make it so that blank ENSD does not break image generation

1 files changed, 4 insertions, 3 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 4a406084..0a9a8f95 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -338,13 +338,14 @@ def slerp(val, low, high):
 

 

 def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):

+    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0

     xs = []

 

     # if we have multiple seeds, this means we are working with batch size>1; this then

     # enables the generation of additional tensors with noise that the sampler will use during its processing.

     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to

     # produce the same images as with two batches [100], [101].

-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):

+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):

         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]

     else:

         sampler_noises = None

@@ -384,8 +385,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:

             cnt = p.sampler.number_of_needed_noises(p)

 

-            if opts.eta_noise_seed_delta > 0:

-                torch.manual_seed(seed + opts.eta_noise_seed_delta)

+            if eta_noise_seed_delta > 0:

+                torch.manual_seed(seed + eta_noise_seed_delta)

 

             for j in range(cnt):

                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))