diff options
| -rw-r--r-- | modules/sd_samplers_common.py | 9 | ||||
| -rw-r--r-- | modules/sd_vae_taesd.py | 2 |
2 files changed, 6 insertions, 5 deletions
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index ceda6a35..d99c933d 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -35,13 +35,14 @@ def single_sample_to_image(sample, approximation=None): elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
elif approximation == 3:
- x_sample = sd_vae_taesd.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
- x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample) # returns value in [-2, 2]
- x_sample = x_sample * 0.5
+ x_sample = sample * 1.5
+ x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
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 = x_sample.astype(np.uint8)
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py index d23812ef..5e8496e8 100644 --- a/modules/sd_vae_taesd.py +++ b/modules/sd_vae_taesd.py @@ -45,7 +45,7 @@ def decoder(): class TAESD(nn.Module): - latent_magnitude = 2 + latent_magnitude = 3 latent_shift = 0.5 def __init__(self, decoder_path="taesd_decoder.pth"): |