Merge branch 'master' of https://github.com/BlenderNeko/ComfyUI

comfy/utils.py

 import torch
 import math
+import einops
 
 def load_torch_file(ckpt, safe_load=False):
     if ckpt.lower().endswith(".safetensors"):
@@ -50,71 +51,81 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
                     sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
     return sd
 
-#slow and inefficient, should be optimized
 def bislerp(samples, width, height):
-    shape = list(samples.shape)
-    width_scale = (shape[3]) / (width )
-    height_scale = (shape[2]) / (height )
-
-    shape[3] = width
-    shape[2] = height
-    out1 = torch.empty(shape, dtype=samples.dtype, layout=samples.layout, device=samples.device)
-
-    def algorithm(in1, in2, t):
-        dims = in1.shape
-        val = t
-
-        #flatten to batches
-        low = in1.reshape(dims[0], -1)
-        high = in2.reshape(dims[0], -1)
-
-        low_weight = torch.norm(low, dim=1, keepdim=True)
-        low_weight[low_weight == 0] = 0.0000000001
-        low_norm = low/low_weight
-        high_weight = torch.norm(high, dim=1, keepdim=True)
-        high_weight[high_weight == 0] = 0.0000000001
-        high_norm = high/high_weight
-
-        dot_prod = (low_norm*high_norm).sum(1)
-        dot_prod[dot_prod > 0.9995] = 0.9995
-        dot_prod[dot_prod < -0.9995] = -0.9995
-        omega = torch.acos(dot_prod)
+    def slerp(b1, b2, r):
+        '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
+        
+        c = b1.shape[-1]
+
+        #norms
+        b1_norms = torch.norm(b1, dim=-1, keepdim=True)
+        b2_norms = torch.norm(b2, dim=-1, keepdim=True)
+
+        #normalize
+        b1_normalized = b1 / b1_norms
+        b2_normalized = b2 / b2_norms
+
+        #zero when norms are zero
+        b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
+        b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
+
+        #slerp
+        dot = (b1_normalized*b2_normalized).sum(1)
+        omega = torch.acos(dot)
         so = torch.sin(omega)
-        res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low_norm + (torch.sin(val*omega)/so).unsqueeze(1) * high_norm
-        res *= (low_weight * (1.0-val) + high_weight * val)
-        return res.reshape(dims)
-
-    for x_dest in range(shape[3]):
-        for y_dest in range(shape[2]):
-            y = (y_dest + 0.5) * height_scale - 0.5
-            x = (x_dest + 0.5) * width_scale - 0.5
-
-            x1 = max(math.floor(x), 0)
-            x2 = min(x1 + 1, samples.shape[3] - 1)
-            wx = x - math.floor(x)
-
-            y1 = max(math.floor(y), 0)
-            y2 = min(y1 + 1, samples.shape[2] - 1)
-            wy = y - math.floor(y)
-
-            in1 = samples[:,:,y1,x1]
-            in2 = samples[:,:,y1,x2]
-            in3 = samples[:,:,y2,x1]
-            in4 = samples[:,:,y2,x2]
-
-            if (x1 == x2) and (y1 == y2):
-                out_value = in1
-            elif (x1 == x2):
-                out_value = algorithm(in1, in3, wy)
-            elif (y1 == y2):
-                out_value = algorithm(in1, in2, wx)
-            else:
-                o1 = algorithm(in1, in2, wx)
-                o2 = algorithm(in3, in4, wx)
-                out_value = algorithm(o1, o2, wy)
-
-            out1[:,:,y_dest,x_dest] = out_value
-    return out1
+
+        #technically not mathematically correct, but more pleasing?
+        res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
+        res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
+
+        #edge cases for same or polar opposites
+        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] 
+        res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
+        return res
+    
+    def generate_bilinear_data(length_old, length_new):
+        coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32)
+        coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
+        ratios = coords_1 - coords_1.floor()
+        coords_1 = coords_1.to(torch.int64)
+        
+        coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1
+        coords_2[:,:,:,-1] -= 1
+        coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
+        coords_2 = coords_2.to(torch.int64)
+        return ratios, coords_1, coords_2
+    
+    n,c,h,w = samples.shape
+    h_new, w_new = (height, width)
+    
+    #linear h
+    ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
+
+    coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w))
+    coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w))
+    ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w))
+
+    pass_1 = einops.rearrange(samples.gather(-2,coords_1), 'n c h w -> (n h w) c')
+    pass_2 = einops.rearrange(samples.gather(-2,coords_2), 'n c h w -> (n h w) c')
+    ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
+
+    result = slerp(pass_1, pass_2, ratios)
+    result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h_new, w=w)
+
+    #linear w
+    ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
+
+    coords_1 = coords_1.expand((n, c, h_new, -1))
+    coords_2 = coords_2.expand((n, c, h_new, -1))
+    ratios = ratios.expand((n, 1, h_new, -1))
+
+    pass_1 = einops.rearrange(result.gather(-1,coords_1), 'n c h w -> (n h w) c')
+    pass_2 = einops.rearrange(result.gather(-1,coords_2), 'n c h w -> (n h w) c')
+    ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
+
+    result = slerp(pass_1, pass_2, ratios)
+    result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h_new, w=w_new)
+    return result
 
 def common_upscale(samples, width, height, upscale_method, crop):
         if crop == "center":