[feat] v2 VideoGenerator: typed fastvideo.api entrypoint over the v2 engine

Mirrors fastvideo.entrypoints.VideoGenerator (from_config(GeneratorConfig) -> generate( GenerationRequest) -> GenerationResult.video_path), reusing the OFFICIAL fastvideo.api config classes so a basic_dmd_new_api.py-style script differs only by importing VideoGenerator from v2. - model_path -> v2 card registry (Wan2.1 / FastWan -> wan21; SFWan -> wan_causal; LTX2 -> ltx2); snapshot_download + stamp_wan21_checkpoints + Engine(cuda) + program; generate maps SamplingConfig -> DiffusionParams -> make_request -> eng.run, saves the [C,T,H,W] decode as an mp4. - Lazy v2.__getattr__ keeps 'import v2' torch-free (verified) so the CPU mini stays green (202+2). - examples/inference/basic/v2_basic_new_api.py runs all three GPU models through this API. - Single-GPU, resident, TORCH_SDPA (EngineConfig offload/num_gpus>1/VSA accepted for parity, not applied). verified: wan21 from_config->generate->mp4 (frames (5,256,256,3) uint8, video_path written).
2026-07-04 17:15:34 +00:00
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+"""v2 typed-API inference example — mirrors ``basic_dmd_new_api.py`` but drives the **v2
+(recipe, runtime) substrate + real torch backend** for the three models brought up on GPU
+(Wan2.1, SF-causal Wan, LTX-2).
+
+The ONLY delta from the upstream example is importing ``VideoGenerator`` from ``v2`` instead of
+``fastvideo`` — the typed config classes are the SAME ``fastvideo.api`` dataclasses.
+
+Run (on a GPU box, with the v2 venv active):
+    python examples/inference/basic/v2_basic_new_api.py
+
+Notes vs upstream: the v2 bring-up runs single-GPU, resident, on the TORCH_SDPA backend (no
+fastvideo-kernel / VSA), so resolutions/steps are modest here for a quick runnable demo. LTX-2 loads
+an 18.88B DiT (slow first load).
+"""
+import os
+import time
+
+from v2 import VideoGenerator
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OffloadConfig,
+    OutputConfig,
+    SamplingConfig,
+)
+
+OUTPUT_PATH = "v2_video_samples"
+
+MODELS = [
+    {
+        "family": "wan21",
+        "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        "prompt": "a red panda surfing on ocean waves at sunset, cinematic, highly detailed",
+        "sampling": SamplingConfig(num_frames=25, height=480, width=832,
+                                   num_inference_steps=30, guidance_scale=5.0, seed=1, fps=16),
+    },
+    {
+        "family": "wan_causal",
+        "model_path": "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers",
+        "prompt": "a cat walking through a sunlit garden, cinematic",
+        "sampling": SamplingConfig(num_frames=25, height=480, width=832,
+                                   num_inference_steps=4, guidance_scale=5.0, seed=1, fps=16),
+    },
+    {
+        "family": "ltx2",
+        "model_path": "FastVideo/LTX2-Distilled-Diffusers",
+        "prompt": "surfers riding ocean waves at sunset, cinematic, highly detailed",
+        "sampling": SamplingConfig(num_frames=9, height=512, width=768,
+                                   num_inference_steps=8, guidance_scale=1.0, seed=1, fps=16),
+    },
+]
+
+
+def run_one(m: dict) -> None:
+    generator_config = GeneratorConfig(
+        model_path=m["model_path"],
+        engine=EngineConfig(
+            num_gpus=1,
+            use_fsdp_inference=False,
+            offload=OffloadConfig(text_encoder=False, dit=False, vae=False, pin_cpu_memory=False),
+        ),
+    )
+    load_start = time.perf_counter()
+    generator = VideoGenerator.from_config(generator_config)
+    load_time = time.perf_counter() - load_start
+
+    request = GenerationRequest(
+        prompt=m["prompt"],
+        sampling=m["sampling"],
+        output=OutputConfig(output_path=OUTPUT_PATH, output_video_name=f"v2_{m['family']}",
+                            save_video=True, return_frames=False),
+    )
+    gen_start = time.perf_counter()
+    result = generator.generate(request)
+    gen_time = time.perf_counter() - gen_start
+
+    print(f"[{m['family']:10s}] load={load_time:6.1f}s  gen={gen_time:6.1f}s  -> {result.video_path}")
+
+
+def main() -> None:
+    for m in MODELS:
+        run_one(m)
+
+
+if __name__ == "__main__":
+    main()