wtz2333andClaude
36135ef09a
Signed-off-by: wtz2333 <2955110911@qq.com> Co-authored-by: Claude <noreply@anthropic.com>
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Single-GPU serving smoke for ``robbyant/lingbot-video-moe-30b-a3b``."""
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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.assertions import assert_video_first_frame_matches
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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 OmniServer, OmniServerParams, OpenAIClientHandler
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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MODEL = "robbyant/lingbot-video-moe-30b-a3b"
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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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SMOKE_DEFAULT_SAMPLING_PARAMS = '{"0":{"num_frames":81,"num_inference_steps":2,"guidance_scale":3.0}}'
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SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
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def _get_server_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=[
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"--model-class-name",
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"LingBotVideoPipeline",
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"--default-sampling-params",
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SMOKE_DEFAULT_SAMPLING_PARAMS,
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],
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),
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id="default",
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marks=SINGLE_CARD_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_server_cases(MODEL), indirect=True)
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def test_text_to_image_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
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openai_client.send_images_generations_http_request(
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{
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"json": {
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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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"size": "320x192",
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"n": 1,
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"response_format": "b64_json",
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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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}
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}
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)
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@pytest.mark.full_model
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@pytest.mark.diffusion
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@pytest.mark.parametrize("omni_server", _get_server_cases(MODEL), indirect=True)
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def test_video_generation_modes_moe(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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"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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synthetic_image = generate_synthetic_image(320, 192, force_regenerate=True, seed=42)
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request_config["form_data"]["prompt"] = "the red block moves slowly while the camera remains fixed"
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# Keep TI2V dimensions model-scoped until /v1/videos forwards raw reference images.
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request_config["form_data"].pop("height")
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request_config["form_data"].pop("width")
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request_config["form_data"]["extra_params"] = json.dumps({"size": "320x192"}, separators=(",", ":"))
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request_config["image_reference"] = f"data:image/jpeg;base64,{synthetic_image['base64']}"
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responses = openai_client.send_video_diffusion_request(request_config)
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assert responses[0].videos
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assert_video_first_frame_matches(
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responses[0].videos[0],
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synthetic_image["np_array"],
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max_mean_absolute_error=0.25,
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)
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