mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-03-12 20:42:45 -07:00
d781af3d1f
removed TrueFace model. added SAEv2 model. Differences from SAE: + default e_ch_dims is now 21 + new encoder produces more stable face and less scale jitter before: https://i.imgur.com/4jUcol8.gifv after: https://i.imgur.com/lyiax49.gifv - scale of the face is less changed within frame size + decoder now has only 1 residual block instead of 2, result is same quality with less decoder size + added mid-full face, which covers 30% more area than half face. + added option " Enable 'true face' training " Enable it only after 50k iters, when the face is sharp enough. the result face will be more like src. The most src-like face with 'true-face-training' you can achieve with DF architecture.
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
from enum import IntEnum
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class FaceType(IntEnum):
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#enumerating in order "next contains prev"
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HALF = 0
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MID_FULL = 1
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FULL = 2
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FULL_NO_ALIGN = 3
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HEAD = 4
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HEAD_NO_ALIGN = 5
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MARK_ONLY = 10, #no align at all, just embedded faceinfo
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@staticmethod
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def fromString (s):
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r = from_string_dict.get (s.lower())
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if r is None:
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raise Exception ('FaceType.fromString value error')
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return r
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@staticmethod
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def toString (face_type):
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return to_string_dict[face_type]
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from_string_dict = {'half_face': FaceType.HALF,
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'midfull_face': FaceType.MID_FULL,
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'full_face': FaceType.FULL,
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'head' : FaceType.HEAD,
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'mark_only' : FaceType.MARK_ONLY,
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'full_face_no_align' : FaceType.FULL_NO_ALIGN,
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'head_no_align' : FaceType.HEAD_NO_ALIGN,
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}
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to_string_dict = { FaceType.HALF : 'half_face',
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FaceType.MID_FULL : 'midfull_face',
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FaceType.FULL : 'full_face',
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FaceType.HEAD : 'head',
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FaceType.MARK_ONLY :'mark_only',
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FaceType.FULL_NO_ALIGN : 'full_face_no_align',
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FaceType.HEAD_NO_ALIGN : 'head_no_align'
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}
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