123 lines
4.0 KiB
Python
123 lines
4.0 KiB
Python
from __future__ import annotations
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import base64
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import gc
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import io
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import os
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from pathlib import Path
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import pytest
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import requests
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import torch
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from PIL import Image
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from tests.e2e.accuracy.helpers import assert_similarity, model_output_dir
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from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
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from tests.helpers.mark import hardware_test
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from tests.helpers.runtime import OmniServer
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pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
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MODEL_ID = "Qwen/Qwen-Image"
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MODEL_ENV_VAR = "QWEN_IMAGE_MODEL"
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PROMPT = "A photo of a cat sitting on a laptop keyboard, digital art style."
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NEGATIVE_PROMPT = "blurry, low quality"
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WIDTH = 512
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HEIGHT = 512
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NUM_INFERENCE_STEPS = 20
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TRUE_CFG_SCALE = 4.0
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SEED = 42
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SSIM_THRESHOLD = 0.97
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PSNR_THRESHOLD = 30.0
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def _model_name() -> str:
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return os.environ.get(MODEL_ENV_VAR, MODEL_ID)
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def _local_files_only(model: str) -> bool:
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return Path(model).exists()
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def _run_vllm_omni_qwen_image(*, model: str, output_path: Path) -> Image.Image:
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server_args = ["--num-gpus", "1", "--stage-init-timeout", "300", "--init-timeout", "900"]
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with OmniServer(model, server_args, use_omni=True) as omni_server:
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response = requests.post(
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f"http://{omni_server.host}:{omni_server.port}/v1/images/generations",
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json={
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"model": omni_server.model,
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"prompt": PROMPT,
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"size": f"{WIDTH}x{HEIGHT}",
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"n": 1,
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"response_format": "b64_json",
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"negative_prompt": NEGATIVE_PROMPT,
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"num_inference_steps": NUM_INFERENCE_STEPS,
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"true_cfg_scale": TRUE_CFG_SCALE,
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"seed": SEED,
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},
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timeout=600,
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)
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response.raise_for_status()
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payload = response.json()
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assert len(payload["data"]) == 1
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image_bytes = base64.b64decode(payload["data"][0]["b64_json"])
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image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
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image.load()
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image.save(output_path)
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return image
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def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> Image.Image:
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run_pre_test_cleanup(enable_force=True)
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pipe: DiffusionPipeline | None = None
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try:
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pipe = DiffusionPipeline.from_pretrained(
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model,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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local_files_only=_local_files_only(model),
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).to("cuda")
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generator = torch.Generator(device="cuda").manual_seed(SEED)
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result = pipe( # pyright: ignore[reportCallIssue]
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prompt=PROMPT,
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negative_prompt=NEGATIVE_PROMPT,
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width=WIDTH,
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height=HEIGHT,
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num_inference_steps=NUM_INFERENCE_STEPS,
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true_cfg_scale=TRUE_CFG_SCALE,
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generator=generator,
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)
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output_image = result.images[0].convert("RGB")
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output_image.save(output_path)
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return output_image
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finally:
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if pipe is not None and hasattr(pipe, "maybe_free_model_hooks"):
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pipe.maybe_free_model_hooks()
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del pipe
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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run_post_test_cleanup(enable_force=True)
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@pytest.mark.benchmark
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@hardware_test(res={"cuda": "H100"}, num_cards=1)
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def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
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model = _model_name()
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output_dir = model_output_dir(accuracy_artifact_root, MODEL_ID)
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vllm_output = _run_vllm_omni_qwen_image(model=model, output_path=output_dir / "vllm_omni.png")
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diffusers_output = _run_diffusers_qwen_image(model=model, output_path=output_dir / "diffusers.png")
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assert_similarity(
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model_name=MODEL_ID,
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vllm_image=vllm_output,
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diffusers_image=diffusers_output,
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width=WIDTH,
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height=HEIGHT,
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ssim_threshold=SSIM_THRESHOLD,
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psnr_threshold=PSNR_THRESHOLD,
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)
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