[feat] add Turbodiffusion I2V pipeline (#984)

examples/inference/basic/basic_turbodiffusion_i2v.py

@@ -0,0 +1,41 @@
+import os
+
+# Set SLA attention backend BEFORE fastvideo imports
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
+
+from fastvideo import VideoGenerator
+
+# Use local model path
+MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
+OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
+
+
+def main() -> None:
+    # TurboDiffusion I2V: 1-4 step image-to-video generation
+    # Uses RCM scheduler with sigma_max=200 for I2V
+    generator = VideoGenerator.from_pretrained(
+        MODEL_PATH,
+        num_gpus=2,
+        override_pipeline_cls_name="TurboDiffusionI2VPipeline",
+    )
+
+    # Example prompt and image for I2V
+    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.")
+
+    # Use an example image path
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+
+
+    video = generator.generate_video(
+        prompt,
+        image_path=image_path,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        num_inference_steps=4,
+        seed=42,
+        guidance_scale=1.0,
+    )
+
+
+if __name__ == "__main__":
+    main()