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inference.py
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import os
import numpy as np
import argparse
import imageio
import torch
from einops import rearrange
from diffusers import DDIMScheduler, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer
import torchvision
from controlnet_aux.processor import Processor
from models.pipeline_controlvideo import ControlVideoPipeline
from models.util import save_videos_grid, read_video
from models.unet import UNet3DConditionModel
from models.controlnet import ControlNetModel3D
from models.RIFE.IFNet_HDv3 import IFNet
device = "cuda"
sd_path = "checkpoints/stable-diffusion-v1-5"
inter_path = "checkpoints/flownet.pkl"
controlnet_dict_version = {
"v10":{
"openpose": "checkpoints/sd-controlnet-openpose",
"depth_midas": "checkpoints/sd-controlnet-depth",
"canny": "checkpoints/sd-controlnet-canny",
},
"v11": {
"softedge_pidinet": "checkpoints/control_v11p_sd15_softedge",
"softedge_pidsafe": "checkpoints/control_v11p_sd15_softedge",
"softedge_hed": "checkpoints/control_v11p_sd15_softedge",
"softedge_hedsafe": "checkpoints/control_v11p_sd15_softedge",
"scribble_hed": "checkpoints/control_v11p_sd15_scribble",
"scribble_pidinet": "checkpoints/control_v11p_sd15_scribble",
"lineart_anime": "checkpoints/control_v11p_sd15_lineart_anime",
"lineart_coarse": "checkpoints/control_v11p_sd15_lineart",
"lineart_realistic": "checkpoints/control_v11p_sd15_lineart",
"depth_midas": "checkpoints/control_v11f1p_sd15_depth",
"depth_leres": "checkpoints/control_v11f1p_sd15_depth",
"depth_leres++": "checkpoints/control_v11f1p_sd15_depth",
"depth_zoe": "checkpoints/control_v11f1p_sd15_depth",
"canny": "checkpoints/control_v11p_sd15_canny",
"openpose": "checkpoints/control_v11p_sd15_openpose",
"openpose_face": "checkpoints/control_v11p_sd15_openpose",
"openpose_faceonly": "checkpoints/control_v11p_sd15_openpose",
"openpose_full": "checkpoints/control_v11p_sd15_openpose",
"openpose_hand": "checkpoints/control_v11p_sd15_openpose",
"normal_bae": "checkpoints/control_v11p_sd15_normalbae"
}
}
# load processor from processor_id
# options are:
# ["canny", "depth_leres", "depth_leres++", "depth_midas", "depth_zoe", "lineart_anime",
# "lineart_coarse", "lineart_realistic", "mediapipe_face", "mlsd", "normal_bae", "normal_midas",
# "openpose", "openpose_face", "openpose_faceonly", "openpose_full", "openpose_hand",
# "scribble_hed, "scribble_pidinet", "shuffle", "softedge_hed", "softedge_hedsafe",
# "softedge_pidinet", "softedge_pidsafe"]
POS_PROMPT = " ,best quality, extremely detailed, HD, ultra-realistic, 8K, HQ, masterpiece, trending on artstation, art, smooth"
NEG_PROMPT = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, deformed body, bloated, ugly, unrealistic"
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str, required=True, help="Text description of target video")
parser.add_argument("--video_path", type=str, required=True, help="Path to a source video")
parser.add_argument("--output_path", type=str, default="./outputs", help="Directory of output")
parser.add_argument("--condition", type=str, default="depth", help="Condition of structure sequence")
parser.add_argument("--video_length", type=int, default=15, help="Length of synthesized video")
parser.add_argument("--height", type=int, default=512, help="Height of synthesized video, and should be a multiple of 32")
parser.add_argument("--width", type=int, default=512, help="Width of synthesized video, and should be a multiple of 32")
parser.add_argument("--smoother_steps", nargs='+', default=[19, 20], type=int, help="Timesteps at which using interleaved-frame smoother")
parser.add_argument("--is_long_video", action='store_true', help="Whether to use hierarchical sampler to produce long video")
parser.add_argument("--seed", type=int, default=42, help="Random seed of generator")
parser.add_argument("--version", type=str, default='v10', choices=["v10", "v11"], help="Version of ControlNet")
parser.add_argument("--frame_rate", type=int, default=None, help="The frame rate of loading input video. Default rate is computed according to video length.")
parser.add_argument("--temp_video_name", type=str, default=None, help="Default video name")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
os.makedirs(args.output_path, exist_ok=True)
# Height and width should be a multiple of 32
args.height = (args.height // 32) * 32
args.width = (args.width // 32) * 32
processor = Processor(args.condition)
controlnet_dict = controlnet_dict_version[args.version]
tokenizer = CLIPTokenizer.from_pretrained(sd_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(sd_path, subfolder="text_encoder").to(dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(sd_path, subfolder="vae").to(dtype=torch.float16)
unet = UNet3DConditionModel.from_pretrained_2d(sd_path, subfolder="unet").to(dtype=torch.float16)
controlnet = ControlNetModel3D.from_pretrained_2d(controlnet_dict[args.condition]).to(dtype=torch.float16)
interpolater = IFNet(ckpt_path=inter_path).to(dtype=torch.float16)
scheduler=DDIMScheduler.from_pretrained(sd_path, subfolder="scheduler")
pipe = ControlVideoPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
controlnet=controlnet, interpolater=interpolater, scheduler=scheduler,
)
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
pipe.to(device)
generator = torch.Generator(device="cuda")
generator.manual_seed(args.seed)
# Step 1. Read a video
video = read_video(video_path=args.video_path, video_length=args.video_length, width=args.width, height=args.height, frame_rate=args.frame_rate)
# Save source video
original_pixels = rearrange(video, "(b f) c h w -> b c f h w", b=1)
save_videos_grid(original_pixels, os.path.join(args.output_path, "source_video.mp4"), rescale=True)
# Step 2. Parse a video to conditional frames
t2i_transform = torchvision.transforms.ToPILImage()
pil_annotation = []
for frame in video:
pil_frame = t2i_transform(frame)
pil_annotation.append(processor(pil_frame, to_pil=True))
# Save condition video
video_cond = [np.array(p).astype(np.uint8) for p in pil_annotation]
imageio.mimsave(os.path.join(args.output_path, f"{args.condition}_condition.mp4"), video_cond, fps=8)
# Reduce memory (optional)
del processor; torch.cuda.empty_cache()
# Step 3. inference
if args.is_long_video:
window_size = int(np.sqrt(args.video_length))
sample = pipe.generate_long_video(args.prompt + POS_PROMPT, video_length=args.video_length, frames=pil_annotation,
num_inference_steps=50, smooth_steps=args.smoother_steps, window_size=window_size,
generator=generator, guidance_scale=12.5, negative_prompt=NEG_PROMPT,
width=args.width, height=args.height
).videos
else:
sample = pipe(args.prompt + POS_PROMPT, video_length=args.video_length, frames=pil_annotation,
num_inference_steps=50, smooth_steps=args.smoother_steps,
generator=generator, guidance_scale=12.5, negative_prompt=NEG_PROMPT,
width=args.width, height=args.height
).videos
args.temp_video_name = args.prompt if args.temp_video_name is None else args.temp_video_name
save_videos_grid(sample, f"{args.output_path}/{args.temp_video_name}.mp4")