import os from PIL import Image import torch import torch.nn as nn from typing import List, Optional import torch.utils.checkpoint from torchvision.transforms import ToPILImage from model_lib.moMA_generator import MoMA_generator from transformers.activations import ACT2FN from huggingface_hub import hf_hub_download from dataset_lib.dataset_eval_MoMA import Dataset_evaluate_MoMA from llava.model.builder import load_pretrained_model from llava.mm_utils import tokenizer_image_token, get_model_name_from_path from llava.constants import IMAGE_TOKEN_INDEX def add_function(model): def my_llava_forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, ): (_,position_ids,attention_mask,_,inputs_embeds,_) = self.prepare_inputs_labels_for_multimodal(input_ids,position_ids,attention_mask,None,None,images) outputs = self.model( input_ids=None, attention_mask=attention_mask, position_ids=position_ids, past_key_values=None, inputs_embeds=inputs_embeds, use_cache=True, output_attentions=False, output_hidden_states=False, return_dict=True, ) return outputs[0] model.my_llava_forward = my_llava_forward class LlamaMLP_mapping(nn.Module): def __init__(self, hidden_size,hidden_size_out): super().__init__() self.hidden_size, self.hidden_size_out = hidden_size,hidden_size_out self.gate_proj = nn.Linear(self.hidden_size, self.hidden_size_out, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.hidden_size_out, bias=False) self.down_proj = nn.Linear(self.hidden_size_out, self.hidden_size_out, bias=False) self.act_fn = ACT2FN["silu"] self.act_fn_output = ACT2FN["tanh"] self.init_linear() def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj def init_linear(self): torch.nn.init.xavier_normal_(self.gate_proj.weight) self.gate_proj.weight.data=self.gate_proj.weight.data/4.0 torch.nn.init.xavier_normal_(self.up_proj.weight) self.up_proj.weight.data=self.up_proj.weight.data/4.0 torch.nn.init.xavier_normal_(self.down_proj.weight) self.down_proj.weight.data=self.down_proj.weight.data/4.0 class MoMA_main_modal(nn.Module): def __init__(self,args): super().__init__() self.args = args self.device = args.device self.moMA_generator = MoMA_generator(self.device,args) self.unet = self.moMA_generator.pipe.unet self.vae = self.moMA_generator.pipe.vae print('Loading MoMA: its Multi-modal LLM...') model_name = get_model_name_from_path(args.model_path) self.tokenizer_llava, self.model_llava, self.image_processor_llava, self.context_len_llava = load_pretrained_model(args.model_path, None, model_name, load_8bit=self.args.load_8bit, load_4bit=self.args.load_4bit, device=args.device) add_function(self.model_llava) self.mapping = LlamaMLP_mapping(4096,1024).to(self.device, dtype=torch.float16) self.load_saved_components() self.freeze_modules() def load_saved_components(self): if not os.path.exists(self.args.load_attn_adapters): print('Loading Attentions and LLM mappings...') hf_hub_download(repo_id=self.args.model_path, filename="attn_adapters_projectors.th",local_dir='/'.join(self.args.load_attn_adapters.split('/')[:-1])) #load attention adapters and self cross attentions state_dict = torch.load(self.args.load_attn_adapters, map_location="cpu") self.moMA_generator.image_proj_model.load_state_dict(state_dict["projectors"]) attn_layers = torch.nn.ModuleList(self.unet.attn_processors.values()) attn_layers.load_state_dict(state_dict["self_cross_attentions"],strict=False) #load LLM projectors self.load_state_dict(state_dict['llm_mapping'],strict=False) def freeze_modules(self): all_modules = [self.moMA_generator.pipe.vae,self.moMA_generator.pipe.text_encoder,self.unet,self.model_llava,self.mapping] for module in all_modules: module.train = False module.requires_grad_(False) def forward_MLLM(self,batch): llava_processeds,subjects,prompts = batch['llava_processed'].half().to(self.device),batch['label'],batch['text'] input_ids,attention_masks,position_ids = [],[],[] for subject,prompt in zip(subjects,prompts): prompt_construct = f"USER: \n A photo of a {subject}. Describe a new image of the same {subject} in: {prompt}. ASSISTANT: *" input_id = tokenizer_image_token(prompt_construct, self.tokenizer_llava, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device) attention_mask = torch.ones(input_id.shape, dtype=torch.long, device=self.device) position_id = torch.tensor(list(range(input_id.shape[-1])), device=self.device) position_ids += [position_id] attention_masks += [attention_mask[0]] input_ids += [input_id[0]] input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in input_ids],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1]) position_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in position_ids],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1]) attention_masks = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[-1]) for i in attention_masks],batch_first=True,padding_value=self.tokenizer_llava.pad_token_id).flip(dims=[1]) output = self.model_llava.my_llava_forward(self.model_llava,input_ids=input_ids,attention_mask=attention_masks,position_ids=position_ids,images=llava_processeds) output = self.mapping(output) return output[:,-1,:] def reset(self): self.moMA_generator.reset_all() def generate_images(self, rgb_path, mask_path, subject, prompt, strength=1.0, num=1, seed=0): batch = Dataset_evaluate_MoMA(rgb_path, prompt, subject, mask_path,self) self.moMA_generator.set_selfAttn_strength(strength) with torch.cuda.amp.autocast(enabled=True, dtype=torch.float16, cache_enabled=True): with torch.no_grad(): ### key steps llava_emb = self.forward_MLLM(batch).clone().detach() img,mask = self.moMA_generator.generate_with_MoMA(batch,llava_emb=llava_emb,seed=seed,device=self.args.device) self.reset() result = ToPILImage()(img[0]) return result