cldm/cldm.py

import einops
import torch
import torch as th
import torch.nn as nn

from ldm.modules.diffusionmodules.util import (
    conv_nd,
    linear,
    zero_module,
    timestep_embedding,
)

from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.ema import LitEma
from contextlib import contextmanager, nullcontext
from cldm.model import load_state_dict
import numpy as np
from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR, OneCycleLR
def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self

class ControlledUnetModel(UNetModel):
    def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, context_glyph= None, **kwargs):
        hs = []
        with torch.no_grad():
            t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
            emb = self.time_embed(t_emb)
            h = x.type(self.dtype)
            for module in self.input_blocks:
                h = module(h, emb, context)
                hs.append(h)
            h = self.middle_block(h, emb, context)

        if control is not None:
            h += control.pop()

        for i, module in enumerate(self.output_blocks):
            if only_mid_control or control is None:
                h = torch.cat([h, hs.pop()], dim=1)
            else:
                h = torch.cat([h, hs.pop() + control.pop()], dim=1)
            h = module(h, emb, context if context_glyph is None else context_glyph)

        h = h.type(x.dtype)
        return self.out(h)


class ControlNet(nn.Module):
    def __init__(
            self,
            image_size,
            in_channels,
            model_channels,
            hint_channels,
            num_res_blocks,
            attention_resolutions,
            dropout=0,
            channel_mult=(1, 2, 4, 8),
            conv_resample=True,
            dims=2,
            use_checkpoint=False,
            use_fp16=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,
    ):
        super().__init__()
        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.dims = dims
        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        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.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        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(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])

        self.input_hint_block = TimestepEmbedSequential(
            conv_nd(dims, hint_channels, 16, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 16, 16, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            conv_nd(dims, 32, 32, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
            nn.SiLU(),
            conv_nd(dims, 96, 96, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
            nn.SiLU(),
            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
        )

        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,
                    )
                ]
                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(
                            AttentionBlock(
                                ch,
                                use_checkpoint=use_checkpoint,
                                num_heads=num_heads,
                                num_head_channels=dim_head,
                                use_new_attention_order=use_new_attention_order,
                            ) if not use_spatial_transformer else SpatialTransformer(
                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
                                use_checkpoint=use_checkpoint
                            )
                        )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self.zero_convs.append(self.make_zero_conv(ch))
                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,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                self.zero_convs.append(self.make_zero_conv(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,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=dim_head,
                use_new_attention_order=use_new_attention_order,
            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
                disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
                use_checkpoint=use_checkpoint
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self.middle_block_out = self.make_zero_conv(ch)
        self._feature_size += ch

    def make_zero_conv(self, channels):
        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))

    def forward(self, x, hint, timesteps, context, **kwargs):
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        guided_hint = self.input_hint_block(hint, emb, context)

        outs = []

        h = x.type(self.dtype)
        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
            if guided_hint is not None:
                h = module(h, emb, context)
                h += guided_hint
                guided_hint = None
            else:
                h = module(h, emb, context)
            outs.append(zero_conv(h, emb, context))

        h = self.middle_block(h, emb, context)
        outs.append(self.middle_block_out(h, emb, context))

        return outs


class ControlLDM(LatentDiffusion):

    def __init__(self, 
                 control_stage_config, 
                 control_key, only_mid_control, 
                 sd_locked = True, concat_textemb = False, 
                 trans_textemb=False, trans_textemb_config = None, 
                 learnable_conscale = False, guess_mode=False,
                 sep_lr = False, decoder_lr = 1.0**-4, 
                 add_glyph_control = False, glyph_control_config = None, glycon_wd = 0.2, glycon_lr = 1.0**-4, glycon_sched = "lambda", 
                 glyph_control_key = "centered_hint", sep_cond_txt = False, exchange_cond_txt = False,
                 max_step = None, multiple_optimizers = False, deepspeed = False, trans_glyph_lr = 1.0**-5,
                 *args, **kwargs
                 ): #sep_cap_for_2b = False
        use_ema = kwargs.pop("use_ema", False)
        ckpt_path = kwargs.pop("ckpt_path", None)
        reset_ema = kwargs.pop("reset_ema", False)
        only_model= kwargs.pop("only_model", False)
        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
        keep_num_ema_updates = kwargs.pop("keep_num_ema_updates", False)
        ignore_keys = kwargs.pop("ignore_keys", [])

        super().__init__(*args, use_ema=False, **kwargs)
        self.control_model = instantiate_from_config(control_stage_config)
        self.control_key = control_key
        self.only_mid_control = only_mid_control
        self.learnable_conscale = learnable_conscale
        conscale_init = [1.0] * 13 if not guess_mode else [(0.825 ** float(12 - i)) for i in range(13)]
        if learnable_conscale:
            # self.control_scales = nn.Parameter(torch.ones(13), requires_grad=True)
            self.control_scales = nn.Parameter(torch.Tensor(conscale_init), requires_grad=True)
        else: # TODO: register the buffer
            self.control_scales = conscale_init #[1.0] * 13 
        self.sd_locked = sd_locked
        self.concat_textemb = concat_textemb
        # update
        self.trans_textemb = False
        if trans_textemb and trans_textemb_config is not None:
            self.trans_textemb = True
            self.instantiate_trans_textemb_model(trans_textemb_config)
        # self.sep_cap_for_2b = sep_cap_for_2b

        self.sep_lr = sep_lr
        self.decoder_lr = decoder_lr
        self.sep_cond_txt = sep_cond_txt
        self.exchange_cond_txt = exchange_cond_txt
        # update (4.18)
        self.multiple_optimizers = multiple_optimizers
        self.add_glyph_control = False
        self.glyph_control_key = glyph_control_key
        self.freeze_glyph_image_encoder = True
        self.glyph_image_encoder_type = "CLIP"
        self.max_step = max_step
        self.trans_glyph_embed = False
        self.trans_glyph_lr = trans_glyph_lr
        if deepspeed:
            try: 
                from deepspeed.ops.adam import FusedAdam, DeepSpeedCPUAdam
                self.optimizer = DeepSpeedCPUAdam #FusedAdam
            except:
                print("could not import FuseAdam from deepspeed")
                self.optimizer = torch.optim.AdamW
        else:
            self.optimizer = torch.optim.AdamW
        
        if add_glyph_control and glyph_control_config is not None:
            self.add_glyph_control = True
            self.glycon_wd = glycon_wd
            self.glycon_lr = glycon_lr
            self.glycon_sched = glycon_sched
            self.instantiate_glyph_control_model(glyph_control_config)
            if self.glyph_control_model.trans_glyph_emb_model is not None:
                self.trans_glyph_embed = True       
    
        self.use_ema = use_ema
        if self.use_ema: #TODO: trainable glyph Image encoder
            # assert self.sd_locked == True
            self.model_ema = LitEma(self.control_model, init_num_updates= 0)
            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
            if not self.sd_locked: # Update
                self.model_diffoutblock_ema = LitEma(self.model.diffusion_model.output_blocks, init_num_updates= 0)
                print(f"Keeping diffoutblock EMAs of {len(list(self.model_diffoutblock_ema.buffers()))}.")
                self.model_diffout_ema = LitEma(self.model.diffusion_model.out, init_num_updates= 0)
                print(f"Keeping diffout EMAs of {len(list(self.model_diffout_ema.buffers()))}.")
            if not self.freeze_glyph_image_encoder:
                self.model_glyphcon_ema = LitEma(self.glyph_control_model.image_encoder, init_num_updates=0)
                print(f"Keeping glyphcon EMAs of {len(list(self.model_glyphcon_ema.buffers()))}.")
            if self.trans_glyph_embed:
                self.model_transglyph_ema = LitEma(self.glyph_control_model.trans_glyph_emb_model, init_num_updates=0)
                print(f"Keeping glyphcon EMAs of {len(list(self.model_transglyph_ema.buffers()))}.")
        
        if ckpt_path is not None:
            ema_num_updates = self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
            self.restarted_from_ckpt = True
            # if reset_ema:
            #     assert self.use_ema
            if self.use_ema and reset_ema:
                print(
                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
                self.model_ema = LitEma(self.control_model, init_num_updates= ema_num_updates if keep_num_ema_updates else 0)
                if not self.sd_locked: # Update
                    self.model_diffoutblock_ema = LitEma(self.model.diffusion_model.output_blocks, init_num_updates= ema_num_updates if keep_num_ema_updates else 0)
                    self.model_diffout_ema = LitEma(self.model.diffusion_model.out, init_num_updates= ema_num_updates if keep_num_ema_updates else 0)
                if not self.freeze_glyph_image_encoder:
                    self.model_glyphcon_ema = LitEma(self.glyph_control_model.image_encoder, init_num_updates= ema_num_updates if keep_num_ema_updates else 0)
                if self.trans_glyph_embed:
                    self.model_transglyph_ema = LitEma(self.glyph_control_model.trans_glyph_emb_model, init_num_updates= ema_num_updates if keep_num_ema_updates else 0)

        if reset_num_ema_updates:
            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
            assert self.use_ema
            self.model_ema.reset_num_updates()
            if not self.sd_locked: # Update
                self.model_diffoutblock_ema.reset_num_updates()
                self.model_diffout_ema.reset_num_updates()
            if not self.freeze_glyph_image_encoder:
                self.model_glyphcon_ema.reset_num_updates()
            if self.trans_glyph_embed:
                self.model_transglyph_ema.reset_num_updates()
        

        # self.freeze_unet()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema: # TODO: fix the bug while adding transemb_model or trainable control scales
            self.model_ema.store(self.control_model.parameters())
            self.model_ema.copy_to(self.control_model)
            if not self.sd_locked: # Update
                self.model_diffoutblock_ema.store(self.model.diffusion_model.output_blocks.parameters())
                self.model_diffoutblock_ema.copy_to(self.model.diffusion_model.output_blocks)
                self.model_diffout_ema.store(self.model.diffusion_model.out.parameters())
                self.model_diffout_ema.copy_to(self.model.diffusion_model.out)
            if not self.freeze_glyph_image_encoder:
                self.model_glyphcon_ema.store(self.glyph_control_model.image_encoder.parameters())
                self.model_glyphcon_ema.copy_to(self.glyph_control_model.image_encoder)
            if self.trans_glyph_embed:
                self.model_transglyph_ema.store(self.glyph_control_model.trans_glyph_emb_model.parameters())
                self.model_transglyph_ema.copy_to(self.glyph_control_model.trans_glyph_emb_model)

            if context is not None:
                print(f"{context}: Switched ControlNet to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.control_model.parameters())
                if not self.sd_locked: # Update
                    self.model_diffoutblock_ema.restore(self.model.diffusion_model.output_blocks.parameters())
                    self.model_diffout_ema.restore(self.model.diffusion_model.out.parameters())
                if not self.freeze_glyph_image_encoder:
                    self.model_glyphcon_ema.restore(self.glyph_control_model.image_encoder.parameters())
                if self.trans_glyph_embed:
                    self.model_transglyph_ema.restore(self.glyph_control_model.trans_glyph_emb_model.parameters())
                if context is not None:
                    print(f"{context}: Restored training weights of ControlNet")

    @torch.no_grad()
    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):

        if path.endswith("model_states.pt"):
            sd = torch.load(path, map_location='cpu')["module"]
        else:
            # sd = load_state_dict(path, location='cpu') # abandoned
            sd = torch.load(path, map_location="cpu")
            if "state_dict" in list(sd.keys()):
                sd = sd["state_dict"]
    
        keys_ = list(sd.keys())[:]
        for k in keys_:
            if k.startswith("module."):
                nk = k[7:]
                sd[nk] = sd[k]
                del sd[k]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        # missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
        #     sd, strict=False)
        if not only_model:
            missing, unexpected = self.load_state_dict(sd, strict=False)  
        elif path.endswith(".bin"):
            missing, unexpected = self.model.diffusion_model.load_state_dict(sd, strict=False)
        elif path.endswith(".ckpt"):
            missing, unexpected = self.model.load_state_dict(sd, strict=False)

        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            print(f"Missing Keys:\n {missing}")
        if len(unexpected) > 0:
            print(f"\nUnexpected Keys:\n {unexpected}")
        
        if "model_ema.num_updates" in sd and "model_ema.num_updates" not in unexpected:
            return sd["model_ema.num_updates"].item()
        else: 
            return 0

    def instantiate_trans_textemb_model(self, config):
        model = instantiate_from_config(config) 
        params = []
        for i in range(model.emb_num):
            if model.trans_trainable[i]:
                params += list(model.trans_list[i].parameters())
            else:
                for param in model.trans_list[i].parameters():
                    param.requires_grad = False 
        self.trans_textemb_model = model
        self.trans_textemb_params = params
    
    # add
    def instantiate_glyph_control_model(self, config):
        model = instantiate_from_config(config) 
        # params = []
        self.freeze_glyph_image_encoder = model.freeze_image_encoder #image_encoder.freeze_model
        self.glyph_control_model = model
        self.glyph_image_encoder_type = model.image_encoder_type
            # self.glyph_control_optim = torch.optim.AdamW([
            #     {"params": gain_or_bias_params, "weight_decay": 0.}, # "lr": self.glycon_lr},
            #     {"params": rest_params, "weight_decay": self.glycon_wd}  #, "lr": self.glycon_lr},
            # ],
            # lr = self.glycon_lr
            # )
            # params += list(model.image_encoder.parameters())
        
        

    @torch.no_grad()
    def get_input(self, batch, k, bs=None, *args, **kwargs):
        x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
        control = batch[self.control_key]
        if bs is not None:
            control = control[:bs]
        control = control.to(self.device)
        control = einops.rearrange(control, 'b h w c -> b c h w')
        control = control.to(memory_format=torch.contiguous_format).float()
        
        if self.add_glyph_control: 
            assert self.glyph_control_key in batch.keys()
            glyph_control = batch[self.glyph_control_key]
            if bs is not None:
                glyph_control = glyph_control[:bs]
            glycon_samples = []
            for glycon_sample in glyph_control:
                glycon_sample = glycon_sample.to(self.device)
                glycon_sample = einops.rearrange(glycon_sample, 'b h w c -> b c h w')
                glycon_sample = glycon_sample.to(memory_format=torch.contiguous_format).float()
                glycon_samples.append(glycon_sample)
            # return x, dict(c_crossattn=[c], c_concat=[control])
            return x, dict(c_crossattn=[c] if not isinstance(c, list) else c, c_concat=[control], c_glyph=glycon_samples)
        return x, dict(c_crossattn=[c] if not isinstance(c, list) else c, c_concat=[control])

    def apply_model(self, x_noisy, t, cond, *args, **kwargs):
        assert isinstance(cond, dict)
        diffusion_model = self.model.diffusion_model
        
        #update
        embdim_list = []
        for c in cond["c_crossattn"]:
            embdim_list.append(c.shape[-1])
        embdim_list = np.array(embdim_list)
        if np.sum(embdim_list != diffusion_model.context_dim):
            assert self.trans_textemb 
            
        if self.trans_textemb:
            assert self.trans_textemb_model
            cond_txt_list = self.trans_textemb_model(cond["c_crossattn"])
            # if len(cond_txt_list) == 2:
            #     print("cond_txt_2 max: {}".format(torch.max(torch.abs(cond_txt_list[1]))))
        else:
            cond_txt_list = cond["c_crossattn"]
        

        assert len(cond_txt_list) > 0
        if self.sep_cond_txt:
            cond_txt = cond_txt_list[0]
            cond_txt_2 = None if len(cond_txt_list) == 1 else cond_txt_list[1]
        else:
            if len(cond_txt_list) > 1:
                cond_txt = cond_txt_list[0] # input text embedding of the pretrained SD 
                if not self.concat_textemb:
                    # currently len(cond_txt_list) <= 2 
                    cond_txt_2 = torch.cat(cond_txt_list[1:], 1) # input text embedding of the ControlNet branch
                else:
                    cond_txt_2 = torch.cat(cond_txt_list, 1)
                if self.exchange_cond_txt:
                    txt_buffer = cond_txt
                    cond_txt = cond_txt_2
                    cond_txt_2 = txt_buffer                   
                print("len cond_txt_list: {} | cond_txt_1 shape: {} | cond_txt_2 shape: {}".format(len(cond_txt_list), cond_txt.shape, cond_txt_2.shape))
            else:
                cond_txt = torch.cat(cond_txt_list, 1)
                cond_txt_2 = None

        context_glyph = None
        if self.add_glyph_control:
            assert "c_glyph" in cond.keys()
            if cond["c_glyph"] is not None:
                context_glyph = self.glyph_control_model(cond["c_glyph"], text_embed = cond_txt_list[-1] if len(cond_txt_list) == 3 else cond_txt)
            else:
                context_glyph = cond_txt_list[-1] if len(cond_txt_list) == 3 else cond_txt
        # if cond_txt_2 is None:
        #     print("cond_txt_2 is None")

        if cond['c_concat'] is None:
            eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control, context_glyph = context_glyph)
        else:
            control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt if cond_txt_2 is None else cond_txt_2)
            control = [c * scale for c, scale in zip(control, self.control_scales)]
            eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control, context_glyph=context_glyph)

        return eps
    
    @torch.no_grad()
    def get_unconditional_conditioning(self, N):
        return self.get_learned_conditioning([""] * N)

    # Maybe not useful: modify the codes to fit the separate input captions
    # @torch.no_grad()
    # def get_unconditional_conditioning(self, N):
    #     return self.get_learned_conditioning([""] * N) if not self.sep_cap_for_2b else self.get_learned_conditioning([[""] * N, [""] * N])
    # TODO: adapt to new model
    @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)
        c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
        N = min(z.shape[0], N)
        n_row = min(z.shape[0], n_row)
        log["reconstruction"] = self.decode_first_stage(z)
        log["control"] = c_cat * 2.0 - 1.0
        log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], 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]},
                                                     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]}
            samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
                                             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

        return log
    # TODO: adapt to new model
    @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, **kwargs)
        return samples, intermediates
    # add 
    def training_step(self, batch, batch_idx, optimizer_idx=0):
        loss = super().training_step(batch, batch_idx, optimizer_idx)
        if self.use_scheduler and not self.sd_locked and self.sep_lr:
            decoder_lr = self.optimizers().param_groups[1]["lr"]
            self.log('decoder_lr_abs', decoder_lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
            if self.trans_glyph_embed and self.freeze_glyph_image_encoder:
                trans_glyph_embed_lr = self.optimizers().param_groups[2]["lr"]
                self.log('trans_glyph_embed_lr_abs', trans_glyph_embed_lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
        return loss
    
    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.control_model.parameters())
        if self.trans_textemb:
            params += self.trans_textemb_params #list(self.trans_textemb_model.parameters())
        
        if self.learnable_conscale:
            params += [self.control_scales]
        
        params_wlr = []
        decoder_params = None
        if not self.sd_locked:
            decoder_params = list(self.model.diffusion_model.output_blocks.parameters())
            decoder_params += list(self.model.diffusion_model.out.parameters())
            if not self.sep_lr:
                params.extend(decoder_params)
                decoder_params = None
                
        params_wlr.append({"params": params, "lr": lr})
        if decoder_params is not None:
            params_wlr.append({"params": decoder_params, "lr": self.decoder_lr})
        
        
        # if not self.sep_lr:
        #     opt = torch.optim.AdamW(params, lr=lr)
        # else:
        #     opt = torch.optim.AdamW(
        #         [
        #         {"params": params},
        #         {"params": decoder_params, "lr": self.decoder_lr} 
        #         ], lr=lr
        #     )
        if not self.freeze_glyph_image_encoder:
            if self.glyph_image_encoder_type == "CLIP":
                # assert self.sep_lr
                # follow the training codes in the OpenClip repo
                # https://github.com/mlfoundations/open_clip/blob/main/src/training/main.py#L303
                exclude = lambda n, p: p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
                include = lambda n, p: not exclude(n, p)
                
                # named_parameters = list(model.image_encoder.named_parameters())
                named_parameters = list(self.glyph_control_model.image_encoder.named_parameters())
                gain_or_bias_params = [p for n, p in named_parameters if exclude(n, p) and p.requires_grad]
                rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad]
                self.glyph_control_params_wlr = [
                    {"params": gain_or_bias_params, "weight_decay": 0., "lr": self.glycon_lr},
                    {"params": rest_params, "weight_decay": self.glycon_wd, "lr": self.glycon_lr},
                ]
        if not self.freeze_glyph_image_encoder and not self.multiple_optimizers:
            params_wlr.extend(self.glyph_control_params_wlr)
        
        if self.trans_glyph_embed:
            trans_glyph_params = list(self.glyph_control_model.trans_glyph_emb_model.parameters())
            params_wlr.append({"params": trans_glyph_params, "lr": self.trans_glyph_lr})
        # opt = torch.optim.AdamW(params_wlr) 
        opt = self.optimizer(params_wlr)
        opts = [opt]
        if not self.freeze_glyph_image_encoder and self.multiple_optimizers:
            glyph_control_opt = self.optimizer(self.glyph_control_params_wlr) #torch.optim.AdamW(self.glyph_control_params_wlr) 
            opts.append(glyph_control_opt)

        # updated
        schedulers = []
        if self.use_scheduler:
            assert 'target' in self.scheduler_config
            scheduler_func = instantiate_from_config(self.scheduler_config)
            print("Setting up LambdaLR scheduler...")
            schedulers = [
                {
                    'scheduler': LambdaLR(
                opt, 
                lr_lambda= [scheduler_func.schedule] * len(params_wlr) #if not self.sep_lr else [scheduler_func.schedule, scheduler_func.schedule]
                ),
                    'interval': 'step',
                    'frequency': 1
                }]
            
            if not self.freeze_glyph_image_encoder and self.multiple_optimizers:
                if self.glycon_sched == "cosine" and self.max_step is not None:
                    glyph_scheduler = CosineAnnealingLR(glyph_control_opt, T_max=self.max_step) #: max_step
                elif self.glycon_sched == "onecycle" and self.max_step is not None:
                    glyph_scheduler = OneCycleLR(
                        glyph_control_opt,
                        max_lr=self.glycon_lr,
                        total_steps=self.max_step, #: max_step
                        pct_start=0.0001,
                        anneal_strategy="cos" #'linear'
                    )
                # elif self.glycon_sched == "lambda":
                else:
                    glyph_scheduler = LambdaLR(
                        glyph_control_opt, 
                        lr_lambda = [scheduler_func.schedule] * len(self.glyph_control_params_wlr)
                    )
                schedulers.append(
                    {
                        "scheduler": glyph_scheduler,
                        "interval": 'step',
                        'frequency': 1
                    }
                )
        return opts, schedulers
        
    # TODO: adapt to new model
    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()

    # ema
    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self.control_model)
            if not self.sd_locked: # Update
                self.model_diffoutblock_ema(self.model.diffusion_model.output_blocks)
                self.model_diffout_ema(self.model.diffusion_model.out)
            if not self.freeze_glyph_image_encoder:
                self.model_glyphcon_ema(self.glyph_control_model.image_encoder)
            if self.trans_glyph_embed:
                self.model_transglyph_ema(self.glyph_control_model.trans_glyph_emb_model)
        if self.log_all_grad_norm:
            zeroconvs = list(self.control_model.input_hint_block.named_parameters())[-2:]
            zeroconvs.extend(
                list(self.control_model.zero_convs.named_parameters())                
            )
            for item in zeroconvs:
                self.log(
                    "zero_convs/{}_norm".format(item[0]),
                    item[1].cpu().detach().norm().item(),
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )
                self.log(
                    "zero_convs/{}_max".format(item[0]),
                    torch.max(item[1].cpu().detach()).item(), #TODO: lack torch.abs
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                )
            gradnorm_list = []
            for param_group in self.trainer.optimizers[0].param_groups:
                for p in param_group['params']:
                    # assert p.requires_grad and p.grad is not None
                    if p.requires_grad and p.grad is not None:
                        grad_norm_v = p.grad.cpu().detach().norm().item()
                        gradnorm_list.append(grad_norm_v)
            # for name, p in self.named_parameters():
            #     if p.requires_grad and p.grad is not None:
            #         grad_norm_v = p.grad.detach().norm().item()
            #         gradnorm_list.append(grad_norm_v)
            #         if "textemb_merge_model" in name:
            #             self.log("all_gradients/{}_norm".format(name),
            #             gradnorm_list[-1],
            #             prog_bar=False, logger=True, on_step=True, on_epoch=False
            #             )
            #         # if grad_norm_v > 0.1:
            #         #     print("the norm of gradient w.r.t {} > 0.1: {:.2f}".format
            #         #           (
            #         #         name, grad_norm_v
            #         #           ))
            if len(gradnorm_list):
                self.log("all_gradients/grad_norm_mean", 
                    np.mean(gradnorm_list), 
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                )
                self.log("all_gradients/grad_norm_max", 
                    np.max(gradnorm_list), 
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                )
                self.log("all_gradients/grad_norm_min", 
                    np.min(gradnorm_list), 
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                ) 
                self.log("all_gradients/param_num", 
                    len(gradnorm_list), 
                    prog_bar=False, logger=True, on_step=True, on_epoch=False
                )

            if self.trans_textemb:
                for name, p in self.trans_textemb_model.named_parameters():
                    if p.requires_grad and p.grad is not None:
                        self.log(
                            "trans_textemb_gradient_norm/{}".format(name), 
                            p.grad.cpu().detach().norm().item(), 
                            prog_bar=False, logger=True, on_step=True, on_epoch=False
                        )
                    self.log(
                        "trans_textemb_params/{}_norm".format(name), 
                        p.cpu().detach().norm().item(), 
                        prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )
                    self.log(
                        "trans_textemb_params/{}_abs_max".format(name),
                        torch.max(torch.abs(p.cpu().detach())).item(),
                        prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )
            if self.trans_glyph_embed:
                for name, p in self.glyph_control_model.trans_glyph_emb_model.named_parameters():
                    if p.requires_grad and p.grad is not None:
                        self.log(
                            "trans_glyph_embed_gradient_norm/{}".format(name), 
                            p.grad.cpu().detach().norm().item(), 
                            prog_bar=False, logger=True, on_step=True, on_epoch=False
                        )
                    self.log(
                        "trans_glyph_embed_params/{}_norm".format(name), 
                        p.cpu().detach().norm().item(), 
                        prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )
                    self.log(
                        "trans_glyph_embed_params/{}_abs_max".format(name),
                        torch.max(torch.abs(p.cpu().detach())).item(),
                        prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )

            if self.learnable_conscale:
                for i in range(len(self.control_scales)):
                    self.log(
                        "control_scale/control_{}".format(i),
                        self.control_scales[i],
                        prog_bar=False, logger=True, on_step=True, on_epoch=False
                    )
            del gradnorm_list
            del zeroconvs

    # def freeze_unet(self):
    #     # Have some bugs
    #     self.model.eval()
    #     # self.model.train = disabled_train
    #     for param in self.model.parameters():
    #         param.requires_grad = False

    #     if not self.sd_locked:
    #         self.model.diffusion_model.output_blocks.train()
    #         self.model.diffusion_model.out.train()
    #         for param in self.model.diffusion_model.out.parameters():
    #             param.requires_grad = True
    #         for param in self.model.diffusion_model.output_blocks.parameters():
    #             param.requires_grad = True