[Test] Add Stable Audio offline e2e TeaCache Test (#2377)

Signed-off-by: Zhang <jianmusings@gmail.com>
Signed-off-by: Zhang Jian <jianmusings@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

examples/offline_inference/text_to_audio/README.md

@@ -23,6 +23,7 @@ python text_to_audio.py \
   --guidance-scale 7.0 \
   --audio-length 10.0 \
   --num-inference-steps 100 \
+  --cache-backend tea_cache \
   --output stable_audio_output.wav
 ```
 
@@ -34,4 +35,5 @@ Key arguments:
 - `--guidance-scale`: classifier-free guidance scale.
 - `--audio-length`: audio duration in seconds.
 - `--num-inference-steps`: diffusion sampling steps.(more steps = higher quality, slower).
+- `--cache-backend`: cache acceleration backend. Stable Audio currently supports `tea_cache`.
 - `--output`: path to save the generated WAV file.

examples/offline_inference/text_to_audio/text_to_audio.py

@@ -11,6 +11,7 @@ Usage:
     python text_to_audio.py --prompt "The sound of a dog barking"
     python text_to_audio.py --prompt "A piano playing a gentle melody" --audio-length 10.0
     python text_to_audio.py --prompt "Thunder and rain sounds" --negative-prompt "Low quality"
+    python text_to_audio.py --prompt "A soft synth pad" --cache-backend tea_cache
 """
 
 import argparse
@@ -90,6 +91,23 @@ def parse_args() -> argparse.Namespace:
         default=44100,
         help="Sample rate for output audio (Stable Audio uses 44100 Hz).",
     )
+    parser.add_argument(
+        "--cache-backend",
+        type=str,
+        default=None,
+        choices=["tea_cache"],
+        help=(
+            "Cache backend to use for acceleration. "
+            "Stable Audio currently supports 'tea_cache'. "
+            "Default: None (no cache acceleration)."
+        ),
+    )
+    parser.add_argument(
+        "--tea-cache-rel-l1-thresh",
+        type=float,
+        default=0.2,
+        help="[tea_cache] Threshold for accumulated relative L1 distance.",
+    )
     parser.add_argument(
         "--enable-diffusion-pipeline-profiler",
         action="store_true",
@@ -124,6 +142,11 @@ def save_audio(audio_data: np.ndarray, output_path: str, sample_rate: int = 4410
 def main():
     args = parse_args()
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
+    cache_config = None
+    if args.cache_backend == "tea_cache":
+        cache_config = {
+            "rel_l1_thresh": args.tea_cache_rel_l1_thresh,
+        }
 
     print(f"\n{'=' * 60}")
     print("Stable Audio Open - Text-to-Audio Generation")
@@ -134,12 +157,15 @@ def main():
     print(f"  Audio length: {args.audio_length}s")
     print(f"  Inference steps: {args.num_inference_steps}")
     print(f"  Guidance scale: {args.guidance_scale}")
+    print(f"  Cache backend: {args.cache_backend if args.cache_backend else 'None (no acceleration)'}")
     print(f"  Seed: {args.seed}")
     print(f"{'=' * 60}\n")
 
     # Initialize Omni with Stable Audio model
     omni = Omni(
         model=args.model,
+        cache_backend=args.cache_backend,
+        cache_config=cache_config,
         enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
     )