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ROCm/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.10.rst
peterjunpark 3a43bacdda Update xdit diffusion inference history (#5808) 
* Update xdit diffusion inference history

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:orphan:
.. meta::
:description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
prebuilt and optimized docker images.
:keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
************************
xDiT diffusion inference
************************
.. _xdit-video-diffusion-2510:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set docker = data.xdit_diffusion_inference.docker %}
{% set model_groups = data.xdit_diffusion_inference.model_groups%}
The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
a prebuilt, optimized inference environment based on `xDiT
<https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion-based
video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
and MI300X (gfx942) GPUs.
This image is based on ROCm {{docker.ROCm}} preview release via `TheRock <https://github.com/ROCm/TheRock>`_
and includes the following software components:
.. tab-set::
.. tab-item:: {{ docker.pull_tag }}
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
What's new
==========
- Initial ROCm-enabled xDiT Docker release for diffusion inference.
- Supported architectures: gfx942 and gfx950 (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X).
- Supported workloads: Wan 2.1, Wan 2.2, HunyuanVideo, and Flux models.
.. _xdit-video-diffusion-supported-models-2510:
Supported models
================
The following models are supported for inference performance benchmarking.
Some instructions, commands, and recommendations in this documentation might
vary by model -- select one to get started.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set docker = data.xdit_diffusion_inference.docker %}
{% set model_groups = data.xdit_diffusion_inference.model_groups%}
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length == 1 %}
<div class="col-12 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{ model.mad_tag }}
.. note::
To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
external license agreement through a third party.
{% endfor %}
{% endfor %}
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA
auto-balancing, you can skip this step. Otherwise, complete the procedures in
the `System validation and optimization
<https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/system-setup/prerequisite-system-validation.html>`__
guide to properly configure your system settings before starting.
Pull the Docker image
=====================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set docker = data.xdit_diffusion_inference.docker %}
For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
Pull the image using the following command:
.. code-block:: shell
docker pull {{ docker.pull_tag }}
Validate and benchmark
======================
Once the image has been downloaded you can follow these steps to
run benchmarks and generate outputs.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set model_groups = data.xdit_diffusion_inference.model_groups %}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
The following commands are written for {{ model.model }}.
See :ref:`xdit-video-diffusion-supported-models-2510` to switch to another available model.
{% endfor %}
{% endfor %}
.. _xdit-video-diffusion-setup-2510:
Prepare the model
-----------------
.. note::
If you're using ROCm MAD to :ref:`run your model
<xdit-video-diffusion-run-2510>`, you can skip this section. MAD will handle
starting the container and downloading required models inside the container.
You can either use an existing Hugging Face cache or download the model fresh inside the container.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set docker = data.xdit_diffusion_inference.docker %}
{% set model_groups = data.xdit_diffusion_inference.model_groups%}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. tab-set::
.. tab-item:: Option 1: Use existing Hugging Face cache
If you already have models downloaded on your host system, you can mount your existing cache.
1. Set your Hugging Face cache location.
.. code-block:: shell
export HF_HOME=/your/hf_cache/location
2. Download the model (if not already cached).
.. code-block:: shell
huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
3. Launch the container with mounted cache.
.. code-block:: shell
docker run \
-it --rm \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--user root \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--ipc=host \
--network host \
--privileged \
--shm-size 128G \
--name pytorch-xdit \
-e HSA_NO_SCRATCH_RECLAIM=1 \
-e OMP_NUM_THREADS=16 \
-e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-e HF_HOME=/app/huggingface_models \
-v $HF_HOME:/app/huggingface_models \
{{ docker.pull_tag }}
.. tab-item:: Option 2: Download inside container
If you prefer to keep the container self-contained or don't have an existing cache.
1. Launch the container
.. code-block:: shell
docker run \
-it --rm \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--user root \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--ipc=host \
--network host \
--privileged \
--shm-size 128G \
--name pytorch-xdit \
-e HSA_NO_SCRATCH_RECLAIM=1 \
-e OMP_NUM_THREADS=16 \
-e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
{{ docker.pull_tag }}
2. Inside the container, set the Hugging Face cache location and download the model.
.. code-block:: shell
export HF_HOME=/app/huggingface_models
huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
.. warning::
Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
{% endfor %}
{% endfor %}
.. _xdit-video-diffusion-run-2510:
Run inference
=============
You can benchmark models through `MAD <https://github.com/ROCm/MAD>`__-integrated automation or standalone
torchrun commands.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
{% set model_groups = data.xdit_diffusion_inference.model_groups%}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{ model.mad_tag }}
.. tab-set::
.. tab-item:: MAD-integrated benchmarking
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
2. On the host machine, use this command to run the performance benchmark test on
the `{{model.model}} <{{ model.url }}>`_ model using one node.
.. code-block:: shell
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{model.mad_tag}} \
--keep-model-dir \
--live-output
MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
and ``{{ model.mad_tag }}_serving.csv``.
.. tab-item:: Standalone benchmarking
To run the benchmarks for {{ model.model }}, use the following command:
.. code-block:: shell
{% if model.model == "Hunyuan Video" %}
cd /app/Hunyuanvideo
mkdir results
torchrun --nproc_per_node=8 run.py \
--model tencent/HunyuanVideo \
--prompt "In the large cage, two puppies were wagging their tails at each other." \
--height 720 --width 1280 --num_frames 129 \
--num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
--ulysses_degree 8 \
--enable_tiling --enable_slicing \
--use_torch_compile \
--bench_output results
{% endif %}
{% if model.model == "Wan2.1" %}
cd Wan2.1
mkdir results
torchrun --nproc_per_node=8 run.py \
--task i2v-14B \
--size 720*1280 --frame_num 81 \
--ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
--image "/app/Wan2.1/examples/i2v_input.JPG" \
--ulysses_size 8 --ring_size 1 \
--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
--benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
--offload_model 0 \
--vae_dtype bfloat16 \
--allow_tf32 \
--compile
{% endif %}
{% if model.model == "Wan2.2" %}
cd Wan2.2
mkdir results
torchrun --nproc_per_node=8 run.py \
--task i2v-A14B \
--size 720*1280 --frame_num 81 \
--ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
--image "/app/Wan2.2/examples/i2v_input.JPG" \
--ulysses_size 8 --ring_size 1 \
--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
--benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
--offload_model 0 \
--vae_dtype bfloat16 \
--allow_tf32 \
--compile
{% endif %}
{% if model.model == "FLUX.1" %}
cd Flux
mkdir results
torchrun --nproc_per_node=8 /app/Flux/run.py \
--model black-forest-labs/FLUX.1-dev \
--seed 42 \
--prompt "A small cat" \
--height 1024 \
--width 1024 \
--num_inference_steps 25 \
--max_sequence_length 256 \
--warmup_steps 5 \
--no_use_resolution_binning \
--ulysses_degree 8 \
--use_torch_compile \
--num_repetitions 1 \
--benchmark_output_directory results
{% endif %}
The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% endif %}
{% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
{% endfor %}
{% endfor %}
Further reading
===============
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- For a list of other ready-made Docker images for AI with ROCm, see `AMD
Infinity Hub
<https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`__.
Previous versions
=================
See :doc:`xdit-history` to find documentation for previous releases
of xDiT diffusion inference performance testing.
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