import torch from diffusers import DiffusionPipeline from typing import List, Optional, Tuple, Union, Dict, Any from diffusers import logging from diffusers.utils.torch_utils import randn_tensor from diffusers.models import AutoencoderKL from transformers import CLIPTokenizer, CLIPTextModel from scheduler.t2v_turbo_scheduler import T2VTurboScheduler logger = logging.get_logger(__name__) # pylint: disable=invalid-name class T2VTurboMSPipeline(DiffusionPipeline): def __init__( self, unet, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, scheduler: T2VTurboScheduler, ): super().__init__() self.register_modules( unet=unet, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler, ) self.vae_scale_factor = 8 def _encode_prompt( self, prompt, device, num_videos_per_prompt, prompt_embeds: None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_videos_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. """ if prompt_embeds is None: with torch.no_grad(): text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(self.text_encoder.device) prompt_embeds = self.text_encoder(text_input_ids)[0] prompt_embeds = prompt_embeds.to(device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) prompt_embeds = prompt_embeds.view( bs_embed * num_videos_per_prompt, seq_len, -1 ) # Don't need to get uncond prompt embedding because of LCM Guided Distillation return prompt_embeds def prepare_latents( self, batch_size, num_channels_latents, frames, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size, num_channels_latents, frames, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if latents is None: latents = randn_tensor( shape, generator=generator, device=device, dtype=dtype ) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32): """ see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: timesteps: torch.Tensor: generate embedding vectors at these timesteps embedding_dim: int: dimension of the embeddings to generate dtype: data type of the generated embeddings Returns: embedding vectors with shape `(len(timesteps), embedding_dim)` """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = 256, width: Optional[int] = 256, frames: int = 16, guidance_scale: float = 7.5, num_videos_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, num_inference_steps: int = 4, lcm_origin_steps: int = 50, prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", ): # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG) # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, device, num_videos_per_prompt, prompt_embeds=prompt_embeds, ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps) timesteps = self.scheduler.timesteps # 5. Prepare latent variable num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_videos_per_prompt, num_channels_latents, frames, height, width, prompt_embeds.dtype, device, generator, latents, ) bs = batch_size * num_videos_per_prompt # 6. Get Guidance Scale Embedding w = torch.tensor(guidance_scale).repeat(bs) w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device) # 7. LCM MultiStep Sampling Loop: with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): ts = torch.full((bs,), t, device=device, dtype=torch.long) # model prediction (v-prediction, eps, x) model_pred = self.unet( latents, ts, timestep_cond=w_embedding, encoder_hidden_states=prompt_embeds.float(), ).sample # compute the previous noisy sample x_t -> x_t-1 latents, denoised = self.scheduler.step( model_pred, i, t, latents, return_dict=False ) progress_bar.update() if not output_type == "latent": t = denoised.shape[2] z = denoised.to(self.vae.dtype) / self.vae.config.scaling_factor videos = torch.cat( [self.vae.decode(z[:, :, i])[0].unsqueeze(2) for i in range(t)], dim=2, ) else: videos = denoised return videos