_id.py

@@ -104,6 +104,22 @@ class LoRAIPAdapterAttnProcessor(nn.Module):
     ):
         residual = hidden_states
 
+        # separate ip_hidden_states from encoder_hidden_states
+        if encoder_hidden_states is not None:
+            if isinstance(encoder_hidden_states, tuple):
+                encoder_hidden_states, ip_hidden_states = encoder_hidden_states
+            else:
+                deprecation_message = (
+                    "You have passed a tensor as `encoder_hidden_states`.This is deprecated and will be removed in a future release."
+                    " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to supress this warning."
+                )
+                deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False)
+                end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]
+                encoder_hidden_states, ip_hidden_states = (
+                    encoder_hidden_states[:, :end_pos, :],
+                    [encoder_hidden_states[:, end_pos:, :]],
+                )
+
         if attn.spatial_norm is not None:
             hidden_states = attn.spatial_norm(hidden_states, temb)
 
@@ -125,15 +141,8 @@ class LoRAIPAdapterAttnProcessor(nn.Module):
 
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
         key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states)
         value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states)
@@ -233,6 +242,22 @@ class LoRAIPAdapterAttnProcessor2_0(nn.Module):
     ):
         residual = hidden_states
 
+        # separate ip_hidden_states from encoder_hidden_states
+        if encoder_hidden_states is not None:
+            if isinstance(encoder_hidden_states, tuple):
+                encoder_hidden_states, ip_hidden_states = encoder_hidden_states
+            else:
+                deprecation_message = (
+                    "You have passed a tensor as `encoder_hidden_states`.This is deprecated and will be removed in a future release."
+                    " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to supress this warning."
+                )
+                deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False)
+                end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]
+                encoder_hidden_states, ip_hidden_states = (
+                    encoder_hidden_states[:, :end_pos, :],
+                    [encoder_hidden_states[:, end_pos:, :]],
+                )
+
         if attn.spatial_norm is not None:
             hidden_states = attn.spatial_norm(hidden_states, temb)
 
@@ -259,15 +284,8 @@ class LoRAIPAdapterAttnProcessor2_0(nn.Module):
 
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
         key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states)
         value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states)
@@ -951,30 +969,6 @@ class IPAdapterFaceIDStableDiffusionPipeline(
 
         return prompt_embeds, negative_prompt_embeds
 
-    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        if output_hidden_states:
-            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_enc_hidden_states = self.image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                num_images_per_prompt, dim=0
-            )
-            return image_enc_hidden_states, uncond_image_enc_hidden_states
-        else:
-            image_embeds = self.image_encoder(image).image_embeds
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_embeds = torch.zeros_like(image_embeds)
-
-            return image_embeds, uncond_image_embeds
-
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
             has_nsfw_concept = None
@@ -1302,7 +1296,6 @@ class IPAdapterFaceIDStableDiffusionPipeline(
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
             image_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated image embeddings.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
             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`):
@@ -1411,7 +1404,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
         if image_embeds is not None:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0).to(
+            image_embeds = torch.stack([image_embeds] * num_images_per_prompt, dim=0).to(
                 device=device, dtype=prompt_embeds.dtype
             )
             negative_image_embeds = torch.zeros_like(image_embeds)