[CI] add diffusion ci (#174)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>
Signed-off-by: Kevin H. Luu <khluu000@gmail.com>
Co-authored-by: Kevin H. Luu <khluu000@gmail.com>
This commit is contained in:
Jiangyun ZhuandKevin H. Luu authored and GitHub committed 2025-12-04 15:23:22 +00:00
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commit 630cbe1ee6
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steps:
- label: ":docker: Build image"
key: image-build
commands:
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
- "docker build --file docker/Dockerfile.ci -t vllm-omni-ci ."
- "docker tag vllm-omni-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
- "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
agents:
queue: "cpu_queue_premerge_us_east_1"
- label: "Simple Unit Test"
commands:
- ".buildkite/scripts/simple_test.sh"
agents:
queue: "cpu_queue_premerge"
- label: "Diffusion Model Test"
depends_on: image-build
commands:
- pytest -s -v tests/single_stage/test_diffusion_model.py
agents:
queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
plugins:
- docker#v5.2.0:
image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
always-pull: true
propagate-environment: true
environment:
- "HF_HOME=/fsx/hf_cache"
volumes:
- "/fsx/hf_cache:/fsx/hf_cache"
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@@ -156,9 +156,6 @@ cython_debug/
# cursor
.cursor/
# docker
docker/
# scripts
/scripts/
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ARG VLLM_BASE_IMAGE=vllm/vllm-openai
ARG VLLM_BASE_TAG=v0.11.0
FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
ARG APP_DIR=/workspace/vllm-omni
WORKDIR ${APP_DIR}
COPY . .
# Install vllm-omni into the same uv-managed Python environment used by the base image.
RUN uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"
RUN ln -sf /usr/bin/python3 /usr/bin/python
ENTRYPOINT []
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import pytest
import torch
from vllm_omni import Omni
models = ["Tongyi-MAI/Z-Image-Turbo"]
@pytest.mark.parametrize("model_name", models)
def test_diffusion_model(model_name: str):
m = Omni(model=model_name)
# high resolution may cause OOM on L4
height = 256
width = 256
images = m.generate(
"a photo of a cat sitting on a laptop keyboard",
height=height,
width=width,
num_inference_steps=2,
guidance_scale=0.0,
generator=torch.Generator("cuda").manual_seed(42),
num_outputs_per_prompt=2,
)
assert len(images) == 2
# check image size
assert images[0].width == width
assert images[0].height == height
images[0].save("z_image_output.png")