first

app.py

@@ -218,7 +218,7 @@ with gr.Blocks() as demo:
     with gr.Tab(label="1 Edit mask"):
         with gr.Row():
             with gr.Column():
-                canvas = gr.Image(value = None, type="numpy", tool="sketch", label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
+                canvas = gr.Image(value = None, type="numpy",  label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
                 input_folder = gr.Textbox(value="example1", label="input folder", interactive= True, )
                 
                 segment_button  = gr.Button("1.1 Run segmentation")
@@ -283,7 +283,7 @@ with gr.Blocks() as demo:
     with gr.Tab(label="2 Optimization"):
         with gr.Row():
             with gr.Column():
-                canvas_opt = gr.Image(value = canvas.value, type="pil", tool="sketch", label="Loaded Image", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
+                canvas_opt = gr.Image(value = canvas.value, type="pil",  label="Loaded Image", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
             
             with gr.Column():
                 gr.Markdown("""<p style="text-align: center; font-size: 20px">Optimization settings (SD)</p>""")

main.py

@@ -3,7 +3,7 @@ import torch
 import numpy as np
 import argparse
 from peft import LoraConfig
-from pipeline_dedit_sdxl import DEditSDXLPipeline
+from old.pipeline_dedit_sdxl import DEditSDXLPipeline
 from pipeline_dedit_sd import DEditSDPipeline
 from utils import load_image, load_mask, load_mask_edit
 from utils_mask import process_mask_move_torch, process_mask_remove_torch, mask_union_torch, mask_substract_torch, create_outer_edge_mask_torch

pipeline_dedit_sdxl.py

@@ -1,875 +0,0 @@
-import torch
-from utils import import_model_class_from_model_name_or_path
-from transformers import AutoTokenizer
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    StableDiffusionXLPipeline,
-    UNet2DConditionModel,
-)
-from accelerate import Accelerator
-from tqdm.auto import tqdm
-from utils import sdxl_prepare_input_decom, save_images
-import torch.nn.functional as F
-import itertools
-from peft import LoraConfig
-from controller import GroupedCAController, register_attention_disentangled_control, DummyController
-from utils import  image2latent, latent2image
-import matplotlib.pyplot as plt
-from utils_mask import  check_mask_overlap_torch
-
-device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-max_length = 40
-class DEditSDXLPipeline:
-    def __init__(
-        self,
-        mask_list,
-        mask_label_list, 
-        mask_list_2 = None,
-        mask_label_list_2 = None, 
-        resolution = 1024,
-        num_tokens = 1
-    ):
-        super().__init__()
-        model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-        self.model_id = model_id
-        self.tokenizer       = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer", use_fast=False)
-        self.tokenizer_2     = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer_2",  use_fast=False)
-        text_encoder_cls_one = import_model_class_from_model_name_or_path(model_id, subfolder = "text_encoder")
-        text_encoder_cls_two = import_model_class_from_model_name_or_path(model_id, subfolder="text_encoder_2")
-        self.text_encoder    = text_encoder_cls_one.from_pretrained(model_id, subfolder="text_encoder" ).to(device)
-        self.text_encoder_2  = text_encoder_cls_two.from_pretrained(model_id, subfolder="text_encoder_2").to(device)
-        self.unet =  UNet2DConditionModel.from_pretrained(model_id, subfolder="unet"  )
-        self.unet.ca_dim = 2048
-        self.vae  = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix")
-        self.scheduler = DDPMScheduler.from_pretrained(model_id , subfolder="scheduler")
-        
-        self.mixed_precision = "fp16"
-        self.resolution = resolution
-        self.num_tokens = num_tokens
-        
-        self.mask_list = mask_list
-        self.mask_label_list = mask_label_list
-        notation_token_list = [phrase.split(" ")[-1] for phrase in mask_label_list]
-        placeholder_token_list = ["#"+word+"{}".format(widx) for widx, word in enumerate(notation_token_list)]
-        self.set_string_list, placeholder_token_ids = self.add_tokens(placeholder_token_list)
-        self.min_added_id = min(placeholder_token_ids)
-        self.max_added_id = max(placeholder_token_ids)
-
-        if mask_list_2 is not None:
-            self.mask_list_2 = mask_list_2
-            self.mask_label_list_2 = mask_label_list_2
-            notation_token_list_2  = [phrase.split(" ")[-1] for phrase in mask_label_list_2]
-            
-            placeholder_token_list_2 = ["$"+word+"{}".format(widx) for widx, word in enumerate(notation_token_list_2)]
-            self.set_string_list_2, placeholder_token_ids_2 = self.add_tokens(placeholder_token_list_2)
-            self.max_added_id = max(placeholder_token_ids_2)
-
-    def add_tokens_text_encoder_random_init(self, placeholder_token, num_tokens=1):
-        # Add the placeholder token in tokenizer
-        placeholder_tokens = [placeholder_token]
-        # add dummy tokens for multi-vector
-        additional_tokens = []
-        for i in range(1, num_tokens):
-            additional_tokens.append(f"{placeholder_token}_{i}")
-        placeholder_tokens += additional_tokens
-        num_added_tokens = self.tokenizer.add_tokens(placeholder_tokens) # 49408
-        num_added_tokens = self.tokenizer_2.add_tokens(placeholder_tokens) # 49408
-
-        if num_added_tokens != num_tokens:
-            raise ValueError(
-                f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
-                " `placeholder_token` that is not already in the tokenizer."
-            )
-        placeholder_token_ids = self.tokenizer.convert_tokens_to_ids(placeholder_tokens)
-        placeholder_token_ids_2 = self.tokenizer_2.convert_tokens_to_ids(placeholder_tokens)  
-        assert placeholder_token_ids == placeholder_token_ids_2, "Two text encoders are expected to have same vocabs"
-        
-        self.text_encoder.resize_token_embeddings(len(self.tokenizer))
-        token_embeds = self.text_encoder.get_input_embeddings().weight.data
-        std, mean = torch.std_mean(token_embeds)
-        with torch.no_grad():
-            for token_id in placeholder_token_ids:         
-                token_embeds[token_id] = torch.randn_like(token_embeds[token_id])*std + mean
-
-        self.text_encoder_2.resize_token_embeddings(len(self.tokenizer))
-        token_embeds = self.text_encoder_2.get_input_embeddings().weight.data
-        std, mean = torch.std_mean(token_embeds)
-        with torch.no_grad():
-            for token_id in placeholder_token_ids:            
-                token_embeds[token_id] = torch.randn_like(token_embeds[token_id])*std + mean
-        
-        set_string = " ".join(self.tokenizer.convert_ids_to_tokens(placeholder_token_ids))
-
-        return set_string, placeholder_token_ids
-
-    def add_tokens(self, placeholder_token_list):
-        set_string_list = []
-        placeholder_token_ids_list = []
-        for str_idx in range(len(placeholder_token_list)):
-            placeholder_token = placeholder_token_list[str_idx]
-            set_string, placeholder_token_ids = self.add_tokens_text_encoder_random_init(placeholder_token,  num_tokens=self.num_tokens)
-            set_string_list.append(set_string)
-            placeholder_token_ids_list.append(placeholder_token_ids)
-        placeholder_token_ids = list(itertools.chain(*placeholder_token_ids_list)) 
-        return set_string_list, placeholder_token_ids
-
-    def train_emb(
-        self, 
-        image_gt,
-        set_string_list,
-        gradient_accumulation_steps = 5,
-        embedding_learning_rate = 1e-4,
-        max_emb_train_steps = 100,
-        train_batch_size = 1,
-        train_full_lora = False
-    ):
-        decom_controller =  GroupedCAController(mask_list = self.mask_list)
-        register_attention_disentangled_control(self.unet, decom_controller)
-        
-        accelerator = Accelerator(mixed_precision=self.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps)
-        self.vae.requires_grad_(False)
-        self.unet.requires_grad_(False)
-
-        self.text_encoder.requires_grad_(True)
-        self.text_encoder_2.requires_grad_(True)
-        
-        self.text_encoder.text_model.encoder.requires_grad_(False)
-        self.text_encoder.text_model.final_layer_norm.requires_grad_(False)
-        self.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
-        
-        self.text_encoder_2.text_model.encoder.requires_grad_(False)
-        self.text_encoder_2.text_model.final_layer_norm.requires_grad_(False)
-        self.text_encoder_2.text_model.embeddings.position_embedding.requires_grad_(False)
-
-        weight_dtype = torch.float32
-        if accelerator.mixed_precision == "fp16":
-            weight_dtype = torch.float16
-        elif accelerator.mixed_precision == "bf16":
-            weight_dtype = torch.bfloat16
-
-        self.unet.to(device, dtype=weight_dtype)
-        self.vae.to(device, dtype=weight_dtype)
-
-        trainable_embmat_list_1 = [param for param in self.text_encoder.get_input_embeddings().parameters()]
-        trainable_embmat_list_2 = [param for param in self.text_encoder_2.get_input_embeddings().parameters()]
-
-        optimizer = torch.optim.AdamW(trainable_embmat_list_1 + trainable_embmat_list_2, lr=embedding_learning_rate)
-        
-        self.text_encoder, self.text_encoder_2, optimizer = accelerator.prepare(self.text_encoder, self.text_encoder_2, optimizer)
-
-        orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder)  .get_input_embeddings().weight.data.clone()
-        orig_embeds_params_2 = accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight.data.clone()
-
-        self.text_encoder.train()
-        self.text_encoder_2.train()
-        
-        effective_emb_train_steps = max_emb_train_steps//gradient_accumulation_steps
-        
-        if accelerator.is_main_process:
-            accelerator.init_trackers("DEdit EmbSteps", config={
-                    "embedding_learning_rate": embedding_learning_rate,
-                    "text_embedding_optimization_steps": effective_emb_train_steps,
-                })
-        global_step = 0
-        noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0" , subfolder="scheduler")
-        progress_bar = tqdm(range(0, effective_emb_train_steps), initial = global_step, desc="EmbSteps")
-        latents0 = image2latent(image_gt, vae = self.vae, dtype=weight_dtype)
-        latents0 = latents0.repeat(train_batch_size, 1, 1, 1)
-        
-        for _ in range(max_emb_train_steps):
-            with accelerator.accumulate(self.text_encoder, self.text_encoder_2):
-                latents = latents0.clone().detach()
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-                encoder_hidden_states_list, add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
-                    set_string_list, 
-                    self.tokenizer, 
-                    self.tokenizer_2,
-                    self.text_encoder, 
-                    self.text_encoder_2, 
-                    length = max_length, 
-                    bsz = train_batch_size, 
-                    weight_dtype = weight_dtype
-                )
-
-                model_pred = self.unet(
-                    noisy_latents,
-                    timesteps, 
-                    encoder_hidden_states = encoder_hidden_states_list,
-                    cross_attention_kwargs = None,
-                    added_cond_kwargs={"text_embeds": add_text_embeds, "time_ids": add_time_ids},
-                    return_dict=False
-                )[0]
-                loss = F.mse_loss(model_pred.float(), noise.float(), reduction="mean")
-                accelerator.backward(loss)
-                optimizer.step()
-                optimizer.zero_grad()
-
-                index_no_updates = torch.ones((len(self.tokenizer),), dtype=torch.bool)
-                index_no_updates[self.min_added_id : self.max_added_id + 1] = False
-                with torch.no_grad():
-                    accelerator.unwrap_model(self.text_encoder).get_input_embeddings().weight[
-                        index_no_updates] = orig_embeds_params_1[index_no_updates]
-
-                index_no_updates = torch.ones((len(self.tokenizer_2),), dtype=torch.bool)
-                index_no_updates[self.min_added_id : self.max_added_id + 1] = False
-                with torch.no_grad():
-                    accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight[
-                        index_no_updates] = orig_embeds_params_2[index_no_updates]
-                        
-            logs = {"loss": loss.detach().item(), "lr": embedding_learning_rate}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-                
-            if global_step >= max_emb_train_steps:
-                break
-        accelerator.wait_for_everyone()
-        accelerator.end_training()
-        self.text_encoder   =  accelerator.unwrap_model(self.text_encoder).to(dtype = weight_dtype)
-        self.text_encoder_2 =  accelerator.unwrap_model(self.text_encoder_2).to(dtype = weight_dtype)
-
-    def train_model(
-        self, 
-        image_gt, 
-        set_string_list,
-        gradient_accumulation_steps = 5,
-        max_diffusion_train_steps = 100,
-        diffusion_model_learning_rate = 1e-5,
-        train_batch_size = 1,
-        train_full_lora = False,
-        lora_rank = 4,
-        lora_alpha = 4
-    ):
-        self.unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet").to(device)
-        self.unet.ca_dim = 2048
-        decom_controller =  GroupedCAController(mask_list = self.mask_list)
-        register_attention_disentangled_control(self.unet, decom_controller)
-        
-        mixed_precision = "fp16"
-        accelerator = Accelerator(gradient_accumulation_steps = gradient_accumulation_steps, mixed_precision = mixed_precision)
-        
-        weight_dtype = torch.float32
-        if accelerator.mixed_precision == "fp16":
-            weight_dtype = torch.float16
-        elif accelerator.mixed_precision == "bf16":
-            weight_dtype = torch.bfloat16
-        
-        self.vae.requires_grad_(False)
-        self.vae.to(device, dtype=weight_dtype)
-        
-        self.unet.requires_grad_(False)
-        self.unet.train()
-        
-        self.text_encoder.requires_grad_(False)
-        self.text_encoder_2.requires_grad_(False)
-        
-        if not train_full_lora:
-            trainable_params_list = []
-            for _, module in self.unet.named_modules():
-                module_name = type(module).__name__
-                if module_name == "Attention":
-                    if module.to_k.in_features == 2048: # this is cross attention:
-                        module.to_k.weight.requires_grad = True
-                        trainable_params_list.append(module.to_k.weight)
-                        if module.to_k.bias is not None:
-                            module.to_k.bias.requires_grad = True
-                            trainable_params_list.append(module.to_k.bias)                 
-                        module.to_v.weight.requires_grad = True
-                        trainable_params_list.append(module.to_v.weight)
-                        if module.to_v.bias is not None:
-                            module.to_v.bias.requires_grad = True
-                            trainable_params_list.append(module.to_v.bias)
-                        module.to_q.weight.requires_grad = True
-                        trainable_params_list.append(module.to_q.weight)
-                        if module.to_q.bias is not None:
-                            module.to_q.bias.requires_grad = True
-                            trainable_params_list.append(module.to_q.bias)
-        else:
-            unet_lora_config = LoraConfig(
-                r=lora_rank,
-                lora_alpha=lora_alpha,
-                init_lora_weights="gaussian",
-                target_modules=["to_k", "to_q", "to_v", "to_out.0"],
-            )
-            self.unet.add_adapter(unet_lora_config) 
-            print("training full parameters using lora!")
-            trainable_params_list = list(filter(lambda p: p.requires_grad, self.unet.parameters()))
-            
-        self.text_encoder.to(device, dtype=weight_dtype)
-        self.text_encoder_2.to(device, dtype=weight_dtype)         
-        optimizer = torch.optim.AdamW(trainable_params_list, lr=diffusion_model_learning_rate)                                 
-        self.unet, optimizer = accelerator.prepare(self.unet, optimizer)
-        psum2 = sum(p.numel() for p in trainable_params_list)
-
-        effective_diffusion_train_steps = max_diffusion_train_steps // gradient_accumulation_steps
-        if accelerator.is_main_process:
-            accelerator.init_trackers("textual_inversion", config={
-                    "diffusion_model_learning_rate": diffusion_model_learning_rate,
-                    "diffusion_model_optimization_steps": effective_diffusion_train_steps,
-                })
-    
-        global_step = 0
-        progress_bar = tqdm( range(0, effective_diffusion_train_steps),initial=global_step, desc="ModelSteps")
-
-        noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0" , subfolder="scheduler")
-        
-        latents0 = image2latent(image_gt, vae = self.vae, dtype=weight_dtype)
-        latents0 = latents0.repeat(train_batch_size, 1, 1, 1)
-
-        with torch.no_grad():
-            encoder_hidden_states_list, add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
-                set_string_list,
-                self.tokenizer,
-                self.tokenizer_2,
-                self.text_encoder,
-                self.text_encoder_2,
-                length = max_length, 
-                bsz = train_batch_size,
-                weight_dtype = weight_dtype
-            )
-
-        for _ in range(max_diffusion_train_steps):
-            with accelerator.accumulate(self.unet):
-                latents = latents0.clone().detach()
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-                model_pred = self.unet(
-                    noisy_latents,
-                    timesteps, 
-                    encoder_hidden_states=encoder_hidden_states_list,
-                    cross_attention_kwargs=None, return_dict=False,
-                    added_cond_kwargs={"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                )[0]
-                loss = F.mse_loss(model_pred.float(), noise.float(), reduction="mean")
-                accelerator.backward(loss)
-                optimizer.step()
-                optimizer.zero_grad()
-
-            logs = {"loss": loss.detach().item(), "lr": diffusion_model_learning_rate}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-            if global_step >=max_diffusion_train_steps:
-                break
-        accelerator.wait_for_everyone()
-        accelerator.end_training()
-        self.unet = accelerator.unwrap_model(self.unet).to(dtype = weight_dtype)
-
-    def train_emb_2imgs(
-        self,
-        image_gt_1,
-        image_gt_2, 
-        set_string_list_1,
-        set_string_list_2,
-        gradient_accumulation_steps = 5,
-        embedding_learning_rate = 1e-4,
-        max_emb_train_steps = 100,
-        train_batch_size = 1,
-        train_full_lora = False
-    ):
-        decom_controller_1 = GroupedCAController(mask_list = self.mask_list)
-        decom_controller_2 = GroupedCAController(mask_list = self.mask_list_2)
-        accelerator = Accelerator(mixed_precision=self.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps)
-        self.vae.requires_grad_(False)
-        self.unet.requires_grad_(False)
-
-        self.text_encoder.requires_grad_(True)
-        self.text_encoder_2.requires_grad_(True)
-        
-        self.text_encoder.text_model.encoder.requires_grad_(False)
-        self.text_encoder.text_model.final_layer_norm.requires_grad_(False)
-        self.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
-        
-        self.text_encoder_2.text_model.encoder.requires_grad_(False)
-        self.text_encoder_2.text_model.final_layer_norm.requires_grad_(False)
-        self.text_encoder_2.text_model.embeddings.position_embedding.requires_grad_(False)
-
-        weight_dtype = torch.float32
-        if accelerator.mixed_precision == "fp16":
-            weight_dtype = torch.float16
-        elif accelerator.mixed_precision == "bf16":
-            weight_dtype = torch.bfloat16
-
-        self.unet.to(device, dtype=weight_dtype)
-        self.vae.to(device, dtype=weight_dtype)
-
-        
-        trainable_embmat_list_1 = [param for param in self.text_encoder.get_input_embeddings().parameters()]
-        trainable_embmat_list_2 = [param for param in self.text_encoder_2.get_input_embeddings().parameters()]
-
-        optimizer = torch.optim.AdamW(trainable_embmat_list_1 + trainable_embmat_list_2, lr=embedding_learning_rate)
-        self.text_encoder, self.text_encoder_2,  optimizer= accelerator.prepare(self.text_encoder, self.text_encoder_2,  optimizer)  ###
-        orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder)  .get_input_embeddings().weight.data.clone()
-        orig_embeds_params_2 = accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight.data.clone()
-
-        self.text_encoder.train()
-        self.text_encoder_2.train()
-        
-        effective_emb_train_steps = max_emb_train_steps//gradient_accumulation_steps
-        
-        if accelerator.is_main_process:
-            accelerator.init_trackers("EmbFt", config={
-                    "embedding_learning_rate": embedding_learning_rate,
-                    "text_embedding_optimization_steps": effective_emb_train_steps,
-                })
-        
-        global_step = 0
-        
-        noise_scheduler = DDPMScheduler.from_pretrained(self.model_id , subfolder="scheduler")
-        progress_bar = tqdm(range(0, effective_emb_train_steps),initial=global_step,desc="EmbSteps")
-        latents0_1 = image2latent(image_gt_1, vae = self.vae, dtype=weight_dtype)
-        latents0_1 = latents0_1.repeat(train_batch_size,1,1,1)
-        
-        latents0_2 = image2latent(image_gt_2, vae = self.vae, dtype=weight_dtype)
-        latents0_2 = latents0_2.repeat(train_batch_size,1,1,1)
-        
-        for step  in range(max_emb_train_steps):
-            with accelerator.accumulate(self.text_encoder, self.text_encoder_2):
-                latents_1 = latents0_1.clone().detach()
-                noise_1 = torch.randn_like(latents_1)
-                
-                latents_2 = latents0_2.clone().detach()
-                noise_2 = torch.randn_like(latents_2)
-                
-                bsz = latents_1.shape[0]
-
-                timesteps_1 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_1.device)
-                timesteps_1 = timesteps_1.long()
-                noisy_latents_1 = noise_scheduler.add_noise(latents_1, noise_1, timesteps_1)
-                
-                timesteps_2 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_2.device)
-                timesteps_2 = timesteps_2.long()
-                noisy_latents_2 = noise_scheduler.add_noise(latents_2, noise_2, timesteps_2)  
-                
-                register_attention_disentangled_control(self.unet, decom_controller_1)
-                encoder_hidden_states_list_1, add_text_embeds_1, add_time_ids_1 = sdxl_prepare_input_decom(
-                    set_string_list_1, 
-                    self.tokenizer,
-                    self.tokenizer_2,
-                    self.text_encoder,
-                    self.text_encoder_2, 
-                    length = max_length,
-                    bsz = train_batch_size,
-                    weight_dtype = weight_dtype
-                )
-                
-                model_pred_1 = self.unet(
-                    noisy_latents_1,
-                    timesteps_1, 
-                    encoder_hidden_states=encoder_hidden_states_list_1,
-                    cross_attention_kwargs=None,
-                    added_cond_kwargs={"text_embeds": add_text_embeds_1, "time_ids": add_time_ids_1},
-                    return_dict=False
-                )[0]
-
-                register_attention_disentangled_control(self.unet, decom_controller_2)
-                # import pdb; pdb.set_trace()
-                encoder_hidden_states_list_2, add_text_embeds_2, add_time_ids_2 = sdxl_prepare_input_decom(
-                    set_string_list_2, 
-                    self.tokenizer,
-                    self.tokenizer_2,
-                    self.text_encoder,
-                    self.text_encoder_2, 
-                    length = max_length,
-                    bsz = train_batch_size,
-                    weight_dtype = weight_dtype
-                )
-                
-                model_pred_2 = self.unet(
-                    noisy_latents_2,
-                    timesteps_2, 
-                    encoder_hidden_states = encoder_hidden_states_list_2,
-                    cross_attention_kwargs=None,
-                    added_cond_kwargs={"text_embeds": add_text_embeds_2, "time_ids": add_time_ids_2},
-                    return_dict=False
-                )[0]
-
-                loss_1 = F.mse_loss(model_pred_1.float(), noise_1.float(), reduction="mean") /2
-                loss_2 = F.mse_loss(model_pred_2.float(), noise_2.float(), reduction="mean") /2
-                loss = loss_1 + loss_2
-                accelerator.backward(loss)
-                optimizer.step()
-                optimizer.zero_grad()
-
-                index_no_updates = torch.ones((len(self.tokenizer),), dtype=torch.bool)
-                index_no_updates[self.min_added_id : self.max_added_id + 1] = False
-                with torch.no_grad():
-                    accelerator.unwrap_model(self.text_encoder).get_input_embeddings().weight[
-                        index_no_updates] = orig_embeds_params_1[index_no_updates]
-                index_no_updates = torch.ones((len(self.tokenizer_2),), dtype=torch.bool)
-                index_no_updates[self.min_added_id : self.max_added_id + 1] = False
-                with torch.no_grad():
-                    accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight[
-                        index_no_updates] = orig_embeds_params_2[index_no_updates]
-                        
-            logs = {"loss": loss.detach().item(), "lr": embedding_learning_rate}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-                
-            if global_step >= max_emb_train_steps:
-                break
-        accelerator.wait_for_everyone()
-        accelerator.end_training()
-        self.text_encoder = accelerator.unwrap_model(self.text_encoder)  .to(dtype = weight_dtype)
-        self.text_encoder_2 = accelerator.unwrap_model(self.text_encoder_2).to(dtype = weight_dtype)
-
-    def train_model_2imgs(
-        self,
-        image_gt_1,
-        image_gt_2,
-        set_string_list_1,
-        set_string_list_2,
-        gradient_accumulation_steps = 5,
-        max_diffusion_train_steps = 100,
-        diffusion_model_learning_rate = 1e-5,
-        train_batch_size = 1,
-        train_full_lora = False,
-        lora_rank = 4,
-        lora_alpha = 4
-    ):
-        self.unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet").to(device)
-        self.unet.ca_dim = 2048
-        decom_controller_1 = GroupedCAController(mask_list = self.mask_list)
-        decom_controller_2 = GroupedCAController(mask_list = self.mask_list_2)
-        
-        mixed_precision = "fp16"
-        accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps,mixed_precision=mixed_precision)
-        
-        weight_dtype = torch.float32
-        if accelerator.mixed_precision == "fp16":
-            weight_dtype = torch.float16
-        elif accelerator.mixed_precision == "bf16":
-            weight_dtype = torch.bfloat16
-        
-        
-        self.vae.requires_grad_(False)
-        self.vae.to(device, dtype=weight_dtype)
-        self.unet.requires_grad_(False)
-        self.unet.train()
-        
-        self.text_encoder.requires_grad_(False)
-        self.text_encoder_2.requires_grad_(False)
-        if not train_full_lora:
-            trainable_params_list = []
-            for name, module in self.unet.named_modules():
-                module_name = type(module).__name__
-                if module_name == "Attention":
-                    if module.to_k.in_features == 2048: # this is cross attention:
-                        module.to_k.weight.requires_grad = True
-                        trainable_params_list.append(module.to_k.weight)
-                        if module.to_k.bias is not None:
-                            module.to_k.bias.requires_grad = True
-                            trainable_params_list.append(module.to_k.bias)
-                            
-                        module.to_v.weight.requires_grad = True
-                        trainable_params_list.append(module.to_v.weight)
-                        if module.to_v.bias is not None:
-                            module.to_v.bias.requires_grad = True
-                            trainable_params_list.append(module.to_v.bias)
-                        module.to_q.weight.requires_grad = True
-                        trainable_params_list.append(module.to_q.weight)
-                        if module.to_q.bias is not None:
-                            module.to_q.bias.requires_grad = True
-                            trainable_params_list.append(module.to_q.bias)
-        else:
-            unet_lora_config = LoraConfig(
-                r = lora_rank,
-                lora_alpha = lora_alpha,
-                init_lora_weights="gaussian",
-                target_modules=["to_k", "to_q", "to_v", "to_out.0"],
-            )
-            self.unet.add_adapter(unet_lora_config) 
-            print("training full parameters using lora!")
-            trainable_params_list = list(filter(lambda p: p.requires_grad, self.unet.parameters()))
-            
-        self.text_encoder.to(device, dtype=weight_dtype)
-        self.text_encoder_2.to(device, dtype=weight_dtype)         
-        optimizer = torch.optim.AdamW(trainable_params_list, lr=diffusion_model_learning_rate)                                 
-        self.unet, optimizer = accelerator.prepare(self.unet, optimizer)
-        psum2 = sum(p.numel() for p in trainable_params_list)
-
-        effective_diffusion_train_steps = max_diffusion_train_steps // gradient_accumulation_steps
-        if accelerator.is_main_process:
-            accelerator.init_trackers("ModelFt", config={
-                    "diffusion_model_learning_rate": diffusion_model_learning_rate,
-                    "diffusion_model_optimization_steps": effective_diffusion_train_steps,
-                })
-    
-        global_step = 0
-        progress_bar = tqdm(range(0, effective_diffusion_train_steps),initial=global_step, desc="ModelSteps")
-        noise_scheduler = DDPMScheduler.from_pretrained(self.model_id, subfolder="scheduler")
-        
-        latents0_1 = image2latent(image_gt_1, vae = self.vae, dtype=weight_dtype)
-        latents0_1 = latents0_1.repeat(train_batch_size, 1, 1, 1)
-
-        latents0_2 = image2latent(image_gt_2, vae = self.vae, dtype=weight_dtype)
-        latents0_2 = latents0_2.repeat(train_batch_size,1, 1, 1)
-        
-        with torch.no_grad():
-            encoder_hidden_states_list_1, add_text_embeds_1, add_time_ids_1 = sdxl_prepare_input_decom(
-                set_string_list_1,
-                self.tokenizer,
-                self.tokenizer_2,
-                self.text_encoder,
-                self.text_encoder_2,
-                length = max_length, 
-                bsz = train_batch_size, 
-                weight_dtype = weight_dtype
-            )
-            encoder_hidden_states_list_2, add_text_embeds_2, add_time_ids_2 = sdxl_prepare_input_decom(
-                set_string_list_2,
-                self.tokenizer,
-                self.tokenizer_2,
-                self.text_encoder,
-                self.text_encoder_2,
-                length = max_length, 
-                bsz = train_batch_size,
-                weight_dtype = weight_dtype
-            )
-            
-        for _ in range(max_diffusion_train_steps):
-            with accelerator.accumulate(self.unet):
-                latents_1 = latents0_1.clone().detach()
-                noise_1 = torch.randn_like(latents_1)
-                bsz = latents_1.shape[0]
-                timesteps_1 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_1.device)
-                timesteps_1 = timesteps_1.long()
-                noisy_latents_1 = noise_scheduler.add_noise(latents_1, noise_1, timesteps_1)
-                
-                latents_2 = latents0_2.clone().detach()
-                noise_2 = torch.randn_like(latents_2)
-                bsz = latents_2.shape[0]
-                timesteps_2 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_2.device)
-                timesteps_2 = timesteps_2.long()
-                noisy_latents_2 = noise_scheduler.add_noise(latents_2, noise_2, timesteps_2)
-                
-                register_attention_disentangled_control(self.unet, decom_controller_1)
-                model_pred_1 = self.unet(
-                    noisy_latents_1,
-                    timesteps_1, 
-                    encoder_hidden_states = encoder_hidden_states_list_1,
-                    cross_attention_kwargs = None,
-                    return_dict = False,
-                    added_cond_kwargs = {"text_embeds": add_text_embeds_1, "time_ids": add_time_ids_1}
-                )[0]
-                
-                register_attention_disentangled_control(self.unet, decom_controller_2)
-                model_pred_2 = self.unet(
-                    noisy_latents_2,
-                    timesteps_2, 
-                    encoder_hidden_states = encoder_hidden_states_list_2,
-                    cross_attention_kwargs = None,
-                    return_dict=False,
-                    added_cond_kwargs={"text_embeds": add_text_embeds_2, "time_ids": add_time_ids_2}
-                )[0]
-                
-                loss_1 = F.mse_loss(model_pred_1.float(), noise_1.float(), reduction="mean")
-                loss_2 = F.mse_loss(model_pred_2.float(), noise_2.float(), reduction="mean")
-                loss = loss_1 + loss_2
-                accelerator.backward(loss)
-                optimizer.step()
-                optimizer.zero_grad()
-                
-
-            logs = {"loss": loss.detach().item(), "lr": diffusion_model_learning_rate}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-                
-            if global_step >=max_diffusion_train_steps:
-                break
-        accelerator.wait_for_everyone()
-        accelerator.end_training()
-        self.unet = accelerator.unwrap_model(self.unet).to(dtype = weight_dtype)
-
-    @torch.no_grad()
-    def backward_zT_to_z0_euler_decom(
-        self,
-        zT,
-        cond_emb_list,
-        cond_add_text_embeds,
-        add_time_ids,
-        uncond_emb=None,
-        guidance_scale = 1,
-        num_sampling_steps = 20,
-        cond_controller = None,
-        uncond_controller = None,
-        mask_hard = None, 
-        mask_soft = None,
-        orig_image = None,
-        return_intermediate = False,
-        strength = 1
-    ):
-        latent_cur = zT
-        if uncond_emb is None:
-            uncond_emb = torch.zeros(zT.shape[0], 77, 2048).to(dtype = zT.dtype, device = zT.device)
-            uncond_add_text_embeds = torch.zeros(1, 1280).to(dtype = zT.dtype, device = zT.device)
-        if mask_soft is not None:
-            init_latents_orig = image2latent(orig_image, self.vae, dtype=self.vae.dtype)
-            length = init_latents_orig.shape[-1]
-            noise = torch.randn_like(init_latents_orig)
-            mask_soft = torch.nn.functional.interpolate(mask_soft.float().unsqueeze(0).unsqueeze(0), (length, length)).to(self.vae.dtype) ###
-        if mask_hard is not None:
-            init_latents_orig = image2latent(orig_image, self.vae, dtype=self.vae.dtype)
-            length = init_latents_orig.shape[-1]
-            noise = torch.randn_like(init_latents_orig) 
-            mask_hard = torch.nn.functional.interpolate(mask_hard.float().unsqueeze(0).unsqueeze(0), (length, length)).to(self.vae.dtype) ###
-
-        intermediate_list = [latent_cur.detach()]
-        for i in tqdm(range(num_sampling_steps)):
-            t = self.scheduler.timesteps[i]
-            latent_input = self.scheduler.scale_model_input(latent_cur, t)
-            
-            register_attention_disentangled_control(self.unet, uncond_controller)
-            noise_pred_uncond = self.unet(latent_input, t, 
-                            encoder_hidden_states=uncond_emb,
-                            added_cond_kwargs={"text_embeds": uncond_add_text_embeds, "time_ids": add_time_ids},
-                            return_dict=False,)[0]
-            
-            register_attention_disentangled_control(self.unet, cond_controller)
-            noise_pred_cond = self.unet(latent_input, t, 
-                    encoder_hidden_states=cond_emb_list,
-                    added_cond_kwargs={"text_embeds": cond_add_text_embeds, "time_ids": add_time_ids},
-                    return_dict=False,)[0]
-            
-            noise_pred =  noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
-            latent_cur = self.scheduler.step(noise_pred, t, latent_cur, generator = None, return_dict=False)[0]
-            if return_intermediate is True:
-                intermediate_list.append(latent_cur)
-            if mask_hard is not None and mask_soft is not None and i <= strength *num_sampling_steps:
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
-                mask = mask_soft.to(latent_cur.device, latent_cur.dtype) + mask_hard.to(latent_cur.device, latent_cur.dtype)
-                latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
-
-            elif mask_hard is not None and mask_soft is not None and i > strength *num_sampling_steps:
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
-                mask = mask_hard.to(latent_cur.device, latent_cur.dtype)
-                latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
-
-            elif mask_hard is None and mask_soft is not None and i <= strength *num_sampling_steps:
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
-                mask = mask_soft.to(latent_cur.device, latent_cur.dtype)
-                latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
-                
-            elif mask_hard is None and mask_soft is not None and i > strength *num_sampling_steps:
-                pass
-            
-            elif mask_hard is not None and mask_soft is None:
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
-                mask = mask_hard.to(latent_cur.dtype)
-                latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
-                
-            else: # hard and soft are both none
-                pass
-            
-        if return_intermediate is True:
-            return latent_cur, intermediate_list
-        else:
-            return latent_cur
-        
-    @torch.no_grad()
-    def sampling(
-        self,
-        set_string_list, 
-        cond_controller = None, 
-        uncond_controller = None,
-        guidance_scale = 7,
-        num_sampling_steps = 20,
-        mask_hard = None,
-        mask_soft = None,
-        orig_image = None,
-        strength = 1.,
-        num_imgs = 1,
-        normal_token_id_list = [],
-        seed = 1
-    ):
-        weight_dtype = torch.float16
-        self.scheduler.set_timesteps(num_sampling_steps)
-        self.unet.to(device, dtype=weight_dtype)
-        self.vae.to(device, dtype=weight_dtype)
-        self.text_encoder.to(device, dtype=weight_dtype)
-        self.text_encoder_2.to(device, dtype=weight_dtype)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-
-        vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        zT = torch.randn(num_imgs, 4, self.resolution//vae_scale_factor,self.resolution//vae_scale_factor).to(device,dtype=weight_dtype)
-        zT = zT * self.scheduler.init_noise_sigma   
-        
-        cond_emb_list, cond_add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
-            set_string_list, 
-            self.tokenizer,
-            self.tokenizer_2,
-            self.text_encoder,
-            self.text_encoder_2, 
-            length = max_length,
-            bsz = num_imgs,
-            weight_dtype = weight_dtype,
-            normal_token_id_list = normal_token_id_list
-        )
-
-        z0 = self.backward_zT_to_z0_euler_decom(zT, cond_emb_list, cond_add_text_embeds, add_time_ids,
-                guidance_scale = guidance_scale, num_sampling_steps = num_sampling_steps,
-                cond_controller = cond_controller, uncond_controller = uncond_controller, 
-                mask_hard = mask_hard, mask_soft = mask_soft,  orig_image =orig_image, strength = strength
-             )
-        x0 = latent2image(z0, vae = self.vae)
-        return x0
-    
-    @torch.no_grad()                 
-    def inference_with_mask(
-        self,
-        save_path,
-        guidance_scale = 3,
-        num_sampling_steps = 50,
-        strength = 1,
-        mask_soft = None,
-        mask_hard= None,
-        orig_image=None,
-        mask_list = None,
-        num_imgs = 1,
-        seed = 1,
-        set_string_list = None
-    ):
-        if mask_list is not None:
-            mask_list = [m.to(device) for m in mask_list]
-        else:
-            mask_list = self.mask_list
-        if set_string_list is not None:
-            self.set_string_list = set_string_list
-
-        if mask_hard is not None and mask_soft is not None:
-            check_mask_overlap_torch(mask_hard, mask_soft)
-        null_controller = DummyController()
-        decom_controller = GroupedCAController(mask_list = mask_list)
-        x0 = self.sampling(
-            self.set_string_list, 
-            guidance_scale = guidance_scale,
-            num_sampling_steps = num_sampling_steps,
-            strength = strength,
-            cond_controller = decom_controller,
-            uncond_controller = null_controller, 
-            mask_soft = mask_soft,
-            mask_hard = mask_hard,
-            orig_image = orig_image,
-            num_imgs = num_imgs,
-            seed = seed
-        )
-        save_images(x0, save_path)