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# Diffusers Benchmarks
Welcome to Diffusers Benchmarks. These benchmarks are use to obtain latency and memory information of the most popular models across different scenarios such as:
* Base case i.e., when using `torch.bfloat16` and `torch.nn.functional.scaled_dot_product_attention`.
* Base + `torch.compile()`
* NF4 quantization
* Layerwise upcasting
Instead of full diffusion pipelines, only the forward pass of the respective model classes (such as `FluxTransformer2DModel`) is tested with the real checkpoints (such as `"black-forest-labs/FLUX.1-dev"`).
The entrypoint to running all the currently available benchmarks is in `run_all.py`. However, one can run the individual benchmarks, too, e.g., `python benchmarking_flux.py`. It should produce a CSV file containing various information about the benchmarks run.
The benchmarks are run on a weekly basis and the CI is defined in [benchmark.yml](../.github/workflows/benchmark.yml).
## Running the benchmarks manually
First set up `torch` and install `diffusers` from the root of the directory:
```py
pip install -e ".[quality,test]"
```
Then make sure the other dependencies are installed:
```sh
cd benchmarks/
pip install -r requirements.txt
```
We need to be authenticated to access some of the checkpoints used during benchmarking:
```sh
hf auth login
```
We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).
Then you can either launch the entire benchmarking suite by running:
```sh
python run_all.py
```
Or, you can run the individual benchmarks.
## Customizing the benchmarks
We define "scenarios" to cover the most common ways in which these models are used. You can
define a new scenario, modifying an existing benchmark file:
```py
BenchmarkScenario(
name=f"{CKPT_ID}-bnb-8bit",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
"quantization_config": BitsAndBytesConfig(load_in_8bit=True),
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=model_init_fn,
)
```
You can also configure a new model-level benchmark and add it to the existing suite. To do so, just defining a valid benchmarking file like `benchmarking_flux.py` should be enough.
Happy benchmarking 🧨 |