jinx/ComfyUI
1
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-08-03 07:26:31 +08:00
Code Issues Activity

Tiled upscaling with the upscale models.

Browse Source
This commit is contained in:
comfyanonymous
2023-03-11 14:04:13 -05:00
parent 905857edd8
commit 9db2e97b47
3 changed files with 28 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
comfy/utils.py
View File

@@ -16,3 +16,28 @@ def common_upscale(samples, width, height, upscale_method, crop):
else:
s = samples
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
@torch.inference_mode()
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4):
output = torch.empty((samples.shape[0], 3, samples.shape[2] * upscale_amount, samples.shape[3] * upscale_amount), device="cpu")
for b in range(samples.shape[0]):
s = samples[b:b+1]
out = torch.zeros((s.shape[0], 3, s.shape[2] * upscale_amount, s.shape[3] * upscale_amount), device="cpu")
out_div = torch.zeros((s.shape[0], 3, s.shape[2] * upscale_amount, s.shape[3] * upscale_amount), device="cpu")
for y in range(0, s.shape[2], tile_y - overlap):
for x in range(0, s.shape[3], tile_x - overlap):
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
ps = function(s_in).cpu()
mask = torch.ones_like(ps)
feather = overlap * upscale_amount
for t in range(feather):
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
out[:,:,y*upscale_amount:(y+tile_y)*upscale_amount,x*upscale_amount:(x+tile_x)*upscale_amount] += ps * mask
out_div[:,:,y*upscale_amount:(y+tile_y)*upscale_amount,x*upscale_amount:(x+tile_x)*upscale_amount] += mask
output[b:b+1] = out/out_div
return output