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Multi dimension tiled scale function and tiled VAE audio encoding fallback.

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comfyanonymous 2024-06-22 11:45:58 -04:00
parent 887a6341ed
commit 4ef1479dcd
2 changed files with 51 additions and 41 deletions
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31
comfy/sd.py
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@ -298,25 +298,9 @@ class VAE:
/ 3.0) / 3.0)
return output return output
def decode_tiled_1d(self, samples, tile_x=128, overlap=64): def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
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) return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_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
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 / output_mult
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): 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) steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
@ -331,6 +315,10 @@ class VAE:
samples /= 3.0 samples /= 3.0
return samples return samples
def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
def decode(self, samples_in): def decode(self, samples_in):
try: try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
@ -374,7 +362,10 @@ class VAE:
except model_management.OOM_EXCEPTION as e: except model_management.OOM_EXCEPTION as e:
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
samples = self.encode_tiled_(pixel_samples) if len(pixel_samples.shape) == 3:
samples = self.encode_tiled_1d(pixel_samples)
else:
samples = self.encode_tiled_(pixel_samples)
return samples return samples

61
comfy/utils.py
View File

@ -6,6 +6,7 @@ import safetensors.torch
import numpy as np import numpy as np
from PIL import Image from PIL import Image
import logging import logging
import itertools
def load_torch_file(ckpt, safe_load=False, device=None): def load_torch_file(ckpt, safe_load=False, device=None):
if device is None: if device is None:
@ -506,34 +507,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
@torch.inference_mode() @torch.inference_mode()
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) dims = len(tile)
output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
for b in range(samples.shape[0]): for b in range(samples.shape[0]):
s = samples[b:b+1] s = samples[b:b+1]
out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
for y in range(0, s.shape[2], tile_y - overlap):
for x in range(0, s.shape[3], tile_x - overlap):
x = max(0, min(s.shape[-1] - overlap, x))
y = max(0, min(s.shape[-2] - overlap, y))
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
ps = function(s_in).to(output_device) for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
mask = torch.ones_like(ps) s_in = s
feather = round(overlap * upscale_amount) upscaled = []
for t in range(feather):
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) for d in range(dims):
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) l = min(tile[d], s.shape[d + 2] - pos)
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) s_in = s_in.narrow(d + 2, pos, l)
out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask upscaled.append(round(pos * upscale_amount))
out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask ps = function(s_in).to(output_device)
if pbar is not None: mask = torch.ones_like(ps)
pbar.update(1) feather = round(overlap * upscale_amount)
for t in range(feather):
for d in range(2, dims + 2):
m = mask.narrow(d, t, 1)
m *= ((1.0/feather) * (t + 1))
m = mask.narrow(d, mask.shape[d] -1 -t, 1)
m *= ((1.0/feather) * (t + 1))
o = out
o_d = out_div
for d in range(dims):
o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
o += ps * mask
o_d += mask
if pbar is not None:
pbar.update(1)
output[b:b+1] = out/out_div output[b:b+1] = out/out_div
return output return output
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
PROGRESS_BAR_ENABLED = True PROGRESS_BAR_ENABLED = True
def set_progress_bar_enabled(enabled): def set_progress_bar_enabled(enabled):
global PROGRESS_BAR_ENABLED global PROGRESS_BAR_ENABLED