Merge branch 'release_candidate'

1 files changed, 29 insertions, 6 deletions

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

+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)

 

 

@@ -58,6 +62,25 @@ def store_latent(decoded):
             shared.state.assign_current_image(sample_to_image(decoded))

 

 

+def is_sampler_using_eta_noise_seed_delta(p):

+    """returns whether sampler from config will use eta noise seed delta for image creation"""

+

+    sampler_config = sd_samplers.find_sampler_config(p.sampler_name)

+

+    eta = p.eta

+

+    if eta is None and p.sampler is not None:

+        eta = p.sampler.eta

+

+    if eta is None and sampler_config is not None:

+        eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0

+

+    if eta == 0:

+        return False

+

+    return sampler_config.options.get("uses_ensd", False)

+

+

 class InterruptedException(BaseException):

     pass