import torch from utils import import_model_class_from_model_name_or_path from transformers import AutoTokenizer from diffusers import ( AutoencoderKL, DDPMScheduler, DDIMScheduler, UNet2DConditionModel, ) from accelerate import Accelerator from tqdm.auto import tqdm from utils import sd_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') class DEditSDPipeline: def __init__( self, mask_list, mask_label_list, mask_list_2 = None, mask_label_list_2 = None, resolution = 512, num_tokens = 1 ): super().__init__() model_id = "CompVis/stable-diffusion-v1-4" self.model_id = model_id self.tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer", use_fast=False) text_encoder_cls_one = import_model_class_from_model_name_or_path(model_id, subfolder = "text_encoder") self.text_encoder = text_encoder_cls_one.from_pretrained(model_id, subfolder="text_encoder" ).to(device) self.unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet") self.unet.ca_dim = 768 self.vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae") self.scheduler = DDPMScheduler.from_pretrained(model_id , subfolder="scheduler") self.scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=True, rescale_betas_zero_snr = False, ) 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 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) 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 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, ): 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.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) 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()] optimizer = torch.optim.AdamW(trainable_embmat_list_1, lr=embedding_learning_rate) self.text_encoder, optimizer = accelerator.prepare(self.text_encoder, optimizer) orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder).get_input_embeddings().weight.data.clone() self.text_encoder.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 = self.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): 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 = sd_prepare_input_decom( set_string_list, self.tokenizer, self.text_encoder, length = 40, bsz = train_batch_size, weight_dtype = weight_dtype ) model_pred = self.unet( noisy_latents, timesteps, encoder_hidden_states = encoder_hidden_states_list, ).sample 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] 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) 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 = 768 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) 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 == self.unet.ca_dim: # 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) 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 = sd_prepare_input_decom( set_string_list, self.tokenizer, self.text_encoder, length = 40, 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, ).sample 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, ): 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.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) 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()] optimizer = torch.optim.AdamW(trainable_embmat_list_1, lr=embedding_learning_rate) self.text_encoder, optimizer= accelerator.prepare(self.text_encoder, optimizer) ### orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder) .get_input_embeddings().weight.data.clone() self.text_encoder.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): 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 = sd_prepare_input_decom( set_string_list_1, self.tokenizer, self.text_encoder, length = 40, 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, ).sample register_attention_disentangled_control(self.unet, decom_controller_2) # import pdb; pdb.set_trace() encoder_hidden_states_list_2= sd_prepare_input_decom( set_string_list_2, self.tokenizer, self.text_encoder, length = 40, 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, ).sample 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] 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) 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 = 768 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) 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 == self.unet.ca_dim: # 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) 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 = sd_prepare_input_decom( set_string_list_1, self.tokenizer, self.text_encoder, length = 40, bsz = train_batch_size, weight_dtype = weight_dtype ) encoder_hidden_states_list_2 = sd_prepare_input_decom( set_string_list_2, self.tokenizer, self.text_encoder, length = 40, 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, ).sample 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, ).sample 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, 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, self.unet.ca_dim).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, ).sample register_attention_disentangled_control(self.unet, cond_controller) noise_pred_cond = self.unet( latent_input, t, encoder_hidden_states=cond_emb_list, ).sample 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) 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 = sd_prepare_input_decom( set_string_list, self.tokenizer, self.text_encoder, length = 40, 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, 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) from PIL import Image return Image.open("example_tmp/text/out_text_0.png")