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Remove einops.

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comfyanonymous 2023-05-25 18:42:56 -04:00
parent 87ab25fac7
commit eb4bd7711a
1 changed files with 8 additions and 9 deletions
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17
comfy/utils.py
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@ -1,6 +1,5 @@
import torch import torch
import math import math
import einops
def load_torch_file(ckpt, safe_load=False): def load_torch_file(ckpt, safe_load=False):
if ckpt.lower().endswith(".safetensors"): if ckpt.lower().endswith(".safetensors"):
@ -104,12 +103,12 @@ def bislerp(samples, width, height):
coords_2 = coords_2.expand((n, c, h, -1)) coords_2 = coords_2.expand((n, c, h, -1))
ratios = ratios.expand((n, 1, h, -1)) ratios = ratios.expand((n, 1, h, -1))
pass_1 = einops.rearrange(samples.gather(-1,coords_1), 'n c h w -> (n h w) c') pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
pass_2 = einops.rearrange(samples.gather(-1,coords_2), 'n c h w -> (n h w) c') pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c') ratios = ratios.movedim(1, -1).reshape((-1,1))
result = slerp(pass_1, pass_2, ratios) result = slerp(pass_1, pass_2, ratios)
result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h, w=w_new) result = result.reshape(n, h, w_new, c).movedim(-1, 1)
#linear h #linear h
ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new) ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
@ -117,12 +116,12 @@ def bislerp(samples, width, height):
coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
pass_1 = einops.rearrange(result.gather(-2,coords_1), 'n c h w -> (n h w) c') pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
pass_2 = einops.rearrange(result.gather(-2,coords_2), 'n c h w -> (n h w) c') pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c') ratios = ratios.movedim(1, -1).reshape((-1,1))
result = slerp(pass_1, pass_2, ratios) 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) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
return result return result
def common_upscale(samples, width, height, upscale_method, crop): def common_upscale(samples, width, height, upscale_method, crop):