# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Comprehensive tests of diffusion features that are available in online serving mode and are supported by Qwen-Image-Layered model. Kept cases (maximum feature coverage, no cpu-offload): sp_001 : cache_dit + Ulysses-SP 2 (2×H100) cfg_parallel_002 : cache_dit + CFG-Parallel 2 (2×H100) layers_guard_001 : layerwise offload + layers=3 (1×H100, issue #1969 guard) Total distinct features covered: cache_dit, Ulysses-SP, CFG-Parallel, layerwise-offload. """ import pytest from tests.helpers.mark import hardware_marks from tests.helpers.media import decode_b64_image, generate_synthetic_image from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data pytestmark = [pytest.mark.diffusion, pytest.mark.full_model] MODEL = "Qwen/Qwen-Image-Layered" EDIT_PROMPT = "Decompose this image into layers." EMPTY_EDIT_PROMPT = "" NEGATIVE_PROMPT = "blurry, low quality, distorted" PARALLEL_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2) SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}) # ── Feature combination cases (2 cards) ───────────────────────────────────── # sp_001 : cache_dit + Ulysses-SP 2 # cfg_parallel_002: cache_dit + CFG-Parallel 2 FEATURE_CASES = [ pytest.param( OmniServerParams( model=MODEL, server_args=[ "--cache-backend", "cache_dit", "--ulysses-degree", "2", ], ), id="sp_001", marks=PARALLEL_FEATURE_MARKS, ), pytest.param( OmniServerParams( model=MODEL, server_args=[ "--cache-backend", "cache_dit", "--cfg-parallel-size", "2", ], ), id="cfg_parallel_001", marks=PARALLEL_FEATURE_MARKS, ), pytest.param( OmniServerParams( model=MODEL, server_args=[ "--use-hsdp", "--hsdp-shard-size", "2", ], ), id="parallel_hsdp", marks=PARALLEL_FEATURE_MARKS, ), ] @pytest.mark.parametrize("omni_server", FEATURE_CASES, indirect=True) def test_feature(omni_server: OmniServer, openai_client: OpenAIClientHandler): """Test feature combinations with Qwen-Image-Layered.""" image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}" messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT) request_config = { "model": omni_server.model, "messages": messages, "extra_body": { "num_inference_steps": 2, "negative_prompt": NEGATIVE_PROMPT, "true_cfg_scale": 4.0, "seed": 42, }, } openai_client.send_diffusion_request(request_config) # ── Guard: Issue #1969 – layered output must return `layers` images ───────── # https://github.com/vllm-project/vllm-omni/issues/1969 # Qwen-Image-Layered should return exactly `layers` images per request, # not just 1. We bypass send_diffusion_request() because its built-in # assertion checks `num_outputs_per_prompt` (default 1), which does not # match the layered semantics where `layers` images are expected. # --------------------------------------------------------------------------- LAYERS_GUARD_CASES = [ pytest.param( OmniServerParams( model=MODEL, server_args=["--enable-layerwise-offload"], ), 3, id="layers_guard_001_layers3", marks=SINGLE_CARD_FEATURE_MARKS, ), ] def _collect_image_url_items(openai_client: OpenAIClientHandler, request_config: dict): """Send a request and return all image_url content items from the API response. Handles both object-style (Pydantic) and dict-style (raw JSON) content items, because the OpenAI SDK may return either depending on how the server serializes the multimodal content list. """ chat_completion = openai_client.client.chat.completions.create( model=request_config["model"], messages=request_config["messages"], extra_body=request_config.get("extra_body"), ) image_items = [] for choice in chat_completion.choices: content = getattr(choice.message, "content", None) assert content is not None, "API response content is None" if isinstance(content, str): pytest.fail( f"API response content is a plain string, expected a list of image items. " f"Content preview: {content[:200]}" ) for item in content: # Dict-style: {'type': 'image_url', 'image_url': {'url': '...'}} if isinstance(item, dict): if item.get("type") == "image_url" and item.get("image_url") is not None: image_items.append(item) # Object-style (Pydantic model from OpenAI SDK) elif hasattr(item, "image_url") and item.image_url is not None: image_items.append(item) return image_items @pytest.mark.parametrize( "omni_server, expected_layers", LAYERS_GUARD_CASES, indirect=["omni_server"], ) def test_layered_output_image_count( omni_server: OmniServer, openai_client: OpenAIClientHandler, expected_layers: int, ): """Guard for https://github.com/vllm-project/vllm-omni/issues/1969 The bug: API flattened the multi-layer output into a single image. Expected: API response content must be a list containing exactly ``layers`` image_url items, one per decomposed layer. """ image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}" messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT) request_config = { "model": omni_server.model, "messages": messages, "extra_body": { "num_inference_steps": 2, "negative_prompt": NEGATIVE_PROMPT, "true_cfg_scale": 4.0, "seed": 42, "layers": expected_layers, }, } image_items = _collect_image_url_items(openai_client, request_config) # Core assertion: the API must NOT collapse multiple layers into 1 image assert len(image_items) != 1 or expected_layers == 1, ( f"Issue #1969 regression: API returned only 1 image instead of a list of {expected_layers} layer images" ) # Exact count must match the requested layers assert len(image_items) == expected_layers, ( f"Expected {expected_layers} image_url items in response, got {len(image_items)}" ) # Verify each item is a decodable image for i, item in enumerate(image_items): if isinstance(item, dict): url = item["image_url"]["url"] else: url = item.image_url.url assert url.startswith("data:image"), f"image_url item {i} is not a data URI: {url[:80]}" b64 = url.split(",", 1)[1] img = decode_b64_image(b64) assert img is not None, f"Failed to decode image at index {i}" # ── Issue #1966 server do not support empty prompt ───────────────────────── # https://github.com/vllm-project/vllm-omni/issues/1966 # case with empty prompt # --------------------------------------------------------------------------- PROMPT_CASES = [ pytest.param( OmniServerParams( model=MODEL, server_args=["--vae-use-slicing", "--vae-use-tiling"], ), id="prompt_001", marks=SINGLE_CARD_FEATURE_MARKS, ), ] @pytest.mark.parametrize("omni_server", PROMPT_CASES, indirect=True) def test_empty_prompt(omni_server: OmniServer, openai_client: OpenAIClientHandler): """Test feature combinations with Qwen-Image-Layered.""" image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}" messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EMPTY_EDIT_PROMPT) request_config = { "model": omni_server.model, "messages": messages, "extra_body": { "num_inference_steps": 2, "negative_prompt": NEGATIVE_PROMPT, "true_cfg_scale": 4.0, "seed": 42, }, } openai_client.send_diffusion_request(request_config)