[Model] Add Index-AniSora I2V support (V1 5B + V2 14B)

docs/models/supported_models.md

@@ -33,6 +33,8 @@ th {
 | `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `AniSoraI2VCogVideoXPipeline` | AniSora-I2V (5B) | `Disty0/Index-anisora-5B-diffusers` | ✅︎ | ✅︎ | | |
+| `AniSoraV2I2VPipeline` | AniSora-I2V (14B) | `aardsoul-music/Wan2.1-Anisora-14B` | ✅︎ | ✅︎ | | |
 | `LTX2Pipeline` | LTX-2-T2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
 | `LTX2ImageToVideoPipeline` | LTX-2-I2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
 | `LTX2TwoStagesPipeline` | LTX-2-T2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |

docs/user_guide/diffusion_features.md

@@ -145,11 +145,13 @@ The following tables show which models support each feature:
 | **Helios** | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 | **HunyuanVideo-1.5 T2V I2V** | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ (decode) | ✅ | ❌ |
 | **DreamID-Omni** | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+| **AniSora V1 (5B)** | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+| **AniSora V2 (14B)** | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 
 **Frame Interpolation Support**
 
 - **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines
-- **Not supported**: Wan2.1-VACE, LTX-2, LTX-2.3, Helios, HunyuanVideo-1.5, DreamID-Omni
+- **Not supported**: Wan2.1-VACE, LTX-2, LTX-2.3, Helios, HunyuanVideo-1.5, DreamID-Omni, AniSora
 
 ### AudioGen
 

tests/e2e/offline_inference/test_anisora_i2v.py

@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+E2E offline inference tests for Index-AniSora I2V models.
+
+Covers:
+- V1 (5B, CogVideoX-based): AniSoraI2VCogVideoXPipeline
+- V2 (14B, Wan2.1-based):   AniSoraV2I2VPipeline
+- TP=2 for both models (requires 2 GPUs)
+"""
+
+import gc
+import os
+import sys
+from pathlib import Path
+
+import PIL.Image
+import pytest
+import torch
+
+# ruff: noqa: E402
+REPO_ROOT = Path(__file__).resolve().parents[3]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from tests.utils import hardware_test
+from vllm_omni import Omni
+from vllm_omni.diffusion.data import DiffusionParallelConfig
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.outputs import OmniRequestOutput
+from vllm_omni.platforms import current_omni_platform
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+MODEL_V1 = "Disty0/Index-anisora-5B-diffusers"
+MODEL_V2 = "aardsoul-music/Wan2.1-Anisora-14B"
+
+# V1: vae_scale_factor_temporal=4 → num_frames % 4 == 1, e.g. 5, 9, 13 ...
+# V2: same constraint (Wan2.1 VAE)
+NUM_FRAMES = 5
+HEIGHT = 480
+WIDTH = 720
+SEED = 42
+
+
+def _dummy_image() -> PIL.Image.Image:
+    """Create a small solid-color image for testing."""
+    return PIL.Image.new("RGB", (WIDTH, HEIGHT), color=(100, 149, 237))
+
+
+def _assert_video_output(output, num_frames: int, height: int, width: int) -> None:
+    assert output is not None
+    if isinstance(output, OmniRequestOutput):
+        assert output.final_output_type == "image"
+        assert output.request_output is not None
+        frames = output.request_output.images[0]
+    else:
+        frames = output
+    assert frames is not None
+    assert hasattr(frames, "shape"), f"Expected tensor, got {type(frames)}"
+    # shape: (batch, num_frames, height, width, channels)
+    # Pipeline may round num_frames up for VAE temporal alignment
+    assert frames.shape[1] >= num_frames, f"Expected >= {num_frames} frames, got {frames.shape[1]}"
+    assert frames.shape[2] == height
+    assert frames.shape[3] == width
+
+
+def _cleanup(model):
+    """Shut down model workers and free GPU memory between tests."""
+    model.shutdown()
+    del model
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+# ---------------------------------------------------------------------------
+# V1 (5B CogVideoX) — single GPU
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.diffusion
+@hardware_test(res={"cuda": "H100", "rocm": "MI325"})
+def test_anisora_v1_offline_single_gpu():
+    """V1 (5B) offline inference on a single GPU."""
+    model = Omni(model=MODEL_V1)
+    image = _dummy_image()
+    outputs = model.generate(
+        {"prompt": "a cat sitting calmly", "multi_modal_data": {"image": image}},
+        OmniDiffusionSamplingParams(
+            height=HEIGHT,
+            width=WIDTH,
+            num_frames=NUM_FRAMES,
+            num_inference_steps=2,
+            guidance_scale=6.0,
+            generator=torch.Generator(current_omni_platform.device_type).manual_seed(SEED),
+        ),
+    )
+    result = outputs[0] if isinstance(outputs, list) else outputs
+    _assert_video_output(result, NUM_FRAMES, HEIGHT, WIDTH)
+    _cleanup(model)
+
+
+# ---------------------------------------------------------------------------
+# V1 (5B CogVideoX) — SP=2 (Ulysses)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.diffusion
+@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
+def test_anisora_v1_offline_sp2():
+    """V1 (5B) offline inference with sequence_parallel_size=2 (Ulysses)."""
+    model = Omni(
+        model=MODEL_V1,
+        parallel_config=DiffusionParallelConfig(sequence_parallel_size=2, ulysses_degree=2),
+    )
+    image = _dummy_image()
+    outputs = model.generate(
+        {"prompt": "a cat sitting calmly", "multi_modal_data": {"image": image}},
+        OmniDiffusionSamplingParams(
+            height=HEIGHT,
+            width=WIDTH,
+            num_frames=NUM_FRAMES,
+            num_inference_steps=2,
+            guidance_scale=6.0,
+            generator=torch.Generator(current_omni_platform.device_type).manual_seed(SEED),
+        ),
+    )
+    result = outputs[0] if isinstance(outputs, list) else outputs
+    _assert_video_output(result, NUM_FRAMES, HEIGHT, WIDTH)
+    _cleanup(model)
+
+
+# ---------------------------------------------------------------------------
+# V1 (5B CogVideoX) — FP8 quantization, single GPU
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.diffusion
+@hardware_test(res={"cuda": "H100", "rocm": "MI325"})
+def test_anisora_v1_offline_fp8():
+    """V1 (5B) offline inference with FP8 quantization (W8A8)."""
+    model = Omni(model=MODEL_V1, quantization="fp8")
+    image = _dummy_image()
+    outputs = model.generate(
+        {"prompt": "a cat sitting calmly", "multi_modal_data": {"image": image}},
+        OmniDiffusionSamplingParams(
+            height=HEIGHT,
+            width=WIDTH,
+            num_frames=NUM_FRAMES,
+            num_inference_steps=2,
+            guidance_scale=6.0,
+            generator=torch.Generator(current_omni_platform.device_type).manual_seed(SEED),
+        ),
+    )
+    result = outputs[0] if isinstance(outputs, list) else outputs
+    _assert_video_output(result, NUM_FRAMES, HEIGHT, WIDTH)
+    _cleanup(model)