1 files changed, 10 insertions, 6 deletions
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 92880caf..763829f1 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -2,7 +2,7 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers
+from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
 
 from modules.shared import opts, state
 import modules.shared as shared
@@ -22,7 +22,7 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
 
 
 def single_sample_to_image(sample, approximation=None):
@@ -30,15 +30,19 @@ def single_sample_to_image(sample, approximation=None):
         approximation = approximation_indexes.get(opts.show_progress_type, 0)
 
     if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample)
+        x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
     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() * 0.5 + 0.5
+    elif approximation == 3:
+        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 = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
 
-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+    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)
+
     return Image.fromarray(x_sample)