Rework MPS randn fix, add randn_like fix

torch.manual_seed() already sets a CPU generator, so there is no reason to create a CPU generator manually. torch.randn_like also needs a MPS fix for k-diffusion, but a torch hijack with randn_like already exists so it can also be used for that.

modules/devices.py

@@ -66,24 +66,15 @@ dtype_vae = torch.float16
 
 
 def randn(seed, shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
-    if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        generator.manual_seed(seed)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
     torch.manual_seed(seed)
+    if device.type == 'mps':
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
     if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 

modules/sd_samplers.py

@@ -365,7 +365,10 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        return torch.randn_like(x)
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
 
 
 # MPS fix for randn in torchsde
@@ -429,8 +432,7 @@ class KDiffusionSampler:
         self.model_wrap.step = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
         extra_params_kwargs = {}
         for param_name in self.extra_params: