disable neonpixel optimizations on M1 hardware (#414)

* disable neonpixel optimizations on M1 hardware

* fix typo that was causing random noise images on m1

ldm/modules/attention.py

@@ -1,9 +1,10 @@
-from inspect import isfunction
 import math
+from inspect import isfunction
+
 import torch
 import torch.nn.functional as F
-from torch import nn, einsum
 from einops import rearrange, repeat
+from torch import nn, einsum
 
 from ldm.modules.diffusionmodules.util import checkpoint
 
@@ -13,7 +14,7 @@ def exists(val):
 
 
 def uniq(arr):
-    return{el: True for el in arr}.keys()
+    return {el: True for el in arr}.keys()
 
 
 def default(val, d):
@@ -82,14 +83,14 @@ class LinearAttention(nn.Module):
         super().__init__()
         self.heads = heads
         hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
         self.to_out = nn.Conv2d(hidden_dim, dim, 1)
 
     def forward(self, x):
         b, c, h, w = x.shape
         qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3)
+        k = k.softmax(dim=-1)
         context = torch.einsum('bhdn,bhen->bhde', k, v)
         out = torch.einsum('bhde,bhdn->bhen', context, q)
         out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
@@ -131,12 +132,12 @@ class SpatialSelfAttention(nn.Module):
         v = self.v(h_)
 
         # compute attention
-        b,c,h,w = q.shape
+        b, c, h, w = q.shape
         q = rearrange(q, 'b c h w -> b (h w) c')
         k = rearrange(k, 'b c h w -> b c (h w)')
         w_ = torch.einsum('bij,bjk->bik', q, k)
 
-        w_ = w_ * (int(c)**(-0.5))
+        w_ = w_ * (int(c) ** (-0.5))
         w_ = torch.nn.functional.softmax(w_, dim=2)
 
         # attend to values
@@ -146,7 +147,7 @@ class SpatialSelfAttention(nn.Module):
         h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
         h_ = self.proj_out(h_)
 
-        return x+h_
+        return x + h_
 
 
 class CrossAttention(nn.Module):
@@ -174,6 +175,7 @@ class CrossAttention(nn.Module):
         context = default(context, x)
         k = self.to_k(context)
         v = self.to_v(context)
+        device_type = x.device.type
         del context, x
 
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
@@ -188,9 +190,11 @@ class CrossAttention(nn.Module):
             sim.masked_fill_(~mask, max_neg_value)
             del mask
 
-        # attention, what we cannot get enough of, by halves
-        sim[4:] = sim[4:].softmax(dim=-1)
-        sim[:4] = sim[:4].softmax(dim=-1)
+        if device_type == 'mps':      #special case for M1 - disable neonsecret optimization
+            sim = sim.softmax(dim=-1)
+        else:
+            sim[4:] = sim[4:].softmax(dim=-1)
+            sim[:4] = sim[:4].softmax(dim=-1)
 
         sim = einsum('b i j, b j d -> b i d', sim, v)
         sim = rearrange(sim, '(b h) n d -> b n (h d)', h=h)
@@ -200,7 +204,8 @@ class CrossAttention(nn.Module):
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
         super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head,
+                                    dropout=dropout)  # is a self-attention
         self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
         self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
                                     heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
@@ -228,6 +233,7 @@ class SpatialTransformer(nn.Module):
     Then apply standard transformer action.
     Finally, reshape to image
     """
+
     def __init__(self, in_channels, n_heads, d_head,
                  depth=1, dropout=0., context_dim=None):
         super().__init__()
@@ -243,7 +249,7 @@ class SpatialTransformer(nn.Module):
 
         self.transformer_blocks = nn.ModuleList(
             [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-                for d in range(depth)]
+             for d in range(depth)]
         )
 
         self.proj_out = zero_module(nn.Conv2d(inner_dim,