Custom VAE

VAE/conf.py

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+"""
+List of all VAE configs, with training parts stripped.
+"""
+vae_conf = {
+	"kl-f4": {
+		"type"             : "AutoencoderKL",
+		"embed_scale"      : 4,
+		"embed_dim"        : 3,
+		"z_channels"       : 3,
+		"double_z"         : True,
+		"resolution"       : 256,
+		"in_channels"      : 3,
+		"out_ch"           : 3,
+		"ch"               : 128, 
+		"ch_mult"          : [1,2,4],
+		"num_res_blocks"   : 2,
+		"attn_resolutions" : [],
+	},
+	"kl-f8": { # Default SD1.5 VAE
+		"type"             : "AutoencoderKL",
+		"embed_scale"      : 8,
+		"embed_dim"        : 4,
+		"z_channels"       : 4,
+		"double_z"         : True,
+		"resolution"       : 256,
+		"in_channels"      : 3,
+		"out_ch"           : 3,
+		"ch"               : 128, 
+		"ch_mult"          : [1,2,4,4],
+		"num_res_blocks"   : 2,
+		"attn_resolutions" : [],
+	},
+	"kl-f16": {
+		"type"             : "AutoencoderKL",
+		"embed_scale"      : 16,
+		"embed_dim"        : 16,
+		"z_channels"       : 16,
+		"double_z"         : True,
+		"resolution"       : 256,
+		"in_channels"      : 3,
+		"out_ch"           : 3,
+		"ch"               : 128, 
+		"ch_mult"          : [1,1,2,2,4],
+		"num_res_blocks"   : 2,
+		"attn_resolutions" : [16],
+	},
+	"kl-f32": {
+		"type"             : "AutoencoderKL",
+		"embed_scale"      : 32,
+		"embed_dim"        : 64,
+		"z_channels"       : 64,
+		"double_z"         : True,
+		"resolution"       : 256,
+		"in_channels"      : 3,
+		"out_ch"           : 3,
+		"ch"               : 128, 
+		"ch_mult"          : [1,1,2,2,4,4],
+		"num_res_blocks"   : 2,
+		"attn_resolutions" : [16,8],
+	},
+}

VAE/loader.py

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+import torch
+import comfy.sd
+import comfy.utils
+from comfy import model_management
+from comfy import diffusers_convert
+from .models.kl import AutoencoderKL
+
+vae_dtype_dict = {
+	"auto" : model_management.vae_device(),
+	"fp32" : torch.float32,
+	"fp16" : torch.float16,
+	"bf16" : torch.bfloat16,
+}
+
+class EXVAE(comfy.sd.VAE):
+	def __init__(self, model_path, model_conf, dtype=None):
+		sd = comfy.utils.load_torch_file(model_path)
+		if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+			sd = diffusers_convert.convert_vae_state_dict(sd)
+
+		self.latent_dim   = model_conf["embed_dim"]
+		self.latent_scale = model_conf["embed_scale"]
+
+		if model_conf["type"] == "AutoencoderKL":
+			model = AutoencoderKL(config=model_conf)
+
+		self.first_stage_model = model.eval()
+		m, u = self.first_stage_model.load_state_dict(sd, strict=False)
+		if len(m) > 0: print("Missing VAE keys", m)
+		if len(u) > 0: print("Leftover VAE keys", u)
+
+		self.device = model_management.vae_device()
+		self.offload_device = model_management.vae_offload_device()
+		self.vae_dtype = vae_dtype_dict.get(dtype, "auto")
+		self.first_stage_model.to(self.vae_dtype)
+
+	### Encode/Decode functions below needed due to source repo having 4 VAE channels and a scale factor of 8 hardcoded
+	def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
+		steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+		steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+		steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+		pbar = comfy.utils.ProgressBar(steps)
+
+		decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
+		output = torch.clamp((
+			(comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
+			comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
+			 comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.latent_scale, pbar = pbar))
+			/ 3.0) / 2.0, min=0.0, max=1.0)
+		return output
+
+	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 // 2, tile_y * 2, overlap)
+		steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+		pbar = comfy.utils.ProgressBar(steps)
+
+		encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
+		samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
+		samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
+		samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
+		samples /= 3.0
+		return samples
+
+	def decode(self, samples_in):
+		self.first_stage_model = self.first_stage_model.to(self.device)
+		try:
+			memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7
+			model_management.free_memory(memory_used, self.device)
+			free_memory = model_management.get_free_memory(self.device)
+			batch_number = int(free_memory / memory_used)
+			batch_number = max(1, batch_number)
+
+			pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.latent_scale), round(samples_in.shape[3] * self.latent_scale)), device="cpu")
+			for x in range(0, samples_in.shape[0], batch_number):
+				samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
+				pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0)
+		except model_management.OOM_EXCEPTION as e:
+			print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+			pixel_samples = self.decode_tiled_(samples_in)
+
+		self.first_stage_model = self.first_stage_model.to(self.offload_device)
+		pixel_samples = pixel_samples.cpu().movedim(1,-1)
+		return pixel_samples
+
+	def encode(self, pixel_samples):
+		self.first_stage_model = self.first_stage_model.to(self.device)
+		pixel_samples = pixel_samples.movedim(-1,1)
+		try:
+			memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.7 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
+			model_management.free_memory(memory_used, self.device)
+			free_memory = model_management.get_free_memory(self.device)
+			batch_number = int(free_memory / memory_used)
+			batch_number = max(1, batch_number)
+			samples = torch.empty((pixel_samples.shape[0], self.first_stage_model.embed_dim, round(pixel_samples.shape[2] // self.latent_scale), round(pixel_samples.shape[3] // self.latent_scale)), device="cpu")
+			for x in range(0, pixel_samples.shape[0], batch_number):
+				pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
+				samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).cpu().float()
+
+		except model_management.OOM_EXCEPTION as e:
+			print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+			samples = self.encode_tiled_(pixel_samples)
+
+		self.first_stage_model = self.first_stage_model.to(self.offload_device)
+		return samples
+

VAE/models/LICENSE-Latent-Diffusion

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+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

VAE/models/kl.py

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+import torch
+import numpy as np
+from torch import nn
+
+
+def Normalize(in_channels, num_groups=32):
+	return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+def nonlinearity(x): # swish
+	return x*torch.sigmoid(x)
+
+
+class AutoencoderKL(nn.Module):
+	def __init__(self, config):
+		super().__init__()
+		self.embed_dim = config["embed_dim"]
+		self.encoder = Encoder(**config)
+		self.decoder = Decoder(**config)
+		assert config["double_z"]
+		self.quant_conv = torch.nn.Conv2d(2*config["z_channels"], 2*self.embed_dim, 1)
+		self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, config["z_channels"], 1)
+
+	def encode(self, x):
+		h = self.encoder(x)
+		moments = self.quant_conv(h)
+		posterior = DiagonalGaussianDistribution(moments)
+		return posterior.sample()
+
+	def decode(self, z):
+		z = self.post_quant_conv(z)
+		dec = self.decoder(z)
+		return dec
+
+	def forward(self, input, sample_posterior=True):
+		posterior = self.encode(input)
+		if sample_posterior:
+			z = posterior.sample()
+		else:
+			z = posterior.mode()
+		dec = self.decode(z)
+		return dec, posterior
+
+
+class Encoder(nn.Module):
+	def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+				 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+				 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+				 **ignore_kwargs):
+		super().__init__()
+		if use_linear_attn: attn_type = "linear"
+		self.ch = ch
+		self.temb_ch = 0
+		self.num_resolutions = len(ch_mult)
+		self.num_res_blocks = num_res_blocks
+		self.resolution = resolution
+		self.in_channels = in_channels
+
+		# downsampling
+		self.conv_in = torch.nn.Conv2d(in_channels,
+									   self.ch,
+									   kernel_size=3,
+									   stride=1,
+									   padding=1)
+
+		curr_res = resolution
+		in_ch_mult = (1,)+tuple(ch_mult)
+		self.in_ch_mult = in_ch_mult
+		self.down = nn.ModuleList()
+		for i_level in range(self.num_resolutions):
+			block = nn.ModuleList()
+			attn = nn.ModuleList()
+			block_in = ch*in_ch_mult[i_level]
+			block_out = ch*ch_mult[i_level]
+			for i_block in range(self.num_res_blocks):
+				block.append(ResnetBlock(in_channels=block_in,
+										 out_channels=block_out,
+										 temb_channels=self.temb_ch,
+										 dropout=dropout))
+				block_in = block_out
+				if curr_res in attn_resolutions:
+					attn.append(make_attn(block_in, attn_type=attn_type))
+			down = nn.Module()
+			down.block = block
+			down.attn = attn
+			if i_level != self.num_resolutions-1:
+				down.downsample = Downsample(block_in, resamp_with_conv)
+				curr_res = curr_res // 2
+			self.down.append(down)
+
+		# middle
+		self.mid = nn.Module()
+		self.mid.block_1 = ResnetBlock(in_channels=block_in,
+									   out_channels=block_in,
+									   temb_channels=self.temb_ch,
+									   dropout=dropout)
+		self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+		self.mid.block_2 = ResnetBlock(in_channels=block_in,
+									   out_channels=block_in,
+									   temb_channels=self.temb_ch,
+									   dropout=dropout)
+
+		# end
+		self.norm_out = Normalize(block_in)
+		self.conv_out = torch.nn.Conv2d(block_in,
+										2*z_channels if double_z else z_channels,
+										kernel_size=3,
+										stride=1,
+										padding=1)
+
+	def forward(self, x):
+		# timestep embedding
+		temb = None
+
+		# downsampling
+		hs = [self.conv_in(x)]
+		for i_level in range(self.num_resolutions):
+			for i_block in range(self.num_res_blocks):
+				h = self.down[i_level].block[i_block](hs[-1], temb)
+				if len(self.down[i_level].attn) > 0:
+					h = self.down[i_level].attn[i_block](h)
+				hs.append(h)
+			if i_level != self.num_resolutions-1:
+				hs.append(self.down[i_level].downsample(hs[-1]))
+
+		# middle
+		h = hs[-1]
+		h = self.mid.block_1(h, temb)
+		h = self.mid.attn_1(h)
+		h = self.mid.block_2(h, temb)
+
+		# end
+		h = self.norm_out(h)
+		h = nonlinearity(h)
+		h = self.conv_out(h)
+		return h
+
+
+class Decoder(nn.Module):
+	def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+				 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+				 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+				 attn_type="vanilla", post_quant_conv=None, **ignorekwargs):
+		super().__init__()
+		if use_linear_attn: attn_type = "linear"
+		self.ch = ch
+		self.temb_ch = 0
+		self.num_resolutions = len(ch_mult)
+		self.num_res_blocks = num_res_blocks
+		self.resolution = resolution
+		self.in_channels = in_channels
+		self.give_pre_end = give_pre_end
+		self.tanh_out = tanh_out
+		self.post_quant_conv = post_quant_conv
+
+		# compute in_ch_mult, block_in and curr_res at lowest res
+		in_ch_mult = (1,)+tuple(ch_mult)
+		block_in = ch*ch_mult[self.num_resolutions-1]
+		curr_res = resolution // 2**(self.num_resolutions-1)
+		self.z_shape = (1,z_channels,curr_res,curr_res)
+		print("Working with z of shape {} = {} dimensions.".format(
+			self.z_shape, np.prod(self.z_shape)))
+
+		# z to block_in
+		self.conv_in = torch.nn.Conv2d(z_channels,
+									   block_in,
+									   kernel_size=3,
+									   stride=1,
+									   padding=1)
+
+		# middle
+		self.mid = nn.Module()
+		self.mid.block_1 = ResnetBlock(in_channels=block_in,
+									   out_channels=block_in,
+									   temb_channels=self.temb_ch,
+									   dropout=dropout)
+		self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+		self.mid.block_2 = ResnetBlock(in_channels=block_in,
+									   out_channels=block_in,
+									   temb_channels=self.temb_ch,
+									   dropout=dropout)
+
+		# upsampling
+		self.up = nn.ModuleList()
+		for i_level in reversed(range(self.num_resolutions)):
+			block = nn.ModuleList()
+			attn = nn.ModuleList()
+			block_out = ch*ch_mult[i_level]
+			for i_block in range(self.num_res_blocks+1):
+				block.append(ResnetBlock(in_channels=block_in,
+										 out_channels=block_out,
+										 temb_channels=self.temb_ch,
+										 dropout=dropout))
+				block_in = block_out
+				if curr_res in attn_resolutions:
+					attn.append(make_attn(block_in, attn_type=attn_type))
+			up = nn.Module()
+			up.block = block
+			up.attn = attn
+			if i_level != 0:
+				up.upsample = Upsample(block_in, resamp_with_conv)
+				curr_res = curr_res * 2
+			self.up.insert(0, up) # prepend to get consistent order
+
+		# end
+		self.norm_out = Normalize(block_in)
+		self.conv_out = torch.nn.Conv2d(block_in,
+										out_ch,
+										kernel_size=3,
+										stride=1,
+										padding=1)
+
+	def forward(self, z):
+		#assert z.shape[1:] == self.z_shape[1:]
+		self.last_z_shape = z.shape
+
+		# timestep embedding
+		temb = None
+
+		# z to block_in
+		h = self.conv_in(z)
+
+		# middle
+		h = self.mid.block_1(h, temb)
+		h = self.mid.attn_1(h)
+		h = self.mid.block_2(h, temb)
+
+		# upsampling
+		for i_level in reversed(range(self.num_resolutions)):
+			for i_block in range(self.num_res_blocks+1):
+				h = self.up[i_level].block[i_block](h, temb)
+				if len(self.up[i_level].attn) > 0:
+					h = self.up[i_level].attn[i_block](h)
+			if i_level != 0:
+				h = self.up[i_level].upsample(h)
+
+		# end
+		if self.give_pre_end:
+			return h
+
+		h = self.norm_out(h)
+		h = nonlinearity(h)
+		h = self.conv_out(h)
+		if self.tanh_out:
+			h = torch.tanh(h)
+		return h
+
+
+class DiagonalGaussianDistribution(object):
+	def __init__(self, parameters, deterministic=False):
+		self.parameters = parameters
+		self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+		self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+		self.deterministic = deterministic
+		self.std = torch.exp(0.5 * self.logvar)
+		self.var = torch.exp(self.logvar)
+		if self.deterministic:
+			self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+	def sample(self):
+		x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+		return x
+
+	def kl(self, other=None):
+		if self.deterministic:
+			return torch.Tensor([0.])
+		else:
+			if other is None:
+				return 0.5 * torch.sum(torch.pow(self.mean, 2)
+									   + self.var - 1.0 - self.logvar,
+									   dim=[1, 2, 3])
+			else:
+				return 0.5 * torch.sum(
+					torch.pow(self.mean - other.mean, 2) / other.var
+					+ self.var / other.var - 1.0 - self.logvar + other.logvar,
+					dim=[1, 2, 3])
+
+	def nll(self, sample, dims=[1,2,3]):
+		if self.deterministic:
+			return torch.Tensor([0.])
+		logtwopi = np.log(2.0 * np.pi)
+		return 0.5 * torch.sum(
+			logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+			dim=dims)
+
+	def mode(self):
+		return self.mean
+
+
+class Upsample(nn.Module):
+	def __init__(self, in_channels, with_conv):
+		super().__init__()
+		self.with_conv = with_conv
+		if self.with_conv:
+			self.conv = torch.nn.Conv2d(in_channels,
+										in_channels,
+										kernel_size=3,
+										stride=1,
+										padding=1)
+
+	def forward(self, x):
+		# BF16 fix
+		xh = x.to(torch.float32)
+		xh = torch.nn.functional.interpolate(xh, scale_factor=2.0, mode="nearest")
+		x = xh.to(x.dtype)
+
+		if self.with_conv:
+			x = self.conv(x)
+		return x
+
+
+class Downsample(nn.Module):
+	def __init__(self, in_channels, with_conv):
+		super().__init__()
+		self.with_conv = with_conv
+		if self.with_conv:
+			# no asymmetric padding in torch conv, must do it ourselves
+			self.conv = torch.nn.Conv2d(in_channels,
+										in_channels,
+										kernel_size=3,
+										stride=2,
+										padding=0)
+
+	def forward(self, x):
+		if self.with_conv:
+			pad = (0,1,0,1)
+			x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+			x = self.conv(x)
+		else:
+			x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+		return x
+		
+
+class ResnetBlock(nn.Module):
+	def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+				 dropout, temb_channels=512):
+		super().__init__()
+		self.in_channels = in_channels
+		out_channels = in_channels if out_channels is None else out_channels
+		self.out_channels = out_channels
+		self.use_conv_shortcut = conv_shortcut
+
+		self.norm1 = Normalize(in_channels)
+		self.conv1 = torch.nn.Conv2d(in_channels,
+									 out_channels,
+									 kernel_size=3,
+									 stride=1,
+									 padding=1)
+		if temb_channels > 0:
+			self.temb_proj = torch.nn.Linear(temb_channels,
+											 out_channels)
+		self.norm2 = Normalize(out_channels)
+		self.dropout = torch.nn.Dropout(dropout)
+		self.conv2 = torch.nn.Conv2d(out_channels,
+									 out_channels,
+									 kernel_size=3,
+									 stride=1,
+									 padding=1)
+		if self.in_channels != self.out_channels:
+			if self.use_conv_shortcut:
+				self.conv_shortcut = torch.nn.Conv2d(in_channels,
+													 out_channels,
+													 kernel_size=3,
+													 stride=1,
+													 padding=1)
+			else:
+				self.nin_shortcut = torch.nn.Conv2d(in_channels,
+													out_channels,
+													kernel_size=1,
+													stride=1,
+													padding=0)
+
+	def forward(self, x, temb):
+		h = x
+		h = self.norm1(h)
+		h = nonlinearity(h)
+		h = self.conv1(h)
+
+		if temb is not None:
+			h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+		h = self.norm2(h)
+		h = nonlinearity(h)
+		h = self.dropout(h)
+		h = self.conv2(h)
+
+		if self.in_channels != self.out_channels:
+			if self.use_conv_shortcut:
+				x = self.conv_shortcut(x)
+			else:
+				x = self.nin_shortcut(x)
+
+		return x+h
+
+
+class AttnBlock(nn.Module):
+	def __init__(self, in_channels):
+		super().__init__()
+		self.in_channels = in_channels
+
+		self.norm = Normalize(in_channels)
+		self.q = torch.nn.Conv2d(in_channels,
+								 in_channels,
+								 kernel_size=1,
+								 stride=1,
+								 padding=0)
+		self.k = torch.nn.Conv2d(in_channels,
+								 in_channels,
+								 kernel_size=1,
+								 stride=1,
+								 padding=0)
+		self.v = torch.nn.Conv2d(in_channels,
+								 in_channels,
+								 kernel_size=1,
+								 stride=1,
+								 padding=0)
+		self.proj_out = torch.nn.Conv2d(in_channels,
+										in_channels,
+										kernel_size=1,
+										stride=1,
+										padding=0)
+
+
+	def forward(self, x):
+		h_ = x
+		h_ = self.norm(h_)
+		q = self.q(h_)
+		k = self.k(h_)
+		v = self.v(h_)
+
+		# compute attention
+		b,c,h,w = q.shape
+		q = q.reshape(b,c,h*w)
+		q = q.permute(0,2,1)   # b,hw,c
+		k = k.reshape(b,c,h*w) # b,c,hw
+		w_ = torch.bmm(q,k)	 # b,hw,hw	w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+		# w_ = w_ * (int(c)**(-0.5))
+		w_ = w_ * (c**(-0.5))
+		w_ = torch.nn.functional.softmax(w_, dim=2)
+
+		# attend to values
+		v = v.reshape(b,c,h*w)
+		w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
+		h_ = torch.bmm(v,w_)	 # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+		h_ = h_.reshape(b,c,h,w)
+
+		h_ = self.proj_out(h_)
+
+		return x+h_
+
+def make_attn(in_channels, attn_type="vanilla"):
+	assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
+	print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+	if attn_type == "vanilla":
+		return AttnBlock(in_channels)
+	elif attn_type == "none":
+		return nn.Identity(in_channels)
+	else:
+		return LinAttnBlock(in_channels)

VAE/nodes.py

@@ -0,0 +1,29 @@
+import folder_paths
+
+from .conf import vae_conf
+from .loader import EXVAE
+
+class ExtraVAELoader:
+	@classmethod
+	def INPUT_TYPES(s):
+		return {
+			"required": {
+				"vae_name": (folder_paths.get_filename_list("vae"),),
+				"vae_type": (list(vae_conf.keys()), {"default":"kl-f8"}),
+				"dtype"   : (["auto","fp32","fp16","bf16"],),
+			}
+		}
+	RETURN_TYPES = ("VAE",)
+	FUNCTION = "load_vae"
+	CATEGORY = "ExtraModels"
+	TITLE = "ExtraVAELoader"
+
+	def load_vae(self, vae_name, vae_type, dtype):
+		model_path = folder_paths.get_full_path("vae", vae_name)
+		model_conf = vae_conf[vae_type]
+		vae = EXVAE(model_path, model_conf, dtype)
+		return (vae,)
+
+NODE_CLASS_MAPPINGS = {
+	"ExtraVAELoader" : ExtraVAELoader,
+}

__init__.py

@@ -13,6 +13,10 @@ else:
 	# DiT
 	from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
 	NODE_CLASS_MAPPINGS.update(DiT_Nodes)
-	
+
+	# VAE
+	from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
+	NODE_CLASS_MAPPINGS.update(VAE_Nodes)
+
 	NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
 	__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']