wan_video_new.py

import torch, warnings, glob, os, types
import numpy as np
from PIL import Image
from einops import repeat, reduce
from typing import Optional, Union
from dataclasses import dataclass
from modelscope import snapshot_download
from einops import rearrange
import numpy as np
from PIL import Image
from tqdm import tqdm
from typing import Optional
from typing_extensions import Literal

from ..utils import BasePipeline, ModelConfig, PipelineUnit, PipelineUnitRunner
from ..models import ModelManager, load_state_dict
from ..models.wan_video_dit import WanModel, RMSNorm, sinusoidal_embedding_1d
from ..models.wan_video_text_encoder import WanTextEncoder, T5RelativeEmbedding, T5LayerNorm
from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample
from ..models.wan_video_image_encoder import WanImageEncoder
from ..models.wan_video_vace import VaceWanModel
from ..models.wan_video_motion_controller import WanMotionControllerModel
from ..schedulers.flow_match import FlowMatchScheduler
from ..prompters import WanPrompter
from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear, WanAutoCastLayerNorm
from ..lora import GeneralLoRALoader
from ..classifiers.open_logo import OpenLogoClassifier
from ..models.logo_detector import LogoDetector



class WanVideoPipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.bfloat16, tokenizer_path=None):
        super().__init__(
            device=device, torch_dtype=torch_dtype,
            height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1
        )
        self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True)
        self.prompter = WanPrompter(tokenizer_path=tokenizer_path)
        self.text_encoder: WanTextEncoder = None
        self.image_encoder: WanImageEncoder = None
        self.dit: WanModel = None
        self.dit2: WanModel = None
        self.vae: WanVideoVAE = None
        self.motion_controller: WanMotionControllerModel = None
        self.vace: VaceWanModel = None
        self.classifier: OpenLogoClassifier = None
        self.in_iteration_models = ("dit", "motion_controller", "vace")
        self.in_iteration_models_2 = ("dit2", "motion_controller", "vace")
        self.unit_runner = PipelineUnitRunner()
        self.units = [
            WanVideoUnit_ShapeChecker(),
            WanVideoUnit_NoiseInitializer(),
            WanVideoUnit_InputVideoEmbedder(),
            WanVideoUnit_PromptEmbedder(),
            WanVideoUnit_ImageEmbedderVAE(),
            WanVideoUnit_ImageEmbedderCLIP(),
            WanVideoUnit_ImageEmbedderFused(),
            WanVideoUnit_FunControl(),
            WanVideoUnit_FunReference(),
            WanVideoUnit_FunCameraControl(),
            WanVideoUnit_SpeedControl(),
            WanVideoUnit_VACE(),
            WanVideoUnit_UnifiedSequenceParallel(),
            WanVideoUnit_TeaCache(),
            WanVideoUnit_CfgMerger(),
        ]
        self.model_fn = model_fn_wan_video
        
    
    def load_lora(self, module, path, alpha=1):
        loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device)
        lora = load_state_dict(path, torch_dtype=self.torch_dtype, device=self.device)
        loader.load(module, lora, alpha=alpha)

        
    def load_classifier(self, model_path, **kwargs):
        self.classifier = OpenLogoClassifier(model_path, device=self.device, **kwargs)


    def training_loss(self, **inputs):
        max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * self.scheduler.num_train_timesteps)
        min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * self.scheduler.num_train_timesteps)
        timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
        timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device)
        
        inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep)
        training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep)
        
        noise_pred = self.model_fn(**inputs, timestep=timestep)
        
        loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
        loss = loss * self.scheduler.training_weight(timestep)
        return loss


    def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5):
        self.vram_management_enabled = True
        if num_persistent_param_in_dit is not None:
            vram_limit = None
        else:
            if vram_limit is None:
                vram_limit = self.get_vram()
            vram_limit = vram_limit - vram_buffer
        if self.text_encoder is not None:
            dtype = next(iter(self.text_encoder.parameters())).dtype
            enable_vram_management(
                self.text_encoder,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Embedding: AutoWrappedModule,
                    T5RelativeEmbedding: AutoWrappedModule,
                    T5LayerNorm: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
        if self.dit is not None:
            dtype = next(iter(self.dit.parameters())).dtype
            device = "cpu" if vram_limit is not None else self.device
            enable_vram_management(
                self.dit,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv3d: AutoWrappedModule,
                    torch.nn.LayerNorm: WanAutoCastLayerNorm,
                    RMSNorm: AutoWrappedModule,
                    torch.nn.Conv2d: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device=device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                max_num_param=num_persistent_param_in_dit,
                overflow_module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
        if self.dit2 is not None:
            dtype = next(iter(self.dit2.parameters())).dtype
            device = "cpu" if vram_limit is not None else self.device
            enable_vram_management(
                self.dit2,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv3d: AutoWrappedModule,
                    torch.nn.LayerNorm: WanAutoCastLayerNorm,
                    RMSNorm: AutoWrappedModule,
                    torch.nn.Conv2d: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device=device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                max_num_param=num_persistent_param_in_dit,
                overflow_module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
        if self.vae is not None:
            dtype = next(iter(self.vae.parameters())).dtype
            enable_vram_management(
                self.vae,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv2d: AutoWrappedModule,
                    RMS_norm: AutoWrappedModule,
                    CausalConv3d: AutoWrappedModule,
                    Upsample: AutoWrappedModule,
                    torch.nn.SiLU: AutoWrappedModule,
                    torch.nn.Dropout: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device=self.device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
            )
        if self.image_encoder is not None:
            dtype = next(iter(self.image_encoder.parameters())).dtype
            enable_vram_management(
                self.image_encoder,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv2d: AutoWrappedModule,
                    torch.nn.LayerNorm: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=dtype,
                    computation_device=self.device,
                ),
            )
        if self.motion_controller is not None:
            dtype = next(iter(self.motion_controller.parameters())).dtype
            enable_vram_management(
                self.motion_controller,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=dtype,
                    computation_device=self.device,
                ),
            )
        if self.vace is not None:
            device = "cpu" if vram_limit is not None else self.device
            enable_vram_management(
                self.vace,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv3d: AutoWrappedModule,
                    torch.nn.LayerNorm: AutoWrappedModule,
                    RMSNorm: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device=device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
            
            
    def initialize_usp(self):
        import torch.distributed as dist
        from xfuser.core.distributed import initialize_model_parallel, init_distributed_environment
        dist.init_process_group(backend="nccl", init_method="env://")
        init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
        initialize_model_parallel(
            sequence_parallel_degree=dist.get_world_size(),
            ring_degree=1,
            ulysses_degree=dist.get_world_size(),
        )
        torch.cuda.set_device(dist.get_rank())
            
            
    def enable_usp(self):
        from xfuser.core.distributed import get_sequence_parallel_world_size
        from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward

        for block in self.dit.blocks:
            block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
        self.dit.forward = types.MethodType(usp_dit_forward, self.dit)
        if self.dit2 is not None:
            for block in self.dit2.blocks:
                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
            self.dit2.forward = types.MethodType(usp_dit_forward, self.dit2)
        self.sp_size = get_sequence_parallel_world_size()
        self.use_unified_sequence_parallel = True


    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
        device: Union[str, torch.device] = "cuda",
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*"),
        redirect_common_files: bool = True,
        use_usp=False,
    ):
        # Redirect model path
        if redirect_common_files:
            redirect_dict = {
                "models_t5_umt5-xxl-enc-bf16.pth": "Wan-AI/Wan2.1-T2V-1.3B",
                "Wan2.1_VAE.pth": "Wan-AI/Wan2.1-T2V-1.3B",
                "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth": "Wan-AI/Wan2.1-I2V-14B-480P",
            }
            for model_config in model_configs:
                if model_config.origin_file_pattern is None or model_config.model_id is None:
                    continue
                if model_config.origin_file_pattern in redirect_dict and model_config.model_id != redirect_dict[model_config.origin_file_pattern]:
                    print(f"To avoid repeatedly downloading model files, ({model_config.model_id}, {model_config.origin_file_pattern}) is redirected to ({redirect_dict[model_config.origin_file_pattern]}, {model_config.origin_file_pattern}). You can use `redirect_common_files=False` to disable file redirection.")
                    model_config.model_id = redirect_dict[model_config.origin_file_pattern]
        
        # Initialize pipeline
        pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
        if use_usp: pipe.initialize_usp()
        
        # Download and load models
        model_manager = ModelManager()
        for model_config in model_configs:
            model_config.download_if_necessary(use_usp=use_usp)
            model_manager.load_model(
                model_config.path,
                device=model_config.offload_device or device,
                torch_dtype=model_config.offload_dtype or torch_dtype
            )
        
        # Load models
        pipe.text_encoder = model_manager.fetch_model("wan_video_text_encoder")
        dit = model_manager.fetch_model("wan_video_dit", index=2)
        if isinstance(dit, list):
            pipe.dit, pipe.dit2 = dit
        else:
            pipe.dit = dit
        pipe.vae = model_manager.fetch_model("wan_video_vae")
        pipe.image_encoder = model_manager.fetch_model("wan_video_image_encoder")
        pipe.motion_controller = model_manager.fetch_model("wan_video_motion_controller")
        pipe.vace = model_manager.fetch_model("wan_video_vace")
        
        # Size division factor
        if pipe.vae is not None:
            pipe.height_division_factor = pipe.vae.upsampling_factor * 2
            pipe.width_division_factor = pipe.vae.upsampling_factor * 2

        # Initialize tokenizer
        tokenizer_config.download_if_necessary(use_usp=use_usp)
        pipe.prompter.fetch_models(pipe.text_encoder)
        pipe.prompter.fetch_tokenizer(tokenizer_config.path)
        
        # Unified Sequence Parallel
        if use_usp: pipe.enable_usp()
        return pipe


    def get_guidance_strength(self, progress_id, num_inference_steps, schedule_name="late_step_emphasis"):
        if schedule_name == "late_step_emphasis":
            # Start guidance after 60% of the steps
            start_step = int(num_inference_steps * 0.6)
            if progress_id < start_step:
                return 0.0
            else:
                # Ramp up from 0 to 1
                return (progress_id - start_step) / (num_inference_steps - start_step)
        else:
            return 1.0


    @torch.no_grad()
    def __call__(
        self,
        # Prompt
        prompt: str,
        negative_prompt: Optional[str] = "",
        # Image-to-video
        input_image: Optional[Image.Image] = None,
        # First-last-frame-to-video
        end_image: Optional[Image.Image] = None,
        # Video-to-video
        input_video: Optional[list[Image.Image]] = None,
        denoising_strength: Optional[float] = 1.0,
        # ControlNet
        control_video: Optional[list[Image.Image]] = None,
        reference_image: Optional[Image.Image] = None,
        # Camera control
        camera_control_direction: Optional[Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"]] = None,
        camera_control_speed: Optional[float] = 1/54,
        camera_control_origin: Optional[tuple] = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0),
        # VACE
        vace_video: Optional[list[Image.Image]] = None,
        vace_video_mask: Optional[Image.Image] = None,
        vace_reference_image: Optional[Image.Image] = None,
        vace_scale: Optional[float] = 1.0,
        # Randomness
        seed: Optional[int] = None,
        rand_device: Optional[str] = "cpu",
        # Shape
        height: Optional[int] = 480,
        width: Optional[int] = 832,
        num_frames=81,
        # Classifier-free guidance
        cfg_scale: Optional[float] = 5.0,
        cfg_merge: Optional[bool] = False,
        # Classifier guidance
        classifier_guidance_scale: float = 0.0,
        logo_mask: Optional[torch.Tensor] = None,
        classifier_class_id: int = 0,
        guidance_schedule: str = "late_step_emphasis",
        logo_detector_path: Optional[str] = None,
        # Boundary
        switch_DiT_boundary: Optional[float] = 0.875,
        # Scheduler
        num_inference_steps: Optional[int] = 50,
        sigma_shift: Optional[float] = 5.0,
        # Speed control
        motion_bucket_id: Optional[int] = None,
        # VAE tiling
        tiled: Optional[bool] = True,
        tile_size: Optional[tuple[int, int]] = (30, 52),
        tile_stride: Optional[tuple[int, int]] = (15, 26),
        # Sliding window
        sliding_window_size: Optional[int] = None,
        sliding_window_stride: Optional[int] = None,
        # Teacache
        tea_cache_l1_thresh: Optional[float] = None,
        tea_cache_model_id: Optional[str] = "",
        # progress_bar
        progress_bar_cmd=tqdm,
    ):
        # Scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
        
        # Inputs
        inputs_posi = {
            "prompt": prompt,
            "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps,
        }
        inputs_nega = {
            "negative_prompt": negative_prompt,
            "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps,
        }
        inputs_shared = {
            "input_image": input_image,
            "end_image": end_image,
            "input_video": input_video, "denoising_strength": denoising_strength,
            "control_video": control_video, "reference_image": reference_image,
            "camera_control_direction": camera_control_direction, "camera_control_speed": camera_control_speed, "camera_control_origin": camera_control_origin,
            "vace_video": vace_video, "vace_video_mask": vace_video_mask, "vace_reference_image": vace_reference_image, "vace_scale": vace_scale,
            "seed": seed, "rand_device": rand_device,
            "height": height, "width": width, "num_frames": num_frames,
            "cfg_scale": cfg_scale, "cfg_merge": cfg_merge,
            "sigma_shift": sigma_shift,
            "motion_bucket_id": motion_bucket_id,
            "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
            "sliding_window_size": sliding_window_size, "sliding_window_stride": sliding_window_stride,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)

        # Logo detection
        if logo_detector_path is not None:
            logo_detector = LogoDetector(logo_detector_path, device=self.device)
            if input_image is not None:
                input_image_tensor = self.preprocess_image(input_image.resize((width, height)))
                logo_mask = logo_detector.get_logo_mask(input_image_tensor)
                logo_mask = logo_mask.to(dtype=self.torch_dtype, device=self.device)

        # Denoise
        self.load_models_to_device(self.in_iteration_models)
        models = {name: getattr(self, name) for name in self.in_iteration_models}
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            # Switch DiT if necessary
            if timestep.item() < switch_DiT_boundary * self.scheduler.num_train_timesteps and self.dit2 is not None and not models["dit"] is self.dit2:
                self.load_models_to_device(self.in_iteration_models_2)
                models["dit"] = self.dit2
                
            # Timestep
            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
            
            # Inference
            noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep)
            if cfg_scale != 1.0:
                if cfg_merge:
                    noise_pred_posi, noise_pred_nega = noise_pred_posi.chunk(2, dim=0)
                else:
                    noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep)
                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
            else:
                noise_pred = noise_pred_posi

            # Classifier guidance
            if self.classifier is not None and classifier_guidance_scale > 0.0:
                g_t = self.get_guidance_strength(progress_id, num_inference_steps, guidance_schedule)
                if g_t > 0:
                    latents_for_grad = inputs_shared["latents"].detach().requires_grad_(True)
                    grad = self.classifier.compute_gradient(
                        latents=latents_for_grad,
                        pipeline=self,
                        timestep=timestep,
                        class_id=classifier_class_id,
                        mask=logo_mask,
                    )

                    alpha_t = self.scheduler.get_alpha_t(timestep)
                    sigma_t = self.scheduler.get_sigma_t(timestep)

                    guidance = -alpha_t * g_t * classifier_guidance_scale * sigma_t * grad
                    noise_pred = noise_pred + guidance

            # Scheduler
            inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"])
            if "first_frame_latents" in inputs_shared:
                inputs_shared["latents"][:, :, 0:1] = inputs_shared["first_frame_latents"]
        
        # VACE (TODO: remove it)
        if vace_reference_image is not None:
            inputs_shared["latents"] = inputs_shared["latents"][:, :, 1:]

        # Decode
        self.load_models_to_device(['vae'])
        video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        video = self.vae_output_to_video(video)
        self.load_models_to_device([])

        return video



class WanVideoUnit_ShapeChecker(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=("height", "width", "num_frames"))

    def process(self, pipe: WanVideoPipeline, height, width, num_frames):
        height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
        return {"height": height, "width": width, "num_frames": num_frames}



class WanVideoUnit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=("height", "width", "num_frames", "seed", "rand_device", "vace_reference_image"))

    def process(self, pipe: WanVideoPipeline, height, width, num_frames, seed, rand_device, vace_reference_image):
        length = (num_frames - 1) // 4 + 1
        if vace_reference_image is not None:
            length += 1
        shape = (1, pipe.vae.model.z_dim, length, height // pipe.vae.upsampling_factor, width // pipe.vae.upsampling_factor)
        noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device)
        if vace_reference_image is not None:
            noise = torch.concat((noise[:, :, -1:], noise[:, :, :-1]), dim=2)
        return {"noise": noise}
    


class WanVideoUnit_InputVideoEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("input_video", "noise", "tiled", "tile_size", "tile_stride", "vace_reference_image"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, input_video, noise, tiled, tile_size, tile_stride, vace_reference_image):
        if input_video is None:
            return {"latents": noise}
        pipe.load_models_to_device(["vae"])
        input_video = pipe.preprocess_video(input_video)
        input_latents = pipe.vae.encode(input_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
        if vace_reference_image is not None:
            vace_reference_image = pipe.preprocess_video([vace_reference_image])
            vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device).to(dtype=pipe.torch_dtype, device=pipe.device)
            input_latents = torch.concat([vace_reference_latents, input_latents], dim=2)
        if pipe.scheduler.training:
            return {"latents": noise, "input_latents": input_latents}
        else:
            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
            return {"latents": latents}



class WanVideoUnit_PromptEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
            input_params_posi={"prompt": "prompt", "positive": "positive"},
            input_params_nega={"prompt": "negative_prompt", "positive": "positive"},
            onload_model_names=("text_encoder",)
        )

    def process(self, pipe: WanVideoPipeline, prompt, positive) -> dict:
        pipe.load_models_to_device(self.onload_model_names)
        prompt_emb = pipe.prompter.encode_prompt(prompt, positive=positive, device=pipe.device)
        return {"context": prompt_emb}



class WanVideoUnit_ImageEmbedder(PipelineUnit):
    """
    Deprecated
    """
    def __init__(self):
        super().__init__(
            input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("image_encoder", "vae")
        )

    def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride):
        if input_image is None or pipe.image_encoder is None:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
        clip_context = pipe.image_encoder.encode_image([image])
        msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
        msk[:, 1:] = 0
        if end_image is not None:
            end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
            vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1)
            if pipe.dit.has_image_pos_emb:
                clip_context = torch.concat([clip_context, pipe.image_encoder.encode_image([end_image])], dim=1)
            msk[:, -1:] = 1
        else:
            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)

        msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
        msk = msk.transpose(1, 2)[0]
        
        y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        y = torch.concat([msk, y])
        y = y.unsqueeze(0)
        clip_context = clip_context.to(dtype=pipe.torch_dtype, device=pipe.device)
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"clip_feature": clip_context, "y": y}



class WanVideoUnit_ImageEmbedderCLIP(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("input_image", "end_image", "height", "width"),
            onload_model_names=("image_encoder",)
        )

    def process(self, pipe: WanVideoPipeline, input_image, end_image, height, width):
        if input_image is None or pipe.image_encoder is None or not pipe.dit.require_clip_embedding:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
        clip_context = pipe.image_encoder.encode_image([image])
        if end_image is not None:
            end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
            if pipe.dit.has_image_pos_emb:
                clip_context = torch.concat([clip_context, pipe.image_encoder.encode_image([end_image])], dim=1)
        clip_context = clip_context.to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"clip_feature": clip_context}
    


class WanVideoUnit_ImageEmbedderVAE(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride):
        if input_image is None or not pipe.dit.require_vae_embedding:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
        msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
        msk[:, 1:] = 0
        if end_image is not None:
            end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
            vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1)
            msk[:, -1:] = 1
        else:
            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)

        msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
        msk = msk.transpose(1, 2)[0]
        
        y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        y = torch.concat([msk, y])
        y = y.unsqueeze(0)
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"y": y}



class WanVideoUnit_ImageEmbedderFused(PipelineUnit):
    """
    Encode input image to latents using VAE. This unit is for Wan-AI/Wan2.2-TI2V-5B.
    """
    def __init__(self):
        super().__init__(
            input_params=("input_image", "latents", "height", "width", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, input_image, latents, height, width, tiled, tile_size, tile_stride):
        if input_image is None or not pipe.dit.fuse_vae_embedding_in_latents:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        image = pipe.preprocess_image(input_image.resize((width, height))).transpose(0, 1)
        z = pipe.vae.encode([image], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        latents[:, :, 0: 1] = z
        return {"latents": latents, "fuse_vae_embedding_in_latents": True, "first_frame_latents": z}



class WanVideoUnit_FunControl(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y", "latents"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y, latents):
        if control_video is None:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        control_video = pipe.preprocess_video(control_video)
        control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
        control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device)
        y_dim = pipe.dit.in_dim-control_latents.shape[1]-latents.shape[1]
        if clip_feature is None or y is None:
            clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device)
            y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
        else:
            y = y[:, -y_dim:]
        y = torch.concat([control_latents, y], dim=1)
        return {"clip_feature": clip_feature, "y": y}
    


class WanVideoUnit_FunReference(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("reference_image", "height", "width", "reference_image"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, reference_image, height, width):
        if reference_image is None:
            return {}
        pipe.load_models_to_device(["vae"])
        reference_image = reference_image.resize((width, height))
        reference_latents = pipe.preprocess_video([reference_image])
        reference_latents = pipe.vae.encode(reference_latents, device=pipe.device)
        if pipe.image_encoder is None:
            return {"reference_latents": reference_latents}
        clip_feature = pipe.preprocess_image(reference_image)
        clip_feature = pipe.image_encoder.encode_image([clip_feature])
        return {"reference_latents": reference_latents, "clip_feature": clip_feature}



class WanVideoUnit_FunCameraControl(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image, tiled, tile_size, tile_stride):
        if camera_control_direction is None:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates(
            camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin)
        
        control_camera_video = camera_control_plucker_embedding[:num_frames].permute([3, 0, 1, 2]).unsqueeze(0)
        control_camera_latents = torch.concat(
            [
                torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
                control_camera_video[:, :, 1:]
            ], dim=2
        ).transpose(1, 2)
        b, f, c, h, w = control_camera_latents.shape
        control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
        control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
        control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype)
        
        input_image = input_image.resize((width, height))
        input_latents = pipe.preprocess_video([input_image])
        input_latents = pipe.vae.encode(input_latents, device=pipe.device)
        y = torch.zeros_like(latents).to(pipe.device)
        y[:, :, :1] = input_latents
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)

        if y.shape[1] != pipe.dit.in_dim - latents.shape[1]:
            image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
            y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
            msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
            msk[:, 1:] = 0
            msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
            msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
            msk = msk.transpose(1, 2)[0]
            y = torch.cat([msk,y])
            y = y.unsqueeze(0)
            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"control_camera_latents_input": control_camera_latents_input, "y": y}



class WanVideoUnit_SpeedControl(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=("motion_bucket_id",))

    def process(self, pipe: WanVideoPipeline, motion_bucket_id):
        if motion_bucket_id is None:
            return {}
        motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"motion_bucket_id": motion_bucket_id}



class WanVideoUnit_VACE(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("vace_video", "vace_video_mask", "vace_reference_image", "vace_scale", "height", "width", "num_frames", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(
        self,
        pipe: WanVideoPipeline,
        vace_video, vace_video_mask, vace_reference_image, vace_scale,
        height, width, num_frames,
        tiled, tile_size, tile_stride
    ):
        if vace_video is not None or vace_video_mask is not None or vace_reference_image is not None:
            pipe.load_models_to_device(["vae"])
            if vace_video is None:
                vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=pipe.torch_dtype, device=pipe.device)
            else:
                vace_video = pipe.preprocess_video(vace_video)
            
            if vace_video_mask is None:
                vace_video_mask = torch.ones_like(vace_video)
            else:
                vace_video_mask = pipe.preprocess_video(vace_video_mask, min_value=0, max_value=1)
            
            inactive = vace_video * (1 - vace_video_mask) + 0 * vace_video_mask
            reactive = vace_video * vace_video_mask + 0 * (1 - vace_video_mask)
            inactive = pipe.vae.encode(inactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
            reactive = pipe.vae.encode(reactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
            vace_video_latents = torch.concat((inactive, reactive), dim=1)
            
            vace_mask_latents = rearrange(vace_video_mask[0,0], "T (H P) (W Q) -> 1 (P Q) T H W", P=8, Q=8)
            vace_mask_latents = torch.nn.functional.interpolate(vace_mask_latents, size=((vace_mask_latents.shape[2] + 3) // 4, vace_mask_latents.shape[3], vace_mask_latents.shape[4]), mode='nearest-exact')
            
            if vace_reference_image is None:
                pass
            else:
                vace_reference_image = pipe.preprocess_video([vace_reference_image])
                vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
                vace_reference_latents = torch.concat((vace_reference_latents, torch.zeros_like(vace_reference_latents)), dim=1)
                vace_video_latents = torch.concat((vace_reference_latents, vace_video_latents), dim=2)
                vace_mask_latents = torch.concat((torch.zeros_like(vace_mask_latents[:, :, :1]), vace_mask_latents), dim=2)
            
            vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1)
            return {"vace_context": vace_context, "vace_scale": vace_scale}
        else:
            return {"vace_context": None, "vace_scale": vace_scale}



class WanVideoUnit_UnifiedSequenceParallel(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=())

    def process(self, pipe: WanVideoPipeline):
        if hasattr(pipe, "use_unified_sequence_parallel"):
            if pipe.use_unified_sequence_parallel:
                return {"use_unified_sequence_parallel": True}
        return {}



class WanVideoUnit_TeaCache(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
            input_params_posi={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"},
            input_params_nega={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"},
        )

    def process(self, pipe: WanVideoPipeline, num_inference_steps, tea_cache_l1_thresh, tea_cache_model_id):
        if tea_cache_l1_thresh is None:
            return {}
        return {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id)}



class WanVideoUnit_CfgMerger(PipelineUnit):
    def __init__(self):
        super().__init__(take_over=True)
        self.concat_tensor_names = ["context", "clip_feature", "y", "reference_latents"]

    def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega):
        if not inputs_shared["cfg_merge"]:
            return inputs_shared, inputs_posi, inputs_nega
        for name in self.concat_tensor_names:
            tensor_posi = inputs_posi.get(name)
            tensor_nega = inputs_nega.get(name)
            tensor_shared = inputs_shared.get(name)
            if tensor_posi is not None and tensor_nega is not None:
                inputs_shared[name] = torch.concat((tensor_posi, tensor_nega), dim=0)
            elif tensor_shared is not None:
                inputs_shared[name] = torch.concat((tensor_shared, tensor_shared), dim=0)
        inputs_posi.clear()
        inputs_nega.clear()
        return inputs_shared, inputs_posi, inputs_nega



class TeaCache:
    def __init__(self, num_inference_steps, rel_l1_thresh, model_id):
        self.num_inference_steps = num_inference_steps
        self.step = 0
        self.accumulated_rel_l1_distance = 0
        self.previous_modulated_input = None
        self.rel_l1_thresh = rel_l1_thresh
        self.previous_residual = None
        self.previous_hidden_states = None
        
        self.coefficients_dict = {
            "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02],
            "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01],
            "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04,  1.40286849e+03, -1.35890334e+01, 1.32517977e-01],
            "Wan2.1-I2V-14B-720P": [ 8.10705460e+03,  2.13393892e+03, -3.72934672e+02,  1.66203073e+01, -4.17769401e-02],
        }
        if model_id not in self.coefficients_dict:
            supported_model_ids = ", ".join([i for i in self.coefficients_dict])
            raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).")
        self.coefficients = self.coefficients_dict[model_id]

    def check(self, dit: WanModel, x, t_mod):
        modulated_inp = t_mod.clone()
        if self.step == 0 or self.step == self.num_inference_steps - 1:
            should_calc = True
            self.accumulated_rel_l1_distance = 0
        else:
            coefficients = self.coefficients
            rescale_func = np.poly1d(coefficients)
            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
                should_calc = False
            else:
                should_calc = True
                self.accumulated_rel_l1_distance = 0
        self.previous_modulated_input = modulated_inp
        self.step += 1
        if self.step == self.num_inference_steps:
            self.step = 0
        if should_calc:
            self.previous_hidden_states = x.clone()
        return not should_calc

    def store(self, hidden_states):
        self.previous_residual = hidden_states - self.previous_hidden_states
        self.previous_hidden_states = None

    def update(self, hidden_states):
        hidden_states = hidden_states + self.previous_residual
        return hidden_states



class TemporalTiler_BCTHW:
    def __init__(self):
        pass

    def build_1d_mask(self, length, left_bound, right_bound, border_width):
        x = torch.ones((length,))
        if border_width == 0:
            return x
        
        shift = 0.5
        if not left_bound:
            x[:border_width] = (torch.arange(border_width) + shift) / border_width
        if not right_bound:
            x[-border_width:] = torch.flip((torch.arange(border_width) + shift) / border_width, dims=(0,))
        return x

    def build_mask(self, data, is_bound, border_width):
        _, _, T, _, _ = data.shape
        t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
        mask = repeat(t, "T -> 1 1 T 1 1")
        return mask
    
    def run(self, model_fn, sliding_window_size, sliding_window_stride, computation_device, computation_dtype, model_kwargs, tensor_names, batch_size=None):
        tensor_names = [tensor_name for tensor_name in tensor_names if model_kwargs.get(tensor_name) is not None]
        tensor_dict = {tensor_name: model_kwargs[tensor_name] for tensor_name in tensor_names}
        B, C, T, H, W = tensor_dict[tensor_names[0]].shape
        if batch_size is not None:
            B *= batch_size
        data_device, data_dtype = tensor_dict[tensor_names[0]].device, tensor_dict[tensor_names[0]].dtype
        value = torch.zeros((B, C, T, H, W), device=data_device, dtype=data_dtype)
        weight = torch.zeros((1, 1, T, 1, 1), device=data_device, dtype=data_dtype)
        for t in range(0, T, sliding_window_stride):
            if t - sliding_window_stride >= 0 and t - sliding_window_stride + sliding_window_size >= T:
                continue
            t_ = min(t + sliding_window_size, T)
            model_kwargs.update({
                tensor_name: tensor_dict[tensor_name][:, :, t: t_:, :].to(device=computation_device, dtype=computation_dtype) \
                    for tensor_name in tensor_names
            })
            model_output = model_fn(**model_kwargs).to(device=data_device, dtype=data_dtype)
            mask = self.build_mask(
                model_output,
                is_bound=(t == 0, t_ == T),
                border_width=(sliding_window_size - sliding_window_stride,)
            ).to(device=data_device, dtype=data_dtype)
            value[:, :, t: t_, :, :] += model_output * mask
            weight[:, :, t: t_, :, :] += mask
        value /= weight
        model_kwargs.update(tensor_dict)
        return value



def model_fn_wan_video(
    dit: WanModel,
    motion_controller: WanMotionControllerModel = None,
    vace: VaceWanModel = None,
    latents: torch.Tensor = None,
    timestep: torch.Tensor = None,
    context: torch.Tensor = None,
    clip_feature: Optional[torch.Tensor] = None,
    y: Optional[torch.Tensor] = None,
    reference_latents = None,
    vace_context = None,
    vace_scale = 1.0,
    tea_cache: TeaCache = None,
    use_unified_sequence_parallel: bool = False,
    motion_bucket_id: Optional[torch.Tensor] = None,
    sliding_window_size: Optional[int] = None,
    sliding_window_stride: Optional[int] = None,
    cfg_merge: bool = False,
    use_gradient_checkpointing: bool = False,
    use_gradient_checkpointing_offload: bool = False,
    control_camera_latents_input = None,
    fuse_vae_embedding_in_latents: bool = False,
    **kwargs,
):
    if sliding_window_size is not None and sliding_window_stride is not None:
        model_kwargs = dict(
            dit=dit,
            motion_controller=motion_controller,
            vace=vace,
            latents=latents,
            timestep=timestep,
            context=context,
            clip_feature=clip_feature,
            y=y,
            reference_latents=reference_latents,
            vace_context=vace_context,
            vace_scale=vace_scale,
            tea_cache=tea_cache,
            use_unified_sequence_parallel=use_unified_sequence_parallel,
            motion_bucket_id=motion_bucket_id,
        )
        return TemporalTiler_BCTHW().run(
            model_fn_wan_video,
            sliding_window_size, sliding_window_stride,
            latents.device, latents.dtype,
            model_kwargs=model_kwargs,
            tensor_names=["latents", "y"],
            batch_size=2 if cfg_merge else 1
        )
    
    if use_unified_sequence_parallel:
        import torch.distributed as dist
        from xfuser.core.distributed import (get_sequence_parallel_rank,
                                            get_sequence_parallel_world_size,
                                            get_sp_group)

    # Timestep
    if dit.seperated_timestep and fuse_vae_embedding_in_latents:
        timestep = torch.concat([
            torch.zeros((1, latents.shape[3] * latents.shape[4] // 4), dtype=latents.dtype, device=latents.device),
            torch.ones((latents.shape[2] - 1, latents.shape[3] * latents.shape[4] // 4), dtype=latents.dtype, device=latents.device) * timestep
        ]).flatten()
        t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep).unsqueeze(0))
        if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
            t_chunks = torch.chunk(t, get_sequence_parallel_world_size(), dim=1)
            t_chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, t_chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in t_chunks]
            t = t_chunks[get_sequence_parallel_rank()]
        t_mod = dit.time_projection(t).unflatten(2, (6, dit.dim))
    else:
        t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
        t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
    
    # Motion Controller
    if motion_bucket_id is not None and motion_controller is not None:
        t_mod = t_mod + motion_controller(motion_bucket_id).unflatten(1, (6, dit.dim))
    context = dit.text_embedding(context)

    x = latents
    # Merged cfg
    if x.shape[0] != context.shape[0]:
        x = torch.concat([x] * context.shape[0], dim=0)
    if timestep.shape[0] != context.shape[0]:
        timestep = torch.concat([timestep] * context.shape[0], dim=0)

    # Image Embedding
    if y is not None and dit.require_vae_embedding:
        x = torch.cat([x, y], dim=1)
    if clip_feature is not None and dit.require_clip_embedding:
        clip_embdding = dit.img_emb(clip_feature)
        context = torch.cat([clip_embdding, context], dim=1)

    # Add camera control
    x, (f, h, w) = dit.patchify(x, control_camera_latents_input)
    
    # Reference image
    if reference_latents is not None:
        if len(reference_latents.shape) == 5:
            reference_latents = reference_latents[:, :, 0]
        reference_latents = dit.ref_conv(reference_latents).flatten(2).transpose(1, 2)
        x = torch.concat([reference_latents, x], dim=1)
        f += 1
    
    freqs = torch.cat([
        dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
        dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
        dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
    ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
    
    # TeaCache
    if tea_cache is not None:
        tea_cache_update = tea_cache.check(dit, x, t_mod)
    else:
        tea_cache_update = False
        
    if vace_context is not None:
        vace_hints = vace(x, vace_context, context, t_mod, freqs)
    
    # blocks
    if use_unified_sequence_parallel:
        if dist.is_initialized() and dist.get_world_size() > 1:
            chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
            pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
            chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
            x = chunks[get_sequence_parallel_rank()]
    if tea_cache_update:
        x = tea_cache.update(x)
    else:
        def create_custom_forward(module):
            def custom_forward(*inputs):
                return module(*inputs)
            return custom_forward
        
        for block_id, block in enumerate(dit.blocks):
            if use_gradient_checkpointing_offload:
                with torch.autograd.graph.save_on_cpu():
                    x = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        x, context, t_mod, freqs,
                        use_reentrant=False,
                    )
            elif use_gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    x, context, t_mod, freqs,
                    use_reentrant=False,
                )
            else:
                x = block(x, context, t_mod, freqs)
            if vace_context is not None and block_id in vace.vace_layers_mapping:
                current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]]
                if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
                    current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
                    current_vace_hint = torch.nn.functional.pad(current_vace_hint, (0, 0, 0, chunks[0].shape[1] - current_vace_hint.shape[1]), value=0)
                x = x + current_vace_hint * vace_scale
        if tea_cache is not None:
            tea_cache.store(x)
            
    x = dit.head(x, t)
    if use_unified_sequence_parallel:
        if dist.is_initialized() and dist.get_world_size() > 1:
            x = get_sp_group().all_gather(x, dim=1)
            x = x[:, :-pad_shape] if pad_shape > 0 else x
    # Remove reference latents
    if reference_latents is not None:
        x = x[:, reference_latents.shape[1]:]
        f -= 1
    x = dit.unpatchify(x, (f, h, w))
    return x