Encode batches separately
Significantly reduces VRAM. This makes encoding more inline with how decoding currently functions.
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@ -92,6 +92,14 @@ def images_tensor_to_samples(image, approximation=None, model=None):
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model = shared.sd_model
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model = shared.sd_model
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image = image.to(shared.device, dtype=devices.dtype_vae)
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image = image.to(shared.device, dtype=devices.dtype_vae)
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image = image * 2 - 1
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image = image * 2 - 1
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if len(image) > 1:
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x_latent = torch.stack([
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model.get_first_stage_encoding(
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model.encode_first_stage(torch.unsqueeze(img, 0))
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)[0]
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for img in image
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])
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else:
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x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
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x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
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return x_latent
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return x_latent
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