Do not watermark or postprocess SDXL latent output

src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py

@@ -129,13 +129,14 @@ def __call__(self, components, state: PipelineState) -> PipelineState:
         else:
             block_state.images = block_state.latents
 
-        # apply watermark if available
-        if hasattr(components, "watermark") and components.watermark is not None:
-            block_state.images = components.watermark.apply_watermark(block_state.images)
+        if not block_state.output_type == "latent":
+            # apply watermark if available
+            if hasattr(components, "watermark") and components.watermark is not None:
+                block_state.images = components.watermark.apply_watermark(block_state.images)
 
-        block_state.images = components.image_processor.postprocess(
-            block_state.images, output_type=block_state.output_type
-        )
+            block_state.images = components.image_processor.postprocess(
+                block_state.images, output_type=block_state.output_type
+            )
 
         self.set_block_state(state, block_state)
 

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py

@@ -1521,11 +1521,12 @@ def denoising_value_valid(dnv):
         else:
             image = latents
 
-        # apply watermark if available
-        if self.watermark is not None:
-            image = self.watermark.apply_watermark(image)
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
 
-        image = self.image_processor.postprocess(image, output_type=output_type)
+            image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload all models
         self.maybe_free_model_hooks()

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1477,11 +1477,12 @@ def denoising_value_valid(dnv):
         else:
             image = latents
 
-        # apply watermark if available
-        if self.watermark is not None:
-            image = self.watermark.apply_watermark(image)
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
 
-        image = self.image_processor.postprocess(image, output_type=output_type)
+            image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload all models
         self.maybe_free_model_hooks()

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py

@@ -357,6 +357,25 @@ def test_stable_diffusion_xl_img2img_negative_conditions(self):
 
         assert (image_slice_with_no_neg_conditions - image_slice_with_neg_conditions).abs().max() > 1e-4
 
+    def test_latent_output_skips_watermark_and_postprocess(self):
+        # Regression: `output_type="latent"` sets `image = latents`, but this pipeline still ran the
+        # watermarker and `image_processor.postprocess` over it, treating raw latents as decoded RGB.
+        # The text-to-image pipeline already guards both behind `output_type != "latent"`.
+        class SentinelWatermark:
+            def apply_watermark(self, images):
+                raise AssertionError("watermark must not be applied to latent output")
+
+        sd_pipe = self.get_pipeline()
+        sd_pipe.watermark = SentinelWatermark()
+
+        inputs = self.get_dummy_inputs()
+        inputs["output_type"] = "latent"
+        output = sd_pipe(**inputs).images
+
+        assert torch.is_tensor(output)
+        # Raw latents keep the VAE latent channel count; postprocess would have produced 3 channels.
+        assert output.shape[1] == sd_pipe.unet.config.in_channels
+
     def test_pipeline_interrupt(self):
         sd_pipe = self.get_pipeline().to(torch_device)