UNet2DConditionModel to onnx with torch.onnx faild

### Describe the bug

I want to convert a unet to onnx using the way as [example](https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py), i can get unet result as ret,but when run into torch.onnx.export , an error reported, the code is here.

### Reproduction

import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline
from diffusers import DiffusionPipeline
from transformers import CLIPTextModel,CLIPTextModelWithProjection
from diffusers import AutoencoderKL,UNet2DConditionModel

@torch.no_grad()
def export_unetxl(device='cuda'):
model_opts = {'torch_dtype': torch.float16,'use_safetensors':False}
unet_model_dir='RunDiffusion/Juggernaut-XI-v11'
model= UNet2DConditionModel.from_pretrained(unet_model_dir,subfolder="unet", **model_opts).to(device)
torch_inference=''
# model = optimize_checkpoint(model, torch_inference)
onnx_path = 'juggernaut-v11-unet.onnx'

input_names= [
"sample",
"timestep",
"encoder_hidden_states",
"text_embeds",
"time_ids"
]
output_names= [
"latent"
]
dynamic_axes={
"sample": {
"0": "2B",
"1": "C",
"2": "H",
"3": "W"
},
"timestep": { "0": "2B" },
"encoder_hidden_states": {
"0": "2B",
"1": "unet_hidden_sequence"
},
'latent': {0: '2B',"1": "C", 2: 'H', 3: 'W'},
"text_embeds": {
"0": "2B",
"1": "unet_text_embeds_size"
},
"time_ids": {
"0": "2B",
"1": "unet_time_ids_size"
}
}
shape_dict= {
"sample": [
2,
4,
128,
128
],
"encoder_hidden_states": [
2,
77,
2048
],
"latent": [
2,
4,
128,
128
],
"text_embeds": [
2,
1280
],
"time_ids": [
2,
6
]
}
xB=2
dtype = torch.float16
batch_size=1
unet_dim=4
latent_height=128
latent_width=128
text_maxlen=77
time_dim=6
embedding_dim=2048
opt_level=19
sample_input=(
torch.randn((xB*batch_size,unet_dim, latent_height, latent_width), dtype=dtype, device=device),
torch.randn((1,)).to(device=device, dtype=dtype),
torch.randn((xB*batch_size,text_maxlen,embedding_dim), dtype=dtype, device=device),
# torch.rand((xB* batch_size, 1280), dtype=dtype),
# torch.rand((xB* batch_size, time_dim), dtype=dtype)
{
'added_cond_kwargs': {
'text_embeds': torch.randn((xB*batch_size, 1280), dtype=dtype, device=device),
'time_ids' : torch.randn((xB*batch_size, time_dim), dtype=dtype, device=device)
}
}
)


ret = model(torch.randn((xB*batch_size,unet_dim, latent_height, latent_width), dtype=dtype, device=device),
torch.randn((1,)).to(device=device, dtype=dtype),
torch.randn((xB*batch_size,text_maxlen,embedding_dim), dtype=dtype, device=device),
added_cond_kwargs= {
'text_embeds': torch.randn((xB*batch_size, 1280), dtype=dtype, device=device),
'time_ids' : torch.randn((xB*batch_size, time_dim), dtype=dtype, device=device)
}
)
# with torch.inference_mode():
torch.onnx.export(model,
sample_input,
onnx_path,
export_params=True,
verbose = False,
do_constant_folding=True,
opset_version=opt_level,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
)

export_unetxl()

### Logs

```shell
diffusers.models.unets.unet_2d_condition.UNet2DConditionModel::/torch.nn.modules.normalization.GroupNorm::conv_norm_out # /home/sd/miniconda3/envs/trt/lib/python3.11/site-packages/torch/nn/functional.py:2955:0
%input : Half(2, 320, 128, 128, strides=[5242880, 16384, 128, 1], requires_grad=0, device=cuda:0) = aten::silu(%input.1095), scope: diffusers.models.unets.unet_2d_condition.UNet2DConditionModel::/torch.nn.modules.activation.SiLU::act # /home/sd/miniconda3/envs/trt/lib/python3.11/site-packages/torch/nn/functional.py:2380:0
%107415 : Half(2, 4, 128, 128, strides=[65536, 16384, 128, 1], requires_grad=0, device=cuda:0) = aten::_convolution(%input, %conv_out.weight, %conv_out.bias, %107429, %107429, %107429, %108606, %107432, %108608, %108606, %108606, %108614, %108614), scope: diffusers.models.unets.unet_2d_condition.UNet2DConditionModel::/torch.nn.modules.conv.Conv2d::conv_out # /home/sd/miniconda3/envs/trt/lib/python3.11/site-packages/torch/nn/modules/conv.py:549:0
return (%107415)
, {'sample': {'0': '2B', '1': 'C', '2': 'H', '3': 'W'}, 'timestep': {'0': '2B'}, 'encoder_hidden_states': {'0': '2B', '1': 'unet_hidden_sequence'}, 'latent': {0: '2B', '1': 'C', 2: 'H', 3: 'W'}, 'text_embeds': {'0': '2B', '1': 'unet_text_embeds_size'}, 'time_ids': {'0': '2B', '1': 'unet_time_ids_size'}}, ['sample', 'timestep', 'encoder_hidden_states', 'text_embeds', 'time_ids']
```

### System Info

No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 20.04.6 LTS
Release: 20.04
Codename: focal

diffusers 0.32.0.dev0
torch 2.5.0
torch2trt_dynamic 0.6.0
torchmetrics 1.5.1
torchvision 0.20.0
transformers 4.42.2
onnx 1.17.0
onnx-graphsurgeon 0.5.2
onnxconverter-common 1.14.0
onnxmltools 1.12.0
onnxruntime 1.20.0
onnxruntime_extensions 0.12.0
onnxruntime-gpu 1.19.2
onnxscript 0.1.0.dev20241107
onnxslim 0.1.35

### Who can help?

@yiyixuxu @sayakpaul @DN6

Start with scripts/convert_stable_diffusion_checkpoint_to_onnx.py and the UNet2DConditionModel call shown in the report, then inspect the torch.onnx.export invocation and its inputs. Reproduce the failure with the listed package versions and capture the actual export error; done means the UNet conversion completes successfully with the intended inputs and output names.

Rédigé par le modèle d'indexation à partir du texte de l'issue.