from typing import Type import torch import os from util import isinstance_str, batch_cosine_sim def register_pivotal(diffusion_model, is_pivotal): for _, module in diffusion_model.named_modules(): # If for some reason this has a different name, create an issue and I'll fix it if isinstance_str(module, "BasicTransformerBlock"): setattr(module, "pivotal_pass", is_pivotal) def register_batch_idx(diffusion_model, batch_idx): for _, module in diffusion_model.named_modules(): # If for some reason this has a different name, create an issue and I'll fix it if isinstance_str(module, "BasicTransformerBlock"): setattr(module, "batch_idx", batch_idx) def register_time(model, t): conv_module = model.unet.up_blocks[1].resnets[1] setattr(conv_module, 't', t) down_res_dict = {0: [0, 1], 1: [0, 1], 2: [0, 1]} up_res_dict = {1: [0, 1, 2], 2: [0, 1, 2], 3: [0, 1, 2]} for res in up_res_dict: for block in up_res_dict[res]: module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn1 setattr(module, 't', t) module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn2 setattr(module, 't', t) for res in down_res_dict: for block in down_res_dict[res]: module = model.unet.down_blocks[res].attentions[block].transformer_blocks[0].attn1 setattr(module, 't', t) module = model.unet.down_blocks[res].attentions[block].transformer_blocks[0].attn2 setattr(module, 't', t) module = model.unet.mid_block.attentions[0].transformer_blocks[0].attn1 setattr(module, 't', t) module = model.unet.mid_block.attentions[0].transformer_blocks[0].attn2 setattr(module, 't', t) def load_source_latents_t(t, latents_path): latents_t_path = os.path.join(latents_path, f'noisy_latents_{t}.pt') assert os.path.exists(latents_t_path), f'Missing latents at t {t} path {latents_t_path}' latents = torch.load(latents_t_path) return latents def register_conv_injection(model, injection_schedule): def conv_forward(self): def forward(input_tensor, temb): hidden_states = input_tensor hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) if self.upsample is not None: # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: input_tensor = input_tensor.contiguous() hidden_states = hidden_states.contiguous() input_tensor = self.upsample(input_tensor) hidden_states = self.upsample(hidden_states) elif self.downsample is not None: input_tensor = self.downsample(input_tensor) hidden_states = self.downsample(hidden_states) hidden_states = self.conv1(hidden_states) if temb is not None: temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] if temb is not None and self.time_embedding_norm == "default": hidden_states = hidden_states + temb hidden_states = self.norm2(hidden_states) if temb is not None and self.time_embedding_norm == "scale_shift": scale, shift = torch.chunk(temb, 2, dim=1) hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.injection_schedule is not None and (self.t in self.injection_schedule or self.t == 1000): source_batch_size = int(hidden_states.shape[0] // 3) # inject unconditional hidden_states[source_batch_size:2 * source_batch_size] = hidden_states[:source_batch_size] # inject conditional hidden_states[2 * source_batch_size:] = hidden_states[:source_batch_size] if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) output_tensor = (input_tensor + hidden_states) / self.output_scale_factor return output_tensor return forward conv_module = model.unet.up_blocks[1].resnets[1] conv_module.forward = conv_forward(conv_module) setattr(conv_module, 'injection_schedule', injection_schedule) def register_extended_attention_pnp(model, injection_schedule): def sa_forward(self): to_out = self.to_out if type(to_out) is torch.nn.modules.container.ModuleList: to_out = self.to_out[0] else: to_out = self.to_out def forward(x, encoder_hidden_states=None): batch_size, sequence_length, dim = x.shape h = self.heads n_frames = batch_size // 3 is_cross = encoder_hidden_states is not None encoder_hidden_states = encoder_hidden_states if is_cross else x q = self.to_q(x) k = self.to_k(encoder_hidden_states) v = self.to_v(encoder_hidden_states) if self.injection_schedule is not None and (self.t in self.injection_schedule or self.t == 1000): # inject unconditional q[n_frames:2 * n_frames] = q[:n_frames] k[n_frames:2 * n_frames] = k[:n_frames] # inject conditional q[2 * n_frames:] = q[:n_frames] k[2 * n_frames:] = k[:n_frames] k_source = k[:n_frames] k_uncond = k[n_frames:2 * n_frames].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) k_cond = k[2 * n_frames:].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) v_source = v[:n_frames] v_uncond = v[n_frames:2 * n_frames].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) v_cond = v[2 * n_frames:].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) q_source = self.head_to_batch_dim(q[:n_frames]) q_uncond = self.head_to_batch_dim(q[n_frames:2 * n_frames]) q_cond = self.head_to_batch_dim(q[2 * n_frames:]) k_source = self.head_to_batch_dim(k_source) k_uncond = self.head_to_batch_dim(k_uncond) k_cond = self.head_to_batch_dim(k_cond) v_source = self.head_to_batch_dim(v_source) v_uncond = self.head_to_batch_dim(v_uncond) v_cond = self.head_to_batch_dim(v_cond) q_src = q_source.view(n_frames, h, sequence_length, dim // h) k_src = k_source.view(n_frames, h, sequence_length, dim // h) v_src = v_source.view(n_frames, h, sequence_length, dim // h) q_uncond = q_uncond.view(n_frames, h, sequence_length, dim // h) k_uncond = k_uncond.view(n_frames, h, sequence_length * n_frames, dim // h) v_uncond = v_uncond.view(n_frames, h, sequence_length * n_frames, dim // h) q_cond = q_cond.view(n_frames, h, sequence_length, dim // h) k_cond = k_cond.view(n_frames, h, sequence_length * n_frames, dim // h) v_cond = v_cond.view(n_frames, h, sequence_length * n_frames, dim // h) out_source_all = [] out_uncond_all = [] out_cond_all = [] single_batch = n_frames<=12 b = n_frames if single_batch else 1 for frame in range(0, n_frames, b): out_source = [] out_uncond = [] out_cond = [] for j in range(h): sim_source_b = torch.bmm(q_src[frame: frame+ b, j], k_src[frame: frame+ b, j].transpose(-1, -2)) * self.scale sim_uncond_b = torch.bmm(q_uncond[frame: frame+ b, j], k_uncond[frame: frame+ b, j].transpose(-1, -2)) * self.scale sim_cond = torch.bmm(q_cond[frame: frame+ b, j], k_cond[frame: frame+ b, j].transpose(-1, -2)) * self.scale out_source.append(torch.bmm(sim_source_b.softmax(dim=-1), v_src[frame: frame+ b, j])) out_uncond.append(torch.bmm(sim_uncond_b.softmax(dim=-1), v_uncond[frame: frame+ b, j])) out_cond.append(torch.bmm(sim_cond.softmax(dim=-1), v_cond[frame: frame+ b, j])) out_source = torch.cat(out_source, dim=0) out_uncond = torch.cat(out_uncond, dim=0) out_cond = torch.cat(out_cond, dim=0) if single_batch: out_source = out_source.view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out_uncond = out_uncond.view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out_cond = out_cond.view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out_source_all.append(out_source) out_uncond_all.append(out_uncond) out_cond_all.append(out_cond) out_source = torch.cat(out_source_all, dim=0) out_uncond = torch.cat(out_uncond_all, dim=0) out_cond = torch.cat(out_cond_all, dim=0) out = torch.cat([out_source, out_uncond, out_cond], dim=0) out = self.batch_to_head_dim(out) return to_out(out) return forward for _, module in model.unet.named_modules(): if isinstance_str(module, "BasicTransformerBlock"): module.attn1.forward = sa_forward(module.attn1) setattr(module.attn1, 'injection_schedule', []) res_dict = {1: [1, 2], 2: [0, 1, 2], 3: [0, 1, 2]} # we are injecting attention in blocks 4 - 11 of the decoder, so not in the first block of the lowest resolution for res in res_dict: for block in res_dict[res]: module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn1 module.forward = sa_forward(module) setattr(module, 'injection_schedule', injection_schedule) def register_extended_attention(model): def sa_forward(self): to_out = self.to_out if type(to_out) is torch.nn.modules.container.ModuleList: to_out = self.to_out[0] else: to_out = self.to_out def forward(x, encoder_hidden_states=None): batch_size, sequence_length, dim = x.shape h = self.heads n_frames = batch_size // 3 is_cross = encoder_hidden_states is not None encoder_hidden_states = encoder_hidden_states if is_cross else x q = self.to_q(x) k = self.to_k(encoder_hidden_states) v = self.to_v(encoder_hidden_states) k_source = k[:n_frames] k_uncond = k[n_frames: 2*n_frames].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) k_cond = k[2*n_frames:].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) v_source = v[:n_frames] v_uncond = v[n_frames:2*n_frames].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) v_cond = v[2*n_frames:].reshape(1, n_frames * sequence_length, -1).repeat(n_frames, 1, 1) q_source = self.head_to_batch_dim(q[:n_frames]) q_uncond = self.head_to_batch_dim(q[n_frames: 2*n_frames]) q_cond = self.head_to_batch_dim(q[2 * n_frames:]) k_source = self.head_to_batch_dim(k_source) k_uncond = self.head_to_batch_dim(k_uncond) k_cond = self.head_to_batch_dim(k_cond) v_source = self.head_to_batch_dim(v_source) v_uncond = self.head_to_batch_dim(v_uncond) v_cond = self.head_to_batch_dim(v_cond) out_source = [] out_uncond = [] out_cond = [] q_src = q_source.view(n_frames, h, sequence_length, dim // h) k_src = k_source.view(n_frames, h, sequence_length, dim // h) v_src = v_source.view(n_frames, h, sequence_length, dim // h) q_uncond = q_uncond.view(n_frames, h, sequence_length, dim // h) k_uncond = k_uncond.view(n_frames, h, sequence_length * n_frames, dim // h) v_uncond = v_uncond.view(n_frames, h, sequence_length * n_frames, dim // h) q_cond = q_cond.view(n_frames, h, sequence_length, dim // h) k_cond = k_cond.view(n_frames, h, sequence_length * n_frames, dim // h) v_cond = v_cond.view(n_frames, h, sequence_length * n_frames, dim // h) for j in range(h): sim_source_b = torch.bmm(q_src[:, j], k_src[:, j].transpose(-1, -2)) * self.scale sim_uncond_b = torch.bmm(q_uncond[:, j], k_uncond[:, j].transpose(-1, -2)) * self.scale sim_cond = torch.bmm(q_cond[:, j], k_cond[:, j].transpose(-1, -2)) * self.scale out_source.append(torch.bmm(sim_source_b.softmax(dim=-1), v_src[:, j])) out_uncond.append(torch.bmm(sim_uncond_b.softmax(dim=-1), v_uncond[:, j])) out_cond.append(torch.bmm(sim_cond.softmax(dim=-1), v_cond[:, j])) out_source = torch.cat(out_source, dim=0).view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out_uncond = torch.cat(out_uncond, dim=0).view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out_cond = torch.cat(out_cond, dim=0).view(h, n_frames,sequence_length, dim // h).permute(1, 0, 2, 3).reshape(h * n_frames, sequence_length, -1) out = torch.cat([out_source, out_uncond, out_cond], dim=0) out = self.batch_to_head_dim(out) return to_out(out) return forward for _, module in model.unet.named_modules(): if isinstance_str(module, "BasicTransformerBlock"): module.attn1.forward = sa_forward(module.attn1) res_dict = {1: [1, 2], 2: [0, 1, 2], 3: [0, 1, 2]} # we are injecting attention in blocks 4 - 11 of the decoder, so not in the first block of the lowest resolution for res in res_dict: for block in res_dict[res]: module = model.unet.up_blocks[res].attentions[block].transformer_blocks[0].attn1 module.forward = sa_forward(module) def make_tokenflow_attention_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: class TokenFlowBlock(block_class): def forward( self, hidden_states, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, timestep=None, cross_attention_kwargs=None, class_labels=None, ) -> torch.Tensor: batch_size, sequence_length, dim = hidden_states.shape n_frames = batch_size // 3 mid_idx = n_frames // 2 hidden_states = hidden_states.view(3, n_frames, sequence_length, dim) if self.use_ada_layer_norm: norm_hidden_states = self.norm1(hidden_states, timestep) elif self.use_ada_layer_norm_zero: norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) else: norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states.view(3, n_frames, sequence_length, dim) if self.pivotal_pass: self.pivot_hidden_states = norm_hidden_states else: idx1 = [] idx2 = [] batch_idxs = [self.batch_idx] if self.batch_idx > 0: batch_idxs.append(self.batch_idx - 1) sim = batch_cosine_sim(norm_hidden_states[0].reshape(-1, dim), self.pivot_hidden_states[0][batch_idxs].reshape(-1, dim)) if len(batch_idxs) == 2: sim1, sim2 = sim.chunk(2, dim=1) # sim: n_frames * seq_len, len(batch_idxs) * seq_len idx1.append(sim1.argmax(dim=-1)) # n_frames * seq_len idx2.append(sim2.argmax(dim=-1)) # n_frames * seq_len else: idx1.append(sim.argmax(dim=-1)) idx1 = torch.stack(idx1 * 3, dim=0) # 3, n_frames * seq_len idx1 = idx1.squeeze(1) if len(batch_idxs) == 2: idx2 = torch.stack(idx2 * 3, dim=0) # 3, n_frames * seq_len idx2 = idx2.squeeze(1) # 1. Self-Attention cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} if self.pivotal_pass: # norm_hidden_states.shape = 3, n_frames * seq_len, dim self.attn_output = self.attn1( norm_hidden_states.view(batch_size, sequence_length, dim), encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, **cross_attention_kwargs, ) # 3, n_frames * seq_len, dim - > 3 * n_frames, seq_len, dim self.kf_attn_output = self.attn_output else: batch_kf_size, _, _ = self.kf_attn_output.shape self.attn_output = self.kf_attn_output.view(3, batch_kf_size // 3, sequence_length, dim)[:, batch_idxs] # 3, n_frames, seq_len, dim --> 3, len(batch_idxs), seq_len, dim if self.use_ada_layer_norm_zero: self.attn_output = gate_msa.unsqueeze(1) * self.attn_output # gather values from attn_output, using idx as indices, and get a tensor of shape 3, n_frames, seq_len, dim if not self.pivotal_pass: if len(batch_idxs) == 2: attn_1, attn_2 = self.attn_output[:, 0], self.attn_output[:, 1] attn_output1 = attn_1.gather(dim=1, index=idx1.unsqueeze(-1).repeat(1, 1, dim)) attn_output2 = attn_2.gather(dim=1, index=idx2.unsqueeze(-1).repeat(1, 1, dim)) s = torch.arange(0, n_frames).to(idx1.device) + batch_idxs[0] * n_frames # distance from the pivot p1 = batch_idxs[0] * n_frames + n_frames // 2 p2 = batch_idxs[1] * n_frames + n_frames // 2 d1 = torch.abs(s - p1) d2 = torch.abs(s - p2) # weight w1 = d2 / (d1 + d2) w1 = torch.sigmoid(w1) w1 = w1.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).repeat(3, 1, sequence_length, dim) attn_output1 = attn_output1.view(3, n_frames, sequence_length, dim) attn_output2 = attn_output2.view(3, n_frames, sequence_length, dim) attn_output = w1 * attn_output1 + (1 - w1) * attn_output2 else: attn_output = self.attn_output[:,0].gather(dim=1, index=idx1.unsqueeze(-1).repeat(1, 1, dim)) attn_output = attn_output.reshape( batch_size, sequence_length, dim) # 3 * n_frames, seq_len, dim else: attn_output = self.attn_output hidden_states = hidden_states.reshape(batch_size, sequence_length, dim) # 3 * n_frames, seq_len, dim hidden_states = attn_output + hidden_states if self.attn2 is not None: norm_hidden_states = ( self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) ) # 2. Cross-Attention attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states # 3. Feed-forward norm_hidden_states = self.norm3(hidden_states) if self.use_ada_layer_norm_zero: norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] ff_output = self.ff(norm_hidden_states) if self.use_ada_layer_norm_zero: ff_output = gate_mlp.unsqueeze(1) * ff_output hidden_states = ff_output + hidden_states return hidden_states return TokenFlowBlock def set_tokenflow( model: torch.nn.Module): """ Sets the tokenflow attention blocks in a model. """ for _, module in model.named_modules(): if isinstance_str(module, "BasicTransformerBlock"): make_tokenflow_block_fn = make_tokenflow_attention_block module.__class__ = make_tokenflow_block_fn(module.__class__) # Something needed for older versions of diffusers if not hasattr(module, "use_ada_layer_norm_zero"): module.use_ada_layer_norm = False module.use_ada_layer_norm_zero = False return model