[Model] Add native SANA-Video 2B T2V and I2V support (#5508)

Signed-off-by: leo <92263211+napleon-liu@users.noreply.github.com>
Signed-off-by: leo <92263211+liuyao0322@users.noreply.github.com>
Signed-off-by: liuyao0322 <yaoliu548926@gmail.com>
Co-authored-by: leo <92263211+napleon-liu@users.noreply.github.com>

tests/model_tests/diffusion/diff_model_builders.py

@@ -1,7 +1,16 @@
 from functools import partial
 from pathlib import Path
 
-from tests.helpers.tiny_model import build_tiny_from_configs
+import torch
+from diffusers import (
+    AutoencoderKLWan,
+    DPMSolverMultistepScheduler,
+    SanaVideoPipeline,
+    SanaVideoTransformer3DModel,
+)
+from transformers import Gemma2Config, Gemma2Model, GemmaTokenizerFast
+
+from tests.helpers.tiny_model import _get_tiny_model_path, build_tiny_from_configs
 
 TINY_CONFIGS_DIR = Path(__file__).parent / "tiny_configs"
 
@@ -40,6 +49,72 @@ def tiny_ltx2_builder() -> str:
     return build_tiny_from_configs("LTX2Pipeline", "Lightricks/LTX-2", TINY_CONFIGS_DIR / "LTX2Pipeline")
 
 
+def tiny_sana_video_builder() -> str:
+    """Build a tiny 480p SANA-Video model without downloading model weights.
+
+    The tokenizer is the only component loaded from the upstream repository.
+    All modules with weights are initialized locally from intentionally small
+    configs, and the scheduler is constructed from its weight-free config.
+    """
+    model_id = "Efficient-Large-Model/SANA-Video_2B_480p_diffusers"
+    model_dir = _get_tiny_model_path("SanaVideoPipeline")
+
+    tokenizer = GemmaTokenizerFast.from_pretrained(model_id, subfolder="tokenizer")
+    scheduler = DPMSolverMultistepScheduler(
+        algorithm_type="dpmsolver++",
+        flow_shift=8.0,
+        prediction_type="flow_prediction",
+        use_flow_sigmas=True,
+    )
+    text_encoder = Gemma2Model(
+        Gemma2Config(
+            vocab_size=256000,
+            hidden_size=32,
+            intermediate_size=64,
+            num_hidden_layers=1,
+            num_attention_heads=4,
+            num_key_value_heads=2,
+            head_dim=8,
+            max_position_embeddings=512,
+            sliding_window=256,
+            layer_types=["sliding_attention"],
+        )
+    )
+    transformer = SanaVideoTransformer3DModel(
+        in_channels=16,
+        out_channels=16,
+        num_attention_heads=2,
+        attention_head_dim=16,
+        num_layers=1,
+        num_cross_attention_heads=2,
+        cross_attention_head_dim=16,
+        cross_attention_dim=32,
+        caption_channels=32,
+        mlp_ratio=2.0,
+        sample_size=30,
+        patch_size=(1, 2, 2),
+    )
+    vae = AutoencoderKLWan(
+        base_dim=8,
+        decoder_base_dim=8,
+        z_dim=16,
+        dim_mult=[1, 1, 1, 1],
+        num_res_blocks=1,
+        temperal_downsample=[False, True, True],
+        latents_mean=[0.0] * 16,
+        latents_std=[1.0] * 16,
+    )
+    pipeline = SanaVideoPipeline(
+        tokenizer=tokenizer,
+        text_encoder=text_encoder,
+        vae=vae,
+        transformer=transformer,
+        scheduler=scheduler,
+    )
+    pipeline.to(dtype=torch.bfloat16).save_pretrained(model_dir)
+    return model_dir
+
+
 def _shrink_flux_clip_text_encoder(config: dict) -> dict:
     config["num_hidden_layers"] = 2
     return config