tokenflow / tokenflow_utils.py
cocktailpeanut's picture
Duplicate from weizmannscience/tokenflow
7a86a0a
from typing import Type
import torch
import os
from utils 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