[docs] Add xDiT in section optimization

docs/source/en/_toctree.yml

@@ -161,6 +161,8 @@
     title: DeepCache
   - local: optimization/tgate
     title: TGATE
+  - local: optimization/xdit
+    title: xDiT
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to
       title: JAX/Flax

docs/source/en/optimization/xdit.md

@@ -0,0 +1,126 @@
+# xDiT
+
+xDiT is an inference engine designed for the parallel deployment of DiTs on large scale. xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as GPU kernel accelerations.
+
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/methods/xdit_overview.png">
+</div>
+You can install xDiT using the following command:
+
+
+```bash
+pip install xfuser
+```
+
+Here's an example of using xDiT to accelerate the inference of a diffusers model:
+
+```diff
+ import torch
+ from diffusers import StableDiffusion3Pipeline
+
+ from xfuser import xFuserArgs, xDiTParallel
+ from xfuser.config import FlexibleArgumentParser
+ from xfuser.core.distributed import get_world_group
+
+ def main():
++    parser = FlexibleArgumentParser(description="xFuser Arguments")
++    args = xFuserArgs.add_cli_args(parser).parse_args()
++    engine_args = xFuserArgs.from_cli_args(args)
++    engine_config, input_config = engine_args.create_config()
+
+     local_rank = get_world_group().local_rank
+     pipe = StableDiffusion3Pipeline.from_pretrained(
+         pretrained_model_name_or_path=engine_config.model_config.model,
+         torch_dtype=torch.float16,
+     ).to(f"cuda:{local_rank}")
+
+# do anything you want with pipeline here
+
++    pipe = xDiTParallel(pipe, engine_config, input_config)
+
+     pipe(
+         height=input_config.height,
+         width=input_config.height,
+         prompt=input_config.prompt,
+         num_inference_steps=input_config.num_inference_steps,
+         output_type=input_config.output_type,
+         generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
+     )
+
++    if input_config.output_type == "pil":
++        pipe.save("results", "stable_diffusion_3")
+
+if __name__ == "__main__":
+    main()
+
+```
+
+As you can see, we only need to use xFuserArgs from xDiT to get configuration parameters, and pass these parameters along with the pipeline object from the diffusers library into xDiTParallel to complete the parallelization of a specific pipeline in diffusers.
+
+
+xDiT runtime parameters can be viewed in the command line using -h, and detailed introductions can also be found on the [xDiT Github page](https://github.com/xdit-project/xDiT?tab=readme-ov-file#2-usage).
+
+
+xDiT needs to be launched using torchrun to support its multi-node, multi-GPU parallel capabilities. For example, the following command can be used for 8-GPU parallel inference:
+
+```bash
+torchrun --nproc_per_node=8 ./inference.py --model models/FLUX.1-dev --data_parallel_degree 2 --ulysses_degree 2 --ring_degree 2 --prompt "A snowy mountain" "A small dog" --num_inference_steps 50
+```
+
+# Supported Models
+
+We have supported a subset of diffusers models in xDiT, including the most popular models such as Flux.1, Stable Diffusion 3, etc. The latest supported models can be found on https://github.com/xdit-project/xDiT?tab=readme-ov-file#-supported-dits
+
+# Benchmark
+
+We tested different models on various machines. Here is some of the data:
+
+
+## Flux.1-schnell
+* Flux.1-schnell with 4 steps on 8 * L40
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/flux/Flux-2k-L40.png">
+</div>
+
+
+* Flux.1-schnell with 4 steps on 8 * A100
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/flux/Flux-2K-A100.png">
+</div>
+
+## Stable Diffusion 3
+* Stable Diffusion 3 with 20 steps on 8 * L40
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/sd3/L40-SD3.png">
+</div>
+
+* Stable Diffusion 3 with 20 steps on 8 * A100
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/sd3/A100-SD3.png">
+</div>
+
+## HunyuanDiT
+* HunyuanDiT with 20 steps on 8 * L40
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/hunuyuandit/L40-HunyuanDiT.png">
+</div>
+
+* HunyuanDiT with 50 steps on 8 * A100
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/hunuyuandit/A100-HunyuanDiT.png">
+</div>
+
+* HunyuanDiT with 50 steps on 4 * T4
+<div class="flex justify-center">
+    <img src="https://github.com/xdit-project/xDiT/raw/main/assets/performance/hunuyuandit/T4-HunyuanDiT.png">
+</div>
+
+More detailed performance metric can be found on our [github page](https://github.com/xdit-project/xDiT?tab=readme-ov-file#perf).
+
+# Reference
+
+[xDiT-project](https://github.com/xdit-project/xDiT)
+
+[USP: A Unified Sequence Parallelism Approach for Long Context Generative AI](https://arxiv.org/abs/2405.07719)
+
+[PipeFusion: Displaced Patch Pipeline Parallelism for Inference of Diffusion Transformer Models](https://arxiv.org/abs/2405.14430)