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