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 ..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.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder', 'motion_controller']
        self.height_division_factor = 16
        self.width_division_factor = 16
        self.use_unified_sequence_parallel = False


    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,
                ),
            )
        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,
                ),
            )
        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")


    @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}
    
    
    def encode_image(self, image, end_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
        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)
            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 = 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}
    
    # diffSynth-Studio代码支持输入Control Video
    def encode_control_video(self, control_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
        control_video = self.preprocess_images(control_video) # f=49,1,c=3,h,w -> 下一行: 1,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
    
    # clip_feature
    def image_clip_feature(self, image, height, width):
        image = self.preprocess_image(image.resize((width, height))).to(self.device)
        # image: b,c,h,w
        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
    
    def prepare_controlnet_kwargs(self, control_video, num_frames, height, width, clip_feature=None, y=None, more_config=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:
            if clip_feature is None:
                clip_feature = torch.zeros((1, 257, 1280), dtype=self.torch_dtype, device=self.device)
            if y is None:
                y0 = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=self.torch_dtype, device=self.device)
            elif more_config == 'encode_y':
                y0 = self.encode_control_video(y, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
            else:
                y0 = y
            # if more_config == 'inp':
                # y = torch.concat([y0, control_latents], dim=1)
            y = torch.concat([control_latents, y0], dim=1)
            # torch.Size([1, 257, 1280]) torch.Size([1, 32, 13, 100, 240])
        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}


    @torch.no_grad()
    def __call__(
        self,
        prompt,
        negative_prompt="",
        input_image=None,
        end_image=None,
        input_video=None,
        control_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,
        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,

        with_clip_feature = True, #+
        cond_latents2 = None, #+
        more_config = 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)
        else: # input_image=None, image_emb=None
            image_emb = {}
            
        # ControlNet #* clip_feature
        if control_video is not None:
            self.load_models_to_device(["image_encoder", "vae"])
            if with_clip_feature:
                clip_feature = self.image_clip_feature(control_video[0], height, width)
            else:
                clip_feature = None
            # image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, clip_feature, **image_emb, **tiler_kwargs)
            # 推理时调用
            image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, clip_feature, 
                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) # return {}
        
        # 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"])
        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,
                x=latents, timestep=timestep,
                **prompt_emb_posi, **image_emb, **extra_input,
                **tea_cache_posi, **usp_kwargs, **motion_kwargs
            )
            if cfg_scale != 1.0:
                noise_pred_nega = model_fn_wan_video(
                    self.dit, motion_controller=self.motion_controller,
                    x=latents, timestep=timestep,
                    **prompt_emb_nega, **image_emb, **extra_input,
                    **tea_cache_nega, **usp_kwargs, **motion_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)

        # 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



def model_fn_wan_video(
    dit: WanModel,
    motion_controller: WanMotionControllerModel = None,
    x: torch.Tensor = None,
    timestep: torch.Tensor = None,
    context: torch.Tensor = None,
    clip_feature: Optional[torch.Tensor] = None,
    y: Optional[torch.Tensor] = None,
    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:
        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
    if tea_cache is not None:
        tea_cache_update = tea_cache.check(dit, x, t_mod)
    else:
        tea_cache_update = False
    
    # 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 in dit.blocks:
            x = block(x, context, t_mod, freqs)
        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 = dit.unpatchify(x, (f, h, w))
    return x