fix nspect vulnerbility

.gitignore

@@ -92,6 +92,7 @@ coverage.xml
 *.pt2
 examples/torchtrt_aoti_example/torchtrt_aoti_example
 CLAUDE.md
+package-lock.json
 # clangd compilation database (generated by `bazel run //:refresh_compile_commands`)
 /compile_commands.json
 /external/

docker/ngc_test/requirements.txt

@@ -8,6 +8,6 @@ parameterized>=0.2.0
 pytest>=9.0.3
 pytest-xdist>=3.6.1
 pyyaml
-transformers>=5.4.0
+transformers>=5.15.1
 timm
-# torchvision nvidia-modelopt should use the one already installed inside the dlfw docker container
+# torchvision nvidia-modelopt should use the one already installed inside the dlfw docker container

examples/apps/README.md

@@ -8,7 +8,7 @@ This demo showcases the Flux image generation model accelerated using Torch-Tens
 1. Install the required dependencies:
 
 ```bash
-pip install gradio==5.29.0 nvidia-modelopt==0.27.1 diffusers==0.33.1 accelerate==1.3.0
+pip install gradio==5.29.0 nvidia-modelopt==0.27.1 diffusers==0.38.0 accelerate==1.3.0
 ```
 
 ## Usage
@@ -58,4 +58,4 @@ python flux_demo.py --low_vram_mode
 You can combine these options as needed. For example:
 ```bash
 python flux_demo.py --dtype fp8 --dynamic_shapes --low_vram_mode
-```
+```

examples/distributed_inference/requirement.txt

@@ -1,4 +1,4 @@
 accelerate
-transformers
-diffusers
-tensorrt-llm
+transformers>=5.15.1
+diffusers>=0.38.0
+tensorrt-llm

examples/dynamo/requirements.txt

@@ -1,7 +1,7 @@
 cupy==13.1.0
 triton==2.3.0
-diffusers==0.30.3
-transformers>=5.4.0
+diffusers==0.38.0
+transformers>=5.15.1
 matplotlib
 pandas
 huggingface_hub

examples/dynamo/torch_export_flux_dev.py

@@ -13,7 +13,7 @@
 
 .. code-block:: python
 
-    pip install sentencepiece=="0.2.0" transformers=="4.48.2" accelerate=="1.3.0" diffusers=="0.32.2" protobuf=="5.29.3"
+    pip install sentencepiece=="0.2.0" transformers>="5.15.1" accelerate=="1.3.0" diffusers=="0.38.0" protobuf=="5.29.3"
 
 There are different components of the ``FLUX.1-dev`` pipeline such as ``transformer``, ``vae``, ``text_encoder``, ``tokenizer`` and ``scheduler``. In this example,
 we demonstrate optimizing the ``transformer`` component of the model (which typically consumes >95% of the e2e diffusion latency)

pyproject.toml

@@ -98,7 +98,7 @@ test = [
 
 test-ext = [
     "timm>=1.0.3",
-    "transformers>=5.4.0",
+    "transformers>=5.15.1",
     "torchvision>=0.30.0.dev,<0.31.0",
     "flashinfer-python; sys_platform == 'linux' and python_version >= '3.11' and python_version < '3.13'",
 ]
@@ -178,7 +178,7 @@ conflicts = [
     ],
 ]
 constraint-dependencies = [
-    "transformers>=5.4.0",
+    "transformers>=5.15.1",
 ]
 
 [tool.uv.sources]

tests/modules/requirements.txt

@@ -1,2 +1,2 @@
 timm==0.9.12
-transformers>=5.4.0
+transformers>=5.15.1

toolchains/jp_workspaces/test_requirements.txt

@@ -5,5 +5,5 @@ parameterized>=0.2.0
 pytest>=9.0.3
 pytest-xdist>=3.6.1
 pyyaml
-transformers>=5.4.0
-# TODO: currently timm torchvision nvidia-modelopt does not have distributions for jetson
+transformers>=5.15.1
+# TODO: currently timm torchvision nvidia-modelopt does not have distributions for jetson

tools/perf/Flux/create_env.sh

@@ -20,7 +20,7 @@ pip install tensorrt==10.14.1.48 --force-reinstall
 pip3 install --pre  torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130
 
 
-pip install sentencepiece=="0.2.0" transformers=="4.48.2" accelerate=="1.3.0" diffusers=="0.32.2" protobuf=="5.29.3"
+pip install sentencepiece=="0.2.0" transformers>="5.15.1" accelerate=="1.3.0" diffusers=="0.38.0" protobuf=="5.29.3"
 
 pip install notebook
 pip install gradio safetensors peft pyinstrument

tools/perf/requirements.txt

@@ -3,7 +3,7 @@ argparse
 pyyaml
 onnx
 pandas
-transformers>=5.4.0
-diffusers==0.34.0
+transformers>=5.15.1
+diffusers==0.38.0
 timm==0.9.8
 monai==1.6.0