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another way to use backend (#638)
* another way to use backend * another way to use backend
This commit is contained in:
lllyasviel
@@ -0,0 +1,223 @@
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import torch
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# import pytorch_lightning as pl
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import torch.nn.functional as F
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from contextlib import contextmanager
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from comfy.ldm.modules.diffusionmodules.model import Encoder, Decoder
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from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from comfy.ldm.util import instantiate_from_config
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from comfy.ldm.modules.ema import LitEma
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# class AutoencoderKL(pl.LightningModule):
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class AutoencoderKL(torch.nn.Module):
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def __init__(self,
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ddconfig,
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lossconfig,
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embed_dim,
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ckpt_path=None,
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ignore_keys=[],
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image_key="image",
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colorize_nlabels=None,
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monitor=None,
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ema_decay=None,
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learn_logvar=False
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):
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super().__init__()
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self.learn_logvar = learn_logvar
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self.image_key = image_key
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self.encoder = Encoder(**ddconfig)
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self.decoder = Decoder(**ddconfig)
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self.loss = instantiate_from_config(lossconfig)
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assert ddconfig["double_z"]
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self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
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self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
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self.embed_dim = embed_dim
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if colorize_nlabels is not None:
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assert type(colorize_nlabels)==int
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self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
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if monitor is not None:
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self.monitor = monitor
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self.use_ema = ema_decay is not None
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if self.use_ema:
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self.ema_decay = ema_decay
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assert 0. < ema_decay < 1.
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self.model_ema = LitEma(self, decay=ema_decay)
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print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
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def init_from_ckpt(self, path, ignore_keys=list()):
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if path.lower().endswith(".safetensors"):
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import safetensors.torch
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sd = safetensors.torch.load_file(path, device="cpu")
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else:
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sd = torch.load(path, map_location="cpu")["state_dict"]
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keys = list(sd.keys())
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for k in keys:
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for ik in ignore_keys:
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if k.startswith(ik):
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print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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self.load_state_dict(sd, strict=False)
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print(f"Restored from {path}")
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@contextmanager
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def ema_scope(self, context=None):
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if self.use_ema:
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self.model_ema.store(self.parameters())
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self.model_ema.copy_to(self)
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if context is not None:
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print(f"{context}: Switched to EMA weights")
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try:
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yield None
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finally:
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if self.use_ema:
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self.model_ema.restore(self.parameters())
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if context is not None:
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print(f"{context}: Restored training weights")
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def on_train_batch_end(self, *args, **kwargs):
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if self.use_ema:
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self.model_ema(self)
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def encode(self, x):
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h = self.encoder(x)
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moments = self.quant_conv(h)
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posterior = DiagonalGaussianDistribution(moments)
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return posterior
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def decode(self, z):
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z = self.post_quant_conv(z)
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dec = self.decoder(z)
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return dec
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def forward(self, input, sample_posterior=True):
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posterior = self.encode(input)
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if sample_posterior:
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z = posterior.sample()
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else:
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z = posterior.mode()
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dec = self.decode(z)
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return dec, posterior
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def get_input(self, batch, k):
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x = batch[k]
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if len(x.shape) == 3:
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x = x[..., None]
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x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
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return x
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def training_step(self, batch, batch_idx, optimizer_idx):
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inputs = self.get_input(batch, self.image_key)
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reconstructions, posterior = self(inputs)
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if optimizer_idx == 0:
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# train encoder+decoder+logvar
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aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train")
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self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
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return aeloss
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if optimizer_idx == 1:
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# train the discriminator
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discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
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last_layer=self.get_last_layer(), split="train")
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self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
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return discloss
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def validation_step(self, batch, batch_idx):
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log_dict = self._validation_step(batch, batch_idx)
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with self.ema_scope():
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log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
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return log_dict
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def _validation_step(self, batch, batch_idx, postfix=""):
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inputs = self.get_input(batch, self.image_key)
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reconstructions, posterior = self(inputs)
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aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
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last_layer=self.get_last_layer(), split="val"+postfix)
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discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
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last_layer=self.get_last_layer(), split="val"+postfix)
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self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
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self.log_dict(log_dict_ae)
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self.log_dict(log_dict_disc)
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return self.log_dict
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def configure_optimizers(self):
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lr = self.learning_rate
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ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
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self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
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if self.learn_logvar:
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print(f"{self.__class__.__name__}: Learning logvar")
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ae_params_list.append(self.loss.logvar)
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opt_ae = torch.optim.Adam(ae_params_list,
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lr=lr, betas=(0.5, 0.9))
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opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
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lr=lr, betas=(0.5, 0.9))
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return [opt_ae, opt_disc], []
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def get_last_layer(self):
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return self.decoder.conv_out.weight
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@torch.no_grad()
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def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
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log = dict()
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x = self.get_input(batch, self.image_key)
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x = x.to(self.device)
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if not only_inputs:
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xrec, posterior = self(x)
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if x.shape[1] > 3:
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# colorize with random projection
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assert xrec.shape[1] > 3
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x = self.to_rgb(x)
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xrec = self.to_rgb(xrec)
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log["samples"] = self.decode(torch.randn_like(posterior.sample()))
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log["reconstructions"] = xrec
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if log_ema or self.use_ema:
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with self.ema_scope():
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xrec_ema, posterior_ema = self(x)
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if x.shape[1] > 3:
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# colorize with random projection
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assert xrec_ema.shape[1] > 3
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xrec_ema = self.to_rgb(xrec_ema)
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log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
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log["reconstructions_ema"] = xrec_ema
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log["inputs"] = x
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return log
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def to_rgb(self, x):
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assert self.image_key == "segmentation"
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if not hasattr(self, "colorize"):
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self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
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x = F.conv2d(x, weight=self.colorize)
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x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
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return x
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class IdentityFirstStage(torch.nn.Module):
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def __init__(self, *args, vq_interface=False, **kwargs):
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self.vq_interface = vq_interface
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super().__init__()
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def encode(self, x, *args, **kwargs):
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return x
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def decode(self, x, *args, **kwargs):
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return x
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def quantize(self, x, *args, **kwargs):
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if self.vq_interface:
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return x, None, [None, None, None]
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return x
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def forward(self, x, *args, **kwargs):
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return x
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@@ -0,0 +1,418 @@
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"""SAMPLING ONLY."""
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import torch
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import numpy as np
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from tqdm import tqdm
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from comfy.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
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class DDIMSampler(object):
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def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
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super().__init__()
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self.model = model
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self.ddpm_num_timesteps = model.num_timesteps
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self.schedule = schedule
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self.device = device
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self.parameterization = kwargs.get("parameterization", "eps")
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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != self.device:
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attr = attr.float().to(self.device)
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setattr(self, name, attr)
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def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
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ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
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num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
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self.make_schedule_timesteps(ddim_timesteps, ddim_eta=ddim_eta, verbose=verbose)
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def make_schedule_timesteps(self, ddim_timesteps, ddim_eta=0., verbose=True):
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self.ddim_timesteps = torch.tensor(ddim_timesteps)
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alphas_cumprod = self.model.alphas_cumprod
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assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.device)
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
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# ddim sampling parameters
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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta,verbose=verbose)
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self.register_buffer('ddim_sigmas', ddim_sigmas)
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self.register_buffer('ddim_alphas', ddim_alphas)
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self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
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self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
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sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
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(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
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1 - self.alphas_cumprod / self.alphas_cumprod_prev))
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self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
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@torch.no_grad()
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def sample_custom(self,
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ddim_timesteps,
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conditioning=None,
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callback=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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dynamic_threshold=None,
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ucg_schedule=None,
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denoise_function=None,
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extra_args=None,
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to_zero=True,
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end_step=None,
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disable_pbar=False,
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**kwargs
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):
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self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose)
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samples, intermediates = self.ddim_sampling(conditioning, x_T.shape,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask, x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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x_T=x_T,
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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold,
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ucg_schedule=ucg_schedule,
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denoise_function=denoise_function,
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extra_args=extra_args,
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to_zero=to_zero,
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end_step=end_step,
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disable_pbar=disable_pbar
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)
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return samples, intermediates
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@torch.no_grad()
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def sample(self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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dynamic_threshold=None,
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ucg_schedule=None,
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**kwargs
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):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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ctmp = conditioning[list(conditioning.keys())[0]]
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while isinstance(ctmp, list): ctmp = ctmp[0]
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cbs = ctmp.shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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elif isinstance(conditioning, list):
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for ctmp in conditioning:
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if ctmp.shape[0] != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for DDIM sampling is {size}, eta {eta}')
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samples, intermediates = self.ddim_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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mask=mask, x0=x0,
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ddim_use_original_steps=False,
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noise_dropout=noise_dropout,
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temperature=temperature,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs,
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x_T=x_T,
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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold,
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ucg_schedule=ucg_schedule,
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denoise_function=None,
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extra_args=None
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)
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return samples, intermediates
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def q_sample(self, x_start, t, noise=None):
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if noise is None:
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noise = torch.randn_like(x_start)
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return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
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extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
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@torch.no_grad()
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def ddim_sampling(self, cond, shape,
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x_T=None, ddim_use_original_steps=False,
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||||
callback=None, timesteps=None, quantize_denoised=False,
|
||||
mask=None, x0=None, img_callback=None, log_every_t=100,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
|
||||
ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False):
|
||||
device = self.model.alphas_cumprod.device
|
||||
b = shape[0]
|
||||
if x_T is None:
|
||||
img = torch.randn(shape, device=device)
|
||||
else:
|
||||
img = x_T
|
||||
|
||||
if timesteps is None:
|
||||
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
||||
elif timesteps is not None and not ddim_use_original_steps:
|
||||
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
||||
timesteps = self.ddim_timesteps[:subset_end]
|
||||
|
||||
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
||||
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else timesteps.flip(0)
|
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
||||
# print(f"Running DDIM Sampling with {total_steps} timesteps")
|
||||
|
||||
iterator = tqdm(time_range[:end_step], desc='DDIM Sampler', total=end_step, disable=disable_pbar)
|
||||
|
||||
for i, step in enumerate(iterator):
|
||||
index = total_steps - i - 1
|
||||
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
||||
|
||||
if mask is not None:
|
||||
assert x0 is not None
|
||||
img_orig = self.q_sample(x0, ts) # TODO: deterministic forward pass?
|
||||
img = img_orig * mask + (1. - mask) * img
|
||||
|
||||
if ucg_schedule is not None:
|
||||
assert len(ucg_schedule) == len(time_range)
|
||||
unconditional_guidance_scale = ucg_schedule[i]
|
||||
|
||||
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
||||
quantize_denoised=quantize_denoised, temperature=temperature,
|
||||
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
||||
corrector_kwargs=corrector_kwargs,
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
unconditional_conditioning=unconditional_conditioning,
|
||||
dynamic_threshold=dynamic_threshold, denoise_function=denoise_function, extra_args=extra_args)
|
||||
img, pred_x0 = outs
|
||||
if callback: callback(i)
|
||||
if img_callback: img_callback(pred_x0, i)
|
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1:
|
||||
intermediates['x_inter'].append(img)
|
||||
intermediates['pred_x0'].append(pred_x0)
|
||||
|
||||
if to_zero:
|
||||
img = pred_x0
|
||||
else:
|
||||
if ddim_use_original_steps:
|
||||
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
||||
else:
|
||||
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
||||
img /= sqrt_alphas_cumprod[index - 1]
|
||||
|
||||
return img, intermediates
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
||||
unconditional_guidance_scale=1., unconditional_conditioning=None,
|
||||
dynamic_threshold=None, denoise_function=None, extra_args=None):
|
||||
b, *_, device = *x.shape, x.device
|
||||
|
||||
if denoise_function is not None:
|
||||
model_output = denoise_function(x, t, **extra_args)
|
||||
elif unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
||||
model_output = self.model.apply_model(x, t, c)
|
||||
else:
|
||||
x_in = torch.cat([x] * 2)
|
||||
t_in = torch.cat([t] * 2)
|
||||
if isinstance(c, dict):
|
||||
assert isinstance(unconditional_conditioning, dict)
|
||||
c_in = dict()
|
||||
for k in c:
|
||||
if isinstance(c[k], list):
|
||||
c_in[k] = [torch.cat([
|
||||
unconditional_conditioning[k][i],
|
||||
c[k][i]]) for i in range(len(c[k]))]
|
||||
else:
|
||||
c_in[k] = torch.cat([
|
||||
unconditional_conditioning[k],
|
||||
c[k]])
|
||||
elif isinstance(c, list):
|
||||
c_in = list()
|
||||
assert isinstance(unconditional_conditioning, list)
|
||||
for i in range(len(c)):
|
||||
c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
|
||||
else:
|
||||
c_in = torch.cat([unconditional_conditioning, c])
|
||||
model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
|
||||
model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
|
||||
|
||||
if self.parameterization == "v":
|
||||
e_t = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * model_output + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
|
||||
else:
|
||||
e_t = model_output
|
||||
|
||||
if score_corrector is not None:
|
||||
assert self.parameterization == "eps", 'not implemented'
|
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
||||
# select parameters corresponding to the currently considered timestep
|
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
||||
|
||||
# current prediction for x_0
|
||||
if self.parameterization != "v":
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
||||
else:
|
||||
pred_x0 = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * x - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * model_output
|
||||
|
||||
if quantize_denoised:
|
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
||||
|
||||
if dynamic_threshold is not None:
|
||||
raise NotImplementedError()
|
||||
|
||||
# direction pointing to x_t
|
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
||||
if noise_dropout > 0.:
|
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
||||
return x_prev, pred_x0
|
||||
|
||||
@torch.no_grad()
|
||||
def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
|
||||
unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
|
||||
num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
|
||||
|
||||
assert t_enc <= num_reference_steps
|
||||
num_steps = t_enc
|
||||
|
||||
if use_original_steps:
|
||||
alphas_next = self.alphas_cumprod[:num_steps]
|
||||
alphas = self.alphas_cumprod_prev[:num_steps]
|
||||
else:
|
||||
alphas_next = self.ddim_alphas[:num_steps]
|
||||
alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
|
||||
|
||||
x_next = x0
|
||||
intermediates = []
|
||||
inter_steps = []
|
||||
for i in tqdm(range(num_steps), desc='Encoding Image'):
|
||||
t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
|
||||
if unconditional_guidance_scale == 1.:
|
||||
noise_pred = self.model.apply_model(x_next, t, c)
|
||||
else:
|
||||
assert unconditional_conditioning is not None
|
||||
e_t_uncond, noise_pred = torch.chunk(
|
||||
self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
|
||||
torch.cat((unconditional_conditioning, c))), 2)
|
||||
noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
|
||||
|
||||
xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
|
||||
weighted_noise_pred = alphas_next[i].sqrt() * (
|
||||
(1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
|
||||
x_next = xt_weighted + weighted_noise_pred
|
||||
if return_intermediates and i % (
|
||||
num_steps // return_intermediates) == 0 and i < num_steps - 1:
|
||||
intermediates.append(x_next)
|
||||
inter_steps.append(i)
|
||||
elif return_intermediates and i >= num_steps - 2:
|
||||
intermediates.append(x_next)
|
||||
inter_steps.append(i)
|
||||
if callback: callback(i)
|
||||
|
||||
out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
|
||||
if return_intermediates:
|
||||
out.update({'intermediates': intermediates})
|
||||
return x_next, out
|
||||
|
||||
@torch.no_grad()
|
||||
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None, max_denoise=False):
|
||||
# fast, but does not allow for exact reconstruction
|
||||
# t serves as an index to gather the correct alphas
|
||||
if use_original_steps:
|
||||
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
||||
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
|
||||
else:
|
||||
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
||||
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
||||
|
||||
if noise is None:
|
||||
noise = torch.randn_like(x0)
|
||||
if max_denoise:
|
||||
noise_multiplier = 1.0
|
||||
else:
|
||||
noise_multiplier = extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
|
||||
|
||||
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + noise_multiplier * noise)
|
||||
|
||||
@torch.no_grad()
|
||||
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
|
||||
use_original_steps=False, callback=None):
|
||||
|
||||
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
|
||||
timesteps = timesteps[:t_start]
|
||||
|
||||
time_range = np.flip(timesteps)
|
||||
total_steps = timesteps.shape[0]
|
||||
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
||||
|
||||
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
||||
x_dec = x_latent
|
||||
for i, step in enumerate(iterator):
|
||||
index = total_steps - i - 1
|
||||
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
|
||||
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
unconditional_conditioning=unconditional_conditioning)
|
||||
if callback: callback(i)
|
||||
return x_dec
|
||||
@@ -0,0 +1 @@
|
||||
from .sampler import DPMSolverSampler
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,96 @@
|
||||
"""SAMPLING ONLY."""
|
||||
import torch
|
||||
|
||||
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
|
||||
|
||||
MODEL_TYPES = {
|
||||
"eps": "noise",
|
||||
"v": "v"
|
||||
}
|
||||
|
||||
|
||||
class DPMSolverSampler(object):
|
||||
def __init__(self, model, device=torch.device("cuda"), **kwargs):
|
||||
super().__init__()
|
||||
self.model = model
|
||||
self.device = device
|
||||
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
|
||||
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
|
||||
|
||||
def register_buffer(self, name, attr):
|
||||
if type(attr) == torch.Tensor:
|
||||
if attr.device != self.device:
|
||||
attr = attr.to(self.device)
|
||||
setattr(self, name, attr)
|
||||
|
||||
@torch.no_grad()
|
||||
def sample(self,
|
||||
S,
|
||||
batch_size,
|
||||
shape,
|
||||
conditioning=None,
|
||||
callback=None,
|
||||
normals_sequence=None,
|
||||
img_callback=None,
|
||||
quantize_x0=False,
|
||||
eta=0.,
|
||||
mask=None,
|
||||
x0=None,
|
||||
temperature=1.,
|
||||
noise_dropout=0.,
|
||||
score_corrector=None,
|
||||
corrector_kwargs=None,
|
||||
verbose=True,
|
||||
x_T=None,
|
||||
log_every_t=100,
|
||||
unconditional_guidance_scale=1.,
|
||||
unconditional_conditioning=None,
|
||||
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
||||
**kwargs
|
||||
):
|
||||
if conditioning is not None:
|
||||
if isinstance(conditioning, dict):
|
||||
ctmp = conditioning[list(conditioning.keys())[0]]
|
||||
while isinstance(ctmp, list): ctmp = ctmp[0]
|
||||
if isinstance(ctmp, torch.Tensor):
|
||||
cbs = ctmp.shape[0]
|
||||
if cbs != batch_size:
|
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
||||
elif isinstance(conditioning, list):
|
||||
for ctmp in conditioning:
|
||||
if ctmp.shape[0] != batch_size:
|
||||
print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}")
|
||||
else:
|
||||
if isinstance(conditioning, torch.Tensor):
|
||||
if conditioning.shape[0] != batch_size:
|
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
||||
|
||||
# sampling
|
||||
C, H, W = shape
|
||||
size = (batch_size, C, H, W)
|
||||
|
||||
print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
|
||||
|
||||
device = self.model.betas.device
|
||||
if x_T is None:
|
||||
img = torch.randn(size, device=device)
|
||||
else:
|
||||
img = x_T
|
||||
|
||||
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
|
||||
|
||||
model_fn = model_wrapper(
|
||||
lambda x, t, c: self.model.apply_model(x, t, c),
|
||||
ns,
|
||||
model_type=MODEL_TYPES[self.model.parameterization],
|
||||
guidance_type="classifier-free",
|
||||
condition=conditioning,
|
||||
unconditional_condition=unconditional_conditioning,
|
||||
guidance_scale=unconditional_guidance_scale,
|
||||
)
|
||||
|
||||
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
|
||||
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2,
|
||||
lower_order_final=True)
|
||||
|
||||
return x.to(device), None
|
||||
@@ -0,0 +1,245 @@
|
||||
"""SAMPLING ONLY."""
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from functools import partial
|
||||
|
||||
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
|
||||
from ldm.models.diffusion.sampling_util import norm_thresholding
|
||||
|
||||
|
||||
class PLMSSampler(object):
|
||||
def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs):
|
||||
super().__init__()
|
||||
self.model = model
|
||||
self.ddpm_num_timesteps = model.num_timesteps
|
||||
self.schedule = schedule
|
||||
self.device = device
|
||||
|
||||
def register_buffer(self, name, attr):
|
||||
if type(attr) == torch.Tensor:
|
||||
if attr.device != self.device:
|
||||
attr = attr.to(self.device)
|
||||
setattr(self, name, attr)
|
||||
|
||||
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
|
||||
if ddim_eta != 0:
|
||||
raise ValueError('ddim_eta must be 0 for PLMS')
|
||||
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
|
||||
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
|
||||
alphas_cumprod = self.model.alphas_cumprod
|
||||
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
|
||||
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
|
||||
|
||||
self.register_buffer('betas', to_torch(self.model.betas))
|
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
||||
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
|
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others
|
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
|
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
|
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
|
||||
|
||||
# ddim sampling parameters
|
||||
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
|
||||
ddim_timesteps=self.ddim_timesteps,
|
||||
eta=ddim_eta,verbose=verbose)
|
||||
self.register_buffer('ddim_sigmas', ddim_sigmas)
|
||||
self.register_buffer('ddim_alphas', ddim_alphas)
|
||||
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
|
||||
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
|
||||
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
|
||||
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
|
||||
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
|
||||
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
|
||||
|
||||
@torch.no_grad()
|
||||
def sample(self,
|
||||
S,
|
||||
batch_size,
|
||||
shape,
|
||||
conditioning=None,
|
||||
callback=None,
|
||||
normals_sequence=None,
|
||||
img_callback=None,
|
||||
quantize_x0=False,
|
||||
eta=0.,
|
||||
mask=None,
|
||||
x0=None,
|
||||
temperature=1.,
|
||||
noise_dropout=0.,
|
||||
score_corrector=None,
|
||||
corrector_kwargs=None,
|
||||
verbose=True,
|
||||
x_T=None,
|
||||
log_every_t=100,
|
||||
unconditional_guidance_scale=1.,
|
||||
unconditional_conditioning=None,
|
||||
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
||||
dynamic_threshold=None,
|
||||
**kwargs
|
||||
):
|
||||
if conditioning is not None:
|
||||
if isinstance(conditioning, dict):
|
||||
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
||||
if cbs != batch_size:
|
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
||||
else:
|
||||
if conditioning.shape[0] != batch_size:
|
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
||||
|
||||
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
||||
# sampling
|
||||
C, H, W = shape
|
||||
size = (batch_size, C, H, W)
|
||||
print(f'Data shape for PLMS sampling is {size}')
|
||||
|
||||
samples, intermediates = self.plms_sampling(conditioning, size,
|
||||
callback=callback,
|
||||
img_callback=img_callback,
|
||||
quantize_denoised=quantize_x0,
|
||||
mask=mask, x0=x0,
|
||||
ddim_use_original_steps=False,
|
||||
noise_dropout=noise_dropout,
|
||||
temperature=temperature,
|
||||
score_corrector=score_corrector,
|
||||
corrector_kwargs=corrector_kwargs,
|
||||
x_T=x_T,
|
||||
log_every_t=log_every_t,
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
unconditional_conditioning=unconditional_conditioning,
|
||||
dynamic_threshold=dynamic_threshold,
|
||||
)
|
||||
return samples, intermediates
|
||||
|
||||
@torch.no_grad()
|
||||
def plms_sampling(self, cond, shape,
|
||||
x_T=None, ddim_use_original_steps=False,
|
||||
callback=None, timesteps=None, quantize_denoised=False,
|
||||
mask=None, x0=None, img_callback=None, log_every_t=100,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
||||
unconditional_guidance_scale=1., unconditional_conditioning=None,
|
||||
dynamic_threshold=None):
|
||||
device = self.model.betas.device
|
||||
b = shape[0]
|
||||
if x_T is None:
|
||||
img = torch.randn(shape, device=device)
|
||||
else:
|
||||
img = x_T
|
||||
|
||||
if timesteps is None:
|
||||
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
||||
elif timesteps is not None and not ddim_use_original_steps:
|
||||
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
||||
timesteps = self.ddim_timesteps[:subset_end]
|
||||
|
||||
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
||||
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
|
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
||||
print(f"Running PLMS Sampling with {total_steps} timesteps")
|
||||
|
||||
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
|
||||
old_eps = []
|
||||
|
||||
for i, step in enumerate(iterator):
|
||||
index = total_steps - i - 1
|
||||
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
||||
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
|
||||
|
||||
if mask is not None:
|
||||
assert x0 is not None
|
||||
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
|
||||
img = img_orig * mask + (1. - mask) * img
|
||||
|
||||
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
||||
quantize_denoised=quantize_denoised, temperature=temperature,
|
||||
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
||||
corrector_kwargs=corrector_kwargs,
|
||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
||||
unconditional_conditioning=unconditional_conditioning,
|
||||
old_eps=old_eps, t_next=ts_next,
|
||||
dynamic_threshold=dynamic_threshold)
|
||||
img, pred_x0, e_t = outs
|
||||
old_eps.append(e_t)
|
||||
if len(old_eps) >= 4:
|
||||
old_eps.pop(0)
|
||||
if callback: callback(i)
|
||||
if img_callback: img_callback(pred_x0, i)
|
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1:
|
||||
intermediates['x_inter'].append(img)
|
||||
intermediates['pred_x0'].append(pred_x0)
|
||||
|
||||
return img, intermediates
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
|
||||
dynamic_threshold=None):
|
||||
b, *_, device = *x.shape, x.device
|
||||
|
||||
def get_model_output(x, t):
|
||||
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
||||
e_t = self.model.apply_model(x, t, c)
|
||||
else:
|
||||
x_in = torch.cat([x] * 2)
|
||||
t_in = torch.cat([t] * 2)
|
||||
c_in = torch.cat([unconditional_conditioning, c])
|
||||
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
|
||||
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
||||
|
||||
if score_corrector is not None:
|
||||
assert self.model.parameterization == "eps"
|
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
||||
|
||||
return e_t
|
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
||||
|
||||
def get_x_prev_and_pred_x0(e_t, index):
|
||||
# select parameters corresponding to the currently considered timestep
|
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
||||
|
||||
# current prediction for x_0
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
||||
if quantize_denoised:
|
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
||||
if dynamic_threshold is not None:
|
||||
pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
|
||||
# direction pointing to x_t
|
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
||||
if noise_dropout > 0.:
|
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
||||
return x_prev, pred_x0
|
||||
|
||||
e_t = get_model_output(x, t)
|
||||
if len(old_eps) == 0:
|
||||
# Pseudo Improved Euler (2nd order)
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
|
||||
e_t_next = get_model_output(x_prev, t_next)
|
||||
e_t_prime = (e_t + e_t_next) / 2
|
||||
elif len(old_eps) == 1:
|
||||
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (3 * e_t - old_eps[-1]) / 2
|
||||
elif len(old_eps) == 2:
|
||||
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
|
||||
elif len(old_eps) >= 3:
|
||||
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
|
||||
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
|
||||
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
|
||||
|
||||
return x_prev, pred_x0, e_t
|
||||
@@ -0,0 +1,22 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
def append_dims(x, target_dims):
|
||||
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
|
||||
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
|
||||
dims_to_append = target_dims - x.ndim
|
||||
if dims_to_append < 0:
|
||||
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
|
||||
return x[(...,) + (None,) * dims_to_append]
|
||||
|
||||
|
||||
def norm_thresholding(x0, value):
|
||||
s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
|
||||
return x0 * (value / s)
|
||||
|
||||
|
||||
def spatial_norm_thresholding(x0, value):
|
||||
# b c h w
|
||||
s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
|
||||
return x0 * (value / s)
|
||||
@@ -0,0 +1,702 @@
|
||||
from inspect import isfunction
|
||||
import math
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn, einsum
|
||||
from einops import rearrange, repeat
|
||||
from typing import Optional, Any
|
||||
|
||||
from .diffusionmodules.util import checkpoint
|
||||
from .sub_quadratic_attention import efficient_dot_product_attention
|
||||
|
||||
from comfy import model_management
|
||||
|
||||
if model_management.xformers_enabled():
|
||||
import xformers
|
||||
import xformers.ops
|
||||
|
||||
from comfy.cli_args import args
|
||||
import comfy.ops
|
||||
|
||||
# CrossAttn precision handling
|
||||
if args.dont_upcast_attention:
|
||||
print("disabling upcasting of attention")
|
||||
_ATTN_PRECISION = "fp16"
|
||||
else:
|
||||
_ATTN_PRECISION = "fp32"
|
||||
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
|
||||
def uniq(arr):
|
||||
return{el: True for el in arr}.keys()
|
||||
|
||||
|
||||
def default(val, d):
|
||||
if exists(val):
|
||||
return val
|
||||
return d
|
||||
|
||||
|
||||
def max_neg_value(t):
|
||||
return -torch.finfo(t.dtype).max
|
||||
|
||||
|
||||
def init_(tensor):
|
||||
dim = tensor.shape[-1]
|
||||
std = 1 / math.sqrt(dim)
|
||||
tensor.uniform_(-std, std)
|
||||
return tensor
|
||||
|
||||
|
||||
# feedforward
|
||||
class GEGLU(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x):
|
||||
x, gate = self.proj(x).chunk(2, dim=-1)
|
||||
return x * F.gelu(gate)
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = default(dim_out, dim)
|
||||
project_in = nn.Sequential(
|
||||
operations.Linear(dim, inner_dim, dtype=dtype, device=device),
|
||||
nn.GELU()
|
||||
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
project_in,
|
||||
nn.Dropout(dropout),
|
||||
operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
"""
|
||||
Zero out the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().zero_()
|
||||
return module
|
||||
|
||||
|
||||
def Normalize(in_channels, dtype=None, device=None):
|
||||
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
|
||||
|
||||
|
||||
class SpatialSelfAttention(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 = rearrange(q, 'b c h w -> b (h w) c')
|
||||
k = rearrange(k, 'b c h w -> b c (h w)')
|
||||
w_ = torch.einsum('bij,bjk->bik', q, k)
|
||||
|
||||
w_ = w_ * (int(c)**(-0.5))
|
||||
w_ = torch.nn.functional.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = rearrange(v, 'b c h w -> b c (h w)')
|
||||
w_ = rearrange(w_, 'b i j -> b j i')
|
||||
h_ = torch.einsum('bij,bjk->bik', v, w_)
|
||||
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
|
||||
|
||||
class CrossAttentionBirchSan(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
query = self.to_q(x)
|
||||
context = default(context, x)
|
||||
key = self.to_k(context)
|
||||
if value is not None:
|
||||
value = self.to_v(value)
|
||||
else:
|
||||
value = self.to_v(context)
|
||||
|
||||
del context, x
|
||||
|
||||
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
|
||||
del key
|
||||
value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
|
||||
dtype = query.dtype
|
||||
upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
|
||||
if upcast_attention:
|
||||
bytes_per_token = torch.finfo(torch.float32).bits//8
|
||||
else:
|
||||
bytes_per_token = torch.finfo(query.dtype).bits//8
|
||||
batch_x_heads, q_tokens, _ = query.shape
|
||||
_, _, k_tokens = key_t.shape
|
||||
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
|
||||
|
||||
mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
|
||||
|
||||
chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
|
||||
|
||||
kv_chunk_size_min = None
|
||||
|
||||
#not sure at all about the math here
|
||||
#TODO: tweak this
|
||||
if mem_free_total > 8192 * 1024 * 1024 * 1.3:
|
||||
query_chunk_size_x = 1024 * 4
|
||||
elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
|
||||
query_chunk_size_x = 1024 * 2
|
||||
else:
|
||||
query_chunk_size_x = 1024
|
||||
kv_chunk_size_min_x = None
|
||||
kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
|
||||
if kv_chunk_size_x < 1024:
|
||||
kv_chunk_size_x = None
|
||||
|
||||
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
|
||||
# the big matmul fits into our memory limit; do everything in 1 chunk,
|
||||
# i.e. send it down the unchunked fast-path
|
||||
query_chunk_size = q_tokens
|
||||
kv_chunk_size = k_tokens
|
||||
else:
|
||||
query_chunk_size = query_chunk_size_x
|
||||
kv_chunk_size = kv_chunk_size_x
|
||||
kv_chunk_size_min = kv_chunk_size_min_x
|
||||
|
||||
hidden_states = efficient_dot_product_attention(
|
||||
query,
|
||||
key_t,
|
||||
value,
|
||||
query_chunk_size=query_chunk_size,
|
||||
kv_chunk_size=kv_chunk_size,
|
||||
kv_chunk_size_min=kv_chunk_size_min,
|
||||
use_checkpoint=self.training,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
|
||||
hidden_states = hidden_states.to(dtype)
|
||||
|
||||
hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
|
||||
|
||||
out_proj, dropout = self.to_out
|
||||
hidden_states = out_proj(hidden_states)
|
||||
hidden_states = dropout(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class CrossAttentionDoggettx(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context)
|
||||
if value is not None:
|
||||
v_in = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
|
||||
mem_free_total = model_management.get_free_memory(q.device)
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
||||
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
||||
|
||||
if steps > 64:
|
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
||||
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
|
||||
|
||||
# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
|
||||
first_op_done = False
|
||||
cleared_cache = False
|
||||
while True:
|
||||
try:
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
if _ATTN_PRECISION =="fp32":
|
||||
with torch.autocast(enabled=False, device_type = 'cuda'):
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
|
||||
else:
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
|
||||
first_op_done = True
|
||||
|
||||
s2 = s1.softmax(dim=-1).to(v.dtype)
|
||||
del s1
|
||||
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
break
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
if first_op_done == False:
|
||||
model_management.soft_empty_cache(True)
|
||||
if cleared_cache == False:
|
||||
cleared_cache = True
|
||||
print("out of memory error, emptying cache and trying again")
|
||||
continue
|
||||
steps *= 2
|
||||
if steps > 64:
|
||||
raise e
|
||||
print("out of memory error, increasing steps and trying again", steps)
|
||||
else:
|
||||
raise e
|
||||
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
if value is not None:
|
||||
v = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
# force cast to fp32 to avoid overflowing
|
||||
if _ATTN_PRECISION =="fp32":
|
||||
with torch.autocast(enabled=False, device_type = 'cuda'):
|
||||
q, k = q.float(), k.float()
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
else:
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
del q, k
|
||||
|
||||
if exists(mask):
|
||||
mask = rearrange(mask, 'b ... -> b (...)')
|
||||
max_neg_value = -torch.finfo(sim.dtype).max
|
||||
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
||||
sim.masked_fill_(~mask, max_neg_value)
|
||||
|
||||
# attention, what we cannot get enough of
|
||||
sim = sim.softmax(dim=-1)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', sim, v)
|
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
||||
class MemoryEfficientCrossAttention(nn.Module):
|
||||
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
if value is not None:
|
||||
v = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
b, _, _ = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.unsqueeze(3)
|
||||
.reshape(b, t.shape[1], self.heads, self.dim_head)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b * self.heads, t.shape[1], self.dim_head)
|
||||
.contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
# actually compute the attention, what we cannot get enough of
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.unsqueeze(0)
|
||||
.reshape(b, self.heads, out.shape[1], self.dim_head)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b, out.shape[1], self.heads * self.dim_head)
|
||||
)
|
||||
return self.to_out(out)
|
||||
|
||||
class CrossAttentionPytorch(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
if value is not None:
|
||||
v = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
b, _, _ = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
|
||||
)
|
||||
|
||||
return self.to_out(out)
|
||||
|
||||
if model_management.xformers_enabled():
|
||||
print("Using xformers cross attention")
|
||||
CrossAttention = MemoryEfficientCrossAttention
|
||||
elif model_management.pytorch_attention_enabled():
|
||||
print("Using pytorch cross attention")
|
||||
CrossAttention = CrossAttentionPytorch
|
||||
else:
|
||||
if args.use_split_cross_attention:
|
||||
print("Using split optimization for cross attention")
|
||||
CrossAttention = CrossAttentionDoggettx
|
||||
else:
|
||||
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
|
||||
CrossAttention = CrossAttentionBirchSan
|
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
|
||||
disable_self_attn=False, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.disable_self_attn = disable_self_attn
|
||||
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
||||
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn
|
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
|
||||
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
|
||||
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none
|
||||
self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.checkpoint = checkpoint
|
||||
self.n_heads = n_heads
|
||||
self.d_head = d_head
|
||||
|
||||
def forward(self, x, context=None, transformer_options={}):
|
||||
return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
|
||||
|
||||
def _forward(self, x, context=None, transformer_options={}):
|
||||
extra_options = {}
|
||||
block = None
|
||||
block_index = 0
|
||||
if "current_index" in transformer_options:
|
||||
extra_options["transformer_index"] = transformer_options["current_index"]
|
||||
if "block_index" in transformer_options:
|
||||
block_index = transformer_options["block_index"]
|
||||
extra_options["block_index"] = block_index
|
||||
if "original_shape" in transformer_options:
|
||||
extra_options["original_shape"] = transformer_options["original_shape"]
|
||||
if "block" in transformer_options:
|
||||
block = transformer_options["block"]
|
||||
extra_options["block"] = block
|
||||
if "cond_or_uncond" in transformer_options:
|
||||
extra_options["cond_or_uncond"] = transformer_options["cond_or_uncond"]
|
||||
if "patches" in transformer_options:
|
||||
transformer_patches = transformer_options["patches"]
|
||||
else:
|
||||
transformer_patches = {}
|
||||
|
||||
extra_options["n_heads"] = self.n_heads
|
||||
extra_options["dim_head"] = self.d_head
|
||||
|
||||
if "patches_replace" in transformer_options:
|
||||
transformer_patches_replace = transformer_options["patches_replace"]
|
||||
else:
|
||||
transformer_patches_replace = {}
|
||||
|
||||
n = self.norm1(x)
|
||||
if self.disable_self_attn:
|
||||
context_attn1 = context
|
||||
else:
|
||||
context_attn1 = None
|
||||
value_attn1 = None
|
||||
|
||||
if "attn1_patch" in transformer_patches:
|
||||
patch = transformer_patches["attn1_patch"]
|
||||
if context_attn1 is None:
|
||||
context_attn1 = n
|
||||
value_attn1 = context_attn1
|
||||
for p in patch:
|
||||
n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
|
||||
|
||||
if block is not None:
|
||||
transformer_block = (block[0], block[1], block_index)
|
||||
else:
|
||||
transformer_block = None
|
||||
attn1_replace_patch = transformer_patches_replace.get("attn1", {})
|
||||
block_attn1 = transformer_block
|
||||
if block_attn1 not in attn1_replace_patch:
|
||||
block_attn1 = block
|
||||
|
||||
if block_attn1 in attn1_replace_patch:
|
||||
if context_attn1 is None:
|
||||
context_attn1 = n
|
||||
value_attn1 = n
|
||||
n = self.attn1.to_q(n)
|
||||
context_attn1 = self.attn1.to_k(context_attn1)
|
||||
value_attn1 = self.attn1.to_v(value_attn1)
|
||||
n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
|
||||
n = self.attn1.to_out(n)
|
||||
else:
|
||||
n = self.attn1(n, context=context_attn1, value=value_attn1)
|
||||
|
||||
if "attn1_output_patch" in transformer_patches:
|
||||
patch = transformer_patches["attn1_output_patch"]
|
||||
for p in patch:
|
||||
n = p(n, extra_options)
|
||||
|
||||
x += n
|
||||
if "middle_patch" in transformer_patches:
|
||||
patch = transformer_patches["middle_patch"]
|
||||
for p in patch:
|
||||
x = p(x, extra_options)
|
||||
|
||||
n = self.norm2(x)
|
||||
|
||||
context_attn2 = context
|
||||
value_attn2 = None
|
||||
if "attn2_patch" in transformer_patches:
|
||||
patch = transformer_patches["attn2_patch"]
|
||||
value_attn2 = context_attn2
|
||||
for p in patch:
|
||||
n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
|
||||
|
||||
attn2_replace_patch = transformer_patches_replace.get("attn2", {})
|
||||
block_attn2 = transformer_block
|
||||
if block_attn2 not in attn2_replace_patch:
|
||||
block_attn2 = block
|
||||
|
||||
if block_attn2 in attn2_replace_patch:
|
||||
if value_attn2 is None:
|
||||
value_attn2 = context_attn2
|
||||
n = self.attn2.to_q(n)
|
||||
context_attn2 = self.attn2.to_k(context_attn2)
|
||||
value_attn2 = self.attn2.to_v(value_attn2)
|
||||
n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
|
||||
n = self.attn2.to_out(n)
|
||||
else:
|
||||
n = self.attn2(n, context=context_attn2, value=value_attn2)
|
||||
|
||||
if "attn2_output_patch" in transformer_patches:
|
||||
patch = transformer_patches["attn2_output_patch"]
|
||||
for p in patch:
|
||||
n = p(n, extra_options)
|
||||
|
||||
x += n
|
||||
x = self.ff(self.norm3(x)) + x
|
||||
return x
|
||||
|
||||
|
||||
class SpatialTransformer(nn.Module):
|
||||
"""
|
||||
Transformer block for image-like data.
|
||||
First, project the input (aka embedding)
|
||||
and reshape to b, t, d.
|
||||
Then apply standard transformer action.
|
||||
Finally, reshape to image
|
||||
NEW: use_linear for more efficiency instead of the 1x1 convs
|
||||
"""
|
||||
def __init__(self, in_channels, n_heads, d_head,
|
||||
depth=1, dropout=0., context_dim=None,
|
||||
disable_self_attn=False, use_linear=False,
|
||||
use_checkpoint=True, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
if exists(context_dim) and not isinstance(context_dim, list):
|
||||
context_dim = [context_dim] * depth
|
||||
self.in_channels = in_channels
|
||||
inner_dim = n_heads * d_head
|
||||
self.norm = Normalize(in_channels, dtype=dtype, device=device)
|
||||
if not use_linear:
|
||||
self.proj_in = operations.Conv2d(in_channels,
|
||||
inner_dim,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
|
||||
for d in range(depth)]
|
||||
)
|
||||
if not use_linear:
|
||||
self.proj_out = operations.Conv2d(inner_dim,in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.use_linear = use_linear
|
||||
|
||||
def forward(self, x, context=None, transformer_options={}):
|
||||
# note: if no context is given, cross-attention defaults to self-attention
|
||||
if not isinstance(context, list):
|
||||
context = [context] * len(self.transformer_blocks)
|
||||
b, c, h, w = x.shape
|
||||
x_in = x
|
||||
x = self.norm(x)
|
||||
if not self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
|
||||
if self.use_linear:
|
||||
x = self.proj_in(x)
|
||||
for i, block in enumerate(self.transformer_blocks):
|
||||
transformer_options["block_index"] = i
|
||||
x = block(x, context=context[i], transformer_options=transformer_options)
|
||||
if self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
|
||||
if not self.use_linear:
|
||||
x = self.proj_out(x)
|
||||
return x + x_in
|
||||
|
||||
@@ -0,0 +1,737 @@
|
||||
# pytorch_diffusion + derived encoder decoder
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import rearrange
|
||||
from typing import Optional, Any
|
||||
|
||||
from ..attention import MemoryEfficientCrossAttention
|
||||
from comfy import model_management
|
||||
import comfy.ops
|
||||
|
||||
if model_management.xformers_enabled_vae():
|
||||
import xformers
|
||||
import xformers.ops
|
||||
|
||||
def get_timestep_embedding(timesteps, embedding_dim):
|
||||
"""
|
||||
This matches the implementation in Denoising Diffusion Probabilistic Models:
|
||||
From Fairseq.
|
||||
Build sinusoidal embeddings.
|
||||
This matches the implementation in tensor2tensor, but differs slightly
|
||||
from the description in Section 3.5 of "Attention Is All You Need".
|
||||
"""
|
||||
assert len(timesteps.shape) == 1
|
||||
|
||||
half_dim = embedding_dim // 2
|
||||
emb = math.log(10000) / (half_dim - 1)
|
||||
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
|
||||
emb = emb.to(device=timesteps.device)
|
||||
emb = timesteps.float()[:, None] * emb[None, :]
|
||||
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
||||
if embedding_dim % 2 == 1: # zero pad
|
||||
emb = torch.nn.functional.pad(emb, (0,1,0,0))
|
||||
return emb
|
||||
|
||||
|
||||
def nonlinearity(x):
|
||||
# swish
|
||||
return x*torch.sigmoid(x)
|
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32):
|
||||
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
self.conv = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
try:
|
||||
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
except: #operation not implemented for bf16
|
||||
b, c, h, w = x.shape
|
||||
out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device)
|
||||
split = 8
|
||||
l = out.shape[1] // split
|
||||
for i in range(0, out.shape[1], l):
|
||||
out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype)
|
||||
del x
|
||||
x = out
|
||||
|
||||
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 = comfy.ops.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.swish = torch.nn.SiLU(inplace=True)
|
||||
self.norm1 = Normalize(in_channels)
|
||||
self.conv1 = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if temb_channels > 0:
|
||||
self.temb_proj = comfy.ops.Linear(temb_channels,
|
||||
out_channels)
|
||||
self.norm2 = Normalize(out_channels)
|
||||
self.dropout = torch.nn.Dropout(dropout, inplace=True)
|
||||
self.conv2 = comfy.ops.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 = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
else:
|
||||
self.nin_shortcut = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x, temb):
|
||||
h = x
|
||||
h = self.norm1(h)
|
||||
h = self.swish(h)
|
||||
h = self.conv1(h)
|
||||
|
||||
if temb is not None:
|
||||
h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
|
||||
|
||||
h = self.norm2(h)
|
||||
h = self.swish(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
|
||||
|
||||
def slice_attention(q, k, v):
|
||||
r1 = torch.zeros_like(k, device=q.device)
|
||||
scale = (int(q.shape[-1])**(-0.5))
|
||||
|
||||
mem_free_total = model_management.get_free_memory(q.device)
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
|
||||
while True:
|
||||
try:
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = torch.bmm(q[:, i:end], k) * scale
|
||||
|
||||
s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
|
||||
del s1
|
||||
|
||||
r1[:, :, i:end] = torch.bmm(v, s2)
|
||||
del s2
|
||||
break
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
model_management.soft_empty_cache(True)
|
||||
steps *= 2
|
||||
if steps > 128:
|
||||
raise e
|
||||
print("out of memory error, increasing steps and trying again", steps)
|
||||
|
||||
return r1
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.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
|
||||
v = v.reshape(b,c,h*w)
|
||||
|
||||
r1 = slice_attention(q, k, v)
|
||||
h_ = r1.reshape(b,c,h,w)
|
||||
del r1
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
|
||||
class MemoryEfficientAttnBlock(nn.Module):
|
||||
"""
|
||||
Uses xformers efficient implementation,
|
||||
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
||||
Note: this is a single-head self-attention operation
|
||||
"""
|
||||
#
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
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, k, v = map(
|
||||
lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
try:
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
||||
out = out.transpose(1, 2).reshape(B, C, H, W)
|
||||
except NotImplementedError as e:
|
||||
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
|
||||
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
||||
class MemoryEfficientAttnBlockPytorch(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
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, k, v = map(
|
||||
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
try:
|
||||
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
||||
out = out.transpose(2, 3).reshape(B, C, H, W)
|
||||
except model_management.OOM_EXCEPTION as e:
|
||||
print("scaled_dot_product_attention OOMed: switched to slice attention")
|
||||
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
|
||||
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
||||
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
|
||||
def forward(self, x, context=None, mask=None):
|
||||
b, c, h, w = x.shape
|
||||
x = rearrange(x, 'b c h w -> b (h w) c')
|
||||
out = super().forward(x, context=context, mask=mask)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
|
||||
return x + out
|
||||
|
||||
|
||||
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
||||
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
|
||||
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
|
||||
attn_type = "vanilla-xformers"
|
||||
if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
|
||||
attn_type = "vanilla-pytorch"
|
||||
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
|
||||
if attn_type == "vanilla":
|
||||
assert attn_kwargs is None
|
||||
return AttnBlock(in_channels)
|
||||
elif attn_type == "vanilla-xformers":
|
||||
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
|
||||
return MemoryEfficientAttnBlock(in_channels)
|
||||
elif attn_type == "vanilla-pytorch":
|
||||
return MemoryEfficientAttnBlockPytorch(in_channels)
|
||||
elif type == "memory-efficient-cross-attn":
|
||||
attn_kwargs["query_dim"] = in_channels
|
||||
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
|
||||
elif attn_type == "none":
|
||||
return nn.Identity(in_channels)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class Model(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, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
|
||||
super().__init__()
|
||||
if use_linear_attn: attn_type = "linear"
|
||||
self.ch = ch
|
||||
self.temb_ch = self.ch*4
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.use_timestep = use_timestep
|
||||
if self.use_timestep:
|
||||
# timestep embedding
|
||||
self.temb = nn.Module()
|
||||
self.temb.dense = nn.ModuleList([
|
||||
comfy.ops.Linear(self.ch,
|
||||
self.temb_ch),
|
||||
comfy.ops.Linear(self.temb_ch,
|
||||
self.temb_ch),
|
||||
])
|
||||
|
||||
# downsampling
|
||||
self.conv_in = comfy.ops.Conv2d(in_channels,
|
||||
self.ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
curr_res = resolution
|
||||
in_ch_mult = (1,)+tuple(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)
|
||||
|
||||
# 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]
|
||||
skip_in = ch*ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
if i_block == self.num_res_blocks:
|
||||
skip_in = ch*in_ch_mult[i_level]
|
||||
block.append(ResnetBlock(in_channels=block_in+skip_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 = comfy.ops.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x, t=None, context=None):
|
||||
#assert x.shape[2] == x.shape[3] == self.resolution
|
||||
if context is not None:
|
||||
# assume aligned context, cat along channel axis
|
||||
x = torch.cat((x, context), dim=1)
|
||||
if self.use_timestep:
|
||||
# timestep embedding
|
||||
assert t is not None
|
||||
temb = get_timestep_embedding(t, self.ch)
|
||||
temb = self.temb.dense[0](temb)
|
||||
temb = nonlinearity(temb)
|
||||
temb = self.temb.dense[1](temb)
|
||||
else:
|
||||
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)
|
||||
|
||||
# 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](
|
||||
torch.cat([h, hs.pop()], dim=1), 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
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h)
|
||||
return h
|
||||
|
||||
def get_last_layer(self):
|
||||
return self.conv_out.weight
|
||||
|
||||
|
||||
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 = comfy.ops.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 = comfy.ops.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
|
||||
h = 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](h, temb)
|
||||
if len(self.down[i_level].attn) > 0:
|
||||
h = self.down[i_level].attn[i_block](h)
|
||||
if i_level != self.num_resolutions-1:
|
||||
h = self.down[i_level].downsample(h)
|
||||
|
||||
# middle
|
||||
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", **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
|
||||
|
||||
# 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 = comfy.ops.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 = comfy.ops.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
|
||||
@@ -0,0 +1,664 @@
|
||||
from abc import abstractmethod
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch as th
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .util import (
|
||||
checkpoint,
|
||||
avg_pool_nd,
|
||||
zero_module,
|
||||
normalization,
|
||||
timestep_embedding,
|
||||
)
|
||||
from ..attention import SpatialTransformer
|
||||
from comfy.ldm.util import exists
|
||||
import comfy.ops
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
Any module where forward() takes timestep embeddings as a second argument.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the module to `x` given `emb` timestep embeddings.
|
||||
"""
|
||||
|
||||
|
||||
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
|
||||
"""
|
||||
A sequential module that passes timestep embeddings to the children that
|
||||
support it as an extra input.
|
||||
"""
|
||||
|
||||
def forward(self, x, emb, context=None, transformer_options={}, output_shape=None):
|
||||
for layer in self:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, emb)
|
||||
elif isinstance(layer, SpatialTransformer):
|
||||
x = layer(x, context, transformer_options)
|
||||
elif isinstance(layer, Upsample):
|
||||
x = layer(x, output_shape=output_shape)
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
#This is needed because accelerate makes a copy of transformer_options which breaks "current_index"
|
||||
def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
|
||||
for layer in ts:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, emb)
|
||||
elif isinstance(layer, SpatialTransformer):
|
||||
x = layer(x, context, transformer_options)
|
||||
transformer_options["current_index"] += 1
|
||||
elif isinstance(layer, Upsample):
|
||||
x = layer(x, output_shape=output_shape)
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
class Upsample(nn.Module):
|
||||
"""
|
||||
An upsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
upsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
if use_conv:
|
||||
self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, output_shape=None):
|
||||
assert x.shape[1] == self.channels
|
||||
if self.dims == 3:
|
||||
shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
|
||||
if output_shape is not None:
|
||||
shape[1] = output_shape[3]
|
||||
shape[2] = output_shape[4]
|
||||
else:
|
||||
shape = [x.shape[2] * 2, x.shape[3] * 2]
|
||||
if output_shape is not None:
|
||||
shape[0] = output_shape[2]
|
||||
shape[1] = output_shape[3]
|
||||
|
||||
x = F.interpolate(x, size=shape, mode="nearest")
|
||||
if self.use_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
class Downsample(nn.Module):
|
||||
"""
|
||||
A downsampling layer with an optional convolution.
|
||||
:param channels: channels in the inputs and outputs.
|
||||
:param use_conv: a bool determining if a convolution is applied.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
||||
downsampling occurs in the inner-two dimensions.
|
||||
"""
|
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.dims = dims
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
if use_conv:
|
||||
self.op = operations.conv_nd(
|
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
assert self.channels == self.out_channels
|
||||
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
|
||||
|
||||
def forward(self, x):
|
||||
assert x.shape[1] == self.channels
|
||||
return self.op(x)
|
||||
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
"""
|
||||
A residual block that can optionally change the number of channels.
|
||||
:param channels: the number of input channels.
|
||||
:param emb_channels: the number of timestep embedding channels.
|
||||
:param dropout: the rate of dropout.
|
||||
:param out_channels: if specified, the number of out channels.
|
||||
:param use_conv: if True and out_channels is specified, use a spatial
|
||||
convolution instead of a smaller 1x1 convolution to change the
|
||||
channels in the skip connection.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param use_checkpoint: if True, use gradient checkpointing on this module.
|
||||
:param up: if True, use this block for upsampling.
|
||||
:param down: if True, use this block for downsampling.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
use_conv=False,
|
||||
use_scale_shift_norm=False,
|
||||
dims=2,
|
||||
use_checkpoint=False,
|
||||
up=False,
|
||||
down=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=comfy.ops
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
self.use_conv = use_conv
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.use_scale_shift_norm = use_scale_shift_norm
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
self.updown = up or down
|
||||
|
||||
if up:
|
||||
self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
|
||||
elif down:
|
||||
self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
|
||||
else:
|
||||
self.h_upd = self.x_upd = nn.Identity()
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
operations.Linear(
|
||||
emb_channels,
|
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
|
||||
),
|
||||
)
|
||||
self.out_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
nn.Dropout(p=dropout),
|
||||
zero_module(
|
||||
operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
),
|
||||
)
|
||||
|
||||
if self.out_channels == channels:
|
||||
self.skip_connection = nn.Identity()
|
||||
elif use_conv:
|
||||
self.skip_connection = operations.conv_nd(
|
||||
dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the block to a Tensor, conditioned on a timestep embedding.
|
||||
:param x: an [N x C x ...] Tensor of features.
|
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
return checkpoint(
|
||||
self._forward, (x, emb), self.parameters(), self.use_checkpoint
|
||||
)
|
||||
|
||||
|
||||
def _forward(self, x, emb):
|
||||
if self.updown:
|
||||
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
||||
h = in_rest(x)
|
||||
h = self.h_upd(h)
|
||||
x = self.x_upd(x)
|
||||
h = in_conv(h)
|
||||
else:
|
||||
h = self.in_layers(x)
|
||||
emb_out = self.emb_layers(emb).type(h.dtype)
|
||||
while len(emb_out.shape) < len(h.shape):
|
||||
emb_out = emb_out[..., None]
|
||||
if self.use_scale_shift_norm:
|
||||
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
|
||||
scale, shift = th.chunk(emb_out, 2, dim=1)
|
||||
h = out_norm(h) * (1 + scale) + shift
|
||||
h = out_rest(h)
|
||||
else:
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
class Timestep(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
|
||||
def forward(self, t):
|
||||
return timestep_embedding(t, self.dim)
|
||||
|
||||
|
||||
class UNetModel(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_classes: if specified (as an int), then this model will be
|
||||
class-conditional with `num_classes` classes.
|
||||
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_size,
|
||||
in_channels,
|
||||
model_channels,
|
||||
out_channels,
|
||||
num_res_blocks,
|
||||
attention_resolutions,
|
||||
dropout=0,
|
||||
channel_mult=(1, 2, 4, 8),
|
||||
conv_resample=True,
|
||||
dims=2,
|
||||
num_classes=None,
|
||||
use_checkpoint=False,
|
||||
use_fp16=False,
|
||||
use_bf16=False,
|
||||
num_heads=-1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
use_scale_shift_norm=False,
|
||||
resblock_updown=False,
|
||||
use_new_attention_order=False,
|
||||
use_spatial_transformer=False, # custom transformer support
|
||||
transformer_depth=1, # custom transformer support
|
||||
context_dim=None, # custom transformer support
|
||||
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
|
||||
legacy=True,
|
||||
disable_self_attentions=None,
|
||||
num_attention_blocks=None,
|
||||
disable_middle_self_attn=False,
|
||||
use_linear_in_transformer=False,
|
||||
adm_in_channels=None,
|
||||
transformer_depth_middle=None,
|
||||
device=None,
|
||||
operations=comfy.ops,
|
||||
):
|
||||
super().__init__()
|
||||
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
|
||||
if use_spatial_transformer:
|
||||
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
|
||||
|
||||
if context_dim is not None:
|
||||
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
|
||||
# from omegaconf.listconfig import ListConfig
|
||||
# if type(context_dim) == ListConfig:
|
||||
# context_dim = list(context_dim)
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
if num_heads == -1:
|
||||
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
|
||||
if num_head_channels == -1:
|
||||
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
|
||||
|
||||
self.image_size = image_size
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
if isinstance(transformer_depth, int):
|
||||
transformer_depth = len(channel_mult) * [transformer_depth]
|
||||
if transformer_depth_middle is None:
|
||||
transformer_depth_middle = transformer_depth[-1]
|
||||
if isinstance(num_res_blocks, int):
|
||||
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
|
||||
else:
|
||||
if len(num_res_blocks) != len(channel_mult):
|
||||
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
|
||||
"as a list/tuple (per-level) with the same length as channel_mult")
|
||||
self.num_res_blocks = num_res_blocks
|
||||
if disable_self_attentions is not None:
|
||||
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
|
||||
assert len(disable_self_attentions) == len(channel_mult)
|
||||
if num_attention_blocks is not None:
|
||||
assert len(num_attention_blocks) == len(self.num_res_blocks)
|
||||
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
|
||||
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
|
||||
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
|
||||
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
|
||||
f"attention will still not be set.")
|
||||
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.num_classes = num_classes
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.dtype = th.float16 if use_fp16 else th.float32
|
||||
self.dtype = th.bfloat16 if use_bf16 else self.dtype
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.predict_codebook_ids = n_embed is not None
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
|
||||
if self.num_classes is not None:
|
||||
if isinstance(self.num_classes, int):
|
||||
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
||||
elif self.num_classes == "continuous":
|
||||
print("setting up linear c_adm embedding layer")
|
||||
self.label_emb = nn.Linear(1, time_embed_dim)
|
||||
elif self.num_classes == "sequential":
|
||||
assert adm_in_channels is not None
|
||||
self.label_emb = nn.Sequential(
|
||||
nn.Sequential(
|
||||
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise ValueError()
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
)
|
||||
]
|
||||
)
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
for level, mult in enumerate(channel_mult):
|
||||
for nr in range(self.num_res_blocks[level]):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=mult * model_channels,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
if exists(disable_self_attentions):
|
||||
disabled_sa = disable_self_attentions[level]
|
||||
else:
|
||||
disabled_sa = False
|
||||
|
||||
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
|
||||
layers.append(SpatialTransformer(
|
||||
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
|
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
down=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
SpatialTransformer( # always uses a self-attn
|
||||
ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
|
||||
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, mult in list(enumerate(channel_mult))[::-1]:
|
||||
for i in range(self.num_res_blocks[level] + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=model_channels * mult,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
if ds in attention_resolutions:
|
||||
if num_head_channels == -1:
|
||||
dim_head = ch // num_heads
|
||||
else:
|
||||
num_heads = ch // num_head_channels
|
||||
dim_head = num_head_channels
|
||||
if legacy:
|
||||
#num_heads = 1
|
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
||||
if exists(disable_self_attentions):
|
||||
disabled_sa = disable_self_attentions[level]
|
||||
else:
|
||||
disabled_sa = False
|
||||
|
||||
if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
|
||||
layers.append(
|
||||
SpatialTransformer(
|
||||
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
|
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
if level and i == self.num_res_blocks[level]:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
up=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
|
||||
)
|
||||
if self.predict_codebook_ids:
|
||||
self.id_predictor = nn.Sequential(
|
||||
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
|
||||
operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
|
||||
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
|
||||
)
|
||||
|
||||
def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param context: conditioning plugged in via crossattn
|
||||
:param y: an [N] Tensor of labels, if class-conditional.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
transformer_options["original_shape"] = list(x.shape)
|
||||
transformer_options["current_index"] = 0
|
||||
transformer_patches = transformer_options.get("patches", {})
|
||||
|
||||
assert (y is not None) == (
|
||||
self.num_classes is not None
|
||||
), "must specify y if and only if the model is class-conditional"
|
||||
hs = []
|
||||
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
|
||||
emb = self.time_embed(t_emb)
|
||||
|
||||
if self.num_classes is not None:
|
||||
assert y.shape[0] == x.shape[0]
|
||||
emb = emb + self.label_emb(y)
|
||||
|
||||
h = x.type(self.dtype)
|
||||
for id, module in enumerate(self.input_blocks):
|
||||
transformer_options["block"] = ("input", id)
|
||||
h = forward_timestep_embed(module, h, emb, context, transformer_options)
|
||||
if control is not None and 'input' in control and len(control['input']) > 0:
|
||||
ctrl = control['input'].pop()
|
||||
if ctrl is not None:
|
||||
h += ctrl
|
||||
hs.append(h)
|
||||
transformer_options["block"] = ("middle", 0)
|
||||
h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
|
||||
if control is not None and 'middle' in control and len(control['middle']) > 0:
|
||||
ctrl = control['middle'].pop()
|
||||
if ctrl is not None:
|
||||
h += ctrl
|
||||
|
||||
for id, module in enumerate(self.output_blocks):
|
||||
transformer_options["block"] = ("output", id)
|
||||
hsp = hs.pop()
|
||||
if control is not None and 'output' in control and len(control['output']) > 0:
|
||||
ctrl = control['output'].pop()
|
||||
if ctrl is not None:
|
||||
hsp += ctrl
|
||||
|
||||
if "output_block_patch" in transformer_patches:
|
||||
patch = transformer_patches["output_block_patch"]
|
||||
for p in patch:
|
||||
h, hsp = p(h, hsp, transformer_options)
|
||||
|
||||
h = th.cat([h, hsp], dim=1)
|
||||
del hsp
|
||||
if len(hs) > 0:
|
||||
output_shape = hs[-1].shape
|
||||
else:
|
||||
output_shape = None
|
||||
h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
|
||||
h = h.type(x.dtype)
|
||||
if self.predict_codebook_ids:
|
||||
return self.id_predictor(h)
|
||||
else:
|
||||
return self.out(h)
|
||||
@@ -0,0 +1,81 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
|
||||
from .util import extract_into_tensor, make_beta_schedule
|
||||
from comfy.ldm.util import default
|
||||
|
||||
|
||||
class AbstractLowScaleModel(nn.Module):
|
||||
# for concatenating a downsampled image to the latent representation
|
||||
def __init__(self, noise_schedule_config=None):
|
||||
super(AbstractLowScaleModel, self).__init__()
|
||||
if noise_schedule_config is not None:
|
||||
self.register_schedule(**noise_schedule_config)
|
||||
|
||||
def register_schedule(self, beta_schedule="linear", timesteps=1000,
|
||||
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
|
||||
cosine_s=cosine_s)
|
||||
alphas = 1. - betas
|
||||
alphas_cumprod = np.cumprod(alphas, axis=0)
|
||||
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
||||
|
||||
timesteps, = betas.shape
|
||||
self.num_timesteps = int(timesteps)
|
||||
self.linear_start = linear_start
|
||||
self.linear_end = linear_end
|
||||
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
|
||||
|
||||
to_torch = partial(torch.tensor, dtype=torch.float32)
|
||||
|
||||
self.register_buffer('betas', to_torch(betas))
|
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
||||
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
|
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others
|
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
|
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
|
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
|
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
|
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
|
||||
|
||||
def q_sample(self, x_start, t, noise=None):
|
||||
noise = default(noise, lambda: torch.randn_like(x_start))
|
||||
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
|
||||
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
|
||||
|
||||
def forward(self, x):
|
||||
return x, None
|
||||
|
||||
def decode(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class SimpleImageConcat(AbstractLowScaleModel):
|
||||
# no noise level conditioning
|
||||
def __init__(self):
|
||||
super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
|
||||
self.max_noise_level = 0
|
||||
|
||||
def forward(self, x):
|
||||
# fix to constant noise level
|
||||
return x, torch.zeros(x.shape[0], device=x.device).long()
|
||||
|
||||
|
||||
class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
|
||||
def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
|
||||
super().__init__(noise_schedule_config=noise_schedule_config)
|
||||
self.max_noise_level = max_noise_level
|
||||
|
||||
def forward(self, x, noise_level=None):
|
||||
if noise_level is None:
|
||||
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
|
||||
else:
|
||||
assert isinstance(noise_level, torch.Tensor)
|
||||
z = self.q_sample(x, noise_level)
|
||||
return z, noise_level
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,278 @@
|
||||
# adopted from
|
||||
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
|
||||
# and
|
||||
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
||||
# and
|
||||
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
|
||||
#
|
||||
# thanks!
|
||||
|
||||
|
||||
import os
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import repeat
|
||||
|
||||
from comfy.ldm.util import instantiate_from_config
|
||||
import comfy.ops
|
||||
|
||||
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
||||
if schedule == "linear":
|
||||
betas = (
|
||||
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
|
||||
)
|
||||
|
||||
elif schedule == "cosine":
|
||||
timesteps = (
|
||||
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
|
||||
)
|
||||
alphas = timesteps / (1 + cosine_s) * np.pi / 2
|
||||
alphas = torch.cos(alphas).pow(2)
|
||||
alphas = alphas / alphas[0]
|
||||
betas = 1 - alphas[1:] / alphas[:-1]
|
||||
betas = np.clip(betas, a_min=0, a_max=0.999)
|
||||
|
||||
elif schedule == "squaredcos_cap_v2": # used for karlo prior
|
||||
# return early
|
||||
return betas_for_alpha_bar(
|
||||
n_timestep,
|
||||
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
|
||||
)
|
||||
|
||||
elif schedule == "sqrt_linear":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
|
||||
elif schedule == "sqrt":
|
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
|
||||
else:
|
||||
raise ValueError(f"schedule '{schedule}' unknown.")
|
||||
return betas.numpy()
|
||||
|
||||
|
||||
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
|
||||
if ddim_discr_method == 'uniform':
|
||||
c = num_ddpm_timesteps // num_ddim_timesteps
|
||||
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
|
||||
elif ddim_discr_method == 'quad':
|
||||
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
|
||||
else:
|
||||
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
|
||||
|
||||
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
|
||||
# add one to get the final alpha values right (the ones from first scale to data during sampling)
|
||||
steps_out = ddim_timesteps + 1
|
||||
if verbose:
|
||||
print(f'Selected timesteps for ddim sampler: {steps_out}')
|
||||
return steps_out
|
||||
|
||||
|
||||
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
|
||||
# select alphas for computing the variance schedule
|
||||
alphas = alphacums[ddim_timesteps]
|
||||
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
|
||||
|
||||
# according the the formula provided in https://arxiv.org/abs/2010.02502
|
||||
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
|
||||
if verbose:
|
||||
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
|
||||
print(f'For the chosen value of eta, which is {eta}, '
|
||||
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
|
||||
return sigmas, alphas, alphas_prev
|
||||
|
||||
|
||||
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
|
||||
"""
|
||||
Create a beta schedule that discretizes the given alpha_t_bar function,
|
||||
which defines the cumulative product of (1-beta) over time from t = [0,1].
|
||||
:param num_diffusion_timesteps: the number of betas to produce.
|
||||
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
|
||||
produces the cumulative product of (1-beta) up to that
|
||||
part of the diffusion process.
|
||||
:param max_beta: the maximum beta to use; use values lower than 1 to
|
||||
prevent singularities.
|
||||
"""
|
||||
betas = []
|
||||
for i in range(num_diffusion_timesteps):
|
||||
t1 = i / num_diffusion_timesteps
|
||||
t2 = (i + 1) / num_diffusion_timesteps
|
||||
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
|
||||
return np.array(betas)
|
||||
|
||||
|
||||
def extract_into_tensor(a, t, x_shape):
|
||||
b, *_ = t.shape
|
||||
out = a.gather(-1, t)
|
||||
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
||||
|
||||
|
||||
def checkpoint(func, inputs, params, flag):
|
||||
"""
|
||||
Evaluate a function without caching intermediate activations, allowing for
|
||||
reduced memory at the expense of extra compute in the backward pass.
|
||||
:param func: the function to evaluate.
|
||||
:param inputs: the argument sequence to pass to `func`.
|
||||
:param params: a sequence of parameters `func` depends on but does not
|
||||
explicitly take as arguments.
|
||||
:param flag: if False, disable gradient checkpointing.
|
||||
"""
|
||||
if flag:
|
||||
args = tuple(inputs) + tuple(params)
|
||||
return CheckpointFunction.apply(func, len(inputs), *args)
|
||||
else:
|
||||
return func(*inputs)
|
||||
|
||||
|
||||
class CheckpointFunction(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, run_function, length, *args):
|
||||
ctx.run_function = run_function
|
||||
ctx.input_tensors = list(args[:length])
|
||||
ctx.input_params = list(args[length:])
|
||||
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
|
||||
"dtype": torch.get_autocast_gpu_dtype(),
|
||||
"cache_enabled": torch.is_autocast_cache_enabled()}
|
||||
with torch.no_grad():
|
||||
output_tensors = ctx.run_function(*ctx.input_tensors)
|
||||
return output_tensors
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, *output_grads):
|
||||
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
|
||||
with torch.enable_grad(), \
|
||||
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
|
||||
# Fixes a bug where the first op in run_function modifies the
|
||||
# Tensor storage in place, which is not allowed for detach()'d
|
||||
# Tensors.
|
||||
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
|
||||
output_tensors = ctx.run_function(*shallow_copies)
|
||||
input_grads = torch.autograd.grad(
|
||||
output_tensors,
|
||||
ctx.input_tensors + ctx.input_params,
|
||||
output_grads,
|
||||
allow_unused=True,
|
||||
)
|
||||
del ctx.input_tensors
|
||||
del ctx.input_params
|
||||
del output_tensors
|
||||
return (None, None) + input_grads
|
||||
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an [N x dim] Tensor of positional embeddings.
|
||||
"""
|
||||
if not repeat_only:
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
||||
).to(device=timesteps.device)
|
||||
args = timesteps[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
else:
|
||||
embedding = repeat(timesteps, 'b -> b d', d=dim)
|
||||
return embedding
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
"""
|
||||
Zero out the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().zero_()
|
||||
return module
|
||||
|
||||
|
||||
def scale_module(module, scale):
|
||||
"""
|
||||
Scale the parameters of a module and return it.
|
||||
"""
|
||||
for p in module.parameters():
|
||||
p.detach().mul_(scale)
|
||||
return module
|
||||
|
||||
|
||||
def mean_flat(tensor):
|
||||
"""
|
||||
Take the mean over all non-batch dimensions.
|
||||
"""
|
||||
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
||||
|
||||
|
||||
def normalization(channels, dtype=None):
|
||||
"""
|
||||
Make a standard normalization layer.
|
||||
:param channels: number of input channels.
|
||||
:return: an nn.Module for normalization.
|
||||
"""
|
||||
return GroupNorm32(32, channels, dtype=dtype)
|
||||
|
||||
|
||||
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
|
||||
class SiLU(nn.Module):
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class GroupNorm32(nn.GroupNorm):
|
||||
def forward(self, x):
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D convolution module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.Conv1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return comfy.ops.Conv2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.Conv3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
def linear(*args, **kwargs):
|
||||
"""
|
||||
Create a linear module.
|
||||
"""
|
||||
return comfy.ops.Linear(*args, **kwargs)
|
||||
|
||||
|
||||
def avg_pool_nd(dims, *args, **kwargs):
|
||||
"""
|
||||
Create a 1D, 2D, or 3D average pooling module.
|
||||
"""
|
||||
if dims == 1:
|
||||
return nn.AvgPool1d(*args, **kwargs)
|
||||
elif dims == 2:
|
||||
return nn.AvgPool2d(*args, **kwargs)
|
||||
elif dims == 3:
|
||||
return nn.AvgPool3d(*args, **kwargs)
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
class HybridConditioner(nn.Module):
|
||||
|
||||
def __init__(self, c_concat_config, c_crossattn_config):
|
||||
super().__init__()
|
||||
self.concat_conditioner = instantiate_from_config(c_concat_config)
|
||||
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
|
||||
|
||||
def forward(self, c_concat, c_crossattn):
|
||||
c_concat = self.concat_conditioner(c_concat)
|
||||
c_crossattn = self.crossattn_conditioner(c_crossattn)
|
||||
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
|
||||
|
||||
|
||||
def noise_like(shape, device, repeat=False):
|
||||
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
|
||||
noise = lambda: torch.randn(shape, device=device)
|
||||
return repeat_noise() if repeat else noise()
|
||||
@@ -0,0 +1,92 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
class AbstractDistribution:
|
||||
def sample(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
def mode(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class DiracDistribution(AbstractDistribution):
|
||||
def __init__(self, value):
|
||||
self.value = value
|
||||
|
||||
def sample(self):
|
||||
return self.value
|
||||
|
||||
def mode(self):
|
||||
return self.value
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
def normal_kl(mean1, logvar1, mean2, logvar2):
|
||||
"""
|
||||
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
|
||||
Compute the KL divergence between two gaussians.
|
||||
Shapes are automatically broadcasted, so batches can be compared to
|
||||
scalars, among other use cases.
|
||||
"""
|
||||
tensor = None
|
||||
for obj in (mean1, logvar1, mean2, logvar2):
|
||||
if isinstance(obj, torch.Tensor):
|
||||
tensor = obj
|
||||
break
|
||||
assert tensor is not None, "at least one argument must be a Tensor"
|
||||
|
||||
# Force variances to be Tensors. Broadcasting helps convert scalars to
|
||||
# Tensors, but it does not work for torch.exp().
|
||||
logvar1, logvar2 = [
|
||||
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
|
||||
for x in (logvar1, logvar2)
|
||||
]
|
||||
|
||||
return 0.5 * (
|
||||
-1.0
|
||||
+ logvar2
|
||||
- logvar1
|
||||
+ torch.exp(logvar1 - logvar2)
|
||||
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
|
||||
)
|
||||
@@ -0,0 +1,80 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class LitEma(nn.Module):
|
||||
def __init__(self, model, decay=0.9999, use_num_upates=True):
|
||||
super().__init__()
|
||||
if decay < 0.0 or decay > 1.0:
|
||||
raise ValueError('Decay must be between 0 and 1')
|
||||
|
||||
self.m_name2s_name = {}
|
||||
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
|
||||
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
|
||||
else torch.tensor(-1, dtype=torch.int))
|
||||
|
||||
for name, p in model.named_parameters():
|
||||
if p.requires_grad:
|
||||
# remove as '.'-character is not allowed in buffers
|
||||
s_name = name.replace('.', '')
|
||||
self.m_name2s_name.update({name: s_name})
|
||||
self.register_buffer(s_name, p.clone().detach().data)
|
||||
|
||||
self.collected_params = []
|
||||
|
||||
def reset_num_updates(self):
|
||||
del self.num_updates
|
||||
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
|
||||
|
||||
def forward(self, model):
|
||||
decay = self.decay
|
||||
|
||||
if self.num_updates >= 0:
|
||||
self.num_updates += 1
|
||||
decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
|
||||
|
||||
one_minus_decay = 1.0 - decay
|
||||
|
||||
with torch.no_grad():
|
||||
m_param = dict(model.named_parameters())
|
||||
shadow_params = dict(self.named_buffers())
|
||||
|
||||
for key in m_param:
|
||||
if m_param[key].requires_grad:
|
||||
sname = self.m_name2s_name[key]
|
||||
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
|
||||
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
|
||||
else:
|
||||
assert not key in self.m_name2s_name
|
||||
|
||||
def copy_to(self, model):
|
||||
m_param = dict(model.named_parameters())
|
||||
shadow_params = dict(self.named_buffers())
|
||||
for key in m_param:
|
||||
if m_param[key].requires_grad:
|
||||
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
|
||||
else:
|
||||
assert not key in self.m_name2s_name
|
||||
|
||||
def store(self, parameters):
|
||||
"""
|
||||
Save the current parameters for restoring later.
|
||||
Args:
|
||||
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
||||
temporarily stored.
|
||||
"""
|
||||
self.collected_params = [param.clone() for param in parameters]
|
||||
|
||||
def restore(self, parameters):
|
||||
"""
|
||||
Restore the parameters stored with the `store` method.
|
||||
Useful to validate the model with EMA parameters without affecting the
|
||||
original optimization process. Store the parameters before the
|
||||
`copy_to` method. After validation (or model saving), use this to
|
||||
restore the former parameters.
|
||||
Args:
|
||||
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
||||
updated with the stored parameters.
|
||||
"""
|
||||
for c_param, param in zip(self.collected_params, parameters):
|
||||
param.data.copy_(c_param.data)
|
||||
@@ -0,0 +1,35 @@
|
||||
from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
|
||||
from ..diffusionmodules.openaimodel import Timestep
|
||||
import torch
|
||||
|
||||
class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
|
||||
def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
if clip_stats_path is None:
|
||||
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
|
||||
else:
|
||||
clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
|
||||
self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
|
||||
self.register_buffer("data_std", clip_std[None, :], persistent=False)
|
||||
self.time_embed = Timestep(timestep_dim)
|
||||
|
||||
def scale(self, x):
|
||||
# re-normalize to centered mean and unit variance
|
||||
x = (x - self.data_mean) * 1. / self.data_std
|
||||
return x
|
||||
|
||||
def unscale(self, x):
|
||||
# back to original data stats
|
||||
x = (x * self.data_std) + self.data_mean
|
||||
return x
|
||||
|
||||
def forward(self, x, noise_level=None):
|
||||
if noise_level is None:
|
||||
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
|
||||
else:
|
||||
assert isinstance(noise_level, torch.Tensor)
|
||||
x = self.scale(x)
|
||||
z = self.q_sample(x, noise_level)
|
||||
z = self.unscale(z)
|
||||
noise_level = self.time_embed(noise_level)
|
||||
return z, noise_level
|
||||
@@ -0,0 +1,250 @@
|
||||
# original source:
|
||||
# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
|
||||
# license:
|
||||
# MIT
|
||||
# credit:
|
||||
# Amin Rezaei (original author)
|
||||
# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
|
||||
# implementation of:
|
||||
# Self-attention Does Not Need O(n2) Memory":
|
||||
# https://arxiv.org/abs/2112.05682v2
|
||||
|
||||
from functools import partial
|
||||
import torch
|
||||
from torch import Tensor
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
import math
|
||||
|
||||
try:
|
||||
from typing import Optional, NamedTuple, List, Protocol
|
||||
except ImportError:
|
||||
from typing import Optional, NamedTuple, List
|
||||
from typing_extensions import Protocol
|
||||
|
||||
from torch import Tensor
|
||||
from typing import List
|
||||
|
||||
from comfy import model_management
|
||||
|
||||
def dynamic_slice(
|
||||
x: Tensor,
|
||||
starts: List[int],
|
||||
sizes: List[int],
|
||||
) -> Tensor:
|
||||
slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
|
||||
return x[slicing]
|
||||
|
||||
class AttnChunk(NamedTuple):
|
||||
exp_values: Tensor
|
||||
exp_weights_sum: Tensor
|
||||
max_score: Tensor
|
||||
|
||||
class SummarizeChunk(Protocol):
|
||||
@staticmethod
|
||||
def __call__(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
) -> AttnChunk: ...
|
||||
|
||||
class ComputeQueryChunkAttn(Protocol):
|
||||
@staticmethod
|
||||
def __call__(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
) -> Tensor: ...
|
||||
|
||||
def _summarize_chunk(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
scale: float,
|
||||
upcast_attention: bool,
|
||||
) -> AttnChunk:
|
||||
if upcast_attention:
|
||||
with torch.autocast(enabled=False, device_type = 'cuda'):
|
||||
query = query.float()
|
||||
key_t = key_t.float()
|
||||
attn_weights = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key_t,
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
else:
|
||||
attn_weights = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key_t,
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
max_score, _ = torch.max(attn_weights, -1, keepdim=True)
|
||||
max_score = max_score.detach()
|
||||
torch.exp(attn_weights - max_score, out=attn_weights)
|
||||
exp_weights = attn_weights.to(value.dtype)
|
||||
exp_values = torch.bmm(exp_weights, value)
|
||||
max_score = max_score.squeeze(-1)
|
||||
return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
|
||||
|
||||
def _query_chunk_attention(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
summarize_chunk: SummarizeChunk,
|
||||
kv_chunk_size: int,
|
||||
) -> Tensor:
|
||||
batch_x_heads, k_channels_per_head, k_tokens = key_t.shape
|
||||
_, _, v_channels_per_head = value.shape
|
||||
|
||||
def chunk_scanner(chunk_idx: int) -> AttnChunk:
|
||||
key_chunk = dynamic_slice(
|
||||
key_t,
|
||||
(0, 0, chunk_idx),
|
||||
(batch_x_heads, k_channels_per_head, kv_chunk_size)
|
||||
)
|
||||
value_chunk = dynamic_slice(
|
||||
value,
|
||||
(0, chunk_idx, 0),
|
||||
(batch_x_heads, kv_chunk_size, v_channels_per_head)
|
||||
)
|
||||
return summarize_chunk(query, key_chunk, value_chunk)
|
||||
|
||||
chunks: List[AttnChunk] = [
|
||||
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
|
||||
]
|
||||
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
|
||||
chunk_values, chunk_weights, chunk_max = acc_chunk
|
||||
|
||||
global_max, _ = torch.max(chunk_max, 0, keepdim=True)
|
||||
max_diffs = torch.exp(chunk_max - global_max)
|
||||
chunk_values *= torch.unsqueeze(max_diffs, -1)
|
||||
chunk_weights *= max_diffs
|
||||
|
||||
all_values = chunk_values.sum(dim=0)
|
||||
all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
|
||||
return all_values / all_weights
|
||||
|
||||
# TODO: refactor CrossAttention#get_attention_scores to share code with this
|
||||
def _get_attention_scores_no_kv_chunking(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
scale: float,
|
||||
upcast_attention: bool,
|
||||
) -> Tensor:
|
||||
if upcast_attention:
|
||||
with torch.autocast(enabled=False, device_type = 'cuda'):
|
||||
query = query.float()
|
||||
key_t = key_t.float()
|
||||
attn_scores = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key_t,
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
else:
|
||||
attn_scores = torch.baddbmm(
|
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
|
||||
query,
|
||||
key_t,
|
||||
alpha=scale,
|
||||
beta=0,
|
||||
)
|
||||
|
||||
try:
|
||||
attn_probs = attn_scores.softmax(dim=-1)
|
||||
del attn_scores
|
||||
except model_management.OOM_EXCEPTION:
|
||||
print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
|
||||
attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
|
||||
torch.exp(attn_scores, out=attn_scores)
|
||||
summed = torch.sum(attn_scores, dim=-1, keepdim=True)
|
||||
attn_scores /= summed
|
||||
attn_probs = attn_scores
|
||||
|
||||
hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value)
|
||||
return hidden_states_slice
|
||||
|
||||
class ScannedChunk(NamedTuple):
|
||||
chunk_idx: int
|
||||
attn_chunk: AttnChunk
|
||||
|
||||
def efficient_dot_product_attention(
|
||||
query: Tensor,
|
||||
key_t: Tensor,
|
||||
value: Tensor,
|
||||
query_chunk_size=1024,
|
||||
kv_chunk_size: Optional[int] = None,
|
||||
kv_chunk_size_min: Optional[int] = None,
|
||||
use_checkpoint=True,
|
||||
upcast_attention=False,
|
||||
):
|
||||
"""Computes efficient dot-product attention given query, transposed key, and value.
|
||||
This is efficient version of attention presented in
|
||||
https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
|
||||
Args:
|
||||
query: queries for calculating attention with shape of
|
||||
`[batch * num_heads, tokens, channels_per_head]`.
|
||||
key_t: keys for calculating attention with shape of
|
||||
`[batch * num_heads, channels_per_head, tokens]`.
|
||||
value: values to be used in attention with shape of
|
||||
`[batch * num_heads, tokens, channels_per_head]`.
|
||||
query_chunk_size: int: query chunks size
|
||||
kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
|
||||
kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
|
||||
use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
|
||||
Returns:
|
||||
Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
|
||||
"""
|
||||
batch_x_heads, q_tokens, q_channels_per_head = query.shape
|
||||
_, _, k_tokens = key_t.shape
|
||||
scale = q_channels_per_head ** -0.5
|
||||
|
||||
kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
|
||||
if kv_chunk_size_min is not None:
|
||||
kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
|
||||
|
||||
def get_query_chunk(chunk_idx: int) -> Tensor:
|
||||
return dynamic_slice(
|
||||
query,
|
||||
(0, chunk_idx, 0),
|
||||
(batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
|
||||
)
|
||||
|
||||
summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
|
||||
summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
|
||||
compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
|
||||
_get_attention_scores_no_kv_chunking,
|
||||
scale=scale,
|
||||
upcast_attention=upcast_attention
|
||||
) if k_tokens <= kv_chunk_size else (
|
||||
# fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
|
||||
partial(
|
||||
_query_chunk_attention,
|
||||
kv_chunk_size=kv_chunk_size,
|
||||
summarize_chunk=summarize_chunk,
|
||||
)
|
||||
)
|
||||
|
||||
if q_tokens <= query_chunk_size:
|
||||
# fast-path for when there's just 1 query chunk
|
||||
return compute_query_chunk_attn(
|
||||
query=query,
|
||||
key_t=key_t,
|
||||
value=value,
|
||||
)
|
||||
|
||||
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
|
||||
# and pass slices to be mutated, instead of torch.cat()ing the returned slices
|
||||
res = torch.cat([
|
||||
compute_query_chunk_attn(
|
||||
query=get_query_chunk(i * query_chunk_size),
|
||||
key_t=key_t,
|
||||
value=value,
|
||||
) for i in range(math.ceil(q_tokens / query_chunk_size))
|
||||
], dim=1)
|
||||
return res
|
||||
@@ -0,0 +1,197 @@
|
||||
import importlib
|
||||
|
||||
import torch
|
||||
from torch import optim
|
||||
import numpy as np
|
||||
|
||||
from inspect import isfunction
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
|
||||
def log_txt_as_img(wh, xc, size=10):
|
||||
# wh a tuple of (width, height)
|
||||
# xc a list of captions to plot
|
||||
b = len(xc)
|
||||
txts = list()
|
||||
for bi in range(b):
|
||||
txt = Image.new("RGB", wh, color="white")
|
||||
draw = ImageDraw.Draw(txt)
|
||||
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
|
||||
nc = int(40 * (wh[0] / 256))
|
||||
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
|
||||
|
||||
try:
|
||||
draw.text((0, 0), lines, fill="black", font=font)
|
||||
except UnicodeEncodeError:
|
||||
print("Cant encode string for logging. Skipping.")
|
||||
|
||||
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
|
||||
txts.append(txt)
|
||||
txts = np.stack(txts)
|
||||
txts = torch.tensor(txts)
|
||||
return txts
|
||||
|
||||
|
||||
def ismap(x):
|
||||
if not isinstance(x, torch.Tensor):
|
||||
return False
|
||||
return (len(x.shape) == 4) and (x.shape[1] > 3)
|
||||
|
||||
|
||||
def isimage(x):
|
||||
if not isinstance(x,torch.Tensor):
|
||||
return False
|
||||
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
|
||||
|
||||
|
||||
def exists(x):
|
||||
return x is not None
|
||||
|
||||
|
||||
def default(val, d):
|
||||
if exists(val):
|
||||
return val
|
||||
return d() if isfunction(d) else d
|
||||
|
||||
|
||||
def mean_flat(tensor):
|
||||
"""
|
||||
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
|
||||
Take the mean over all non-batch dimensions.
|
||||
"""
|
||||
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
||||
|
||||
|
||||
def count_params(model, verbose=False):
|
||||
total_params = sum(p.numel() for p in model.parameters())
|
||||
if verbose:
|
||||
print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
|
||||
return total_params
|
||||
|
||||
|
||||
def instantiate_from_config(config):
|
||||
if not "target" in config:
|
||||
if config == '__is_first_stage__':
|
||||
return None
|
||||
elif config == "__is_unconditional__":
|
||||
return None
|
||||
raise KeyError("Expected key `target` to instantiate.")
|
||||
return get_obj_from_str(config["target"])(**config.get("params", dict()))
|
||||
|
||||
|
||||
def get_obj_from_str(string, reload=False):
|
||||
module, cls = string.rsplit(".", 1)
|
||||
if reload:
|
||||
module_imp = importlib.import_module(module)
|
||||
importlib.reload(module_imp)
|
||||
return getattr(importlib.import_module(module, package=None), cls)
|
||||
|
||||
|
||||
class AdamWwithEMAandWings(optim.Optimizer):
|
||||
# credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
|
||||
def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
|
||||
weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
|
||||
ema_power=1., param_names=()):
|
||||
"""AdamW that saves EMA versions of the parameters."""
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
if not 0.0 <= betas[0] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
|
||||
if not 0.0 <= betas[1] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
|
||||
if not 0.0 <= weight_decay:
|
||||
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
|
||||
if not 0.0 <= ema_decay <= 1.0:
|
||||
raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps,
|
||||
weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
|
||||
ema_power=ema_power, param_names=param_names)
|
||||
super().__init__(params, defaults)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super().__setstate__(state)
|
||||
for group in self.param_groups:
|
||||
group.setdefault('amsgrad', False)
|
||||
|
||||
@torch.no_grad()
|
||||
def step(self, closure=None):
|
||||
"""Performs a single optimization step.
|
||||
Args:
|
||||
closure (callable, optional): A closure that reevaluates the model
|
||||
and returns the loss.
|
||||
"""
|
||||
loss = None
|
||||
if closure is not None:
|
||||
with torch.enable_grad():
|
||||
loss = closure()
|
||||
|
||||
for group in self.param_groups:
|
||||
params_with_grad = []
|
||||
grads = []
|
||||
exp_avgs = []
|
||||
exp_avg_sqs = []
|
||||
ema_params_with_grad = []
|
||||
state_sums = []
|
||||
max_exp_avg_sqs = []
|
||||
state_steps = []
|
||||
amsgrad = group['amsgrad']
|
||||
beta1, beta2 = group['betas']
|
||||
ema_decay = group['ema_decay']
|
||||
ema_power = group['ema_power']
|
||||
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
params_with_grad.append(p)
|
||||
if p.grad.is_sparse:
|
||||
raise RuntimeError('AdamW does not support sparse gradients')
|
||||
grads.append(p.grad)
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
# State initialization
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
||||
if amsgrad:
|
||||
# Maintains max of all exp. moving avg. of sq. grad. values
|
||||
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
||||
# Exponential moving average of parameter values
|
||||
state['param_exp_avg'] = p.detach().float().clone()
|
||||
|
||||
exp_avgs.append(state['exp_avg'])
|
||||
exp_avg_sqs.append(state['exp_avg_sq'])
|
||||
ema_params_with_grad.append(state['param_exp_avg'])
|
||||
|
||||
if amsgrad:
|
||||
max_exp_avg_sqs.append(state['max_exp_avg_sq'])
|
||||
|
||||
# update the steps for each param group update
|
||||
state['step'] += 1
|
||||
# record the step after step update
|
||||
state_steps.append(state['step'])
|
||||
|
||||
optim._functional.adamw(params_with_grad,
|
||||
grads,
|
||||
exp_avgs,
|
||||
exp_avg_sqs,
|
||||
max_exp_avg_sqs,
|
||||
state_steps,
|
||||
amsgrad=amsgrad,
|
||||
beta1=beta1,
|
||||
beta2=beta2,
|
||||
lr=group['lr'],
|
||||
weight_decay=group['weight_decay'],
|
||||
eps=group['eps'],
|
||||
maximize=False)
|
||||
|
||||
cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
|
||||
for param, ema_param in zip(params_with_grad, ema_params_with_grad):
|
||||
ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
|
||||
|
||||
return loss
|
||||
Reference in New Issue
Block a user