From 0d5dc9a6e7f6362e423a06bf0e75dd5854025394 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 9 Aug 2023 08:43:31 +0300
Subject: rework RNG to use generators instead of generating noises beforehand

---
 modules/devices.py | 81 ++----------------------------------------------------
 1 file changed, 2 insertions(+), 79 deletions(-)

(limited to 'modules/devices.py')

diff --git a/modules/devices.py b/modules/devices.py
index 00a00b18..ce59dc53 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors, rng_philox
+from modules import errors
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -96,84 +96,6 @@ def cond_cast_float(input):
 nv_rng = None
 
 
-def randn(seed, shape):
-    """Generate a tensor with random numbers from a normal distribution using seed.
-
-    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
-
-    from modules.shared import opts
-
-    manual_seed(seed)
-
-    if opts.randn_source == "NV":
-        return torch.asarray(nv_rng.randn(shape), device=device)
-
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-
-    return torch.randn(shape, device=device)
-
-
-def randn_local(seed, shape):
-    """Generate a tensor with random numbers from a normal distribution using seed.
-
-    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
-
-    from modules.shared import opts
-
-    if opts.randn_source == "NV":
-        rng = rng_philox.Generator(seed)
-        return torch.asarray(rng.randn(shape), device=device)
-
-    local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device
-    local_generator = torch.Generator(local_device).manual_seed(int(seed))
-    return torch.randn(shape, device=local_device, generator=local_generator).to(device)
-
-
-def randn_like(x):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
-
-    Use either randn() or manual_seed() to initialize the generator."""
-
-    from modules.shared import opts
-
-    if opts.randn_source == "NV":
-        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
-
-    if opts.randn_source == "CPU" or x.device.type == 'mps':
-        return torch.randn_like(x, device=cpu).to(x.device)
-
-    return torch.randn_like(x)
-
-
-def randn_without_seed(shape):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
-
-    Use either randn() or manual_seed() to initialize the generator."""
-
-    from modules.shared import opts
-
-    if opts.randn_source == "NV":
-        return torch.asarray(nv_rng.randn(shape), device=device)
-
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-
-    return torch.randn(shape, device=device)
-
-
-def manual_seed(seed):
-    """Set up a global random number generator using the specified seed."""
-    from modules.shared import opts
-
-    if opts.randn_source == "NV":
-        global nv_rng
-        nv_rng = rng_philox.Generator(seed)
-        return
-
-    torch.manual_seed(seed)
-
-
 def autocast(disable=False):
     from modules import shared
 
@@ -236,3 +158,4 @@ def first_time_calculation():
     x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
     conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
     conv2d(x)
+
-- 
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