- fix setup_venv.sh for benchmarks/imports etc.
- fix torch benchmarks in SharkBenchmarkRunner
- generate SD artifacts using build_tools/stable_diffusion_testing.py and --import_mlir
- decouple SD gen from tank/generate_sharktank for now
* Fix sharktank generation and add batch_size pytest option for torch.
* Disable torch dynamo until py3.11 supported
* Compile torchmodel without dynamo if torch.compile fails
* Use release versions of TF/Keras for importer.
* Pin torchvision and remove debug prints.
* Remove duplicates from torch model list.
* Update generate_sharktank.py
* xfail a few models that fail sharktank generation/ numerics
* Rollback T5 models for torch as the inputs give some issues that aren't trivial to resolve
* xfail efficientnet-b0 on torch+cuda -- see CUDA requesting shared memory size larger than allowed size openxla/iree#12771
