from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDMotionModel from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator from ..prompts import SDPrompter from ..schedulers import EnhancedDDIMScheduler from .dancer import lets_dance from typing import List import torch from tqdm import tqdm from PIL import Image import numpy as np def lets_dance_with_long_video( unet: SDUNet, motion_modules: SDMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, timestep = None, encoder_hidden_states = None, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, animatediff_batch_size = 16, animatediff_stride = 8, device = "cuda", vram_limit_level = 0, ): 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[batch_id: batch_id_].to(device), 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, device=device, vram_limit_level=vram_limit_level ).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) / 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 + 1) # output hidden_states = torch.stack([h for h, _ in hidden_states_output]) return hidden_states class SDVideoPipeline(torch.nn.Module): def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True): super().__init__() self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear") self.prompter = SDPrompter() self.device = device self.torch_dtype = torch_dtype # 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.motion_modules: SDMotionModel = None def fetch_main_models(self, model_manager: ModelManager): self.text_encoder = model_manager.text_encoder self.unet = model_manager.unet self.vae_decoder = model_manager.vae_decoder self.vae_encoder = model_manager.vae_encoder # load textual inversion self.prompter.load_textual_inversion(model_manager.textual_inversion_dict) def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): controlnet_units = [] for config in controlnet_config_units: controlnet_unit = ControlNetUnit( Annotator(config.processor_id), model_manager.get_model_with_model_path(config.model_path), config.scale ) controlnet_units.append(controlnet_unit) self.controlnet = MultiControlNetManager(controlnet_units) def fetch_motion_modules(self, model_manager: ModelManager): if "motion_modules" in model_manager.model: self.motion_modules = model_manager.motion_modules def fetch_beautiful_prompt(self, model_manager: ModelManager): if "beautiful_prompt" in model_manager.model: self.prompter.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"]) @staticmethod def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]): pipe = SDVideoPipeline( device=model_manager.device, torch_dtype=model_manager.torch_dtype, use_animatediff="motion_modules" in model_manager.model ) pipe.fetch_main_models(model_manager) pipe.fetch_motion_modules(model_manager) pipe.fetch_beautiful_prompt(model_manager) pipe.fetch_controlnet_models(model_manager, controlnet_config_units) return pipe def preprocess_image(self, image): image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) return image def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] image = image.cpu().permute(1, 2, 0).numpy() image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) return image def decode_images(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_images(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.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu() latents.append(latent) 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, controlnet_frames=None, denoising_strength=1.0, height=512, width=512, num_inference_steps=20, vram_limit_level=0, progress_bar_cmd=tqdm, progress_bar_st=None, ): # Prepare scheduler self.scheduler.set_timesteps(num_inference_steps, denoising_strength) # Prepare latent tensors noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype) if input_frames is None: latents = noise else: latents = self.encode_images(input_frames) latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) # Encode prompts prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True).cpu() prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False).cpu() prompt_emb_posi = prompt_emb_posi.repeat(num_frames, 1, 1) prompt_emb_nega = prompt_emb_nega.repeat(num_frames, 1, 1) # Prepare ControlNets if controlnet_frames is not None: 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) # Denoise for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): timestep = torch.IntTensor((timestep,))[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, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames, device=self.device, vram_limit_level=vram_limit_level ) noise_pred_nega = lets_dance_with_long_video( self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames, device=self.device, vram_limit_level=vram_limit_level ) noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) # DDIM 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_images(latents) return output_frames