fpack_generate_video.py

import argparse
from datetime import datetime
import gc
import json
import random
import os
import re
import time
import math
import copy
from typing import Tuple, Optional, List, Union, Any, Dict

import torch
from safetensors.torch import load_file, save_file
from safetensors import safe_open
from PIL import Image
import cv2
import numpy as np
import torchvision.transforms.functional as TF
from transformers import LlamaModel
from tqdm import tqdm

from networks import lora_framepack
from hunyuan_model.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
from frame_pack import hunyuan
from frame_pack.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked, load_packed_model
from frame_pack.utils import crop_or_pad_yield_mask, resize_and_center_crop, soft_append_bcthw
from frame_pack.bucket_tools import find_nearest_bucket
from frame_pack.clip_vision import hf_clip_vision_encode
from frame_pack.k_diffusion_hunyuan import sample_hunyuan
from dataset import image_video_dataset

try:
    from lycoris.kohya import create_network_from_weights
except:
    pass

from utils.device_utils import clean_memory_on_device
from hv_generate_video import save_images_grid, save_videos_grid, synchronize_device
from wan_generate_video import merge_lora_weights
from frame_pack.framepack_utils import load_vae, load_text_encoder1, load_text_encoder2, load_image_encoders
from dataset.image_video_dataset import load_video

import logging

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


class GenerationSettings:
    def __init__(self, device: torch.device, dit_weight_dtype: Optional[torch.dtype] = None):
        self.device = device
        self.dit_weight_dtype = dit_weight_dtype


def parse_args() -> argparse.Namespace:
    """parse command line arguments"""
    parser = argparse.ArgumentParser(description="Wan 2.1 inference script")

    # WAN arguments
    # parser.add_argument("--ckpt_dir", type=str, default=None, help="The path to the checkpoint directory (Wan 2.1 official).")
    parser.add_argument(
        "--sample_solver", type=str, default="unipc", choices=["unipc", "dpm++", "vanilla"], help="The solver used to sample."
    )

    parser.add_argument("--dit", type=str, default=None, help="DiT directory or path")
    parser.add_argument("--vae", type=str, default=None, help="VAE directory or path")
    parser.add_argument("--text_encoder1", type=str, required=True, help="Text Encoder 1 directory or path")
    parser.add_argument("--text_encoder2", type=str, required=True, help="Text Encoder 2 directory or path")
    parser.add_argument("--image_encoder", type=str, required=True, help="Image Encoder directory or path")
    # LoRA
    parser.add_argument("--lora_weight", type=str, nargs="*", required=False, default=None, help="LoRA weight path")
    parser.add_argument("--lora_multiplier", type=float, nargs="*", default=1.0, help="LoRA multiplier")
    parser.add_argument("--include_patterns", type=str, nargs="*", default=None, help="LoRA module include patterns")
    parser.add_argument("--exclude_patterns", type=str, nargs="*", default=None, help="LoRA module exclude patterns")
    parser.add_argument(
        "--save_merged_model",
        type=str,
        default=None,
        help="Save merged model to path. If specified, no inference will be performed.",
    )

    # inference
    parser.add_argument(
        "--prompt",
        type=str,
        default=None,
        help="prompt for generation. If `;;;` is used, it will be split into sections. Example: `section_index:prompt` or "
        "`section_index:prompt;;;section_index:prompt;;;...`, section_index can be `0` or `-1` or `0-2`, `-1` means last section, `0-2` means from 0 to 2 (inclusive).",
    )
    parser.add_argument(
        "--negative_prompt",
        type=str,
        default=None,
        help="negative prompt for generation, default is empty string. should not change.",
    )
    parser.add_argument("--video_size", type=int, nargs=2, default=[256, 256], help="video size, height and width")
    parser.add_argument("--video_seconds", type=float, default=5.0, help="video length, Default is 5.0 seconds")
    parser.add_argument("--fps", type=int, default=30, help="video fps, Default is 30")
    parser.add_argument("--infer_steps", type=int, default=25, help="number of inference steps, Default is 25")
    parser.add_argument("--save_path", type=str, required=True, help="path to save generated video")
    parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
    # parser.add_argument(
    #     "--cpu_noise", action="store_true", help="Use CPU to generate noise (compatible with ComfyUI). Default is False."
    # )
    parser.add_argument("--latent_window_size", type=int, default=9, help="latent window size, default is 9. should not change.")
    parser.add_argument(
        "--embedded_cfg_scale", type=float, default=10.0, help="Embeded CFG scale (distilled CFG Scale), default is 10.0"
    )
    parser.add_argument(
        "--guidance_scale",
        type=float,
        default=1.0,
        help="Guidance scale for classifier free guidance. Default is 1.0, should not change.",
    )
    parser.add_argument("--guidance_rescale", type=float, default=0.0, help="CFG Re-scale, default is 0.0. Should not change.")
    # parser.add_argument("--video_path", type=str, default=None, help="path to video for video2video inference")
    parser.add_argument("--image_path", type=str, default=None, help="path to image for image2video inference")
    parser.add_argument("--end_image_path", type=str, default=None, help="path to end image for image2video inference")
    # parser.add_argument(
    #     "--control_path",
    #     type=str,
    #     default=None,
    #     help="path to control video for inference with controlnet. video file or directory with images",
    # )
    # parser.add_argument("--trim_tail_frames", type=int, default=0, help="trim tail N frames from the video before saving")

    # # Flow Matching
    # parser.add_argument(
    #     "--flow_shift",
    #     type=float,
    #     default=None,
    #     help="Shift factor for flow matching schedulers. Default depends on task.",
    # )

    parser.add_argument("--fp8", action="store_true", help="use fp8 for DiT model")
    parser.add_argument("--fp8_scaled", action="store_true", help="use scaled fp8 for DiT, only for fp8")
    # parser.add_argument("--fp8_fast", action="store_true", help="Enable fast FP8 arithmetic (RTX 4XXX+), only for fp8_scaled")
    parser.add_argument("--fp8_llm", action="store_true", help="use fp8 for Text Encoder 1 (LLM)")
    parser.add_argument(
        "--device", type=str, default=None, help="device to use for inference. If None, use CUDA if available, otherwise use CPU"
    )
    parser.add_argument(
        "--attn_mode",
        type=str,
        default="torch",
        choices=["flash", "torch", "sageattn", "xformers", "sdpa"],  #  "flash2", "flash3",
        help="attention mode",
    )
    parser.add_argument("--vae_chunk_size", type=int, default=None, help="chunk size for CausalConv3d in VAE")
    parser.add_argument(
        "--vae_spatial_tile_sample_min_size", type=int, default=None, help="spatial tile sample min size for VAE, default 256"
    )
    parser.add_argument("--bulk_decode", action="store_true", help="decode all frames at once")
    parser.add_argument("--blocks_to_swap", type=int, default=0, help="number of blocks to swap in the model")
    parser.add_argument(
        "--output_type", type=str, default="video", choices=["video", "images", "latent", "both"], help="output type"
    )
    parser.add_argument("--no_metadata", action="store_true", help="do not save metadata")
    parser.add_argument("--latent_path", type=str, nargs="*", default=None, help="path to latent for decode. no inference")
    parser.add_argument("--lycoris", action="store_true", help="use lycoris for inference")
    # parser.add_argument("--compile", action="store_true", help="Enable torch.compile")
    # parser.add_argument(
    #     "--compile_args",
    #     nargs=4,
    #     metavar=("BACKEND", "MODE", "DYNAMIC", "FULLGRAPH"),
    #     default=["inductor", "max-autotune-no-cudagraphs", "False", "False"],
    #     help="Torch.compile settings",
    # )

    # New arguments for batch and interactive modes
    parser.add_argument("--from_file", type=str, default=None, help="Read prompts from a file")
    parser.add_argument("--interactive", action="store_true", help="Interactive mode: read prompts from console")

    args = parser.parse_args()

    # Validate arguments
    if args.from_file and args.interactive:
        raise ValueError("Cannot use both --from_file and --interactive at the same time")

    if args.prompt is None and not args.from_file and not args.interactive:
        raise ValueError("Either --prompt, --from_file or --interactive must be specified")

    return args


def parse_prompt_line(line: str) -> Dict[str, Any]:
    """Parse a prompt line into a dictionary of argument overrides

    Args:
        line: Prompt line with options

    Returns:
        Dict[str, Any]: Dictionary of argument overrides
    """
    # TODO common function with hv_train_network.line_to_prompt_dict
    parts = line.split(" --")
    prompt = parts[0].strip()

    # Create dictionary of overrides
    overrides = {"prompt": prompt}

    for part in parts[1:]:
        if not part.strip():
            continue
        option_parts = part.split(" ", 1)
        option = option_parts[0].strip()
        value = option_parts[1].strip() if len(option_parts) > 1 else ""

        # Map options to argument names
        if option == "w":
            overrides["video_size_width"] = int(value)
        elif option == "h":
            overrides["video_size_height"] = int(value)
        elif option == "f":
            overrides["video_seconds"] = float(value)
        elif option == "d":
            overrides["seed"] = int(value)
        elif option == "s":
            overrides["infer_steps"] = int(value)
        elif option == "g" or option == "l":
            overrides["guidance_scale"] = float(value)
        # elif option == "fs":
        #     overrides["flow_shift"] = float(value)
        elif option == "i":
            overrides["image_path"] = value
        elif option == "cn":
            overrides["control_path"] = value
        elif option == "n":
            overrides["negative_prompt"] = value

    return overrides


def apply_overrides(args: argparse.Namespace, overrides: Dict[str, Any]) -> argparse.Namespace:
    """Apply overrides to args

    Args:
        args: Original arguments
        overrides: Dictionary of overrides

    Returns:
        argparse.Namespace: New arguments with overrides applied
    """
    args_copy = copy.deepcopy(args)

    for key, value in overrides.items():
        if key == "video_size_width":
            args_copy.video_size[1] = value
        elif key == "video_size_height":
            args_copy.video_size[0] = value
        else:
            setattr(args_copy, key, value)

    return args_copy


def check_inputs(args: argparse.Namespace) -> Tuple[int, int, int]:
    """Validate video size and length

    Args:
        args: command line arguments

    Returns:
        Tuple[int, int, float]: (height, width, video_seconds)
    """
    height = args.video_size[0]
    width = args.video_size[1]

    video_seconds = args.video_seconds

    if height % 8 != 0 or width % 8 != 0:
        raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

    return height, width, video_seconds


# region DiT model


def load_dit_model(args: argparse.Namespace, device: torch.device) -> HunyuanVideoTransformer3DModelPacked:
    """load DiT model

    Args:
        args: command line arguments
        device: device to use
        dit_dtype: data type for the model
        dit_weight_dtype: data type for the model weights. None for as-is

    Returns:
        HunyuanVideoTransformer3DModelPacked: DiT model
    """
    loading_device = "cpu"
    if args.blocks_to_swap == 0 and not args.fp8_scaled and args.lora_weight is None:
        loading_device = device

    # do not fp8 optimize because we will merge LoRA weights
    model = load_packed_model(device, args.dit, args.attn_mode, loading_device)
    return model


def optimize_model(model: HunyuanVideoTransformer3DModelPacked, args: argparse.Namespace, device: torch.device) -> None:
    """optimize the model (FP8 conversion, device move etc.)

    Args:
        model: dit model
        args: command line arguments
        device: device to use
    """
    if args.fp8_scaled:
        # load state dict as-is and optimize to fp8
        state_dict = model.state_dict()

        # if no blocks to swap, we can move the weights to GPU after optimization on GPU (omit redundant CPU->GPU copy)
        move_to_device = args.blocks_to_swap == 0  # if blocks_to_swap > 0, we will keep the model on CPU
        state_dict = model.fp8_optimization(state_dict, device, move_to_device, use_scaled_mm=False)  # args.fp8_fast)

        info = model.load_state_dict(state_dict, strict=True, assign=True)
        logger.info(f"Loaded FP8 optimized weights: {info}")

        if args.blocks_to_swap == 0:
            model.to(device)  # make sure all parameters are on the right device (e.g. RoPE etc.)
    else:
        # simple cast to dit_dtype
        target_dtype = None  # load as-is (dit_weight_dtype == dtype of the weights in state_dict)
        target_device = None

        if args.fp8:
            target_dtype = torch.float8e4m3fn

        if args.blocks_to_swap == 0:
            logger.info(f"Move model to device: {device}")
            target_device = device

        if target_device is not None and target_dtype is not None:
            model.to(target_device, target_dtype)  # move and cast  at the same time. this reduces redundant copy operations

    # if args.compile:
    #     compile_backend, compile_mode, compile_dynamic, compile_fullgraph = args.compile_args
    #     logger.info(
    #         f"Torch Compiling[Backend: {compile_backend}; Mode: {compile_mode}; Dynamic: {compile_dynamic}; Fullgraph: {compile_fullgraph}]"
    #     )
    #     torch._dynamo.config.cache_size_limit = 32
    #     for i in range(len(model.blocks)):
    #         model.blocks[i] = torch.compile(
    #             model.blocks[i],
    #             backend=compile_backend,
    #             mode=compile_mode,
    #             dynamic=compile_dynamic.lower() in "true",
    #             fullgraph=compile_fullgraph.lower() in "true",
    #         )

    if args.blocks_to_swap > 0:
        logger.info(f"Enable swap {args.blocks_to_swap} blocks to CPU from device: {device}")
        model.enable_block_swap(args.blocks_to_swap, device, supports_backward=False)
        model.move_to_device_except_swap_blocks(device)
        model.prepare_block_swap_before_forward()
    else:
        # make sure the model is on the right device
        model.to(device)

    model.eval().requires_grad_(False)
    clean_memory_on_device(device)


# endregion


def decode_latent(
    latent_window_size: int,
    total_latent_sections: int,
    bulk_decode: bool,
    vae: AutoencoderKLCausal3D,
    latent: torch.Tensor,
    device: torch.device,
) -> torch.Tensor:
    logger.info(f"Decoding video...")
    if latent.ndim == 4:
        latent = latent.unsqueeze(0)  # add batch dimension

    vae.to(device)
    if not bulk_decode:
        latent_window_size = latent_window_size  # default is 9
        # total_latent_sections = (args.video_seconds * 30) / (latent_window_size * 4)
        # total_latent_sections = int(max(round(total_latent_sections), 1))
        num_frames = latent_window_size * 4 - 3

        latents_to_decode = []
        latent_frame_index = 0
        for i in range(total_latent_sections - 1, -1, -1):
            is_last_section = i == total_latent_sections - 1
            generated_latent_frames = (num_frames + 3) // 4 + (1 if is_last_section else 0)
            section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)

            section_latent = latent[:, :, latent_frame_index : latent_frame_index + section_latent_frames, :, :]
            latents_to_decode.append(section_latent)

            latent_frame_index += generated_latent_frames

        latents_to_decode = latents_to_decode[::-1]  # reverse the order of latents to decode

        history_pixels = None
        for latent in tqdm(latents_to_decode):
            if history_pixels is None:
                history_pixels = hunyuan.vae_decode(latent, vae).cpu()
            else:
                overlapped_frames = latent_window_size * 4 - 3
                current_pixels = hunyuan.vae_decode(latent, vae).cpu()
                history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)
            clean_memory_on_device(device)
    else:
        # bulk decode
        logger.info(f"Bulk decoding")
        history_pixels = hunyuan.vae_decode(latent, vae).cpu()
    vae.to("cpu")

    print(f"Decoded. Pixel shape {history_pixels.shape}")
    return history_pixels[0]  # remove batch dimension


def prepare_i2v_inputs(
    args: argparse.Namespace,
    device: torch.device,
    vae: AutoencoderKLCausal3D,
    encoded_context: Optional[Dict] = None,
    encoded_context_n: Optional[Dict] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[dict, dict]]:
    """Prepare inputs for I2V

    Args:
        args: command line arguments
        config: model configuration
        device: device to use
        vae: VAE model, used for image encoding
        encoded_context: Pre-encoded text context

    Returns:
        Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Tuple[dict, dict]]:
            (noise, context, context_null, y, (arg_c, arg_null))
    """

    height, width, video_seconds = check_inputs(args)

    # prepare image
    def preprocess_image(image_path: str):
        image = Image.open(image_path).convert("RGB")

        image_np = np.array(image)  # PIL to numpy, HWC

        image_np = image_video_dataset.resize_image_to_bucket(image_np, (width, height))
        image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0  # -1 to 1.0, HWC
        image_tensor = image_tensor.permute(2, 0, 1)[None, :, None]  # HWC -> CHW -> NCFHW, N=1, C=3, F=1
        return image_tensor, image_np

    img_tensor, img_np = preprocess_image(args.image_path)
    if args.end_image_path is not None:
        end_img_tensor, end_img_np = preprocess_image(args.end_image_path)
    else:
        end_img_tensor, end_img_np = None, None

    # configure negative prompt
    n_prompt = args.negative_prompt if args.negative_prompt else ""

    if encoded_context is None:
        # load text encoder
        tokenizer1, text_encoder1 = load_text_encoder1(args, args.fp8_llm, device)
        tokenizer2, text_encoder2 = load_text_encoder2(args)
        text_encoder2.to(device)

        # parse section prompts
        section_prompts = {}
        if ";;;" in args.prompt:
            section_prompt_strs = args.prompt.split(";;;")
            for section_prompt_str in section_prompt_strs:
                if ":" not in section_prompt_str:
                    start = end = 0
                    prompt_str = section_prompt_str.strip()
                else:
                    index_str, prompt_str = section_prompt_str.split(":", 1)
                    index_str = index_str.strip()
                    prompt_str = prompt_str.strip()

                    m = re.match(r"^(-?\d+)(-\d+)?$", index_str)
                    if m:
                        start = int(m.group(1))
                        end = int(m.group(2)[1:]) if m.group(2) is not None else start
                    else:
                        start = end = 0
                        prompt_str = section_prompt_str.strip()
                for i in range(start, end + 1):
                    section_prompts[i] = prompt_str
        else:
            section_prompts[0] = args.prompt

        # assert 0 in section_prompts, "Section prompts must contain section 0"
        if 0 not in section_prompts:
            # use smallest section index. prefer positive index over negative index
            # if all section indices are negative, use the smallest negative index
            indices = list(section_prompts.keys())
            if all(i < 0 for i in indices):
                section_index = min(indices)
            else:
                section_index = min(i for i in indices if i >= 0)
            section_prompts[0] = section_prompts[section_index]
        print(section_prompts)

        logger.info(f"Encoding prompt")
        llama_vecs = {}
        llama_attention_masks = {}
        clip_l_poolers = {}
        with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad():
            for index, prompt in section_prompts.items():
                llama_vec, clip_l_pooler = hunyuan.encode_prompt_conds(prompt, text_encoder1, text_encoder2, tokenizer1, tokenizer2)
                llama_vec = llama_vec.cpu()
                clip_l_pooler = clip_l_pooler.cpu()

                llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)

                llama_vecs[index] = llama_vec
                llama_attention_masks[index] = llama_attention_mask
                clip_l_poolers[index] = clip_l_pooler

        if args.guidance_scale == 1.0:
            llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vecs[0]), torch.zeros_like(clip_l_poolers[0])
        else:
            with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad():
                llama_vec_n, clip_l_pooler_n = hunyuan.encode_prompt_conds(
                    n_prompt, text_encoder1, text_encoder2, tokenizer1, tokenizer2
                )
                llama_vec_n = llama_vec_n.cpu()
                clip_l_pooler_n = clip_l_pooler_n.cpu()

        llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)

        # free text encoder and clean memory
        del text_encoder1, text_encoder2, tokenizer1, tokenizer2
        clean_memory_on_device(device)

        # load image encoder
        feature_extractor, image_encoder = load_image_encoders(args)
        image_encoder.to(device)

        # encode image with image encoder
        with torch.no_grad():
            image_encoder_output = hf_clip_vision_encode(img_np, feature_extractor, image_encoder)
        image_encoder_last_hidden_state = image_encoder_output.last_hidden_state.cpu()

        if end_img_np is not None:
            with torch.no_grad():
                end_image_encoder_output = hf_clip_vision_encode(end_img_np, feature_extractor, image_encoder)
            end_image_encoder_last_hidden_state = end_image_encoder_output.last_hidden_state.cpu()
        else:
            end_image_encoder_last_hidden_state = None

        # free image encoder and clean memory
        del image_encoder, feature_extractor
        clean_memory_on_device(device)
    else:
        # Use pre-encoded context
        llama_vecs = encoded_context["llama_vecs"]
        llama_attention_masks = encoded_context["llama_attention_masks"]
        clip_l_poolers = encoded_context["clip_l_poolers"]
        llama_vec_n = encoded_context_n["llama_vec"]
        llama_attention_mask_n = encoded_context_n["llama_attention_mask"]
        clip_l_pooler_n = encoded_context_n["clip_l_pooler"]
        image_encoder_last_hidden_state = encoded_context["image_encoder_last_hidden_state"]

    # # end frame image
    # if args.end_image_path is not None:
    #     end_img = Image.open(args.end_image_path).convert("RGB")
    #     end_img_cv2 = np.array(end_img)  # PIL to numpy
    # else:
    #     end_img = None
    #     end_img_cv2 = None
    # has_end_image = end_img is not None

    # VAE encoding
    logger.info(f"Encoding image to latent space")
    vae.to(device)
    start_latent = hunyuan.vae_encode(img_tensor, vae).cpu()
    if end_img_tensor is not None:
        end_latent = hunyuan.vae_encode(end_img_tensor, vae).cpu()
    else:
        end_latent = None
    vae.to("cpu")  # move VAE to CPU to save memory
    clean_memory_on_device(device)

    # prepare model input arguments
    arg_c = {}
    for index in llama_vecs.keys():
        llama_vec = llama_vecs[index]
        llama_attention_mask = llama_attention_masks[index]
        clip_l_pooler = clip_l_poolers[index]
        arg_c_i = {
            "llama_vec": llama_vec,
            "llama_attention_mask": llama_attention_mask,
            "clip_l_pooler": clip_l_pooler,
            "image_encoder_last_hidden_state": image_encoder_last_hidden_state,
            "end_image_encoder_last_hidden_state": end_image_encoder_last_hidden_state,
            "prompt": section_prompts[index],  # for debugging
        }
        arg_c[index] = arg_c_i

    arg_null = {
        "llama_vec": llama_vec_n,
        "llama_attention_mask": llama_attention_mask_n,
        "clip_l_pooler": clip_l_pooler_n,
        "image_encoder_last_hidden_state": image_encoder_last_hidden_state,
        "end_image_encoder_last_hidden_state": end_image_encoder_last_hidden_state,
    }

    return height, width, video_seconds, start_latent, end_latent, arg_c, arg_null


# def setup_scheduler(args: argparse.Namespace, config, device: torch.device) -> Tuple[Any, torch.Tensor]:
#     """setup scheduler for sampling

#     Args:
#         args: command line arguments
#         config: model configuration
#         device: device to use

#     Returns:
#         Tuple[Any, torch.Tensor]: (scheduler, timesteps)
#     """
#     if args.sample_solver == "unipc":
#         scheduler = FlowUniPCMultistepScheduler(num_train_timesteps=config.num_train_timesteps, shift=1, use_dynamic_shifting=False)
#         scheduler.set_timesteps(args.infer_steps, device=device, shift=args.flow_shift)
#         timesteps = scheduler.timesteps
#     elif args.sample_solver == "dpm++":
#         scheduler = FlowDPMSolverMultistepScheduler(
#             num_train_timesteps=config.num_train_timesteps, shift=1, use_dynamic_shifting=False
#         )
#         sampling_sigmas = get_sampling_sigmas(args.infer_steps, args.flow_shift)
#         timesteps, _ = retrieve_timesteps(scheduler, device=device, sigmas=sampling_sigmas)
#     elif args.sample_solver == "vanilla":
#         scheduler = FlowMatchDiscreteScheduler(num_train_timesteps=config.num_train_timesteps, shift=args.flow_shift)
#         scheduler.set_timesteps(args.infer_steps, device=device)
#         timesteps = scheduler.timesteps

#         # FlowMatchDiscreteScheduler does not support generator argument in step method
#         org_step = scheduler.step

#         def step_wrapper(
#             model_output: torch.Tensor,
#             timestep: Union[int, torch.Tensor],
#             sample: torch.Tensor,
#             return_dict: bool = True,
#             generator=None,
#         ):
#             return org_step(model_output, timestep, sample, return_dict=return_dict)

#         scheduler.step = step_wrapper
#     else:
#         raise NotImplementedError("Unsupported solver.")

#     return scheduler, timesteps


def generate(args: argparse.Namespace, gen_settings: GenerationSettings, shared_models: Optional[Dict] = None) -> torch.Tensor:
    """main function for generation

    Args:
        args: command line arguments
        shared_models: dictionary containing pre-loaded models and encoded data

    Returns:
        torch.Tensor: generated latent
    """
    device, dit_weight_dtype = (gen_settings.device, gen_settings.dit_weight_dtype)

    # prepare seed
    seed = args.seed if args.seed is not None else random.randint(0, 2**32 - 1)
    args.seed = seed  # set seed to args for saving

    # Check if we have shared models
    if shared_models is not None:
        # Use shared models and encoded data
        vae = shared_models.get("vae")
        model = shared_models.get("model")
        encoded_context = shared_models.get("encoded_contexts", {}).get(args.prompt)
        n_prompt = args.negative_prompt if args.negative_prompt else ""
        encoded_context_n = shared_models.get("encoded_contexts", {}).get(n_prompt)

        height, width, video_seconds, start_latent, end_latent, context, context_null = prepare_i2v_inputs(
            args, device, vae, encoded_context, encoded_context_n
        )
    else:
        # prepare inputs without shared models
        vae = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, device)
        height, width, video_seconds, start_latent, end_latent, context, context_null = prepare_i2v_inputs(args, device, vae)

        # load DiT model
        model = load_dit_model(args, device)

        # merge LoRA weights
        if args.lora_weight is not None and len(args.lora_weight) > 0:
            merge_lora_weights(lora_framepack, model, args, device)  # ugly hack to common merge_lora_weights function
            # if we only want to save the model, we can skip the rest
            if args.save_merged_model:
                return None

        # optimize model: fp8 conversion, block swap etc.
        optimize_model(model, args, device)

    # sampling
    latent_window_size = args.latent_window_size  # default is 9
    # ex: (5s * 30fps) / (9 * 4) = 4.16 -> 4 sections, 60s -> 1800 / 36 = 50 sections
    total_latent_sections = (video_seconds * 30) / (latent_window_size * 4)
    total_latent_sections = int(max(round(total_latent_sections), 1))

    # set random generator
    seed_g = torch.Generator(device="cpu")
    seed_g.manual_seed(seed)
    num_frames = latent_window_size * 4 - 3

    logger.info(
        f"Video size: {height}x{width}@{video_seconds} (HxW@seconds), fps: {args.fps}, "
        f"infer_steps: {args.infer_steps}, frames per generation: {num_frames}"
    )

    history_latents = torch.zeros((1, 16, 1 + 2 + 16, height // 8, width // 8), dtype=torch.float32)
    # history_pixels = None
    total_generated_latent_frames = 0

    latent_paddings = reversed(range(total_latent_sections))

    if total_latent_sections > 4:
        # In theory the latent_paddings should follow the above sequence, but it seems that duplicating some
        # items looks better than expanding it when total_latent_sections > 4
        # One can try to remove below trick and just
        # use `latent_paddings = list(reversed(range(total_latent_sections)))` to compare
        # 4 sections: 3, 2, 1, 0. 50 sections: 3, 2, 2, ... 2, 1, 0
        latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0]

    for section_index_reverse, latent_padding in enumerate(latent_paddings):
        section_index = total_latent_sections - 1 - section_index_reverse

        is_last_section = latent_padding == 0
        is_first_section = section_index_reverse == 0
        latent_padding_size = latent_padding * latent_window_size

        logger.info(f"latent_padding_size = {latent_padding_size}, is_last_section = {is_last_section}")

        reference_start_latent = start_latent
        apply_end_image = args.end_image_path is not None and is_first_section
        if apply_end_image:
            latent_padding_size = 0
            reference_start_latent = end_latent
            logger.info(f"Apply experimental end image, latent_padding_size = {latent_padding_size}")

        # sum([1, 3, 9, 1, 2, 16]) = 32
        indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0)
        (
            clean_latent_indices_pre,
            blank_indices,
            latent_indices,
            clean_latent_indices_post,
            clean_latent_2x_indices,
            clean_latent_4x_indices,
        ) = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1)
        clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1)

        clean_latents_pre = reference_start_latent.to(history_latents)
        clean_latents_post, clean_latents_2x, clean_latents_4x = history_latents[:, :, : 1 + 2 + 16, :, :].split([1, 2, 16], dim=2)
        clean_latents = torch.cat([clean_latents_pre, clean_latents_post], dim=2)

        # if use_teacache:
        #     transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
        # else:
        #     transformer.initialize_teacache(enable_teacache=False)

        section_index_from_last = -(section_index_reverse + 1)  # -1, -2 ...
        if section_index_from_last in context:
            prompt_index = section_index_from_last
        elif section_index in context:
            prompt_index = section_index
        else:
            prompt_index = 0
        context_for_index = context[prompt_index]
        # if args.section_prompts is not None:
        logger.info(f"Section {section_index}: {context_for_index['prompt']}")

        llama_vec = context_for_index["llama_vec"].to(device, dtype=torch.bfloat16)
        llama_attention_mask = context_for_index["llama_attention_mask"].to(device)
        clip_l_pooler = context_for_index["clip_l_pooler"].to(device, dtype=torch.bfloat16)

        if not apply_end_image:
            image_encoder_last_hidden_state = context_for_index["image_encoder_last_hidden_state"].to(device, dtype=torch.bfloat16)
        else:
            image_encoder_last_hidden_state = context_for_index["end_image_encoder_last_hidden_state"].to(
                device, dtype=torch.bfloat16
            )

        llama_vec_n = context_null["llama_vec"].to(device, dtype=torch.bfloat16)
        llama_attention_mask_n = context_null["llama_attention_mask"].to(device)
        clip_l_pooler_n = context_null["clip_l_pooler"].to(device, dtype=torch.bfloat16)

        generated_latents = sample_hunyuan(
            transformer=model,
            sampler=args.sample_solver,
            width=width,
            height=height,
            frames=num_frames,
            real_guidance_scale=args.guidance_scale,
            distilled_guidance_scale=args.embedded_cfg_scale,
            guidance_rescale=args.guidance_rescale,
            # shift=3.0,
            num_inference_steps=args.infer_steps,
            generator=seed_g,
            prompt_embeds=llama_vec,
            prompt_embeds_mask=llama_attention_mask,
            prompt_poolers=clip_l_pooler,
            negative_prompt_embeds=llama_vec_n,
            negative_prompt_embeds_mask=llama_attention_mask_n,
            negative_prompt_poolers=clip_l_pooler_n,
            device=device,
            dtype=torch.bfloat16,
            image_embeddings=image_encoder_last_hidden_state,
            latent_indices=latent_indices,
            clean_latents=clean_latents,
            clean_latent_indices=clean_latent_indices,
            clean_latents_2x=clean_latents_2x,
            clean_latent_2x_indices=clean_latent_2x_indices,
            clean_latents_4x=clean_latents_4x,
            clean_latent_4x_indices=clean_latent_4x_indices,
        )

        if is_last_section:
            generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)

        total_generated_latent_frames += int(generated_latents.shape[2])
        history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)

        real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :]

        logger.info(f"Generated. Latent shape {real_history_latents.shape}")

        # # TODO support saving intermediate video
        # clean_memory_on_device(device)
        # vae.to(device)
        # if history_pixels is None:
        #     history_pixels = hunyuan.vae_decode(real_history_latents, vae).cpu()
        # else:
        #     section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
        #     overlapped_frames = latent_window_size * 4 - 3
        #     current_pixels = hunyuan.vae_decode(real_history_latents[:, :, :section_latent_frames], vae).cpu()
        #     history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)
        # vae.to("cpu")
        # # if not is_last_section:
        # #     # save intermediate video
        # #     save_video(history_pixels[0], args, total_generated_latent_frames)
        # print(f"Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}")

    # Only clean up shared models if they were created within this function
    if shared_models is None:
        # free memory
        del model
        # del scheduler
        synchronize_device(device)

    # wait for 5 seconds until block swap is done
    logger.info("Waiting for 5 seconds to finish block swap")
    time.sleep(5)

    gc.collect()
    clean_memory_on_device(device)

    return vae, real_history_latents


def save_latent(latent: torch.Tensor, args: argparse.Namespace, height: int, width: int) -> str:
    """Save latent to file

    Args:
        latent: Latent tensor
        args: command line arguments
        height: height of frame
        width: width of frame

    Returns:
        str: Path to saved latent file
    """
    save_path = args.save_path
    os.makedirs(save_path, exist_ok=True)
    time_flag = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

    seed = args.seed
    video_seconds = args.video_seconds
    latent_path = f"{save_path}/{time_flag}_{seed}_latent.safetensors"

    if args.no_metadata:
        metadata = None
    else:
        metadata = {
            "seeds": f"{seed}",
            "prompt": f"{args.prompt}",
            "height": f"{height}",
            "width": f"{width}",
            "video_seconds": f"{video_seconds}",
            "infer_steps": f"{args.infer_steps}",
            "guidance_scale": f"{args.guidance_scale}",
            "latent_window_size": f"{args.latent_window_size}",
            "embedded_cfg_scale": f"{args.embedded_cfg_scale}",
            "guidance_rescale": f"{args.guidance_rescale}",
            "sample_solver": f"{args.sample_solver}",
            "latent_window_size": f"{args.latent_window_size}",
            "fps": f"{args.fps}",
        }
        if args.negative_prompt is not None:
            metadata["negative_prompt"] = f"{args.negative_prompt}"

    sd = {"latent": latent.contiguous()}
    save_file(sd, latent_path, metadata=metadata)
    logger.info(f"Latent saved to: {latent_path}")

    return latent_path


def save_video(
    video: torch.Tensor, args: argparse.Namespace, original_base_name: Optional[str] = None, latent_frames: Optional[int] = None
) -> str:
    """Save video to file

    Args:
        video: Video tensor
        args: command line arguments
        original_base_name: Original base name (if latents are loaded from files)

    Returns:
        str: Path to saved video file
    """
    save_path = args.save_path
    os.makedirs(save_path, exist_ok=True)
    time_flag = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

    seed = args.seed
    original_name = "" if original_base_name is None else f"_{original_base_name}"
    latent_frames = "" if latent_frames is None else f"_{latent_frames}"
    video_path = f"{save_path}/{time_flag}_{seed}{original_name}{latent_frames}.mp4"

    video = video.unsqueeze(0)
    save_videos_grid(video, video_path, fps=args.fps, rescale=True)
    logger.info(f"Video saved to: {video_path}")

    return video_path


def save_images(sample: torch.Tensor, args: argparse.Namespace, original_base_name: Optional[str] = None) -> str:
    """Save images to directory

    Args:
        sample: Video tensor
        args: command line arguments
        original_base_name: Original base name (if latents are loaded from files)

    Returns:
        str: Path to saved images directory
    """
    save_path = args.save_path
    os.makedirs(save_path, exist_ok=True)
    time_flag = datetime.fromtimestamp(time.time()).strftime("%Y%m%d-%H%M%S")

    seed = args.seed
    original_name = "" if original_base_name is None else f"_{original_base_name}"
    image_name = f"{time_flag}_{seed}{original_name}"
    sample = sample.unsqueeze(0)
    save_images_grid(sample, save_path, image_name, rescale=True)
    logger.info(f"Sample images saved to: {save_path}/{image_name}")

    return f"{save_path}/{image_name}"


def save_output(
    args: argparse.Namespace,
    vae: AutoencoderKLCausal3D,
    latent: torch.Tensor,
    device: torch.device,
    original_base_names: Optional[List[str]] = None,
) -> None:
    """save output

    Args:
        args: command line arguments
        vae: VAE model
        latent: latent tensor
        device: device to use
        original_base_names: original base names (if latents are loaded from files)
    """
    height, width = latent.shape[-2], latent.shape[-1]  # BCTHW
    height *= 8
    width *= 8
    # print(f"Saving output. Latent shape {latent.shape}; pixel shape {height}x{width}")
    if args.output_type == "latent" or args.output_type == "both":
        # save latent
        save_latent(latent, args, height, width)
    if args.output_type == "latent":
        return

    total_latent_sections = (args.video_seconds * 30) / (args.latent_window_size * 4)
    total_latent_sections = int(max(round(total_latent_sections), 1))
    video = decode_latent(args.latent_window_size, total_latent_sections, args.bulk_decode, vae, latent, device)

    if args.output_type == "video" or args.output_type == "both":
        # save video
        original_name = "" if original_base_names is None else f"_{original_base_names[0]}"
        save_video(video, args, original_name)

    elif args.output_type == "images":
        # save images
        original_name = "" if original_base_names is None else f"_{original_base_names[0]}"
        save_images(video, args, original_name)


def preprocess_prompts_for_batch(prompt_lines: List[str], base_args: argparse.Namespace) -> List[Dict]:
    """Process multiple prompts for batch mode

    Args:
        prompt_lines: List of prompt lines
        base_args: Base command line arguments

    Returns:
        List[Dict]: List of prompt data dictionaries
    """
    prompts_data = []

    for line in prompt_lines:
        line = line.strip()
        if not line or line.startswith("#"):  # Skip empty lines and comments
            continue

        # Parse prompt line and create override dictionary
        prompt_data = parse_prompt_line(line)
        logger.info(f"Parsed prompt data: {prompt_data}")
        prompts_data.append(prompt_data)

    return prompts_data


def get_generation_settings(args: argparse.Namespace) -> GenerationSettings:
    device = torch.device(args.device)

    dit_weight_dtype = None  # default
    if args.fp8_scaled:
        dit_weight_dtype = None  # various precision weights, so don't cast to specific dtype
    elif args.fp8:
        dit_weight_dtype = torch.float8_e4m3fn

    logger.info(f"Using device: {device}, DiT weight weight precision: {dit_weight_dtype}")

    gen_settings = GenerationSettings(device=device, dit_weight_dtype=dit_weight_dtype)
    return gen_settings


def main():
    # Parse arguments
    args = parse_args()

    # Check if latents are provided
    latents_mode = args.latent_path is not None and len(args.latent_path) > 0

    # Set device
    device = args.device if args.device is not None else "cuda" if torch.cuda.is_available() else "cpu"
    device = torch.device(device)
    logger.info(f"Using device: {device}")
    args.device = device

    if latents_mode:
        # Original latent decode mode
        original_base_names = []
        latents_list = []
        seeds = []

        assert len(args.latent_path) == 1, "Only one latent path is supported for now"

        for latent_path in args.latent_path:
            original_base_names.append(os.path.splitext(os.path.basename(latent_path))[0])
            seed = 0

            if os.path.splitext(latent_path)[1] != ".safetensors":
                latents = torch.load(latent_path, map_location="cpu")
            else:
                latents = load_file(latent_path)["latent"]
                with safe_open(latent_path, framework="pt") as f:
                    metadata = f.metadata()
                if metadata is None:
                    metadata = {}
                logger.info(f"Loaded metadata: {metadata}")

                if "seeds" in metadata:
                    seed = int(metadata["seeds"])
                if "height" in metadata and "width" in metadata:
                    height = int(metadata["height"])
                    width = int(metadata["width"])
                    args.video_size = [height, width]
                if "video_seconds" in metadata:
                    args.video_seconds = float(metadata["video_seconds"])

            seeds.append(seed)
            logger.info(f"Loaded latent from {latent_path}. Shape: {latents.shape}")

            if latents.ndim == 5:  # [BCTHW]
                latents = latents.squeeze(0)  # [CTHW]

            latents_list.append(latents)

        latent = torch.stack(latents_list, dim=0)  # [N, ...], must be same shape

        args.seed = seeds[0]

        vae = load_vae(args.vae, args.vae_chunk_size, args.vae_spatial_tile_sample_min_size, device)
        save_output(args, vae, latent, device, original_base_names)

    elif args.from_file:
        # Batch mode from file

        # Read prompts from file
        with open(args.from_file, "r", encoding="utf-8") as f:
            prompt_lines = f.readlines()

        # Process prompts
        prompts_data = preprocess_prompts_for_batch(prompt_lines, args)
        # process_batch_prompts(prompts_data, args)
        raise NotImplementedError("Batch mode is not implemented yet.")

    elif args.interactive:
        # Interactive mode
        # process_interactive(args)
        raise NotImplementedError("Interactive mode is not implemented yet.")

    else:
        # Single prompt mode (original behavior)

        # Generate latent
        gen_settings = get_generation_settings(args)
        vae, latent = generate(args, gen_settings)
        # print(f"Generated latent shape: {latent.shape}")

        # # Save latent and video
        # if args.save_merged_model:
        #     return

        save_output(args, vae, latent[0], device)

    logger.info("Done!")


if __name__ == "__main__":
    main()