MuseV/scripts/gradio/gradio_text2video.py

import argparse
import copy
import os
from pathlib import Path
import logging
from collections import OrderedDict
from pprint import pprint
import random
import gradio as gr
from argparse import Namespace

import numpy as np
from omegaconf import OmegaConf, SCMode
import torch
from einops import rearrange, repeat
import cv2
from PIL import Image
from diffusers.models.autoencoder_kl import AutoencoderKL

from mmcm.utils.load_util import load_pyhon_obj
from mmcm.utils.seed_util import set_all_seed
from mmcm.utils.signature import get_signature_of_string
from mmcm.utils.task_util import fiss_tasks, generate_tasks as generate_tasks_from_table
from mmcm.vision.utils.data_type_util import is_video, is_image, read_image_as_5d
from mmcm.utils.str_util import clean_str_for_save
from mmcm.vision.data.video_dataset import DecordVideoDataset
from musev.auto_prompt.util import generate_prompts


from musev.models.facein_loader import load_facein_extractor_and_proj_by_name
from musev.models.referencenet_loader import load_referencenet_by_name
from musev.models.ip_adapter_loader import (
    load_ip_adapter_vision_clip_encoder_by_name,
    load_vision_clip_encoder_by_name,
    load_ip_adapter_image_proj_by_name,
)
from musev.models.ip_adapter_face_loader import (
    load_ip_adapter_face_extractor_and_proj_by_name,
)
from musev.pipelines.pipeline_controlnet_predictor import (
    DiffusersPipelinePredictor,
)
from musev.models.referencenet import ReferenceNet2D
from musev.models.unet_loader import load_unet_by_name
from musev.utils.util import save_videos_grid_with_opencv
from musev import logger

use_v2v_predictor = False
if use_v2v_predictor:
    from gradio_video2video import sd_predictor as video_sd_predictor

logger.setLevel("INFO")

file_dir = os.path.dirname(__file__)
PROJECT_DIR = os.path.join(os.path.dirname(__file__), "../..")
DATA_DIR = os.path.join(PROJECT_DIR, "data")
CACHE_PATH = "./t2v_input_image"


# TODO：use group to group arguments


args_dict = {
    "add_static_video_prompt": False,
    "context_batch_size": 1,
    "context_frames": 12,
    "context_overlap": 4,
    "context_schedule": "uniform_v2",
    "context_stride": 1,
    "cross_attention_dim": 768,
    "face_image_path": None,
    "facein_model_cfg_path": "../../configs/model/facein.py",
    "facein_model_name": None,
    "facein_scale": 1.0,
    "fix_condition_images": False,
    "fixed_ip_adapter_image": True,
    "fixed_refer_face_image": True,
    "fixed_refer_image": True,
    "fps": 4,
    "guidance_scale": 7.5,
    "height": None,
    "img_length_ratio": 1.0,
    "img_weight": 0.001,
    "interpolation_factor": 1,
    "ip_adapter_face_model_cfg_path": "../../configs/model/ip_adapter.py",
    "ip_adapter_face_model_name": None,
    "ip_adapter_face_scale": 1.0,
    "ip_adapter_model_cfg_path": "../../configs/model/ip_adapter.py",
    "ip_adapter_model_name": "musev_referencenet",
    "ip_adapter_scale": 1.0,
    "ipadapter_image_path": None,
    "lcm_model_cfg_path": "../../configs/model/lcm_model.py",
    "lcm_model_name": None,
    "log_level": "INFO",
    "motion_speed": 8.0,
    "n_batch": 1,
    "n_cols": 3,
    "n_repeat": 1,
    "n_vision_condition": 1,
    "need_hist_match": False,
    "need_img_based_video_noise": True,
    "need_redraw": False,
    "negative_prompt": "V2",
    "negprompt_cfg_path": "../../configs/model/negative_prompt.py",
    "noise_type": "video_fusion",
    "num_inference_steps": 30,
    "output_dir": "./results/",
    "overwrite": False,
    "prompt_only_use_image_prompt": False,
    "record_mid_video_latents": False,
    "record_mid_video_noises": False,
    "redraw_condition_image": False,
    "redraw_condition_image_with_facein": True,
    "redraw_condition_image_with_ip_adapter_face": True,
    "redraw_condition_image_with_ipdapter": True,
    "redraw_condition_image_with_referencenet": True,
    "referencenet_image_path": None,
    "referencenet_model_cfg_path": "../../configs/model/referencenet.py",
    "referencenet_model_name": "musev_referencenet",
    "save_filetype": "mp4",
    "save_images": False,
    "sd_model_cfg_path": "../../configs/model/T2I_all_model.py",
    "sd_model_name": "majicmixRealv6Fp16",
    "seed": None,
    "strength": 0.8,
    "target_datas": "boy_dance2",
    "test_data_path": "../../configs/infer/testcase_video_famous.yaml",
    "time_size": 24,
    "unet_model_cfg_path": "../../configs/model/motion_model.py",
    "unet_model_name": "musev_referencenet",
    "use_condition_image": True,
    "use_video_redraw": True,
    "vae_model_path": "../../checkpoints/vae/sd-vae-ft-mse",
    "video_guidance_scale": 3.5,
    "video_guidance_scale_end": None,
    "video_guidance_scale_method": "linear",
    "video_negative_prompt": "V2",
    "video_num_inference_steps": 10,
    "video_overlap": 1,
    "vision_clip_extractor_class_name": "ImageClipVisionFeatureExtractor",
    "vision_clip_model_path": "../../checkpoints/IP-Adapter/models/image_encoder",
    "w_ind_noise": 0.5,
    "width": None,
    "write_info": False,
}
args = Namespace(**args_dict)
print("args")
pprint(args)
print("\n")

logger.setLevel(args.log_level)
overwrite = args.overwrite
cross_attention_dim = args.cross_attention_dim
time_size = args.time_size  # 一次视频生成的帧数
n_batch = args.n_batch  # 按照time_size的尺寸 生成n_batch次，总帧数 = time_size * n_batch
fps = args.fps
# need_redraw = args.need_redraw  # 视频重绘视频使用视频网络
# use_video_redraw = args.use_video_redraw  # 视频重绘视频使用视频网络
fix_condition_images = args.fix_condition_images
use_condition_image = args.use_condition_image  # 当 test_data 中有图像时，作为初始图像
redraw_condition_image = args.redraw_condition_image  # 用于视频生成的首帧是否使用重绘后的
need_img_based_video_noise = (
    args.need_img_based_video_noise
)  # 视频加噪过程中是否使用首帧 condition_images
img_weight = args.img_weight
height = args.height  # 如果测试数据中没有单独指定宽高，则默认这里
width = args.width  # 如果测试数据中没有单独指定宽高，则默认这里
img_length_ratio = args.img_length_ratio  # 如果测试数据中没有单独指定图像宽高比resize比例，则默认这里
n_cols = args.n_cols
noise_type = args.noise_type
strength = args.strength  # 首帧重绘程度参数
video_guidance_scale = args.video_guidance_scale  # 视频 condition与 uncond的权重参数
guidance_scale = args.guidance_scale  # 时序条件帧 condition与uncond的权重参数
video_num_inference_steps = args.video_num_inference_steps  # 视频迭代次数
num_inference_steps = args.num_inference_steps  # 时序条件帧 重绘参数
seed = args.seed
save_filetype = args.save_filetype
save_images = args.save_images
sd_model_cfg_path = args.sd_model_cfg_path
sd_model_name = (
    args.sd_model_name
    if args.sd_model_name in ["all", "None"]
    else args.sd_model_name.split(",")
)
unet_model_cfg_path = args.unet_model_cfg_path
unet_model_name = args.unet_model_name
test_data_path = args.test_data_path
target_datas = (
    args.target_datas if args.target_datas == "all" else args.target_datas.split(",")
)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16
negprompt_cfg_path = args.negprompt_cfg_path
video_negative_prompt = args.video_negative_prompt
negative_prompt = args.negative_prompt
motion_speed = args.motion_speed
need_hist_match = args.need_hist_match
video_guidance_scale_end = args.video_guidance_scale_end
video_guidance_scale_method = args.video_guidance_scale_method
add_static_video_prompt = args.add_static_video_prompt
n_vision_condition = args.n_vision_condition
lcm_model_cfg_path = args.lcm_model_cfg_path
lcm_model_name = args.lcm_model_name
referencenet_model_cfg_path = args.referencenet_model_cfg_path
referencenet_model_name = args.referencenet_model_name
ip_adapter_model_cfg_path = args.ip_adapter_model_cfg_path
ip_adapter_model_name = args.ip_adapter_model_name
vision_clip_model_path = args.vision_clip_model_path
vision_clip_extractor_class_name = args.vision_clip_extractor_class_name
facein_model_cfg_path = args.facein_model_cfg_path
facein_model_name = args.facein_model_name
ip_adapter_face_model_cfg_path = args.ip_adapter_face_model_cfg_path
ip_adapter_face_model_name = args.ip_adapter_face_model_name

fixed_refer_image = args.fixed_refer_image
fixed_ip_adapter_image = args.fixed_ip_adapter_image
fixed_refer_face_image = args.fixed_refer_face_image
redraw_condition_image_with_referencenet = args.redraw_condition_image_with_referencenet
redraw_condition_image_with_ipdapter = args.redraw_condition_image_with_ipdapter
redraw_condition_image_with_facein = args.redraw_condition_image_with_facein
redraw_condition_image_with_ip_adapter_face = (
    args.redraw_condition_image_with_ip_adapter_face
)
w_ind_noise = args.w_ind_noise
ip_adapter_scale = args.ip_adapter_scale
facein_scale = args.facein_scale
ip_adapter_face_scale = args.ip_adapter_face_scale
face_image_path = args.face_image_path
ipadapter_image_path = args.ipadapter_image_path
referencenet_image_path = args.referencenet_image_path
vae_model_path = args.vae_model_path
prompt_only_use_image_prompt = args.prompt_only_use_image_prompt
# serial_denoise parameter start
record_mid_video_noises = args.record_mid_video_noises
record_mid_video_latents = args.record_mid_video_latents
video_overlap = args.video_overlap
# serial_denoise parameter end
# parallel_denoise parameter start
context_schedule = args.context_schedule
context_frames = args.context_frames
context_stride = args.context_stride
context_overlap = args.context_overlap
context_batch_size = args.context_batch_size
interpolation_factor = args.interpolation_factor
n_repeat = args.n_repeat

# parallel_denoise parameter end

b = 1
negative_embedding = [
    ["../../checkpoints/embedding/badhandv4.pt", "badhandv4"],
    [
        "../../checkpoints/embedding/ng_deepnegative_v1_75t.pt",
        "ng_deepnegative_v1_75t",
    ],
    [
        "../../checkpoints/embedding/EasyNegativeV2.safetensors",
        "EasyNegativeV2",
    ],
    [
        "../../checkpoints/embedding/bad_prompt_version2-neg.pt",
        "bad_prompt_version2-neg",
    ],
]
prefix_prompt = ""
suffix_prompt = ", beautiful, masterpiece, best quality"
suffix_prompt = ""


# sd model parameters

if sd_model_name != "None":
    # 使用 cfg_path 里的sd_model_path
    sd_model_params_dict_src = load_pyhon_obj(sd_model_cfg_path, "MODEL_CFG")
    sd_model_params_dict = {
        k: v
        for k, v in sd_model_params_dict_src.items()
        if sd_model_name == "all" or k in sd_model_name
    }
else:
    # 使用命令行给的sd_model_path, 需要单独设置 sd_model_name 为None，
    sd_model_name = os.path.basename(sd_model_cfg_path).split(".")[0]
    sd_model_params_dict = {sd_model_name: {"sd": sd_model_cfg_path}}
    sd_model_params_dict_src = sd_model_params_dict
if len(sd_model_params_dict) == 0:
    raise ValueError(
        "has not target model, please set one of {}".format(
            " ".join(list(sd_model_params_dict_src.keys()))
        )
    )
print("running model, T2I SD")
pprint(sd_model_params_dict)

# lcm
if lcm_model_name is not None:
    lcm_model_params_dict_src = load_pyhon_obj(lcm_model_cfg_path, "MODEL_CFG")
    print("lcm_model_params_dict_src")
    lcm_lora_dct = lcm_model_params_dict_src[lcm_model_name]
else:
    lcm_lora_dct = None
print("lcm: ", lcm_model_name, lcm_lora_dct)


# motion net parameters
if os.path.isdir(unet_model_cfg_path):
    unet_model_path = unet_model_cfg_path
elif os.path.isfile(unet_model_cfg_path):
    unet_model_params_dict_src = load_pyhon_obj(unet_model_cfg_path, "MODEL_CFG")
    print("unet_model_params_dict_src", unet_model_params_dict_src.keys())
    unet_model_path = unet_model_params_dict_src[unet_model_name]["unet"]
else:
    raise ValueError(f"expect dir or file, but given {unet_model_cfg_path}")
print("unet: ", unet_model_name, unet_model_path)


# referencenet
if referencenet_model_name is not None:
    if os.path.isdir(referencenet_model_cfg_path):
        referencenet_model_path = referencenet_model_cfg_path
    elif os.path.isfile(referencenet_model_cfg_path):
        referencenet_model_params_dict_src = load_pyhon_obj(
            referencenet_model_cfg_path, "MODEL_CFG"
        )
        print(
            "referencenet_model_params_dict_src",
            referencenet_model_params_dict_src.keys(),
        )
        referencenet_model_path = referencenet_model_params_dict_src[
            referencenet_model_name
        ]["net"]
    else:
        raise ValueError(f"expect dir or file, but given {referencenet_model_cfg_path}")
else:
    referencenet_model_path = None
print("referencenet: ", referencenet_model_name, referencenet_model_path)


# ip_adapter
if ip_adapter_model_name is not None:
    ip_adapter_model_params_dict_src = load_pyhon_obj(
        ip_adapter_model_cfg_path, "MODEL_CFG"
    )
    print("ip_adapter_model_params_dict_src", ip_adapter_model_params_dict_src.keys())
    ip_adapter_model_params_dict = ip_adapter_model_params_dict_src[
        ip_adapter_model_name
    ]
else:
    ip_adapter_model_params_dict = None
print("ip_adapter: ", ip_adapter_model_name, ip_adapter_model_params_dict)


# facein
if facein_model_name is not None:
    facein_model_params_dict_src = load_pyhon_obj(facein_model_cfg_path, "MODEL_CFG")
    print("facein_model_params_dict_src", facein_model_params_dict_src.keys())
    facein_model_params_dict = facein_model_params_dict_src[facein_model_name]
else:
    facein_model_params_dict = None
print("facein: ", facein_model_name, facein_model_params_dict)

# ip_adapter_face
if ip_adapter_face_model_name is not None:
    ip_adapter_face_model_params_dict_src = load_pyhon_obj(
        ip_adapter_face_model_cfg_path, "MODEL_CFG"
    )
    print(
        "ip_adapter_face_model_params_dict_src",
        ip_adapter_face_model_params_dict_src.keys(),
    )
    ip_adapter_face_model_params_dict = ip_adapter_face_model_params_dict_src[
        ip_adapter_face_model_name
    ]
else:
    ip_adapter_face_model_params_dict = None
print(
    "ip_adapter_face: ", ip_adapter_face_model_name, ip_adapter_face_model_params_dict
)


# negative_prompt
def get_negative_prompt(negative_prompt, cfg_path=None, n: int = 10):
    name = negative_prompt[:n]
    if cfg_path is not None and cfg_path not in ["None", "none"]:
        dct = load_pyhon_obj(cfg_path, "Negative_Prompt_CFG")
        negative_prompt = dct[negative_prompt]["prompt"]

    return name, negative_prompt


negtive_prompt_length = 10
video_negative_prompt_name, video_negative_prompt = get_negative_prompt(
    video_negative_prompt,
    cfg_path=negprompt_cfg_path,
    n=negtive_prompt_length,
)
negative_prompt_name, negative_prompt = get_negative_prompt(
    negative_prompt,
    cfg_path=negprompt_cfg_path,
    n=negtive_prompt_length,
)

print("video_negprompt", video_negative_prompt_name, video_negative_prompt)
print("negprompt", negative_prompt_name, negative_prompt)

output_dir = args.output_dir
os.makedirs(output_dir, exist_ok=True)


# test_data_parameters
def load_yaml(path):
    tasks = OmegaConf.to_container(
        OmegaConf.load(path), structured_config_mode=SCMode.INSTANTIATE, resolve=True
    )
    return tasks


# if test_data_path.endswith(".yaml"):
#     test_datas_src = load_yaml(test_data_path)
# elif test_data_path.endswith(".csv"):
#     test_datas_src = generate_tasks_from_table(test_data_path)
# else:
#     raise ValueError("expect yaml or csv, but given {}".format(test_data_path))

# test_datas = [
#     test_data
#     for test_data in test_datas_src
#     if target_datas == "all" or test_data.get("name", None) in target_datas
# ]

# test_datas = fiss_tasks(test_datas)
# test_datas = generate_prompts(test_datas)

# n_test_datas = len(test_datas)
# if n_test_datas == 0:
#     raise ValueError(
#         "n_test_datas == 0, set target_datas=None or set atleast one of {}".format(
#             " ".join(list(d.get("name", "None") for d in test_datas_src))
#         )
#     )
# print("n_test_datas", n_test_datas)
# # pprint(test_datas)


def read_image(path):
    name = os.path.basename(path).split(".")[0]
    image = read_image_as_5d(path)
    return image, name


def read_image_lst(path):
    images_names = [read_image(x) for x in path]
    images, names = zip(*images_names)
    images = np.concatenate(images, axis=2)
    name = "_".join(names)
    return images, name


def read_image_and_name(path):
    if isinstance(path, str):
        path = [path]
    images, name = read_image_lst(path)
    return images, name


if referencenet_model_name is not None and not use_v2v_predictor:
    referencenet = load_referencenet_by_name(
        model_name=referencenet_model_name,
        # sd_model=sd_model_path,
        # sd_model="../../checkpoints//Moore-AnimateAnyone/AnimateAnyone/reference_unet.pth",
        sd_referencenet_model=referencenet_model_path,
        cross_attention_dim=cross_attention_dim,
    )
else:
    referencenet = None
    referencenet_model_name = "no"

if vision_clip_extractor_class_name is not None and not use_v2v_predictor:
    vision_clip_extractor = load_vision_clip_encoder_by_name(
        ip_image_encoder=vision_clip_model_path,
        vision_clip_extractor_class_name=vision_clip_extractor_class_name,
    )
    logger.info(
        f"vision_clip_extractor, name={vision_clip_extractor_class_name}, path={vision_clip_model_path}"
    )
else:
    vision_clip_extractor = None
    logger.info(f"vision_clip_extractor, None")

if ip_adapter_model_name is not None and not use_v2v_predictor:
    ip_adapter_image_proj = load_ip_adapter_image_proj_by_name(
        model_name=ip_adapter_model_name,
        ip_image_encoder=ip_adapter_model_params_dict.get(
            "ip_image_encoder", vision_clip_model_path
        ),
        ip_ckpt=ip_adapter_model_params_dict["ip_ckpt"],
        cross_attention_dim=cross_attention_dim,
        clip_embeddings_dim=ip_adapter_model_params_dict["clip_embeddings_dim"],
        clip_extra_context_tokens=ip_adapter_model_params_dict[
            "clip_extra_context_tokens"
        ],
        ip_scale=ip_adapter_model_params_dict["ip_scale"],
        device=device,
    )
else:
    ip_adapter_image_proj = None
    ip_adapter_model_name = "no"

for model_name, sd_model_params in sd_model_params_dict.items():
    lora_dict = sd_model_params.get("lora", None)
    model_sex = sd_model_params.get("sex", None)
    model_style = sd_model_params.get("style", None)
    sd_model_path = sd_model_params["sd"]
    test_model_vae_model_path = sd_model_params.get("vae", vae_model_path)

    unet = (
        load_unet_by_name(
            model_name=unet_model_name,
            sd_unet_model=unet_model_path,
            sd_model=sd_model_path,
            # sd_model="../../checkpoints//Moore-AnimateAnyone/AnimateAnyone/denoising_unet.pth",
            cross_attention_dim=cross_attention_dim,
            need_t2i_facein=facein_model_name is not None,
            # facein 目前没参与训练，但在unet中定义了，载入相关参数会报错，所以用strict控制
            strict=not (facein_model_name is not None),
            need_t2i_ip_adapter_face=ip_adapter_face_model_name is not None,
        )
        if not use_v2v_predictor
        else None
    )

    if facein_model_name is not None and not use_v2v_predictor:
        (
            face_emb_extractor,
            facein_image_proj,
        ) = load_facein_extractor_and_proj_by_name(
            model_name=facein_model_name,
            ip_image_encoder=facein_model_params_dict["ip_image_encoder"],
            ip_ckpt=facein_model_params_dict["ip_ckpt"],
            cross_attention_dim=cross_attention_dim,
            clip_embeddings_dim=facein_model_params_dict["clip_embeddings_dim"],
            clip_extra_context_tokens=facein_model_params_dict[
                "clip_extra_context_tokens"
            ],
            ip_scale=facein_model_params_dict["ip_scale"],
            device=device,
            # facein目前没有参与unet中的训练，需要单独载入参数
            unet=unet,
        )
    else:
        face_emb_extractor = None
        facein_image_proj = None

    if ip_adapter_face_model_name is not None and not use_v2v_predictor:
        (
            ip_adapter_face_emb_extractor,
            ip_adapter_face_image_proj,
        ) = load_ip_adapter_face_extractor_and_proj_by_name(
            model_name=ip_adapter_face_model_name,
            ip_image_encoder=ip_adapter_face_model_params_dict["ip_image_encoder"],
            ip_ckpt=ip_adapter_face_model_params_dict["ip_ckpt"],
            cross_attention_dim=cross_attention_dim,
            clip_embeddings_dim=ip_adapter_face_model_params_dict[
                "clip_embeddings_dim"
            ],
            clip_extra_context_tokens=ip_adapter_face_model_params_dict[
                "clip_extra_context_tokens"
            ],
            ip_scale=ip_adapter_face_model_params_dict["ip_scale"],
            device=device,
            unet=unet,  # ip_adapter_face 目前没有参与unet中的训练，需要单独载入参数
        )
    else:
        ip_adapter_face_emb_extractor = None
        ip_adapter_face_image_proj = None

    print("test_model_vae_model_path", test_model_vae_model_path)

    sd_predictor = (
        DiffusersPipelinePredictor(
            sd_model_path=sd_model_path,
            unet=unet,
            lora_dict=lora_dict,
            lcm_lora_dct=lcm_lora_dct,
            device=device,
            dtype=torch_dtype,
            negative_embedding=negative_embedding,
            referencenet=referencenet,
            ip_adapter_image_proj=ip_adapter_image_proj,
            vision_clip_extractor=vision_clip_extractor,
            facein_image_proj=facein_image_proj,
            face_emb_extractor=face_emb_extractor,
            vae_model=test_model_vae_model_path,
            ip_adapter_face_emb_extractor=ip_adapter_face_emb_extractor,
            ip_adapter_face_image_proj=ip_adapter_face_image_proj,
        )
        if not use_v2v_predictor
        else video_sd_predictor
    )
    if use_v2v_predictor:
        print(
            "text2video use video_sd_predictor, sd_predictor type is ",
            type(sd_predictor),
        )
    logger.debug(f"load sd_predictor"),

    # TODO:这里修改为gradio
import cuid


def generate_cuid():
    return cuid.cuid()


def online_t2v_inference(
    prompt,
    image_np,
    seed,
    fps,
    w,
    h,
    video_len,
    img_edge_ratio: float = 1.0,
    progress=gr.Progress(track_tqdm=True),
):
    progress(0, desc="Starting...")
    # Save the uploaded image to a specified path
    if not os.path.exists(CACHE_PATH):
        os.makedirs(CACHE_PATH)
    image_cuid = generate_cuid()

    image_path = os.path.join(CACHE_PATH, f"{image_cuid}.jpg")
    image = Image.fromarray(image_np)
    image.save(image_path)

    time_size = int(video_len)
    test_data = {
        "name": image_cuid,
        "prompt": prompt,
        # 'video_path': None,
        "condition_images": image_path,
        "refer_image": image_path,
        "ipadapter_image": image_path,
        "height": h,
        "width": w,
        "img_length_ratio": img_edge_ratio,
        # 'style': 'anime',
        # 'sex': 'female'
    }
    batch = []
    texts = []
    print("\n test_data", test_data, model_name)
    test_data_name = test_data.get("name", test_data)
    prompt = test_data["prompt"]
    prompt = prefix_prompt + prompt + suffix_prompt
    prompt_hash = get_signature_of_string(prompt, length=5)
    test_data["prompt_hash"] = prompt_hash
    test_data_height = test_data.get("height", height)
    test_data_width = test_data.get("width", width)
    test_data_condition_images_path = test_data.get("condition_images", None)
    test_data_condition_images_index = test_data.get("condition_images_index", None)
    test_data_redraw_condition_image = test_data.get(
        "redraw_condition_image", redraw_condition_image
    )
    # read condition_image
    if (
        test_data_condition_images_path is not None
        and use_condition_image
        and (
            isinstance(test_data_condition_images_path, list)
            or (
                isinstance(test_data_condition_images_path, str)
                and is_image(test_data_condition_images_path)
            )
        )
    ):
        (
            test_data_condition_images,
            test_data_condition_images_name,
        ) = read_image_and_name(test_data_condition_images_path)
        condition_image_height = test_data_condition_images.shape[3]
        condition_image_width = test_data_condition_images.shape[4]
        logger.debug(
            f"test_data_condition_images use {test_data_condition_images_path}"
        )
    else:
        test_data_condition_images = None
        test_data_condition_images_name = "no"
        condition_image_height = None
        condition_image_width = None
        logger.debug(f"test_data_condition_images is None")

    # 当没有指定生成视频的宽高时，使用输入条件的宽高，优先使用 condition_image，低优使用 video
    if test_data_height in [None, -1]:
        test_data_height = condition_image_height

    if test_data_width in [None, -1]:
        test_data_width = condition_image_width

    test_data_img_length_ratio = float(
        test_data.get("img_length_ratio", img_length_ratio)
    )
    # 为了和video2video保持对齐，使用64而不是8作为宽、高最小粒度
    # test_data_height = int(test_data_height * test_data_img_length_ratio // 8 * 8)
    # test_data_width = int(test_data_width * test_data_img_length_ratio // 8 * 8)
    test_data_height = int(test_data_height * test_data_img_length_ratio // 64 * 64)
    test_data_width = int(test_data_width * test_data_img_length_ratio // 64 * 64)
    pprint(test_data)
    print(f"test_data_height={test_data_height}")
    print(f"test_data_width={test_data_width}")
    # continue
    test_data_style = test_data.get("style", None)
    test_data_sex = test_data.get("sex", None)
    # 如果使用|进行多参数任务设置时对应的字段是字符串类型，需要显式转换浮点数。
    test_data_motion_speed = float(test_data.get("motion_speed", motion_speed))
    test_data_w_ind_noise = float(test_data.get("w_ind_noise", w_ind_noise))
    test_data_img_weight = float(test_data.get("img_weight", img_weight))
    logger.debug(f"test_data_condition_images_path {test_data_condition_images_path}")
    logger.debug(f"test_data_condition_images_index {test_data_condition_images_index}")
    test_data_refer_image_path = test_data.get("refer_image", referencenet_image_path)
    test_data_ipadapter_image_path = test_data.get(
        "ipadapter_image", ipadapter_image_path
    )
    test_data_refer_face_image_path = test_data.get("face_image", face_image_path)

    if negprompt_cfg_path is not None:
        if "video_negative_prompt" in test_data:
            (
                test_data_video_negative_prompt_name,
                test_data_video_negative_prompt,
            ) = get_negative_prompt(
                test_data.get(
                    "video_negative_prompt",
                ),
                cfg_path=negprompt_cfg_path,
                n=negtive_prompt_length,
            )
        else:
            test_data_video_negative_prompt_name = video_negative_prompt_name
            test_data_video_negative_prompt = video_negative_prompt
        if "negative_prompt" in test_data:
            (
                test_data_negative_prompt_name,
                test_data_negative_prompt,
            ) = get_negative_prompt(
                test_data.get(
                    "negative_prompt",
                ),
                cfg_path=negprompt_cfg_path,
                n=negtive_prompt_length,
            )
        else:
            test_data_negative_prompt_name = negative_prompt_name
            test_data_negative_prompt = negative_prompt
    else:
        test_data_video_negative_prompt = test_data.get(
            "video_negative_prompt", video_negative_prompt
        )
        test_data_video_negative_prompt_name = test_data_video_negative_prompt[
            :negtive_prompt_length
        ]
        test_data_negative_prompt = test_data.get("negative_prompt", negative_prompt)
        test_data_negative_prompt_name = test_data_negative_prompt[
            :negtive_prompt_length
        ]

    # 准备 test_data_refer_image
    if referencenet is not None:
        if test_data_refer_image_path is None:
            test_data_refer_image = test_data_condition_images
            test_data_refer_image_name = test_data_condition_images_name
            logger.debug(f"test_data_refer_image use test_data_condition_images")
        else:
            test_data_refer_image, test_data_refer_image_name = read_image_and_name(
                test_data_refer_image_path
            )
            logger.debug(f"test_data_refer_image use {test_data_refer_image_path}")
    else:
        test_data_refer_image = None
        test_data_refer_image_name = "no"
        logger.debug(f"test_data_refer_image is None")

    # 准备 test_data_ipadapter_image
    if vision_clip_extractor is not None:
        if test_data_ipadapter_image_path is None:
            test_data_ipadapter_image = test_data_condition_images
            test_data_ipadapter_image_name = test_data_condition_images_name

            logger.debug(f"test_data_ipadapter_image use test_data_condition_images")
        else:
            (
                test_data_ipadapter_image,
                test_data_ipadapter_image_name,
            ) = read_image_and_name(test_data_ipadapter_image_path)
            logger.debug(
                f"test_data_ipadapter_image use f{test_data_ipadapter_image_path}"
            )
    else:
        test_data_ipadapter_image = None
        test_data_ipadapter_image_name = "no"
        logger.debug(f"test_data_ipadapter_image is None")

    # 准备 test_data_refer_face_image
    if facein_image_proj is not None or ip_adapter_face_image_proj is not None:
        if test_data_refer_face_image_path is None:
            test_data_refer_face_image = test_data_condition_images
            test_data_refer_face_image_name = test_data_condition_images_name

            logger.debug(f"test_data_refer_face_image use test_data_condition_images")
        else:
            (
                test_data_refer_face_image,
                test_data_refer_face_image_name,
            ) = read_image_and_name(test_data_refer_face_image_path)
            logger.debug(
                f"test_data_refer_face_image use f{test_data_refer_face_image_path}"
            )
    else:
        test_data_refer_face_image = None
        test_data_refer_face_image_name = "no"
        logger.debug(f"test_data_refer_face_image is None")

    # # 当模型的sex、style与test_data同时存在且不相等时，就跳过这个测试用例
    # if (
    #     model_sex is not None
    #     and test_data_sex is not None
    #     and model_sex != test_data_sex
    # ) or (
    #     model_style is not None
    #     and test_data_style is not None
    #     and model_style != test_data_style
    # ):
    #     print("model doesnt match test_data")
    #     print("model name: ", model_name)
    #     print("test_data: ", test_data)
    #     continue
    if add_static_video_prompt:
        test_data_video_negative_prompt = "static video, {}".format(
            test_data_video_negative_prompt
        )
    for i_num in range(n_repeat):
        test_data_seed = random.randint(0, 1e8) if seed in [None, -1] else seed
        cpu_generator, gpu_generator = set_all_seed(int(test_data_seed))
        save_file_name = (
            f"m={model_name}_rm={referencenet_model_name}_case={test_data_name}"
            f"_w={test_data_width}_h={test_data_height}_t={time_size}_nb={n_batch}"
            f"_s={test_data_seed}_p={prompt_hash}"
            f"_w={test_data_img_weight}"
            f"_ms={test_data_motion_speed}"
            f"_s={strength}_g={video_guidance_scale}"
            f"_c-i={test_data_condition_images_name[:5]}_r-c={test_data_redraw_condition_image}"
            f"_w={test_data_w_ind_noise}_{test_data_video_negative_prompt_name}"
            f"_r={test_data_refer_image_name[:3]}_ip={test_data_refer_image_name[:3]}_f={test_data_refer_face_image_name[:3]}"
        )

        save_file_name = clean_str_for_save(save_file_name)
        output_path = os.path.join(
            output_dir,
            f"{save_file_name}.{save_filetype}",
        )
        if os.path.exists(output_path) and not overwrite:
            print("existed", output_path)
            continue

        print("output_path", output_path)
        out_videos = sd_predictor.run_pipe_text2video(
            video_length=time_size,
            prompt=prompt,
            width=test_data_width,
            height=test_data_height,
            generator=gpu_generator,
            noise_type=noise_type,
            negative_prompt=test_data_negative_prompt,
            video_negative_prompt=test_data_video_negative_prompt,
            max_batch_num=n_batch,
            strength=strength,
            need_img_based_video_noise=need_img_based_video_noise,
            video_num_inference_steps=video_num_inference_steps,
            condition_images=test_data_condition_images,
            fix_condition_images=fix_condition_images,
            video_guidance_scale=video_guidance_scale,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            redraw_condition_image=test_data_redraw_condition_image,
            img_weight=test_data_img_weight,
            w_ind_noise=test_data_w_ind_noise,
            n_vision_condition=n_vision_condition,
            motion_speed=test_data_motion_speed,
            need_hist_match=need_hist_match,
            video_guidance_scale_end=video_guidance_scale_end,
            video_guidance_scale_method=video_guidance_scale_method,
            vision_condition_latent_index=test_data_condition_images_index,
            refer_image=test_data_refer_image,
            fixed_refer_image=fixed_refer_image,
            redraw_condition_image_with_referencenet=redraw_condition_image_with_referencenet,
            ip_adapter_image=test_data_ipadapter_image,
            refer_face_image=test_data_refer_face_image,
            fixed_refer_face_image=fixed_refer_face_image,
            facein_scale=facein_scale,
            redraw_condition_image_with_facein=redraw_condition_image_with_facein,
            ip_adapter_face_scale=ip_adapter_face_scale,
            redraw_condition_image_with_ip_adapter_face=redraw_condition_image_with_ip_adapter_face,
            fixed_ip_adapter_image=fixed_ip_adapter_image,
            ip_adapter_scale=ip_adapter_scale,
            redraw_condition_image_with_ipdapter=redraw_condition_image_with_ipdapter,
            prompt_only_use_image_prompt=prompt_only_use_image_prompt,
            # need_redraw=need_redraw,
            # use_video_redraw=use_video_redraw,
            # serial_denoise parameter start
            record_mid_video_noises=record_mid_video_noises,
            record_mid_video_latents=record_mid_video_latents,
            video_overlap=video_overlap,
            # serial_denoise parameter end
            # parallel_denoise parameter start
            context_schedule=context_schedule,
            context_frames=context_frames,
            context_stride=context_stride,
            context_overlap=context_overlap,
            context_batch_size=context_batch_size,
            interpolation_factor=interpolation_factor,
            # parallel_denoise parameter end
        )
        out = np.concatenate([out_videos], axis=0)
        texts = ["out"]
        save_videos_grid_with_opencv(
            out,
            output_path,
            texts=texts,
            fps=fps,
            tensor_order="b c t h w",
            n_cols=n_cols,
            write_info=args.write_info,
            save_filetype=save_filetype,
            save_images=save_images,
        )
        print("Save to", output_path)
        print("\n" * 2)
        return output_path