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Supports TAESD models in safetensors format
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@@ -20,7 +20,7 @@ class SD15(LatentFormat):
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[-0.2829, 0.1762, 0.2721],
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[-0.2120, -0.2616, -0.7177]
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]
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self.taesd_decoder_name = "taesd_decoder.pth"
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self.taesd_decoder_name = "taesd_decoder"
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class SDXL(LatentFormat):
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def __init__(self):
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@@ -32,4 +32,4 @@ class SDXL(LatentFormat):
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[ 0.0568, 0.1687, -0.0755],
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[-0.3112, -0.2359, -0.2076]
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]
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self.taesd_decoder_name = "taesdxl_decoder.pth"
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self.taesd_decoder_name = "taesdxl_decoder"
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@@ -50,9 +50,17 @@ class TAESD(nn.Module):
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self.encoder = Encoder()
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self.decoder = Decoder()
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if encoder_path is not None:
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self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True))
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if encoder_path.lower().endswith(".safetensors"):
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import safetensors.torch
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self.encoder.load_state_dict(safetensors.torch.load_file(encoder_path, device="cpu"))
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else:
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self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True))
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if decoder_path is not None:
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self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True))
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if decoder_path.lower().endswith(".safetensors"):
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import safetensors.torch
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self.decoder.load_state_dict(safetensors.torch.load_file(decoder_path, device="cpu"))
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else:
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self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True))
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@staticmethod
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def scale_latents(x):
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