import random import numpy as np from tqdm import tqdm import os import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from einops import repeat import time from tools.torch_tools import wav_to_fbank, sinusoidal_positional_embedding from audioldm.audio.stft import TacotronSTFT from audioldm.variational_autoencoder import AutoencoderKL from audioldm.utils import default_audioldm_config, get_metadata from transformers import CLIPTokenizer, AutoTokenizer, T5Tokenizer from transformers import CLIPTextModel, T5EncoderModel, AutoModel from transformers import CLIPVisionModelWithProjection, CLIPTextModelWithProjection from transformers import CLIPProcessor, CLIPModel import diffusers from diffusers.utils.torch_utils import randn_tensor from diffusers import DDPMScheduler, UNet2DConditionModel from diffusers import AutoencoderKL as DiffuserAutoencoderKL from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode, RandomResizedCrop from diffusers import AudioLDMPipeline def build_pretrained_models(name): checkpoint = torch.load(name, map_location="cpu") scale_factor = checkpoint["state_dict"]["scale_factor"].item() vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k} config = default_audioldm_config(name) vae_config = config["model"]["params"]["first_stage_config"]["params"] vae_config["scale_factor"] = scale_factor vae = AutoencoderKL(**vae_config) vae.load_state_dict(vae_state_dict) fn_STFT = TacotronSTFT( config["preprocessing"]["stft"]["filter_length"], config["preprocessing"]["stft"]["hop_length"], config["preprocessing"]["stft"]["win_length"], config["preprocessing"]["mel"]["n_mel_channels"], config["preprocessing"]["audio"]["sampling_rate"], config["preprocessing"]["mel"]["mel_fmin"], config["preprocessing"]["mel"]["mel_fmax"], ) vae.eval() fn_STFT.eval() return vae, fn_STFT class EffNetb3(nn.Module): def __init__(self, pretrained_model_path, embedding_dim=1024, pretrained=True): super(EffNetb3, self).__init__() self.model_name = 'effnetb3' self.pretrained = pretrained # Create model # self.effnet = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'efficientnet_b3', pretrained=self.pretrained) # torch.save(self.effnet, 'model.pth') self.effnet = torch.hub.load(pretrained_model_path, 'efficientnet_b3', trust_repo=True, source='local') #self.effnet.conv_stem = nn.Conv2d(1, 40, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) self.embedder = nn.Conv2d(384, embedding_dim, kernel_size=1, stride=1, padding=0) def forward(self, x): #out = self.effnet(x) out = self.effnet.conv_stem(x) out = self.effnet.bn1(out) out = self.effnet.act1(out) for i in range(len(self.effnet.blocks)): out = self.effnet.blocks[i](out) out = self.embedder(out) return out class EffNetb3_last_layer(nn.Module): def __init__(self, pretrained_model_path, embedding_dim=1024, pretrained=True): super(EffNetb3_last_layer, self).__init__() self.model_name = 'effnetb3' self.pretrained = pretrained self.effnet = torch.hub.load(pretrained_model_path, 'efficientnet_b3', trust_repo=True, source='local') self.effnet.classifier = nn.Linear(1536, embedding_dim) def forward(self, x): out = self.effnet(x) return out.unsqueeze(-1) class Clip4Video(nn.Module): def __init__(self, model, embedding_dim=1024, pretrained=True, pe=False): super(Clip4Video, self).__init__() self.pretrained = pretrained self.clip_vision = CLIPVisionModelWithProjection.from_pretrained(model) self.clip_text = CLIPTextModelWithProjection.from_pretrained(model) self.tokenizer = AutoTokenizer.from_pretrained(model) input_dim = 512 if "clip-vit-base" in model else 768 self.linear_layer = nn.Linear(input_dim, embedding_dim) self.pe = sinusoidal_positional_embedding(30, input_dim) if pe else None print("*****PE*****") if pe else print("*****W/O PE*****") def forward(self, text=None, image=None, video=None): assert text is not None or image is not None or video is not None, "At least one of text, image or video should be provided" if text is not None and video is None: inputs = self.tokenizer([text], padding=True, truncation=True, return_tensors="pt", max_length=77).to(self.clip_text.device) out = self.clip_text(**inputs) out = out.text_embeds.repeat(20, 1) elif video is not None and text is None: out = self.clip_vision(video.to(self.clip_vision.device)) # input video x: t * 3 * w * h out = out.image_embeds # t * 512 if self.pe is not None: out = out + self.pe[:out.shape[0], :].to(self.clip_vision.device) # out['last_hidden_state'].shape # t * 50 * 768 # out['image_embeds'].shape # t * 512 elif text is not None and video is not None: text_inputs = self.tokenizer([text], padding=True, truncation=True, return_tensors="pt", max_length=77).to(self.clip_text.device) video_out = self.clip_vision(video.to(self.clip_vision.device)) video_out = video_out.image_embeds text_out = self.clip_text(**text_inputs) text_out = text_out.text_embeds.repeat(video_out.shape[0], 1) # out = text_out + video_out # concat out = torch.cat([text_out, video_out], dim=0) out = self.linear_layer(out) # t * 1024 return out class AudioDiffusion(nn.Module): def __init__( self, fea_encoder_name, scheduler_name, unet_model_name=None, unet_model_config_path=None, snr_gamma=None, freeze_text_encoder=True, uncondition=False, img_pretrained_model_path=None, task=None, embedding_dim=1024, pe=False ): super().__init__() assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required" self.fea_encoder_name = fea_encoder_name self.scheduler_name = scheduler_name self.unet_model_name = unet_model_name self.unet_model_config_path = unet_model_config_path self.snr_gamma = snr_gamma self.freeze_text_encoder = freeze_text_encoder self.uncondition = uncondition self.task = task self.pe = pe # https://huggingface.co./docs/diffusers/v0.14.0/en/api/schedulers/overview self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") if unet_model_config_path: unet_config = UNet2DConditionModel.load_config(unet_model_config_path) print("unet_config", unet_config) self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet") self.set_from = "random" print("UNet initialized randomly.") else: self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet") self.set_from = "pre-trained" self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4)) self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8)) print("UNet initialized from stable diffusion checkpoint.") if self.task == "text2audio": if "stable-diffusion" in self.fea_encoder_name: self.tokenizer = CLIPTokenizer.from_pretrained(self.fea_encoder_name, subfolder="tokenizer") self.text_encoder = CLIPTextModel.from_pretrained(self.fea_encoder_name, subfolder="text_encoder") elif "t5" in self.fea_encoder_name and "Chinese" not in self.fea_encoder_name: self.tokenizer = AutoTokenizer.from_pretrained(self.fea_encoder_name) self.text_encoder = T5EncoderModel.from_pretrained(self.fea_encoder_name) elif "Chinese" in self.fea_encoder_name: self.tokenizer = T5Tokenizer.from_pretrained(self.fea_encoder_name) self.text_encoder = T5EncoderModel.from_pretrained(self.fea_encoder_name) elif "clap" in self.fea_encoder_name: self.tokenizer = RobertaTokenizer.from_pretrained("roberta-base") self.CLAP_model = laion_clap.CLAP_Module(enable_fusion=False) self.CLAP_model.load_ckpt(self.fea_encoder_name) elif "clip-vit" in self.fea_encoder_name: # self.CLIP_model = CLIPModel.from_pretrained(self.fea_encoder_name) # self.CLIP_processor = CLIPProcessor.from_pretrained(self.fea_encoder_name) self.CLIP_model = CLIPTextModelWithProjection.from_pretrained(self.fea_encoder_name) self.tokenizer = AutoTokenizer.from_pretrained(self.fea_encoder_name) if "base" in self.fea_encoder_name: self.linear_layer = nn.Linear(512, embedding_dim) else: self.linear_layer = nn.Linear(768, embedding_dim) else: self.tokenizer = AutoTokenizer.from_pretrained(self.fea_encoder_name) self.text_encoder = AutoModel.from_pretrained(self.fea_encoder_name) elif self.task == "image2audio": if "clip-vit" in self.fea_encoder_name: self.CLIP_model = CLIPModel.from_pretrained(self.fea_encoder_name) self.CLIP_processor = CLIPProcessor.from_pretrained(self.fea_encoder_name) self.linear_layer = nn.Linear(512, embedding_dim) # self.img_fea_extractor = EffNetb3(img_pretrained_model_path) else: self.img_fea_extractor = EffNetb3_last_layer(img_pretrained_model_path) elif self.task == "video2audio": self.vid_fea_extractor = Clip4Video(model=self.fea_encoder_name, embedding_dim=embedding_dim, pe=pe) def compute_snr(self, timesteps): """ Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849 """ alphas_cumprod = self.noise_scheduler.alphas_cumprod sqrt_alphas_cumprod = alphas_cumprod**0.5 sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5 # Expand the tensors. # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026 sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float() while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape): sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None] alpha = sqrt_alphas_cumprod.expand(timesteps.shape) sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float() while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape): sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None] sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape) # Compute SNR. snr = (alpha / sigma) ** 2 return snr def encode_text(self, prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) if self.freeze_text_encoder: with torch.no_grad(): encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] else: encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] boolean_encoder_mask = (attention_mask == 1).to(device) return encoder_hidden_states, boolean_encoder_mask def encode_text_CLAP(self, prompt): device = self.text_encoder.device batch = self.tokenizer(prompt, padding="max_length", truncation=True, max_length=self.tokenizer.model_max_length, return_tensors="pt") input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) if self.freeze_text_encoder: with torch.no_grad(): encoder_hidden_states = self.CLAP_model.model.get_text_embedding(prompt) else: encoder_hidden_states = self.CLAP_model.model.get_text_embedding(prompt) boolean_encoder_mask = (attention_mask == 1).to(device) return encoder_hidden_states, boolean_encoder_mask def encode_image(self, prompt, device): if "clip-vit" in self.fea_encoder_name: with torch.no_grad(): inputs = self.CLIP_processor(text=["aaa"], images=prompt, return_tensors="pt", padding=True).to(device) encoder_hidden_states = self.CLIP_model(**inputs).image_embeds encoder_hidden_states = self.linear_layer(encoder_hidden_states) # b * 1024 encoder_hidden_states = encoder_hidden_states.unsqueeze(1).to(device) else: img_fea = self.img_fea_extractor(prompt) encoder_hidden_states = img_fea.view(img_fea.shape[0], img_fea.shape[1], -1).permute(0, 2, 1) boolean_encoder_mask = torch.ones((encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]), dtype=torch.bool) boolean_encoder_mask = boolean_encoder_mask.to(device) return encoder_hidden_states, boolean_encoder_mask def encode_video(self, video_batch, text=None, device=None): vid_feas = [] for i, video in enumerate(video_batch): if text: vid_fea = self.vid_fea_extractor(video=video, text=text[i]) # t * fea_dim else: vid_fea = self.vid_fea_extractor(video=video) vid_feas.append(vid_fea) padding = 0 size = max(v.size(0) for v in vid_feas) batch_size = len(vid_feas) embed_size = vid_feas[0].size(1) encoder_hidden_states = vid_feas[0].new(batch_size, size, embed_size).fill_(padding) boolean_encoder_mask = torch.ones((batch_size, size), dtype=torch.bool) def copy_tensor(src, dst): assert dst.numel() == src.numel() dst.copy_(src) for i, v in enumerate(vid_feas): copy_tensor(v, encoder_hidden_states[i][: len(v)]) boolean_encoder_mask[i, len(v):] = False return encoder_hidden_states.to(device), boolean_encoder_mask.to(device) def encode_text_CLIP(self, prompt, device): # tmp_image = np.ones((512, 512, 3)) # with torch.no_grad(): # inputs = self.CLIP_processor(text=prompt, images=tmp_image, return_tensors="pt", padding=True, max_length=77, truncation=True).to(device) # encoder_hidden_states = self.CLIP_model(**inputs).text_embeds # b * 768 text_inputs = self.tokenizer(prompt, padding=True, truncation=True, return_tensors="pt", max_length=77).to(device) encoder_hidden_states = self.CLIP_model(**text_inputs).text_embeds encoder_hidden_states = self.linear_layer(encoder_hidden_states) # b * 1024 encoder_hidden_states = encoder_hidden_states.unsqueeze(1).to(device) boolean_encoder_mask = torch.ones((encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]), dtype=torch.bool) boolean_encoder_mask = boolean_encoder_mask.to(device) return encoder_hidden_states, boolean_encoder_mask def forward(self, latents, text=None, video=None, image=None, validation_mode=False, device=None): num_train_timesteps = self.noise_scheduler.num_train_timesteps self.noise_scheduler.set_timesteps(num_train_timesteps, device=device) # encoder_hidden_states.shape [b, t, f] if self.task == "text2audio": if "clip-vit" in self.fea_encoder_name: encoder_hidden_states, boolean_encoder_mask = self.encode_text_CLIP(text, device) else: encoder_hidden_states, boolean_encoder_mask = self.encode_text(text) if self.uncondition: mask_indices = [k for k in range(len(text)) if random.random() < 0.1] # mask_indices = [k for k in range(len(prompt))] if len(mask_indices) > 0: encoder_hidden_states[mask_indices] = 0 elif self.task == "image2audio": encoder_hidden_states, boolean_encoder_mask = self.encode_image(image, device=device) elif self.task == "video2audio": encoder_hidden_states, boolean_encoder_mask = self.encode_video(video, text, device=device) bsz = latents.shape[0] if validation_mode: timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device) else: # Sample a random timestep for each instance timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device) timesteps = timesteps.long() noise = torch.randn_like(latents) noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps) # Get the target for loss depending on the prediction type if self.noise_scheduler.config.prediction_type == "epsilon": target = noise elif self.noise_scheduler.config.prediction_type == "v_prediction": target = self.noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}") if self.set_from == "random": model_pred = self.unet( noisy_latents, timesteps, encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample elif self.set_from == "pre-trained": compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() model_pred = self.unet( compressed_latents, timesteps, encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() if self.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py snr = self.compute_snr(timesteps) mse_loss_weights = ( torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr ) loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() return loss @torch.no_grad() def inference(self, inference_scheduler, text=None, video=None, image=None, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, disable_progress=True, device=None): start = time.time() classifier_free_guidance = guidance_scale > 1.0 #print("ldm time 0", time.time()-start, prompt) if self.task == "text2audio": batch_size = len(text) * num_samples_per_prompt if classifier_free_guidance: if "clip-vit" in self.fea_encoder_name: encoder_hidden_states, boolean_encoder_mask = self.encode_text_clip_classifier_free(text, num_samples_per_prompt, device=device) else: encoder_hidden_states, boolean_encoder_mask = self.encode_text_classifier_free(text, num_samples_per_prompt) else: encoder_hidden_states, boolean_encoder_mask = self.encode_text(text) encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_samples_per_prompt, 0) boolean_encoder_mask = boolean_encoder_mask.repeat_interleave(num_samples_per_prompt, 0) elif self.task == "image2audio": if classifier_free_guidance: encoder_hidden_states, boolean_encoder_mask = self.encode_image_classifier_free(image, num_samples_per_prompt, device=device) else: encoder_hidden_states, boolean_encoder_mask = self.encode_image_no_grad(image, device=device) encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_samples_per_prompt, 0) boolean_encoder_mask = boolean_encoder_mask.repeat_interleave(num_samples_per_prompt, 0) elif self.task == "video2audio": batch_size = len(video) * num_samples_per_prompt encoder_hidden_states, boolean_encoder_mask = self.encode_video_classifier_free(video, text, num_samples_per_prompt, device=device) # import pdb;pdb.set_trace() #print("ldm time 1", time.time()-start) inference_scheduler.set_timesteps(num_steps, device=device) timesteps = inference_scheduler.timesteps num_channels_latents = self.unet.in_channels latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, encoder_hidden_states.dtype, device) num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order progress_bar = tqdm(range(num_steps), disable=disable_progress) #print("ldm time 2", time.time()-start, timesteps) for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t) #print("ldm emu", i, time.time()-start) noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample # perform guidance if classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = inference_scheduler.step(noise_pred, t, latents).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0): progress_bar.update(1) #print("ldm time 3", time.time()-start) if self.set_from == "pre-trained": latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() return latents def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device): shape = (batch_size, num_channels_latents, 256, 16) latents = randn_tensor(shape, generator=None, device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * inference_scheduler.init_noise_sigma return latents def encode_text_classifier_free(self, prompt, num_samples_per_prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) with torch.no_grad(): prompt_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0) # get unconditional embeddings for classifier free guidance uncond_tokens = [""] * len(prompt) max_length = prompt_embeds.shape[1] uncond_batch = self.tokenizer( uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_batch.input_ids.to(device) uncond_attention_mask = uncond_batch.attention_mask.to(device) with torch.no_grad(): negative_prompt_embeds = self.text_encoder( input_ids=uncond_input_ids, attention_mask=uncond_attention_mask )[0] negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_mask = torch.cat([uncond_attention_mask, attention_mask]) boolean_prompt_mask = (prompt_mask == 1).to(device) # import pdb;pdb.set_trace() return prompt_embeds, boolean_prompt_mask def encode_image_no_grad(self, prompt, device): with torch.no_grad(): img_fea = self.img_fea_extractor(prompt) encoder_hidden_states = img_fea.view(img_fea.shape[0], img_fea.shape[1], -1).permute(0, 2, 1) boolean_encoder_mask = torch.ones((encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]), dtype=torch.bool) boolean_encoder_mask = boolean_encoder_mask.to(device) return encoder_hidden_states, boolean_encoder_mask def encode_text_clip_classifier_free(self, prompt, num_samples_per_prompt, device): # 如果想测试输入文本的效果，就用下面两行 with torch.no_grad(): encoder_hidden_states, boolean_encoder_mask = self.encode_text_CLIP(prompt, device) # if "clip-vit" in self.fea_encoder_name: # with torch.no_grad(): # inputs = self.CLIP_processor(text=['aaa'], images=prompt, return_tensors="pt", padding=True).to(device) # encoder_hidden_states = self.CLIP_model(**inputs).image_embeds # b * 768 # encoder_hidden_states = self.linear_layer(encoder_hidden_states) # b * 1024 # encoder_hidden_states = encoder_hidden_states.unsqueeze(1).to(device) # boolean_encoder_mask = torch.ones((encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]), dtype=torch.bool) # boolean_encoder_mask = boolean_encoder_mask.to(device) b, t, n = encoder_hidden_states.shape attention_mask = boolean_encoder_mask.to(device) prompt_embeds = encoder_hidden_states.repeat_interleave(num_samples_per_prompt, 0) attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0) negative_prompt_embeds = encoder_hidden_states.new(b, t, n).fill_(0) uncond_attention_mask = torch.ones((b, t), dtype=torch.bool).to(device) negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) boolean_prompt_mask = torch.cat([uncond_attention_mask, attention_mask]) return prompt_embeds.to(device), boolean_prompt_mask.to(device) def encode_image_classifier_free(self, prompt, num_samples_per_prompt, device): with torch.no_grad(): if "clip-vit" in self.fea_encoder_name: inputs = self.CLIP_processor(text=["aaa"], images=prompt, return_tensors="pt", padding=True).to(device) img_fea = self.CLIP_model(**inputs).image_embeds img_fea = self.linear_layer(img_fea) else: img_fea = self.img_fea_extractor(prompt) encoder_hidden_states = img_fea.view(img_fea.shape[0], img_fea.shape[1], -1).permute(0, 2, 1) b, t, n = encoder_hidden_states.shape boolean_encoder_mask = torch.ones((b, t), dtype=torch.bool) attention_mask = boolean_encoder_mask.to(device) prompt_embeds = encoder_hidden_states.repeat_interleave(num_samples_per_prompt, 0) attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0) negative_prompt_embeds = encoder_hidden_states.new(b, t, n).fill_(0) uncond_attention_mask = torch.ones((b, t), dtype=torch.bool).to(device) negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) boolean_prompt_mask = torch.cat([uncond_attention_mask, attention_mask]) return prompt_embeds.to(device), boolean_prompt_mask.to(device) def encode_video_classifier_free(self, video_batch, text_batch, num_samples_per_prompt, device): vid_feas = [] for i, video in enumerate(video_batch): if text_batch: vid_fea = self.vid_fea_extractor(video=video.to(device), text=text_batch[i]) else: vid_fea = self.vid_fea_extractor(video=video.to(device)) vid_feas.append(vid_fea) padding = 0 size = max(v.size(0) for v in vid_feas) batch_size = len(vid_feas) embed_size = vid_feas[0].size(1) encoder_hidden_states = vid_feas[0].new(batch_size, size, embed_size).fill_(padding) boolean_encoder_mask = torch.ones((batch_size, size), dtype=torch.bool) def copy_tensor(src, dst): assert dst.numel() == src.numel() dst.copy_(src) for i, v in enumerate(vid_feas): copy_tensor(v, encoder_hidden_states[i][: len(v)]) boolean_encoder_mask[i, len(v):] = False b, t, n = encoder_hidden_states.shape negative_prompt_embeds = encoder_hidden_states.new(b, t, n).fill_(0) uncond_attention_mask = torch.ones((b, t), dtype=torch.bool) negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes encoder_hidden_states = torch.cat([negative_prompt_embeds, encoder_hidden_states]) boolean_encoder_mask = torch.cat([uncond_attention_mask, boolean_encoder_mask]) return encoder_hidden_states.to(device), boolean_encoder_mask.to(device)