[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_mps.py

@@ -1,40 +1,56 @@
 from fastvideo import VideoGenerator, PipelineConfig
-from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.api import (
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OffloadConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
 
 def main():
     config = PipelineConfig.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
     config.text_encoder_precisions = ["fp16"]
-    
-    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        pipeline_config=config,
-        use_fsdp_inference=False,      # Disable FSDP for MPS
-        dit_cpu_offload=True,          
-        text_encoder_cpu_offload=True,    
-        pin_cpu_memory=True,           
-        disable_autocast=False,        
-        num_gpus=1,      
+
+    generator = VideoGenerator.from_config(
+        GeneratorConfig(
+            model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+            engine=EngineConfig(
+                num_gpus=1,
+                use_fsdp_inference=False,      # Disable FSDP for MPS
+                disable_autocast=False,
+                offload=OffloadConfig(
+                    dit=True,
+                    text_encoder=True,
+                    pin_cpu_memory=True,
+                ),
+            ),
+            pipeline=PipelineSelection(
+                experimental={"pipeline_config": config},
+            ),
+        )
     )
 
-    # Create sampling parameters with reduced number of frames
-    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-    sampling_param.num_frames = 25  # Reduce from default 81 to 25 frames bc we have to use the SDPA attn backend for mps
-    sampling_param.height = 256
-    sampling_param.width = 256
+    # Reduce from default 81 to 25 frames bc we have to use the SDPA attn backend for mps
+    sampling = SamplingConfig(
+        num_frames=25,
+        height=256,
+        width=256,
+    )
 
     prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
              "wide with interest. The playful yet serene atmosphere is complemented by soft "
              "natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
-    
-    video = generator.generate_video(prompt, sampling_param=sampling_param)
+
+    video = generator.generate(GenerationRequest(prompt=prompt, sampling=sampling))
 
     prompt2 = ("A majestic lion strides across the golden savanna, its powerful frame "
         "glistening under the warm afternoon sun. The tall grass ripples gently in "
         "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
         "embodying the raw energy of the wild. Low angle, steady tracking shot, "
         "cinematic.")
-    
-    video2 = generator.generate_video(prompt2, sampling_param=sampling_param)
+
+    video2 = generator.generate(GenerationRequest(prompt=prompt2, sampling=sampling))
 
 if __name__ == "__main__":
     main()