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new file mode 100644
index 0000000000000000000000000000000000000000..749f89322b9e53553fd5cff15f8750f1e900b1ed
--- /dev/null
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diff --git a/assets/manual_poses/camera_O.txt b/assets/manual_poses/camera_O.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6de034cd4d0b501d0b68eb3bd6a78d54245f7df
--- /dev/null
+++ b/assets/manual_poses/camera_O.txt
@@ -0,0 +1,49 @@
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diff --git a/assets/manual_poses/camera_R.txt b/assets/manual_poses/camera_R.txt
new file mode 100644
index 0000000000000000000000000000000000000000..72320f6ac15f3615730df769c2107268119a8a48
--- /dev/null
+++ b/assets/manual_poses/camera_R.txt
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diff --git a/assets/manual_poses/camera_U.txt b/assets/manual_poses/camera_U.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1452d13666c4d61ee3b2d0dddd7228f2f49afd94
--- /dev/null
+++ b/assets/manual_poses/camera_U.txt
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diff --git a/assets/manual_poses_PanTiltSpin/Pan_Left_90.000000.txt b/assets/manual_poses_PanTiltSpin/Pan_Left_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6686a25755dc0bb57bd727652c23a317cc1121ca
--- /dev/null
+++ b/assets/manual_poses_PanTiltSpin/Pan_Left_90.000000.txt
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diff --git a/assets/manual_poses_PanTiltSpin/Pan_Right_90.000000.txt b/assets/manual_poses_PanTiltSpin/Pan_Right_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..afe02ebf883d5c84f0583f21ddfd54707adb28f9
--- /dev/null
+++ b/assets/manual_poses_PanTiltSpin/Pan_Right_90.000000.txt
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diff --git a/assets/manual_poses_PanTiltSpin/Spin_AntiClockwise_90.000000.txt b/assets/manual_poses_PanTiltSpin/Spin_AntiClockwise_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..765471623bb78a3a5f03aad9d963e4a09524537d
--- /dev/null
+++ b/assets/manual_poses_PanTiltSpin/Spin_AntiClockwise_90.000000.txt
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diff --git a/assets/manual_poses_PanTiltSpin/Spin_Clockwise_90.000000.txt b/assets/manual_poses_PanTiltSpin/Spin_Clockwise_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c271a0cab7402e41fc0143eae07993027eb8a2f0
--- /dev/null
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diff --git a/assets/manual_poses_PanTiltSpin/Tilt_Down_90.000000.txt b/assets/manual_poses_PanTiltSpin/Tilt_Down_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dee0092ad6997ff3b72652554fac37241e527678
--- /dev/null
+++ b/assets/manual_poses_PanTiltSpin/Tilt_Down_90.000000.txt
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diff --git a/assets/manual_poses_PanTiltSpin/Tilt_Up_90.000000.txt b/assets/manual_poses_PanTiltSpin/Tilt_Up_90.000000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dde6afdc39c3ea43d9801425a85ad2e36a61dcee
--- /dev/null
+++ b/assets/manual_poses_PanTiltSpin/Tilt_Up_90.000000.txt
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new file mode 100644
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diff --git a/assets/re10k_poses/ffa95c3b40609c76.txt b/assets/re10k_poses/ffa95c3b40609c76.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0d0c33a07301d0a84819c0fd3d0a99ebf19ea4dd
--- /dev/null
+++ b/assets/re10k_poses/ffa95c3b40609c76.txt
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diff --git a/inference/cli_demo.py b/inference/cli_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..37dfcfc75e120635aef0034407ef35667c927368
--- /dev/null
+++ b/inference/cli_demo.py
@@ -0,0 +1,237 @@
+"""
+This script demonstrates how to generate a video using the CogVideoX model with the Hugging Face `diffusers` pipeline.
+The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
+and video-to-video (v2v), depending on the input data and different weight.
+
+- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
+
+Running the Script:
+To run the script, use the following command with appropriate arguments:
+
+```bash
+$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
+```
+
+You can change `pipe.enable_sequential_cpu_offload()` to `pipe.enable_model_cpu_offload()` to speed up inference, but this will use more GPU memory
+
+Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+
+"""
+
+import argparse
+import logging
+from typing import Literal, Optional
+
+import torch
+
+from diffusers import (
+    CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
+    CogVideoXVideoToVideoPipeline,
+)
+from diffusers.utils import export_to_video, load_image, load_video
+
+
+logging.basicConfig(level=logging.INFO)
+
+# Recommended resolution for each model (width, height)
+RESOLUTION_MAP = {
+    # cogvideox1.5-*
+    "cogvideox1.5-5b-i2v": (768, 1360),
+    "cogvideox1.5-5b": (768, 1360),
+    # cogvideox-*
+    "cogvideox-5b-i2v": (480, 720),
+    "cogvideox-5b": (480, 720),
+    "cogvideox-2b": (480, 720),
+}
+
+
+def generate_video(
+    prompt: str,
+    model_path: str,
+    lora_path: str = None,
+    lora_rank: int = 128,
+    num_frames: int = 81,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    output_path: str = "./output.mp4",
+    image_or_video_path: str = "",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bfloat16,
+    generate_type: str = Literal["t2v", "i2v", "v2v"],  # i2v: image to video, v2v: video to video
+    seed: int = 42,
+    fps: int = 16,
+):
+    """
+    Generates a video based on the given prompt and saves it to the specified path.
+
+    Parameters:
+    - prompt (str): The description of the video to be generated.
+    - model_path (str): The path of the pre-trained model to be used.
+    - lora_path (str): The path of the LoRA weights to be used.
+    - lora_rank (int): The rank of the LoRA weights.
+    - output_path (str): The path where the generated video will be saved.
+    - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
+    - width (int): The width of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - height (int): The height of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
+    - num_videos_per_prompt (int): Number of videos to generate per prompt.
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
+    - generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
+    - seed (int): The seed for reproducibility.
+    - fps (int): The frames per second for the generated video.
+    """
+
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
+    # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
+    # function to use Multi GPUs.
+
+    image = None
+    video = None
+
+    model_name = model_path.split("/")[-1].lower()
+    desired_resolution = RESOLUTION_MAP[model_name]
+    if width is None or height is None:
+        height, width = desired_resolution
+        logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m")
+    elif (height, width) != desired_resolution:
+        if generate_type == "i2v":
+            # For i2v models, use user-defined width and height
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
+        else:
+            # Otherwise, use the recommended width and height
+            logging.warning(
+                f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m"
+            )
+            height, width = desired_resolution
+
+    if generate_type == "i2v":
+        pipe = CogVideoXImageToVideoPipeline.from_pretrained(model_path, torch_dtype=dtype)
+        image = load_image(image=image_or_video_path)
+    elif generate_type == "t2v":
+        pipe = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype)
+    else:
+        pipe = CogVideoXVideoToVideoPipeline.from_pretrained(model_path, torch_dtype=dtype)
+        video = load_video(image_or_video_path)
+
+    # If you're using with lora, add this code
+    if lora_path:
+        pipe.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name="test_1")
+        pipe.fuse_lora(components=["transformer"], lora_scale=1 / lora_rank)
+
+    # 2. Set Scheduler.
+    # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
+    # We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B.
+    # using `CogVideoXDPMScheduler` for CogVideoX-5B / CogVideoX-5B-I2V.
+
+    # pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+    # 3. Enable CPU offload for the model.
+    # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
+    # and enable to("cuda")
+    # pipe.to("cuda")
+
+    # pipe.enable_model_cpu_offload()
+    pipe.enable_sequential_cpu_offload()
+    pipe.vae.enable_slicing()
+    pipe.vae.enable_tiling()
+
+    # 4. Generate the video frames based on the prompt.
+    # `num_frames` is the Number of frames to generate.
+    if generate_type == "i2v":
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            image=image,
+            # The path of the image, the resolution of video will be the same as the image for CogVideoX1.5-5B-I2V, otherwise it will be 720 * 480
+            num_videos_per_prompt=num_videos_per_prompt,  # Number of videos to generate per prompt
+            num_inference_steps=num_inference_steps,  # Number of inference steps
+            num_frames=num_frames,  # Number of frames to generate
+            use_dynamic_cfg=True,  # This id used for DPM scheduler, for DDIM scheduler, it should be False
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(seed),  # Set the seed for reproducibility
+        ).frames[0]
+    elif generate_type == "t2v":
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            num_videos_per_prompt=num_videos_per_prompt,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            use_dynamic_cfg=True,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(seed),
+        ).frames[0]
+    else:
+        video_generate = pipe(
+            height=height,
+            width=width,
+            prompt=prompt,
+            video=video,  # The path of the video to be used as the background of the video
+            num_videos_per_prompt=num_videos_per_prompt,
+            num_inference_steps=num_inference_steps,
+            num_frames=num_frames,
+            use_dynamic_cfg=True,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator().manual_seed(seed),  # Set the seed for reproducibility
+        ).frames[0]
+    export_to_video(video_generate, output_path, fps=fps)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
+    parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
+    parser.add_argument(
+        "--image_or_video_path",
+        type=str,
+        default=None,
+        help="The path of the image to be used as the background of the video",
+    )
+    parser.add_argument(
+        "--model_path", type=str, default="THUDM/CogVideoX1.5-5B", help="Path of the pre-trained model use"
+    )
+    parser.add_argument("--lora_path", type=str, default=None, help="The path of the LoRA weights to be used")
+    parser.add_argument("--lora_rank", type=int, default=128, help="The rank of the LoRA weights")
+    parser.add_argument("--output_path", type=str, default="./output.mp4", help="The path save generated video")
+    parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
+    parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--num_frames", type=int, default=81, help="Number of steps for the inference process")
+    parser.add_argument("--width", type=int, default=None, help="The width of the generated video")
+    parser.add_argument("--height", type=int, default=None, help="The height of the generated video")
+    parser.add_argument("--fps", type=int, default=16, help="The frames per second for the generated video")
+    parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
+    parser.add_argument("--generate_type", type=str, default="t2v", help="The type of video generation")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="The data type for computation")
+    parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
+
+    args = parser.parse_args()
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
+    generate_video(
+        prompt=args.prompt,
+        model_path=args.model_path,
+        lora_path=args.lora_path,
+        lora_rank=args.lora_rank,
+        output_path=args.output_path,
+        num_frames=args.num_frames,
+        width=args.width,
+        height=args.height,
+        image_or_video_path=args.image_or_video_path,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        num_videos_per_prompt=args.num_videos_per_prompt,
+        dtype=dtype,
+        generate_type=args.generate_type,
+        seed=args.seed,
+        fps=args.fps,
+    )
diff --git a/inference/flovd_ddp_demo.py b/inference/flovd_ddp_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e4fa8ce55e5b8b2234dd4c1b0f9dd266696ca70
--- /dev/null
+++ b/inference/flovd_ddp_demo.py
@@ -0,0 +1,817 @@
+"""
+This script demonstrates how to generate a video using the CogVideoX model with the Hugging Face `diffusers` pipeline.
+The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
+and video-to-video (v2v), depending on the input data and different weight.
+
+- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
+
+Running the Script:
+To run the script, use the following command with appropriate arguments:
+
+```bash
+$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
+```
+
+You can change `pipe.enable_sequential_cpu_offload()` to `pipe.enable_model_cpu_offload()` to speed up inference, but this will use more GPU memory
+
+Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+
+"""
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
+import argparse
+import logging
+import os
+import sys
+from typing import Literal, Optional
+from pathlib import Path
+import json
+from datetime import timedelta
+import random
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from einops import rearrange, repeat
+import math
+import numpy as np
+from PIL import Image
+
+import torch
+
+from diffusers import (
+    CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
+    CogVideoXVideoToVideoPipeline,
+    AutoencoderKLCogVideoX,
+    CogVideoXTransformer3DModel,
+)
+from diffusers.utils import export_to_video, load_image, load_video
+from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
+
+sys.path.append(os.path.abspath(os.path.join(sys.path[0], "../")))
+from finetune.pipeline.flovd_FVSM_cogvideox_controlnet_pipeline import FloVDCogVideoXControlnetImageToVideoPipeline
+from finetune.pipeline.flovd_OMSM_cogvideox_pipeline import FloVDOMSMCogVideoXImageToVideoPipeline
+from finetune.schemas import Components, Args
+from finetune.modules.cogvideox_controlnet import CogVideoXControlnet
+from finetune.modules.cogvideox_custom_model import CustomCogVideoXTransformer3DModel
+from transformers import AutoTokenizer, T5EncoderModel
+
+from finetune.modules.camera_sampler import SampleManualCam
+from finetune.modules.camera_flow_generator import CameraFlowGenerator
+from finetune.modules.utils import get_camera_flow_generator_input, forward_bilinear_splatting, flow_to_color
+from finetune.modules.depth_warping.depth_warping import unnormalize_intrinsic
+
+from finetune.datasets.utils import (
+    preprocess_image_with_resize,
+    preprocess_video_with_resize,
+)
+
+
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import torch.distributed as dist
+from torch.utils.data.distributed import DistributedSampler
+
+import pdb
+sys.path.append(os.path.abspath(os.path.join(sys.path[-1], 'finetune'))) # for camera flow generator
+
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+logging.basicConfig(level=logging.INFO)
+
+# Recommended resolution for each model (width, height)
+RESOLUTION_MAP = {
+    # cogvideox1.5-*
+    "cogvideox1.5-5b-i2v": (768, 1360),
+    "cogvideox1.5-5b": (768, 1360),
+    # cogvideox-*
+    "cogvideox-5b-i2v": (480, 720),
+    "cogvideox-5b": (480, 720),
+    "cogvideox-2b": (480, 720),
+}
+
+
+
+
+def init_dist(launcher="slurm", backend='nccl', port=29500, **kwargs):
+    """Initializes distributed environment."""
+    if launcher == 'pytorch':
+        rank = int(os.environ['RANK'])
+        num_gpus = torch.cuda.device_count()
+        local_rank = rank % num_gpus
+        torch.cuda.set_device(local_rank)
+        dist.init_process_group(backend=backend, timeout=timedelta(minutes=30), **kwargs)
+
+    elif launcher == 'slurm':
+        proc_id = int(os.environ['SLURM_PROCID'])
+        ntasks = int(os.environ['SLURM_NTASKS'])
+        node_list = os.environ['SLURM_NODELIST']
+        num_gpus = torch.cuda.device_count()
+        local_rank = proc_id % num_gpus
+        torch.cuda.set_device(local_rank)
+        addr = subprocess.getoutput(
+            f'scontrol show hostname {node_list} | head -n1')
+        os.environ['MASTER_ADDR'] = addr
+        os.environ['WORLD_SIZE'] = str(ntasks)
+        os.environ['RANK'] = str(proc_id)
+        port = os.environ.get('PORT', port)
+        os.environ['MASTER_PORT'] = str(port)
+        dist.init_process_group(backend=backend, timeout=timedelta(minutes=30))
+
+    else:
+        raise NotImplementedError(f'Not implemented launcher type: `{launcher}`!')
+    # https://github.com/pytorch/pytorch/issues/98763
+    # torch.cuda.set_device(local_rank)
+
+    return local_rank
+
+
+def load_cogvideox_flovd_FVSM_controlnet_pipeline(controlnet_path, backbone_path, device, dtype):
+    controlnet_sd = torch.load(controlnet_path, map_location='cpu')['module']
+    
+    tokenizer = AutoTokenizer.from_pretrained(backbone_path, subfolder="tokenizer")
+    text_encoder = T5EncoderModel.from_pretrained(backbone_path, subfolder="text_encoder")
+    transformer = CustomCogVideoXTransformer3DModel.from_pretrained(backbone_path, subfolder="transformer")
+    vae = AutoencoderKLCogVideoX.from_pretrained(backbone_path, subfolder="vae")
+    scheduler = CogVideoXDPMScheduler.from_pretrained(backbone_path, subfolder="scheduler")
+    
+    additional_kwargs = {
+        'num_layers': 6,
+        'out_proj_dim_factor': 64,
+        'out_proj_dim_zero_init': True,
+        'notextinflow': True,
+    }
+    controlnet = CogVideoXControlnet.from_pretrained(backbone_path, subfolder="transformer", **additional_kwargs)
+    controlnet.eval()
+    
+    missing, unexpected = controlnet.load_state_dict(controlnet_sd)
+    
+    if len(missing) != 0 or len(unexpected) != 0:
+        print(f"Missing keys : {missing}")
+        print(f"Unexpected keys : {unexpected}")
+        
+    pipe = FloVDCogVideoXControlnetImageToVideoPipeline(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            controlnet=controlnet,
+            scheduler=scheduler,
+    )
+    
+    # pipe.enable_model_cpu_offload(device=device)
+    pipe = pipe.to(device, dtype)
+    
+    return pipe
+
+def load_cogvideox_flovd_OMSM_lora_pipeline(omsm_path, backbone_path, transformer_lora_config, device, dtype):
+    tokenizer = AutoTokenizer.from_pretrained(backbone_path, subfolder="tokenizer")
+    text_encoder = T5EncoderModel.from_pretrained(backbone_path, subfolder="text_encoder")
+    transformer = CogVideoXTransformer3DModel.from_pretrained(backbone_path, subfolder="transformer")
+    vae = AutoencoderKLCogVideoX.from_pretrained(backbone_path, subfolder="vae")
+    scheduler = CogVideoXDPMScheduler.from_pretrained(backbone_path, subfolder="scheduler")
+
+    # 1) Load Lora weight
+    transformer.add_adapter(transformer_lora_config)
+
+    lora_state_dict = FloVDOMSMCogVideoXImageToVideoPipeline.lora_state_dict(omsm_path)
+    transformer_state_dict = {
+        f'{k.replace("transformer.", "")}': v
+        for k, v in lora_state_dict.items()
+        if k.startswith("transformer.")
+    }
+    incompatible_keys = set_peft_model_state_dict(transformer, transformer_state_dict, adapter_name="default")
+    if incompatible_keys is not None:
+        # check only for unexpected keys
+        unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+        if unexpected_keys:
+            logger.warning(
+                f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                f" {unexpected_keys}. "
+            )
+
+    # 2) Load Other weight
+    load_path = os.path.join(omsm_path, "selected_blocks.safetensors")
+    if os.path.exists(load_path):
+        tensor_dict = load_file(load_path)
+        
+        block_state_dicts = {}
+        for k, v in tensor_dict.items():
+            block_name, param_name = k.split(".", 1)
+            if block_name not in block_state_dicts:
+                block_state_dicts[block_name] = {}
+            block_state_dicts[block_name][param_name] = v
+        
+        for block_name, state_dict in block_state_dicts.items():
+            if hasattr(transformer, block_name):
+                getattr(transformer, block_name).load_state_dict(state_dict)
+            else:
+                raise ValueError(f"Transformer has no attribute '{block_name}'")
+    
+    
+    pipe = FloVDOMSMCogVideoXImageToVideoPipeline(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+    )
+    
+    # pipe.load_lora_weights(omsm_path, weight_name="pytorch_lora_weights.safetensors", adapter_name="test_1")
+    # pipe.fuse_lora(components=["transformer"], lora_scale=1.0)
+    
+    # pipe.enable_model_cpu_offload(device=device)
+    pipe = pipe.to(device, dtype)
+    
+    return pipe
+
+
+class I2VFlowDataset_Inference(Dataset):
+    def __init__(
+        self, 
+        max_num_frames: int, 
+        height: int, 
+        width: int, 
+        data_root: str,
+        max_num_videos: int = None,
+    ) -> None:
+        
+        self.train_resolution = (int(max_num_frames), int(height), int(width))
+        
+        data_root = Path(data_root)
+        metadata_path = data_root / "metadata_reformat.jsonl"
+        assert metadata_path.is_file(), "For this dataset type, you need metadata.jsonl in the root path"
+        
+        metadata = []
+        with open(metadata_path, "r") as f:
+            for line in f:
+                metadata.append( json.loads(line) )
+        
+        metadata = random.sample(metadata, max_num_videos)
+
+        self.prompts = [x["prompt"] for x in metadata]
+        self.prompt_embeddings = [data_root / "prompt_embeddings_revised" / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.videos = [data_root / "video_latent" / "x".join(str(x) for x in self.train_resolution) / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.images = [data_root / "first_frames" / (x["hash_code"] + '.png') for x in metadata]
+        self.flows = [data_root / "flow_direct_f_latent" / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.masks = [data_root / "valid_mask" / (x["hash_code"] + '.bin') for x in metadata]
+        
+        self.max_num_frames = max_num_frames
+        self.height = height
+        self.width = width
+
+        self.__frame_transforms = transforms.Compose([transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0)])
+        self.__image_transforms = self.__frame_transforms
+        
+        self.length = len(self.videos)
+
+        print(f"Dataset size: {self.length}")
+        
+    def __len__(self) -> int:
+        return self.length
+    
+    def load_data_pair(self, index):
+        prompt_embedding_path = self.prompt_embeddings[index]
+        encoded_video_path = self.videos[index]
+        encoded_flow_path = self.flows[index]
+        
+        prompt_embedding = load_file(prompt_embedding_path)["prompt_embedding"] 
+        encoded_video = load_file(encoded_video_path)["encoded_video"] # CFHW
+        encoded_flow = load_file(encoded_flow_path)["encoded_flow_f"] # CFHW
+    
+        return prompt_embedding, encoded_video, encoded_flow
+
+    def __getitem__(self, index: int) -> Dict[str, Any]:
+        while True:
+            try:
+                prompt_embedding, encoded_video, encoded_flow = self.load_data_pair(index)
+                break
+            except Exception as e:
+                print(f"Error loading {self.prompt_embeddings[index]}: {str(e)}")
+                index = random.randint(0, self.length - 1)
+            
+        image_path = self.images[index]
+        prompt = self.prompts[index]
+        
+        _, image = self.preprocess(None, image_path)
+        image = self.image_transform(image)
+        
+        
+        # shape of encoded_video: [C, F, H, W]
+        # shape and scale of image: [C, H, W], [-1,1]
+        return {
+            "image": image,
+            "prompt": prompt,
+            "prompt_embedding": prompt_embedding,
+            "encoded_video": encoded_video,
+            "encoded_flow": encoded_flow,
+            "video_metadata": {
+                "num_frames": encoded_video.shape[1],
+                "height": encoded_video.shape[2],
+                "width": encoded_video.shape[3],
+            },
+        }
+    
+    def preprocess(self, video_path: Path | None, image_path: Path | None) -> Tuple[torch.Tensor, torch.Tensor]:
+        if video_path is not None:
+            video = preprocess_video_with_resize(video_path, self.max_num_frames, self.height, self.width)
+        else:
+            video = None
+        if image_path is not None:
+            image = preprocess_image_with_resize(image_path, self.height, self.width)
+        else:
+            image = None
+        return video, image
+    
+    def video_transform(self, frames: torch.Tensor) -> torch.Tensor:
+        return torch.stack([self.__frame_transforms(f) for f in frames], dim=0)
+
+    def image_transform(self, image: torch.Tensor) -> torch.Tensor:
+        return self.__image_transforms(image)
+
+def initialize_flow_generator(target):
+    depth_estimator_kwargs = {
+        "target": target,
+        "kwargs": {
+            "ckpt_path": '/workspace/workspace/checkpoints/depth_anything/depth_anything_v2_metric_hypersim_vitb.pth',
+            "model_config": {
+                "max_depth": 20,
+                "encoder": 'vitb',
+                "features": 128,
+                "out_channels": [96, 192, 384, 768],
+            }
+
+        }
+    }
+
+    return CameraFlowGenerator(depth_estimator_kwargs)
+
+def integrate_flow(camera_flow, object_flow, depth_ctxt, camera_flow_generator, camera_flow_generator_input):
+    # camera_flow: (BF)CHW
+    # object_flow: (BF)CHW
+    # depth_ctxt: B1HW
+    
+    B, F = camera_flow_generator_input["target"]["intrinsics"].shape[:2]
+    H, W = object_flow.shape[-2:]
+    
+    c2w_ctxt = repeat(camera_flow_generator_input["context"]["extrinsics"], "b t h w -> (b v t) h w", v=F) # No need to apply inverse as it is an eye matrix.
+    c2w_trgt = rearrange(torch.inverse(camera_flow_generator_input["target"]["extrinsics"]), "b t h w -> (b t) h w")
+    intrinsics_ctxt = unnormalize_intrinsic(repeat(camera_flow_generator_input["context"]["intrinsics"], "b t h w -> (b v t) h w", v=F), size=(H, W))
+        
+    with torch.cuda.amp.autocast(enabled=False):
+        warped_object_flow = camera_flow_generator.depth_warping_module.warper.forward_warp_displacement(
+            depth1=repeat(depth_ctxt, "b c h w -> (b f) c h w", f=F),
+            flow1=object_flow, 
+            transformation1=c2w_ctxt, 
+            transformation2=c2w_trgt, 
+            intrinsic1=intrinsics_ctxt, 
+            intrinsic2=None,
+        )
+    
+    integrated_flow = camera_flow + warped_object_flow
+    
+    return integrated_flow
+    
+def save_flow(flow, filename, fps=16):
+    # flow: (BF)CHW, arbitrary scale
+    flow_RGB = flow_to_color(flow) # BF,C,H,W (B=1)
+
+    frame_list = []
+    for frame in flow_RGB:
+        frame = (frame.permute(1,2,0).float().detach().cpu().numpy()).astype(np.uint8).clip(0,255)
+        frame_list.append(Image.fromarray(frame))
+    
+    export_to_video(frame_list, filename, fps=fps)
+
+def save_flow_warped_video(image, flow, filename, fps=16):
+    # image: CHW, 0~255 scale
+    # flow: (BF)CHW, arbitrary scale
+    warped_video = forward_bilinear_splatting(repeat(image, 'c h w -> f c h w', f=flow.size(0)), flow.to(torch.float))
+    
+    frame_list = []
+    for frame in warped_video:
+        frame = (frame.permute(1,2,0).float().detach().cpu().numpy()).astype(np.uint8).clip(0,255)
+        frame_list.append(Image.fromarray(frame))
+    
+    export_to_video(frame_list, filename, fps=fps)
+
+def generate_video(
+    # prompt: str,
+    launcher: str,
+    port: int,
+    data_root: str,
+    fvsm_path: str,
+    omsm_path: str,
+    num_frames: int = 81,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    output_path: str = "./output.mp4",
+    image_path: str = "",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 42,
+    fps: int = 16,
+    controlnet_guidance_end: float = 0.4,
+    max_num_videos: int = None,
+    use_dynamic_cfg: bool = False,
+    pose_type: str = "manual",
+    speed: float = 0.5,
+    use_flow_integration: bool = False,
+):
+    """
+    Generates a video based on the given prompt and saves it to the specified path.
+
+    Parameters:
+    - prompt (str): The description of the video to be generated.
+    - lora_path (str): The path of the LoRA weights to be used.
+    - lora_rank (int): The rank of the LoRA weights.
+    - output_path (str): The path where the generated video will be saved.
+    - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
+    - width (int): The width of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - height (int): The height of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
+    - num_videos_per_prompt (int): Number of videos to generate per prompt.
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
+    - generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
+    - seed (int): The seed for reproducibility.
+    - fps (int): The frames per second for the generated video.
+    """
+    
+    # Distributed
+    local_rank = init_dist(launcher=launcher, port=port)
+    global_rank = dist.get_rank()
+    num_processes = dist.get_world_size()
+    is_main_process = global_rank == 0
+    
+    torch.manual_seed(seed)
+    random.seed(seed)
+    
+    if is_main_process:
+        os.makedirs(os.path.join(output_path, 'generated_videos'), exist_ok=True)
+        os.makedirs(os.path.join(output_path, 'generated_flow_videos'), exist_ok=True)
+        os.makedirs(os.path.join(output_path, 'flow_warped_videos'), exist_ok=True)
+
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
+    # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
+    # function to use Multi GPUs.
+
+    image = None
+    video = None
+
+    model_name = "cogvideox-5b-i2v".lower()
+    desired_resolution = RESOLUTION_MAP[model_name]
+    if width is None or height is None:
+        height, width = desired_resolution
+        logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m")
+    elif (height, width) != desired_resolution:
+        if generate_type == "i2v":
+            # For i2v models, use user-defined width and height
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
+
+    """
+        # Prepare Dataset Class..
+    """
+    # image = load_image(image=image_or_video_path)
+    
+    # prompt
+    # first image
+    # camera parameters
+    dataset = I2VFlowDataset_Inference(
+        max_num_frames=num_frames,
+        height=height,
+        width=width,
+        data_root=data_root,
+        max_num_videos=max_num_videos,
+    )
+    
+    
+    distributed_sampler = DistributedSampler(
+        dataset,
+        num_replicas=num_processes,
+        rank=global_rank,
+        shuffle=False,
+        seed=seed,
+    )
+    
+    # DataLoaders creation:
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        sampler=distributed_sampler,
+        num_workers=4,
+        pin_memory=True,
+        drop_last=False,
+    )
+    
+
+    """
+        # Prepare Pipeline
+    """
+    transformer_lora_config = LoraConfig(
+        r=128,
+        lora_alpha=64,
+        init_lora_weights=True,
+        target_modules=["to_q", "to_k", "to_v", "to_out.0", "norm1.linear", "norm2.linear", "ff.net.2"],
+    )
+    
+    print(f'Constructing pipeline')
+    pipe_omsm = load_cogvideox_flovd_OMSM_lora_pipeline(omsm_path, backbone_path="THUDM/CogVideoX-5b-I2V", transformer_lora_config=transformer_lora_config, device=local_rank, dtype=dtype)   
+    pipe_fvsm = load_cogvideox_flovd_FVSM_controlnet_pipeline(fvsm_path, backbone_path="THUDM/CogVideoX-5b-I2V", device=local_rank, dtype=dtype)   
+    print(f'Done loading pipeline')
+    
+    assert pose_type in ['re10k', 'manual'], "Choose other pose_type between ['re10k', 'manual']"
+    if pose_type == 're10k':
+        root_path = "./manual_poses_re10k"
+    else:
+        root_path = "./manual_poses"
+        
+    CameraSampler = SampleManualCam(pose_type=pose_type, root_path=root_path)
+    camera_flow_generator_target = 'finetune.modules.depth_warping.depth_warping.DepthWarping_wrapper'
+    camera_flow_generator = initialize_flow_generator(camera_flow_generator_target).to(local_rank)
+    #--------------------------------------------------------------------------------------------------------
+
+
+    # 2. Set Scheduler.
+    # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
+    # We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B.
+    # using `CogVideoXDPMScheduler` for CogVideoX-5B / CogVideoX-5B-I2V.
+
+    # pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe_fvsm.scheduler = CogVideoXDPMScheduler.from_config(pipe_fvsm.scheduler.config, timestep_spacing="trailing")
+    pipe_omsm.scheduler = CogVideoXDPMScheduler.from_config(pipe_omsm.scheduler.config, timestep_spacing="trailing")
+
+    # 3. Enable CPU offload for the model.
+    # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
+    # and enable to("cuda")
+    # pipe.to("cuda")
+
+    # pipe_fvsm.enable_model_cpu_offload()
+    # pipe_omsm.enable_model_cpu_offload()
+    # pipe_fvsm.enable_sequential_cpu_offload()
+    # pipe_omsm.enable_sequential_cpu_offload()
+    
+    pipe_fvsm.vae.enable_slicing()
+    pipe_fvsm.vae.enable_tiling()
+    pipe_omsm.vae.enable_slicing()
+    pipe_omsm.vae.enable_tiling()
+    
+    dataloader.sampler.set_epoch(1)
+    dist.barrier()
+    
+    output_video_path = os.path.join(output_path, 'generated_videos')
+    output_flow_path = os.path.join(output_path, 'generated_flow_videos')
+    output_warped_video_path = os.path.join(output_path, 'flow_warped_videos')
+    
+    data_iter = iter(dataloader)
+    for step in tqdm(range(0, len(dataloader))):
+        batch = next(data_iter)
+        
+        prompt = batch["prompt"][0]
+        image = batch["image"].to(local_rank)
+        prompt_embedding = batch["prompt_embedding"].to(local_rank)
+        prompt_short = prompt[:20].strip()
+        
+        # if step < 10:
+        #     step += 1
+        #     continue
+        
+        # Get Camera flow
+        camparam, cam_name = CameraSampler.sample() # W2C
+        image_torch_255 = ((image.detach().clone()+1)/2. * 255.).squeeze(0)
+        camera_flow_generator_input = get_camera_flow_generator_input(image_torch_255, camparam, device=local_rank, speed=speed)
+        image_torch = ((image_torch_255.unsqueeze(0) / 255.) * 2. - 1.).to(local_rank)
+        
+        with torch.no_grad():
+            with torch.cuda.amp.autocast(enabled=True, dtype=dtype):
+                
+                # camera_flow, log_dict = camera_flow_generator(image_torch, camera_flow_generator_input)
+                # camera_flow = camera_flow.to(local_rank, dtype)
+                
+                # camera_flow_latent = rearrange(encode_flow(camera_flow, pipe_omsm.vae, flow_scale_factor=[60, 36]), 'b c f h w -> b f c h w').to(local_rank, dtype)
+                
+                flow_latent = pipe_omsm(
+                    num_frames=num_frames,
+                    height=height,
+                    width=width,
+                    prompt=None,
+                    prompt_embeds=prompt_embedding,
+                    image=image,
+                    generator=torch.Generator().manual_seed(seed),
+                    num_inference_steps=num_inference_steps,
+                    use_dynamic_cfg=use_dynamic_cfg,
+                    output_type='latent'
+                ).frames[0]
+                object_flow = decode_flow(flow_latent.detach().clone().unsqueeze(0).to(local_rank), pipe_omsm.vae, flow_scale_factor=[60, 36]) # BF,C,H,W
+                
+                if use_flow_integration:
+                    # Integrate camera (from 3D warping) and object (from OMSM) flow maps
+                    # Using segmentation model will be implemented later..
+                    
+                    camera_flow, log_dict = camera_flow_generator(image_torch, camera_flow_generator_input)
+                    camera_flow = camera_flow.to(local_rank, dtype)
+
+                    integrated_flow = integrate_flow(camera_flow, object_flow, log_dict['depth_ctxt'], camera_flow_generator, camera_flow_generator_input)
+                    integrated_flow_latent = rearrange(encode_flow(integrated_flow, pipe_omsm.vae, flow_scale_factor=[60, 36]), 'b c f h w -> b f c h w').to(local_rank, dtype)
+                else:
+                    integrated_flow_latent = rearrange(flow_latent, '(b f) c h w -> b f c h w', b=image.size(0))
+                
+                # 4. Generate the video frames based on the prompt.
+                # `num_frames` is the Number of frames to generate.
+                video_generate = pipe_fvsm(
+                    num_frames=num_frames,
+                    height=height,
+                    width=width,
+                    prompt=None,
+                    prompt_embeds=prompt_embedding,
+                    image=image,
+                    flow_latent=integrated_flow_latent,
+                    valid_mask=None,
+                    generator=torch.Generator().manual_seed(seed),
+                    num_inference_steps=num_inference_steps,
+                    controlnet_guidance_start = 0.0,
+                    controlnet_guidance_end = controlnet_guidance_end,
+                    use_dynamic_cfg=use_dynamic_cfg,
+                ).frames[0]
+
+        # Save logs
+        # 1) Synthesized flow (object_flow)
+        save_path = os.path.join(output_flow_path, f"{prompt_short}_DCFG-{use_dynamic_cfg}_ContGuide-{controlnet_guidance_end}_{cam_name}.mp4")
+        save_flow(object_flow, filename=save_path, fps=fps)
+        
+        # 2) Flow-Warped Video
+        save_path = os.path.join(output_warped_video_path, f"{prompt_short}_DCFG-{use_dynamic_cfg}_ContGuide-{controlnet_guidance_end}_{cam_name}.mp4")
+        save_flow_warped_video(image_torch_255, object_flow, filename=save_path, fps=fps)
+
+        # 3) Flow-Cond. Synthesized Video
+        save_path = os.path.join(output_video_path, f"{prompt_short}_DCFG-{use_dynamic_cfg}_ContGuide-{controlnet_guidance_end}_{cam_name}.mp4")
+        export_to_video(video_generate, save_path, fps=fps)
+        
+        dist.barrier()
+        
+        step += 1
+
+
+#--------------------------------------------------------------------------------------------------
+def encode_video(video: torch.Tensor, vae) -> torch.Tensor:
+    # shape of input video: [B, C, F, H, W]
+    video = video.to(vae.device, dtype=vae.dtype)
+    latent_dist = vae.encode(video).latent_dist
+    latent = latent_dist.sample() * vae.config.scaling_factor
+    return latent
+
+def encode_flow(flow, vae, flow_scale_factor):
+    # flow: BF,C,H,W
+    # flow_scale_factor [sf_x, sf_y]
+    assert flow.ndim == 4
+    num_frames, _, height, width = flow.shape
+
+    # Normalize optical flow
+    # ndim: 4 -> 5
+    flow = rearrange(flow, '(b f) c h w -> b f c h w', b=1)
+    flow_norm = adaptive_normalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    # ndim: 5 -> 4
+    flow_norm = rearrange(flow_norm, 'b f c h w -> (b f) c h w', b=1)
+
+    # Duplicate mean value for third channel
+    num_frames, _, H, W = flow_norm.shape
+    flow_norm_extended = torch.empty((num_frames, 3, height, width)).to(flow_norm)
+    flow_norm_extended[:,:2] = flow_norm
+    flow_norm_extended[:,-1:] = flow_norm.mean(dim=1, keepdim=True)
+    flow_norm_extended = rearrange(flow_norm_extended, '(b f) c h w -> b c f h w', f=num_frames)
+
+    return encode_video(flow_norm_extended, vae)
+
+
+def decode_flow(flow_latent, vae, flow_scale_factor):
+    flow_latent = flow_latent.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+    flow_latent = 1 / vae.config.scaling_factor * flow_latent
+    
+    flow = vae.decode(flow_latent).sample # BCFHW
+
+    # discard third channel (which is a mean value of f_x and f_y)
+    flow = flow[:,:2].detach().clone()
+
+    # Unnormalize optical flow
+    flow = rearrange(flow, 'b c f h w -> b f c h w')
+    flow = adaptive_unnormalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    flow = rearrange(flow, 'b f c h w -> (b f) c h w')
+    return flow # BF,C,H,W
+
+def adaptive_normalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    b, f, c, h, w = flow.shape
+    
+    max_clip_x = math.sqrt(w/sf_x) * 1.0
+    max_clip_y = math.sqrt(h/sf_y) * 1.0
+    
+    flow_norm = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_x_norm = torch.sign(flow_x) * torch.sqrt(torch.abs(flow_x)/sf_x + 1e-7)
+    flow_y_norm = torch.sign(flow_y) * torch.sqrt(torch.abs(flow_y)/sf_y + 1e-7)
+
+    flow_norm[:, :, 0] = torch.clamp(flow_x_norm, min=-max_clip_x, max=max_clip_x)
+    flow_norm[:, :, 1] = torch.clamp(flow_y_norm, min=-max_clip_y, max=max_clip_y)
+
+    return flow_norm
+
+
+def adaptive_unnormalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    
+    flow_orig = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_orig[:, :, 0] = torch.sign(flow_x) * sf_x * (flow_x**2 - 1e-7)
+    flow_orig[:, :, 1] = torch.sign(flow_y) * sf_y * (flow_y**2 - 1e-7)
+    
+    return flow_orig
+
+#--------------------------------------------------------------------------------------------------
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
+    # parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
+    parser.add_argument("--image_path", type=str, default=None, help="The path of the image to be used as the background of the video",)
+    parser.add_argument("--data_root", type=str, required=True, help="The path of the dataset root",)
+    parser.add_argument("--fvsm_path", type=str, required=True, help="Path of the pre-trained model use")
+    parser.add_argument("--omsm_path", type=str, required=True, help="Path of the pre-trained model use")
+    parser.add_argument("--output_path", type=str, default="./output.mp4", help="The path save generated video")
+    parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
+    parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--num_frames", type=int, default=49, help="Number of steps for the inference process")
+    parser.add_argument("--width", type=int, default=None, help="The width of the generated video")
+    parser.add_argument("--height", type=int, default=None, help="The height of the generated video")
+    parser.add_argument("--fps", type=int, default=16, help="The frames per second for the generated video")
+    parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="The data type for computation")
+    parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
+    parser.add_argument("--controlnet_guidance_end", type=float, default=0.4, help="Controlnet guidance end during sampling")
+    parser.add_argument("--max_num_videos", type=int, default=None, help="# of videos for inference")
+    parser.add_argument("--use_dynamic_cfg", action='store_true')
+    parser.add_argument("--pose_type", type=str, default='manual', help="pose type in the inference time")
+    parser.add_argument("--speed", type=float, default=0.5, help="pose type in the inference time")
+    parser.add_argument("--use_flow_integration", action='store_true')
+    
+    
+    # DDP args
+    parser.add_argument("--launcher", type=str, choices=["pytorch", "slurm"], default="pytorch")
+    parser.add_argument("--world_size", default=1, type=int,
+                        help="number of the distributed processes.")
+    parser.add_argument('--local-rank', type=int, default=-1,
+                        help='Replica rank on the current node. This field is required '
+                             'by `torch.distributed.launch`.')
+    parser.add_argument("--global_seed", default=42, type=int,
+                        help="seed")
+    parser.add_argument("--port", type=int)
+    parser.add_argument("--local_rank", type=int, help="Local rank. Necessary for using the torch.distributed.launch utility.")
+
+
+    args = parser.parse_args()
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
+    
+    
+    generate_video(
+        # prompt=args.prompt,
+        launcher=args.launcher,
+        port=args.port,
+        data_root=args.data_root,
+        fvsm_path=args.fvsm_path,
+        omsm_path=args.omsm_path,
+        output_path=args.output_path,
+        num_frames=args.num_frames,
+        width=args.width,
+        height=args.height,
+        image_path=args.image_path,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        num_videos_per_prompt=args.num_videos_per_prompt,
+        dtype=dtype,
+        seed=args.seed,
+        fps=args.fps,
+        controlnet_guidance_end=args.controlnet_guidance_end,
+        max_num_videos=args.max_num_videos,
+        use_dynamic_cfg=args.use_dynamic_cfg,
+        pose_type=args.pose_type,
+        speed=args.speed,
+        use_flow_integration=args.use_flow_integration,
+    )
diff --git a/inference/flovd_demo.py b/inference/flovd_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..34ffc955e8459599a1296cefa9110668318c9c77
--- /dev/null
+++ b/inference/flovd_demo.py
@@ -0,0 +1,594 @@
+"""
+This script demonstrates how to generate a video using the CogVideoX model with the Hugging Face `diffusers` pipeline.
+The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
+and video-to-video (v2v), depending on the input data and different weight.
+
+- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
+
+Running the Script:
+To run the script, use the following command with appropriate arguments:
+
+```bash
+$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
+```
+
+You can change `pipe.enable_sequential_cpu_offload()` to `pipe.enable_model_cpu_offload()` to speed up inference, but this will use more GPU memory
+
+Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+
+"""
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
+import argparse
+import logging
+import os
+import sys
+from typing import Literal, Optional
+from pathlib import Path
+import json
+from datetime import timedelta
+import random
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from einops import rearrange, repeat
+import math
+import numpy as np
+from PIL import Image
+
+import torch
+
+from diffusers import (
+    CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
+    CogVideoXVideoToVideoPipeline,
+    AutoencoderKLCogVideoX,
+    CogVideoXTransformer3DModel,
+)
+from diffusers.utils import export_to_video, load_image, load_video
+from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
+
+sys.path.append(os.path.abspath(os.path.join(sys.path[0], "../")))
+from finetune.pipeline.flovd_FVSM_cogvideox_controlnet_pipeline import FloVDCogVideoXControlnetImageToVideoPipeline
+from finetune.pipeline.flovd_OMSM_cogvideox_pipeline import FloVDOMSMCogVideoXImageToVideoPipeline
+from finetune.schemas import Components, Args
+from finetune.modules.cogvideox_controlnet import CogVideoXControlnet
+from finetune.modules.cogvideox_custom_model import CustomCogVideoXTransformer3DModel
+from transformers import AutoTokenizer, T5EncoderModel
+
+from finetune.modules.camera_sampler import SampleManualCam
+from finetune.modules.camera_flow_generator import CameraFlowGenerator
+from finetune.modules.utils import get_camera_flow_generator_input, forward_bilinear_splatting, flow_to_color
+from finetune.modules.depth_warping.depth_warping import unnormalize_intrinsic
+
+from finetune.datasets.utils import (
+    preprocess_image_with_resize,
+    preprocess_video_with_resize,
+)
+
+
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+
+import pdb
+sys.path.append(os.path.abspath(os.path.join(sys.path[-1], 'finetune'))) # for camera flow generator
+
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+logging.basicConfig(level=logging.INFO)
+
+# Recommended resolution for each model (width, height)
+RESOLUTION_MAP = {
+    # cogvideox1.5-*
+    "cogvideox1.5-5b-i2v": (768, 1360),
+    "cogvideox1.5-5b": (768, 1360),
+    # cogvideox-*
+    "cogvideox-5b-i2v": (480, 720),
+    "cogvideox-5b": (480, 720),
+    "cogvideox-2b": (480, 720),
+}
+
+
+
+
+def load_cogvideox_flovd_FVSM_controlnet_pipeline(controlnet_path, backbone_path, device, dtype):
+    controlnet_sd = torch.load(controlnet_path, map_location='cpu')['module']
+    
+    tokenizer = AutoTokenizer.from_pretrained(backbone_path, subfolder="tokenizer")
+    text_encoder = T5EncoderModel.from_pretrained(backbone_path, subfolder="text_encoder")
+    transformer = CustomCogVideoXTransformer3DModel.from_pretrained(backbone_path, subfolder="transformer")
+    vae = AutoencoderKLCogVideoX.from_pretrained(backbone_path, subfolder="vae")
+    scheduler = CogVideoXDPMScheduler.from_pretrained(backbone_path, subfolder="scheduler")
+    
+    additional_kwargs = {
+        'num_layers': 6,
+        'out_proj_dim_factor': 64,
+        'out_proj_dim_zero_init': True,
+        'notextinflow': True,
+    }
+    controlnet = CogVideoXControlnet.from_pretrained(backbone_path, subfolder="transformer", **additional_kwargs)
+    controlnet.eval()
+    
+    missing, unexpected = controlnet.load_state_dict(controlnet_sd)
+    
+    if len(missing) != 0 or len(unexpected) != 0:
+        print(f"Missing keys : {missing}")
+        print(f"Unexpected keys : {unexpected}")
+        
+    pipe = FloVDCogVideoXControlnetImageToVideoPipeline(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            controlnet=controlnet,
+            scheduler=scheduler,
+    )
+    
+    # pipe.enable_model_cpu_offload(device=device)
+    pipe = pipe.to(device, dtype)
+    
+    return pipe
+
+def load_cogvideox_flovd_OMSM_lora_pipeline(omsm_path, backbone_path, transformer_lora_config, device, dtype):
+    tokenizer = AutoTokenizer.from_pretrained(backbone_path, subfolder="tokenizer")
+    text_encoder = T5EncoderModel.from_pretrained(backbone_path, subfolder="text_encoder")
+    transformer = CogVideoXTransformer3DModel.from_pretrained(backbone_path, subfolder="transformer")
+    vae = AutoencoderKLCogVideoX.from_pretrained(backbone_path, subfolder="vae")
+    scheduler = CogVideoXDPMScheduler.from_pretrained(backbone_path, subfolder="scheduler")
+
+    # 1) Load Lora weight
+    transformer.add_adapter(transformer_lora_config)
+
+    lora_state_dict = FloVDOMSMCogVideoXImageToVideoPipeline.lora_state_dict(omsm_path)
+    transformer_state_dict = {
+        f'{k.replace("transformer.", "")}': v
+        for k, v in lora_state_dict.items()
+        if k.startswith("transformer.")
+    }
+    incompatible_keys = set_peft_model_state_dict(transformer, transformer_state_dict, adapter_name="default")
+    if incompatible_keys is not None:
+        # check only for unexpected keys
+        unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+        if unexpected_keys:
+            logger.warning(
+                f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                f" {unexpected_keys}. "
+            )
+
+    # 2) Load Other weight
+    load_path = os.path.join(omsm_path, "selected_blocks.safetensors")
+    if os.path.exists(load_path):
+        tensor_dict = load_file(load_path)
+        
+        block_state_dicts = {}
+        for k, v in tensor_dict.items():
+            block_name, param_name = k.split(".", 1)
+            if block_name not in block_state_dicts:
+                block_state_dicts[block_name] = {}
+            block_state_dicts[block_name][param_name] = v
+        
+        for block_name, state_dict in block_state_dicts.items():
+            if hasattr(transformer, block_name):
+                getattr(transformer, block_name).load_state_dict(state_dict)
+            else:
+                raise ValueError(f"Transformer has no attribute '{block_name}'")
+    
+    
+    pipe = FloVDOMSMCogVideoXImageToVideoPipeline(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+    )
+    
+    # pipe.load_lora_weights(omsm_path, weight_name="pytorch_lora_weights.safetensors", adapter_name="test_1")
+    # pipe.fuse_lora(components=["transformer"], lora_scale=1.0)
+    
+    # pipe.enable_model_cpu_offload(device=device)
+    pipe = pipe.to(device, dtype)
+    
+    return pipe
+
+
+def initialize_flow_generator(target, ckpt_path):
+    depth_estimator_kwargs = {
+        "target": target,
+        "kwargs": {
+            "ckpt_path": ckpt_path, # './ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth',
+            "model_config": {
+                "max_depth": 20,
+                "encoder": 'vitb',
+                "features": 128,
+                "out_channels": [96, 192, 384, 768],
+            }
+
+        }
+    }
+
+    return CameraFlowGenerator(depth_estimator_kwargs)
+
+def integrate_flow(camera_flow, object_flow, depth_ctxt, camera_flow_generator, camera_flow_generator_input):
+    # camera_flow: (BF)CHW
+    # object_flow: (BF)CHW
+    # depth_ctxt: B1HW
+    
+    B, F = camera_flow_generator_input["target"]["intrinsics"].shape[:2]
+    H, W = object_flow.shape[-2:]
+    
+    c2w_ctxt = repeat(camera_flow_generator_input["context"]["extrinsics"], "b t h w -> (b v t) h w", v=F) # No need to apply inverse as it is an eye matrix.
+    c2w_trgt = rearrange(torch.inverse(camera_flow_generator_input["target"]["extrinsics"]), "b t h w -> (b t) h w")
+    intrinsics_ctxt = unnormalize_intrinsic(repeat(camera_flow_generator_input["context"]["intrinsics"], "b t h w -> (b v t) h w", v=F), size=(H, W))
+        
+    with torch.cuda.amp.autocast(enabled=False):
+        warped_object_flow = camera_flow_generator.depth_warping_module.warper.forward_warp_displacement(
+            depth1=repeat(depth_ctxt, "b c h w -> (b f) c h w", f=F),
+            flow1=object_flow, 
+            transformation1=c2w_ctxt, 
+            transformation2=c2w_trgt, 
+            intrinsic1=intrinsics_ctxt, 
+            intrinsic2=None,
+        )
+    
+    integrated_flow = camera_flow + warped_object_flow
+    
+    return integrated_flow
+    
+def save_flow(flow, filename, fps=16):
+    # flow: (BF)CHW, arbitrary scale
+    flow_RGB = flow_to_color(flow) # BF,C,H,W (B=1)
+
+    frame_list = []
+    for frame in flow_RGB:
+        frame = (frame.permute(1,2,0).float().detach().cpu().numpy()).astype(np.uint8).clip(0,255)
+        frame_list.append(Image.fromarray(frame))
+    
+    export_to_video(frame_list, filename, fps=fps)
+
+def save_flow_warped_video(image, flow, filename, fps=16):
+    # image: CHW, 0~255 scale
+    # flow: (BF)CHW, arbitrary scale
+    warped_video = forward_bilinear_splatting(repeat(image, 'c h w -> f c h w', f=flow.size(0)), flow.to(torch.float))
+    
+    frame_list = []
+    for frame in warped_video:
+        frame = (frame.permute(1,2,0).float().detach().cpu().numpy()).astype(np.uint8).clip(0,255)
+        frame_list.append(Image.fromarray(frame))
+    
+    export_to_video(frame_list, filename, fps=fps)
+
+def generate_video(
+    prompt: str,
+    fvsm_path: str,
+    omsm_path: str,
+    num_frames: int = 81,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    output_path: str = "./output.mp4",
+    image_path: str = "",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 42,
+    fps: int = 16,
+    controlnet_guidance_end: float = 0.4,
+    use_dynamic_cfg: bool = False,
+    pose_type: str = "manual",
+    speed: float = 0.5,
+    use_flow_integration: bool = False,
+    cam_pose_name: str = None,
+    depth_ckpt_path: str = "./ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth",
+):
+    """
+    Generates a video based on the given prompt and saves it to the specified path.
+
+    Parameters:
+    - prompt (str): The description of the video to be generated.
+    - lora_path (str): The path of the LoRA weights to be used.
+    - lora_rank (int): The rank of the LoRA weights.
+    - output_path (str): The path where the generated video will be saved.
+    - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
+    - width (int): The width of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - height (int): The height of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
+    - num_videos_per_prompt (int): Number of videos to generate per prompt.
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
+    - generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
+    - seed (int): The seed for reproducibility.
+    - fps (int): The frames per second for the generated video.
+    """
+    
+    local_rank = 'cuda'
+    
+    torch.manual_seed(seed)
+    random.seed(seed)
+    
+    os.makedirs(os.path.join(output_path, 'generated_videos'), exist_ok=True)
+
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
+    # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
+    # function to use Multi GPUs.
+
+    image = None
+    video = None
+
+    model_name = "cogvideox-5b-i2v".lower()
+    desired_resolution = RESOLUTION_MAP[model_name]
+    if width is None or height is None:
+        height, width = desired_resolution
+        logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m")
+    elif (height, width) != desired_resolution:
+        if generate_type == "i2v":
+            # For i2v models, use user-defined width and height
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
+
+    
+
+    """
+        # Prepare Pipeline
+    """
+    transformer_lora_config = LoraConfig(
+        r=128,
+        lora_alpha=64,
+        init_lora_weights=True,
+        target_modules=["to_q", "to_k", "to_v", "to_out.0", "norm1.linear", "norm2.linear", "ff.net.2"],
+    )
+    
+    print(f'Constructing pipeline')
+    pipe_omsm = load_cogvideox_flovd_OMSM_lora_pipeline(omsm_path, backbone_path="THUDM/CogVideoX-5b-I2V", transformer_lora_config=transformer_lora_config, device=local_rank, dtype=dtype)   
+    pipe_fvsm = load_cogvideox_flovd_FVSM_controlnet_pipeline(fvsm_path, backbone_path="THUDM/CogVideoX-5b-I2V", device=local_rank, dtype=dtype)   
+    print(f'Done loading pipeline')
+    
+    """
+        # Prepare inputs
+    """
+    image = load_image(image=image_path)
+    
+    assert pose_type in ['re10k', 'manual'], "Choose other pose_type between ['re10k', 'manual']"
+    if pose_type == 're10k':
+        root_path = "./assets/re10k_poses"
+    else:
+        root_path = "./assets/manual_poses"
+        
+    CameraSampler = SampleManualCam(pose_type=pose_type, root_path=root_path)
+    camera_flow_generator_target = 'finetune.modules.depth_warping.depth_warping.DepthWarping_wrapper'
+    camera_flow_generator = initialize_flow_generator(camera_flow_generator_target, ckpt_path=depth_ckpt_path).to(local_rank)
+    #--------------------------------------------------------------------------------------------------------
+
+
+    # 2. Set Scheduler.
+    # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
+    # We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B.
+    # using `CogVideoXDPMScheduler` for CogVideoX-5B / CogVideoX-5B-I2V.
+
+    pipe_fvsm.scheduler = CogVideoXDPMScheduler.from_config(pipe_fvsm.scheduler.config, timestep_spacing="trailing")
+    pipe_omsm.scheduler = CogVideoXDPMScheduler.from_config(pipe_omsm.scheduler.config, timestep_spacing="trailing")
+
+    # 3. Enable CPU offload for the model.
+    # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
+    # and enable to("cuda")
+    # pipe.to("cuda")
+
+    # pipe_fvsm.enable_model_cpu_offload()
+    # pipe_omsm.enable_model_cpu_offload()
+    # pipe_fvsm.enable_sequential_cpu_offload()
+    # pipe_omsm.enable_sequential_cpu_offload()
+    
+    pipe_fvsm.vae.enable_slicing()
+    pipe_fvsm.vae.enable_tiling()
+    pipe_omsm.vae.enable_slicing()
+    pipe_omsm.vae.enable_tiling()
+    
+    
+    output_video_path = os.path.join(output_path, 'generated_videos')
+    
+    """
+        # Inference time
+    """
+    image = rearrange((torch.tensor(np.array(image)).to(torch.float) / 255. * 2. - 1.).unsqueeze(0), 'b h w c -> b c h w')
+    image = image.to(local_rank)
+    prompt_short = prompt[:30].strip().replace(" ", "_")
+    
+    # Get Camera flow
+    camparam, cam_name = CameraSampler.sample(name=cam_pose_name) # W2C
+    image_torch_255 = ((image.detach().clone()+1)/2. * 255.).squeeze(0)
+    camera_flow_generator_input = get_camera_flow_generator_input(image_torch_255, camparam, device=local_rank, speed=speed)
+    image_torch = ((image_torch_255.unsqueeze(0) / 255.) * 2. - 1.).to(local_rank)
+    
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(enabled=True, dtype=dtype):
+            
+            flow_latent = pipe_omsm(
+                num_frames=num_frames,
+                height=height,
+                width=width,
+                prompt=prompt,
+                image=image,
+                generator=torch.Generator().manual_seed(seed),
+                num_inference_steps=num_inference_steps,
+                use_dynamic_cfg=use_dynamic_cfg,
+                output_type='latent'
+            ).frames[0]
+            object_flow = decode_flow(flow_latent.detach().clone().unsqueeze(0).to(local_rank), pipe_omsm.vae, flow_scale_factor=[60, 36]) # BF,C,H,W
+            
+            if use_flow_integration:
+                # Integrate camera (from 3D warping) and object (from OMSM) flow maps
+                # Using segmentation model will be implemented later..
+                
+                camera_flow, log_dict = camera_flow_generator(image_torch, camera_flow_generator_input)
+                camera_flow = camera_flow.to(local_rank, dtype)
+
+                integrated_flow = integrate_flow(camera_flow, object_flow, log_dict['depth_ctxt'], camera_flow_generator, camera_flow_generator_input)
+                integrated_flow_latent = rearrange(encode_flow(integrated_flow, pipe_omsm.vae, flow_scale_factor=[60, 36]), 'b c f h w -> b f c h w').to(local_rank, dtype)
+            else:
+                integrated_flow_latent = rearrange(flow_latent, '(b f) c h w -> b f c h w', b=image.size(0))
+            
+            # 4. Generate the video frames based on the prompt.
+            # `num_frames` is the Number of frames to generate.
+            video_generate = pipe_fvsm(
+                num_frames=num_frames,
+                height=height,
+                width=width,
+                prompt=prompt,
+                image=image,
+                flow_latent=integrated_flow_latent,
+                valid_mask=None,
+                generator=torch.Generator().manual_seed(seed),
+                num_inference_steps=num_inference_steps,
+                controlnet_guidance_start = 0.0,
+                controlnet_guidance_end = controlnet_guidance_end,
+                use_dynamic_cfg=use_dynamic_cfg,
+            ).frames[0]
+        
+    # Save Result
+    save_path = os.path.join(output_video_path, f"{prompt_short}_{cam_name}.mp4")
+    export_to_video(video_generate, save_path, fps=fps)
+    
+
+
+#--------------------------------------------------------------------------------------------------
+def encode_video(video: torch.Tensor, vae) -> torch.Tensor:
+    # shape of input video: [B, C, F, H, W]
+    video = video.to(vae.device, dtype=vae.dtype)
+    latent_dist = vae.encode(video).latent_dist
+    latent = latent_dist.sample() * vae.config.scaling_factor
+    return latent
+
+def encode_flow(flow, vae, flow_scale_factor):
+    # flow: BF,C,H,W
+    # flow_scale_factor [sf_x, sf_y]
+    assert flow.ndim == 4
+    num_frames, _, height, width = flow.shape
+
+    # Normalize optical flow
+    # ndim: 4 -> 5
+    flow = rearrange(flow, '(b f) c h w -> b f c h w', b=1)
+    flow_norm = adaptive_normalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    # ndim: 5 -> 4
+    flow_norm = rearrange(flow_norm, 'b f c h w -> (b f) c h w', b=1)
+
+    # Duplicate mean value for third channel
+    num_frames, _, H, W = flow_norm.shape
+    flow_norm_extended = torch.empty((num_frames, 3, height, width)).to(flow_norm)
+    flow_norm_extended[:,:2] = flow_norm
+    flow_norm_extended[:,-1:] = flow_norm.mean(dim=1, keepdim=True)
+    flow_norm_extended = rearrange(flow_norm_extended, '(b f) c h w -> b c f h w', f=num_frames)
+
+    return encode_video(flow_norm_extended, vae)
+
+
+def decode_flow(flow_latent, vae, flow_scale_factor):
+    flow_latent = flow_latent.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+    flow_latent = 1 / vae.config.scaling_factor * flow_latent
+    
+    flow = vae.decode(flow_latent).sample # BCFHW
+
+    # discard third channel (which is a mean value of f_x and f_y)
+    flow = flow[:,:2].detach().clone()
+
+    # Unnormalize optical flow
+    flow = rearrange(flow, 'b c f h w -> b f c h w')
+    flow = adaptive_unnormalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    flow = rearrange(flow, 'b f c h w -> (b f) c h w')
+    return flow # BF,C,H,W
+
+def adaptive_normalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    b, f, c, h, w = flow.shape
+    
+    max_clip_x = math.sqrt(w/sf_x) * 1.0
+    max_clip_y = math.sqrt(h/sf_y) * 1.0
+    
+    flow_norm = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_x_norm = torch.sign(flow_x) * torch.sqrt(torch.abs(flow_x)/sf_x + 1e-7)
+    flow_y_norm = torch.sign(flow_y) * torch.sqrt(torch.abs(flow_y)/sf_y + 1e-7)
+
+    flow_norm[:, :, 0] = torch.clamp(flow_x_norm, min=-max_clip_x, max=max_clip_x)
+    flow_norm[:, :, 1] = torch.clamp(flow_y_norm, min=-max_clip_y, max=max_clip_y)
+
+    return flow_norm
+
+
+def adaptive_unnormalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    
+    flow_orig = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_orig[:, :, 0] = torch.sign(flow_x) * sf_x * (flow_x**2 - 1e-7)
+    flow_orig[:, :, 1] = torch.sign(flow_y) * sf_y * (flow_y**2 - 1e-7)
+    
+    return flow_orig
+
+#--------------------------------------------------------------------------------------------------
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
+    parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
+    parser.add_argument("--image_path", type=str, default=None, help="The path of the image to be used as the background of the video",)
+    parser.add_argument("--fvsm_path", type=str, required=True, help="Path of the pre-trained model use")
+    parser.add_argument("--omsm_path", type=str, required=True, help="Path of the pre-trained model use")
+    parser.add_argument("--output_path", type=str, default="./output.mp4", help="The path save generated video")
+    parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
+    parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--num_frames", type=int, default=49, help="Number of steps for the inference process")
+    parser.add_argument("--width", type=int, default=None, help="The width of the generated video")
+    parser.add_argument("--height", type=int, default=None, help="The height of the generated video")
+    parser.add_argument("--fps", type=int, default=16, help="The frames per second for the generated video")
+    parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="The data type for computation")
+    parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
+    parser.add_argument("--controlnet_guidance_end", type=float, default=0.4, help="Controlnet guidance end during sampling")
+    parser.add_argument("--use_dynamic_cfg", action='store_true')
+    parser.add_argument("--pose_type", type=str, default='manual', help="pose type in the inference time")
+    parser.add_argument("--speed", type=float, default=0.5, help="pose type in the inference time")
+    parser.add_argument("--use_flow_integration", action='store_true')
+    parser.add_argument("--cam_pose_name", type=str, required=False, default=None, help="Camera trajectory name")
+    parser.add_argument("--depth_ckpt_path", type=str, required=False, default="./ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth", help="Camera trajectory name")
+
+    args = parser.parse_args()
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
+    
+    
+    generate_video(
+        prompt=args.prompt,
+        fvsm_path=args.fvsm_path,
+        omsm_path=args.omsm_path,
+        output_path=args.output_path,
+        num_frames=args.num_frames,
+        width=args.width,
+        height=args.height,
+        image_path=args.image_path,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        num_videos_per_prompt=args.num_videos_per_prompt,
+        dtype=dtype,
+        seed=args.seed,
+        fps=args.fps,
+        controlnet_guidance_end=args.controlnet_guidance_end,
+        use_dynamic_cfg=args.use_dynamic_cfg,
+        pose_type=args.pose_type,
+        speed=args.speed,
+        use_flow_integration=args.use_flow_integration,
+        cam_pose_name=args.cam_pose_name,
+        depth_ckpt_path=args.depth_ckpt_path
+    )
diff --git a/inference/flovd_fvsm_ddp_demo.py b/inference/flovd_fvsm_ddp_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..12153d9cee6255834e93c295e0d9a42d78b7d51b
--- /dev/null
+++ b/inference/flovd_fvsm_ddp_demo.py
@@ -0,0 +1,640 @@
+"""
+This script demonstrates how to generate a video using the CogVideoX model with the Hugging Face `diffusers` pipeline.
+The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
+and video-to-video (v2v), depending on the input data and different weight.
+
+- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
+- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
+
+Running the Script:
+To run the script, use the following command with appropriate arguments:
+
+```bash
+$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
+```
+
+You can change `pipe.enable_sequential_cpu_offload()` to `pipe.enable_model_cpu_offload()` to speed up inference, but this will use more GPU memory
+
+Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+
+"""
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
+import argparse
+import logging
+import os
+import sys
+from typing import Literal, Optional
+from pathlib import Path
+import json
+from datetime import timedelta
+import random
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from einops import rearrange, repeat
+import math
+
+import torch
+
+from diffusers import (
+    CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
+    CogVideoXVideoToVideoPipeline,
+    AutoencoderKLCogVideoX
+)
+from diffusers.utils import export_to_video, load_image, load_video
+
+sys.path.append(os.path.abspath(os.path.join(sys.path[0], "../")))
+from finetune.pipeline.flovd_FVSM_cogvideox_controlnet_pipeline import FloVDCogVideoXControlnetImageToVideoPipeline
+from finetune.schemas import Components, Args
+from finetune.modules.cogvideox_controlnet import CogVideoXControlnet
+from finetune.modules.cogvideox_custom_model import CustomCogVideoXTransformer3DModel
+from transformers import AutoTokenizer, T5EncoderModel
+
+from finetune.modules.camera_sampler import SampleManualCam
+from finetune.modules.camera_flow_generator import CameraFlowGenerator
+from finetune.modules.utils import get_camera_flow_generator_input, forward_bilinear_splatting
+
+from finetune.datasets.utils import (
+    preprocess_image_with_resize,
+    preprocess_video_with_resize,
+)
+
+
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import torch.distributed as dist
+from torch.utils.data.distributed import DistributedSampler
+
+import pdb
+sys.path.append(os.path.abspath(os.path.join(sys.path[-1], 'finetune'))) # for camera flow generator
+
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+logging.basicConfig(level=logging.INFO)
+
+# Recommended resolution for each model (width, height)
+RESOLUTION_MAP = {
+    # cogvideox1.5-*
+    "cogvideox1.5-5b-i2v": (768, 1360),
+    "cogvideox1.5-5b": (768, 1360),
+    # cogvideox-*
+    "cogvideox-5b-i2v": (480, 720),
+    "cogvideox-5b": (480, 720),
+    "cogvideox-2b": (480, 720),
+}
+
+
+
+
+def init_dist(launcher="slurm", backend='nccl', port=29500, **kwargs):
+    """Initializes distributed environment."""
+    if launcher == 'pytorch':
+        rank = int(os.environ['RANK'])
+        num_gpus = torch.cuda.device_count()
+        local_rank = rank % num_gpus
+        torch.cuda.set_device(local_rank)
+        dist.init_process_group(backend=backend, timeout=timedelta(minutes=30), **kwargs)
+
+    elif launcher == 'slurm':
+        proc_id = int(os.environ['SLURM_PROCID'])
+        ntasks = int(os.environ['SLURM_NTASKS'])
+        node_list = os.environ['SLURM_NODELIST']
+        num_gpus = torch.cuda.device_count()
+        local_rank = proc_id % num_gpus
+        torch.cuda.set_device(local_rank)
+        addr = subprocess.getoutput(
+            f'scontrol show hostname {node_list} | head -n1')
+        os.environ['MASTER_ADDR'] = addr
+        os.environ['WORLD_SIZE'] = str(ntasks)
+        os.environ['RANK'] = str(proc_id)
+        port = os.environ.get('PORT', port)
+        os.environ['MASTER_PORT'] = str(port)
+        dist.init_process_group(backend=backend, timeout=timedelta(minutes=30))
+
+    else:
+        raise NotImplementedError(f'Not implemented launcher type: `{launcher}`!')
+    # https://github.com/pytorch/pytorch/issues/98763
+    # torch.cuda.set_device(local_rank)
+
+    return local_rank
+
+
+def load_cogvideox_flovd_FVSM_controlnet_pipeline(controlnet_path, backbone_path, device, dtype):
+    controlnet_sd = torch.load(controlnet_path)['module']
+    
+    tokenizer = AutoTokenizer.from_pretrained(backbone_path, subfolder="tokenizer")
+    text_encoder = T5EncoderModel.from_pretrained(backbone_path, subfolder="text_encoder")
+    transformer = CustomCogVideoXTransformer3DModel.from_pretrained(backbone_path, subfolder="transformer")
+    vae = AutoencoderKLCogVideoX.from_pretrained(backbone_path, subfolder="vae")
+    scheduler = CogVideoXDPMScheduler.from_pretrained(backbone_path, subfolder="scheduler")
+    
+    additional_kwargs = {
+        'num_layers': 6,
+        'out_proj_dim_factor': 64,
+        'out_proj_dim_zero_init': True,
+        'notextinflow': True,
+    }
+    controlnet = CogVideoXControlnet.from_pretrained(backbone_path, subfolder="transformer", **additional_kwargs)
+    controlnet.eval()
+    
+    missing, unexpected = controlnet.load_state_dict(controlnet_sd)
+    
+    if len(missing) != 0 or len(unexpected) != 0:
+        print(f"Missing keys : {missing}")
+        print(f"Unexpected keys : {unexpected}")
+        
+    pipe = FloVDCogVideoXControlnetImageToVideoPipeline(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            controlnet=controlnet,
+            scheduler=scheduler,
+    )
+    
+    # pipe.enable_model_cpu_offload(device=device)
+    pipe = pipe.to(device, dtype)
+    
+    return pipe
+
+class I2VFlowDataset_Inference(Dataset):
+    def __init__(
+        self, 
+        max_num_frames: int, 
+        height: int, 
+        width: int, 
+        data_root: str,
+        max_num_videos: int = None,
+    ) -> None:
+        
+        self.train_resolution = (int(max_num_frames), int(height), int(width))
+        
+        data_root = Path(data_root)
+        metadata_path = data_root / "metadata_revised.jsonl"
+        assert metadata_path.is_file(), "For this dataset type, you need metadata.jsonl in the root path"
+        
+        metadata = []
+        with open(metadata_path, "r") as f:
+            for line in f:
+                metadata.append( json.loads(line) )
+        
+        metadata = random.sample(metadata, max_num_videos)
+
+        self.prompts = [x["prompt"] for x in metadata]
+        self.prompt_embeddings = [data_root / "prompt_embeddings_revised" / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.videos = [data_root / "video_latent" / "x".join(str(x) for x in self.train_resolution) / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.images = [data_root / "first_frames" / (x["hash_code"] + '.png') for x in metadata]
+        self.flows = [data_root / "flow_direct_f_latent" / (x["hash_code"] + '.safetensors') for x in metadata]
+        self.masks = [data_root / "valid_mask" / (x["hash_code"] + '.bin') for x in metadata]
+        
+        self.max_num_frames = max_num_frames
+        self.height = height
+        self.width = width
+
+        self.__frame_transforms = transforms.Compose([transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0)])
+        self.__image_transforms = self.__frame_transforms
+        
+        self.length = len(self.videos)
+
+        print(f"Dataset size: {self.length}")
+        
+    def __len__(self) -> int:
+        return self.length
+    
+    def load_data_pair(self, index):
+        prompt_embedding_path = self.prompt_embeddings[index]
+        encoded_video_path = self.videos[index]
+        encoded_flow_path = self.flows[index]
+        
+        prompt_embedding = load_file(prompt_embedding_path)["prompt_embedding"] 
+        encoded_video = load_file(encoded_video_path)["encoded_video"] # CFHW
+        encoded_flow = load_file(encoded_flow_path)["encoded_flow_f"] # CFHW
+    
+        return prompt_embedding, encoded_video, encoded_flow
+
+    def __getitem__(self, index: int) -> Dict[str, Any]:
+        while True:
+            try:
+                prompt_embedding, encoded_video, encoded_flow = self.load_data_pair(index)
+                break
+            except Exception as e:
+                print(f"Error loading {self.prompt_embeddings[index]}: {str(e)}")
+                index = random.randint(0, self.length - 1)
+            
+        image_path = self.images[index]
+        prompt = self.prompts[index]
+        
+        _, image = self.preprocess(None, image_path)
+        image = self.image_transform(image)
+        
+        
+        # shape of encoded_video: [C, F, H, W]
+        # shape and scale of image: [C, H, W], [-1,1]
+        return {
+            "image": image,
+            "prompt": prompt,
+            "prompt_embedding": prompt_embedding,
+            "encoded_video": encoded_video,
+            "encoded_flow": encoded_flow,
+            "video_metadata": {
+                "num_frames": encoded_video.shape[1],
+                "height": encoded_video.shape[2],
+                "width": encoded_video.shape[3],
+            },
+        }
+    
+    def preprocess(self, video_path: Path | None, image_path: Path | None) -> Tuple[torch.Tensor, torch.Tensor]:
+        if video_path is not None:
+            video = preprocess_video_with_resize(video_path, self.max_num_frames, self.height, self.width)
+        else:
+            video = None
+        if image_path is not None:
+            image = preprocess_image_with_resize(image_path, self.height, self.width)
+        else:
+            image = None
+        return video, image
+    
+    def video_transform(self, frames: torch.Tensor) -> torch.Tensor:
+        return torch.stack([self.__frame_transforms(f) for f in frames], dim=0)
+
+    def image_transform(self, image: torch.Tensor) -> torch.Tensor:
+        return self.__image_transforms(image)
+
+def initialize_flow_generator(target):
+    depth_estimator_kwargs = {
+        "target": target,
+        "kwargs": {
+            "ckpt_path": '/workspace/workspace/checkpoints/depth_anything/depth_anything_v2_metric_hypersim_vitb.pth',
+            "model_config": {
+                "max_depth": 20,
+                "encoder": 'vitb',
+                "features": 128,
+                "out_channels": [96, 192, 384, 768],
+            }
+
+        }
+    }
+
+    return CameraFlowGenerator(depth_estimator_kwargs)
+
+def generate_video(
+    # prompt: str,
+    launcher: str,
+    port: int,
+    data_root: str,
+    model_path: str,
+    num_frames: int = 81,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    output_path: str = "./output.mp4",
+    image_path: str = "",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 42,
+    fps: int = 16,
+    controlnet_guidance_end: float = 0.4,
+    max_num_videos: int = None,
+    use_dynamic_cfg: bool = False,
+    pose_type: str = "manual",
+    speed: float = 0.5,
+):
+    """
+    Generates a video based on the given prompt and saves it to the specified path.
+
+    Parameters:
+    - prompt (str): The description of the video to be generated.
+    - model_path (str): The path of the pre-trained model to be used.
+    - lora_path (str): The path of the LoRA weights to be used.
+    - lora_rank (int): The rank of the LoRA weights.
+    - output_path (str): The path where the generated video will be saved.
+    - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
+    - width (int): The width of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - height (int): The height of the generated video, applicable only for CogVideoX1.5-5B-I2V
+    - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
+    - num_videos_per_prompt (int): Number of videos to generate per prompt.
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
+    - generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
+    - seed (int): The seed for reproducibility.
+    - fps (int): The frames per second for the generated video.
+    """
+    
+    # Distributed
+    local_rank = init_dist(launcher=launcher, port=port)
+    global_rank = dist.get_rank()
+    num_processes = dist.get_world_size()
+    is_main_process = global_rank == 0
+    
+    torch.manual_seed(seed)
+    random.seed(seed)
+    
+    if is_main_process:
+        os.makedirs(os.path.join(output_path, 'generated_videos'), exist_ok=True)
+
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
+    # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
+    # function to use Multi GPUs.
+
+    image = None
+    video = None
+
+    model_name = "cogvideox-5b-i2v".lower()
+    desired_resolution = RESOLUTION_MAP[model_name]
+    if width is None or height is None:
+        height, width = desired_resolution
+        logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m")
+    elif (height, width) != desired_resolution:
+        if generate_type == "i2v":
+            # For i2v models, use user-defined width and height
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
+
+    """
+        # Prepare Dataset Class..
+    """
+    # image = load_image(image=image_or_video_path)
+    
+    # prompt
+    # first image
+    # camera parameters
+    dataset = I2VFlowDataset_Inference(
+        max_num_frames=num_frames,
+        height=height,
+        width=width,
+        data_root=data_root,
+        max_num_videos=max_num_videos,
+    )
+    
+    
+    distributed_sampler = DistributedSampler(
+        dataset,
+        num_replicas=num_processes,
+        rank=global_rank,
+        shuffle=False,
+        seed=seed,
+    )
+    
+    # DataLoaders creation:
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        sampler=distributed_sampler,
+        num_workers=4,
+        pin_memory=True,
+        drop_last=False,
+    )
+    
+
+    """
+        # Prepare Pipeline
+    """
+    print(f'Constructing pipeline')
+    pipe = load_cogvideox_flovd_FVSM_controlnet_pipeline(model_path, backbone_path="THUDM/CogVideoX-5b-I2V", device=local_rank, dtype=dtype)   
+    
+    assert pose_type in ['re10k', 'manual'], "Choose other pose_type between ['re10k', 'manual']"
+    if pose_type == 're10k':
+        root_path = "./manual_poses_re10k"
+    else:
+        root_path = "./manual_poses"
+        
+    CameraSampler = SampleManualCam(pose_type=pose_type, root_path=root_path)
+    camera_flow_generator_target = 'finetune.modules.depth_warping.depth_warping.DepthWarping_wrapper'
+    camera_flow_generator = initialize_flow_generator(camera_flow_generator_target).to(local_rank)
+    #--------------------------------------------------------------------------------------------------------
+
+
+    # 2. Set Scheduler.
+    # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
+    # We recommend using `CogVideoXDDIMScheduler` for CogVideoX-2B.
+    # using `CogVideoXDPMScheduler` for CogVideoX-5B / CogVideoX-5B-I2V.
+
+    # pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+    pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+    # 3. Enable CPU offload for the model.
+    # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
+    # and enable to("cuda")
+    # pipe.to("cuda")
+
+    # pipe.enable_model_cpu_offload()
+    # pipe.enable_sequential_cpu_offload()
+    pipe.vae.enable_slicing()
+    pipe.vae.enable_tiling()
+    
+    dataloader.sampler.set_epoch(1)
+    dist.barrier()
+    
+    output_path = os.path.join(output_path, 'generated_videos')
+    
+    data_iter = iter(dataloader)
+    for step in tqdm(range(0, len(dataloader))):
+        batch = next(data_iter)
+        
+        prompt = batch["prompt"][0]
+        image = batch["image"].to(local_rank)
+        prompt_embedding = batch["prompt_embedding"].to(local_rank)
+        prompt_short = prompt[:20].strip()
+        
+        # if step < 10:
+        #     step += 1
+        #     continue
+        
+        # Get Camera flow
+        camparam, cam_name = CameraSampler.sample()
+        image_torch = ((image.detach().clone()+1)/2. * 255.).squeeze(0)
+        camera_flow_generator_input = get_camera_flow_generator_input(image_torch, camparam, device=local_rank, speed=speed)
+        image_torch = ((image_torch.unsqueeze(0) / 255.) * 2. - 1.).to(local_rank)
+        
+        with torch.no_grad():
+            with torch.cuda.amp.autocast(enabled=True, dtype=dtype):
+                camera_flow, log_dict = camera_flow_generator(image_torch, camera_flow_generator_input)
+                camera_flow = camera_flow.to(local_rank, dtype)
+                
+                camera_flow_latent = rearrange(encode_flow(camera_flow, pipe.vae, flow_scale_factor=[60, 36]), 'b c f h w -> b f c h w').to(local_rank, dtype)
+                
+                # 4. Generate the video frames based on the prompt.
+                # `num_frames` is the Number of frames to generate.
+                video_generate = pipe(
+                    num_frames=num_frames,
+                    height=height,
+                    width=width,
+                    prompt=None,
+                    prompt_embeds=prompt_embedding,
+                    image=image,
+                    flow_latent=camera_flow_latent,
+                    valid_mask=None,
+                    generator=torch.Generator().manual_seed(seed),
+                    num_inference_steps=50,
+                    controlnet_guidance_start = 0.0,
+                    controlnet_guidance_end = controlnet_guidance_end,
+                    use_dynamic_cfg=use_dynamic_cfg,
+                ).frames[0]
+
+                
+        save_path = os.path.join(output_path, f"{prompt_short}_DCFG-{use_dynamic_cfg}_ContGuide-{controlnet_guidance_end}_{cam_name}.mp4")
+        export_to_video(video_generate, save_path, fps=fps)
+        
+        dist.barrier()
+        
+        step += 1
+
+
+#--------------------------------------------------------------------------------------------------
+def encode_video(video: torch.Tensor, vae) -> torch.Tensor:
+    # shape of input video: [B, C, F, H, W]
+    video = video.to(vae.device, dtype=vae.dtype)
+    latent_dist = vae.encode(video).latent_dist
+    latent = latent_dist.sample() * vae.config.scaling_factor
+    return latent
+
+def encode_flow(flow, vae, flow_scale_factor):
+    # flow: BF,C,H,W
+    # flow_scale_factor [sf_x, sf_y]
+    assert flow.ndim == 4
+    num_frames, _, height, width = flow.shape
+
+    # Normalize optical flow
+    # ndim: 4 -> 5
+    flow = rearrange(flow, '(b f) c h w -> b f c h w', b=1)
+    flow_norm = adaptive_normalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    # ndim: 5 -> 4
+    flow_norm = rearrange(flow_norm, 'b f c h w -> (b f) c h w', b=1)
+
+    # Duplicate mean value for third channel
+    num_frames, _, H, W = flow_norm.shape
+    flow_norm_extended = torch.empty((num_frames, 3, height, width)).to(flow_norm)
+    flow_norm_extended[:,:2] = flow_norm
+    flow_norm_extended[:,-1:] = flow_norm.mean(dim=1, keepdim=True)
+    flow_norm_extended = rearrange(flow_norm_extended, '(b f) c h w -> b c f h w', f=num_frames)
+
+    return encode_video(flow_norm_extended, vae)
+
+
+def decode_flow(flow_latent, vae, flow_scale_factor):
+    flow_latent = flow_latent.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+    flow_latent = 1 / vae.config.scaling_factor * flow_latent
+    
+    flow = vae.decode(flow_latent).sample # BCFHW
+
+    # discard third channel (which is a mean value of f_x and f_y)
+    flow = flow[:,:2].detach().clone()
+
+    # Unnormalize optical flow
+    flow = rearrange(flow, 'b c f h w -> b f c h w')
+    flow = adaptive_unnormalize(flow, flow_scale_factor[0], flow_scale_factor[1])
+
+    flow = rearrange(flow, 'b f c h w -> (b f) c h w')
+    return flow # BF,C,H,W
+
+def adaptive_normalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    b, f, c, h, w = flow.shape
+    
+    max_clip_x = math.sqrt(w/sf_x) * 1.0
+    max_clip_y = math.sqrt(h/sf_y) * 1.0
+    
+    flow_norm = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_x_norm = torch.sign(flow_x) * torch.sqrt(torch.abs(flow_x)/sf_x + 1e-7)
+    flow_y_norm = torch.sign(flow_y) * torch.sqrt(torch.abs(flow_y)/sf_y + 1e-7)
+
+    flow_norm[:, :, 0] = torch.clamp(flow_x_norm, min=-max_clip_x, max=max_clip_x)
+    flow_norm[:, :, 1] = torch.clamp(flow_y_norm, min=-max_clip_y, max=max_clip_y)
+
+    return flow_norm
+
+
+def adaptive_unnormalize(flow, sf_x, sf_y):
+    # x: BFCHW, optical flow
+    assert flow.ndim == 5, 'Set the shape of the flow input as (B, F, C, H, W)'
+    assert sf_x is not None and sf_y is not None
+    
+    flow_orig = flow.detach().clone()
+    flow_x = flow[:, :, 0].detach().clone()
+    flow_y = flow[:, :, 1].detach().clone()
+    
+    flow_orig[:, :, 0] = torch.sign(flow_x) * sf_x * (flow_x**2 - 1e-7)
+    flow_orig[:, :, 1] = torch.sign(flow_y) * sf_y * (flow_y**2 - 1e-7)
+    
+    return flow_orig
+
+#--------------------------------------------------------------------------------------------------
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
+    # parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
+    parser.add_argument("--image_path", type=str, default=None, help="The path of the image to be used as the background of the video",)
+    parser.add_argument("--data_root", type=str, required=True, help="The path of the dataset root",)
+    parser.add_argument("--model_path", type=str, default="THUDM/CogVideoX1.5-5B", help="Path of the pre-trained model use")
+    parser.add_argument("--output_path", type=str, default="./output.mp4", help="The path save generated video")
+    parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
+    parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--num_frames", type=int, default=49, help="Number of steps for the inference process")
+    parser.add_argument("--width", type=int, default=None, help="The width of the generated video")
+    parser.add_argument("--height", type=int, default=None, help="The height of the generated video")
+    parser.add_argument("--fps", type=int, default=16, help="The frames per second for the generated video")
+    parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="The data type for computation")
+    parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
+    parser.add_argument("--controlnet_guidance_end", type=float, default=0.4, help="Controlnet guidance end during sampling")
+    parser.add_argument("--max_num_videos", type=int, default=None, help="# of videos for inference")
+    parser.add_argument("--use_dynamic_cfg", action='store_true')
+    parser.add_argument("--pose_type", type=str, default='manual', help="pose type in the inference time")
+    parser.add_argument("--speed", type=float, default=0.5, help="pose type in the inference time")
+    
+    
+    # DDP args
+    parser.add_argument("--launcher", type=str, choices=["pytorch", "slurm"], default="pytorch")
+    parser.add_argument("--world_size", default=1, type=int,
+                        help="number of the distributed processes.")
+    parser.add_argument('--local-rank', type=int, default=-1,
+                        help='Replica rank on the current node. This field is required '
+                             'by `torch.distributed.launch`.')
+    parser.add_argument("--global_seed", default=42, type=int,
+                        help="seed")
+    parser.add_argument("--port", type=int)
+    parser.add_argument("--local_rank", type=int, help="Local rank. Necessary for using the torch.distributed.launch utility.")
+
+
+    args = parser.parse_args()
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
+    
+    
+    generate_video(
+        # prompt=args.prompt,
+        launcher=args.launcher,
+        port=args.port,
+        data_root=args.data_root,
+        model_path=args.model_path,
+        output_path=args.output_path,
+        num_frames=args.num_frames,
+        width=args.width,
+        height=args.height,
+        image_path=args.image_path,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        num_videos_per_prompt=args.num_videos_per_prompt,
+        dtype=dtype,
+        seed=args.seed,
+        fps=args.fps,
+        controlnet_guidance_end=args.controlnet_guidance_end,
+        max_num_videos=args.max_num_videos,
+        use_dynamic_cfg=args.use_dynamic_cfg,
+        pose_type=args.pose_type,
+        speed=args.speed,
+    )
diff --git a/inference/inference_scripts/flovd_demo.sh b/inference/inference_scripts/flovd_demo.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0b222613410540c1e63d04ef013a54ed1c6376d4
--- /dev/null
+++ b/inference/inference_scripts/flovd_demo.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+
+FVSM_PATH="path/to/FVSM/blahblah.pt" # e.g., ./ckpt/FVSM/FloVD_FVSM_Controlnet.pt
+OMSM_PATH="path/to/OMSM/directory" # e.g., ./ckpt/OMMSM/
+DEPTH_CKPT_PATH="path/to/depth_estimator.pth" # e.g., './ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth'
+
+POSE_TYPE="re10k" # Choose between ['re10k', 'manual']
+OUTPUT_PATH="./results/"
+CONTROLNET_GUIDANCE_END=0.4
+SPEED=1.0
+
+PROMPT="A chef in a white coat and glasses preparing a meal in a professional kitchen. He is seen standing at a counter, surrounded by various ingredients and cookware, including bowls, plates, and utensils. The chef is focused on his task, carefully selecting and arranging the ingredients on the counter. The kitchen is well-equipped with stainless steel appliances, such as a refrigerator, oven, and microwave, and features a ventilation system to ensure a clean and efficient environment. The chef's meticulous preparation process is captured as he carefully selects and organizes the ingredients, indicating a high level of culinary skill and attention to detail."
+IMAGE_PATH="./assets/example_image/input1.png"
+CAM_POSE_NAME="1593596b99e2dde9.txt"
+
+python inference/flovd_demo.py \
+    --prompt "$PROMPT" \
+    --image_path $IMAGE_PATH \
+    --fvsm_path $FVSM_PATH \
+    --omsm_path $OMSM_PATH \
+    --output_path $OUTPUT_PATH \
+    --controlnet_guidance_end $CONTROLNET_GUIDANCE_END \
+    --use_flow_integration \
+    --pose_type $POSE_TYPE \
+    --speed $SPEED \
+    --cam_pose_name $CAM_POSE_NAME \
+    --depth_ckpt_path $DEPTH_CKPT_PATH \
+
+
+PROMPT="A stunning and untouched coastal landscape. It begins with a view of a rugged rock formation emerging from the sea, surrounded by churning waves. As the video progresses, the scene shifts to a solitary rock formation standing in the midst of the sea, with the waves crashing against the shore. The natural beauty of the coastline is highlighted by the absence of any human activity. The video then presents a panoramic view of the coastline, revealing the dynamic interplay between the sea and the land. The scene is characterized by the presence of a solitary rock formation, which stands out against the backdrop of the sea and the overcast sky. The video concludes with a view of the coastline, emphasizing the natural beauty and the absence of any human activity."
+IMAGE_PATH="./assets/example_image/input2.png"
+CAM_POSE_NAME="6b6d20c6a46b9fe9.txt"
+
+python inference/flovd_demo.py \
+    --prompt "$PROMPT" \
+    --image_path $IMAGE_PATH \
+    --fvsm_path $FVSM_PATH \
+    --omsm_path $OMSM_PATH \
+    --output_path $OUTPUT_PATH \
+    --controlnet_guidance_end $CONTROLNET_GUIDANCE_END \
+    --use_flow_integration \
+    --pose_type $POSE_TYPE \
+    --speed $SPEED \
+    --cam_pose_name $CAM_POSE_NAME \
+    --depth_ckpt_path $DEPTH_CKPT_PATH \
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..09bc849926ce8a8868cf7853a4f58e9229176bfa
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,27 @@
+[tool.ruff]
+line-length = 119
+
+[tool.ruff.lint]
+# Never enforce `E501` (line length violations).
+ignore = ["C901", "E501", "E741", "F402", "F823"]
+select = ["C", "E", "F", "I", "W"]
+
+# Ignore import violations in all `__init__.py` files.
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+
+[tool.ruff.format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..585dfabc88b1aadcfc40d8a33f154f2caa2917f5
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,21 @@
+diffusers>=0.32.1
+accelerate>=1.1.1
+transformers>=4.46.2
+numpy==1.26.0
+torch==2.6.0
+torchvision>=0.20.0
+sentencepiece>=0.2.0
+SwissArmyTransformer>=0.4.12
+gradio>=5.5.0
+imageio>=2.35.1
+imageio-ffmpeg>=0.5.1
+openai>=1.54.0
+moviepy>=2.0.0
+scikit-video>=1.1.11
+pydantic>=2.10.3
+xformers==0.0.29.post3
+wandb
+peft
+opencv-python
+decord
+pyav
\ No newline at end of file