第一次完整测例跑完

scripts/evaluation/base_model_inference.py

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+import argparse, os, glob
+import datetime, time
+import pandas as pd
+import torch
+import torchvision
+import torchvision.transforms as transforms
+import random
+
+from pytorch_lightning import seed_everything
+from PIL import Image
+from omegaconf import OmegaConf
+from tqdm import tqdm
+from einops import rearrange, repeat
+from collections import OrderedDict
+
+from unifolm_wma.models.samplers.ddim import DDIMSampler
+from unifolm_wma.utils.utils import instantiate_from_config
+
+
+def get_filelist(data_dir: str, postfixes: list[str]) -> list[str]:
+    """
+    Get list of files in `data_dir` with extensions in `postfixes`.
+
+    Args:
+        data_dir (str): Directory path.
+        postfixes (list[str]): List of file extensions (e.g., ['csv', 'jpg']).
+
+    Returns:
+        list[str]: Sorted list of matched file paths.
+    """
+    patterns = [
+        os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes
+    ]
+    file_list = []
+    for pattern in patterns:
+        file_list.extend(glob.glob(pattern))
+    file_list.sort()
+    return file_list
+
+
+def load_model_checkpoint(model: torch.nn.Module,
+                          ckpt: str) -> torch.nn.Module:
+    """
+    Load model weights from checkpoint file.
+
+    Args:
+        model (torch.nn.Module): The model to load weights into.
+        ckpt (str): Path to the checkpoint file.
+
+    Returns:
+        torch.nn.Module: Model with weights loaded.
+    """
+    state_dict = torch.load(ckpt, map_location="cpu")
+    if "state_dict" in list(state_dict.keys()):
+        state_dict = state_dict["state_dict"]
+        try:
+            loaded = model.load_state_dict(state_dict, strict=False)
+            print("Missing keys:")
+            for k in loaded.missing_keys:
+                print(f"  {k}")
+            print("Unexpected keys:")
+            for k in loaded.unexpected_keys:
+                print(f"  {k}")
+
+        except:
+            # Rename the keys for 256x256 model
+            new_pl_sd = OrderedDict()
+            for k, v in state_dict.items():
+                new_pl_sd[k] = v
+
+            for k in list(new_pl_sd.keys()):
+                if "framestride_embed" in k:
+                    new_key = k.replace("framestride_embed", "fps_embedding")
+                    new_pl_sd[new_key] = new_pl_sd[k]
+                    del new_pl_sd[k]
+            model.load_state_dict(new_pl_sd, strict=False)
+    else:
+        new_pl_sd = OrderedDict()
+        for key in state_dict['module'].keys():
+            new_pl_sd[key[16:]] = state_dict['module'][key]
+        model.load_state_dict(new_pl_sd)
+    print('>>> model checkpoint loaded.')
+    return model
+
+
+def load_prompts(prompt_file: str) -> list[str]:
+    """
+    Load prompts from a text file, one per line.
+
+    Args:
+        prompt_file (str): Path to the prompt file.
+
+    Returns:
+        list[str]: List of prompt strings.
+    """
+    f = open(prompt_file, 'r')
+    prompt_list = []
+    for idx, line in enumerate(f.readlines()):
+        l = line.strip()
+        if len(l) != 0:
+            prompt_list.append(l)
+        f.close()
+    return prompt_list
+
+
+def load_data_prompts(
+    data_dir: str,
+    savedir: str,
+    video_size: tuple[int, int] = (256, 256),
+    video_frames: int = 16
+) -> tuple[list[str], list[torch.Tensor], list[str], list[float], list[float],
+           list[int]]:
+    """
+    Load image prompts, process them into video format, and retrieve metadata.
+
+    Args:
+        data_dir (str): Directory containing images and CSV file.
+        savedir (str): Output directory to check if inference was already done.
+        video_size (tuple[int, int], optional): Target size of video frames.
+        video_frames (int, optional): Number of frames in each video.
+
+    Returns:
+        tuple: (filenames, video tensors, prompts, fps values, fs values, num_generations)
+    """
+
+    transform = transforms.Compose([
+        transforms.Resize(min(video_size)),
+        transforms.CenterCrop(video_size),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+    ])
+
+    # Load prompt csv
+    prompt_file = get_filelist(data_dir, ['csv'])
+    assert len(prompt_file) > 0, "Error: found NO image prompt file!"
+
+    # Load image prompts
+    file_list = get_filelist(data_dir, ['jpg', 'png', 'jpeg', 'JPEG', 'PNG'])
+    data_list = []
+    filename_list = []
+    prompt_list = []
+    fps_list = []
+    fs_list = []
+    num_gen_list = []
+    prompt_csv = pd.read_csv(prompt_file[0])
+    n_samples = len(file_list)
+
+    for idx in range(n_samples):
+        image = Image.open(file_list[idx]).convert('RGB')
+        image_tensor = transform(image).unsqueeze(1)
+        frame_tensor = repeat(image_tensor,
+                              'c t h w -> c (repeat t) h w',
+                              repeat=video_frames)
+        _, filename = os.path.split(file_list[idx])
+
+        if not is_inferenced(savedir, filename):
+            video_id = filename[:-4]
+            prompt_csv['videoid'] = prompt_csv['videoid'].map(str)
+            if not (prompt_csv['videoid'] == video_id).any():
+                continue
+            data_list.append(frame_tensor)
+            filename_list.append(filename)
+            ins = prompt_csv[prompt_csv['videoid'] ==
+                             video_id]['instruction'].values[0]
+            prompt_list.append(ins)
+            fps = prompt_csv[prompt_csv['videoid'] ==
+                             video_id]['fps'].values[0]
+            fps_list.append(fps)
+            fs = prompt_csv[prompt_csv['videoid'] == video_id]['fs'].values[0]
+            fs_list.append(fs)
+            num_gen = prompt_csv[prompt_csv['videoid'] ==
+                                 video_id]['num_gen'].values[0]
+            num_gen_list.append(int(num_gen))
+
+    return filename_list, data_list, prompt_list, fps_list, fs_list, num_gen_list
+
+
+def is_inferenced(save_dir: str, filename: str) -> bool:
+    """
+    Check if a result video already exists.
+
+    Args:
+        save_dir (str): Directory where results are saved.
+        filename (str): Base filename to check.
+
+    Returns:
+        bool: True if file exists, else False.
+    """
+    video_file = os.path.join(save_dir, f"{filename[:-4]}.mp4")
+    return os.path.exists(video_file)
+
+
+def save_results_seperate(prompt: str | list[str],
+                          samples: torch.Tensor,
+                          filename: str,
+                          fakedir: str,
+                          fps: int = 8) -> None:
+    """
+    Save generated video samples as .mp4 files.
+
+    Args:
+        prompt (str | list[str]): The prompt text.
+        samples (torch.Tensor): Generated video tensor of shape [B, C, T, H, W].
+        filename (str): Output filename.
+        fakedir (str): Directory to save output videos.
+        fps (int, optional): Frames per second.
+
+    Returns:
+        None
+    """
+    prompt = prompt[0] if isinstance(prompt, list) else prompt
+
+    # Save video
+    videos = [samples]
+    savedirs = [fakedir]
+    for idx, video in enumerate(videos):
+        if video is None:
+            continue
+        video = video.detach().cpu()
+        video = torch.clamp(video.float(), -1., 1.)
+        n = video.shape[0]
+        for i in range(n):
+            grid = video[i, ...]
+            grid = (grid + 1.0) / 2.0
+            grid = (grid * 255).to(torch.uint8).permute(1, 2, 3, 0)
+            path = os.path.join(savedirs[idx], f'{filename.split(".")[0]}.mp4')
+            torchvision.io.write_video(path,
+                                       grid,
+                                       fps=fps,
+                                       video_codec='h264',
+                                       options={'crf': '0'})
+
+
+def get_latent_z(model: torch.nn.Module, videos: torch.Tensor) -> torch.Tensor:
+    """
+    Encode videos to latent space.
+
+    Args:
+        model (torch.nn.Module): Model with encode_first_stage function.
+        videos (torch.Tensor): Video tensor of shape [B, C, T, H, W].
+
+    Returns:
+        torch.Tensor: Latent representation of shape [B, C, T, H, W].
+    """
+    b, c, t, h, w = videos.shape
+    x = rearrange(videos, 'b c t h w -> (b t) c h w')
+    z = model.encode_first_stage(x)
+    z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
+    return z
+
+
+def image_guided_synthesis(model: torch.nn.Module,
+                           prompts: list[str],
+                           videos: torch.Tensor,
+                           noise_shape: list[int],
+                           ddim_steps: int = 50,
+                           ddim_eta: float = 1.0,
+                           unconditional_guidance_scale: float = 1.0,
+                           fs: int | None = None,
+                           text_input: bool = False,
+                           timestep_spacing: str = 'uniform',
+                           guidance_rescale: float = 0.0,
+                           **kwargs) -> torch.Tensor:
+    """
+    Run DDIM-based image-to-video synthesis with hybrid/text+image guidance.
+
+    Args:
+        model (torch.nn.Module): Diffusion model.
+        prompts (list[str]): Text prompts.
+        videos (torch.Tensor): Input images/videos of shape [B, C, T, H, W].
+        noise_shape (list[int]): Latent noise shape [B, C, T, H, W].
+        ddim_steps (int, optional): Number of DDIM steps.
+        ddim_eta (float, optional): Eta value for DDIM.
+        unconditional_guidance_scale (float, optional): Guidance scale.
+        fs (int | None, optional): FPS input for sampler.
+        text_input (bool, optional): If True, use text guidance.
+        timestep_spacing (str, optional): Timestep schedule spacing.
+        guidance_rescale (float, optional): Rescale guidance effect.
+        **kwargs: Additional sampler args.
+
+    Returns:
+        torch.Tensor: Synthesized videos of shape [B, 1, C, T, H, W].
+    """
+
+    ddim_sampler = DDIMSampler(model)
+    batch_size = noise_shape[0]
+    fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
+
+    if not text_input:
+        prompts = [""] * batch_size
+
+    b, c, t, h, w = videos.shape
+    img = videos[:, :, 0]
+    img_emb = model.embedder(img)
+    img_emb = model.image_proj_model(img_emb)
+    img_emb = rearrange(img_emb, 'b (t l) c -> (b t) l c', t=t)
+    cond_emb = model.get_learned_conditioning(prompts)
+    cond_emb = cond_emb.repeat_interleave(repeats=t, dim=0)
+
+    cond = {"c_crossattn": [torch.cat([cond_emb, img_emb], dim=1)]}
+    if model.model.conditioning_key == 'hybrid':
+        z = get_latent_z(model, videos)
+        img_cat_cond = z[:, :, :1, :, :]
+        img_cat_cond = repeat(img_cat_cond,
+                              'b c t h w -> b c (repeat t) h w',
+                              repeat=z.shape[2])
+        cond["c_concat"] = [img_cat_cond]
+
+    uc = None
+    cond_mask = None
+    kwargs.update({"unconditional_conditioning_img_nonetext": None})
+
+    batch_variants = []
+    if ddim_sampler is not None:
+        samples, _, _, _ = ddim_sampler.sample(
+            S=ddim_steps,
+            batch_size=batch_size,
+            shape=noise_shape[1:],
+            conditioning=cond,
+            eta=ddim_eta,
+            mask=cond_mask,
+            x0=None,
+            verbose=False,
+            unconditional_guidance_scale=unconditional_guidance_scale,
+            unconditional_conditioning=uc,
+            fs=fs,
+            timestep_spacing=timestep_spacing,
+            guidance_rescale=guidance_rescale,
+            **kwargs)
+
+        # Reconstruct from latent to pixel space
+        batch_images = model.decode_first_stage(samples)
+        batch_variants.append(batch_images)
+
+    batch_variants = torch.stack(batch_variants)
+    return batch_variants.permute(1, 0, 2, 3, 4, 5)
+
+
+def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
+    """
+    Run inference pipeline on prompts and image inputs.
+
+    Args:
+        args (argparse.Namespace): Parsed command-line arguments.
+        gpu_num (int): Number of GPUs.
+        gpu_no (int): Index of the current GPU.
+
+    Returns:
+        None
+    """
+    # Load config
+    config = OmegaConf.load(args.config)
+    # Set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set"
+    config['model']['params']['wma_config']['params'][
+        'use_checkpoint'] = False
+    model = instantiate_from_config(config.model)
+    model = model.cuda(gpu_no)
+    model.perframe_ae = args.perframe_ae
+    assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
+    model = load_model_checkpoint(model, args.ckpt_path)
+    model.eval()
+
+    # Run over data
+    assert (args.height % 16 == 0) and (
+        args.width % 16
+        == 0), "Error: image size [h,w] should be multiples of 16!"
+    assert args.bs == 1, "Current implementation only support [batch size = 1]!"
+
+    # Get latent noise shape
+    h, w = args.height // 8, args.width // 8
+    channels = model.model.diffusion_model.out_channels
+    n_frames = args.video_length
+    print(f'>>> Generate {n_frames} frames under each generation ...')
+    noise_shape = [args.bs, channels, n_frames, h, w]
+
+    fakedir = os.path.join(args.savedir, "samples")
+    os.makedirs(fakedir, exist_ok=True)
+
+    # Prompt file setting
+    assert os.path.exists(args.prompt_dir), "Error: prompt file Not Found!"
+    filename_list, data_list, prompt_list, fps_list, fs_list, num_gen_list = load_data_prompts(
+        args.prompt_dir,
+        args.savedir,
+        video_size=(args.height, args.width),
+        video_frames=n_frames)
+
+    num_samples = len(prompt_list)
+    samples_split = num_samples // gpu_num
+    print('>>> Prompts testing [rank:%d] %d/%d samples loaded.' %
+          (gpu_no, samples_split, num_samples))
+
+    indices = list(range(samples_split * gpu_no, samples_split * (gpu_no + 1)))
+    fps_list_rank = [fps_list[i] for i in indices]
+    fs_list_rank = [fs_list[i] for i in indices]
+    prompt_list_rank = [prompt_list[i] for i in indices]
+    data_list_rank = [data_list[i] for i in indices]
+    filename_list_rank = [filename_list[i] for i in indices]
+
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        # Create a new result csv
+        for idx, indice in enumerate(
+                tqdm(range(0, len(prompt_list_rank), args.bs),
+                     desc=f'Sample batch')):
+            fps = fps_list_rank[indice:indice + args.bs]
+            fs = fs_list_rank[indice:indice + args.bs]
+            prompts = prompt_list_rank[indice:indice + args.bs]
+            num_gen = num_gen_list[indice:indice + args.bs]
+            videos = data_list_rank[indice:indice + args.bs]
+            filenames = filename_list_rank[indice:indice + args.bs]
+            if isinstance(videos, list):
+                videos = torch.stack(videos, dim=0).to("cuda")
+            else:
+                videos = videos.unsqueeze(0).to("cuda")
+
+            results = []
+            print(
+                f">>> {prompts[0]}, frame_stride:{fs[0]}, and {num_gen[0]} generation ..."
+            )
+            for _ in range(num_gen[0]):
+                batch_samples = image_guided_synthesis(
+                    model, prompts, videos, noise_shape, args.ddim_steps,
+                    args.ddim_eta, args.unconditional_guidance_scale,
+                    fps[0] // fs[0], args.text_input, args.timestep_spacing,
+                    args.guidance_rescale)
+                results.extend(batch_samples)
+                videos = repeat(batch_samples[0][:, :, -1, :, :].unsqueeze(2),
+                                'b c t h w -> b c (repeat t) h w',
+                                repeat=batch_samples[0].shape[2])
+            batch_samples = [torch.concat(results, axis=2)]
+
+            # Save each example individually
+            for nn, samples in enumerate(batch_samples):
+                prompt = prompts[nn]
+                filename = filenames[nn]
+                save_results_seperate(prompt,
+                                      samples,
+                                      filename,
+                                      fakedir,
+                                      fps=8)
+
+
+def get_parser() -> argparse.ArgumentParser:
+    """
+    Create and return the argument parser.
+
+    Returns:
+        argparse.ArgumentParser: Parser for command-line arguments.
+    """
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--savedir",
+                        type=str,
+                        default=None,
+                        help="Path to save the results.")
+    parser.add_argument("--ckpt_path",
+                        type=str,
+                        default=None,
+                        help="Path to the model checkpoint.")
+    parser.add_argument("--config",
+                        type=str,
+                        help="Path to the YAML configuration file.")
+    parser.add_argument(
+        "--prompt_dir",
+        type=str,
+        default=None,
+        help="Directory containing videos and corresponding prompts.")
+    parser.add_argument(
+        "--ddim_steps",
+        type=int,
+        default=50,
+        help="Number of DDIM steps. If non-positive, DDPM is used instead.")
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=1.0,
+        help="Eta for DDIM sampling. Set to 0.0 for deterministic results.")
+    parser.add_argument("--bs",
+                        type=int,
+                        default=1,
+                        help="Batch size for inference. Must be 1.")
+    parser.add_argument("--height",
+                        type=int,
+                        default=320,
+                        help="Height of the generated images in pixels.")
+    parser.add_argument("--width",
+                        type=int,
+                        default=512,
+                        help="Width of the generated images in pixels.")
+    parser.add_argument(
+        "--unconditional_guidance_scale",
+        type=float,
+        default=1.0,
+        help="Scale for classifier-free guidance during sampling.")
+    parser.add_argument("--seed",
+                        type=int,
+                        default=123,
+                        help="Random seed for reproducibility.")
+    parser.add_argument("--video_length",
+                        type=int,
+                        default=16,
+                        help="Number of frames in the generated video.")
+    parser.add_argument(
+        "--text_input",
+        action='store_true',
+        default=False,
+        help=
+        "Whether to provide a text prompt as input to the image-to-video model."
+    )
+    parser.add_argument(
+        "--timestep_spacing",
+        type=str,
+        default="uniform",
+        help=
+        "Strategy for timestep scaling. See Table 2 in the paper: 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
+    )
+    parser.add_argument(
+        "--guidance_rescale",
+        type=float,
+        default=0.0,
+        help=
+        "Rescale factor for guidance as discussed in 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
+    )
+    parser.add_argument(
+        "--perframe_ae",
+        action='store_true',
+        default=False,
+        help=
+        "Use per-frame autoencoder decoding to reduce GPU memory usage. Recommended for models with resolutions like 576x1024."
+    )
+    return parser
+
+
+if __name__ == '__main__':
+    parser = get_parser()
+    args = parser.parse_args()
+    seed = args.seed
+    if seed < 0:
+        seed = random.randint(0, 2**31)
+    seed_everything(seed)
+    rank, gpu_num = 0, 1
+    run_inference(args, gpu_num, rank)

scripts/evaluation/eval_utils.py

@@ -0,0 +1,77 @@
+import torch
+import warnings
+import torchvision
+import sys
+import pyarrow as pa
+import logging
+
+from dataclasses import dataclass, field
+from typing import Dict, Any, ClassVar, Deque, Mapping, Union
+from datasets.features.features import register_feature
+from torch.utils.tensorboard.writer import SummaryWriter
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+
+
+@dataclass
+class VideoFrame:
+    """
+    Provides a type for a dataset containing video frames.
+
+    Example:
+
+    ```python
+    data_dict = [{"image": {"path": "videos/episode_0.mp4", "timestamp": 0.3}}]
+    features = {"image": VideoFrame()}
+    Dataset.from_dict(data_dict, features=Features(features))
+    ```
+    """
+
+    pa_type: ClassVar[Any] = pa.struct({
+        "path": pa.string(),
+        "timestamp": pa.float32()
+    })
+    _type: str = field(default="VideoFrame", init=False, repr=False)
+
+    def __call__(self):
+        return self.pa_type
+
+
+with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        "'register_feature' is experimental and might be subject to breaking changes in the future.",
+        category=UserWarning,
+    )
+    register_feature(VideoFrame, "VideoFrame")
+
+
+def populate_queues(
+        queues: Dict[str, Deque[Any]],
+        batch: Mapping[str, Any]) -> Dict[str, Deque[Any]]:
+
+    for key in batch:
+        if key not in queues:
+            continue
+        if len(queues[key]) != queues[key].maxlen:
+            while len(queues[key]) != queues[key].maxlen:
+                queues[key].append(batch[key])
+        else:
+            queues[key].append(batch[key])
+    return queues
+
+
+def log_to_tensorboard(
+        writer: SummaryWriter,
+        data: Union[torch.Tensor, Any],
+        tag: str,
+        fps: int = 10) -> None:
+    if isinstance(data, torch.Tensor) and data.dim() == 5:
+        video = data
+        n = video.shape[0]
+        video = video.permute(2, 0, 1, 3, 4)
+        frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video]
+        grid = torch.stack(frame_grids, dim=0)
+        grid = (grid + 1.0) / 2.0
+        grid = grid.unsqueeze(dim=0)
+        writer.add_video(tag, grid, fps=fps)

scripts/evaluation/real_eval_server.py

@@ -0,0 +1,463 @@
+import argparse, os, sys
+import torch
+import torchvision
+import warnings
+import imageio
+import logging
+import matplotlib.pyplot as plt
+plt.switch_backend('agg')
+import traceback
+import uvicorn
+
+from omegaconf import OmegaConf
+from einops import rearrange, repeat
+from collections import OrderedDict
+from pytorch_lightning import seed_everything
+from torch import nn
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from typing import Any, Dict, Optional, Tuple, List
+from datetime import datetime
+
+from unifolm_wma.utils.utils import instantiate_from_config
+from unifolm_wma.models.samplers.ddim import DDIMSampler
+
+
+def get_device_from_parameters(module: nn.Module) -> torch.device:
+    """Get a module's device by checking one of its parameters.
+
+    Args:
+        module (nn.Module): PyTorch module.
+
+    Returns:
+        torch.device: The device where the module's parameters are stored.
+    """
+    return next(iter(module.parameters())).device
+
+
+def load_model_checkpoint(model: nn.Module, ckpt: str) -> nn.Module:
+    """Load model weights from checkpoint file.
+
+    Args:
+        model (nn.Module): Model to load weights into.
+        ckpt (str): Path to checkpoint file.
+
+    Returns:
+        nn.Module: Model with loaded weights.
+    """
+
+    state_dict = torch.load(ckpt, map_location="cpu")
+    if "state_dict" in list(state_dict.keys()):
+        state_dict = state_dict["state_dict"]
+        try:
+            model.load_state_dict(state_dict, strict=False)
+        except:
+            new_pl_sd = OrderedDict()
+            for k, v in state_dict.items():
+                new_pl_sd[k] = v
+
+            for k in list(new_pl_sd.keys()):
+                if "framestride_embed" in k:
+                    new_key = k.replace("framestride_embed", "fps_embedding")
+                    new_pl_sd[new_key] = new_pl_sd[k]
+                    del new_pl_sd[k]
+            model.load_state_dict(new_pl_sd, strict=False)
+    else:
+        new_pl_sd = OrderedDict()
+        for key in state_dict['module'].keys():
+            new_pl_sd[key[16:]] = state_dict['module'][key]
+        model.load_state_dict(new_pl_sd)
+    print('>>> model checkpoint loaded.')
+    return model
+
+
+def write_video(video_path: str, stacked_frames: List[Any], fps: int) -> None:
+    """Write a video to disk using imageio.
+
+    Args:
+        video_path (str): Path to save the video.
+        stacked_frames (List[Any]): Frames to write.
+        fps (int): Frames per second.
+    """
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore",
+                                "pkg_resources is deprecated as an API",
+                                category=DeprecationWarning)
+        imageio.mimsave(video_path, stacked_frames, fps=fps)
+
+
+def save_results(video: torch.Tensor, filename: str, fps: int = 8) -> None:
+    """Save a video tensor as an MP4 file.
+
+    Args:
+        video (torch.Tensor): Video tensor of shape (B, C, T, H, W).
+        filename (str): Path to save video.
+        fps (int, optional): Frame rate. Defaults to 8.
+
+    """
+    video = video.detach().cpu()
+    video = torch.clamp(video.float(), -1., 1.)
+    n = video.shape[0]
+    video = video.permute(2, 0, 1, 3, 4)
+
+    frame_grids = [
+        torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0)
+        for framesheet in video
+    ]
+    grid = torch.stack(frame_grids, dim=0)
+    grid = (grid + 1.0) / 2.0
+    grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
+    torchvision.io.write_video(filename,
+                               grid,
+                               fps=fps,
+                               video_codec='h264',
+                               options={'crf': '10'})
+
+
+def get_latent_z(model: nn.Module, videos: torch.Tensor) -> torch.Tensor:
+    """Encode videos into latent space.
+
+    Args:
+        model (nn.Module): Model with `encode_first_stage` method.
+        videos (torch.Tensor): Input videos (B, C, T, H, W).
+
+    Returns:
+        torch.Tensor: Latent representation (B, C, T, H, W).
+
+    """
+    b, c, t, h, w = videos.shape
+    x = rearrange(videos, 'b c t h w -> (b t) c h w')
+    z = model.encode_first_stage(x)
+    z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
+    return z
+
+
+def image_guided_synthesis(
+        model: torch.nn.Module,
+        prompts: list[str],
+        observation: Dict[str, torch.Tensor],
+        noise_shape: tuple[int, int, int, int, int],
+        ddim_steps: int = 50,
+        ddim_eta: float = 1.0,
+        unconditional_guidance_scale: float = 1.0,
+        fs: int | None = None,
+        timestep_spacing: str = 'uniform',
+        guidance_rescale: float = 0.0,
+        **kwargs) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Run inference with DDIM sampling.
+
+    Args:
+        model (nn.Module): Diffusion model.
+        prompts (Any): Conditioning text prompts.
+        observation (Dict[str, torch.Tensor]): Observation dictionary.
+        noise_shape (List[int]): Shape of noise tensor.
+        ddim_steps (int, optional): Number of DDIM steps. Defaults to 50.
+        ddim_eta (float, optional): Sampling eta. Defaults to 1.0.
+        unconditional_guidance_scale (float, optional): Guidance scale. Defaults to 1.0.
+        fs (Optional[int], optional): Frame stride or FPS. Defaults to None.
+        timestep_spacing (str, optional): Spacing strategy. Defaults to "uniform".
+        guidance_rescale (float, optional): Guidance rescale. Defaults to 0.0.
+        **kwargs (Any): Additional arguments.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+
+    b, _, t, _, _ = noise_shape
+    ddim_sampler = DDIMSampler(model)
+    batch_size = noise_shape[0]
+    fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
+
+    img = observation['observation.images.top']
+    cond_img = img[:, -1, ...]
+    cond_img_emb = model.embedder(cond_img)
+    cond_img_emb = model.image_proj_model(cond_img_emb)
+
+    if model.model.conditioning_key == 'hybrid':
+        z = get_latent_z(model, img.permute(0, 2, 1, 3, 4))
+        img_cat_cond = z[:, :, -1:, :, :]
+        img_cat_cond = repeat(img_cat_cond,
+                              'b c t h w -> b c (repeat t) h w',
+                              repeat=noise_shape[2])
+        cond = {"c_concat": [img_cat_cond]}
+
+    cond_ins_emb = model.get_learned_conditioning(prompts)
+    cond_state = model.state_projector(observation['observation.state'])
+    cond_state_emb = model.agent_state_pos_emb + cond_state
+
+    cond_action = model.action_projector(observation['action'])
+    cond_action_emb = model.agent_action_pos_emb + cond_action
+    cond_action_emb = torch.zeros_like(cond_action_emb)
+
+    cond["c_crossattn"] = [
+        torch.cat([cond_state_emb, cond_ins_emb, cond_img_emb], dim=1)
+    ]
+    cond["c_crossattn_action"] = [
+        observation['observation.images.top'].permute(
+            0, 2, 1, 3, 4)[:, :, -model.n_obs_steps_acting:],
+        observation['observation.state'][:, -model.n_obs_steps_acting:]
+    ]
+
+    uc = None
+
+    kwargs.update({"unconditional_conditioning_img_nonetext": None})
+
+    cond_mask = None
+    cond_z0 = None
+
+    if ddim_sampler is not None:
+
+        samples, actions, states, intermedia = ddim_sampler.sample(
+            S=ddim_steps,
+            conditioning=cond,
+            batch_size=batch_size,
+            shape=noise_shape[1:],
+            verbose=False,
+            unconditional_guidance_scale=unconditional_guidance_scale,
+            unconditional_conditioning=uc,
+            eta=ddim_eta,
+            cfg_img=None,
+            mask=cond_mask,
+            x0=cond_z0,
+            fs=fs,
+            timestep_spacing=timestep_spacing,
+            guidance_rescale=guidance_rescale,
+            **kwargs)
+
+        # Reconstruct from latent to pixel space
+        batch_images = model.decode_first_stage(samples)
+        batch_variants = batch_images
+
+    return batch_variants, actions, states
+
+
+def run_inference(args: argparse.Namespace, gpu_num: int,
+                  gpu_no: int) -> Tuple[nn.Module, List[int], Any]:
+    """
+    Run inference pipeline on prompts and image inputs.
+
+    Args:
+        args (argparse.Namespace): Parsed command-line arguments.
+        gpu_num (int): Number of GPUs.
+        gpu_no (int): Index of the current GPU.
+
+    Returns:
+        None
+    """
+    # Load config
+    config = OmegaConf.load(args.config)
+    # Set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set"
+    config['model']['params']['wma_config']['params']['use_checkpoint'] = False
+    model = instantiate_from_config(config.model)
+    model.perframe_ae = args.perframe_ae
+    assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
+    model = load_model_checkpoint(model, args.ckpt_path)
+    model = model.cuda(gpu_no)
+    model.eval()
+    print(">>> Model is successfully loaded ...")
+
+    # Build unnomalizer
+    logging.info("***** Configing Data *****")
+    data = instantiate_from_config(config.data)
+    data.setup()
+    print(">>> Dataset is successfully loaded ...")
+
+    ## Run over data
+    assert (args.height % 16 == 0) and (
+        args.width % 16
+        == 0), "Error: image size [h,w] should be multiples of 16!"
+    assert args.bs == 1, "Current implementation only support [batch size = 1]!"
+
+    ## Get latent noise shape
+    h, w = args.height // 8, args.width // 8
+    channels = model.model.diffusion_model.out_channels
+    n_frames = args.video_length
+    print(f'>>> Generate {n_frames} frames under each generation ...')
+    noise_shape = [args.bs, channels, n_frames, h, w]
+
+    return model, noise_shape, data
+
+
+def get_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--savedir",
+                        type=str,
+                        default=None,
+                        help="Path to save the results.")
+    parser.add_argument("--ckpt_path",
+                        type=str,
+                        default=None,
+                        help="Path to the model checkpoint.")
+    parser.add_argument("--config", type=str, help="Path to the config file.")
+    parser.add_argument(
+        "--ddim_steps",
+        type=int,
+        default=50,
+        help="Number of DDIM steps. If non-positive, DDPM is used instead.")
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=1.0,
+        help="Eta for DDIM sampling. Set to 0.0 for deterministic results.")
+    parser.add_argument("--bs",
+                        type=int,
+                        default=1,
+                        help="Batch size for inference. Must be 1.")
+    parser.add_argument("--height",
+                        type=int,
+                        default=320,
+                        help="Height of the generated images in pixels.")
+    parser.add_argument("--width",
+                        type=int,
+                        default=512,
+                        help="Width of the generated images in pixels.")
+    parser.add_argument(
+        "--frame_stride",
+        type=int,
+        default=3,
+        help=
+        "frame stride control for 256 model (larger->larger motion), FPS control for 512 or 1024 model (smaller->larger motion)"
+    )
+    parser.add_argument(
+        "--unconditional_guidance_scale",
+        type=float,
+        default=1.0,
+        help="Scale for classifier-free guidance during sampling.")
+    parser.add_argument("--seed",
+                        type=int,
+                        default=123,
+                        help="Random seed for reproducibility.")
+    parser.add_argument("--video_length",
+                        type=int,
+                        default=16,
+                        help="Number of frames in the generated video.")
+    parser.add_argument(
+        "--timestep_spacing",
+        type=str,
+        default="uniform",
+        help=
+        "Strategy for timestep scaling. See Table 2 in the paper: 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
+    )
+    parser.add_argument(
+        "--guidance_rescale",
+        type=float,
+        default=0.0,
+        help=
+        "Rescale factor for guidance as discussed in 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
+    )
+    parser.add_argument(
+        "--perframe_ae",
+        action='store_true',
+        default=False,
+        help=
+        "Use per-frame autoencoder decoding to reduce GPU memory usage. Recommended for models with resolutions like 576x1024."
+    )
+    return parser
+
+
+class Server:
+
+    def __init__(self, args: argparse.Namespace) -> None:
+        self.model_, self.noise_shape_, self.data_ = run_inference(args, 1, 0)
+        self.args_ = args
+        self.dataset_name = self.data_.dataset_configs['test']['params'][
+            'dataset_name']
+        self.device_ = get_device_from_parameters(self.model_)
+
+    def normalize_image(self, image: torch.Tensor) -> torch.Tensor:
+        return (image / 255 - 0.5) * 2
+
+    def predict_action(self, payload: Dict[str, Any]) -> Any:
+        try:
+            images = payload['observation.images.top']
+            states = payload['observation.state']
+            actions = payload['action']  # Should be all zeros
+            language_instruction = payload['language_instruction']
+
+            images = torch.tensor(images).cuda()
+            images = self.data_.test_datasets[
+                self.dataset_name].spatial_transform(images).unsqueeze(0)
+            images = self.normalize_image(images)
+            print(f"images shape: {images.shape} ...")
+            states = torch.tensor(states)
+            states = self.data_.test_datasets[self.dataset_name].normalizer(
+                {'observation.state': states})['observation.state']
+            states, _ = self.data_.test_datasets[
+                self.dataset_name]._map_to_uni_state(states, "joint position")
+            print(f"states shape: {states.shape} ...")
+            actions = torch.tensor(actions)
+            actions, action_mask = self.data_.test_datasets[
+                self.dataset_name]._map_to_uni_action(actions,
+                                                      "joint position")
+            print(f"actions shape: {actions.shape} ...")
+            print("=" * 20)
+            states = states.unsqueeze(0).cuda()
+            actions = actions.unsqueeze(0).cuda()
+
+            observation = {
+                'observation.images.top': images,
+                'observation.state': states,
+                'action': actions
+            }
+            observation = {
+                key: observation[key].to(self.device_, non_blocking=True)
+                for key in observation
+            }
+
+            args = self.args_
+            pred_videos, pred_action, _ = image_guided_synthesis(
+                self.model_,
+                language_instruction,
+                observation,
+                self.noise_shape_,
+                ddim_steps=args.ddim_steps,
+                ddim_ets=args.ddim_eta,
+                unconditional_guidance_scale=args.unconditional_guidance_scale,
+                fs=30 / args.frame_stride,
+                timestep_spacing=args.timestep_spacing,
+                guidance_rescale=args.guidance_rescale)
+
+            pred_action = pred_action[..., action_mask[0] == 1.0][0].cpu()
+            pred_action = self.data_.test_datasets[
+                self.dataset_name].unnormalizer({'action':
+                                                 pred_action})['action']
+
+            os.makedirs(args.savedir, exist_ok=True)
+            current_time = datetime.now().strftime("%H:%M:%S")
+            video_file = f'{args.savedir}/{current_time}.mp4'
+            save_results(pred_videos.cpu(), video_file)
+
+            response = {
+                'result': 'ok',
+                'action': pred_action.tolist(),
+                'desc': 'success'
+            }
+            return JSONResponse(response)
+
+        except:
+            logging.error(traceback.format_exc())
+            logging.warning(
+                "Your request threw an error; make sure your request complies with the expected format:\n"
+                "{'image': np.ndarray, 'instruction': str}\n"
+                "You can optionally an `unnorm_key: str` to specific the dataset statistics you want to use for "
+                "de-normalizing the output actions.")
+            return {'result': 'error', 'desc': traceback.format_exc()}
+
+    def run(self, host: str = "127.0.0.1", port: int = 8000) -> None:
+        self.app = FastAPI()
+        self.app.post("/predict_action")(self.predict_action)
+        print(">>> Inference server is ready ... ")
+        uvicorn.run(self.app, host=host, port=port)
+        print(">>> Inference server stops ... ")
+        return
+
+
+if __name__ == '__main__':
+    parser = get_parser()
+    args = parser.parse_args()
+    seed = args.seed
+    seed_everything(seed)
+    rank, gpu_num = 0, 1
+    print(">>> Launch inference server ... ")
+    server = Server(args)
+    server.run()