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 guided_diffusion.unet 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 pdb import set_trace as st class ControlledUnetModel(UNetModel): def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, get_attr='', **kwargs): if get_attr != '': # not breaking the forward hooks return getattr(self, get_attr) hs = [] with torch.no_grad(): # fix middle_block, SD t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) if self.roll_out: x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96]) 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) assert control is not None # 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: # st() h = torch.cat([h, hs.pop() + control.pop()], dim=1) h = module(h, emb, context) h = h.type(x.dtype) h = self.out(h) if self.roll_out: return rearrange(h, 'b c h (n w) -> b (n c) h w', n=3) return 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, # * new keys introduced in LDM 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, roll_out=False, ): super().__init__() self.roll_out = roll_out 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_checkpoint = False 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( # f=8 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) # time condition embedding guided_hint = self.input_hint_block(hint, emb, context) # B 320 8 8, if input resolution = 64 if self.roll_out: x = rearrange(x, 'b (n c) h w->b c h (n w)', n=3) # torch.Size([84, 4, 32, 96]) guided_hint = repeat(guided_hint, 'b c h w -> b c h (n w)', n=3) # torch.Size([84, 4, 32, 96]) outs = [] h = x.type(self.dtype) for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: # f=8, shall send in 128x128 img_sr h = module(h, emb, context) # B 320 16 16 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 # ! do not support PL here # class ControlLDM(LatentDiffusion): # def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs): # super().__init__(*args, **kwargs) # self.control_model = instantiate_from_config(control_stage_config) # self.control_key = control_key # self.only_mid_control = only_mid_control # self.control_scales = [1.0] * 13 # @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() # return x, dict(c_crossattn=[c], c_concat=[control]) # def apply_model(self, x_noisy, t, cond, *args, **kwargs): # assert isinstance(cond, dict) # diffusion_model = self.model.diffusion_model # cond_txt = torch.cat(cond['c_crossattn'], 1) # 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) # else: # control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt) # 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) # return eps # @torch.no_grad() # def get_unconditional_conditioning(self, N): # return self.get_learned_conditioning([""] * N) # @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 # @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 # def configure_optimizers(self): # lr = self.learning_rate # params = list(self.control_model.parameters()) # if not self.sd_locked: # params += list(self.model.diffusion_model.output_blocks.parameters()) # params += list(self.model.diffusion_model.out.parameters()) # 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()