add pad channel value and fix channel detection
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@@ -41,11 +41,13 @@ class CustomVAE(VAE):
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self.latent_dim = 2
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self.input_channels = 3
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self.output_channels = 3
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self.pad_channel_value = None
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self.process_input = lambda image: image * 2.0 - 1.0
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self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
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self.working_dtypes = [torch.bfloat16, torch.float32]
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self.disable_offload = False
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self.not_video = False
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self.size = None
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self.downscale_index_formula = None
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self.upscale_index_formula = None
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@@ -247,13 +249,17 @@ class CustomVAE(VAE):
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self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
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self.downscale_index_formula = (4, 8, 8)
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self.input_channels = sd["encoder.conv1.weight"].shape[1]
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self.output_channels = sd["decoder.head.2.weight"].shape[0]
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# self.output_channels = sd["decoder.head.2.weight"].shape[0]
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self.output_channels = self.input_channels
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raw_output_channels = sd["decoder.head.2.weight"].shape[0]
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self.latent_dim = 3
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self.latent_channels = 16
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self.pad_channel_value = 1.0
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ddconfig = {
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"in_channels": self.input_channels,
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"out_channels": self.output_channels,
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# "out_channels": self.output_channels,
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"out_channels": raw_output_channels,
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"dim": 96,
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"z_dim": self.latent_channels,
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"dim_mult": [1, 2, 4, 4],
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