diffsynth/pipelines/wan_video.py

import types
from ..models import ModelManager
from ..models.wan_video_dit import WanModel
from ..models.wan_video_text_encoder import WanTextEncoder
from ..models.wan_video_vae import WanVideoVAE
from ..models.wan_video_image_encoder import WanImageEncoder
from ..models.wan_video_vace import VaceWanModel
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
from ..prompters import WanPrompter
import torch, os
from einops import rearrange
import numpy as np
from PIL import Image
from tqdm import tqdm
from typing import Optional

from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm
from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d
from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample
from ..models.wan_video_motion_controller import WanMotionControllerModel



class WanVideoPipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None):
        super().__init__(device=device, torch_dtype=torch_dtype)
        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.vae: WanVideoVAE = None
        self.motion_controller: WanMotionControllerModel = None
        self.vace: VaceWanModel = None
        self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder', 'motion_controller', 'vace']
        self.height_division_factor = 16
        self.width_division_factor = 16
        self.use_unified_sequence_parallel = False
        
        self.model_fn = model_fn_wan_video #*me


    def enable_vram_management(self, num_persistent_param_in_dit=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,
            ),
        )
        dtype = next(iter(self.dit.parameters())).dtype
        enable_vram_management(
            self.dit,
            module_map = {
                torch.nn.Linear: AutoWrappedLinear,
                torch.nn.Conv3d: AutoWrappedModule,
                torch.nn.LayerNorm: AutoWrappedModule,
                RMSNorm: AutoWrappedModule,
                torch.nn.Conv2d: 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,
            ),
            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,
            ),
        )
        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:
            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=self.device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
            )
        self.enable_cpu_offload()


    def fetch_models(self, model_manager: ModelManager):
        text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True)
        if text_encoder_model_and_path is not None:
            self.text_encoder, tokenizer_path = text_encoder_model_and_path
            self.prompter.fetch_models(self.text_encoder)
            self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl"))
        self.dit = model_manager.fetch_model("wan_video_dit")
        self.vae = model_manager.fetch_model("wan_video_vae")
        self.image_encoder = model_manager.fetch_model("wan_video_image_encoder")
        self.motion_controller = model_manager.fetch_model("wan_video_motion_controller")
        self.vace = model_manager.fetch_model("wan_video_vace")


    @staticmethod
    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, use_usp=False):
        if device is None: device = model_manager.device
        if torch_dtype is None: torch_dtype = model_manager.torch_dtype
        pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
        pipe.fetch_models(model_manager)
        if use_usp:
            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 pipe.dit.blocks:
                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
            pipe.dit.forward = types.MethodType(usp_dit_forward, pipe.dit)
            pipe.sp_size = get_sequence_parallel_world_size()
            pipe.use_unified_sequence_parallel = True
        return pipe
    
    
    def denoising_model(self):
        return self.dit

    def encode_prompt(self, prompt, positive=True):
        prompt_emb = self.prompter.encode_prompt(prompt, positive=positive, device=self.device)
        return {"context": prompt_emb}
    
    # For Inp模型
    def encode_image(self, image, end_image, num_frames, height, width, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
        image = self.preprocess_image(image.resize((width, height))).to(self.device) # 1,c,h,w
        clip_context = self.image_encoder.encode_image([image])
        msk = torch.ones(1, num_frames, height//8, width//8, device=self.device) # 1,f,h1,w1,c=1
        msk[:, 1:] = 0 # 首帧之后置为0
        if end_image is not None:
            end_image = self.preprocess_image(end_image.resize((width, height))).to(self.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 self.dit.has_image_pos_emb:
                clip_context = torch.concat([clip_context, self.image_encoder.encode_image([end_image])], dim=1)
            msk[:, -1:] = 1 # 最后一帧置为1
        else: # 第一帧+剩余帧拼0; c=3,f,h,w
            vae_input = torch.concat( [ image.transpose(0, 1), # 1,c=3,h,w->c=3,1,h,w
                torch.zeros(3, num_frames-1, height, width).to(image.device) ], dim=1)

        # mask说明: 首尾为1; 其余为0-> 保留为1, 生成为0, 应为fg_mask(fg为1)
        # 第一帧重复3次49+3=52 // 4 = 13
        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) # 调整维度
        msk = msk.transpose(1, 2)[0] # 4,f1,h1,w1
        
        y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
        y = y.to(dtype=self.torch_dtype, device=self.device) # c1=16, f1, h1, w1
        y = torch.concat([msk, y])
        y = y.unsqueeze(0)
        clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device)
        y = y.to(dtype=self.torch_dtype, device=self.device)
        return {"clip_feature": clip_context, "y": y}
    
    
    def encode_control_video(self, control_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        control_video = self.preprocess_images(control_video) # 归一化 f=49,1,c=3,h,w -> 下一行: 1(bs),c=3,f=49,h,w
        control_video = torch.stack(control_video, dim=2).to(dtype=self.torch_dtype, device=self.device)
        # print(control_video.shape, control_video.max(), control_video.min())
        # torch.Size([1, 3, 49, 800, 1920]) tensor(0.8125, device='cuda:0', dtype=torch.bfloat16) tensor(-1., device='cuda:0', dtype=torch.bfloat16)
        latents = self.encode_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device)
        return latents
    
    def prepare_reference_image(self, reference_image, height=480, width=832):
        if reference_image is not None:
            self.load_models_to_device(["vae"])
            reference_image = reference_image.resize((width, height))
            reference_image = self.preprocess_images([reference_image]) # f=1,1,c=3,h,w 
            # # 输入: 1(bs),c=3,f=1,h,w
            reference_image = torch.stack(reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device)
            reference_latents = self.vae.encode(reference_image, device=self.device) # 1,c1,f1,h1,w1
            # reference_image: [1, 3, 1, 480, 832], reference_latents: [1, 16, 1, 60, 104])
            return {"reference_latents": reference_latents}
        else:
            return {}
    

    #* clip_feature #me
    def image_clip_feature(self, image, height, width):
        # image: h,w,c -> 1,c=3,h,w (-1,1)
        image = Image.fromarray(image).convert('RGB')
        image = self.preprocess_image(image.resize((width, height))).to(self.device)
        # encode_image输入格式为: # [image]: 1,1,c=3,h,w; 输出clip_feature: 1,257,1280
        clip_feature = self.image_encoder.encode_image( [image] ).to(self.device)
        clip_feature = clip_feature.to(dtype=self.torch_dtype, device=self.device)
        return clip_feature
    
    #me
    def prepare_controlnet_kwargs(self, control_video, num_frames, height, width, clip_feature=None, 
        more_cond=None, cond_mode=None, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        if control_video is not None: # control_video: 
            control_latents = self.encode_control_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
            # control_latents: f
            if clip_feature is None:
                clip_feature = torch.zeros((1, 257, 1280), dtype=self.torch_dtype, device=self.device)
            
            if more_cond is None:
                y0 = torch.zeros((1, 16, (num_frames-1)//4 + 1, height//8, width//8), dtype=self.torch_dtype, device=self.device)
            elif cond_mode in  [ 'v2v', 'v2v_bg_fg' ]:
                y0 = self.encode_control_video(more_cond, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
            else: # cond_mode = inp
                y0 = more_cond.to(dtype=self.torch_dtype, device=self.device)
            
            if cond_mode in [ 'inp', 'v2v_bg_fg', 'test' ]:
                y = torch.concat([y0, control_latents], dim=1)
            else:
                y = torch.concat([control_latents, y0], dim=1)
            # torch.Size([1, 257, 1280]) torch.Size([1, 16+16, 13, 100, 240])
        return {"clip_feature": clip_feature, "y": y}

    # 原代码
    def prepare_controlnet_kwargs0(self, control_video, num_frames, height, width, clip_feature=None, y=None, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        if control_video is not None:
            control_latents = self.encode_control_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
            if clip_feature is None or y is None:
                clip_feature = torch.zeros((1, 257, 1280), dtype=self.torch_dtype, device=self.device)
                y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=self.torch_dtype, device=self.device)
            else:
                y = y[:, -16:]
            y = torch.concat([control_latents, y], dim=1)
        return {"clip_feature": clip_feature, "y": y}


    def tensor2video(self, frames):
        frames = rearrange(frames, "C T H W -> T H W C")
        frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
        frames = [Image.fromarray(frame) for frame in frames]
        return frames
    
    
    def prepare_extra_input(self, latents=None):
        return {}
    
    
    def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        return latents
    
    
    def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        return frames
    
    
    def prepare_unified_sequence_parallel(self):
        return {"use_unified_sequence_parallel": self.use_unified_sequence_parallel}
    
    
    def prepare_motion_bucket_id(self, motion_bucket_id):
        motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=self.torch_dtype, device=self.device)
        return {"motion_bucket_id": motion_bucket_id}
    
    
    def prepare_vace_kwargs(
        self,
        latents,
        vace_video=None, vace_mask=None, vace_reference_image=None, vace_scale=1.0,
        height=480, width=832, num_frames=81,
        seed=None, rand_device="cpu",
        tiled=True, tile_size=(34, 34), tile_stride=(18, 16)
    ):
        if vace_video is not None or vace_mask is not None or vace_reference_image is not None:
            self.load_models_to_device(["vae"])
            if vace_video is None:
                vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=self.torch_dtype, device=self.device)
            else:
                vace_video = self.preprocess_images(vace_video)
                vace_video = torch.stack(vace_video, dim=2).to(dtype=self.torch_dtype, device=self.device)
            
            if vace_mask is None:
                vace_mask = torch.ones_like(vace_video)
            else:
                vace_mask = self.preprocess_images(vace_mask)
                vace_mask = torch.stack(vace_mask, dim=2).to(dtype=self.torch_dtype, device=self.device)
            
            inactive = vace_video * (1 - vace_mask) + 0 * vace_mask
            reactive = vace_video * vace_mask + 0 * (1 - vace_mask)
            inactive = self.encode_video(inactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device)
            reactive = self.encode_video(reactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device)
            vace_video_latents = torch.concat((inactive, reactive), dim=1)
            
            vace_mask_latents = rearrange(vace_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 = self.preprocess_images([vace_reference_image])
                vace_reference_image = torch.stack(vace_reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device)
                vace_reference_latents = self.encode_video(vace_reference_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.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)
                
                noise = self.generate_noise((1, 16, 1, latents.shape[3], latents.shape[4]), seed=seed, device=rand_device, dtype=torch.float32)
                noise = noise.to(dtype=self.torch_dtype, device=self.device)
                latents = torch.concat((noise, latents), dim=2)
            
            vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1)
            return latents, {"vace_context": vace_context, "vace_scale": vace_scale}
        else:
            return latents, {"vace_context": None, "vace_scale": vace_scale}


    @torch.no_grad()
    def __call__(
        self,
        prompt,
        negative_prompt="",
        input_image=None,
        end_image=None,
        input_video=None,
        control_video=None,
        reference_image=None,
        vace_video=None,
        vace_video_mask=None,
        vace_reference_image=None,
        vace_scale=1.0,
        denoising_strength=1.0,
        seed=None,
        rand_device="cpu",
        height=480,
        width=832,
        num_frames=81,
        cfg_scale=5.0,
        num_inference_steps=50,
        sigma_shift=5.0,
        motion_bucket_id=None,
        tiled=True,
        tile_size=(30, 52),
        tile_stride=(15, 26),
        tea_cache_l1_thresh=None,
        tea_cache_model_id="",
        progress_bar_cmd=tqdm,
        progress_bar_st=None,
        
        cond_mode = None, 
    ):
        # Parameter check
        height, width = self.check_resize_height_width(height, width)
        if num_frames % 4 != 1:
            num_frames = (num_frames + 2) // 4 * 4 + 1
            print(f"Only `num_frames % 4 != 1` is acceptable. We round it up to {num_frames}.")
        
        # Tiler parameters
        tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}

        # Scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)

        # Initialize noise
        noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32)
        noise = noise.to(dtype=self.torch_dtype, device=self.device)
        if input_video is not None:
            self.load_models_to_device(['vae'])
            input_video = self.preprocess_images(input_video)
            input_video = torch.stack(input_video, dim=2).to(dtype=self.torch_dtype, device=self.device)
            latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device)
            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
        else:
            latents = noise
        
        # Encode prompts
        self.load_models_to_device(["text_encoder"])
        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
        if cfg_scale != 1.0:
            prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
            
        # Encode image
        if input_image is not None and self.image_encoder is not None:
            self.load_models_to_device(["image_encoder", "vae"])
            image_emb = self.encode_image(input_image, end_image, num_frames, height, width, **tiler_kwargs)
        else:
            image_emb = {}
            
        # Reference image
        if reference_image is not None and cond_mode == 'i2v':
            reference_image_kwargs = self.prepare_reference_image(reference_image, height, width)
            more_cond = None
        else: # reference_image_kwargs和more_cond只有一个有值
            more_cond = reference_image # ref background video (v2v) or mask latents(inp)
            reference_image_kwargs = {}

        
        # ControlNet
        if control_video is not None:
            self.load_models_to_device(["image_encoder", "vae"])
            # image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, **image_emb, **tiler_kwargs)
            #* 输入首帧的clip feature, 有助于保持前景ID
            clip_feature = self.image_clip_feature(control_video[0], height, width)
            # 推理时调用
            
            image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, clip_feature, 
                more_cond=more_cond, cond_mode=cond_mode, **image_emb, **tiler_kwargs)
                # y=cond_latents2, more_config=more_config, **image_emb, **tiler_kwargs)
        
        # Motion Controller
        if self.motion_controller is not None and motion_bucket_id is not None:
            motion_kwargs = self.prepare_motion_bucket_id(motion_bucket_id)
        else:
            motion_kwargs = {}
            
        # Extra input
        extra_input = self.prepare_extra_input(latents)
        
        # VACE
        latents, vace_kwargs = self.prepare_vace_kwargs(
            latents, vace_video, vace_video_mask, vace_reference_image, vace_scale,
            height=height, width=width, num_frames=num_frames, seed=seed, rand_device=rand_device, **tiler_kwargs
        )
        
        # TeaCache
        tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None}
        tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None}
        
        # Unified Sequence Parallel
        usp_kwargs = self.prepare_unified_sequence_parallel()

        # Denoise
        self.load_models_to_device(["dit", "motion_controller", "vace"])
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)

            # Inference
            noise_pred_posi = model_fn_wan_video(
                self.dit, motion_controller=self.motion_controller, vace=self.vace,
                x=latents, timestep=timestep,
                **prompt_emb_posi, **image_emb, **extra_input,
                **tea_cache_posi, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs,
            )
            if cfg_scale != 1.0:
                noise_pred_nega = model_fn_wan_video(
                    self.dit, motion_controller=self.motion_controller, vace=self.vace,
                    x=latents, timestep=timestep,
                    **prompt_emb_nega, **image_emb, **extra_input,
                    **tea_cache_nega, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs,
                )
                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
            else:
                noise_pred = noise_pred_posi

            # Scheduler
            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
            
        if vace_reference_image is not None:
            latents = latents[:, :, 1:]

        # Decode
        self.load_models_to_device(['vae'])
        frames = self.decode_video(latents, **tiler_kwargs)
        self.load_models_to_device([])
        frames = self.tensor2video(frames[0])

        return frames



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


# 旧版前向代码
def model_fn_wan_video0(
    dit: WanModel,
    motion_controller: WanMotionControllerModel = None,
    vace: VaceWanModel = None,
    x: 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,
    **kwargs,
):
    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)
    
    t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
    t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
    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)
    
    if dit.has_image_input: # 只有这使用了y等, 推出dit.has_image_input=True
        x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
        clip_embdding = dit.img_emb(clip_feature)
        context = torch.cat([clip_embdding, context], dim=1)
    
    x, (f, h, w) = dit.patchify(x)
    
    # Reference image
    if reference_latents is not None:
        # reference_latents: bs=1,c1=16,f1=1,h1,w1->取1,c1,h1,w1 -> 过卷积: 1,dit.dim=1536,h1,w1 
        # -> flatten(2): 1,dit.dim,h1*w1 -> 1,h1*w1,dit.dim
        reference_latents = dit.ref_conv(reference_latents[:, :, 0]).flatten(2).transpose(1, 2)
        x = torch.concat([reference_latents, x], dim=1) # 在sequence length维度上拼接
        f += 1 # 时间维度+1: 49//4+1=13, + 1 = 14; 相当于把reference_latents当做第0帧拼在了x的前面
    
    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:
            x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
    if tea_cache_update:
        x = tea_cache.update(x)
    else:
        for block_id, block in enumerate(dit.blocks):
            x = block(x, context, t_mod, freqs)
            if vace_context is not None and block_id in vace.vace_layers_mapping:
                x = x + vace_hints[vace.vace_layers_mapping[block_id]] * vace_scale
        if tea_cache is not None:
            tea_cache.store(x)
            
    if reference_latents is not None:
        x = x[:, reference_latents.shape[1]:]
        f -= 1

    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 = dit.unpatchify(x, (f, h, w))
    return x


# 新版前向代码 copy from https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/pipelines/wan_video_new.py 2025.6.30
def model_fn_wan_video(
    dit: WanModel,
    motion_controller: WanMotionControllerModel = None,
    vace: VaceWanModel = None,
    # latents: torch.Tensor = None,
    x: torch.Tensor = None, #me
    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,
    **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)
    
    t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
    t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
    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 #me注释掉
    # 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)
    
    if dit.has_image_input:# 只有这使用了y等, 推出dit.has_image_input=True
        x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
        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)
    x, (f, h, w) = dit.patchify(x) #me

    
    # Reference image
    if reference_latents is not None:
        # reference_latents: bs=1,c1=16,f1=1,h1,w1->取1,c1,h1,w1 -> 过卷积: 1,dit.dim=1536,h1,w1 
        # -> flatten(2): 1,dit.dim,h1*w1 -> 1,h1*w1,dit.dim
        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) # 在sequence length维度上拼接
        f += 1 # 时间维度+1: 49//4+1=13, + 1 = 14; 相当于把reference_latents当做第0帧拼在了x的前面
    
    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:
            x = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)[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: #* 训练时为ture
                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()]
                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)
    
    # 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