We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Color channel order for watermark embedding
### Describe the bug

The encoder from the invisible watermark library expects input images with the channel order BGR, which is the default in OpenCV. This can be seen [here](https://github.com/ShieldMnt/invisible-watermark/blob/68d0376d94a4701ed240af0841ec12e00676e325/imwatermark/maxDct.py#L21).

As far as I can see from [here](https://github.com/huggingface/diffusers/blob/3369bc810a09a52521bbf8cc1ec77df3a8c682a8/src/diffusers/pipelines/stable_diffusion_xl/watermark.py#L24), diffusers passes the images in RGB order.

The watermark encoder then converts the given image from BGR to YUV. When the image is passed with the wrong channel order, this will lead to unexpected U and V channel values.

### Reproduction

n/a

### Logs

_No response_

### System Info

Python 3.10, diffusers 0.24.0, invisible-watermark-0.2.0

### Who can help?

_No response_

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/diffusers/pipelines/stable_diffusion_xl/watermark.py`
Content:
```
1 import numpy as np
2 import torch
3 
4 from ...utils import is_invisible_watermark_available
5 
6 
7 if is_invisible_watermark_available():
8     from imwatermark import WatermarkEncoder
9 
10 
11 # Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
12 WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
13 # bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
14 WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
15 
16 
17 class StableDiffusionXLWatermarker:
18     def __init__(self):
19         self.watermark = WATERMARK_BITS
20         self.encoder = WatermarkEncoder()
21 
22         self.encoder.set_watermark("bits", self.watermark)
23 
24     def apply_watermark(self, images: torch.FloatTensor):
25         # can't encode images that are smaller than 256
26         if images.shape[-1] < 256:
27             return images
28 
29         images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()
30 
31         images = [self.encoder.encode(image, "dwtDct") for image in images]
32 
33         images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2)
34 
35         images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)
36         return images
37
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/src/diffusers/pipelines/stable_diffusion_xl/watermark.py b/src/diffusers/pipelines/stable_diffusion_xl/watermark.py
--- a/src/diffusers/pipelines/stable_diffusion_xl/watermark.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/watermark.py
@@ -28,9 +28,15 @@
 
         images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()
 
-        images = [self.encoder.encode(image, "dwtDct") for image in images]
+        # Convert RGB to BGR, which is the channel order expected by the watermark encoder.
+        images = images[:, :, :, ::-1]
 
-        images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2)
+        # Add watermark and convert BGR back to RGB
+        images = [self.encoder.encode(image, "dwtDct")[:, :, ::-1] for image in images]
+
+        images = np.array(images)
+
+        images = torch.from_numpy(images).permute(0, 3, 1, 2)
 
         images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)
         return images

{"golden_diff": "diff --git a/src/diffusers/pipelines/stable_diffusion_xl/watermark.py b/src/diffusers/pipelines/stable_diffusion_xl/watermark.py\n--- a/src/diffusers/pipelines/stable_diffusion_xl/watermark.py\n+++ b/src/diffusers/pipelines/stable_diffusion_xl/watermark.py\n@@ -28,9 +28,15 @@\n \n         images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()\n \n-        images = [self.encoder.encode(image, \"dwtDct\") for image in images]\n+        # Convert RGB to BGR, which is the channel order expected by the watermark encoder.\n+        images = images[:, :, :, ::-1]\n \n-        images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2)\n+        # Add watermark and convert BGR back to RGB\n+        images = [self.encoder.encode(image, \"dwtDct\")[:, :, ::-1] for image in images]\n+\n+        images = np.array(images)\n+\n+        images = torch.from_numpy(images).permute(0, 3, 1, 2)\n \n         images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)\n         return images\n", "issue": "Color channel order for watermark embedding\n### Describe the bug\n\nThe encoder from the invisible watermark library expects input images with the channel order BGR, which is the default in OpenCV. This can be seen [here](https://github.com/ShieldMnt/invisible-watermark/blob/68d0376d94a4701ed240af0841ec12e00676e325/imwatermark/maxDct.py#L21).\r\n\r\nAs far as I can see from [here](https://github.com/huggingface/diffusers/blob/3369bc810a09a52521bbf8cc1ec77df3a8c682a8/src/diffusers/pipelines/stable_diffusion_xl/watermark.py#L24), diffusers passes the images in RGB order.\r\n\r\nThe watermark encoder then converts the given image from BGR to YUV. When the image is passed with the wrong channel order, this will lead to unexpected U and V channel values.\n\n### Reproduction\n\nn/a\n\n### Logs\n\n_No response_\n\n### System Info\n\nPython 3.10, diffusers 0.24.0, invisible-watermark-0.2.0\n\n### Who can help?\n\n_No response_\n", "before_files": [{"content": "import numpy as np\nimport torch\n\nfrom ...utils import is_invisible_watermark_available\n\n\nif is_invisible_watermark_available():\n    from imwatermark import WatermarkEncoder\n\n\n# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66\nWATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110\n# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1\nWATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]\n\n\nclass StableDiffusionXLWatermarker:\n    def __init__(self):\n        self.watermark = WATERMARK_BITS\n        self.encoder = WatermarkEncoder()\n\n        self.encoder.set_watermark(\"bits\", self.watermark)\n\n    def apply_watermark(self, images: torch.FloatTensor):\n        # can't encode images that are smaller than 256\n        if images.shape[-1] < 256:\n            return images\n\n        images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()\n\n        images = [self.encoder.encode(image, \"dwtDct\") for image in images]\n\n        images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2)\n\n        images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)\n        return images\n", "path": "src/diffusers/pipelines/stable_diffusion_xl/watermark.py"}], "after_files": [{"content": "import numpy as np\nimport torch\n\nfrom ...utils import is_invisible_watermark_available\n\n\nif is_invisible_watermark_available():\n    from imwatermark import WatermarkEncoder\n\n\n# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66\nWATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110\n# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1\nWATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]\n\n\nclass StableDiffusionXLWatermarker:\n    def __init__(self):\n        self.watermark = WATERMARK_BITS\n        self.encoder = WatermarkEncoder()\n\n        self.encoder.set_watermark(\"bits\", self.watermark)\n\n    def apply_watermark(self, images: torch.FloatTensor):\n        # can't encode images that are smaller than 256\n        if images.shape[-1] < 256:\n            return images\n\n        images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()\n\n        # Convert RGB to BGR, which is the channel order expected by the watermark encoder.\n        images = images[:, :, :, ::-1]\n\n        # Add watermark and convert BGR back to RGB\n        images = [self.encoder.encode(image, \"dwtDct\")[:, :, ::-1] for image in images]\n\n        images = np.array(images)\n\n        images = torch.from_numpy(images).permute(0, 3, 1, 2)\n\n        images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)\n        return images\n", "path": "src/diffusers/pipelines/stable_diffusion_xl/watermark.py"}]}