DeepFaceLab - Optimized version

Russian version / Русская версия

---

Installation

Method 1

• Run the module installation python -m pip install numba==0.53.1 via _internal\python_console.bat.
• Copy the files from this repository inside _internal\DeepFaceLab, replacing the existing files.
• You're amazing! 🎉

Method 2

• Download the ready version via torrent (based on DeepFaceLab_NVIDIA_up_to_RTX2080Ti_build_11_20_2021.exe)

---

New features ✨

• Now in step 7 (merge), saving in jpg format with quality setting 100 is available.
• Added video codec selection for merging images into video at step 8

Performance changes 🚀

Extraction (step 4 and 5)

Estimated speedup: 1.52х

Measured on:

• 2000 pictures (HD 1280x720, 1k with faces, 1k without faces)
• detector s3fd
• image-size 320
• jpeg-quality 100
• output-debug

Sorting (step 4.2 and 5.2)

Estimated speedup:

• Blur: 9.72x (less for a small number of images)
• Motion blur: 1.90x
• Face yaw direction: 8.09x
• Face pitch direction: 8.09x
• Face rect size in source image: 9.15x
• Histogram similarity: 1.32x (less for a small number of images)
• Histogram dissimilarity: 3.00x (more for a small number of images)
• Brightness: 2.29x
• Hue: 2.29x
• Amount of black pixels: 2.47x
• Original filename: 9.58x
• One face in image: 1.00x
• Absolute pixel difference: 1.00x
• Best faces: 9.88x
• Best faces faster: 4.01x

Measured on:

• 10000 images 320x320

Training (step 6)

Small decrease in iteration time. I got this: -10ms (~4%) on the DF 160 model.

Merging (step 7)

Estimated speedup:

• Prepare: 8.22x
• Merge: 1.13x

Measured on:

• 2000 pictures (HD 1280x720, 1k with faces, 1k skip without faces)
• Saving results in jpg format (in my version)
• Number of threads = number of virtual threads + 1

Joining (step 8)

Depends on codec: h264, h265 and its versions accelerated with video card