diffsynth/pipelines/wan_video_recammaster.py

from ..models import ModelManager
from ..models.wan_video_dit import WanModel
from ..models.wan_video_dit_infer import WanModel_infer
from ..models.wan_video_text_encoder import WanTextEncoder
from ..models.wan_video_vae import WanVideoVAE
from ..models.wan_video_image_encoder import WanImageEncoder
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



class WanVideoReCamMasterPipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None,condition_frames=None,target_frames=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.model_names = ['text_encoder', 'dit', 'vae']
        self.height_division_factor = 16
        self.width_division_factor = 16
        self.condition_frames = condition_frames
        self.target_frames = target_frames


    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,
            },
            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,
                ),
            )
        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")


    @staticmethod
    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None):
        if device is None: device = model_manager.device
        if torch_dtype is None: torch_dtype = model_manager.torch_dtype
        pipe = WanVideoReCamMasterPipeline(device=device, torch_dtype=torch_dtype)
        pipe.fetch_models(model_manager)
        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)
        return {"context": prompt_emb}
    
    
    def encode_image(self, image, num_frames, height, width):
        image = self.preprocess_image(image.resize((width, height))).to(self.device)
        clip_context = self.image_encoder.encode_image([image])
        msk = torch.ones(1, num_frames, height//8, width//8, device=self.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]
        
        vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
        y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device)[0]
        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 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



    @torch.no_grad()
    def __call__(
        self,
        prompt,
        negative_prompt="",
        source_video=None,
        target_camera=None,
        input_image=None,
        input_video=None,
        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,
        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,
        condition_frames=None,  # 🔧 新增参数
        target_frames=53,     # 🔧 新增参数
    ):
        # 🔧 设置帧数配置
        if condition_frames is not None:
            self.condition_frames = condition_frames
        if target_frames is not None:
            self.target_frames = target_frames
        
        # Parameter check
        height, width = self.check_resize_height_width(height, width)
        
        # 🔧 修改：根据target_frames调整num_frames
        if num_frames is None:
            num_frames = self.target_frames
            
        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)

        # 🔧 修改：Initialize noise for target frames only
        target_noise_shape = (1, 16, self.target_frames, height//8, width//8)
        noise = self.generate_noise(target_noise_shape, 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:
            # 🔧 初始化target latents
            latents = noise
        # 🔧 修改：Encode source video (condition frames)
        self.load_models_to_device(['vae'])
        source_video = source_video.to(dtype=self.torch_dtype, device=self.device)
        
        # 🔧 确保source_video有足够的帧作为condition
        if source_video.shape[2] < self.condition_frames:
            raise ValueError(f"Source video has {source_video.shape[2]} frames, need at least {self.condition_frames}")
        
        # 🔧 取前condition_frames作为条件
        source_latents = self.encode_video(source_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device)
        source_latents = source_latents[:, :, :self.condition_frames, :, :]

        # 🔧 确保target_camera的长度匹配target_frames
        cam_emb = target_camera.to(dtype=self.torch_dtype, device=self.device)
        if cam_emb.shape[1] != self.target_frames:
            cam_emb = cam_emb[:,:self.target_frames,:]

        # 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, self.target_frames, height, width)  # 🔧 使用target_frames
        else:
            image_emb = {}
            
        # Extra input
        extra_input = self.prepare_extra_input(latents)
        
        # 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}

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

            # 🔧 修改：构造输入 - [target | condition]
            latents_input = torch.cat([source_latents, latents], dim=2)
            
            # Inference
            noise_pred_posi = model_fn_wan_video(
                self.dit, latents_input, timestep=timestep, cam_emb=cam_emb, 
                **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi
            )
            
            if cfg_scale != 1.0:
                noise_pred_nega = model_fn_wan_video(
                    self.dit, latents_input, timestep=timestep, cam_emb=cam_emb, 
                    **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega
                )
                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
            else:
                noise_pred = noise_pred_posi
            # Scheduler step
            latents = self.scheduler.step(noise_pred[:, :, self.condition_frames:, :, :], self.scheduler.timesteps[progress_id], latents_input[:, :, self.condition_frames:, :, :])

        self.load_models_to_device(['vae'])
        frames = self.decode_video(torch.cat([source_latents, latents], dim=2), **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



def model_fn_wan_video(
    dit: WanModel,
    x: torch.Tensor,
    timestep: torch.Tensor,
    cam_emb: torch.Tensor,
    context: torch.Tensor,
    clip_feature: Optional[torch.Tensor] = None,
    y: Optional[torch.Tensor] = None,
    tea_cache: TeaCache = None,
    **kwargs,
):
    t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
    t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
    context = dit.text_embedding(context)
    
    if dit.has_image_input:
        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)
    
    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
    tea_cache=None
    if tea_cache is not None:
        tea_cache_update = tea_cache.check(dit, x, t_mod)
    else:
        tea_cache_update = False
    
    if tea_cache_update:
        x = tea_cache.update(x)
    else:
        # blocks
        for block in dit.blocks:
            x = block(x, context, cam_emb, t_mod, freqs)
        if tea_cache is not None:
            tea_cache.store(x)

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