Option to use CPU for random number generation.

Makes a given manual seed generate the same images across different
platforms, independently of the GPU architecture in use.

Fixes #9613.

4 files changed, 17 insertions, 3 deletions

diff --git a/modules/devices.py b/modules/devices.py
index 52c3e7cd..3bc86a6a 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -92,14 +92,18 @@ def cond_cast_float(input):
 
 
 def randn(seed, shape):
+    from modules.shared import opts
+
     torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.use_cpu_randn or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.use_cpu_randn or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index a1aac7cf..e6a372d5 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -60,3 +60,12 @@ def store_latent(decoded):
 

 class InterruptedException(BaseException):

     pass

+

+if opts.use_cpu_randn:

+    import torchsde._brownian.brownian_interval

+

+    def torchsde_randn(size, dtype, device, seed):

+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))

+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)

+

+    torchsde._brownian.brownian_interval._randn = torchsde_randn

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e9f08518..13f4567a 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -190,7 +190,7 @@ class TorchHijack:
             if noise.shape == x.shape:

                 return noise

 

-        if x.device.type == 'mps':

+        if opts.use_cpu_randn or x.device.type == 'mps':

             return torch.randn_like(x, device=devices.cpu).to(x.device)

         else:

             return torch.randn_like(x)

diff --git a/modules/shared.py b/modules/shared.py
index 5fd0eecb..59b037d5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -331,6 +331,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),

     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),

     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),

+    "use_cpu_randn": OptionInfo(False, "Use CPU for random number generation to make manual seeds generate the same image across platforms. This may change existing seeds."),

 }))

 

 options_templates.update(options_section(('compatibility', "Compatibility"), {