执行export_onnx.py报错

[ahqzy]

1 执行命令：
python -m trt.export_onnx --model-root ./ckpts --infer-mode fa --no-enhance --load-key ema --model DiT-XL/2 --image-size 1024 1024
报错信息：
(hydit) root@L20:~/HunyuanDiT# python -m trt.export_onnx --model-root ./ckpts --infer-mode fa --no-enhance --load-key ema --model DiT-XL/2 --image-size 1024 1024
[2025-09-30 10:56:09,478] [INFO] [real_accelerator.py:239:get_accelerator] Setting ds_accelerator to cuda (auto detect)
2025-09-30 10:56:11.188 | INFO | main:init:42 - Got text-to-image model root path: ckpts/t2i
2025-09-30 10:56:11.188 | INFO | main:load_model:57 - Building HunYuan-DiT model...
2025-09-30 10:56:11.188 | INFO | hydit.modules.models:init:343 - Enable Flash Attention.
2025-09-30 10:56:11.396 | INFO | hydit.modules.models:init:402 - Number of tokens: 4096
2025-09-30 10:56:18.166 | INFO | main:load_model:79 - Loading torch model ckpts/t2i/model/pytorch_model_ema.pt...
Traceback (most recent call last):
File "/root/miniconda3/envs/hydit/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/root/miniconda3/envs/hydit/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/root/HunyuanDiT/trt/export_onnx.py", line 375, in
exporter.export()
File "/root/HunyuanDiT/trt/export_onnx.py", line 90, in export
self.load_model()
File "/root/HunyuanDiT/trt/export_onnx.py", line 81, in load_model
self.model.load_state_dict(state_dict)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2624, in load_state_dict
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for HunYuanDiT:
Missing key(s) in state_dict: "blocks.15.skip_norm.weight", "blocks.15.skip_norm.bias", "blocks.15.skip_linear.weight", "blocks.15.skip_linear.bias", "blocks.16.skip_norm.weight", "blocks.16.skip_norm.bias", "blocks.16.skip_linear.weight", "blocks.16.skip_linear.bias", "blocks.17.skip_norm.weight", "blocks.17.skip_norm.bias", "blocks.17.skip_linear.weight", "blocks.17.skip_linear.bias", "blocks.18.skip_norm.weight", "blocks.18.skip_norm.bias", "blocks.18.skip_linear.weight", "blocks.18.skip_linear.bias", "blocks.19.skip_norm.weight", "blocks.19.skip_norm.bias", "blocks.19.skip_linear.weight", "blocks.19.skip_linear.bias", "blocks.20.skip_norm.weight", "blocks.20.skip_norm.bias", "blocks.20.skip_linear.weight", "blocks.20.skip_linear.bias".
Unexpected key(s) in state_dict: "blocks.28.norm1.weight", "blocks.28.norm1.bias", "blocks.28.attn1.Wqkv.weight", "blocks.28.attn1.Wqkv.bias", "blocks.28.attn1.q_norm.weight", "blocks.28.attn1.q_norm.bias", "blocks.28.attn1.k_norm.weight", "blocks.28.attn1.k_norm.bias", "blocks.28.attn1.out_proj.weight", "blocks.28.attn1.out_proj.bias", "blocks.28.norm2.weight", "blocks.28.norm2.bias", "blocks.28.mlp.fc1.weight", "blocks.28.mlp.fc1.bias", "blocks.28.mlp.fc2.weight", "blocks.28.mlp.fc2.bias", "blocks.28.default_modulation.1.weight", "blocks.28.default_modulation.1.bias", "blocks.28.attn2.q_proj.weight", "blocks.28.attn2.q_proj.bias", "blocks.28.attn2.kv_proj.weight", "blocks.28.attn2.kv_proj.bias", "blocks.28.attn2.q_norm.weight", "blocks.28.attn2.q_norm.bias", "blocks.28.attn2.k_norm.weight", "blocks.28.attn2.k_norm.bias", "blocks.28.attn2.out_proj.weight", "blocks.28.attn2.out_proj.bias", "blocks.28.norm3.weight", "blocks.28.norm3.bias", "blocks.28.skip_norm.weight", "blocks.28.skip_norm.bias", "blocks.28.skip_linear.weight", "blocks.28.skip_linear.bias", "blocks.29.norm1.weight", "blocks.29.norm1.bias", "blocks.29.attn1.Wqkv.weight", "blocks.29.attn1.Wqkv.bias", "blocks.29.attn1.q_norm.weight", "blocks.29.attn1.q_norm.bias", "blocks.29.attn1.k_norm.weight", "blocks.29.attn1.k_norm.bias", "blocks.29.attn1.out_proj.weight", "blocks.29.attn1.out_proj.bias", "blocks.29.norm2.weight", "blocks.29.norm2.bias", "blocks.29.mlp.fc1.weight", "blocks.29.mlp.fc1.bias", "blocks.29.mlp.fc2.weight", "blocks.29.mlp.fc2.bias", "blocks.29.default_modulation.1.weight", "blocks.29.default_modulation.1.bias", "blocks.29.attn2.q_proj.weight", "blocks.29.attn2.q_proj.bias", "blocks.29.attn2.kv_proj.weight", "blocks.29.attn2.kv_proj.bias", "blocks.29.attn2.q_norm.weight", "blocks.29.attn2.q_norm.bias", "blocks.29.attn2.k_norm.weight", "blocks.29.attn2.k_norm.bias", "blocks.29.attn2.out_proj.weight", "blocks.29.attn2.out_proj.bias", "blocks.29.norm3.weight", "blocks.29.norm3.bias", "blocks.29.skip_norm.weight", "blocks.29.skip_norm.bias", "blocks.29.skip_linear.weight", "blocks.29.skip_linear.bias", "blocks.30.norm1.weight", "blocks.30.norm1.bias", "blocks.30.attn1.Wqkv.weight", "blocks.30.attn1.Wqkv.bias", "blocks.30.attn1.q_norm.weight", "blocks.30.attn1.q_norm.bias", "blocks.30.attn1.k_norm.weight", "blocks.30.attn1.k_norm.bias", "blocks.30.attn1.out_proj.weight", "blocks.30.attn1.out_proj.bias", "blocks.30.norm2.weight", "blocks.30.norm2.bias", "blocks.30.mlp.fc1.weight", "blocks.30.mlp.fc1.bias", "blocks.30.mlp.fc2.weight", "blocks.30.mlp.fc2.bias", "blocks.30.default_modulation.1.weight", "blocks.30.default_modulation.1.bias", "blocks.30.attn2.q_proj.weight", "blocks.30.attn2.q_proj.bias", "blocks.30.attn2.kv_proj.weight", "blocks.30.attn2.kv_proj.bias", "blocks.30.attn2.q_norm.weight", "blocks.30.attn2.q_norm.bias", "blocks.30.attn2.k_norm.weight", "blocks.30.attn2.k_norm.bias", "blocks.30.attn2.out_proj.weight", "blocks.30.attn2.out_proj.bias", "blocks.30.norm3.weight", "blocks.30.norm3.bias", "blocks.30.skip_norm.weight", "blocks.30.skip_norm.bias", "blocks.30.skip_linear.weight", "blocks.30.skip_linear.bias", "blocks.31.norm1.weight", "blocks.31.norm1.bias", "blocks.31.attn1.Wqkv.weight", "blocks.31.attn1.Wqkv.bias", "blocks.31.attn1.q_norm.weight", "blocks.31.attn1.q_norm.bias", "blocks.31.attn1.k_norm.weight", "blocks.31.attn1.k_norm.bias", "blocks.31.attn1.out_proj.weight", "blocks.31.attn1.out_proj.bias", "blocks.31.norm2.weight", "blocks.31.norm2.bias", "blocks.31.mlp.fc1.weight", "blocks.31.mlp.fc1.bias", "blocks.31.mlp.fc2.weight", "blocks.31.mlp.fc2.bias", "blocks.31.default_modulation.1.weight", "blocks.31.default_modulation.1.bias", "blocks.31.attn2.q_proj.weight", "blocks.31.attn2.q_proj.bias", "blocks.31.attn2.kv_proj.weight", "blocks.31.attn2.kv_proj.bias", "blocks.31.attn2.q_norm.weight", "blocks.31.attn2.q_norm.bias", "blocks.31.attn2.k_norm.weight", "blocks.31.attn2.k_norm.bias", "blocks.31.attn2.out_proj.weight", "blocks.31.attn2.out_proj.bias", "blocks.31.norm3.weight", "blocks.31.norm3.bias", "blocks.31.skip_norm.weight", "blocks.31.skip_norm.bias", "blocks.31.skip_linear.weight", "blocks.31.skip_linear.bias", "blocks.32.norm1.weight", "blocks.32.norm1.bias", "blocks.32.attn1.Wqkv.weight", "blocks.32.attn1.Wqkv.bias", "blocks.32.attn1.q_norm.weight", "blocks.32.attn1.q_norm.bias", "blocks.32.attn1.k_norm.weight", "blocks.32.attn1.k_norm.bias", "blocks.32.attn1.out_proj.weight", "blocks.32.attn1.out_proj.bias", "blocks.32.norm2.weight", "blocks.32.norm2.bias", "blocks.32.mlp.fc1.weight", "blocks.32.mlp.fc1.bias", "blocks.32.mlp.fc2.weight", "blocks.32.mlp.fc2.bias", "blocks.32.default_modulation.1.weight", "blocks.32.default_modulation.1.bias", "blocks.32.attn2.q_proj.weight", "blocks.32.attn2.q_proj.bias", "blocks.32.attn2.kv_proj.weight", "blocks.32.attn2.kv_proj.bias", "blocks.32.attn2.q_norm.weight", "blocks.32.attn2.q_norm.bias", "blocks.32.attn2.k_norm.weight", "blocks.32.attn2.k_norm.bias", "blocks.32.attn2.out_proj.weight", "blocks.32.attn2.out_proj.bias", "blocks.32.norm3.weight", "blocks.32.norm3.bias", "blocks.32.skip_norm.weight", "blocks.32.skip_norm.bias", "blocks.32.skip_linear.weight", "blocks.32.skip_linear.bias", "blocks.33.norm1.weight", "blocks.33.norm1.bias", "blocks.33.attn1.Wqkv.weight", "blocks.33.attn1.Wqkv.bias", "blocks.33.attn1.q_norm.weight", "blocks.33.attn1.q_norm.bias", "blocks.33.attn1.k_norm.weight", "blocks.33.attn1.k_norm.bias", "blocks.33.attn1.out_proj.weight", "blocks.33.attn1.out_proj.bias", "blocks.33.norm2.weight", "blocks.33.norm2.bias", "blocks.33.mlp.fc1.weight", "blocks.33.mlp.fc1.bias", "blocks.33.mlp.fc2.weight", "blocks.33.mlp.fc2.bias", "blocks.33.default_modulation.1.weight", "blocks.33.default_modulation.1.bias", "blocks.33.attn2.q_proj.weight", "blocks.33.attn2.q_proj.bias", "blocks.33.attn2.kv_proj.weight", "blocks.33.attn2.kv_proj.bias", "blocks.33.attn2.q_norm.weight", "blocks.33.attn2.q_norm.bias", "blocks.33.attn2.k_norm.weight", "blocks.33.attn2.k_norm.bias", "blocks.33.attn2.out_proj.weight", "blocks.33.attn2.out_proj.bias", "blocks.33.norm3.weight", "blocks.33.norm3.bias", "blocks.33.skip_norm.weight", "blocks.33.skip_norm.bias", "blocks.33.skip_linear.weight", "blocks.33.skip_linear.bias", "blocks.34.norm1.weight", "blocks.34.norm1.bias", "blocks.34.attn1.Wqkv.weight", "blocks.34.attn1.Wqkv.bias", "blocks.34.attn1.q_norm.weight", "blocks.34.attn1.q_norm.bias", "blocks.34.attn1.k_norm.weight", "blocks.34.attn1.k_norm.bias", "blocks.34.attn1.out_proj.weight", "blocks.34.attn1.out_proj.bias", "blocks.34.norm2.weight", "blocks.34.norm2.bias", "blocks.34.mlp.fc1.weight", "blocks.34.mlp.fc1.bias", "blocks.34.mlp.fc2.weight", "blocks.34.mlp.fc2.bias", "blocks.34.default_modulation.1.weight", "blocks.34.default_modulation.1.bias", "blocks.34.attn2.q_proj.weight", "blocks.34.attn2.q_proj.bias", "blocks.34.attn2.kv_proj.weight", "blocks.34.attn2.kv_proj.bias", "blocks.34.attn2.q_norm.weight", "blocks.34.attn2.q_norm.bias", "blocks.34.attn2.k_norm.weight", "blocks.34.attn2.k_norm.bias", "blocks.34.attn2.out_proj.weight", "blocks.34.attn2.out_proj.bias", "blocks.34.norm3.weight", "blocks.34.norm3.bias", "blocks.34.skip_norm.weight", "blocks.34.skip_norm.bias", "blocks.34.skip_linear.weight", "blocks.34.skip_linear.bias", "blocks.35.norm1.weight", "blocks.35.norm1.bias", "blocks.35.attn1.Wqkv.weight", "blocks.35.attn1.Wqkv.bias", "blocks.35.attn1.q_norm.weight", "blocks.35.attn1.q_norm.bias", "blocks.35.attn1.k_norm.weight", "blocks.35.attn1.k_norm.bias", "blocks.35.attn1.out_proj.weight", "blocks.35.attn1.out_proj.bias", "blocks.35.norm2.weight", "blocks.35.norm2.bias", "blocks.35.mlp.fc1.weight", "blocks.35.mlp.fc1.bias", "blocks.35.mlp.fc2.weight", "blocks.35.mlp.fc2.bias", "blocks.35.default_modulation.1.weight", "blocks.35.default_modulation.1.bias", "blocks.35.attn2.q_proj.weight", "blocks.35.attn2.q_proj.bias", "blocks.35.attn2.kv_proj.weight", "blocks.35.attn2.kv_proj.bias", "blocks.35.attn2.q_norm.weight", "blocks.35.attn2.q_norm.bias", "blocks.35.attn2.k_norm.weight", "blocks.35.attn2.k_norm.bias", "blocks.35.attn2.out_proj.weight", "blocks.35.attn2.out_proj.bias", "blocks.35.norm3.weight", "blocks.35.norm3.bias", "blocks.35.skip_norm.weight", "blocks.35.skip_norm.bias", "blocks.35.skip_linear.weight", "blocks.35.skip_linear.bias", "blocks.36.norm1.weight", "blocks.36.norm1.bias", "blocks.36.attn1.Wqkv.weight", "blocks.36.attn1.Wqkv.bias", "blocks.36.attn1.q_norm.weight", "blocks.36.attn1.q_norm.bias", "blocks.36.attn1.k_norm.weight", "blocks.36.attn1.k_norm.bias", "blocks.36.attn1.out_proj.weight", "blocks.36.attn1.out_proj.bias", "blocks.36.norm2.weight", "blocks.36.norm2.bias", "blocks.36.mlp.fc1.weight", "blocks.36.mlp.fc1.bias", "blocks.36.mlp.fc2.weight", "blocks.36.mlp.fc2.bias", "blocks.36.default_modulation.1.weight", "blocks.36.default_modulation.1.bias", "blocks.36.attn2.q_proj.weight", "blocks.36.attn2.q_proj.bias", "blocks.36.attn2.kv_proj.weight", "blocks.36.attn2.kv_proj.bias", "blocks.36.attn2.q_norm.weight", "blocks.36.attn2.q_norm.bias", "blocks.36.attn2.k_norm.weight", "blocks.36.attn2.k_norm.bias", "blocks.36.attn2.out_proj.weight", "blocks.36.attn2.out_proj.bias", "blocks.36.norm3.weight", "blocks.36.norm3.bias", "blocks.36.skip_norm.weight", "blocks.36.skip_norm.bias", "blocks.36.skip_linear.weight", "blocks.36.skip_linear.bias", "blocks.37.norm1.weight", "blocks.37.norm1.bias", "blocks.37.attn1.Wqkv.weight", "blocks.37.attn1.Wqkv.bias", "blocks.37.attn1.q_norm.weight", "blocks.37.attn1.q_norm.bias", "blocks.37.attn1.k_norm.weight", "blocks.37.attn1.k_norm.bias", "blocks.37.attn1.out_proj.weight", "blocks.37.attn1.out_proj.bias", "blocks.37.norm2.weight", "blocks.37.norm2.bias", "blocks.37.mlp.fc1.weight", "blocks.37.mlp.fc1.bias", "blocks.37.mlp.fc2.weight", "blocks.37.mlp.fc2.bias", "blocks.37.default_modulation.1.weight", "blocks.37.default_modulation.1.bias", "blocks.37.attn2.q_proj.weight", "blocks.37.attn2.q_proj.bias", "blocks.37.attn2.kv_proj.weight", "blocks.37.attn2.kv_proj.bias", "blocks.37.attn2.q_norm.weight", "blocks.37.attn2.q_norm.bias", "blocks.37.attn2.k_norm.weight", "blocks.37.attn2.k_norm.bias", "blocks.37.attn2.out_proj.weight", "blocks.37.attn2.out_proj.bias", "blocks.37.norm3.weight", "blocks.37.norm3.bias", "blocks.37.skip_norm.weight", "blocks.37.skip_norm.bias", "blocks.37.skip_linear.weight", "blocks.37.skip_linear.bias", "blocks.38.norm1.weight", "blocks.38.norm1.bias", "blocks.38.attn1.Wqkv.weight", "blocks.38.attn1.Wqkv.bias", "blocks.38.attn1.q_norm.weight", "blocks.38.attn1.q_norm.bias", "blocks.38.attn1.k_norm.weight", "blocks.38.attn1.k_norm.bias", "blocks.38.attn1.out_proj.weight", "blocks.38.attn1.out_proj.bias", "blocks.38.norm2.weight", "blocks.38.norm2.bias", "blocks.38.mlp.fc1.weight", "blocks.38.mlp.fc1.bias", "blocks.38.mlp.fc2.weight", "blocks.38.mlp.fc2.bias", "blocks.38.default_modulation.1.weight", "blocks.38.default_modulation.1.bias", "blocks.38.attn2.q_proj.weight", "blocks.38.attn2.q_proj.bias", "blocks.38.attn2.kv_proj.weight", "blocks.38.attn2.kv_proj.bias", "blocks.38.attn2.q_norm.weight", "blocks.38.attn2.q_norm.bias", "blocks.38.attn2.k_norm.weight", "blocks.38.attn2.k_norm.bias", "blocks.38.attn2.out_proj.weight", "blocks.38.attn2.out_proj.bias", "blocks.38.norm3.weight", "blocks.38.norm3.bias", "blocks.38.skip_norm.weight", "blocks.38.skip_norm.bias", "blocks.38.skip_linear.weight", "blocks.38.skip_linear.bias", "blocks.39.norm1.weight", "blocks.39.norm1.bias", "blocks.39.attn1.Wqkv.weight", "blocks.39.attn1.Wqkv.bias", "blocks.39.attn1.q_norm.weight", "blocks.39.attn1.q_norm.bias", "blocks.39.attn1.k_norm.weight", "blocks.39.attn1.k_norm.bias", "blocks.39.attn1.out_proj.weight", "blocks.39.attn1.out_proj.bias", "blocks.39.norm2.weight", "blocks.39.norm2.bias", "blocks.39.mlp.fc1.weight", "blocks.39.mlp.fc1.bias", "blocks.39.mlp.fc2.weight", "blocks.39.mlp.fc2.bias", "blocks.39.default_modulation.1.weight", "blocks.39.default_modulation.1.bias", "blocks.39.attn2.q_proj.weight", "blocks.39.attn2.q_proj.bias", "blocks.39.attn2.kv_proj.weight", "blocks.39.attn2.kv_proj.bias", "blocks.39.attn2.q_norm.weight", "blocks.39.attn2.q_norm.bias", "blocks.39.attn2.k_norm.weight", "blocks.39.attn2.k_norm.bias", "blocks.39.attn2.out_proj.weight", "blocks.39.attn2.out_proj.bias", "blocks.39.norm3.weight", "blocks.39.norm3.bias", "blocks.39.skip_norm.weight", "blocks.39.skip_norm.bias", "blocks.39.skip_linear.weight", "blocks.39.skip_linear.bias".
size mismatch for x_embedder.proj.weight: copying a param with shape torch.Size([1408, 4, 2, 2]) from checkpoint, the shape in current model is torch.Size([1152, 4, 2, 2]).
size mismatch for x_embedder.proj.bias: copying a param with shape torch.Size([1408]) from checkpoint, the shape in current model is torch.Size([1152]).
size mismatch for t_embedder.mlp.0.weight: copying a param with shape torch.Size([1408, 256]) from checkpoint, the shape in current model is torch.Size([1152, 256]).
size mismatch for t_embedder.mlp.0.bias: copying a param with shape torch.Size([1408]) from checkpoint, the shape in current model is torch.Size([1152]).
size mismatch for t_embedder.mlp.2.weight: copying a param with shape torch.Size([1408, 1408]) from checkpoint, the shape in current model is torch.Size([1152, 1152]).
size mismatch for t_embedder.mlp.2.bias: copying a param with shape torch.Size([1408]) from checkpoint, the shape in current model is torch.Size([1152]).
size mismatch for extra_embedder.0.weight: copying a param with shape torch.Size([5632, 1024]) from checkpoint, the shape in current model is torch.Size([4608, 1024]).

2 模型结构列表：

tree ckpts

ckpts
├── asset
│   ├── chinese elements understanding.png
│   ├── cover.png
│   ├── framework.png
│   ├── Hunyuan_DiT_Tech_Report_05140553.pdf
│   ├── logo.png
│   ├── long text understanding.png
│   ├── mllm.png
│   └── radar.png
├── configuration.json
├── dialoggen
│   ├── config.json
│   ├── generation_config.json
│   ├── model-00001-of-00004.safetensors
│   ├── model-00002-of-00004.safetensors
│   ├── model-00003-of-00004.safetensors
│   ├── model-00004-of-00004.safetensors
│   ├── model.safetensors.index.json
│   ├── openai
│   │   └── clip-vit-large-patch14-336
│   │   ├── config.json
│   │   ├── merges.txt
│   │   ├── preprocessor_config.json
│   │   ├── pytorch_model.bin
│   │   ├── README.md
│   │   ├── special_tokens_map.json
│   │   ├── tf_model.h5
│   │   ├── tokenizer_config.json
│   │   ├── tokenizer.json
│   │   └── vocab.json
│   ├── special_tokens_map.json
│   ├── tokenizer_config.json
│   └── tokenizer.model
├── LICENSE
├── LICENSE.txt
├── Notice
├── README.md
└── t2i
├── clip_text_encoder
│   ├── config.json
│   └── pytorch_model.bin
├── model
│   ├── pytorch_model_ema.pt
│   └── pytorch_model_module.pt
├── mt5
│   ├── config.json
│   ├── generation_config.json
│   ├── pytorch_model.bin
│   ├── README.md
│   ├── special_tokens_map.json
│   ├── spiece.model
│   └── tokenizer_config.json
├── sdxl-vae-fp16-fix
│   ├── config.json
│   ├── diffusion_pytorch_model.bin
│   └── diffusion_pytorch_model.safetensors
└── tokenizer
├── special_tokens_map.json
├── tokenizer_config.json
├── vocab_org.txt
└── vocab.txt

10 directories, 51 files

完整报错信息见附件。

MobaXterm_115.190.3.124root_20250930_105628.txt

[ahqzy]

如果修改为DiT-g/2：
python -m trt.export_onnx --model-root ./ckpts --infer-mode fa --no-enhance --load-key module --model DiT-g/2 --image-size 1024 1024

则会报如下错误：

python -m trt.export_onnx --model-root ./ckpts --infer-mode fa --no-enhance --load-key module --model DiT-g/2 --image-size 1024 1024

[2025-09-30 11:09:54,615] [INFO] [real_accelerator.py:239:get_accelerator] Setting ds_accelerator to cuda (auto detect)
2025-09-30 11:09:56.303 | INFO | main:init:42 - Got text-to-image model root path: ckpts/t2i
2025-09-30 11:09:56.303 | INFO | main:load_model:57 - Building HunYuan-DiT model...
2025-09-30 11:09:56.304 | INFO | hydit.modules.models:init:343 - Enable Flash Attention.
2025-09-30 11:09:56.529 | INFO | hydit.modules.models:init:402 - Number of tokens: 4096
2025-09-30 11:10:09.633 | INFO | main:load_model:79 - Loading torch model ckpts/t2i/model/pytorch_model_module.pt...
2025-09-30 11:10:12.272 | INFO | main:load_model:83 - Loading torch model finished
2025-09-30 11:10:12.273 | INFO | main:load_model:84 - ==================================================
2025-09-30 11:10:12.273 | INFO | main:load_model:85 - Model is ready.
2025-09-30 11:10:12.273 | INFO | main:load_model:86 - ==================================================
2025-09-30 11:10:12.306 | INFO | main:export:110 - Exporting ONNX model onnx_model/export/model.onnx...
2025-09-30 11:10:12.306 | INFO | main:export:111 - Exporting ONNX external data onnx_model/export...
/root/HunyuanDiT/trt/export_onnx.py:124: DeprecationWarning: You are using the legacy TorchScript-based ONNX export. Starting in PyTorch 2.9, the new torch.export-based ONNX exporter will be the default. To switch now, set dynamo=True in torch.onnx.export. This new exporter supports features like exporting LLMs with DynamicCache. We encourage you to try it and share feedback to help improve the experience. Learn more about the new export logic: https://pytorch.org/docs/stable/onnx_dynamo.html. For exporting control flow: https://pytorch.org/tutorials/beginner/onnx/export_control_flow_model_to_onnx_tutorial.html.
torch.onnx.export(
Traceback (most recent call last):
File "/root/miniconda3/envs/hydit/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/root/miniconda3/envs/hydit/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/root/HunyuanDiT/trt/export_onnx.py", line 375, in
exporter.export()
File "/root/HunyuanDiT/trt/export_onnx.py", line 124, in export
torch.onnx.export(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/init.py", line 424, in export
export(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/utils.py", line 522, in export
_export(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/utils.py", line 1457, in _export
graph, params_dict, torch_out = _model_to_graph(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/utils.py", line 1080, in _model_to_graph
graph, params, torch_out, module = _create_jit_graph(model, args)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/utils.py", line 964, in _create_jit_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/onnx/utils.py", line 871, in _trace_and_get_graph_from_model
trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/jit/_trace.py", line 1504, in _get_trace_graph
outs = ONNXTracedModule(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/jit/_trace.py", line 138, in forward
graph, _out = torch._C._create_graph_by_tracing(
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/jit/_trace.py", line 129, in wrapper
outs.append(self.inner(*trace_inputs))
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/root/miniconda3/envs/hydit/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1763, in _slow_forward
result = self.forward(*input, **kwargs)
File "/root/HunyuanDiT/hydit/modules/models.py", line 526, in forward
text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256
AttributeError: 'NoneType' object has no attribute 'bool'