Add VideoInpaintPipeline for temporally-consistent diffusion-based video inpainting

Feature request

Introduce a new pipeline to extend existing image inpainting capabilities (StableDiffusionInpaintPipeline) to videos. The goal is to provide a native, GPU-optimized API within Diffusers that performs temporally coherent video inpainting instead of independent per-frame processing.

Motivation

Current video inpainting approaches in the community simply loop over frames and call the image inpainting pipeline repeatedly.

This leads to:

• Temporal flicker and inconsistent textures between frames.
• Poor GPU utilization and high memory overhead.
• Lack of tools to maintain motion coherence or reuse diffusion latents across time.
• A built-in VideoInpaintPipeline would make it possible to remove objects, restore scenes, or creatively edit videos using diffusion models while keeping motion and lighting consistent across frames.

Your contribution

I plan to:

Implement VideoInpaintPipeline as a subclass of DiffusionPipeline, leveraging StableDiffusionInpaintPipeline under the hood.
Add temporal consistency mechanisms, such as latent reuse between frames and optional optical-flow–guided warping (RAFT / GMFlow).
Optimize performance through batched FP16 inference, scheduler noise reuse, and optional torch.compile acceleration.
Provide a clean user API compatible with existing pipelines:

from diffusers import VideoInpaintPipeline

pipe = VideoInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    use_optical_flow=True,
    compile=True,
)

result = pipe(
    video_path="input.mp4",
    mask_path="mask.mp4",
    prompt="replace background with a snowy mountain",
    num_inference_steps=10,
)
result.video.save("output.mp4")

Contribute documentation and tests demonstrating temporal coherence, performance benchmarks, and example notebooks for real-world use.

Empieza leyendo DiffusionPipeline y StableDiffusionInpaintPipeline, que la solicitud identifica como la clase base propuesta y la pipeline subyacente. Aclara los requisitos de consistencia temporal, flujo óptico, batching y compilación antes de la implementación. Done debe incluir la API solicitada para vídeo y máscaras, pruebas de coherencia temporal, benchmarks de rendimiento, documentación y notebooks de ejemplo.