[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/optimizations/text_encoder_quant_example.py

@@ -1,4 +1,7 @@
 from fastvideo import VideoGenerator
+from fastvideo.api import (ComponentConfig, EngineConfig, GenerationRequest,
+                           GeneratorConfig, InputConfig, OffloadConfig,
+                           OutputConfig, PipelineSelection, QuantizationConfig)
 import argparse
 
 OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
@@ -10,28 +13,39 @@ def main(text_encoder_path: str):
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-    generator = VideoGenerator.from_pretrained(
-        model_name,
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=False,
-        # AbsMaxFP8 is the quantization method used by ComfyUI; 
-        # check fastvideo/layers/quantization/* for more quantization methods
-        override_text_encoder_quant="AbsMaxFP8",
-        # for Wan 2.2, this is the path to "umt5_xxl_fp8_e4m3fn_scaled.safetensors"
-        override_text_encoder_safetensors=text_encoder_path,
-        pin_cpu_memory=True,  # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-    )
+    generator = VideoGenerator.from_config(
+        GeneratorConfig(
+            model_path=model_name,
+            # FastVideo will automatically handle distributed setup
+            engine=EngineConfig(
+                num_gpus=1,
+                use_fsdp_inference=True,
+                offload=OffloadConfig(
+                    dit=True,
+                    vae=False,
+                    text_encoder=False,
+                    pin_cpu_memory=
+                    True,  # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
+                ),
+                # AbsMaxFP8 is the quantization method used by ComfyUI;
+                # check fastvideo/layers/quantization/* for more quantization methods
+                quantization=QuantizationConfig(text_encoder_quant="AbsMaxFP8"),
+            ),
+            pipeline=PipelineSelection(
+                components=ComponentConfig(
+                    # for Wan 2.2, this is the path to "umt5_xxl_fp8_e4m3fn_scaled.safetensors"
+                    text_encoder_weights=text_encoder_path, ), ),
+        ))
 
     # I2V is triggered just by passing in an image_path argument
     prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
     image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
-    video = generator.generate_video(
-        prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path
-    )
+    video = generator.generate(
+        GenerationRequest(
+            prompt=prompt,
+            inputs=InputConfig(image_path=image_path),
+            output=OutputConfig(output_path=OUTPUT_PATH, save_video=True),
+        ))
 
 
 if __name__ == "__main__":