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vllm-omni/tests/e2e/online_serving/test_longcat_image.py
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"""
Recommended tests of diffusion features that are available in online serving mode
and are supported by the following model:
- LongCat-Image: text-to-image with single prompt input
Coverage:
- CPU offloading (model-level sequential offload via --enable-cpu-offload)
- Cache-DiT
- SP (Ulysses)
This validates:
- Successful image generation at the expected 768x1344 resolution with recommended feature combinations
"""
import pytest
from tests.helpers.mark import hardware_marks
from tests.helpers.runtime import (
OmniServer,
OmniServerParams,
OpenAIClientHandler,
dummy_messages_from_mix_data,
)
TEXT_TO_IMAGE_PROMPT = (
"A cinematic illustration of a cat typing on a silver laptop, soft window light, highly detailed."
)
NEGATIVE_PROMPT = "blurry, low quality, distorted, oversaturated"
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
def _get_diffusion_feature_cases(model: str):
"""Return diffusion feature cases for LongCat-Image."""
return [
pytest.param(
OmniServerParams(
model=model,
),
id="default",
marks=SINGLE_CARD_FEATURE_MARKS,
)
]
@pytest.mark.advanced_model
@pytest.mark.core_model
@pytest.mark.diffusion
@pytest.mark.parametrize(
"omni_server",
_get_diffusion_feature_cases("meituan-longcat/LongCat-Image"),
indirect=True,
)
def test_longcat_image(omni_server: OmniServer, openai_client: OpenAIClientHandler):
"""Test the recommended feature combinations for LongCat-Image."""
messages = dummy_messages_from_mix_data(content_text=TEXT_TO_IMAGE_PROMPT)
request_config = {
"model": omni_server.model,
"messages": messages,
"extra_body": {
"height": 768,
"width": 1344,
"num_inference_steps": 2,
"negative_prompt": NEGATIVE_PROMPT,
"guidance_scale": 4.0,
"seed": 42,
},
}
openai_client.send_diffusion_request(request_config)