import inspect import math from typing import Any, Callable, Dict, List, Optional, Tuple, Union from typing_extensions import override import PIL import torch from transformers import T5EncoderModel, T5Tokenizer from diffusers import ( AutoencoderKLCogVideoX, CogVideoXDPMScheduler, CogVideoXImageToVideoPipeline, CogVideoXTransformer3DModel, ) from diffusers.pipelines.cogvideo.pipeline_output import CogVideoXPipelineOutput from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import PipelineImageInput from diffusers.pipelines.cogvideo.pipeline_cogvideox_image2video import retrieve_timesteps from diffusers.utils import is_torch_xla_available import pdb if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False class FloVDOMSMCogVideoXImageToVideoPipeline(CogVideoXImageToVideoPipeline): @override def __call__( self, image: PipelineImageInput, prompt: Optional[Union[str, List[str]]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, height: Optional[int] = None, width: Optional[int] = None, 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, 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, ) -> Union[CogVideoXPipelineOutput, 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 `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 8. 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](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. 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. Examples: Returns: [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`: [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] 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._current_timestep = None 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.to(negative_prompt_embeds.device)], dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) self._num_timesteps = len(timesteps) # 5. Prepare latents latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t patch_size_t = self.transformer.config.patch_size_t additional_frames = 0 if patch_size_t is not None and latent_frames % patch_size_t != 0: additional_frames = patch_size_t - latent_frames % patch_size_t num_frames += additional_frames * self.vae_scale_factor_temporal 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, ) # 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. Create ofs embeds if required ofs_emb = None if self.transformer.config.ofs_embed_dim is None else latents.new_full((1,), fill_value=2.0) # 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 for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t 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) 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, ofs=ofs_emb, image_rotary_emb=image_rotary_emb, attention_kwargs=attention_kwargs, return_dict=False, )[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 - 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(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 XLA_AVAILABLE: xm.mark_step() self._current_timestep = None if not output_type == "latent": # Discard any padding frames that were added for CogVideoX 1.5 latents = latents[:, additional_frames:] 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 CogVideoXPipelineOutput(frames=video)