wtz2333andClaude
36135ef09a
Signed-off-by: wtz2333 <2955110911@qq.com> Co-authored-by: Claude <noreply@anthropic.com>
85 lines
2.6 KiB
Python
85 lines
2.6 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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L4 expansion coverage for ``robbyant/lingbot-video-dense-1.3b``.
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This file remains dense-only. Basic T2I/T2V/TI2V serving for both dense and MoE
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checkpoints is covered by ``test_lingbot_video.py`` and
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``test_lingbot_video_moe.py``. Distributed feature rows belong in follow-up
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PRs.
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"""
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import json
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import os
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import pytest
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from tests.helpers.mark import hardware_marks
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from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
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MODEL = "robbyant/lingbot-video-dense-1.3b"
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PROMPT = "a robotic arm picks up a red block"
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NEGATIVE_PROMPT = "low quality, blurry, watermark, text"
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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 [
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pytest.param(
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OmniServerParams(
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model=model,
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server_args=["--model-class-name", "LingBotVideoPipeline"],
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),
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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.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
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def test_cfg_off(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
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request_config = {
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"model": omni_server.model,
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"form_data": {
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"model": omni_server.model,
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"prompt": PROMPT,
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"height": 192,
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"width": 192,
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"num_frames": 9,
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"fps": 24,
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"num_inference_steps": 2,
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"guidance_scale": 1.0,
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"flow_shift": 3.0,
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"seed": 42,
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},
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}
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openai_client.send_video_diffusion_request(request_config)
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@pytest.mark.parametrize("omni_server", _get_diffusion_feature_cases(MODEL), indirect=True)
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def test_batch_cfg_extra_params(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
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request_config = {
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"model": omni_server.model,
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"form_data": {
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"model": omni_server.model,
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"prompt": PROMPT,
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"negative_prompt": NEGATIVE_PROMPT,
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"height": 192,
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"width": 320,
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"num_frames": 9,
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"fps": 24,
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"num_inference_steps": 2,
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"guidance_scale": 3.0,
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"flow_shift": 3.0,
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"seed": 42,
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"extra_params": json.dumps({"batch_cfg": True}, separators=(",", ":")),
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},
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}
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openai_client.send_video_diffusion_request(request_config)
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