import torch import torch.nn as nn import folder_paths import comfy.clip_model import comfy.clip_vision import comfy.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=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 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(comfy.clip_model.CLIPVisionModelProjection): def __init__(self): 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) 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": (folder_paths.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 = folder_paths.get_full_path("photomaker", photomaker_model_name) photomaker_model = PhotoMakerIDEncoder() data = comfy.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, "dynamicPrompts": 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 = comfy.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, }