wangyu
d671e6b0db
Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Online serving tests for ``Qwen/Qwen-Image-Layered`` (layer decomposition).
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- ``test_feature``: default single-GPU smoke via ``_get_diffusion_feature_cases`` (one ``default`` case).
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- ``test_layered_output_image_count``: guard for issue #1969 (multi-layer image count).
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- ``test_empty_prompt``: guard for issue #1966 (empty prompt).
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Broader parallel / cache feature matrices previously lived in ``test_qwen_image_layered_expansion.py``;
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that module is now a shim that points here.
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"""
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import pytest
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from tests.helpers.mark import hardware_marks
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from tests.helpers.media import generate_synthetic_image
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from tests.helpers.runtime import (
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OmniServer,
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OmniServerParams,
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OpenAIClientHandler,
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dummy_messages_from_mix_data,
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)
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MODEL = "Qwen/Qwen-Image-Layered"
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EDIT_PROMPT = "Decompose this image into layers."
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MULTI_EDIT_PROMPT = (
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"Transform the first image into a Dadaism collage art. "
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"Transform the second image into a Vincent van Gogh style painting. "
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"Then juxtapose the two transformed images into a single artwork for visual contrast."
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)
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NEGATIVE_PROMPT = "blurry, low quality, distorted"
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SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
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def _get_diffusion_feature_cases(model: str):
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"""Return a single default ``OmniServerParams`` row (no extra ``server_args``)."""
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return [
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pytest.param(
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OmniServerParams(model=model),
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id="default",
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marks=SINGLE_CARD_FEATURE_MARKS,
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),
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]
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@pytest.mark.slow
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@pytest.mark.diffusion
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@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
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def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
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"""Default Qwen-Image-Layered smoke (single ``default`` server config)."""
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image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
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messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
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request_config = {
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"model": omni_server.model,
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"messages": messages,
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"extra_body": {
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"num_inference_steps": 2,
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"negative_prompt": NEGATIVE_PROMPT,
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"true_cfg_scale": 4.0,
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"seed": 42,
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},
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}
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openai_client.send_diffusion_request(request_config)
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