What are Wav2Lip's practical flags (pads, nosmooth, resize_factor), its training sanity thresholds, and why can't it be used commercially?

Problem. Older GAN lip-sync still appears in many pipelines; its failure modes and licence are often missed. Conditions. Wav2Lip open-source release trained on LRS2. Method. inference.py takes --checkpoint_path, --face (video or image) and --audio (any FFmpeg-readable file or a video whose audio is extracted). Tips from the README: adjust --pads (e.g. --pads 0 20 0 0 adds bottom padding to include the chin); if the mouth is dislocated or two mouths appear, over-smoothing of face detections is the cause - use --nosmooth; use --resize_factor to lower resolution because models were trained on lower-resolution faces, and 720p often looks better than 1080p; the non-GAN model needs more tuning but can be better; Wav2Lip+GAN trades slightly inferior lip-sync for better visual quality. Training sanity: expert-discriminator eval loss about 0.25 and Wav2Lip eval sync loss about 0.2 are said to be needed for good results; fine-tuning on a few minutes of one speaker is stated not to work. Rights. The repo says use is personal/research/non-commercial only and commercial use is strictly prohibited because of LRS2; no LICENSE file was found at the raw path (404). A separate commercial HD model with a 192x288 face is mentioned but belongs to the vendor. Limitations. Face crops are low-resolution by design (inference from resize_factor advice). 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: Thresholds are the authors' claims; no independent check. Commercial status of any other Wav2Lip derivative is outside this source. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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