BruceLoveDecimaland汪志鹏
adf6d0c1ea
Signed-off-by: BruceLoveDecimal <liuqihao970610@gmail.com> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Signed-off-by: liuqihao <liuqihao970610@gmail.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
107 lines
3.7 KiB
Python
107 lines
3.7 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 smoke for SANA-WM image-to-video (first-frame I2V via ``/v1/videos``).
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Boots the server straight from the model id — the Stage-1 repo's
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``model_index.json`` names ``SanaWmPipeline``, so the class resolves on its own
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— submits one async ``/v1/videos`` job with a first-frame reference image, and
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asserts the job completes and returns video bytes.
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From ``tests/``::
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pytest -s -v e2e/online_serving/test_sana_wm.py -m "advanced_model and diffusion" --run-level=advanced_model
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"""
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import base64
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import json
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import os
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from io import BytesIO
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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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from vllm_omni.diffusion.models.sana_wm import (
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SANA_WM_MODEL_ID,
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SANA_WM_OUTPUT_HEIGHT,
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SANA_WM_OUTPUT_WIDTH,
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)
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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MODEL = SANA_WM_MODEL_ID
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PROMPT = "A slow forward camera move through a quiet city street."
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NEGATIVE_PROMPT = "blurry, low quality, distorted, watermark"
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# Smoke knobs: a handful of frames / steps so the job finishes quickly.
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SMOKE_NUM_FRAMES = 9
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SMOKE_NUM_INFERENCE_STEPS = 2
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# First-frame I2V needs a camera trajectory; "w-<n>" is the forward-move action DSL.
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# Passed via the ``sana_wm`` form field (a JSON string parsed server-side); explicit
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# intrinsics avoid the optional Pi3X camera-calibration dependency.
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SANA_WM_PARAMS = {
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"action": f"w-{SMOKE_NUM_FRAMES - 1}",
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"translation_speed": 0.055,
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"rotation_speed_deg": 1.2,
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"intrinsics": {
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"fx": SANA_WM_OUTPUT_WIDTH / 2,
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"fy": SANA_WM_OUTPUT_WIDTH / 2,
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"cx": SANA_WM_OUTPUT_WIDTH / 2,
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"cy": SANA_WM_OUTPUT_HEIGHT / 2,
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},
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}
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SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
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def _first_frame_data_url() -> str:
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"""A solid-color first frame encoded as a PNG ``data:`` URL for I2V."""
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from PIL import Image
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image = Image.new("RGB", (SANA_WM_OUTPUT_WIDTH, SANA_WM_OUTPUT_HEIGHT), (96, 128, 160))
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buffer = BytesIO()
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image.save(buffer, format="PNG")
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encoded = base64.b64encode(buffer.getvalue()).decode("ascii")
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return f"data:image/png;base64,{encoded}"
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def _get_diffusion_feature_cases(model: str):
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"""Single default server row."""
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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.advanced_model
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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_image_to_video_001(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:
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"""Default SANA-WM I2V smoke: async ``/v1/videos`` job completes and returns video bytes."""
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request_config = {
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"model": omni_server.model,
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"image_reference": _first_frame_data_url(),
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"form_data": {
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"prompt": PROMPT,
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"negative_prompt": NEGATIVE_PROMPT,
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# The camera payload rides inside extra_params; there is no
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# top-level sana_wm form field on the video endpoints, so sending
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# one is silently dropped and the request fails in the
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# preprocessor for missing camera control.
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"extra_params": json.dumps({"sana_wm": SANA_WM_PARAMS}),
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"height": SANA_WM_OUTPUT_HEIGHT,
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"width": SANA_WM_OUTPUT_WIDTH,
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"num_frames": SMOKE_NUM_FRAMES,
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"fps": 16,
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"num_inference_steps": SMOKE_NUM_INFERENCE_STEPS,
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"guidance_scale": 5.0,
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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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