Merge branch 'dev' into refiner

1 files changed, 12 insertions, 12 deletions

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index b6ad6830..35c4d657 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -1,5 +1,5 @@
 import inspect

-from collections import namedtuple, deque

+from collections import namedtuple

 import numpy as np

 import torch

 from PIL import Image

@@ -161,10 +161,15 @@ def apply_refiner(sampler):
 

 

 class TorchHijack:

-    def __init__(self, sampler_noises):

-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based

-        # implementation.

-        self.sampler_noises = deque(sampler_noises)

+    """This is here to replace torch.randn_like of k-diffusion.

+

+    k-diffusion has random_sampler argument for most samplers, but not for all, so

+    this is needed to properly replace every use of torch.randn_like.

+

+    We need to replace to make images generated in batches to be same as images generated individually."""

+

+    def __init__(self, p):

+        self.rng = p.rng

 

     def __getattr__(self, item):

         if item == 'randn_like':

@@ -176,12 +181,7 @@ class TorchHijack:
         raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")

 

     def randn_like(self, x):

-        if self.sampler_noises:

-            noise = self.sampler_noises.popleft()

-            if noise.shape == x.shape:

-                return noise

-

-        return devices.randn_like(x)

+        return self.rng.next()

 

 

 class Sampler:

@@ -248,7 +248,7 @@ class Sampler:
         self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)

         self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)

 

-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

+        k_diffusion.sampling.torch = TorchHijack(p)

 

         extra_params_kwargs = {}

         for param_name in self.extra_params: