""" Tests for Stable Diffusion 3.5 medium model. """ import pytest from tests.helpers.mark import hardware_marks from tests.helpers.runtime import ( OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data, ) pytestmark = [pytest.mark.diffusion, pytest.mark.slow] FOUR_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "L4"}, num_cards=4) POSITIVE_PROMPT = "A serene mountain landscape at sunset" NEGATIVE_PROMPT = "blurry, low quality, distorted" # For now, we only test one or two good configurations for this model to keep # the CI light. The test cases focus on the most common feature combinations # that provide good performance improvements. def _get_diffusion_feature_cases(model: str): return [ # CFG Parallel + Tensor Parallel + CPU offload pytest.param( OmniServerParams( model=model, server_args=[ "--cfg-parallel-size", "2", "--tensor-parallel-size", "2", "--enable-cpu-offload", ], ), marks=FOUR_CARD_FEATURE_MARKS, ), # Cache-DiT + CFG Parallel + Tensor Parallel pytest.param( OmniServerParams( model=model, server_args=[ "--cache-backend", "cache_dit", "--cfg-parallel-size", "2", "--tensor-parallel-size", "2", ], ), marks=FOUR_CARD_FEATURE_MARKS, ), ] @pytest.mark.parametrize( "omni_server", _get_diffusion_feature_cases( model="stabilityai/stable-diffusion-3.5-medium", ), indirect=True, ) def test_sd3_medium(omni_server: OmniServer, openai_client: OpenAIClientHandler): messages = dummy_messages_from_mix_data(content_text=POSITIVE_PROMPT) request_config = { "model": omni_server.model, "messages": messages, "extra_body": { "height": 1024, "width": 1024, "num_inference_steps": 28, "negative_prompt": NEGATIVE_PROMPT, "guidance_scale": 4.5, "seed": 42, }, } openai_client.send_diffusion_request(request_config)