can not use custom_pipeline hd_painter with IP_adapter

### Describe the bug

When I use IP_adapter and hd_painter at the same time.
it pop out the RuntimeError: mat1 and mat2 shapes cannot be multiplied (514x1280 and 1024x3072).
It should be successful since both IP_adapter and hd_painter didn't modify the output shape of attention layers.
From log, it seems that the whole computation mixed up both computation of IP_adapter and hd_painter?
Maybe should do one first and then do the other?

### Reproduction

```
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"./stable-diffusion/absolutereality_v181",
use_safetensors=True, torch_dtype=torch.float16, safety_checker=None, custom_pipeline="hd_painter"
)

pipe.to("cuda")
pipe.load_ip_adapter("./", subfolder = "ip_adapter", weight_name="ip-adapter_sd15.bin")
prompt = "plastic bag"
init_image = load_image("./input/bag.jpg")
mask_image = load_image("./mask/bag.jpg")
ip_image = load_image("./input/plastic_bag.jpg")

image = pipe (prompt, init_image, mask_image,
ip_adapter_image = ip_image,
use_rasg = True, use_painta = True,
num_inference_steps=30,
generator=torch.manual_seed(12345)).images[0]

out = make_image_grid([init_image, mask_image, image], rows=1, cols=3)
out.save("hd.jpg")
```

### Logs

```shell
Traceback (most recent call last):
File "/home/loyd_wan/snap/snapd-desktop-integration/83/Desktop/IP-Adapter/hd_painter.py", line 41, in
image = pipe (prompt, init_image, mask_image,
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/root/.cache/huggingface/modules/diffusers_modules/git/hd_painter.py", line 759, in __call__
noise_pred = self.unet(
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_condition.py", line 1168, in forward
encoder_hidden_states = self.process_encoder_hidden_states(
File "/opt/conda/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_condition.py", line 1039, in process_encoder_hidden_states
image_embeds = self.encoder_hid_proj(image_embeds)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 1032, in forward
image_embed = image_projection_layer(image_embed)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/diffusers/models/embeddings.py", line 457, in forward
image_embeds = self.image_embeds(image_embeds)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (514x1280 and 1024x3072)
```

### System Info

Diffusers==v0.28.0
python==3.10

### Who can help?

_No response_

Reproduce the failure with the provided hd_painter pipeline and IP-Adapter example. Start at diffusers/models/unets/unet_2d_condition.py in process_encoder_hidden_states, then trace the projection in diffusers/models/embeddings.py and the call from hd_painter.py line 759. Done means the combined pipeline runs without the reported matrix-shape error, with regression coverage for this combination.

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