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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-10 15:23:32 -07:00
37505d88e3
Fixed bug when SAE can be collapsed during a time. SAE: removed CA weights and encoder/decoder dims. added new options: Encoder dims per channel (21-85 ?:help skip:%d) More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU. Decoder dims per channel (11-85 ?:help skip:%d) More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU. Add residual blocks to decoder? (y/n, ?:help skip:n) : These blocks help to get better details, but require more computing time. Remove gray border? (y/n, ?:help skip:n) : Removes gray border of predicted face, but requires more computing resources.
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import copy
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'''
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You can implement your own Converter, check example ConverterMasked.py
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'''
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class Converter(object):
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TYPE_FACE = 0 #calls convert_face
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TYPE_IMAGE = 1 #calls convert_image without landmarks
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TYPE_IMAGE_WITH_LANDMARKS = 2 #calls convert_image with landmarks
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#overridable
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def __init__(self, predictor_func, type):
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self.predictor_func = predictor_func
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self.type = type
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#overridable
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def on_cli_initialize(self):
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#cli initialization
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pass
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#overridable
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def convert_face (self, img_bgr, img_face_landmarks, debug):
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#return float32 image
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#if debug , return tuple ( images of any size and channels, ...)
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return image
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#overridable
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def convert_image (self, img_bgr, img_landmarks, debug):
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#img_landmarks not None, if input image is png with embedded data
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#return float32 image
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#if debug , return tuple ( images of any size and channels, ...)
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return image
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#overridable
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def dummy_predict(self):
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#do dummy predict here
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pass
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def copy(self):
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return copy.copy(self)
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def copy_and_set_predictor(self, predictor_func):
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result = self.copy()
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result.predictor_func = predictor_func
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return result
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