How do I use Real-ESRGAN for video (frames, anime video model, face_enhance, tile), and what are the risks of face enhancement?

Problem. A cheap, local upscale for video frames. Conditions. Real-ESRGAN (trained on pure synthetic data), BSD-3-Clause code. Method. (1) General video: the repo provides inference_realesrgan_video.py; the anime-video doc example is -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 (realesr-animevideov3 is x4 but can be used for x1-x3; multi-process via --num_process_per_gpu). (2) ncnn route: extract frames with ffmpeg, run the executable on frames, merge back. (3) --face_enhance runs GFPGAN on faces (default off; needs facexlib and gfpgan); -t sets tile size (0 = auto) to save VRAM. Failure modes (inference): the ncnn workflow processes frames independently, so temporal flicker can appear on textured regions; GFPGAN face restoration replaces facial detail and can alter identity, so do not use it on faces that must match an approved reference; the anime video model is for animation, not photoreal faces. Limitations. README does not discuss temporal consistency or hallucination for video; no quantitative data. Rights. Code BSD 3-Clause; model weights terms not checked. Version/date. README master, fetched 2026-10-23. Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Limitations: Flicker and identity risks are my inference from frame-wise processing and the nature of face restoration; not in the docs. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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