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More proper tiled audio decoding.

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comfyanonymous 2024-06-20 16:50:31 -04:00
parent d5efde89b7
commit 1e2839f4d9
1 changed files with 12 additions and 3 deletions
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15
comfy/sd.py
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@ -299,15 +299,24 @@ class VAE:
return output
def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
output_mult = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
for j in range(samples.shape[0]):
for i in range(0, samples.shape[-1], tile_x - overlap):
f = i
t = i + tile_x
output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
o = output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio]
m = torch.ones_like(o)
l = m.shape[-1]
for x in range(overlap):
c = ((x + 1) / overlap)
m[:,:,x:x+1] *= c
m[:,:,l-x-1:l-x] *= c
o += self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float().to(self.output_device) * m
output_mult[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] += m
return output
return output / output_mult
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)