154 lines
4.8 KiB
Python
154 lines
4.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""
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Comprehensive tests of diffusion features that are available in online serving mode
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and are supported by the FluxKontext model.
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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 OmniServer, OmniServerParams, OnlineOmniClient, dummy_messages_from_mix_data
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pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
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EDIT_PROMPT = "Transform this modern, geometrist image into a Vincent van Gogh style impressionist painting."
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NEGATIVE_PROMPT = "blurry, low quality, modern, geometrist"
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MODEL = "black-forest-labs/FLUX.1-Kontext-dev"
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BASE_FEATURE_MARKS = hardware_marks(res={"cuda": "L4"})
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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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# The unsharded transformer alone exceeds the L4's 22 GiB.
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OmniServerParams(model=model, server_args=["--enable-layerwise-offload"]),
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id="base",
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marks=BASE_FEATURE_MARKS,
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),
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]
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_flux_kontext_text_to_image(omni_server: OmniServer, online_client: OnlineOmniClient):
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"""Test text-to-image generation with FluxKontext in regular end-user scenarios."""
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messages = dummy_messages_from_mix_data(content_text="A photo of a cat sitting on a laptop keyboard")
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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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"height": 512,
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"width": 512,
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"num_inference_steps": 2,
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"seed": 42,
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},
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}
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online_client.send_diffusion_request(request_config)
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_flux_kontext_image_edit(omni_server: OmniServer, online_client: OnlineOmniClient):
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"""Test image editing with FluxKontext in regular end-user scenarios."""
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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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"height": 512,
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"width": 512,
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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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online_client.send_diffusion_request(request_config)
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_flux_kontext_image_edit_no_negative(omni_server: OmniServer, online_client: OnlineOmniClient):
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"""Test image editing with FluxKontext without negative prompt."""
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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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"height": 512,
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"width": 512,
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"num_inference_steps": 2,
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"seed": 42,
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},
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}
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online_client.send_diffusion_request(request_config)
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_flux_kontext_high_resolution(omni_server: OmniServer, online_client: OnlineOmniClient):
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"""Test high-resolution generation with FluxKontext."""
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messages = dummy_messages_from_mix_data(content_text="A beautiful landscape with mountains and a lake")
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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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"height": 768,
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"width": 1024,
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"num_inference_steps": 2,
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"seed": 42,
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},
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}
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online_client.send_diffusion_request(request_config)
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_flux_kontext_multiple_outputs(omni_server: OmniServer, online_client: OnlineOmniClient):
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"""Test generating multiple outputs with FluxKontext."""
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messages = dummy_messages_from_mix_data(content_text="A photo of a cat sitting on a laptop keyboard")
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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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"height": 512,
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"width": 512,
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"num_inference_steps": 2,
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"num_outputs_per_prompt": 2,
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"seed": 42,
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},
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}
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online_client.send_diffusion_request(request_config)
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