:orphan:
:no-search:

.. 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
************************

.. caution::

   This documentation does not reflect the latest version of ROCm vLLM
   inference performance documentation. See
   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
   version.

.. _xdit-video-diffusion-2511:

.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml

   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
   {% set model_groups = data.xdit_diffusion_inference.model_groups%}

   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
   and includes the following components:

   .. dropdown:: Software components

      .. 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
==========

.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml

   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
   {% set model_groups = data.xdit_diffusion_inference.model_groups%}

   {% for item in docker.whats_new %}
   * {{ item }}
   {% endfor %}

.. _xdit-video-diffusion-supported-models-2511:

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_25.11-inference-models.yaml

   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
   {% set model_groups = data.xdit_diffusion_inference.model_groups %}

   {# Create a lookup for supported models #}
   {% set supported_lookup = {} %}
   {% for supported in docker.supported_models %}
   {% set _ = supported_lookup.update({supported.group: supported.models}) %}
   {% endfor %}

   .. 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 %}
            {% if model_group.group in supported_lookup %}
                  <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>
            {% endif %}
        {% 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 %}
            {% if model_group.group in supported_lookup %}
            {% set supported_models = supported_lookup[model_group.group] %}
            {% set models = model_group.models %}
            {% for model in models %}
                {% if model.model in supported_models %}
                {% if models|length % 3 == 0 %}
                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.page_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.page_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
                {% endif %}
                {% endif %}
            {% endfor %}
            {% endif %}
        {% endfor %}
              </div>
          </div>
      </div>

   {% for model_group in model_groups %}
       {% for model in model_group.models %}

   .. container:: model-doc {{ model.page_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 :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting.

To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.

Pull the Docker image
=====================

.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-inference-models.yaml

   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}

   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_25.11-inference-models.yaml

   {% for model_group in model_groups %}
     {% for model in model_group.models %}

   .. container:: model-doc {{model.page_tag}}

      The following commands are written for {{ model.model }}.
      See :ref:`xdit-video-diffusion-supported-models-2511` to switch to another available model.

     {% endfor %}
   {% endfor %}

Choose your setup method
------------------------

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_25.11-inference-models.yaml

   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
   {% 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.page_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 %}

Run inference
=============

.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.11-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.page_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 %}

Previous versions
=================

See
:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
to find documentation for previous releases of xDiT diffusion inference
performance testing.
