[refactor]: migrate examples/scripts to typed VideoGenerator API; rename api/compat.py to api/translation.py

Move all examples/inference and scripts off the legacy
VideoGenerator.from_pretrained(model, **kwargs) +
generate_video(prompt, **kwargs) surface onto the typed
from_config(GeneratorConfig(...)) + generate(GenerationRequest(...))
convention, including the Kandinsky-5 and DreamX-World examples added
on main after the original migration. git-mv api/compat.py to
api/translation.py ('compat' implied a temporary shim; the forward
translation is its honest permanent role) and repoint all importers,
tests, and doc links.

examples/inference/basic/basic_sd35_t2i.py

@@ -85,24 +85,35 @@ def main() -> None:
         os.environ["FASTVIDEO_ATTENTION_BACKEND"] = args.backend
 
     from fastvideo import VideoGenerator
+    from fastvideo.api import (
+        EngineConfig, GenerationRequest, GeneratorConfig, OffloadConfig, OutputConfig,
+        ParallelismConfig, PipelineSelection, SamplingConfig,
+    )
 
     os.makedirs(args.out_dir, exist_ok=True)
 
-    init_kwargs = {
-        "num_gpus": args.num_gpus,
-        "workload_type": "t2i",
-        "sp_size": 1,
-        "tp_size": 1,
-        "dit_cpu_offload": False,
-        "dit_layerwise_offload": False,
-        "text_encoder_cpu_offload": False,
-        "vae_cpu_offload": False,
-        "image_encoder_cpu_offload": False,
-        "pin_cpu_memory": False,
-        "use_fsdp_inference": False,
-    }
-
-    generator = VideoGenerator.from_pretrained(model_path=args.model_path, **init_kwargs)
+    generator = VideoGenerator.from_config(
+        GeneratorConfig(
+            model_path=args.model_path,
+            engine=EngineConfig(
+                num_gpus=args.num_gpus,
+                use_fsdp_inference=False,
+                parallelism=ParallelismConfig(
+                    sp_size=1,
+                    tp_size=1,
+                ),
+                offload=OffloadConfig(
+                    dit=False,
+                    dit_layerwise=False,
+                    text_encoder=False,
+                    vae=False,
+                    image_encoder=False,
+                    pin_cpu_memory=False,
+                ),
+            ),
+            pipeline=PipelineSelection(workload_type="t2i"),
+        )
+    )
     try:
         for i, prompt in enumerate(prompts):
             seed = args.seed + i
@@ -113,20 +124,25 @@ def main() -> None:
             output_path = os.path.join(args.out_dir, f"{filename_base}.png")
             print(f"[sd35] prompt_idx={i} seed={seed} output_path={output_path}")
 
-            generation_kwargs = {
-                "output_path": output_path,
-                "height": args.height,
-                "width": args.width,
-                "num_frames": 1,
-                "fps": 1,
-                "num_inference_steps": args.steps,
-                "guidance_scale": args.guidance,
-                "seed": seed,
-                "negative_prompt": args.negative,
-                "save_video": True,
-            }
-
-            generator.generate_video(prompt, **generation_kwargs)
+            generator.generate(
+                GenerationRequest(
+                    prompt=prompt,
+                    negative_prompt=args.negative,
+                    sampling=SamplingConfig(
+                        height=args.height,
+                        width=args.width,
+                        num_frames=1,
+                        fps=1,
+                        num_inference_steps=args.steps,
+                        guidance_scale=args.guidance,
+                        seed=seed,
+                    ),
+                    output=OutputConfig(
+                        output_path=output_path,
+                        save_video=True,
+                    ),
+                )
+            )
 
         print(f"[sd35] done. outputs written to: {args.out_dir}")
     finally: