# Copyright 2023 ByteDance and/or its affiliates.
#
# Copyright (2023) MagicAnimate Authors
#
# ByteDance, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from ByteDance or
# its affiliates is strictly prohibited.
import datetime
import inspect
import os
import numpy as np
from PIL import Image
from omegaconf import OmegaConf
from collections import OrderedDict
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from magicanimate.models.unet_controlnet import UNet3DConditionModel
from magicanimate.models.controlnet import ControlNetModel
from magicanimate.models.appearance_encoder import AppearanceEncoderModel
from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
from magicanimate.pipelines.pipeline_animation import AnimationPipeline
from magicanimate.utils.util import save_videos_grid
from accelerate.utils import set_seed
from magicanimate.utils.videoreader import VideoReader
from einops import rearrange
device = "cuda" if torch.cuda.is_available() else "cpu"
class MagicAnimate():
def __init__(self, config="configs/prompts/animation.yaml") -> None:
print("Initializing MagicAnimate Pipeline...")
*_, func_args = inspect.getargvalues(inspect.currentframe())
func_args = dict(func_args)
config = OmegaConf.load(config)
inference_config = OmegaConf.load(config.inference_config)
motion_module = config.motion_module
### >>> create animation pipeline >>> ###
tokenizer = CLIPTokenizer.from_pretrained(config.pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(config.pretrained_model_path, subfolder="text_encoder")
if config.pretrained_unet_path:
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_unet_path, unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
else:
unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
self.appearance_encoder = AppearanceEncoderModel.from_pretrained(config.pretrained_appearance_encoder_path, subfolder="appearance_encoder").to(device)
self.reference_control_writer = ReferenceAttentionControl(self.appearance_encoder, do_classifier_free_guidance=True, mode='write', fusion_blocks=config.fusion_blocks)
self.reference_control_reader = ReferenceAttentionControl(unet, do_classifier_free_guidance=True, mode='read', fusion_blocks=config.fusion_blocks)
if config.pretrained_vae_path is not None:
vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path)
else:
vae = AutoencoderKL.from_pretrained(config.pretrained_model_path, subfolder="vae")
### Load controlnet
controlnet = ControlNetModel.from_pretrained(config.pretrained_controlnet_path)
vae.to(torch.float16)
unet.to(torch.float16)
text_encoder.to(torch.float16)
controlnet.to(torch.float16)
self.appearance_encoder.to(torch.float16)
if device == "cuda":
unet.enable_xformers_memory_efficient_attention()
self.appearance_encoder.enable_xformers_memory_efficient_attention()
controlnet.enable_xformers_memory_efficient_attention()
self.pipeline = AnimationPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet,
scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
# NOTE: UniPCMultistepScheduler
).to(device)
# 1. unet ckpt
# 1.1 motion module
motion_module_state_dict = torch.load(motion_module, map_location="cpu")
if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
motion_module_state_dict = motion_module_state_dict['state_dict'] if 'state_dict' in motion_module_state_dict else motion_module_state_dict
try:
# extra steps for self-trained models
state_dict = OrderedDict()
for key in motion_module_state_dict.keys():
if key.startswith("module."):
_key = key.split("module.")[-1]
state_dict[_key] = motion_module_state_dict[key]
else:
state_dict[key] = motion_module_state_dict[key]
motion_module_state_dict = state_dict
del state_dict
missing, unexpected = self.pipeline.unet.load_state_dict(motion_module_state_dict, strict=False)
assert len(unexpected) == 0
except:
_tmp_ = OrderedDict()
for key in motion_module_state_dict.keys():
if "motion_modules" in key:
if key.startswith("unet."):
_key = key.split('unet.')[-1]
_tmp_[_key] = motion_module_state_dict[key]
else:
_tmp_[key] = motion_module_state_dict[key]
missing, unexpected = unet.load_state_dict(_tmp_, strict=False)
assert len(unexpected) == 0
del _tmp_
del motion_module_state_dict
self.pipeline.to(device)
self.L = config.L
print("Initialization Done!")
def __call__(self, source_image, motion_sequence, random_seed, step, guidance_scale, size=512):
prompt = n_prompt = ""
random_seed = int(random_seed)
step = int(step)
guidance_scale = float(guidance_scale)
samples_per_video = []
# manually set random seed for reproduction
if random_seed != -1:
torch.manual_seed(random_seed)
set_seed(random_seed)
else:
torch.seed()
if motion_sequence.endswith('.mp4'):
control = VideoReader(motion_sequence).read()
if control[0].shape[0] != size:
control = [np.array(Image.fromarray(c).resize((size, size))) for c in control]
control = np.array(control)
if source_image.shape[0] != size:
source_image = np.array(Image.fromarray(source_image).resize((size, size)))
H, W, C = source_image.shape
init_latents = None
original_length = control.shape[0]
if control.shape[0] % self.L > 0:
control = np.pad(control, ((0, self.L-control.shape[0] % self.L), (0, 0), (0, 0), (0, 0)), mode='edge')
generator = torch.Generator(device=torch.device(device))
generator.manual_seed(torch.initial_seed())
sample = self.pipeline(
prompt,
negative_prompt = n_prompt,
num_inference_steps = step,
guidance_scale = guidance_scale,
width = W,
height = H,
video_length = len(control),
controlnet_condition = control,
init_latents = init_latents,
generator = generator,
appearance_encoder = self.appearance_encoder,
reference_control_writer = self.reference_control_writer,
reference_control_reader = self.reference_control_reader,
source_image = source_image,
).videos
source_images = np.array([source_image] * original_length)
source_images = rearrange(torch.from_numpy(source_images), "t h w c -> 1 c t h w") / 255.0
samples_per_video.append(source_images)
control = control / 255.0
control = rearrange(control, "t h w c -> 1 c t h w")
control = torch.from_numpy(control)
samples_per_video.append(control[:, :, :original_length])
samples_per_video.append(sample[:, :, :original_length])
samples_per_video = torch.cat(samples_per_video)
time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
savedir = f"demo/outputs"
animation_path = f"{savedir}/{time_str}.mp4"
os.makedirs(savedir, exist_ok=True)
save_videos_grid(samples_per_video, animation_path)
return animation_path