[daVinci-MagiHuman][Doc][BugFix] Update model support for daVici-MagiHuman and fix media utils bug (#2542)

Signed-off-by: princepride <wangzhipeng628@gmail.com>

docs/models/supported_models.md

@@ -37,7 +37,7 @@ th {
 | `LTX2TwoStagesPipeline` | LTX-2-T2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
 | `LTX2ImageToVideoTwoStagesPipeline` | LTX-2-I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | |
-| `MagiHumanPipeline` | MagiHuman | `princepride/daVinci-MagiHuman` | ✅︎ | ✅︎ | | |
+| `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | |
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ |
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |

docs/user_guide/diffusion_features.md

@@ -124,7 +124,7 @@ The following tables show which models support each feature:
 
 > Notes:
 > 1. Nextstep_1(T2I) does not support cache acceleration methods such as TeaCache or Cache-DiT.
-> 2. `Tongyi-MAI/Z-Image-Turbo` and `princepride/daVinci-MagiHuman` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
+> 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
 
 ### VideoGen
 

tests/e2e/offline_inference/test_magi_human.py

@@ -47,7 +47,7 @@ def test_magi_human_e2e(run_level):
     if run_level != "advanced_model":
         pytest.skip("MagiHuman e2e test requires advanced_model run level with real weights.")
 
-    model_path = "princepride/daVinci-MagiHuman"
+    model_path = "SII-GAIR/daVinci-MagiHuman-Base-1080p"
 
     omni = Omni(
         model=model_path,

vllm_omni/diffusion/utils/media_utils.py

@@ -50,7 +50,7 @@ def mux_video_audio_bytes(
         if samples.ndim == 1:
             samples = samples.reshape(1, -1)
         elif samples.ndim == 2 and samples.shape[0] > samples.shape[1]:
-            samples = samples.T
+            samples = np.ascontiguousarray(samples.T)
         num_channels = samples.shape[0]
         layout = "stereo" if num_channels >= 2 else "mono"
         a_stream = container.add_stream(audio_codec, rate=audio_sample_rate)