Added Support of Xlabs controlnet to FluxControlNetInpaintPipeline (#9770)

* added xlabs support

Author: SahilCarterr

src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

@@ -932,19 +932,22 @@ def __call__(
             )
             height, width = control_image.shape[-2:]
 
-            # vae encode
-            control_image = self.vae.encode(control_image).latent_dist.sample()
-            control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-            # pack
-            height_control_image, width_control_image = control_image.shape[2:]
-            control_image = self._pack_latents(
-                control_image,
-                batch_size * num_images_per_prompt,
-                num_channels_latents,
-                height_control_image,
-                width_control_image,
-            )
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.input_hint_block is None else True
+            if self.controlnet.input_hint_block is None:
+                # vae encode
+                control_image = self.vae.encode(control_image).latent_dist.sample()
+                control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                # pack
+                height_control_image, width_control_image = control_image.shape[2:]
+                control_image = self._pack_latents(
+                    control_image,
+                    batch_size * num_images_per_prompt,
+                    num_channels_latents,
+                    height_control_image,
+                    width_control_image,
+                )
 
             # set control mode
             if control_mode is not None:
@@ -954,7 +957,9 @@ def __call__(
         elif isinstance(self.controlnet, FluxMultiControlNetModel):
             control_images = []
 
-            for control_image_ in control_image:
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.nets[0].input_hint_block is None else True
+            for i, control_image_ in enumerate(control_image):
                 control_image_ = self.prepare_image(
                     image=control_image_,
                     width=width,
@@ -966,19 +971,20 @@ def __call__(
                 )
                 height, width = control_image_.shape[-2:]
 
-                # vae encode
-                control_image_ = self.vae.encode(control_image_).latent_dist.sample()
-                control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-                # pack
-                height_control_image, width_control_image = control_image_.shape[2:]
-                control_image_ = self._pack_latents(
-                    control_image_,
-                    batch_size * num_images_per_prompt,
-                    num_channels_latents,
-                    height_control_image,
-                    width_control_image,
-                )
+                if self.controlnet.nets[0].input_hint_block is None:
+                    # vae encode
+                    control_image_ = self.vae.encode(control_image_).latent_dist.sample()
+                    control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                    # pack
+                    height_control_image, width_control_image = control_image_.shape[2:]
+                    control_image_ = self._pack_latents(
+                        control_image_,
+                        batch_size * num_images_per_prompt,
+                        num_channels_latents,
+                        height_control_image,
+                        width_control_image,
+                    )
 
                 control_images.append(control_image_)
 
@@ -1129,6 +1135,7 @@ def __call__(
                     img_ids=latent_image_ids,
                     joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
+                    controlnet_blocks_repeat=controlnet_blocks_repeat,
                 )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1