# 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 random import argparse import numpy as np import torch from tqdm import tqdm from omegaconf import OmegaConf from diffusers.utils import is_accelerate_available from packaging import version from transformers import CLIPTextModel, CLIPTokenizer import os from safetensors import safe_open from diffusers.configuration_utils import FrozenDict from diffusers.models import AutoencoderKL from diffusers.pipelines import DiffusionPipeline from diffusers.schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from diffusers.utils import deprecate, logging, BaseOutput from einops import rearrange from animatediff.models.unet import UNet3DConditionModel from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora from animatediff.utils.util import prepare_mask_coef, save_videos_grid from animatediff.models.resnet import InflatedConv3d from PIL import Image PIL_INTERPOLATION = { "linear": Image.Resampling.BILINEAR, "bilinear": Image.Resampling.BILINEAR, "bicubic": Image.Resampling.BICUBIC, "lanczos": Image.Resampling.LANCZOS, "nearest": Image.Resampling.NEAREST, } def preprocess_image(image): if isinstance(image, torch.Tensor): return image elif isinstance(image, Image.Image): image = [image] if isinstance(image[0], Image.Image): w, h = image[0].size w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) if len(image.shape) == 3: image = image.reshape(image.shape[0], image.shape[1], image.shape[2], 1) image = np.array(image).astype(np.float32) / 255.0 image = image.transpose(0, 3, 1, 2) image = 2.0 * image - 1.0 image = torch.from_numpy(image) elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0) return image logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class AnimationPipelineOutput(BaseOutput): videos: Union[torch.Tensor, np.ndarray] class ValidationPipeline(DiffusionPipeline): _optional_components = [] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet3DConditionModel, scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], ): 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.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) 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 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 tqdm(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): if 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 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, 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 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:] 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) 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 latents = latents * self.scheduler.init_noise_sigma return latents @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], use_image: bool, video_length: Optional[int], 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, **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) # 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 text_embeddings = self._encode_prompt( prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt ) # 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( batch_size * num_videos_per_prompt, num_channels_latents, video_length, height, width, text_embeddings.dtype, device, generator, latents, ) latents_dtype = latents.dtype if use_image != False: shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) image = Image.open(f'test_image/init_image{use_image}.png').convert('RGB') image = preprocess_image(image).to(device) if isinstance(generator, list): image_latent = [ self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size) ] image_latent = torch.cat(image_latent, dim=0).to(device=device) else: image_latent = self.vae.encode(image).latent_dist.sample(generator).to(device=device) image_latent = torch.nn.functional.interpolate(image_latent, size=[shape[-2], shape[-1]]) image_latent_padding = image_latent.clone() * 0.18215 mask = torch.zeros((shape[0], 1, shape[2], shape[3], shape[4])).to(device) mask_coef = prepare_mask_coef(video_length, 0, kwargs['mask_sim_range']) add_noise = torch.randn(shape).to(device) masked_image = torch.zeros(shape).to(device) for f in range(video_length): mask[:,:,f,:,:] = mask_coef[f] masked_image[:,:,f,:,:] = image_latent_padding.clone() mask = mask.to(device) else: shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) add_noise = torch.zeros_like(latents).to(device) masked_image = add_noise mask = torch.zeros((shape[0], 1, shape[2], shape[3], shape[4])).to(device) # Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask masked_image = torch.cat([masked_image] * 2) if do_classifier_free_guidance else masked_image # 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) # predict the noise residual noise_pred = self.unet(latent_model_input, mask, masked_image, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype) # noise_pred = [] # import pdb # pdb.set_trace() # for batch_idx in range(latent_model_input.shape[0]): # noise_pred_single = self.unet(latent_model_input[batch_idx:batch_idx+1], t, encoder_hidden_states=text_embeddings[batch_idx:batch_idx+1]).sample.to(dtype=latents_dtype) # noise_pred.append(noise_pred_single) # noise_pred = torch.cat(noise_pred) # 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)