Add Tiny AE live preview

1 files changed, 13 insertions, 8 deletions

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index bc074238..d3dc130c 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

+from modules import devices, processing, images, sd_vae_approx, sd_vae_taesd

 

 from modules.shared import opts, state

 import modules.shared as shared

@@ -22,21 +22,26 @@ 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, "Tiny AE": 1, "Approx NN": 2, "Approx cheap": 3}

 

 

 def single_sample_to_image(sample, approximation=None):

     if approximation is None:

         approximation = approximation_indexes.get(opts.show_progress_type, 0)

 

-    if approximation == 2:

-        x_sample = sd_vae_approx.cheap_approximation(sample)

-    elif approximation == 1:

-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()

+    if approximation == 1:

+        x_sample = sd_vae_taesd.decode()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()

+        x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample)

+        x_sample = torch.clamp((x_sample * 0.25) + 0.5, 0, 1)

     else:

-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]

+        if approximation == 3:

+            x_sample = sd_vae_approx.cheap_approximation(sample)

+        elif approximation == 2:

+            x_sample = sd_vae_approx.model()(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)

 

-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, 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)