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vllm-omni/tests/e2e/online_serving/test_longcat_image_edit.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-Edit: image-to-image edit with a single image + single edit 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 1024x1024 resolution with recommended feature combinations
"""
import pytest
from tests.helpers.mark import hardware_marks
from tests.helpers.media import generate_synthetic_image
from tests.helpers.runtime import (
OmniServer,
OmniServerParams,
OpenAIClientHandler,
dummy_messages_from_mix_data,
)
EDIT_PROMPT = "Transform this modern image into a cinematic animation style with vibrant colors and soft lighting."
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-Edit."""
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-Edit"),
indirect=True,
)
def test_longcat_image_edit(omni_server: OmniServer, openai_client: OpenAIClientHandler):
"""Test the recommended feature combinations for LongCat-Image-Edit."""
image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
request_config = {
"model": omni_server.model,
"messages": messages,
"extra_body": {
"height": 1024,
"width": 1024,
"num_inference_steps": 2,
"negative_prompt": NEGATIVE_PROMPT,
"guidance_scale": 4.0,
"seed": 42,
},
}
openai_client.send_diffusion_request(request_config)