Create tokenflow_utils.py

tokenflow_utils.py

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+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