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# Merge image encoder and fuse module to create an ID Encoder
# send multiple ID images, we can directly obtain the updated text encoder containing a stacked ID embedding
import torch
import torch.nn as nn
import comfy.ops
import comfy.clip_model
import comfy.model_management
from .resampler import FacePerceiverResampler
# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model_v2.py under Apache License Version 2.0
VISION_CONFIG_DICT = {
"hidden_size": 1024,
"image_size": 224,
"intermediate_size": 4096,
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dim": 768,
"hidden_act": "quick_gelu",
}
class MLP(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops):
super().__init__()
if use_residual:
assert in_dim == out_dim
self.layernorm = operations.LayerNorm(in_dim)
self.fc1 = operations.Linear(in_dim, hidden_dim)
self.fc2 = operations.Linear(hidden_dim, out_dim)
self.use_residual = use_residual
self.act_fn = nn.GELU()
def forward(self, x):
residual = x
x = self.layernorm(x)
x = self.fc1(x)
x = self.act_fn(x)
x = self.fc2(x)
if self.use_residual:
x = x + residual
return x
class QFormerPerceiver(nn.Module):
def __init__(self, id_embeddings_dim, cross_attention_dim, num_tokens, embedding_dim=1024, use_residual=True, ratio=4, operations=comfy.ops):
super().__init__()
self.num_tokens = num_tokens
self.cross_attention_dim = cross_attention_dim
self.use_residual = use_residual
print(cross_attention_dim*num_tokens)
self.token_proj = nn.Sequential(
operations.Linear(id_embeddings_dim, id_embeddings_dim*ratio),
nn.GELU(),
operations.Linear(id_embeddings_dim*ratio, cross_attention_dim*num_tokens),
)
self.token_norm = operations.LayerNorm(cross_attention_dim)
self.perceiver_resampler = FacePerceiverResampler(
dim=cross_attention_dim,
depth=4,
dim_head=128,
heads=cross_attention_dim // 128,
embedding_dim=embedding_dim,
output_dim=cross_attention_dim,
ff_mult=4,
)
def forward(self, x, last_hidden_state):
x = self.token_proj(x)
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
x = self.token_norm(x) # cls token
out = self.perceiver_resampler(x, last_hidden_state) # retrieve from patch tokens
if self.use_residual: # TODO: if use_residual is not true
out = x + 1.0 * out
return out
class FuseModule(nn.Module):
def __init__(self, embed_dim, operations):
super().__init__()
self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
self.layer_norm = operations.LayerNorm(embed_dim)
def fuse_fn(self, prompt_embeds, id_embeds):
stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
stacked_id_embeds = self.mlp2(stacked_id_embeds)
stacked_id_embeds = self.layer_norm(stacked_id_embeds)
return stacked_id_embeds
def forward(
self,
prompt_embeds,
id_embeds,
class_tokens_mask,
) -> torch.Tensor:
# id_embeds shape: [b, max_num_inputs, 1, 2048]
id_embeds = id_embeds.to(prompt_embeds.dtype)
num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
batch_size, max_num_inputs = id_embeds.shape[:2]
# seq_length: 77
seq_length = prompt_embeds.shape[1]
# flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
flat_id_embeds = id_embeds.view(
-1, id_embeds.shape[-2], id_embeds.shape[-1]
)
# valid_id_mask [b*max_num_inputs]
valid_id_mask = (
torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
< num_inputs[:, None]
)
valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
class_tokens_mask = class_tokens_mask.view(-1)
valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
# slice out the image token embeddings
image_token_embeds = prompt_embeds[class_tokens_mask]
stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
return updated_prompt_embeds
class PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken(comfy.clip_model.CLIPVisionModelProjection):
def __init__(self, id_embeddings_dim=512):
self.load_device = comfy.model_management.text_encoder_device()
offload_device = comfy.model_management.text_encoder_offload_device()
dtype = comfy.model_management.text_encoder_dtype(self.load_device)
super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast)
self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False)
self.fuse_module = FuseModule(2048, comfy.ops.manual_cast)
cross_attention_dim = 2048
# projection
self.num_tokens = 2
self.cross_attention_dim = cross_attention_dim
self.qformer_perceiver = QFormerPerceiver(
id_embeddings_dim,
cross_attention_dim,
self.num_tokens,
operations=comfy.ops.manual_cast,
)
def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds):
b, num_inputs, c, h, w = id_pixel_values.shape
id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
last_hidden_state = self.vision_model(id_pixel_values)[0]
id_embeds = id_embeds.view(b * num_inputs, -1)
id_embeds = self.qformer_perceiver(id_embeds, last_hidden_state)
id_embeds = id_embeds.view(b, num_inputs, self.num_tokens, -1)
updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
return updated_prompt_embeds
if __name__ == "__main__":
PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken() |