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# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
import torch.nn as nn
import ldm_patched.utils.path_utils
import ldm_patched.modules.clip_model
import ldm_patched.modules.clip_vision
import ldm_patched.modules.ops
# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.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=ldm_patched.modules.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 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(ldm_patched.modules.clip_model.CLIPVisionModelProjection):
def __init__(self):
self.load_device = ldm_patched.modules.model_management.text_encoder_device()
offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)
super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast)
self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False)
self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast)
def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
b, num_inputs, c, h, w = id_pixel_values.shape
id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
shared_id_embeds = self.vision_model(id_pixel_values)[2]
id_embeds = self.visual_projection(shared_id_embeds)
id_embeds_2 = self.visual_projection_2(shared_id_embeds)
id_embeds = id_embeds.view(b, num_inputs, 1, -1)
id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
return updated_prompt_embeds
class PhotoMakerLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker_model_name": (ldm_patched.utils.path_utils.get_filename_list("photomaker"), )}}
RETURN_TYPES = ("PHOTOMAKER",)
FUNCTION = "load_photomaker_model"
CATEGORY = "_for_testing/photomaker"
def load_photomaker_model(self, photomaker_model_name):
photomaker_model_path = ldm_patched.utils.path_utils.get_full_path("photomaker", photomaker_model_name)
photomaker_model = PhotoMakerIDEncoder()
data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True)
if "id_encoder" in data:
data = data["id_encoder"]
photomaker_model.load_state_dict(data)
return (photomaker_model,)
class PhotoMakerEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker": ("PHOTOMAKER",),
"image": ("IMAGE",),
"clip": ("CLIP", ),
"text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_photomaker"
CATEGORY = "_for_testing/photomaker"
def apply_photomaker(self, photomaker, image, clip, text):
special_token = "photomaker"
pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
try:
index = text.split(" ").index(special_token) + 1
except ValueError:
index = -1
tokens = clip.tokenize(text, return_word_ids=True)
out_tokens = {}
for k in tokens:
out_tokens[k] = []
for t in tokens[k]:
f = list(filter(lambda x: x[2] != index, t))
while len(f) < len(t):
f.append(t[-1])
out_tokens[k].append(f)
cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)
if index > 0:
token_index = index - 1
num_id_images = 1
class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
else:
out = cond
return ([[out, {"pooled_output": pooled}]], )
NODE_CLASS_MAPPINGS = {
"PhotoMakerLoader": PhotoMakerLoader,
"PhotoMakerEncode": PhotoMakerEncode,
}
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