generate.py

# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import argparse
from datetime import datetime
import logging
import os
import sys
import warnings
import random
import time
warnings.filterwarnings('ignore')

import torch
import torch_npu
torch_npu.npu.set_compile_mode(jit_compile=False)
torch.npu.config.allow_internal_format=False
from torch_npu.contrib import transfer_to_npu
import torch.distributed as dist
from PIL import Image

import wan
from wan.configs import WAN_CONFIGS, SIZE_CONFIGS, MAX_AREA_CONFIGS, SUPPORTED_SIZES
from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
from wan.utils.utils import cache_video, cache_image, str2bool
from wan.distributed.parallel_mgr import ParallelConfig, init_parallel_env, finalize_parallel_env
from wan.distributed.tp_applicator import TensorParallelApplicator

from mindiesd import CacheConfig, CacheAgent


EXAMPLE_PROMPT = {
    "t2v-1.3B": {
        "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
    },
    "t2v-14B": {
        "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
    },
    "t2i-14B": {
        "prompt": "一个朴素端庄的美人",
    },
    "i2v-14B": {
        "prompt":
            "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
        "image":
            "examples/i2v_input.JPG",
    },
}


def _validate_args(args):
    # Basic check
    assert args.ckpt_dir is not None, "Please specify the checkpoint directory."
    assert args.task in WAN_CONFIGS, f"Unsupport task: {args.task}"
    assert args.task in EXAMPLE_PROMPT, f"Unsupport task: {args.task}"

    # The default sampling steps are 40 for image-to-video tasks and 50 for text-to-video tasks.
    if args.sample_steps is None:
        args.sample_steps = 40 if "i2v" in args.task else 50

    if args.sample_shift is None:
        args.sample_shift = 5.0
        if "i2v" in args.task and args.size in ["832*480", "480*832"]:
            args.sample_shift = 3.0

    # The default number of frames are 1 for text-to-image tasks and 81 for other tasks.
    if args.frame_num is None:
        args.frame_num = 1 if "t2i" in args.task else 81

    # T2I frame_num check
    if "t2i" in args.task:
        assert args.frame_num == 1, f"Unsupport frame_num {args.frame_num} for task {args.task}"

    args.base_seed = args.base_seed if args.base_seed >= 0 else random.randint(
        0, sys.maxsize)
    # Size check
    assert args.size in SUPPORTED_SIZES[
        args.
        task], f"Unsupport size {args.size} for task {args.task}, supported sizes are: {', '.join(SUPPORTED_SIZES[args.task])}"


def _parse_args():
    parser = argparse.ArgumentParser(
        description="Generate a image or video from a text prompt or image using Wan"
    )
    parser.add_argument(
        "--task",
        type=str,
        default="t2v-14B",
        choices=list(WAN_CONFIGS.keys()),
        help="The task to run.")
    parser.add_argument(
        "--size",
        type=str,
        default="1280*720",
        choices=list(SIZE_CONFIGS.keys()),
        help="The area (width*height) of the generated video. For the I2V task, the aspect ratio of the output video will follow that of the input image."
    )
    parser.add_argument(
        "--frame_num",
        type=int,
        default=None,
        help="How many frames to sample from a image or video. The number should be 4n+1"
    )
    parser.add_argument(
        "--ckpt_dir",
        type=str,
        default=None,
        help="The path to the checkpoint directory.")
    parser.add_argument(
        "--offload_model",
        type=str2bool,
        default=None,
        help="Whether to offload the model to CPU after each model forward, reducing GPU memory usage."
    )
    parser.add_argument(
        "--cfg_size",
        type=int,
        default=1,
        help="The size of the cfg parallelism in DiT.")
    parser.add_argument(
        "--ulysses_size",
        type=int,
        default=1,
        help="The size of the ulysses parallelism in DiT.")
    parser.add_argument(
        "--ring_size",
        type=int,
        default=1,
        help="The size of the ring attention parallelism in DiT.")
    parser.add_argument(
        "--tp_size",
        type=int,
        default=1,
        help="The size of the tensor parallelism in DiT.")
    parser.add_argument(
        "--vae_parallel",
        action="store_true",
        default=False,
        help="Whether to use parallel for vae.")
    parser.add_argument(
        "--t5_fsdp",
        action="store_true",
        default=False,
        help="Whether to use FSDP for T5.")
    parser.add_argument(
        "--t5_cpu",
        action="store_true",
        default=False,
        help="Whether to place T5 model on CPU.")
    parser.add_argument(
        "--dit_fsdp",
        action="store_true",
        default=False,
        help="Whether to use FSDP for DiT.")
    parser.add_argument(
        "--save_file",
        type=str,
        default=None,
        help="The file to save the generated image or video to.")
    parser.add_argument(
        "--prompt",
        type=str,
        default=None,
        help="The prompt to generate the image or video from.")
    parser.add_argument(
        "--use_prompt_extend",
        action="store_true",
        default=False,
        help="Whether to use prompt extend.")
    parser.add_argument(
        "--prompt_extend_method",
        type=str,
        default="local_qwen",
        choices=["dashscope", "local_qwen"],
        help="The prompt extend method to use.")
    parser.add_argument(
        "--prompt_extend_model",
        type=str,
        default=None,
        help="The prompt extend model to use.")
    parser.add_argument(
        "--prompt_extend_target_lang",
        type=str,
        default="zh",
        choices=["zh", "en"],
        help="The target language of prompt extend.")
    parser.add_argument(
        "--base_seed",
        type=int,
        default=-1,
        help="The seed to use for generating the image or video.")
    parser.add_argument(
        "--image",
        type=str,
        default=None,
        help="The image to generate the video from.")
    parser.add_argument(
        "--sample_solver",
        type=str,
        default='unipc',
        choices=['unipc', 'dpm++'],
        help="The solver used to sample.")
    parser.add_argument(
        "--sample_steps", type=int, default=None, help="The sampling steps.")
    parser.add_argument(
        "--sample_shift",
        type=float,
        default=None,
        help="Sampling shift factor for flow matching schedulers.")
    parser.add_argument(
        "--sample_guide_scale",
        type=float,
        default=5.0,
        help="Classifier free guidance scale.")
    parser.add_argument(
        "--quant_desc_path",
        type=str,
        help="Path to quantization description file (enables quantization if provided, format: quant_model_description_*.json)"
    )
    
    parser = add_attentioncache_args(parser)
    args = parser.parse_args()

    _validate_args(args)

    # Validate quantization file existence if path is provided
    if args.quant_desc_path:
        if not os.path.exists(args.quant_desc_path):
            raise FileNotFoundError(f"Quantization description file not found: {args.quant_desc_path}")
        logging.info(f"Quantization enabled. Using description file: {args.quant_desc_path}")

    return args


def add_attentioncache_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group(title="Attention Cache args")

    group.add_argument("--use_attentioncache", action='store_true')
    group.add_argument("--attentioncache_ratio", type=float, default=1.2)
    group.add_argument("--attentioncache_interval", type=int, default=4)
    group.add_argument("--start_step", type=int, default=12)
    group.add_argument("--end_step", type=int, default=37)

    return parser


def _init_logging(rank):
    # logging
    if rank == 0:
        # set format
        logging.basicConfig(
            level=logging.INFO,
            format="[%(asctime)s] %(levelname)s: %(message)s",
            handlers=[logging.StreamHandler(stream=sys.stdout)])
    else:
        logging.basicConfig(level=logging.ERROR)


def generate(args):
    rank = int(os.getenv("RANK", 0))
    world_size = int(os.getenv("WORLD_SIZE", 1))
    local_rank = int(os.getenv("LOCAL_RANK", 0))
    device = local_rank
    _init_logging(rank)
    stream = torch.npu.Stream()

    if args.offload_model is None:
        args.offload_model = False if world_size > 1 else True
        logging.info(
            f"offload_model is not specified, set to {args.offload_model}.")
    if world_size > 1:
        torch.cuda.set_device(local_rank)
        dist.init_process_group(
            backend="hccl",
            init_method="env://",
            rank=rank,
            world_size=world_size)
    else:
        assert not (
            args.t5_fsdp or args.dit_fsdp
        ), f"t5_fsdp and dit_fsdp are not supported in non-distributed environments."
        assert not (
            args.cfg_size > 1 or args.ulysses_size > 1 or args.ring_size > 1
        ), f"context parallel are not supported in non-distributed environments."
        assert not (
            args.vae_parallel
        ), f"vae parallel are not supported in non-distributed environments."

    if args.cfg_size > 1 or args.ulysses_size > 1 or args.ring_size > 1 or args.tp_size > 1:
        assert args.cfg_size * args.ulysses_size * args.ring_size * args.tp_size == world_size, f"The number of cfg_size, ulysses_size and ring_size should be equal to the world size."
        sp_degree = args.ulysses_size * args.ring_size
        parallel_config = ParallelConfig(
            sp_degree=sp_degree,
            ulysses_degree=args.ulysses_size,
            ring_degree=args.ring_size,
            tp_degree=args.tp_size,
            use_cfg_parallel=(args.cfg_size==2),
            world_size=world_size,
        )
        init_parallel_env(parallel_config)

    if args.tp_size > 1 and args.dit_fsdp:
        logging.info("DiT using Tensor Parallel, disabled dit_fsdp")
        args.dit_fsdp = False

    if args.use_prompt_extend:
        if args.prompt_extend_method == "dashscope":
            prompt_expander = DashScopePromptExpander(
                model_name=args.prompt_extend_model, is_vl="i2v" in args.task)
        elif args.prompt_extend_method == "local_qwen":
            prompt_expander = QwenPromptExpander(
                model_name=args.prompt_extend_model,
                is_vl="i2v" in args.task,
                device=rank)
        else:
            raise NotImplementedError(
                f"Unsupport prompt_extend_method: {args.prompt_extend_method}")

    cfg = WAN_CONFIGS[args.task]
    if args.ulysses_size > 1:
        assert cfg.num_heads % args.ulysses_size == 0, f"`num_heads` must be divisible by `ulysses_size`."

    logging.info(f"Generation job args: {args}")
    logging.info(f"Generation model config: {cfg}")

    if dist.is_initialized():
        base_seed = [args.base_seed] if rank == 0 else [None]
        dist.broadcast_object_list(base_seed, src=0)
        args.base_seed = base_seed[0]

    if "t2v" in args.task or "t2i" in args.task:
        if args.prompt is None:
            args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
        logging.info(f"Input prompt: {args.prompt}")
        if args.use_prompt_extend:
            logging.info("Extending prompt ...")
            if rank == 0:
                prompt_output = prompt_expander(
                    args.prompt,
                    tar_lang=args.prompt_extend_target_lang,
                    seed=args.base_seed)
                if prompt_output.status == False:
                    logging.info(
                        f"Extending prompt failed: {prompt_output.message}")
                    logging.info("Falling back to original prompt.")
                    input_prompt = args.prompt
                else:
                    input_prompt = prompt_output.prompt
                input_prompt = [input_prompt]
            else:
                input_prompt = [None]
            if dist.is_initialized():
                dist.broadcast_object_list(input_prompt, src=0)
            args.prompt = input_prompt[0]
            logging.info(f"Extended prompt: {args.prompt}")

        logging.info("Creating WanT2V pipeline.")
        wan_t2v = wan.WanT2V(
            config=cfg,
            checkpoint_dir=args.ckpt_dir,
            device_id=device,
            rank=rank,
            t5_fsdp=args.t5_fsdp,
            dit_fsdp=args.dit_fsdp,
            use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
            t5_cpu=args.t5_cpu,
            use_vae_parallel=args.vae_parallel,
        )

        transformer = wan_t2v.model
        if args.tp_size > 1:
            logging.info("Initializing tensor parallel...")
            applicator = TensorParallelApplicator(args.tp_size, device_map="cpu")
            applicator.apply_to_model(transformer)
        wan_t2v.model.to("npu")

        # Apply quantization if description file is provided
        if args.quant_desc_path:
            # Import quantization module only when needed to reduce dependencies
            from mindiesd import quantize
            # Apply quantization
            quantize(
                model=transformer,
                quant_des_path=args.quant_desc_path,
                use_nz=True
            )
            # Ensure quantized model is on the correct device
            transformer = transformer.to(device)
            logging.info("Quantization applied successfully")

        if args.use_attentioncache:
            config = CacheConfig(
                method="attention_cache",
                blocks_count=len(transformer.blocks),
                steps_count=args.sample_steps,
                step_start=args.start_step,
                step_interval=args.attentioncache_interval,
                step_end=args.end_step
            )
        else:
            config = CacheConfig(
                method="attention_cache",
                blocks_count=len(transformer.blocks),
                steps_count=args.sample_steps
            )
        cache = CacheAgent(config)
        if args.dit_fsdp:
            for block in transformer._fsdp_wrapped_module.blocks:
                block._fsdp_wrapped_module.cache = cache
                block._fsdp_wrapped_module.args = args
        else:
            for block in transformer.blocks:
                block.cache = cache
                block.args = args
        
        logging.info(f"Warm up 2 steps...")
        video = wan_t2v.generate(
            args.prompt,
            size=SIZE_CONFIGS[args.size],
            frame_num=args.frame_num,
            shift=args.sample_shift,
            sample_solver=args.sample_solver,
            sampling_steps=2,
            guide_scale=args.sample_guide_scale,
            seed=args.base_seed,
            offload_model=args.offload_model)
        
        stream.synchronize()
        begin = time.time()
        video = wan_t2v.generate(
            args.prompt,
            size=SIZE_CONFIGS[args.size],
            frame_num=args.frame_num,
            shift=args.sample_shift,
            sample_solver=args.sample_solver,
            sampling_steps=args.sample_steps,
            guide_scale=args.sample_guide_scale,
            seed=args.base_seed,
            offload_model=args.offload_model)
        stream.synchronize()
        end = time.time()
        logging.info(f"Generating video used time {end - begin: .4f}s")

    else:
        if args.prompt is None:
            args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
        if args.image is None:
            args.image = EXAMPLE_PROMPT[args.task]["image"]
        logging.info(f"Input prompt: {args.prompt}")
        logging.info(f"Input image: {args.image}")

        img = Image.open(args.image).convert("RGB")
        if args.use_prompt_extend:
            logging.info("Extending prompt ...")
            if rank == 0:
                prompt_output = prompt_expander(
                    args.prompt,
                    tar_lang=args.prompt_extend_target_lang,
                    image=img,
                    seed=args.base_seed)
                if prompt_output.status == False:
                    logging.info(
                        f"Extending prompt failed: {prompt_output.message}")
                    logging.info("Falling back to original prompt.")
                    input_prompt = args.prompt
                else:
                    input_prompt = prompt_output.prompt
                input_prompt = [input_prompt]
            else:
                input_prompt = [None]
            if dist.is_initialized():
                dist.broadcast_object_list(input_prompt, src=0)
            args.prompt = input_prompt[0]
            logging.info(f"Extended prompt: {args.prompt}")

        logging.info("Creating WanI2V pipeline.")
        wan_i2v = wan.WanI2V(
            config=cfg,
            checkpoint_dir=args.ckpt_dir,
            device_id=device,
            rank=rank,
            t5_fsdp=args.t5_fsdp,
            dit_fsdp=args.dit_fsdp,
            use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
            t5_cpu=args.t5_cpu,
            use_vae_parallel=args.vae_parallel,
        )

        transformer = wan_i2v.model
        if args.tp_size > 1:
            logging.info("Initializing tensor parallel...")
            applicator = TensorParallelApplicator(args.tp_size, device_map="cpu")
            applicator.apply_to_model(transformer)
        wan_i2v.model.to("npu")

        # Apply quantization if description file is provided
        if args.quant_desc_path:
            # Import quantization module only when needed to reduce dependencies
            from mindiesd import quantize
            # Apply quantization
            quantize(
                model=transformer,
                quant_des_path=args.quant_desc_path,
                use_nz=True
            )
            # Ensure quantized model is on the correct device
            transformer = transformer.to(device)
            logging.info("Quantization applied successfully")

        if args.use_attentioncache:
            config = CacheConfig(
                method="attention_cache",
                blocks_count=len(transformer.blocks),
                steps_count=args.sample_steps,
                step_start=args.start_step,
                step_interval=args.attentioncache_interval,
                step_end=args.end_step
            )
        else:
            config = CacheConfig(
                method="attention_cache",
                blocks_count=len(transformer.blocks),
                steps_count=args.sample_steps
            )
        cache = CacheAgent(config)
        if args.dit_fsdp:
            for block in transformer._fsdp_wrapped_module.blocks:
                block._fsdp_wrapped_module.cache = cache
                block._fsdp_wrapped_module.args = args
        else:
            for block in transformer.blocks:
                block.cache = cache
                block.args = args
        
        logging.info(f"Warm up 2 steps...")
        video = wan_i2v.generate(
            args.prompt,
            img,
            max_area=MAX_AREA_CONFIGS[args.size],
            frame_num=args.frame_num,
            shift=args.sample_shift,
            sample_solver=args.sample_solver,
            sampling_steps=2,
            guide_scale=args.sample_guide_scale,
            seed=args.base_seed,
            offload_model=args.offload_model)

        logging.info("Generating video ...")
        stream.synchronize()
        begin = time.time()
        video = wan_i2v.generate(
            args.prompt,
            img,
            max_area=MAX_AREA_CONFIGS[args.size],
            frame_num=args.frame_num,
            shift=args.sample_shift,
            sample_solver=args.sample_solver,
            sampling_steps=args.sample_steps,
            guide_scale=args.sample_guide_scale,
            seed=args.base_seed,
            offload_model=args.offload_model)
        stream.synchronize()
        end = time.time()
        logging.info(f"Generating video used time {end - begin: .4f}s")


    if rank == 0:
        if args.save_file is None:
            formatted_time = datetime.now().strftime("%Y%m%d_%H%M%S")
            formatted_prompt = args.prompt.replace(" ", "_").replace("/",
                                                                     "_")[:50]
            suffix = '.png' if "t2i" in args.task else '.mp4'
            args.save_file = f"{args.task}_{args.size.replace('*','x') if sys.platform=='win32' else args.size}_{args.cfg_size}_{args.ulysses_size}_{args.ring_size}_{args.tp_size}_{formatted_prompt}_{formatted_time}" + suffix

        if "t2i" in args.task:
            logging.info(f"Saving generated image to {args.save_file}")
            cache_image(
                tensor=video.squeeze(1)[None],
                save_file=args.save_file,
                nrow=1,
                normalize=True,
                value_range=(-1, 1))
        else:
            logging.info(f"Saving generated video to {args.save_file}")
            cache_video(
                tensor=video[None],
                save_file=args.save_file,
                fps= args.frame_num // 5,
                nrow=1,
                normalize=True,
                value_range=(-1, 1))
    logging.info("Finished.")


if __name__ == "__main__":
    args = _parse_args()
    generate(args)
    finalize_parallel_env()