xDiT diffusion inference#
2025-12-05
15 min read time
The rocm/pytorch-xdit Docker image offers a prebuilt, optimized environment based on 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 7.10.0 (preview) based on TheRock and includes the following components:
Software components
Software component |
Version |
|---|---|
TheRock |
3e3f834 |
rccl |
d23d18f |
composable_kernel |
2570462 |
rocm-libraries |
0588f07 |
rocm-systems |
473025a |
torch |
73adac |
torchvision |
f5c6c2e |
triton |
7416ffc |
accelerate |
34c1779 |
aiter |
de14bec |
diffusers |
40528e9 |
xfuser |
83978b5 |
yunchang |
2c9b712 |
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.
What’s new#
Minor bug fixes and clarifications to READMEs.
Bumps TheRock, AITER, Diffusers, xDiT versions.
Changes Aiter rounding mode for faster gfx942 FWD Attention.
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.
Note
To learn more about your specific model see the Hunyuan Video model card on Hugging Face or visit the GitHub page. Note that some models require access authorization before use via an external license agreement through a third party.
Note
To learn more about your specific model see the Wan2.1 model card on Hugging Face or visit the GitHub page. Note that some models require access authorization before use via an external license agreement through a third party.
Note
To learn more about your specific model see the Wan2.2 model card on Hugging Face or visit the GitHub page. Note that some models require access authorization before use via an external license agreement through a third party.
Note
To learn more about your specific model see the FLUX.1 model card on Hugging Face or visit the GitHub page. Note that some models require access authorization before use via an external license agreement through a third party.
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 guide to properly configure your system settings before starting.
To test for optimal performance, consult the recommended System health benchmarks. This suite of tests will help you verify and fine-tune your system’s configuration.
Pull the Docker image#
For this tutorial, it’s recommended to use the latest rocm/pytorch-xdit:v25.11 Docker image.
Pull the image using the following command:
docker pull rocm/pytorch-xdit:v25.11
Validate and benchmark#
Once the image has been downloaded you can follow these steps to run benchmarks and generate outputs.
Choose your setup method#
You can either use an existing Hugging Face cache or download the model fresh inside the container.
If you already have models downloaded on your host system, you can mount your existing cache.
Set your Hugging Face cache location.
export HF_HOME=/your/hf_cache/location
Download the model (if not already cached).
huggingface-cli download tencent/HunyuanVideo --revision refs/pr/18
Launch the container with mounted cache.
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 \ rocm/pytorch-xdit:v25.11
If you prefer to keep the container self-contained or don’t have an existing cache.
Launch the container
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 \ rocm/pytorch-xdit:v25.11
Inside the container, set the Hugging Face cache location and download the model.
export HF_HOME=/app/huggingface_models huggingface-cli download tencent/HunyuanVideo --revision refs/pr/18
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.
If you already have models downloaded on your host system, you can mount your existing cache.
Set your Hugging Face cache location.
export HF_HOME=/your/hf_cache/location
Download the model (if not already cached).
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P
Launch the container with mounted cache.
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 \ rocm/pytorch-xdit:v25.11
If you prefer to keep the container self-contained or don’t have an existing cache.
Launch the container
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 \ rocm/pytorch-xdit:v25.11
Inside the container, set the Hugging Face cache location and download the model.
export HF_HOME=/app/huggingface_models huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P
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.
If you already have models downloaded on your host system, you can mount your existing cache.
Set your Hugging Face cache location.
export HF_HOME=/your/hf_cache/location
Download the model (if not already cached).
huggingface-cli download Wan-AI/Wan2.2-I2V-A14B
Launch the container with mounted cache.
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 \ rocm/pytorch-xdit:v25.11
If you prefer to keep the container self-contained or don’t have an existing cache.
Launch the container
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 \ rocm/pytorch-xdit:v25.11
Inside the container, set the Hugging Face cache location and download the model.
export HF_HOME=/app/huggingface_models huggingface-cli download Wan-AI/Wan2.2-I2V-A14B
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.
If you already have models downloaded on your host system, you can mount your existing cache.
Set your Hugging Face cache location.
export HF_HOME=/your/hf_cache/location
Download the model (if not already cached).
huggingface-cli download black-forest-labs/FLUX.1-dev
Launch the container with mounted cache.
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 \ rocm/pytorch-xdit:v25.11
If you prefer to keep the container self-contained or don’t have an existing cache.
Launch the container
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 \ rocm/pytorch-xdit:v25.11
Inside the container, set the Hugging Face cache location and download the model.
export HF_HOME=/app/huggingface_models huggingface-cli download black-forest-labs/FLUX.1-dev
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.
Run inference#
Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.
git clone https://github.com/ROCm/MAD cd MAD pip install -r requirements.txt
On the host machine, use this command to run the performance benchmark test on the Hunyuan Video model using one node.
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models" madengine run \ --tags pyt_xdit_hunyuanvideo \ --keep-model-dir \ --live-output
MAD launches a Docker container with the name
container_ci-pyt_xdit_hunyuanvideo. The throughput and serving reports of the
model are collected in the following paths: pyt_xdit_hunyuanvideo_throughput.csv
and pyt_xdit_hunyuanvideo_serving.csv.
To run the benchmarks for Hunyuan Video, use the following command:
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
The generated video will be stored under the results directory. For the actual benchmark step runtimes, see stdout.
Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.
git clone https://github.com/ROCm/MAD cd MAD pip install -r requirements.txt
On the host machine, use this command to run the performance benchmark test on the Wan2.1 model using one node.
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models" madengine run \ --tags pyt_xdit_wan_2_1 \ --keep-model-dir \ --live-output
MAD launches a Docker container with the name
container_ci-pyt_xdit_wan_2_1. The throughput and serving reports of the
model are collected in the following paths: pyt_xdit_wan_2_1_throughput.csv
and pyt_xdit_wan_2_1_serving.csv.
To run the benchmarks for Wan2.1, use the following command:
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
The generated video will be stored under the results directory. For the actual benchmark step runtimes, see results/outputs/rank0_*.json
Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.
git clone https://github.com/ROCm/MAD cd MAD pip install -r requirements.txt
On the host machine, use this command to run the performance benchmark test on the Wan2.2 model using one node.
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models" madengine run \ --tags pyt_xdit_wan_2_2 \ --keep-model-dir \ --live-output
MAD launches a Docker container with the name
container_ci-pyt_xdit_wan_2_2. The throughput and serving reports of the
model are collected in the following paths: pyt_xdit_wan_2_2_throughput.csv
and pyt_xdit_wan_2_2_serving.csv.
To run the benchmarks for Wan2.2, use the following command:
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
The generated video will be stored under the results directory. For the actual benchmark step runtimes, see results/outputs/rank0_*.json
Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.
git clone https://github.com/ROCm/MAD cd MAD pip install -r requirements.txt
On the host machine, use this command to run the performance benchmark test on the FLUX.1 model using one node.
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models" madengine run \ --tags pyt_xdit_flux \ --keep-model-dir \ --live-output
MAD launches a Docker container with the name
container_ci-pyt_xdit_flux. The throughput and serving reports of the
model are collected in the following paths: pyt_xdit_flux_throughput.csv
and pyt_xdit_flux_serving.csv.
To run the benchmarks for FLUX.1, use the following command:
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
The generated video will be stored under the results directory. For the actual benchmark step runtimes, see results/timing.json
You may also use run_usp.py which implements USP without modifying the default diffusers pipeline.
Previous versions#
See xDiT diffusion inference performance testing version history to find documentation for previous releases of xDiT diffusion inference performance testing.