138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from types import SimpleNamespace
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import pytest
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import torch
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from PIL import Image
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from torch import nn
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from tests.diffusion.models.wan2_2.conftest import StubTransformer, StubVAE, noop_progress_bar
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from vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2_vace import (
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Wan22VACEPipeline,
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create_vace_transformer_from_config,
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get_wan22_vace_pre_process_func,
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)
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
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def _make_vace_pipeline() -> Wan22VACEPipeline:
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pipeline = object.__new__(Wan22VACEPipeline)
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nn.Module.__init__(pipeline)
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pipeline.device = torch.device("cpu")
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pipeline.transformer = StubTransformer(in_channels=4, out_channels=4)
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pipeline.transformer_config = pipeline.transformer.config
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pipeline.vae = StubVAE(z_dim=4)
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pipeline.vae_scale_factor_temporal = 4
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pipeline.vae_scale_factor_spatial = 8
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pipeline.progress_bar = noop_progress_bar
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return pipeline
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def test_vace_preprocess_collects_reference_video_and_mask_inputs() -> None:
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preprocess = get_wan22_vace_pre_process_func(SimpleNamespace())
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ref = Image.new("RGB", (320, 160), "green")
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frame = Image.new("RGB", (64, 64), "black")
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mask = Image.new("L", (64, 64), 255)
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request = SimpleNamespace(
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prompts=[
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{
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"prompt": "p",
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"multi_modal_data": {
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"image": ref,
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"video": [frame],
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"mask": mask,
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},
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}
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],
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sampling_params=SimpleNamespace(height=None, width=None),
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)
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result = preprocess(request)
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additional_info = result.prompts[0]["additional_information"]
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assert result.sampling_params.height == 432
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assert result.sampling_params.width == 880
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assert additional_info["reference_images"] == [ref]
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assert additional_info["source_video"] == [frame]
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assert additional_info["mask"] == [mask]
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def test_create_vace_transformer_from_config_maps_vace_specific_keys(monkeypatch) -> None:
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captured = {}
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class FakeVACETransformer:
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def __init__(self, **kwargs) -> None:
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captured.update(kwargs)
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monkeypatch.setattr(
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"vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2_vace.WanVACETransformer3DModel",
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FakeVACETransformer,
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)
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transformer = create_vace_transformer_from_config(
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{
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"patch_size": [1, 2, 2],
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"in_channels": 96,
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"out_channels": 16,
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"vace_layers": [0, 1, 2],
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"vace_in_channels": 132,
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"unknown": "ignored",
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}
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)
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assert isinstance(transformer, FakeVACETransformer)
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assert captured == {
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"patch_size": (1, 2, 2),
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"in_channels": 96,
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"out_channels": 16,
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"vace_layers": [0, 1, 2],
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"vace_in_channels": 132,
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}
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def test_vace_prepare_masks_encodes_spatial_stride_and_reference_padding() -> None:
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pipeline = _make_vace_pipeline()
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mask = torch.ones(1, 3, 5, 16, 16)
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reference_images = [[torch.zeros(3, 16, 16), torch.zeros(3, 16, 16)]]
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encoded = pipeline.prepare_masks(mask, reference_images)
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assert encoded.shape == (1, 64, 4, 2, 2)
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torch.testing.assert_close(encoded[:, :, :2], torch.zeros(1, 64, 2, 2, 2))
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torch.testing.assert_close(encoded[:, :, 2:], torch.ones(1, 64, 2, 2, 2))
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def test_vace_diffuse_passes_context_and_scale_to_cfg_branches() -> None:
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pipeline = _make_vace_pipeline()
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latents = torch.zeros(1, 4, 1, 2, 2)
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vace_context = torch.ones(1, 12, 1, 2, 2)
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calls = []
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def fake_predict_noise_maybe_with_cfg(**kwargs):
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calls.append(kwargs)
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return torch.ones_like(latents)
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pipeline.predict_noise_maybe_with_cfg = fake_predict_noise_maybe_with_cfg # type: ignore[method-assign]
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pipeline.scheduler_step_maybe_with_cfg = lambda noise, t, current, cfg: current + noise # type: ignore[method-assign]
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result = pipeline.diffuse(
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latents=latents,
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timesteps=torch.tensor([5]),
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prompt_embeds=torch.zeros(1, 2, 3),
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negative_prompt_embeds=torch.zeros(1, 2, 3),
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guidance_scale=4.0,
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dtype=torch.float32,
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attention_kwargs={},
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vace_context=vace_context,
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vace_context_scale=0.75,
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)
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assert calls[0]["do_true_cfg"] is True
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assert calls[0]["true_cfg_scale"] == 4.0
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assert calls[0]["positive_kwargs"]["vace_context"] is vace_context
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assert calls[0]["negative_kwargs"]["vace_context_scale"] == 0.75
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torch.testing.assert_close(result, torch.ones_like(latents))
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