from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder, SDMotionModel from ..models.model_manager import ModelManager from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompters import SDPrompter from ..schedulers import EnhancedDDIMScheduler from .sd_image import SDImagePipeline from .dancer import lets_dance from typing import List import torch from tqdm import tqdm def lets_dance_with_long_video( unet: SDUNet, motion_modules: SDMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device="cuda", animatediff_batch_size=16, animatediff_stride=8, ): num_frames = sample.shape[0] hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)] for batch_id in range(0, num_frames, animatediff_stride): batch_id_ = min(batch_id + animatediff_batch_size, num_frames) # process this batch hidden_states_batch = lets_dance( unet, motion_modules, controlnet, sample[batch_id: batch_id_].to(device), timestep, encoder_hidden_states, ipadapter_kwargs_list=ipadapter_kwargs_list, controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None, unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, cross_frame_attention=cross_frame_attention, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, device=device ).cpu() # update hidden_states for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch): bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2) hidden_states, num = hidden_states_output[i] hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias)) hidden_states_output[i] = (hidden_states, num + bias) if batch_id_ == num_frames: break # output hidden_states = torch.stack([h for h, _ in hidden_states_output]) return hidden_states class SDVideoPipeline(SDImagePipeline): def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): super().__init__(device=device, torch_dtype=torch_dtype) self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") self.prompter = SDPrompter() # models self.text_encoder: SDTextEncoder = None self.unet: SDUNet = None self.vae_decoder: SDVAEDecoder = None self.vae_encoder: SDVAEEncoder = None self.controlnet: MultiControlNetManager = None self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None self.ipadapter: SDIpAdapter = None self.motion_modules: SDMotionModel = None def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): # Main models self.text_encoder = model_manager.fetch_model("sd_text_encoder") self.unet = model_manager.fetch_model("sd_unet") self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") self.prompter.fetch_models(self.text_encoder) self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) # ControlNets controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id, device=self.device), model_manager.fetch_model("sd_controlnet", config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = MultiControlNetManager(controlnet_units) # IP-Adapters self.ipadapter = model_manager.fetch_model("sd_ipadapter") self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") # Motion Modules self.motion_modules = model_manager.fetch_model("sd_motion_modules") if self.motion_modules is None: self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): pipe = SDVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype, ) pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) return pipe def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): images = [ self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) for frame_id in range(latents.shape[0]) ] return images def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): latents = [] for image in processed_images: image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) latents.append(latent.cpu()) latents = torch.concat(latents, dim=0) return latents @torch.no_grad() def __call__( self, prompt, negative_prompt="", cfg_scale=7.5, clip_skip=1, num_frames=None, input_frames=None, ipadapter_images=None, ipadapter_scale=1.0, controlnet_frames=None, denoising_strength=1.0, height=512, width=512, num_inference_steps=20, animatediff_batch_size = 16, animatediff_stride = 8, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, smoother=None, smoother_progress_ids=[], tiled=False, tile_size=64, tile_stride=32, progress_bar_cmd=tqdm, progress_bar_st=None, ): # Tiler parameters, batch size ... tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} other_kwargs = { "animatediff_batch_size": animatediff_batch_size, "animatediff_stride": animatediff_stride, "unet_batch_size": unet_batch_size, "controlnet_batch_size": controlnet_batch_size, "cross_frame_attention": cross_frame_attention, } # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors if self.motion_modules is None: noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) else: noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype) if input_frames is None or denoising_strength == 1.0: latents = noise else: latents = self.encode_video(input_frames, **tiler_kwargs) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) # Encode prompts prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) # IP-Adapter if ipadapter_images is not None: ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} else: ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} # Prepare ControlNets if controlnet_frames is not None: if isinstance(controlnet_frames[0], list): controlnet_frames_ = [] for processor_id in range(len(controlnet_frames)): controlnet_frames_.append( torch.stack([ self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) ], dim=1) ) controlnet_frames = torch.concat(controlnet_frames_, dim=0) else: controlnet_frames = torch.stack([ self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) for controlnet_frame in progress_bar_cmd(controlnet_frames) ], dim=1) controlnet_kwargs = {"controlnet_frames": controlnet_frames} else: controlnet_kwargs = {"controlnet_frames": None} # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = timestep.unsqueeze(0).to(self.device) # Classifier-free guidance noise_pred_posi = lets_dance_with_long_video( self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **other_kwargs, **tiler_kwargs, device=self.device, ) noise_pred_nega = lets_dance_with_long_video( self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **other_kwargs, **tiler_kwargs, device=self.device, ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM and smoother if smoother is not None and progress_id in smoother_progress_ids: rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) rendered_frames = self.decode_video(rendered_frames) rendered_frames = smoother(rendered_frames, original_frames=input_frames) target_latents = self.encode_video(rendered_frames) noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) latents = self.scheduler.step(noise_pred, timestep, latents) # UI if progress_bar_st is not None: progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) # Decode image output_frames = self.decode_video(latents, **tiler_kwargs) # Post-process if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): output_frames = smoother(output_frames, original_frames=input_frames) return output_frames