import os import math import inspect import numpy as np from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import torch from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.models import AutoencoderKLCogVideoX from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler from diffusers.utils import BaseOutput, logging from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor from einops import rearrange from PIL import Image from torchvision import transforms from .mvdit_transformer import Transformer3DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_1d_rotary_pos_embed( dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False, linear_factor=1.0, ntk_factor=1.0, repeat_interleave_real=True, freqs_dtype=torch.float32, # torch.float32, torch.float64 (flux) ): """ Precompute the frequency tensor for complex exponentials (cis) with given dimensions. This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64 data type. Args: dim (`int`): Dimension of the frequency tensor. pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar theta (`float`, *optional*, defaults to 10000.0): Scaling factor for frequency computation. Defaults to 10000.0. use_real (`bool`, *optional*): If True, return real part and imaginary part separately. Otherwise, return complex numbers. linear_factor (`float`, *optional*, defaults to 1.0): Scaling factor for the context extrapolation. Defaults to 1.0. ntk_factor (`float`, *optional*, defaults to 1.0): Scaling factor for the NTK-Aware RoPE. Defaults to 1.0. repeat_interleave_real (`bool`, *optional*, defaults to `True`): If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`. Otherwise, they are concateanted with themselves. freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`): the dtype of the frequency tensor. Returns: `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2] """ assert dim % 2 == 0 if isinstance(pos, int): pos = torch.arange(pos) if isinstance(pos, np.ndarray): pos = torch.from_numpy(pos) # type: ignore # [S] theta = theta * ntk_factor freqs = ( 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim)) / linear_factor ) # [D/2] freqs = torch.outer(pos, freqs) # type: ignore # [S, D/2] if use_real and repeat_interleave_real: freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D] return freqs_cos, freqs_sin elif use_real: freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float() # [S, D] freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float() # [S, D] return freqs_cos, freqs_sin else: freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] return freqs_cis def get_3d_rotary_pos_embed( embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: """ RoPE for video tokens with 3D structure. Args: embed_dim: (`int`): The embedding dimension size, corresponding to hidden_size_head. crops_coords (`Tuple[int]`): The top-left and bottom-right coordinates of the crop. grid_size (`Tuple[int]`): The grid size of the spatial positional embedding (height, width). temporal_size (`int`): The size of the temporal dimension. theta (`float`): Scaling factor for frequency computation. Returns: `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`. """ if use_real is not True: raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed") start, stop = crops_coords grid_size_h, grid_size_w = grid_size grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32) grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32) grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32) # Compute dimensions for each axis dim_t = embed_dim // 4 dim_h = embed_dim // 8 * 3 dim_w = embed_dim // 8 * 3 # Temporal frequencies freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True) # Spatial frequencies for height and width freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True) freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True) # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor def combine_time_height_width(freqs_t, freqs_h, freqs_w): freqs_t = freqs_t[:, None, None, :].expand( -1, grid_size_h, grid_size_w, -1 ) # temporal_size, grid_size_h, grid_size_w, dim_t freqs_h = freqs_h[None, :, None, :].expand( temporal_size, -1, grid_size_w, -1 ) # temporal_size, grid_size_h, grid_size_2, dim_h freqs_w = freqs_w[None, None, :, :].expand( temporal_size, grid_size_h, -1, -1 ) # temporal_size, grid_size_h, grid_size_2, dim_w freqs = torch.cat( [freqs_t, freqs_h, freqs_w], dim=-1 ) # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w) freqs = freqs.view( temporal_size * grid_size_h * grid_size_w, -1 ) # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w) return freqs t_cos, t_sin = freqs_t # both t_cos and t_sin has shape: temporal_size, dim_t h_cos, h_sin = freqs_h # both h_cos and h_sin has shape: grid_size_h, dim_h w_cos, w_sin = freqs_w # both w_cos and w_sin has shape: grid_size_w, dim_w cos = combine_time_height_width(t_cos, h_cos, w_cos) sin = combine_time_height_width(t_sin, h_sin, w_sin) return cos, sin def get_3d_motion_spatial_embed( embed_dim: int, num_joints: int, joints_mean: np.ndarray, joints_std: np.ndarray, theta: float = 10000.0 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: """ """ assert embed_dim % 2 == 0 and embed_dim % 3 == 0 def create_rope_pe(dim, pos, freqs_dtype=torch.float32): if isinstance(pos, np.ndarray): pos = torch.from_numpy(pos) freqs = ( 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim)) ) # [D/2] freqs = torch.outer(pos, freqs) # type: ignore # [S, D/2] freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D] freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D] return freqs_cos, freqs_sin # 为每个轴创建位置编码 # relative_pos_x = joints_mean[:, 0] - joints_mean[0, 0] # relative_pos_y = joints_mean[:, 1] - joints_mean[0, 1] # relative_pos_z = joints_mean[:, 2] - joints_mean[0, 2] # normalized_pos_x = relative_pos_x / joints_std[:, 0].mean() # normalized_pos_y = relative_pos_y / joints_std[:, 1].mean() # normalized_pos_z = relative_pos_z / joints_std[:, 2].mean() pos_x = joints_mean[:, 0] pos_y = joints_mean[:, 1] pos_z = joints_mean[:, 2] normalized_pos_x = (pos_x - pos_x.mean()) normalized_pos_y = (pos_y - pos_y.mean()) normalized_pos_z = (pos_z - pos_z.mean()) freqs_cos_x, freqs_sin_x = create_rope_pe(embed_dim // 3, normalized_pos_x) freqs_cos_y, freqs_sin_y = create_rope_pe(embed_dim // 3, normalized_pos_y) freqs_cos_z, freqs_sin_z = create_rope_pe(embed_dim // 3, normalized_pos_z) freqs_cos = torch.cat([freqs_cos_x, freqs_cos_y, freqs_cos_z], dim=-1) freqs_sin = torch.cat([freqs_sin_x, freqs_sin_y, freqs_sin_z], dim=-1) return freqs_cos, freqs_sin # Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): tw = tgt_width th = tgt_height h, w = src r = h / w if r > (th / tw): resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = None, **kwargs, ): """ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: raise ValueError('Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values') if timesteps is not None: accepts_timesteps = 'timesteps' in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f' timestep schedules. Please check whether you are using the correct scheduler.' ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: accept_sigmas = 'sigmas' in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f' sigmas schedules. Please check whether you are using the correct scheduler.' ) scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps @dataclass class MTVCrafterPipelineOutput(BaseOutput): r"""Output class for the MTVCrafter pipeline. Args: frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. """ frames: torch.Tensor class MTVCrafterPipeline(DiffusionPipeline): r"""Pipeline for MTVCrafter. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. transformer ([`Transformer3DModel`]): A image conditioned `Transformer3DModel` to denoise the encoded video latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `transformer` to denoise the encoded video latents. """ _callback_tensor_inputs = [ 'latents', 'prompt_embeds', 'negative_prompt_embeds', ] def __init__( self, vae: AutoencoderKLCogVideoX, transformer: Transformer3DModel, scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler], ): super().__init__() self.register_modules( vae=vae, transformer=transformer, scheduler=scheduler, ) self.vae_scale_factor_spatial = ( 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, 'vae') and self.vae is not None else 8 ) self.vae_scale_factor_temporal = ( self.vae.config.temporal_compression_ratio if hasattr(self, 'vae') and self.vae is not None else 4 ) self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) self.normalize = transforms.Normalize([0.5], [0.5]) @classmethod def from_pretrained( cls, model_path, transformer_model_path=None, scheduler_type='ddim', torch_dtype=None, **kwargs, ): if transformer_model_path is None: transformer_model_path = os.path.join(model_path, 'transformer') transformer = Transformer3DModel.from_pretrained( transformer_model_path, torch_dtype=torch_dtype, **kwargs ) if scheduler_type == 'ddim': scheduler = CogVideoXDDIMScheduler.from_pretrained(model_path, subfolder='scheduler') elif scheduler_type == 'dpm': scheduler = CogVideoXDPMScheduler.from_pretrained(model_path, subfolder='scheduler') else: assert False pipe = super().from_pretrained( model_path, transformer=transformer, scheduler=scheduler, torch_dtype=torch_dtype, **kwargs ) return pipe def prepare_latents( self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None ): shape = ( batch_size, (num_frames - 1) // self.vae_scale_factor_temporal + 1, num_channels_latents, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, ) 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 decode_latents(self, latents: torch.Tensor) -> torch.Tensor: latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] latents = 1 / self.vae.config.scaling_factor * latents frames = self.vae.decode(latents).sample return frames 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 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.latte.pipeline_latte.LattePipeline.check_inputs def check_inputs( self, height, width, callback_on_step_end_tensor_inputs, ): 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_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 ' f'{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}' ) def _prepare_rotary_positional_embeddings( self, height: int, width: int, num_frames: int, device: torch.device, dtype: torch.dtype, ) -> Tuple[torch.Tensor, torch.Tensor]: grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) grid_crops_coords = ((0, 0), (grid_height, grid_width)) freqs_cos, freqs_sin = get_3d_rotary_pos_embed( embed_dim=self.transformer.config.attention_head_dim, crops_coords=grid_crops_coords, grid_size=(grid_height, grid_width), temporal_size=num_frames, ) freqs_cos = freqs_cos.to(device=device, dtype=dtype) freqs_sin = freqs_sin.to(device=device, dtype=dtype) return freqs_cos, freqs_sin def _prepare_motion_embeddings(self, num_frames, num_joints, joints_mean, joints_std, device, dtype): time_embed = get_1d_rotary_pos_embed(self.transformer.config.attention_head_dim // 4, num_frames, use_real=True) time_embed_cos = time_embed[0][:, None, :].expand(-1, num_joints, -1).reshape(num_frames*num_joints, -1) time_embed_sin = time_embed[1][:, None, :].expand(-1, num_joints, -1).reshape(num_frames*num_joints, -1) spatial_motion_embed = get_3d_motion_spatial_embed(self.transformer.config.attention_head_dim // 4 * 3, num_joints, joints_mean, joints_std) spatial_embed_cos = spatial_motion_embed[0][None, :, :].expand(num_frames, -1, -1).reshape(num_frames*num_joints, -1) spatial_embed_sin = spatial_motion_embed[1][None, :, :].expand(num_frames, -1, -1).reshape(num_frames*num_joints, -1) motion_embed_cos = torch.cat([time_embed_cos, spatial_embed_cos], dim=-1).to(device=device, dtype=dtype) motion_embed_sin = torch.cat([time_embed_sin, spatial_embed_sin], dim=-1).to(device=device, dtype=dtype) return motion_embed_cos, motion_embed_sin @property def guidance_scale(self): return self._guidance_scale @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt @torch.no_grad() def __call__( self, prompt: Optional[Union[str, List[str]]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, height: int = 480, width: int = 720, num_frames: int = 49, num_inference_steps: int = 50, timesteps: Optional[List[int]] = None, guidance_scale: float = 6, use_dynamic_cfg: bool = False, num_videos_per_prompt: int = 1, eta: float = 0.0, seed: Optional[int] = -1, 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: str = 'pil', return_dict: bool = True, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ['latents'], max_sequence_length: int = 226, ref_images: List[Image.Image] = None, motion_embeds: Optional[torch.FloatTensor] = None, joint_mean: Optional[np.ndarray] = None, joint_std: Optional[np.ndarray] = None, ) -> Union[MTVCrafterPipelineOutput, Tuple]: """Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. 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`). height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_frames (`int`, defaults to `48`): Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that needs to be satisfied is that of divisibility mentioned above. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance]. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of videos to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)] to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. 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. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int`, defaults to `226`): Maximum sequence length in encoded prompt. Must be consistent with `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. """ if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial # 720 * 480 num_videos_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( height, width, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._interrupt = False # 2. Default 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) elif prompt is None: batch_size = 1 else: batch_size = prompt_embeds.shape[0] device = self._execution_device if seed > 0: generator = torch.Generator(device=device) generator.manual_seed(seed) do_classifier_free_guidance = guidance_scale > 1.0 # 3. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) self._num_timesteps = len(timesteps) # 4. Prepare latents. latent_channels = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_videos_per_prompt, latent_channels, num_frames, height, width, self.vae.dtype, device, generator, latents, ) # [1, x, 16, h/8, w/8] if ref_images is not None: ref_images = rearrange(ref_images.unsqueeze(0), 'b f c h w -> b c f h w') ref_latents = self.vae.encode( ref_images.to(dtype=self.vae.dtype, device=self.vae.device) ).latent_dist.sample() ref_latents = rearrange(ref_latents, 'b c f h w -> b f c h w') if do_classifier_free_guidance: ref_latents = torch.cat([ref_latents, ref_latents], dim=0) motion_embeds = motion_embeds.to(latents.dtype) if motion_embeds is not None and do_classifier_free_guidance: motion_embeds = torch.cat([self.transformer.unconditional_motion_token.unsqueeze(0), motion_embeds], dim=0) # 5. 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) # 6. Create rotary embeds if required image_rotary_emb = ( self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device, dtype=latents.dtype) if self.transformer.config.use_rotary_positional_embeddings else None ) motion_rotary_emb = self._prepare_motion_embeddings(latents.size(1), 24, joint_mean, joint_std, device, dtype=latents.dtype) # 7. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) with self.progress_bar(total=num_inference_steps) as progress_bar: # for DPM-solver++ old_pred_original_sample = None for i, t in enumerate(timesteps): if self.interrupt: continue 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) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) if ref_images is not None: latent_model_input = torch.cat([latent_model_input, ref_latents], dim=2) # predict noise model_output noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep.long(), image_rotary_emb=image_rotary_emb, motion_rotary_emb=motion_rotary_emb, motion_emb=motion_embeds, return_dict=False, )[0] noise_pred = noise_pred.float() # [b, f, c, h, w] # perform guidance if use_dynamic_cfg: self._guidance_scale = 1 + guidance_scale * ( (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 ) if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 if not isinstance(self.scheduler, CogVideoXDPMScheduler): latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] else: latents, old_pred_original_sample = self.scheduler.step( noise_pred, old_pred_original_sample, t, timesteps[i - 1] if i > 0 else None, latents, **extra_step_kwargs, return_dict=False, ) latents = latents.to(self.vae.dtype) # call the callback, if provided if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop('latents', latents) prompt_embeds = callback_outputs.pop('prompt_embeds', prompt_embeds) negative_prompt_embeds = callback_outputs.pop('negative_prompt_embeds', negative_prompt_embeds) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if not output_type == 'latent': video = self.decode_latents(latents) video = self.video_processor.postprocess_video(video=video, output_type=output_type) else: video = latents # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return MTVCrafterPipelineOutput(frames=video)