import einops import torch import torch as th import torch.nn as nn import copy from easydict import EasyDict as edict 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.distributions.distributions import DiagonalGaussianDistribution from .recognizer import TextRecognizer, create_predictor def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) class ControlledUnetModel(UNetModel): def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs): hs = [] with torch.no_grad(): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) if self.use_fp16: t_emb = t_emb.half() 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) h = h.type(x.dtype) return self.out(h) class ControlNet(nn.Module): def __init__( self, image_size, in_channels, model_channels, glyph_channels, position_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.use_fp16 = use_fp16 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.glyph_block = TimestepEmbedSequential( conv_nd(dims, glyph_channels, 8, 3, padding=1), nn.SiLU(), conv_nd(dims, 8, 8, 3, padding=1), nn.SiLU(), conv_nd(dims, 8, 16, 3, padding=1, stride=2), 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(), ) self.position_block = TimestepEmbedSequential( conv_nd(dims, position_channels, 8, 3, padding=1), nn.SiLU(), conv_nd(dims, 8, 8, 3, padding=1), nn.SiLU(), conv_nd(dims, 8, 16, 3, padding=1, stride=2), 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, 64, 3, padding=1, stride=2), nn.SiLU(), ) self.fuse_block = zero_module(conv_nd(dims, 256+64+4, 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, text_info, timesteps, context, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) if self.use_fp16: t_emb = t_emb.half() emb = self.time_embed(t_emb) # guided_hint from text_info B, C, H, W = x.shape glyphs = torch.cat(text_info['glyphs'], dim=1).sum(dim=1, keepdim=True) positions = torch.cat(text_info['positions'], dim=1).sum(dim=1, keepdim=True) enc_glyph = self.glyph_block(glyphs, emb, context) enc_pos = self.position_block(positions, emb, context) guided_hint = self.fuse_block(torch.cat([enc_glyph, enc_pos, text_info['masked_x']], dim=1)) 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, 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): self.use_fp16 = kwargs.pop('use_fp16', False) super().__init__(*args, **kwargs) self.control_model = instantiate_from_config(control_stage_config) self.control_key = control_key self.glyph_key = glyph_key self.position_key = position_key self.only_mid_control = only_mid_control self.control_scales = [1.0] * 13 self.loss_alpha = loss_alpha self.loss_beta = loss_beta self.with_step_weight = with_step_weight self.use_vae_upsample = use_vae_upsample self.latin_weight = latin_weight if embedding_manager_config is not None and embedding_manager_config.params.valid: self.embedding_manager = self.instantiate_embedding_manager(embedding_manager_config, self.cond_stage_model) for param in self.embedding_manager.embedding_parameters(): param.requires_grad = True else: self.embedding_manager = None if self.loss_alpha > 0 or self.loss_beta > 0 or self.embedding_manager: if embedding_manager_config.params.emb_type == 'ocr': self.text_predictor = create_predictor().eval() args = edict() args.rec_image_shape = "3, 48, 320" args.rec_batch_num = 6 args.rec_char_dict_path = './ocr_recog/ppocr_keys_v1.txt' args.use_fp16 = self.use_fp16 self.cn_recognizer = TextRecognizer(args, self.text_predictor) for param in self.text_predictor.parameters(): param.requires_grad = False if self.embedding_manager: self.embedding_manager.recog = self.cn_recognizer @torch.no_grad() def get_input(self, batch, k, bs=None, *args, **kwargs): if self.embedding_manager is None: # fill in full caption self.fill_caption(batch) x, c, mx = super().get_input(batch, self.first_stage_key, mask_k='masked_img', *args, **kwargs) control = batch[self.control_key] # for log_images and loss_alpha, not real control 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() inv_mask = batch['inv_mask'] if bs is not None: inv_mask = inv_mask[:bs] inv_mask = inv_mask.to(self.device) inv_mask = einops.rearrange(inv_mask, 'b h w c -> b c h w') inv_mask = inv_mask.to(memory_format=torch.contiguous_format).float() glyphs = batch[self.glyph_key] gly_line = batch['gly_line'] positions = batch[self.position_key] n_lines = batch['n_lines'] language = batch['language'] texts = batch['texts'] assert len(glyphs) == len(positions) for i in range(len(glyphs)): if bs is not None: glyphs[i] = glyphs[i][:bs] gly_line[i] = gly_line[i][:bs] positions[i] = positions[i][:bs] n_lines = n_lines[:bs] glyphs[i] = glyphs[i].to(self.device) gly_line[i] = gly_line[i].to(self.device) positions[i] = positions[i].to(self.device) glyphs[i] = einops.rearrange(glyphs[i], 'b h w c -> b c h w') gly_line[i] = einops.rearrange(gly_line[i], 'b h w c -> b c h w') positions[i] = einops.rearrange(positions[i], 'b h w c -> b c h w') glyphs[i] = glyphs[i].to(memory_format=torch.contiguous_format).float() gly_line[i] = gly_line[i].to(memory_format=torch.contiguous_format).float() positions[i] = positions[i].to(memory_format=torch.contiguous_format).float() info = {} info['glyphs'] = glyphs info['positions'] = positions info['n_lines'] = n_lines info['language'] = language info['texts'] = texts info['img'] = batch['img'] # nhwc, (-1,1) info['masked_x'] = mx info['gly_line'] = gly_line info['inv_mask'] = inv_mask return x, dict(c_crossattn=[c], c_concat=[control], text_info=info) def apply_model(self, x_noisy, t, cond, *args, **kwargs): assert isinstance(cond, dict) diffusion_model = self.model.diffusion_model _cond = torch.cat(cond['c_crossattn'], 1) _hint = torch.cat(cond['c_concat'], 1) if self.use_fp16: x_noisy = x_noisy.half() control = self.control_model(x=x_noisy, timesteps=t, context=_cond, hint=_hint, text_info=cond['text_info']) control = [c * scale for c, scale in zip(control, self.control_scales)] eps = diffusion_model(x=x_noisy, timesteps=t, context=_cond, control=control, only_mid_control=self.only_mid_control) return eps def instantiate_embedding_manager(self, config, embedder): model = instantiate_from_config(config, embedder=embedder) return model @torch.no_grad() def get_unconditional_conditioning(self, N): return self.get_learned_conditioning(dict(c_crossattn=[[""] * N], text_info=None)) def get_learned_conditioning(self, c): 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: self.embedding_manager.encode_text(c['text_info']) if isinstance(c, dict): cond_txt = c['c_crossattn'][0] else: cond_txt = c if self.embedding_manager is not None: cond_txt = self.cond_stage_model.encode(cond_txt, embedding_manager=self.embedding_manager) 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()