first attempt to produce crrect seeds in batch

1 files changed, 16 insertions, 2 deletions

diff --git a/modules/processing.py b/modules/processing.py
index f33560ee..aab72903 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -119,8 +119,14 @@ def slerp(val, low, high):
     return res

 

 

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

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

     xs = []

+

+    if p is not None and p.sampler is not None and len(seeds) > 1:

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

+    else:

+        sampler_noises = None

+

     for i, seed in enumerate(seeds):

         noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)

 

@@ -155,9 +161,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]

             noise = x

 

+        if sampler_noises is not None:

+            cnt = p.sampler.number_of_needed_noises(p)

 

+            for j in range(cnt):

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

 

         xs.append(noise)

+

+    if sampler_noises is not None:

+        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]

+

     x = torch.stack(xs).to(shared.device)

     return x

 

@@ -254,7 +268,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 comments += model_hijack.comments

 

             # we manually generate all input noises because each one should have a specific seed

-            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)

+            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)

 

             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"