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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
commit
875ccc27f6
@ -35,13 +35,14 @@ def single_sample_to_image(sample, approximation=None):
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elif approximation == 1:
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x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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elif approximation == 3:
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x_sample = sd_vae_taesd.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample) # returns value in [-2, 2]
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x_sample = x_sample * 0.5
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x_sample = sample * 1.5
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x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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else:
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x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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if approximation != 3:
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x_sample = (x_sample + 1.0) / 2.0
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x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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@ -45,7 +45,7 @@ def decoder():
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class TAESD(nn.Module):
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latent_magnitude = 2
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latent_magnitude = 3
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latent_shift = 0.5
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def __init__(self, decoder_path="taesd_decoder.pth"):
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