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HunyuanVideo 1.5 (#10819)

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* init

* update

* Update model.py

* Update model.py

* remove print

* Fix text encoding

* Prevent empty negative prompt

Really doesn't work otherwise

* fp16 works

* I2V

* Update model_base.py

* Update nodes_hunyuan.py

* Better latent rgb factors

* Use the correct sigclip output...

* Support HunyuanVideo1.5 SR model

* whitespaces...

* Proper latent channel count

* SR model fixes

This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already

* vae_refiner: roll the convolution through temporal

Work in progress.

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

* Support HunyuanVideo15 latent resampler

* fix

* Some cleanup

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Proper hyvid15 I2V channels

Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>

* Fix TokenRefiner for fp16

Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.

* Bugfix for the HunyuanVideo15 SR model

* vae_refiner: roll the convolution through temporal II

Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.

Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).

Fixed names, cleaned up code.

* Allow any number of input frames in VAE.

* Better VAE encode mem estimation.

* Lowvram fix.

* Fix hunyuan image 2.1 refiner.

* Fix mistake.

* Name changes.

* Rename.

* Whitespace.

* Fix.

* Fix.

---------

Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>
This commit is contained in:
comfyanonymous
2025-11-20 19:44:43 -08:00
committed by GitHub
parent 10e90a5757
commit 943b3b615d
15 changed files with 777 additions and 126 deletions
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284
comfy/ldm/hunyuan_video/vae_refiner.py
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@@ -4,8 +4,40 @@ import torch.nn.functional as F
from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
import comfy.ops
import comfy.ldm.models.autoencoder
import comfy.model_management
ops = comfy.ops.disable_weight_init
class NoPadConv3d(nn.Module):
def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
super().__init__()
self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
return self.conv(x)
def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
x = xl[0]
xl.clear()
if conv_carry_out is not None:
to_push = x[:, :, -2:, :, :].clone()
conv_carry_out.append(to_push)
if isinstance(op, NoPadConv3d):
if conv_carry_in is None:
x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
else:
carry_len = conv_carry_in[0].shape[2]
x = torch.cat([conv_carry_in.pop(0), x], dim=2)
x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
out = op(x)
return out
class RMS_norm(nn.Module):
def __init__(self, dim):
super().__init__()
@@ -14,7 +46,7 @@ class RMS_norm(nn.Module):
self.gamma = nn.Parameter(torch.empty(shape))
def forward(self, x):
return F.normalize(x, dim=1) * self.scale * self.gamma
return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
class DnSmpl(nn.Module):
def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
@@ -27,11 +59,12 @@ class DnSmpl(nn.Module):
self.tds = tds
self.gs = fct * ic // oc
def forward(self, x):
def forward(self, x, conv_carry_in=None, conv_carry_out=None):
r1 = 2 if self.tds else 1
h = self.conv(x)
h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
if self.tds and self.refiner_vae and conv_carry_in is None:
if self.tds and self.refiner_vae:
hf = h[:, :, :1, :, :]
b, c, f, ht, wd = hf.shape
hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
@@ -39,14 +72,7 @@ class DnSmpl(nn.Module):
hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
hf = torch.cat([hf, hf], dim=1)
hn = h[:, :, 1:, :, :]
b, c, frms, ht, wd = hn.shape
nf = frms // r1
hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6)
hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
h = torch.cat([hf, hn], dim=2)
h = h[:, :, 1:, :, :]
xf = x[:, :, :1, :, :]
b, ci, f, ht, wd = xf.shape
@@ -54,34 +80,32 @@ class DnSmpl(nn.Module):
xf = xf.permute(0, 4, 6, 1, 2, 3, 5)
xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)
B, C, T, H, W = xf.shape
xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2)
xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2)
xn = x[:, :, 1:, :, :]
b, ci, frms, ht, wd = xn.shape
nf = frms // r1
xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6)
xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
B, C, T, H, W = xn.shape
xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
sc = torch.cat([xf, xn], dim=2)
else:
b, c, frms, ht, wd = h.shape
x = x[:, :, 1:, :, :]
nf = frms // r1
h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
if h.shape[2] == 0:
return hf + xf
b, ci, frms, ht, wd = x.shape
nf = frms // r1
sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6)
sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
B, C, T, H, W = sc.shape
sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
b, c, frms, ht, wd = h.shape
nf = frms // r1
h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
return h + sc
b, ci, frms, ht, wd = x.shape
nf = frms // r1
x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
x = x.permute(0, 3, 5, 7, 1, 2, 4, 6)
x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
B, C, T, H, W = x.shape
x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
if self.tds and self.refiner_vae and conv_carry_in is None:
h = torch.cat([hf, h], dim=2)
x = torch.cat([xf, x], dim=2)
return h + x
class UpSmpl(nn.Module):
@@ -94,11 +118,11 @@ class UpSmpl(nn.Module):
self.tus = tus
self.rp = fct * oc // ic
def forward(self, x):
def forward(self, x, conv_carry_in=None, conv_carry_out=None):
r1 = 2 if self.tus else 1
h = self.conv(x)
h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
if self.tus and self.refiner_vae:
if self.tus and self.refiner_vae and conv_carry_in is None:
hf = h[:, :, :1, :, :]
b, c, f, ht, wd = hf.shape
nc = c // (2 * 2)
@@ -107,14 +131,7 @@ class UpSmpl(nn.Module):
hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
hf = hf[:, : hf.shape[1] // 2]
hn = h[:, :, 1:, :, :]
b, c, frms, ht, wd = hn.shape
nc = c // (r1 * 2 * 2)
hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3)
hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
h = torch.cat([hf, hn], dim=2)
h = h[:, :, 1:, :, :]
xf = x[:, :, :1, :, :]
b, ci, f, ht, wd = xf.shape
@@ -125,29 +142,43 @@ class UpSmpl(nn.Module):
xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
xn = x[:, :, 1:, :, :]
xn = xn.repeat_interleave(repeats=self.rp, dim=1)
b, c, frms, ht, wd = xn.shape
nc = c // (r1 * 2 * 2)
xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3)
xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
sc = torch.cat([xf, xn], dim=2)
else:
b, c, frms, ht, wd = h.shape
nc = c // (r1 * 2 * 2)
h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
x = x[:, :, 1:, :, :]
sc = x.repeat_interleave(repeats=self.rp, dim=1)
b, c, frms, ht, wd = sc.shape
nc = c // (r1 * 2 * 2)
sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd)
sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3)
sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2)
b, c, frms, ht, wd = h.shape
nc = c // (r1 * 2 * 2)
h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
return h + sc
x = x.repeat_interleave(repeats=self.rp, dim=1)
b, c, frms, ht, wd = x.shape
nc = c // (r1 * 2 * 2)
x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd)
x = x.permute(0, 4, 5, 1, 6, 2, 7, 3)
x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2)
if self.tus and self.refiner_vae and conv_carry_in is None:
h = torch.cat([hf, h], dim=2)
x = torch.cat([xf, x], dim=2)
return h + x
class HunyuanRefinerResnetBlock(ResnetBlock):
def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm):
super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
def forward(self, x, conv_carry_in=None, conv_carry_out=None):
h = x
h = [ self.swish(self.norm1(x)) ]
h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
h = [ self.dropout(self.swish(self.norm2(h))) ]
h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
if self.in_channels != self.out_channels:
x = self.nin_shortcut(x)
return x+h
class Encoder(nn.Module):
def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
@@ -160,7 +191,7 @@ class Encoder(nn.Module):
self.refiner_vae = refiner_vae
if self.refiner_vae:
conv_op = VideoConv3d
conv_op = NoPadConv3d
norm_op = RMS_norm
else:
conv_op = ops.Conv3d
@@ -175,10 +206,9 @@ class Encoder(nn.Module):
for i, tgt in enumerate(block_out_channels):
stage = nn.Module()
stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
out_channels=tgt,
temb_channels=0,
conv_op=conv_op, norm_op=norm_op)
stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
out_channels=tgt,
conv_op=conv_op, norm_op=norm_op)
for j in range(num_res_blocks)])
ch = tgt
if i < depth:
@@ -188,9 +218,9 @@ class Encoder(nn.Module):
self.down.append(stage)
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.norm_out = norm_op(ch)
self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
@@ -201,31 +231,50 @@ class Encoder(nn.Module):
if not self.refiner_vae and x.shape[2] == 1:
x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
x = self.conv_in(x)
if self.refiner_vae:
xl = [x[:, :, :1, :, :]]
if x.shape[2] > self.ffactor_temporal:
xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2)
x = xl
else:
x = [x]
out = []
for stage in self.down:
for blk in stage.block:
x = blk(x)
if hasattr(stage, 'downsample'):
x = stage.downsample(x)
conv_carry_in = None
x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
for i, x1 in enumerate(x):
conv_carry_out = []
if i == len(x) - 1:
conv_carry_out = None
x1 = [ x1 ]
x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
for stage in self.down:
for blk in stage.block:
x1 = blk(x1, conv_carry_in, conv_carry_out)
if hasattr(stage, 'downsample'):
x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
out.append(x1)
conv_carry_in = conv_carry_out
if len(out) > 1:
out = torch.cat(out, dim=2)
else:
out = out[0]
x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
del out
b, c, t, h, w = x.shape
grp = c // (self.z_channels << 1)
skip = x.view(b, c // grp, grp, t, h, w).mean(2)
out = self.conv_out(F.silu(self.norm_out(x))) + skip
out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip
if self.refiner_vae:
out = self.regul(out)[0]
out = torch.cat((out[:, :, :1], out), dim=2)
out = out.permute(0, 2, 1, 3, 4)
b, f_times_2, c, h, w = out.shape
out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
out = out.permute(0, 2, 1, 3, 4).contiguous()
return out
class Decoder(nn.Module):
@@ -239,7 +288,7 @@ class Decoder(nn.Module):
self.refiner_vae = refiner_vae
if self.refiner_vae:
conv_op = VideoConv3d
conv_op = NoPadConv3d
norm_op = RMS_norm
else:
conv_op = ops.Conv3d
@@ -249,9 +298,9 @@ class Decoder(nn.Module):
self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.up = nn.ModuleList()
depth = (ffactor_spatial >> 1).bit_length()
@@ -259,10 +308,9 @@ class Decoder(nn.Module):
for i, tgt in enumerate(block_out_channels):
stage = nn.Module()
stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
out_channels=tgt,
temb_channels=0,
conv_op=conv_op, norm_op=norm_op)
stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
out_channels=tgt,
conv_op=conv_op, norm_op=norm_op)
for j in range(num_res_blocks + 1)])
ch = tgt
if i < depth:
@@ -275,27 +323,41 @@ class Decoder(nn.Module):
self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
def forward(self, z):
if self.refiner_vae:
z = z.permute(0, 2, 1, 3, 4)
b, f, c, h, w = z.shape
z = z.reshape(b, f, 2, c // 2, h, w)
z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
z = z.permute(0, 2, 1, 3, 4)
z = z[:, :, 1:]
x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
for stage in self.up:
for blk in stage.block:
x = blk(x)
if hasattr(stage, 'upsample'):
x = stage.upsample(x)
if self.refiner_vae:
x = torch.split(x, 2, dim=2)
else:
x = [ x ]
out = []
out = self.conv_out(F.silu(self.norm_out(x)))
conv_carry_in = None
for i, x1 in enumerate(x):
conv_carry_out = []
if i == len(x) - 1:
conv_carry_out = None
for stage in self.up:
for blk in stage.block:
x1 = blk(x1, conv_carry_in, conv_carry_out)
if hasattr(stage, 'upsample'):
x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
x1 = [ F.silu(self.norm_out(x1)) ]
x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out)
out.append(x1)
conv_carry_in = conv_carry_out
del x
if len(out) > 1:
out = torch.cat(out, dim=2)
else:
out = out[0]
if not self.refiner_vae:
if z.shape[-3] == 1:
out = out[:, :, -1:]
return out