# Copyright 2024 ConsisID Authors and The HuggingFace Team. All rights reserved. # # 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 math from typing import Callable, Any, Dict, List, Optional, Tuple, Union import cv2 import numpy as np import PIL import torch from transformers import T5EncoderModel, T5Tokenizer from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import PipelineImageInput from diffusers.loaders import CogVideoXLoraLoaderMixin from diffusers.models import AutoencoderKLCogVideoX, ConsisIDTransformer3DModel from diffusers.models.embeddings import get_3d_rotary_pos_embed from diffusers.pipelines.consisid.pipeline_output import ConsisIDPipelineOutput from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers import CogVideoXDPMScheduler from diffusers.utils import logging, replace_example_docstring from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import ConsisIDPipeline >>> from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer >>> from diffusers.utils import export_to_video >>> from huggingface_hub import snapshot_download >>> snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview") >>> face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = ( ... prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16) ... ) >>> pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> prompt = "A woman adorned with a delicate flower crown, is standing amidst a field of gently swaying wildflowers. Her eyes sparkle with a serene gaze, and a faint smile graces her lips, suggesting a moment of peaceful contentment. The shot is framed from the waist up, highlighting the gentle breeze lightly tousling her hair. The background reveals an expansive meadow under a bright blue sky, capturing the tranquility of a sunny afternoon." >>> image = "https://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/example_images/1.png?raw=true" >>> id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer( ... face_helper_1, ... face_clip_model, ... face_helper_2, ... eva_transform_mean, ... eva_transform_std, ... face_main_model, ... "cuda", ... torch.bfloat16, ... image, ... is_align_face=True, ... ) >>> video = pipe( ... image=image, ... prompt=prompt, ... num_inference_steps=50, ... guidance_scale=6.0, ... use_dynamic_cfg=False, ... id_vit_hidden=id_vit_hidden, ... id_cond=id_cond, ... kps_cond=face_kps, ... generator=torch.Generator("cuda").manual_seed(42), ... ) >>> export_to_video(video.frames[0], "output.mp4", fps=8) ``` """ def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]): """ This function draws keypoints and the limbs connecting them on an image. Parameters: - image_pil (PIL.Image): Input image as a PIL object. - kps (list of tuples): A list of keypoints where each keypoint is a tuple of (x, y) coordinates. - color_list (list of tuples, optional): List of colors (in RGB format) for each keypoint. Default is a set of five colors. Returns: - PIL.Image: Image with the keypoints and limbs drawn. """ stickwidth = 4 limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) kps = np.array(kps) w, h = image_pil.size out_img = np.zeros([h, w, 3]) for i in range(len(limbSeq)): index = limbSeq[i] color = color_list[index[0]] x = kps[index][:, 0] y = kps[index][:, 1] length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) polygon = cv2.ellipse2Poly( (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1 ) out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) out_img = (out_img * 0.6).astype(np.uint8) for idx_kp, kp in enumerate(kps): color = color_list[idx_kp] x, y = kp out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8)) return out_img_pil # 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): """ This function calculates the resize and crop region for an image to fit a target width and height while preserving the aspect ratio. Parameters: - src (tuple): A tuple containing the source image's height (h) and width (w). - tgt_width (int): The target width to resize the image. - tgt_height (int): The target height to resize the image. Returns: - tuple: Two tuples representing the crop region: 1. The top-left coordinates of the crop region. 2. The bottom-right coordinates of the crop region. """ 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, ): r""" 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 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") class ConsisIDPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin): r""" Pipeline for image-to-video generation using ConsisID. 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. text_encoder ([`T5EncoderModel`]): Frozen text-encoder. ConsisID uses [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. tokenizer (`T5Tokenizer`): Tokenizer of class [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). transformer ([`ConsisIDTransformer3DModel`]): A text conditioned `ConsisIDTransformer3DModel` to denoise the encoded video latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `transformer` to denoise the encoded video latents. """ _optional_components = [] model_cpu_offload_seq = "text_encoder->transformer->vae" _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", ] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, vae: AutoencoderKLCogVideoX, transformer: ConsisIDTransformer3DModel, scheduler: CogVideoXDPMScheduler, ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, 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.vae_scaling_factor_image = ( self.vae.config.scaling_factor if hasattr(self, "vae") and self.vae is not None else 0.7 ) self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds def _get_t5_prompt_embeds( self, prompt: Union[str, List[str]] = None, num_videos_per_prompt: int = 1, max_sequence_length: int = 226, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): device = device or self._execution_device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt batch_size = len(prompt) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, add_special_tokens=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[:, max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {max_sequence_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(text_input_ids.to(device))[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) return prompt_embeds # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt def encode_prompt( self, prompt: Union[str, List[str]], negative_prompt: Optional[Union[str, List[str]]] = None, do_classifier_free_guidance: bool = True, num_videos_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, max_sequence_length: int = 226, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded 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`). do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): Whether to use classifier free guidance or not. num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos that should be generated per prompt. torch device to place the resulting embeddings on prompt_embeds (`torch.Tensor`, *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.Tensor`, *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. device: (`torch.device`, *optional*): torch device dtype: (`torch.dtype`, *optional*): torch dtype """ device = device or self._execution_device prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: prompt_embeds = self._get_t5_prompt_embeds( prompt=prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt if 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 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`." ) negative_prompt_embeds = self._get_t5_prompt_embeds( prompt=negative_prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) return prompt_embeds, negative_prompt_embeds def prepare_latents( self, image: torch.Tensor, batch_size: int = 1, num_channels_latents: int = 16, num_frames: int = 13, height: int = 60, width: int = 90, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, generator: Optional[torch.Generator] = None, latents: Optional[torch.Tensor] = None, kps_cond: Optional[torch.Tensor] = 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." ) num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 shape = ( batch_size, num_frames, num_channels_latents, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, ) image = image.unsqueeze(2) # [B, C, F, H, W] if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i]) for i in range(batch_size) ] if kps_cond is not None: kps_cond = kps_cond.unsqueeze(2) kps_cond_latents = [ retrieve_latents(self.vae.encode(kps_cond[i].unsqueeze(0)), generator[i]) for i in range(batch_size) ] else: image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in image] if kps_cond is not None: kps_cond = kps_cond.unsqueeze(2) kps_cond_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in kps_cond] image_latents = torch.cat(image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W] image_latents = self.vae_scaling_factor_image * image_latents if kps_cond is not None: kps_cond_latents = torch.cat(kps_cond_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W] kps_cond_latents = self.vae_scaling_factor_image * kps_cond_latents padding_shape = ( batch_size, num_frames - 2, num_channels_latents, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, ) else: padding_shape = ( batch_size, num_frames - 1, num_channels_latents, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, ) latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype) if kps_cond is not None: image_latents = torch.cat([image_latents, kps_cond_latents, latent_padding], dim=1) else: image_latents = torch.cat([image_latents, latent_padding], dim=1) 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, image_latents # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_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_scaling_factor_image * latents frames = self.vae.decode(latents).sample return frames # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps def get_timesteps(self, num_inference_steps, timesteps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start # 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 def check_inputs( self, image, prompt, height, width, negative_prompt, callback_on_step_end_tensor_inputs, latents=None, prompt_embeds=None, negative_prompt_embeds=None, ): if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list) ): raise ValueError( "`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}" ) 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 {[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 prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) 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}." ) def _prepare_rotary_positional_embeddings( self, height: int, width: int, num_frames: int, device: torch.device, ) -> 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) base_size_width = self.transformer.config.sample_width // self.transformer.config.patch_size base_size_height = self.transformer.config.sample_height // self.transformer.config.patch_size grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size_width, base_size_height ) 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, device=device, ) return freqs_cos, freqs_sin @property def guidance_scale(self): return self._guidance_scale @property def num_timesteps(self): return self._num_timesteps @property def attention_kwargs(self): return self._attention_kwargs @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: PipelineImageInput, 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, guidance_scale: float = 6.0, use_dynamic_cfg: bool = False, num_videos_per_prompt: int = 1, 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: str = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, 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, id_vit_hidden: Optional[torch.Tensor] = None, id_cond: Optional[torch.Tensor] = None, kps_cond: Optional[torch.Tensor] = None, ) -> Union[ConsisIDPipelineOutput, Tuple]: """ Function invoked when calling the pipeline for generation. Args: image (`PipelineImageInput`): The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`. 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.transformer.config.sample_height * self.vae_scale_factor_spatial): The height in pixels of the generated image. This is set to 480 by default for the best results. width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): The width in pixels of the generated image. This is set to 720 by default for the best results. num_frames (`int`, defaults to `49`): Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will contain 1 extra frame because ConsisID 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. guidance_scale (`float`, *optional*, defaults to 6): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). 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. use_dynamic_cfg (`bool`, *optional*, defaults to `False`): If True, dynamically adjusts the guidance scale during inference. This allows the model to use a progressive guidance scale, improving the balance between text-guided generation and image quality over the course of the inference steps. Typically, early inference steps use a higher guidance scale for more faithful image generation, while later steps reduce it for more diverse and natural results. 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)](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 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](https://pillow.readthedocs.io/en/stable/): `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. attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). 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. id_vit_hidden (`Optional[torch.Tensor]`, *optional*): The tensor representing the hidden features extracted from the face model, which are used to condition the local facial extractor. This is crucial for the model to obtain high-frequency information of the face. If not provided, the local facial extractor will not run normally. id_cond (`Optional[torch.Tensor]`, *optional*): The tensor representing the hidden features extracted from the clip model, which are used to condition the local facial extractor. This is crucial for the model to edit facial features If not provided, the local facial extractor will not run normally. kps_cond (`Optional[torch.Tensor]`, *optional*): A tensor that determines whether the global facial extractor use keypoint information for conditioning. If provided, this tensor controls whether facial keypoints such as eyes, nose, and mouth landmarks are used during the generation process. This helps ensure the model retains more facial low-frequency information. Examples: Returns: [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] or `tuple`: [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ 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_height * self.vae_scale_factor_spatial width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial num_frames = num_frames or self.transformer.config.sample_frames num_videos_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( image=image, prompt=prompt, height=height, width=width, negative_prompt=negative_prompt, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, latents=latents, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) self._guidance_scale = guidance_scale self._attention_kwargs = attention_kwargs 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) 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 prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, do_classifier_free_guidance=do_classifier_free_guidance, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, max_sequence_length=max_sequence_length, device=device, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device) self._num_timesteps = len(timesteps) # 5. Prepare latents is_kps = getattr(self.transformer.config, "is_kps", False) kps_cond = kps_cond if is_kps else None if kps_cond is not None: kps_cond = draw_kps(image, kps_cond) kps_cond = self.video_processor.preprocess(kps_cond, height=height, width=width).to( device, dtype=prompt_embeds.dtype ) image = self.video_processor.preprocess(image, height=height, width=width).to( device, dtype=prompt_embeds.dtype ) latent_channels = self.transformer.config.in_channels // 2 latents, image_latents = self.prepare_latents( image, batch_size * num_videos_per_prompt, latent_channels, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, kps_cond, ) # 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. Create rotary embeds if required image_rotary_emb = ( self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) if self.transformer.config.use_rotary_positional_embeddings else None ) # 8. 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 timesteps_cpu = timesteps.cpu() 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) latent_image_input = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) # predict noise model_output noise_pred = self.transformer( hidden_states=latent_model_input, encoder_hidden_states=prompt_embeds, timestep=timestep, image_rotary_emb=image_rotary_emb, attention_kwargs=attention_kwargs, return_dict=False, id_vit_hidden=id_vit_hidden, id_cond=id_cond, )[0] noise_pred = noise_pred.float() # perform guidance if use_dynamic_cfg: self._guidance_scale = 1 + guidance_scale * ( ( 1 - math.cos( math.pi * ((num_inference_steps - timesteps_cpu[i].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(prompt_embeds.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 ConsisIDPipelineOutput(frames=video)