diffusers/benchmarks
Welcome to 🤗 Diffusers Benchmarks! This is dataset where we keep track of the inference latency and memory information of the core models in the diffusers library. Currently, the core models are: Flux Wan LTX SDXL Note that we will continue to extend this list based on their usage. You can analyze the results in this demo. [!IMPORTANT] Instead of benchmarking the entire diffusion pipelines, we only benchmark the forward passes of the diffusion networks under different settings… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/benchmarks.
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1---2license: apache-2.03language:4- en5pretty_name: Diffusers Benchmarks6---7 8<div align="center">9<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/benchmarks_headshot.jpg" width=800/>10</div>11 12Welcome to 🤗 Diffusers Benchmarks!13 14This is dataset where we keep track of the inference latency and memory information of the core models in the `diffusers` library. 15 16Currently, the core models are:17 18* Flux19* Wan20* LTX21* SDXL22 23*Note that we will continue to extend this list based on their usage.*24 25You can analyze the results in [this demo](https://huggingface.co/spaces/diffusers/benchmark-analyzer/).26 27> [!IMPORTANT]28> Instead of benchmarking the entire diffusion pipelines, we only benchmark the forward passes29> of the diffusion networks under different settings (compile, offloading, quantization, etc.).30> We only benchmark the diffusion networks because they are typically the most compute-heavy part31> in a diffusion pipeline.32 33We use [this GitHub Actions workflow](https://github.com/huggingface/diffusers/blob/main/.github/workflows/benchmark.yml) to report the34above numbers automatically. This workflow runs on a biweekly cadence. 35 36[Here](https://github.com/huggingface/diffusers/actions/runs/16065987231) is an example run.