631 lines
28 KiB
Python
631 lines
28 KiB
Python
import os
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from pathlib import Path
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import einops
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import torch
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import torch as th
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import torch.nn as nn
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import copy
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from easydict import EasyDict as edict
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from iopaint.model.anytext.ldm.modules.diffusionmodules.util import (
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conv_nd,
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linear,
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zero_module,
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timestep_embedding,
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)
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from einops import rearrange, repeat
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from iopaint.model.anytext.ldm.modules.attention import SpatialTransformer
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from iopaint.model.anytext.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
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from iopaint.model.anytext.ldm.models.diffusion.ddpm import LatentDiffusion
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from iopaint.model.anytext.ldm.util import log_txt_as_img, exists, instantiate_from_config
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from iopaint.model.anytext.ldm.models.diffusion.ddim import DDIMSampler
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from iopaint.model.anytext.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from .recognizer import TextRecognizer, create_predictor
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CURRENT_DIR = Path(os.path.dirname(os.path.abspath(__file__)))
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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class ControlledUnetModel(UNetModel):
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def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
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hs = []
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with torch.no_grad():
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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if self.use_fp16:
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t_emb = t_emb.half()
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emb = self.time_embed(t_emb)
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h = x.type(self.dtype)
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for module in self.input_blocks:
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h = module(h, emb, context)
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hs.append(h)
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h = self.middle_block(h, emb, context)
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if control is not None:
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h += control.pop()
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for i, module in enumerate(self.output_blocks):
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if only_mid_control or control is None:
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h = torch.cat([h, hs.pop()], dim=1)
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else:
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h = torch.cat([h, hs.pop() + control.pop()], dim=1)
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h = module(h, emb, context)
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h = h.type(x.dtype)
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return self.out(h)
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class ControlNet(nn.Module):
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def __init__(
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self,
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image_size,
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in_channels,
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model_channels,
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glyph_channels,
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position_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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use_checkpoint=False,
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use_fp16=False,
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num_heads=-1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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use_spatial_transformer=False, # custom transformer support
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transformer_depth=1, # custom transformer support
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context_dim=None, # custom transformer support
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n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
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legacy=True,
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disable_self_attentions=None,
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num_attention_blocks=None,
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disable_middle_self_attn=False,
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use_linear_in_transformer=False,
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):
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super().__init__()
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if use_spatial_transformer:
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assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
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if context_dim is not None:
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assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
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from omegaconf.listconfig import ListConfig
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if type(context_dim) == ListConfig:
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context_dim = list(context_dim)
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if num_heads == -1:
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assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
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if num_head_channels == -1:
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assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
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self.dims = dims
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self.image_size = image_size
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self.in_channels = in_channels
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self.model_channels = model_channels
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if isinstance(num_res_blocks, int):
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self.num_res_blocks = len(channel_mult) * [num_res_blocks]
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else:
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if len(num_res_blocks) != len(channel_mult):
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raise ValueError("provide num_res_blocks either as an int (globally constant) or "
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"as a list/tuple (per-level) with the same length as channel_mult")
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self.num_res_blocks = num_res_blocks
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if disable_self_attentions is not None:
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# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
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assert len(disable_self_attentions) == len(channel_mult)
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if num_attention_blocks is not None:
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assert len(num_attention_blocks) == len(self.num_res_blocks)
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assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
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print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
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f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
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f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
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f"attention will still not be set.")
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.use_checkpoint = use_checkpoint
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self.use_fp16 = use_fp16
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self.dtype = th.float16 if use_fp16 else th.float32
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.predict_codebook_ids = n_embed is not None
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
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self.glyph_block = TimestepEmbedSequential(
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conv_nd(dims, glyph_channels, 8, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 8, 8, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 8, 16, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 16, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 32, 32, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 96, 96, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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nn.SiLU(),
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)
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self.position_block = TimestepEmbedSequential(
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conv_nd(dims, position_channels, 8, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 8, 8, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 8, 16, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 16, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 32, 32, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 32, 64, 3, padding=1, stride=2),
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nn.SiLU(),
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)
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self.fuse_block = zero_module(conv_nd(dims, 256+64+4, model_channels, 3, padding=1))
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for nr in range(self.num_res_blocks[level]):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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if legacy:
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# num_heads = 1
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dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
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if exists(disable_self_attentions):
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disabled_sa = disable_self_attentions[level]
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else:
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disabled_sa = False
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if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
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layers.append(
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AttentionBlock(
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ch,
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use_checkpoint=use_checkpoint,
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num_heads=num_heads,
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num_head_channels=dim_head,
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use_new_attention_order=use_new_attention_order,
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) if not use_spatial_transformer else SpatialTransformer(
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ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
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disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self.zero_convs.append(self.make_zero_conv(ch))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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self.zero_convs.append(self.make_zero_conv(ch))
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ds *= 2
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self._feature_size += ch
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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if legacy:
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# num_heads = 1
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dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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AttentionBlock(
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ch,
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use_checkpoint=use_checkpoint,
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num_heads=num_heads,
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num_head_channels=dim_head,
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use_new_attention_order=use_new_attention_order,
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) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
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ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
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disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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)
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self.middle_block_out = self.make_zero_conv(ch)
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self._feature_size += ch
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def make_zero_conv(self, channels):
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return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
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def forward(self, x, hint, text_info, timesteps, context, **kwargs):
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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if self.use_fp16:
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t_emb = t_emb.half()
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emb = self.time_embed(t_emb)
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# guided_hint from text_info
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B, C, H, W = x.shape
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glyphs = torch.cat(text_info['glyphs'], dim=1).sum(dim=1, keepdim=True)
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positions = torch.cat(text_info['positions'], dim=1).sum(dim=1, keepdim=True)
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enc_glyph = self.glyph_block(glyphs, emb, context)
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enc_pos = self.position_block(positions, emb, context)
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guided_hint = self.fuse_block(torch.cat([enc_glyph, enc_pos, text_info['masked_x']], dim=1))
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outs = []
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h = x.type(self.dtype)
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for module, zero_conv in zip(self.input_blocks, self.zero_convs):
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if guided_hint is not None:
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h = module(h, emb, context)
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h += guided_hint
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guided_hint = None
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else:
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h = module(h, emb, context)
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outs.append(zero_conv(h, emb, context))
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h = self.middle_block(h, emb, context)
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outs.append(self.middle_block_out(h, emb, context))
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return outs
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class ControlLDM(LatentDiffusion):
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def __init__(self, control_stage_config, control_key, glyph_key, position_key, only_mid_control, loss_alpha=0, loss_beta=0, with_step_weight=False, use_vae_upsample=False, latin_weight=1.0, embedding_manager_config=None, *args, **kwargs):
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self.use_fp16 = kwargs.pop('use_fp16', False)
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super().__init__(*args, **kwargs)
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self.control_model = instantiate_from_config(control_stage_config)
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self.control_key = control_key
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self.glyph_key = glyph_key
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self.position_key = position_key
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self.only_mid_control = only_mid_control
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self.control_scales = [1.0] * 13
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self.loss_alpha = loss_alpha
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self.loss_beta = loss_beta
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self.with_step_weight = with_step_weight
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self.use_vae_upsample = use_vae_upsample
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self.latin_weight = latin_weight
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if embedding_manager_config is not None and embedding_manager_config.params.valid:
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self.embedding_manager = self.instantiate_embedding_manager(embedding_manager_config, self.cond_stage_model)
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for param in self.embedding_manager.embedding_parameters():
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param.requires_grad = True
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else:
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self.embedding_manager = None
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if self.loss_alpha > 0 or self.loss_beta > 0 or self.embedding_manager:
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if embedding_manager_config.params.emb_type == 'ocr':
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self.text_predictor = create_predictor().eval()
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args = edict()
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args.rec_image_shape = "3, 48, 320"
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args.rec_batch_num = 6
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args.rec_char_dict_path = str(CURRENT_DIR.parent / "ocr_recog" / "ppocr_keys_v1.txt")
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args.use_fp16 = self.use_fp16
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self.cn_recognizer = TextRecognizer(args, self.text_predictor)
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for param in self.text_predictor.parameters():
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param.requires_grad = False
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if self.embedding_manager:
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self.embedding_manager.recog = self.cn_recognizer
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@torch.no_grad()
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def get_input(self, batch, k, bs=None, *args, **kwargs):
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if self.embedding_manager is None: # fill in full caption
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self.fill_caption(batch)
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x, c, mx = super().get_input(batch, self.first_stage_key, mask_k='masked_img', *args, **kwargs)
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control = batch[self.control_key] # for log_images and loss_alpha, not real control
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if bs is not None:
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control = control[:bs]
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control = control.to(self.device)
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control = einops.rearrange(control, 'b h w c -> b c h w')
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control = control.to(memory_format=torch.contiguous_format).float()
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inv_mask = batch['inv_mask']
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if bs is not None:
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inv_mask = inv_mask[:bs]
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inv_mask = inv_mask.to(self.device)
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inv_mask = einops.rearrange(inv_mask, 'b h w c -> b c h w')
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inv_mask = inv_mask.to(memory_format=torch.contiguous_format).float()
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glyphs = batch[self.glyph_key]
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gly_line = batch['gly_line']
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positions = batch[self.position_key]
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n_lines = batch['n_lines']
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language = batch['language']
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texts = batch['texts']
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assert len(glyphs) == len(positions)
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for i in range(len(glyphs)):
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if bs is not None:
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glyphs[i] = glyphs[i][:bs]
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gly_line[i] = gly_line[i][:bs]
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positions[i] = positions[i][:bs]
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n_lines = n_lines[:bs]
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glyphs[i] = glyphs[i].to(self.device)
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gly_line[i] = gly_line[i].to(self.device)
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positions[i] = positions[i].to(self.device)
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glyphs[i] = einops.rearrange(glyphs[i], 'b h w c -> b c h w')
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gly_line[i] = einops.rearrange(gly_line[i], 'b h w c -> b c h w')
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positions[i] = einops.rearrange(positions[i], 'b h w c -> b c h w')
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glyphs[i] = glyphs[i].to(memory_format=torch.contiguous_format).float()
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gly_line[i] = gly_line[i].to(memory_format=torch.contiguous_format).float()
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positions[i] = positions[i].to(memory_format=torch.contiguous_format).float()
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info = {}
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info['glyphs'] = glyphs
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info['positions'] = positions
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info['n_lines'] = n_lines
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info['language'] = language
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info['texts'] = texts
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info['img'] = batch['img'] # nhwc, (-1,1)
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info['masked_x'] = mx
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info['gly_line'] = gly_line
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info['inv_mask'] = inv_mask
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return x, dict(c_crossattn=[c], c_concat=[control], text_info=info)
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def apply_model(self, x_noisy, t, cond, *args, **kwargs):
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assert isinstance(cond, dict)
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|
diffusion_model = self.model.diffusion_model
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|
_cond = torch.cat(cond['c_crossattn'], 1)
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|
_hint = torch.cat(cond['c_concat'], 1)
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if self.use_fp16:
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|
x_noisy = x_noisy.half()
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control = self.control_model(x=x_noisy, timesteps=t, context=_cond, hint=_hint, text_info=cond['text_info'])
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|
control = [c * scale for c, scale in zip(control, self.control_scales)]
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|
eps = diffusion_model(x=x_noisy, timesteps=t, context=_cond, control=control, only_mid_control=self.only_mid_control)
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|
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|
return eps
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|
|
|
def instantiate_embedding_manager(self, config, embedder):
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|
model = instantiate_from_config(config, embedder=embedder)
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|
return model
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|
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|
@torch.no_grad()
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|
def get_unconditional_conditioning(self, N):
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|
return self.get_learned_conditioning(dict(c_crossattn=[[""] * N], text_info=None))
|
|
|
|
def get_learned_conditioning(self, c):
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|
if self.cond_stage_forward is None:
|
|
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
|
|
if self.embedding_manager is not None and c['text_info'] is not None:
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|
self.embedding_manager.encode_text(c['text_info'])
|
|
if isinstance(c, dict):
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|
cond_txt = c['c_crossattn'][0]
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|
else:
|
|
cond_txt = c
|
|
if self.embedding_manager is not None:
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|
cond_txt = self.cond_stage_model.encode(cond_txt, embedding_manager=self.embedding_manager)
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|
else:
|
|
cond_txt = self.cond_stage_model.encode(cond_txt)
|
|
if isinstance(c, dict):
|
|
c['c_crossattn'][0] = cond_txt
|
|
else:
|
|
c = cond_txt
|
|
if isinstance(c, DiagonalGaussianDistribution):
|
|
c = c.mode()
|
|
else:
|
|
c = self.cond_stage_model(c)
|
|
else:
|
|
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
|
|
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
|
|
return c
|
|
|
|
def fill_caption(self, batch, place_holder='*'):
|
|
bs = len(batch['n_lines'])
|
|
cond_list = copy.deepcopy(batch[self.cond_stage_key])
|
|
for i in range(bs):
|
|
n_lines = batch['n_lines'][i]
|
|
if n_lines == 0:
|
|
continue
|
|
cur_cap = cond_list[i]
|
|
for j in range(n_lines):
|
|
r_txt = batch['texts'][j][i]
|
|
cur_cap = cur_cap.replace(place_holder, f'"{r_txt}"', 1)
|
|
cond_list[i] = cur_cap
|
|
batch[self.cond_stage_key] = cond_list
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
|
|
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
|
|
plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
|
|
use_ema_scope=True,
|
|
**kwargs):
|
|
use_ddim = ddim_steps is not None
|
|
|
|
log = dict()
|
|
z, c = self.get_input(batch, self.first_stage_key, bs=N)
|
|
if self.cond_stage_trainable:
|
|
with torch.no_grad():
|
|
c = self.get_learned_conditioning(c)
|
|
c_crossattn = c["c_crossattn"][0][:N]
|
|
c_cat = c["c_concat"][0][:N]
|
|
text_info = c["text_info"]
|
|
text_info['glyphs'] = [i[:N] for i in text_info['glyphs']]
|
|
text_info['gly_line'] = [i[:N] for i in text_info['gly_line']]
|
|
text_info['positions'] = [i[:N] for i in text_info['positions']]
|
|
text_info['n_lines'] = text_info['n_lines'][:N]
|
|
text_info['masked_x'] = text_info['masked_x'][:N]
|
|
text_info['img'] = text_info['img'][:N]
|
|
|
|
N = min(z.shape[0], N)
|
|
n_row = min(z.shape[0], n_row)
|
|
log["reconstruction"] = self.decode_first_stage(z)
|
|
log["masked_image"] = self.decode_first_stage(text_info['masked_x'])
|
|
log["control"] = c_cat * 2.0 - 1.0
|
|
log["img"] = text_info['img'].permute(0, 3, 1, 2) # log source image if needed
|
|
# get glyph
|
|
glyph_bs = torch.stack(text_info['glyphs'])
|
|
glyph_bs = torch.sum(glyph_bs, dim=0) * 2.0 - 1.0
|
|
log["glyph"] = torch.nn.functional.interpolate(glyph_bs, size=(512, 512), mode='bilinear', align_corners=True,)
|
|
# fill caption
|
|
if not self.embedding_manager:
|
|
self.fill_caption(batch)
|
|
captions = batch[self.cond_stage_key]
|
|
log["conditioning"] = log_txt_as_img((512, 512), captions, size=16)
|
|
|
|
if plot_diffusion_rows:
|
|
# get diffusion row
|
|
diffusion_row = list()
|
|
z_start = z[:n_row]
|
|
for t in range(self.num_timesteps):
|
|
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
|
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
|
|
t = t.to(self.device).long()
|
|
noise = torch.randn_like(z_start)
|
|
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
|
|
diffusion_row.append(self.decode_first_stage(z_noisy))
|
|
|
|
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
|
|
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
|
|
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
|
|
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
|
|
log["diffusion_row"] = diffusion_grid
|
|
|
|
if sample:
|
|
# get denoise row
|
|
samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c], "text_info": text_info},
|
|
batch_size=N, ddim=use_ddim,
|
|
ddim_steps=ddim_steps, eta=ddim_eta)
|
|
x_samples = self.decode_first_stage(samples)
|
|
log["samples"] = x_samples
|
|
if plot_denoise_rows:
|
|
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
|
|
log["denoise_row"] = denoise_grid
|
|
|
|
if unconditional_guidance_scale > 1.0:
|
|
uc_cross = self.get_unconditional_conditioning(N)
|
|
uc_cat = c_cat # torch.zeros_like(c_cat)
|
|
uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross['c_crossattn'][0]], "text_info": text_info}
|
|
samples_cfg, tmps = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c_crossattn], "text_info": text_info},
|
|
batch_size=N, ddim=use_ddim,
|
|
ddim_steps=ddim_steps, eta=ddim_eta,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=uc_full,
|
|
)
|
|
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
|
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
|
|
pred_x0 = False # wether log pred_x0
|
|
if pred_x0:
|
|
for idx in range(len(tmps['pred_x0'])):
|
|
pred_x0 = self.decode_first_stage(tmps['pred_x0'][idx])
|
|
log[f"pred_x0_{tmps['index'][idx]}"] = pred_x0
|
|
|
|
return log
|
|
|
|
@torch.no_grad()
|
|
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
|
|
ddim_sampler = DDIMSampler(self)
|
|
b, c, h, w = cond["c_concat"][0].shape
|
|
shape = (self.channels, h // 8, w // 8)
|
|
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, log_every_t=5, **kwargs)
|
|
return samples, intermediates
|
|
|
|
def configure_optimizers(self):
|
|
lr = self.learning_rate
|
|
params = list(self.control_model.parameters())
|
|
if self.embedding_manager:
|
|
params += list(self.embedding_manager.embedding_parameters())
|
|
if not self.sd_locked:
|
|
# params += list(self.model.diffusion_model.input_blocks.parameters())
|
|
# params += list(self.model.diffusion_model.middle_block.parameters())
|
|
params += list(self.model.diffusion_model.output_blocks.parameters())
|
|
params += list(self.model.diffusion_model.out.parameters())
|
|
if self.unlockKV:
|
|
nCount = 0
|
|
for name, param in self.model.diffusion_model.named_parameters():
|
|
if 'attn2.to_k' in name or 'attn2.to_v' in name:
|
|
params += [param]
|
|
nCount += 1
|
|
print(f'Cross attention is unlocked, and {nCount} Wk or Wv are added to potimizers!!!')
|
|
|
|
opt = torch.optim.AdamW(params, lr=lr)
|
|
return opt
|
|
|
|
def low_vram_shift(self, is_diffusing):
|
|
if is_diffusing:
|
|
self.model = self.model.cuda()
|
|
self.control_model = self.control_model.cuda()
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
self.cond_stage_model = self.cond_stage_model.cpu()
|
|
else:
|
|
self.model = self.model.cpu()
|
|
self.control_model = self.control_model.cpu()
|
|
self.first_stage_model = self.first_stage_model.cuda()
|
|
self.cond_stage_model = self.cond_stage_model.cuda()
|