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

comfy/sd.py

@@ -298,25 +298,9 @@ class VAE:
             / 3.0)
         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):
         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
         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):
         try:
             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:
             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
 

comfy/utils.py

@@ -6,6 +6,7 @@ import safetensors.torch
 import numpy as np
 from PIL import Image
 import logging
+import itertools
 
 def load_torch_file(ckpt, safe_load=False, device=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)))
 
 @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]):
         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
     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
 def set_progress_bar_enabled(enabled):
     global PROGRESS_BAR_ENABLED