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Remove einops.
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@ -1,6 +1,5 @@
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import torch
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import torch
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import math
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import math
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import einops
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def load_torch_file(ckpt, safe_load=False):
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def load_torch_file(ckpt, safe_load=False):
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if ckpt.lower().endswith(".safetensors"):
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if ckpt.lower().endswith(".safetensors"):
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@ -104,12 +103,12 @@ def bislerp(samples, width, height):
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coords_2 = coords_2.expand((n, c, h, -1))
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coords_2 = coords_2.expand((n, c, h, -1))
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ratios = ratios.expand((n, 1, h, -1))
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ratios = ratios.expand((n, 1, h, -1))
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pass_1 = einops.rearrange(samples.gather(-1,coords_1), 'n c h w -> (n h w) c')
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pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = einops.rearrange(samples.gather(-1,coords_2), 'n c h w -> (n h w) c')
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pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = slerp(pass_1, pass_2, ratios)
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result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h, w=w_new)
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result = result.reshape(n, h, w_new, c).movedim(-1, 1)
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#linear h
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#linear h
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
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@ -117,12 +116,12 @@ def bislerp(samples, width, height):
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coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
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ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
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pass_1 = einops.rearrange(result.gather(-2,coords_1), 'n c h w -> (n h w) c')
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pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = einops.rearrange(result.gather(-2,coords_2), 'n c h w -> (n h w) c')
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pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = slerp(pass_1, pass_2, ratios)
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result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h_new, w=w_new)
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result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
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return result
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return result
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def common_upscale(samples, width, height, upscale_method, crop):
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def common_upscale(samples, width, height, upscale_method, crop):
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