最新阿里开源视频生成框架Tora部署

Tora是由阿里团队推出的一种基于轨迹导向的扩散变换器(Diffusion Transformer, DiT)技术的AI视频生成框架。

Tora在生成过程中可以接受多种形式的输入,包括文字描述、图片或物体移动的路线,并据此制作出既真实又流畅的视频。

通过引入轨迹控制机制,Tora能够更精确地控制视频中物体的运动模式,解决了现有模型难以生成具有精确一致运动的问题。

Tora采用两阶段训练过程,首先使用密集光流进行训练,然后使用稀疏轨迹进行微调,以提高模型对各种类型轨迹数据的适应性。

Tora模型支持长达204帧、720p分辨率的视频制作,适用于影视制作、动画创作、虚拟现实(VR)、增强现实(AR)及游戏开发等多个领域。

github项目地址:https://github.com/alibaba/Tora。

一、环境安装

1、python环境

建议安装python版本在3.10以上。

2、pip库安装

pip install torch==2.4.0+cu118 torchvision==0.19.0+cu118 torchaudio==2.4.0 --extra-index-url https://download.pytorch.org/whl/cu118

cd modules/SwissArmyTransformer

pip install -e .

cd ../../sat

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

3、CogVideoX-5b模型下载

git lfs install

git clone https://www.modelscope.cn/AI-ModelScope/CogVideoX-5b.git

4、Tora t2v模型下载

https://cloudbook-public-daily.oss-cn-hangzhou.aliyuncs.com/Tora_t2v/mp_rank_00_model_states.pt

二**、功能测试**

1、运行测试

(1)python代码调用测试

复制代码
import argparse
import gc
import json
import math
import os
import pickle
from pathlib import Path
from typing import List, Union

import cv2
import imageio
import numpy as np
import torch
import torchvision.transforms as TT
from arguments import get_args
from diffusion_video import SATVideoDiffusionEngine
from einops import rearrange, repeat
from omegaconf import ListConfig
from torchvision.io import write_video
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import resize
from torchvision.utils import flow_to_image
from tqdm import tqdm
from utils.flow_utils import process_traj
from utils.misc import vis_tensor

from sat import mpu
from sat.arguments import set_random_seed
from sat.model.base_model import get_model
from sat.training.model_io import load_checkpoint

def read_from_cli():
    cnt = 0
    try:
        while True:
            x = input("Please input English text (Ctrl-D quit): ")
            yield x.strip(), cnt
            cnt += 1
    except EOFError as e:
        pass

def read_from_file(p, rank=0, world_size=1):
    with open(p, "r") as fin:
        cnt = -1
        for l in fin:
            cnt += 1
            if cnt % world_size != rank:
                continue
            yield l.strip(), cnt

def get_unique_embedder_keys_from_conditioner(conditioner):
    return list(set([x.input_key for x in conditioner.embedders]))

def get_batch(keys, value_dict, N: Union[List, ListConfig], T=None, device="cuda"):
    batch = {}
    batch_uc = {}

    for key in keys:
        if key == "txt":
            batch["txt"] = np.repeat([value_dict["prompt"]], repeats=math.prod(N)).reshape(N).tolist()
            batch_uc["txt"] = np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N)).reshape(N).tolist()
        else:
            batch[key] = value_dict[key]

    if T is not None:
        batch["num_video_frames"] = T

    for key in batch.keys():
        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
            batch_uc[key] = torch.clone(batch[key])
    return batch, batch_uc

def draw_points(video, points):
    """
    Draw points onto video frames.

    Parameters:
        video (torch.tensor): Video tensor with shape [T, H, W, C], where T is the number of frames,
                            H is the height, W is the width, and C is the number of channels.
        points (list): Positions of points to be drawn as a tensor with shape [N, T, 2],
                            each point contains x and y coordinates.

    Returns:
        torch.tensor: The video tensor after drawing points, maintaining the same shape [T, H, W, C].
    """

    T = video.shape[0]
    N = len(points)
    device = video.device
    dtype = video.dtype
    video = video.cpu().numpy().copy()
    traj = np.zeros(video.shape[-3:], dtype=np.uint8)  # [H, W, C]
    for n in range(N):
        for t in range(1, T):
            cv2.line(traj, tuple(points[n][t - 1]), tuple(points[n][t]), (255, 1, 1), 2)
    for t in range(T):
        mask = traj[..., -1] > 0
        mask = repeat(mask, "h w -> h w c", c=3)
        alpha = 0.7
        video[t][mask] = video[t][mask] * (1 - alpha) + traj[mask] * alpha
        for n in range(N):
            cv2.circle(video[t], tuple(points[n][t]), 3, (160, 230, 100), -1)
    video = torch.from_numpy(video).to(device, dtype)
    return video

def save_video_as_grid_and_mp4(
    video_batch: torch.Tensor,
    save_path: str,
    name: str,
    fps: int = 5,
    args=None,
    key=None,
    traj_points=None,
    prompt="",
):
    os.makedirs(save_path, exist_ok=True)
    p = Path(save_path)

    for i, vid in enumerate(video_batch):
        x = rearrange(vid, "t c h w -> t h w c")
        x = x.mul(255).add(0.5).clamp(0, 255).to("cpu", torch.uint8)  # [T H W C]
        os.makedirs(p / "video", exist_ok=True)
        os.makedirs(p / "prompt", exist_ok=True)
        if traj_points is not None:
            os.makedirs(p / "traj", exist_ok=True)
            os.makedirs(p / "traj_video", exist_ok=True)
            write_video(
                p / "video" / f"{name}_{i:06d}.mp4",
                x,
                fps=fps,
                video_codec="libx264",
                options={"crf": "18"},
            )
            with open(p / "traj" / f"{name}_{i:06d}.pkl", "wb") as f:
                pickle.dump(traj_points, f)
            x = draw_points(x, traj_points)
            write_video(
                p / "traj_video" / f"{name}_{i:06d}.mp4",
                x,
                fps=fps,
                video_codec="libx264",
                options={"crf": "18"},
            )
        else:
            write_video(
                p / "video" / f"{name}_{i:06d}.mp4",
                x,
                fps=fps,
                video_codec="libx264",
                options={"crf": "18"},
            )
        with open(p / "prompt" / f"{name}_{i:06d}.txt", "w") as f:
            f.write(prompt)

def resize_for_rectangle_crop(arr, image_size, reshape_mode="random"):
    if arr.shape[3] / arr.shape[2] > image_size[1] / image_size[0]:
        arr = resize(
            arr,
            size=[image_size[0], int(arr.shape[3] * image_size[0] / arr.shape[2])],
            interpolation=InterpolationMode.BICUBIC,
        )
    else:
        arr = resize(
            arr,
            size=[int(arr.shape[2] * image_size[1] / arr.shape[3]), image_size[1]],
            interpolation=InterpolationMode.BICUBIC,
        )

    h, w = arr.shape[2], arr.shape[3]
    arr = arr.squeeze(0)

    delta_h = h - image_size[0]
    delta_w = w - image_size[1]

    if reshape_mode == "random" or reshape_mode == "none":
        top = np.random.randint(0, delta_h + 1)
        left = np.random.randint(0, delta_w + 1)
    elif reshape_mode == "center":
        top, left = delta_h // 2, delta_w // 2
    else:
        raise NotImplementedError
    arr = TT.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])
    return arr

def sampling_main(args, model_cls):
    if isinstance(model_cls, type):
        model = get_model(args, model_cls)
    else:
        model = model_cls

    load_checkpoint(model, args)
    model.eval()

    if args.input_type == "cli":
        data_iter = read_from_cli()
    elif args.input_type == "txt":
        rank, world_size = mpu.get_data_parallel_rank(), mpu.get_data_parallel_world_size()
        print("rank and world_size", rank, world_size)
        data_iter = read_from_file(args.input_file, rank=rank, world_size=world_size)
    else:
        raise NotImplementedError

    image_size = [480, 720]

    sample_func = model.sample
    T, H, W, C, F = args.sampling_num_frames, image_size[0], image_size[1], args.latent_channels, 8
    num_samples = [1]
    force_uc_zero_embeddings = ["txt"]
    device = model.device
    with torch.no_grad():
        for text, cnt in tqdm(data_iter):
            set_random_seed(args.seed)
            if args.flow_from_prompt:
                text, flow_files = text.split("\t")
            total_num_frames = (T - 1) * 4 + 1  # T is the video latent size, 13 * 4 = 52
            if args.no_flow_injection:
                video_flow = None
            elif args.flow_from_prompt:
                assert args.flow_path is not None, "Flow path must be provided if flow_from_prompt is True"
                p = os.path.join(args.flow_path, flow_files)
                print(f"Flow path: {p}")
                video_flow = (
                    torch.load(p, map_location="cpu", weights_only=True)[:total_num_frames].unsqueeze_(0).cuda()
                )
            elif args.flow_path:
                print(f"Flow path: {args.flow_path}")
                video_flow = torch.load(args.flow_path, map_location=device, weights_only=True)[
                    :total_num_frames
                ].unsqueeze_(0)
            elif args.point_path:
                if type(args.point_path) == str:
                    args.point_path = json.loads(args.point_path)
                print(f"Point path: {args.point_path}")
                video_flow, points = process_traj(args.point_path, total_num_frames, image_size, device=device)
                video_flow = video_flow.unsqueeze_(0)
            else:
                print("No flow injection")
                video_flow = None

            if video_flow is not None:
                model.to("cpu")  # move model to cpu, run vae on gpu only.
                tmp = rearrange(video_flow[0], "T H W C -> T C H W")
                video_flow = flow_to_image(tmp).unsqueeze_(0).to("cuda")  # [1 T C H W]
                if args.vis_traj_features:
                    os.makedirs("samples/flow", exist_ok=True)
                    vis_tensor(tmp, *tmp.shape[-2:], "samples/flow/flow1_vis.gif")
                    imageio.mimwrite(
                        "samples/flow/flow2_vis.gif",
                        rearrange(video_flow[0], "T C H W -> T H W C").cpu(),
                        fps=8,
                        loop=0,
                    )
                del tmp
                video_flow = (
                    rearrange(video_flow / 255.0 * 2 - 1, "B T C H W -> B C T H W").contiguous().to(torch.bfloat16)
                )
                torch.cuda.empty_cache()
                video_flow = video_flow.repeat(2, 1, 1, 1, 1).contiguous()  # for uncondition
                model.first_stage_model.to(device)
                video_flow = model.encode_first_stage(video_flow, None)
                video_flow = video_flow.permute(0, 2, 1, 3, 4).contiguous()
                model.to(device)

            print("rank:", rank, "start to process", text, cnt)
            # TODO: broadcast image2video
            value_dict = {
                "prompt": text,
                "negative_prompt": "",
                "num_frames": torch.tensor(T).unsqueeze(0),
            }

            batch, batch_uc = get_batch(
                get_unique_embedder_keys_from_conditioner(model.conditioner), value_dict, num_samples
            )
            for key in batch:
                if isinstance(batch[key], torch.Tensor):
                    print(key, batch[key].shape)
                elif isinstance(batch[key], list):
                    print(key, [len(l) for l in batch[key]])
                else:
                    print(key, batch[key])
            c, uc = model.conditioner.get_unconditional_conditioning(
                batch,
                batch_uc=batch_uc,
                force_uc_zero_embeddings=force_uc_zero_embeddings,
            )

            for k in c:
                if not k == "crossattn":
                    c[k], uc[k] = map(lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc))
            for index in range(args.batch_size):
                # reload model on GPU
                model.to(device)
                samples_z = sample_func(
                    c,
                    uc=uc,
                    batch_size=1,
                    shape=(T, C, H // F, W // F),
                    video_flow=video_flow,
                )
                samples_z = samples_z.permute(0, 2, 1, 3, 4).contiguous()

                # Unload the model from GPU to save GPU memory
                model.to("cpu")
                torch.cuda.empty_cache()
                first_stage_model = model.first_stage_model
                first_stage_model = first_stage_model.to(device)

                latent = 1.0 / model.scale_factor * samples_z

                # Decode latent serial to save GPU memory
                recons = []
                loop_num = (T - 1) // 2
                for i in range(loop_num):
                    if i == 0:
                        start_frame, end_frame = 0, 3
                    else:
                        start_frame, end_frame = i * 2 + 1, i * 2 + 3
                    if i == loop_num - 1:
                        clear_fake_cp_cache = True
                    else:
                        clear_fake_cp_cache = False
                    with torch.no_grad():
                        recon = first_stage_model.decode(
                            latent[:, :, start_frame:end_frame].contiguous(), clear_fake_cp_cache=clear_fake_cp_cache
                        )

                    recons.append(recon)

                recon = torch.cat(recons, dim=2).to(torch.float32)
                samples_x = recon.permute(0, 2, 1, 3, 4).contiguous()
                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0).cpu()

                save_path = args.output_dir
                name = str(cnt) + "_" + text.replace(" ", "_").replace("/", "")[:60] + f"_{index}_seed{args.seed}"
                if args.flow_from_prompt:
                    name = Path(flow_files).stem
                if mpu.get_model_parallel_rank() == 0:
                    save_video_as_grid_and_mp4(
                        samples,
                        save_path,
                        name,
                        fps=args.sampling_fps,
                        traj_points=locals().get("points", None),
                        prompt=text,
                    )
            del samples_z, samples_x, samples, video_flow, latent, recon, recons, c, uc, batch, batch_uc
            gc.collect()

if __name__ == "__main__":
    if "OMPI_COMM_WORLD_LOCAL_RANK" in os.environ:
        os.environ["LOCAL_RANK"] = os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]
        os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
        os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
    py_parser = argparse.ArgumentParser(add_help=False)
    known, args_list = py_parser.parse_known_args()

    args = get_args(args_list)
    args = argparse.Namespace(**vars(args), **vars(known))
    del args.deepspeed_config
    args.model_config.first_stage_config.params.cp_size = 1
    args.model_config.network_config.params.transformer_args.model_parallel_size = 1
    args.model_config.network_config.params.transformer_args.checkpoint_activations = False
    args.model_config.loss_fn_config.params.sigma_sampler_config.params.uniform_sampling = False
    args.model_config.en_and_decode_n_samples_a_time = 1

    sampling_main(args, model_cls=SATVideoDiffusionEngine)

未完......

更多详细的欢迎关注:杰哥新技术

相关推荐
胡耀超36 分钟前
标签体系设计与管理:从理论基础到智能化实践的综合指南
人工智能·python·深度学习·数据挖掘·大模型·用户画像·语义分析
开-悟40 分钟前
嵌入式编程-使用AI查找BUG的启发
c语言·人工智能·嵌入式硬件·bug
Ailerx41 分钟前
YOLOv13震撼发布:超图增强引领目标检测新纪元
人工智能·yolo·目标检测
大咖分享课1 小时前
开源模型与商用模型协同开发机制设计
人工智能·开源·ai模型
你不知道我是谁?1 小时前
AI 应用于进攻性安全
人工智能·安全
reddingtons2 小时前
Adobe高阶技巧与设计师创意思维的进阶指南
人工智能·adobe·illustrator·设计师·photoshop·创意设计·aftereffects
机器之心2 小时前
刚刚,Grok4跑分曝光:「人类最后考试」拿下45%,是Gemini 2.5两倍,但网友不信
人工智能
蹦蹦跳跳真可爱5892 小时前
Python----大模型(使用api接口调用大模型)
人工智能·python·microsoft·语言模型
小爷毛毛_卓寿杰2 小时前
突破政务文档理解瓶颈:基于多模态大模型的智能解析系统详解
人工智能·llm
Mr.Winter`2 小时前
障碍感知 | 基于3D激光雷达的三维膨胀栅格地图构建(附ROS C++仿真)
人工智能·机器人·自动驾驶·ros·具身智能·环境感知