mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-12-15 01:07:03 +08:00
943b3b615d
* 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>
364 lines
14 KiB
Python
364 lines
14 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
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__()
|
|
shape = (dim, 1, 1, 1)
|
|
self.scale = dim**0.5
|
|
self.gamma = nn.Parameter(torch.empty(shape))
|
|
|
|
def forward(self, x):
|
|
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):
|
|
super().__init__()
|
|
fct = 2 * 2 * 2 if tds else 1 * 2 * 2
|
|
assert oc % fct == 0
|
|
self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1)
|
|
self.refiner_vae = refiner_vae
|
|
|
|
self.tds = tds
|
|
self.gs = fct * ic // oc
|
|
|
|
def forward(self, x, conv_carry_in=None, conv_carry_out=None):
|
|
r1 = 2 if self.tds else 1
|
|
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:
|
|
|
|
hf = h[:, :, :1, :, :]
|
|
b, c, f, ht, wd = hf.shape
|
|
hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
|
|
hf = hf.permute(0, 4, 6, 1, 2, 3, 5)
|
|
hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
|
|
hf = torch.cat([hf, hf], dim=1)
|
|
|
|
h = h[:, :, 1:, :, :]
|
|
|
|
xf = x[:, :, :1, :, :]
|
|
b, ci, f, ht, wd = xf.shape
|
|
xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2)
|
|
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, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2)
|
|
|
|
x = x[:, :, 1:, :, :]
|
|
|
|
if h.shape[2] == 0:
|
|
return hf + xf
|
|
|
|
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)
|
|
|
|
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):
|
|
def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
|
|
super().__init__()
|
|
fct = 2 * 2 * 2 if tus else 1 * 2 * 2
|
|
self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
|
|
self.refiner_vae = refiner_vae
|
|
|
|
self.tus = tus
|
|
self.rp = fct * oc // ic
|
|
|
|
def forward(self, x, conv_carry_in=None, conv_carry_out=None):
|
|
r1 = 2 if self.tus else 1
|
|
h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
|
|
|
|
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)
|
|
hf = hf.reshape(b, 2, 2, nc, f, ht, wd)
|
|
hf = hf.permute(0, 3, 4, 5, 1, 6, 2)
|
|
hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
|
|
hf = hf[:, : hf.shape[1] // 2]
|
|
|
|
h = h[:, :, 1:, :, :]
|
|
|
|
xf = x[:, :, :1, :, :]
|
|
b, ci, f, ht, wd = xf.shape
|
|
xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1)
|
|
b, c, f, ht, wd = xf.shape
|
|
nc = c // (2 * 2)
|
|
xf = xf.reshape(b, 2, 2, nc, f, ht, wd)
|
|
xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
|
|
xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
|
|
|
|
x = x[:, :, 1:, :, :]
|
|
|
|
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.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,
|
|
ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
|
|
super().__init__()
|
|
self.z_channels = z_channels
|
|
self.block_out_channels = block_out_channels
|
|
self.num_res_blocks = num_res_blocks
|
|
self.ffactor_temporal = ffactor_temporal
|
|
|
|
self.refiner_vae = refiner_vae
|
|
if self.refiner_vae:
|
|
conv_op = NoPadConv3d
|
|
norm_op = RMS_norm
|
|
else:
|
|
conv_op = ops.Conv3d
|
|
norm_op = Normalize
|
|
|
|
self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1)
|
|
|
|
self.down = nn.ModuleList()
|
|
ch = block_out_channels[0]
|
|
depth = (ffactor_spatial >> 1).bit_length()
|
|
depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length()
|
|
|
|
for i, tgt in enumerate(block_out_channels):
|
|
stage = nn.Module()
|
|
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:
|
|
nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
|
|
stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
|
|
ch = nxt
|
|
self.down.append(stage)
|
|
|
|
self.mid = nn.Module()
|
|
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 = 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)
|
|
|
|
self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer()
|
|
|
|
def forward(self, x):
|
|
if not self.refiner_vae and x.shape[2] == 1:
|
|
x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
|
|
|
|
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 = []
|
|
|
|
conv_carry_in = None
|
|
|
|
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 = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip
|
|
|
|
if self.refiner_vae:
|
|
out = self.regul(out)[0]
|
|
|
|
return out
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
|
|
ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_):
|
|
super().__init__()
|
|
block_out_channels = block_out_channels[::-1]
|
|
self.z_channels = z_channels
|
|
self.block_out_channels = block_out_channels
|
|
self.num_res_blocks = num_res_blocks
|
|
|
|
self.refiner_vae = refiner_vae
|
|
if self.refiner_vae:
|
|
conv_op = NoPadConv3d
|
|
norm_op = RMS_norm
|
|
else:
|
|
conv_op = ops.Conv3d
|
|
norm_op = Normalize
|
|
|
|
ch = block_out_channels[0]
|
|
self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
|
|
|
|
self.mid = nn.Module()
|
|
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 = 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()
|
|
depth_temporal = (ffactor_temporal >> 1).bit_length()
|
|
|
|
for i, tgt in enumerate(block_out_channels):
|
|
stage = nn.Module()
|
|
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:
|
|
nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
|
|
stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
|
|
ch = nxt
|
|
self.up.append(stage)
|
|
|
|
self.norm_out = norm_op(ch)
|
|
self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
|
|
|
|
def forward(self, z):
|
|
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)))
|
|
|
|
if self.refiner_vae:
|
|
x = torch.split(x, 2, dim=2)
|
|
else:
|
|
x = [ x ]
|
|
out = []
|
|
|
|
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
|
|
|