# Copyright 2024 LLM-grounded Video Diffusion Models (LVD) Team and The HuggingFace Team. All rights reserved. # Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved. # Copyright 2024 The ModelScope Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch import numpy as np from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL from diffusers.models.attention import GatedSelfAttentionDense from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.models.unets import UNet3DConditionModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.text_to_video_synthesis import \ TextToVideoSDPipelineOutput from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import (USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers) from diffusers.utils.torch_utils import randn_tensor from transformers import CLIPTextModel, CLIPTokenizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import TextToVideoSDPipeline >>> from diffusers.utils import export_to_video >>> pipe = TextToVideoSDPipeline.from_pretrained( ... "damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16" ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "Spiderman is surfing" >>> video_frames = pipe(prompt).frames >>> video_path = export_to_video(video_frames) >>> video_path ``` """ def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]: # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 # reshape to ncfhw mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1) std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1) # unnormalize back to [0,1] video = video.mul_(std).add_(mean) video.clamp_(0, 1) # prepare the final outputs i, c, f, h, w = video.shape images = video.permute(2, 3, 0, 4, 1).reshape( f, h, i * w, c ) # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c) # prepare a list of indvidual (consecutive frames) images = images.unbind(dim=0) images = [(image.cpu().numpy() * 255).astype("uint8") for image in images] # f h w c return images class GroundedTextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): r""" Pipeline for text-to-video generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). tokenizer (`CLIPTokenizer`): A [`~transformers.CLIPTokenizer`] to tokenize text. unet ([`UNet3DConditionModel`]): A [`UNet3DConditionModel`] to denoise the encoded video latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ model_cpu_offload_seq = "text_encoder->unet->vae" def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet3DConditionModel, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** ( len(self.vae.config.block_out_channels) - 1) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, **kwargs, ): deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) prompt_embeds_tuple = self.encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, **kwargs, ) # concatenate for backwards comp prompt_embeds = torch.cat( [prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt def encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = 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_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). 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. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. """ # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, LoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) 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] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) 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 untruncated_ids = self.tokenizer( prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1: -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None if clip_skip is None: prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask) prompt_embeds = prompt_embeds[0] else: prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True ) # Access the `hidden_states` first, that contains a tuple of # all the hidden states from the encoder layers. Then index into # the tuple to access the hidden states from the desired layer. prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] # We also need to apply the final LayerNorm here to not mess with the # representations. The `last_hidden_states` that we typically use for # obtaining the final prompt representations passes through the LayerNorm # layer. prompt_embeds = self.text_encoder.text_model.final_layer_norm( prompt_embeds) if self.text_encoder is not None: prompt_embeds_dtype = self.text_encoder.dtype elif self.unet is not None: prompt_embeds_dtype = self.unet.dtype else: prompt_embeds_dtype = prompt_embeds.dtype prompt_embeds = prompt_embeds.to( dtype=prompt_embeds_dtype, 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_images_per_prompt, 1) prompt_embeds = prompt_embeds.view( bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt( uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to( dtype=prompt_embeds_dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat( 1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view( batch_size * num_images_per_prompt, seq_len, -1) if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) return prompt_embeds, negative_prompt_embeds def decode_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents batch_size, channels, num_frames, height, width = latents.shape latents = latents.permute(0, 2, 1, 3, 4).reshape( batch_size * num_frames, channels, height, width) image = self.vae.decode(latents).sample video = ( image[None, :] .reshape( ( batch_size, num_frames, -1, ) + image.shape[2:] ) .permute(0, 2, 1, 3, 4) ) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 video = video.float() return video # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature( self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set( inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, callback_steps, lvd_gligen_phrases, lvd_gligen_boxes, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, num_frames=None, callback_on_step_end_tensor_inputs=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError( f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError( f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if lvd_gligen_boxes: if len(lvd_gligen_phrases) != num_frames or len(lvd_gligen_boxes) != num_frames: raise ValueError( "length of `lvd_gligen_phrases` and `lvd_gligen_boxes` has to be same and match `num_frames`, but" f" got: `lvd_gligen_phrases` {len(lvd_gligen_phrases)}, `lvd_gligen_boxes` {len(lvd_gligen_boxes)}, `num_frames` {num_frames}" ) else: for frame_index, (lvd_gligen_phrases_frame, lvd_gligen_boxes_frame) in enumerate(zip(lvd_gligen_phrases, lvd_gligen_boxes)): if len(lvd_gligen_phrases_frame) != len(lvd_gligen_boxes_frame): raise ValueError( "length of `lvd_gligen_phrases` and `lvd_gligen_boxes` has to be same, but" f" got: `lvd_gligen_phrases` {len(lvd_gligen_phrases_frame)} != `lvd_gligen_boxes` {len(lvd_gligen_boxes_frame)} at frame {frame_index}" ) def prepare_latents( self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None ): shape = ( batch_size, num_channels_latents, num_frames, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) 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 enable_fuser(self, enabled=True): for module in self.unet.modules(): if type(module) is GatedSelfAttentionDense: module.enabled = enabled # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stages where they are being applied. Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. Args: s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. """ if not hasattr(self, "unet"): raise ValueError("The pipeline must have `unet` for using FreeU.") self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu def disable_freeu(self): """Disables the FreeU mechanism if enabled.""" self.unet.disable_freeu() @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_frames: int = 16, num_inference_steps: int = 50, guidance_scale: float = 9.0, lvd_gligen_scheduled_sampling_beta: float = 0.3, lvd_gligen_phrases: List[List[str]] = None, lvd_gligen_boxes: List[List[List[float]]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "np", return_dict: bool = True, callback: Optional[Callable[[ int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, clip_skip: Optional[int] = None, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated video. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated video. num_frames (`int`, *optional*, defaults to 16): The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds amounts to 2 seconds of video. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality videos at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. lvd_gligen_phrases (`List[str]`): The phrases to guide what to include in each of the regions defined by the corresponding `lvd_gligen_boxes`. There should only be one phrase per bounding box. lvd_gligen_boxes (`List[List[float]]`): The bounding boxes that identify rectangular regions of the image that are going to be filled with the content described by the corresponding `lvd_gligen_phrases`. Each rectangular box is defined as a `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1]. lvd_gligen_scheduled_sampling_beta (`float`, defaults to 0.3): Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for scheduled sampling during inference for improved quality and controllability. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. Latents should be of shape `(batch_size, num_channel, num_frames, height, width)`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"np"`): The output format of the generated video. Choose between `torch.FloatTensor` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function is called. If not specified, the callback is called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. Examples: Returns: [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated frames. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor num_images_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, lvd_gligen_phrases, lvd_gligen_boxes, negative_prompt, prompt_embeds, negative_prompt_embeds, num_frames ) # 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 # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get( "scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=clip_skip, ) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, ) # 5.1 Prepare GLIGEN variables if lvd_gligen_boxes: max_objs = 30 boxes_all, text_embeddings_all, masks_all = [], [], [] for lvd_gligen_phrases_frame, lvd_gligen_boxes_frame in zip(lvd_gligen_phrases, lvd_gligen_boxes): if len(lvd_gligen_boxes_frame) > max_objs: warnings.warn( f"More than {max_objs} objects found. Only first {max_objs} objects will be processed.", FutureWarning, ) lvd_gligen_phrases_frame = lvd_gligen_phrases_frame[:max_objs] lvd_gligen_boxes_frame = lvd_gligen_boxes_frame[:max_objs] n_objs = len(lvd_gligen_boxes_frame) if n_objs: # prepare batched input to the PositionNet (boxes, phrases, mask) # Get tokens for phrases from pre-trained CLIPTokenizer tokenizer_inputs = self.tokenizer( lvd_gligen_phrases_frame, padding=True, return_tensors="pt").to(device) # For the token, we use the same pre-trained text encoder # to obtain its text feature _text_embeddings = self.text_encoder( **tokenizer_inputs).pooler_output # For each entity, described in phrases, is denoted with a bounding box, # we represent the location information as (xmin,ymin,xmax,ymax) boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype) boxes[:n_objs] = torch.tensor(lvd_gligen_boxes_frame) text_embeddings = torch.zeros( max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype ) if n_objs: text_embeddings[:n_objs] = _text_embeddings # Generate a mask for each object that is entity described by phrases masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype) masks[:n_objs] = 1 repeat_batch = batch_size * num_images_per_prompt boxes = boxes.unsqueeze(0).expand(repeat_batch, -1, -1).clone() text_embeddings = text_embeddings.unsqueeze( 0).expand(repeat_batch, -1, -1).clone() masks = masks.unsqueeze(0).expand(repeat_batch, -1).clone() if do_classifier_free_guidance: repeat_batch = repeat_batch * 2 boxes = torch.cat([boxes] * 2) text_embeddings = torch.cat([text_embeddings] * 2) masks = torch.cat([masks] * 2) masks[: repeat_batch // 2] = 0 boxes_all.append(boxes) text_embeddings_all.append(text_embeddings) masks_all.append(masks) if cross_attention_kwargs is None: cross_attention_kwargs = {} # In `UNet3DConditionModel`, there is a permute and reshape to merge batch dimension and frame dimension. boxes_all = torch.stack(boxes_all, dim=1).flatten(0, 1) text_embeddings_all = torch.stack( text_embeddings_all, dim=1).flatten(0, 1) masks_all = torch.stack(masks_all, dim=1).flatten(0, 1) cross_attention_kwargs["gligen"] = { "boxes": boxes_all, "positive_embeddings": text_embeddings_all, "masks": masks_all} num_grounding_steps = int( lvd_gligen_scheduled_sampling_beta * len(timesteps)) self.enable_fuser(True) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = len(timesteps) - \ num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Scheduled sampling if i == num_grounding_steps: self.enable_fuser(False) assert latents.shape[1] == 4, f"latent channel mismatch: {latents.shape}" # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat( [latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input( latent_model_input, t) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] # perform guidance if do_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) # reshape latents bsz, channel, frames, width, height = latents.shape latents = latents.permute(0, 2, 1, 3, 4).reshape( bsz * frames, channel, width, height) noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape( bsz * frames, channel, width, height) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( noise_pred, t, latents, **extra_step_kwargs).prev_sample # reshape latents back latents = latents[None, :].reshape( bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) if output_type == "latent": return TextToVideoSDPipelineOutput(frames=latents) video_tensor = self.decode_latents(latents) if output_type == "pt": video = video_tensor else: video = tensor2vid(video_tensor) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return TextToVideoSDPipelineOutput(frames=video)