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Merge pull request #10467 from Sakura-Luna/taesd-a

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Tiny AE fix
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AUTOMATIC1111 2023-05-17 14:45:38 +03:00 committed by GitHub
commit 875ccc27f6
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2 changed files with 6 additions and 5 deletions
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9
modules/sd_samplers_common.py
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@ -35,13 +35,14 @@ def single_sample_to_image(sample, approximation=None):
elif approximation == 1: elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
elif approximation == 3: elif approximation == 3:
x_sample = sd_vae_taesd.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() x_sample = sample * 1.5
x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample) # returns value in [-2, 2] x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
x_sample = x_sample * 0.5
else: else:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) if approximation != 3:
x_sample = (x_sample + 1.0) / 2.0
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8) x_sample = x_sample.astype(np.uint8)

2
modules/sd_vae_taesd.py
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@ -45,7 +45,7 @@ def decoder():
class TAESD(nn.Module): class TAESD(nn.Module):
latent_magnitude = 2 latent_magnitude = 3
latent_shift = 0.5 latent_shift = 0.5
def __init__(self, decoder_path="taesd_decoder.pth"): def __init__(self, decoder_path="taesd_decoder.pth"):