# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py import inspect from typing import Callable, List, Optional, Union from dataclasses import dataclass import PIL.Image import numpy as np import torch from tqdm import tqdm from diffusers.utils import is_accelerate_available from packaging import version from transformers import CLIPTextModel, CLIPTokenizer from diffusers.configuration_utils import FrozenDict from diffusers.models import AutoencoderKL from diffusers import DiffusionPipeline from diffusers.schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from diffusers.utils import deprecate, logging, BaseOutput from einops import rearrange from ..models.unet import UNet3DConditionModel from ..models.sparse_controlnet import SparseControlNetModel import pdb import PIL logger = logging.get_logger(__name__) # pylint: disable=invalid-name # image: either PIL.Image.Image or torch.Tensor. def preprocess_image(image, h=512, w=512): if isinstance(image, torch.Tensor): return image elif isinstance(image, PIL.Image.Image): # image: [1, 512, 512, 3] image = np.array(image.resize((w, h), resample=PIL.Image.LANCZOS))[None, :] image = image.astype(np.float16) * 2 / 255.0 - 1.0 # image: [1, 3, 512, 512] image = image.transpose(0, 3, 1, 2) image = torch.from_numpy(image) else: breakpoint() return image @dataclass class AnimationPipelineOutput(BaseOutput): videos: Union[torch.Tensor, np.ndarray] class AnimationPipeline(DiffusionPipeline): _optional_components = [] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet3DConditionModel, scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], controlnet: Union[SparseControlNetModel, None] = None, torch_dtype=torch.float32, ): super().__init__() if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " "to update the config accordingly as leaving `steps_offset` might led to incorrect results" " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" " file" ) deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False scheduler._internal_dict = FrozenDict(new_config) is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( version.parse(unet.config._diffusers_version).base_version ) < version.parse("0.9.0.dev0") is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: deprecation_message = ( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" " the `unet/config.json` file" ) deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) self.torch_dtype=torch_dtype self.register_modules( vae=vae.to(self.torch_dtype), text_encoder=text_encoder.to(self.torch_dtype), tokenizer=tokenizer, unet=unet.to(self.torch_dtype), scheduler=scheduler, # controlnet=controlnet.to(self.torch_dtype), ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if controlnet!=None: self.controlnet=controlnet.to(self.torch_dtype) def enable_vae_slicing(self): self.vae.enable_slicing() def disable_vae_slicing(self): self.vae.disable_slicing() def enable_sequential_cpu_offload(self, gpu_id=0): if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("Please install accelerate via `pip install accelerate`") device = torch.device(f"cuda:{gpu_id}") for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: if cpu_offloaded_model is not None: cpu_offload(cpu_offloaded_model, device) @property def _execution_device(self): if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): return self.device for module in self.unet.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): batch_size = len(prompt) if isinstance(prompt, list) else 1 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 text_embeddings = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) text_embeddings = text_embeddings[0] # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = text_embeddings.shape text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif 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 max_length = text_input_ids.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 uncond_embeddings = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) uncond_embeddings = uncond_embeddings[0] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = uncond_embeddings.shape[1] uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) # 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 text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) return text_embeddings def encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): batch_size = len(prompt) if isinstance(prompt, list) else 1 # print(batch_size) # exit() 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 text_embeddings = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) text_embeddings = text_embeddings[0] # duplicate text embeddings for each generation per prompt, using mps friendly method bs_embed, seq_len, _ = text_embeddings.shape text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif 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 max_length = text_input_ids.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 uncond_embeddings = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) uncond_embeddings = uncond_embeddings[0] # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = uncond_embeddings.shape[1] uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) # 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 # text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # print("encode here!!!") # print("shape of text_embeddings",text_embeddings.shape) # print("shape of uncond_embeddings",uncond_embeddings.shape) return text_embeddings,uncond_embeddings def decode_latents(self, latents): video_length = latents.shape[2] latents = 1 / 0.18215 * latents latents = rearrange(latents, "b c f h w -> (b f) c h w") # video = self.vae.decode(latents).sample video = [] for frame_idx in range(latents.shape[0]): video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample) video = torch.cat(video) video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) video = (video / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 video = video.cpu().float().numpy() return video 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 def check_inputs(self, prompt, height, width, callback_steps,prompt_embedding): if not isinstance(prompt, str) and not isinstance(prompt, list) and prompt_embedding==None: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") 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 None) or ( 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)}." ) def prepare_latents(self, init_image, init_image_strength, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) if init_image is not None: # init_image: either PIL.Image.Image or torch.Tensor. image = preprocess_image(init_image, height, width) image = image.to(device=device, dtype=dtype) if isinstance(generator, list): init_latents = [ self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.vae.encode(image).latent_dist.sample(generator) else: init_latents = None 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: rand_device = "cpu" if device.type == "mps" else device if isinstance(generator, list): shape = shape # shape = (1,) + shape[1:] # ignore init latents for batch model latents = [ torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(batch_size) ] latents = torch.cat(latents, dim=0).to(device) else: latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) if init_latents is not None: blend_frames = video_length // 2 init_image_strength, init_image_final_weight = init_image_strength for i in range(video_length): dist_to_end = (blend_frames - float(i)) / blend_frames # When i > 0.9 * blend_frames, dist_to_end < 0.1. Then it will be changed to 0.05, # so that the last half of the video still is still initialized with a little bit of init_latents. dist_to_end = max(dist_to_end, init_image_final_weight) # Changed from /30 to /100. # gradully reduce init alpha along video frames (loosen restriction) init_alpha = dist_to_end * init_image_strength / 100 latents[:, :, i, :, :] = init_latents * init_alpha + latents[:, :, i, :, :] * (1 - init_alpha) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler if init_latents is None: latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], video_length: Optional[int], init_image: Union[PIL.Image.Image, torch.Tensor], init_image_strength: float = 1.0, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_videos_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "tensor", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, #support embeddings prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds:Optional[torch.FloatTensor] = None, # support controlnet controlnet_images: torch.FloatTensor = None, controlnet_image_index: list = [0], controlnet_conditioning_scale: Union[float, List[float]] = 1.0, **kwargs, ): # 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 if isinstance(prompt_embeds, (list, tuple)): prompt_embeds_begin, prompt_embeds_end, adaface_anneal_steps = prompt_embeds prompt_embeds = prompt_embeds_begin do_prompt_embeds_annealing = True else: do_prompt_embeds_annealing = False # Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps, prompt_embeds) # Define call parameters # batch_size = 1 if isinstance(prompt, str) else len(prompt) batch_size = 1 if latents is not None: batch_size = latents.shape[0] if isinstance(prompt, list): batch_size = len(prompt) 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 # Encode input prompt prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size if negative_prompt is not None: negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size if prompt_embeds is None: text_embeddings = self._encode_prompt( prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt ) # If do_prompt_embeds_annealing is True, prompt_embeds and text_embeddings will be assigned in the loop below, # and this is just to avoid type error. # Otherwise, text_embeddings won't be replaced. else: text_embeddings = torch.cat([negative_prompt_embeds, prompt_embeds]) # print(text_embeddings.shape) # return # Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # Prepare latent variables num_channels_latents = self.unet.in_channels latents = self.prepare_latents( init_image, init_image_strength, batch_size * num_videos_per_prompt, num_channels_latents, video_length, height, width, text_embeddings.dtype, device, generator, latents, ).to(self.torch_dtype) latents_dtype = latents.dtype # Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # Denoising loop # num_warmup_steps = 0. num_inference_steps: 30. # [958, 925, 892, 859, 826, 793, 760, 727, 694, 661, 628, 595, 562, 529, # 496, 463, 430, 397, 364, 331, 298, 265, 232, 199, 166, 133, 100, 67, # 34, 1] 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): # 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) down_block_additional_residuals = mid_block_additional_residual = None if (getattr(self, "controlnet", None) != None) and (controlnet_images != None): assert controlnet_images.dim() == 5 controlnet_noisy_latents = latent_model_input controlnet_prompt_embeds = text_embeddings controlnet_images = controlnet_images.to(latents.device) controlnet_cond_shape = list(controlnet_images.shape) controlnet_cond_shape[2] = video_length controlnet_cond = torch.zeros(controlnet_cond_shape).to(latents.device).to(latents.dtype) controlnet_conditioning_mask_shape = list(controlnet_cond.shape) controlnet_conditioning_mask_shape[1] = 1 controlnet_conditioning_mask = torch.zeros(controlnet_conditioning_mask_shape).to(latents.device).to(latents.dtype) assert controlnet_images.shape[2] >= len(controlnet_image_index) controlnet_cond[:,:,controlnet_image_index] = controlnet_images[:,:,:len(controlnet_image_index)] controlnet_conditioning_mask[:,:,controlnet_image_index] = 1 down_block_additional_residuals, mid_block_additional_residual = self.controlnet( controlnet_noisy_latents, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=controlnet_cond, conditioning_mask=controlnet_conditioning_mask, conditioning_scale=controlnet_conditioning_scale, guess_mode=False, return_dict=False, ) if do_prompt_embeds_annealing: # i: 0 to num_inference_steps. Anneal the first adaface_anneal_steps steps. # If adaface_anneal_steps == 0, then anneal_factor is always 1. anneal_factor = i / adaface_anneal_steps if i < adaface_anneal_steps else 1 prompt_embeds_annealed = prompt_embeds_begin + anneal_factor * (prompt_embeds_end - prompt_embeds_begin) text_embeddings = torch.cat([negative_prompt_embeds, prompt_embeds_annealed]) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=text_embeddings, down_block_additional_residuals = down_block_additional_residuals, mid_block_additional_residual = mid_block_additional_residual, ).sample.to(dtype=latents_dtype) # 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) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # 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: callback(i, t, latents) # Post-processing video = self.decode_latents(latents) # Convert to tensor if output_type == "tensor": video = torch.from_numpy(video) if not return_dict: return video return AnimationPipelineOutput(videos=video) @torch.no_grad() def video_edit( self, prompt: Union[str, List[str]], video_length: Optional[int], init_image: Union[PIL.Image.Image, torch.Tensor], init_image_strength: float = 1.0, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_videos_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "tensor", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, #support embeddings prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds:Optional[torch.FloatTensor] = None, # support controlnet controlnet_images: torch.FloatTensor = None, controlnet_image_index: list = [0], controlnet_conditioning_scale: Union[float, List[float]] = 1.0, **kwargs, ): # 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 # Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps, prompt_embeds) # Define call parameters # batch_size = 1 if isinstance(prompt, str) else len(prompt) batch_size = 1 if latents is not None: batch_size = latents.shape[0] if isinstance(prompt, list): batch_size = len(prompt) 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 # Encode input prompt prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size if negative_prompt is not None: negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size if prompt_embeds is None: text_embeddings = self._encode_prompt( prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt ) else: text_embeddings = torch.cat([negative_prompt_embeds, prompt_embeds]) # print(text_embeddings.shape) # return # Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # Prepare latent variables num_channels_latents = self.unet.in_channels latents = self.prepare_latents( init_image, init_image_strength, batch_size * num_videos_per_prompt, num_channels_latents, video_length, height, width, text_embeddings.dtype, device, generator, latents, ).to(self.torch_dtype) latents_dtype = latents.dtype # Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 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): # 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) down_block_additional_residuals = mid_block_additional_residual = None if (getattr(self, "controlnet", None) != None) and (controlnet_images != None): assert controlnet_images.dim() == 5 controlnet_noisy_latents = latent_model_input controlnet_prompt_embeds = text_embeddings controlnet_images = controlnet_images.to(latents.device) controlnet_cond_shape = list(controlnet_images.shape) controlnet_cond_shape[2] = video_length controlnet_cond = torch.zeros(controlnet_cond_shape).to(latents.device) controlnet_conditioning_mask_shape = list(controlnet_cond.shape) controlnet_conditioning_mask_shape[1] = 1 controlnet_conditioning_mask = torch.zeros(controlnet_conditioning_mask_shape).to(latents.device) assert controlnet_images.shape[2] >= len(controlnet_image_index) controlnet_cond[:,:,controlnet_image_index] = controlnet_images[:,:,:len(controlnet_image_index)] controlnet_conditioning_mask[:,:,controlnet_image_index] = 1 down_block_additional_residuals, mid_block_additional_residual = self.controlnet( controlnet_noisy_latents, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=controlnet_cond, conditioning_mask=controlnet_conditioning_mask, conditioning_scale=controlnet_conditioning_scale, guess_mode=False, return_dict=False, ) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=text_embeddings, down_block_additional_residuals = down_block_additional_residuals, mid_block_additional_residual = mid_block_additional_residual, ).sample.to(dtype=latents_dtype) # 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) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # 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: callback(i, t, latents) # Post-processing video = self.decode_latents(latents) # Convert to tensor if output_type == "tensor": video = torch.from_numpy(video) if not return_dict: return video return AnimationPipelineOutput(videos=video)