vace/models/wan/wan_vace.py

# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import sys
import gc
import math
import random
import types
import logging
from contextlib import contextmanager
from functools import partial

from PIL import Image
import torchvision.transforms.functional as TF
import torch
import torch.nn.functional as F
import torch.cuda.amp as amp
import torch.distributed as dist
from tqdm import tqdm

from wan.text2video import (WanT2V, T5EncoderModel, WanVAE, shard_model, FlowDPMSolverMultistepScheduler,
                               get_sampling_sigmas, retrieve_timesteps, FlowUniPCMultistepScheduler)
from .modules.model import VaceWanModel
from ..utils.preprocessor import VaceVideoProcessor


class WanVace(WanT2V):
    def __init__(
        self,
        config,
        checkpoint_dir,
        device_id=0,
        rank=0,
        t5_fsdp=False,
        dit_fsdp=False,
        use_usp=False,
        t5_cpu=False,
    ):
        r"""
        Initializes the Wan text-to-video generation model components.

        Args:
            config (EasyDict):
                Object containing model parameters initialized from config.py
            checkpoint_dir (`str`):
                Path to directory containing model checkpoints
            device_id (`int`,  *optional*, defaults to 0):
                Id of target GPU device
            rank (`int`,  *optional*, defaults to 0):
                Process rank for distributed training
            t5_fsdp (`bool`, *optional*, defaults to False):
                Enable FSDP sharding for T5 model
            dit_fsdp (`bool`, *optional*, defaults to False):
                Enable FSDP sharding for DiT model
            use_usp (`bool`, *optional*, defaults to False):
                Enable distribution strategy of USP.
            t5_cpu (`bool`, *optional*, defaults to False):
                Whether to place T5 model on CPU. Only works without t5_fsdp.
        """
        self.device = torch.device(f"cuda:{device_id}")
        self.config = config
        self.rank = rank
        self.t5_cpu = t5_cpu

        self.num_train_timesteps = config.num_train_timesteps
        self.param_dtype = config.param_dtype

        shard_fn = partial(shard_model, device_id=device_id)
        self.text_encoder = T5EncoderModel(
            text_len=config.text_len,
            dtype=config.t5_dtype,
            device=torch.device('cpu'),
            checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
            tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
            shard_fn=shard_fn if t5_fsdp else None)

        self.vae_stride = config.vae_stride
        self.patch_size = config.patch_size
        self.vae = WanVAE(
            vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
            device=self.device)

        logging.info(f"Creating VaceWanModel from {checkpoint_dir}")
        self.model = VaceWanModel.from_pretrained(checkpoint_dir)
        self.model.eval().requires_grad_(False)

        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,
                                                            usp_dit_forward_vace)
            for block in self.model.blocks:
                block.self_attn.forward = types.MethodType(
                    usp_attn_forward, block.self_attn)
            for block in self.model.vace_blocks:
                block.self_attn.forward = types.MethodType(
                    usp_attn_forward, block.self_attn)
            self.model.forward = types.MethodType(usp_dit_forward, self.model)
            self.model.forward_vace = types.MethodType(usp_dit_forward_vace, self.model)
            self.sp_size = get_sequence_parallel_world_size()
        else:
            self.sp_size = 1

        if dist.is_initialized():
            dist.barrier()
        if dit_fsdp:
            self.model = shard_fn(self.model)
        else:
            self.model.to(self.device)

        self.sample_neg_prompt = config.sample_neg_prompt

        self.vid_proc = VaceVideoProcessor(downsample=tuple([x * y for x, y in zip(config.vae_stride, self.patch_size)]),
                                           min_area=480*832,
                                           max_area=480*832,
                                           min_fps=config.sample_fps,
                                           max_fps=config.sample_fps,
                                           zero_start=True,
                                           seq_len=32760,
                                           keep_last=True)

    def vace_encode_frames(self, frames, ref_images, masks=None):
        if ref_images is None:
            ref_images = [None] * len(frames)
        else:
            assert len(frames) == len(ref_images)

        if masks is None:
            latents = self.vae.encode(frames)
        else:
            inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
            reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
            inactive = self.vae.encode(inactive)
            reactive = self.vae.encode(reactive)
            latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]

        cat_latents = []
        for latent, refs in zip(latents, ref_images):
            if refs is not None:
                if masks is None:
                    ref_latent = self.vae.encode(refs)
                else:
                    ref_latent = self.vae.encode(refs)
                    ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
                assert all([x.shape[1] == 1 for x in ref_latent])
                latent = torch.cat([*ref_latent, latent], dim=1)
            cat_latents.append(latent)
        return cat_latents

    def vace_encode_masks(self, masks, ref_images=None):
        if ref_images is None:
            ref_images = [None] * len(masks)
        else:
            assert len(masks) == len(ref_images)

        result_masks = []
        for mask, refs in zip(masks, ref_images):
            c, depth, height, width = mask.shape
            new_depth = int((depth + 3) // self.vae_stride[0])
            height = 2 * (int(height) // (self.vae_stride[1] * 2))
            width = 2 * (int(width) // (self.vae_stride[2] * 2))

            # reshape
            mask = mask[0, :, :, :]
            mask = mask.view(
                depth, height, self.vae_stride[1], width, self.vae_stride[1]
            )  # depth, height, 8, width, 8
            mask = mask.permute(2, 4, 0, 1, 3)  # 8, 8, depth, height, width
            mask = mask.reshape(
                self.vae_stride[1] * self.vae_stride[2], depth, height, width
            )  # 8*8, depth, height, width

            # interpolation
            mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0)

            if refs is not None:
                length = len(refs)
                mask_pad = torch.zeros_like(mask[:, :length, :, :])
                mask = torch.cat((mask_pad, mask), dim=1)
            result_masks.append(mask)
        return result_masks

    def vace_latent(self, z, m):
        return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)]

    def prepare_source(self, src_video, src_mask, src_ref_images, num_frames, image_size, device):
        image_sizes = []
        for i, (sub_src_video, sub_src_mask) in enumerate(zip(src_video, src_mask)):
            if sub_src_mask is not None and sub_src_video is not None:
                src_video[i], src_mask[i], _, _, _ = self.vid_proc.load_video_pair(sub_src_video, sub_src_mask)
                src_video[i] = src_video[i].to(device)
                src_mask[i] = src_mask[i].to(device)
                src_mask[i] = torch.clamp((src_mask[i][:1, :, :, :] + 1) / 2, min=0, max=1)
                image_sizes.append(src_video[i].shape[2:])
            elif sub_src_video is None:
                src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)
                src_mask[i] = torch.ones_like(src_video[i], device=device)
                image_sizes.append(image_size)
            else:
                src_video[i], _, _, _ = self.vid_proc.load_video(sub_src_video)
                src_video[i] = src_video[i].to(device)
                src_mask[i] = torch.ones_like(src_video[i], device=device)
                image_sizes.append(src_video[i].shape[2:])

        for i, ref_images in enumerate(src_ref_images):
            if ref_images is not None:
                image_size = image_sizes[i]
                for j, ref_img in enumerate(ref_images):
                    if ref_img is not None:
                        ref_img = Image.open(ref_img).convert("RGB")
                        ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
                        if ref_img.shape[-2:] != image_size:
                            canvas_height, canvas_width = image_size
                            ref_height, ref_width = ref_img.shape[-2:]
                            white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1]
                            scale = min(canvas_height / ref_height, canvas_width / ref_width)
                            new_height = int(ref_height * scale)
                            new_width = int(ref_width * scale)
                            resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1)
                            top = (canvas_height - new_height) // 2
                            left = (canvas_width - new_width) // 2
                            white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image
                            ref_img = white_canvas
                        src_ref_images[i][j] = ref_img.to(device)
        return src_video, src_mask, src_ref_images

    def decode_latent(self, zs, ref_images=None):
        if ref_images is None:
            ref_images = [None] * len(zs)
        else:
            assert len(zs) == len(ref_images)

        trimed_zs = []
        for z, refs in zip(zs, ref_images):
            if refs is not None:
                z = z[:, len(refs):, :, :]
            trimed_zs.append(z)

        return self.vae.decode(trimed_zs)



    def generate(self,
                 input_prompt,
                 input_frames,
                 input_masks,
                 input_ref_images,
                 size=(1280, 720),
                 frame_num=81,
                 context_scale=1.0,
                 shift=5.0,
                 sample_solver='unipc',
                 sampling_steps=50,
                 guide_scale=5.0,
                 n_prompt="",
                 seed=-1,
                 offload_model=True):
        r"""
        Generates video frames from text prompt using diffusion process.

        Args:
            input_prompt (`str`):
                Text prompt for content generation
            size (tupele[`int`], *optional*, defaults to (1280,720)):
                Controls video resolution, (width,height).
            frame_num (`int`, *optional*, defaults to 81):
                How many frames to sample from a video. The number should be 4n+1
            shift (`float`, *optional*, defaults to 5.0):
                Noise schedule shift parameter. Affects temporal dynamics
            sample_solver (`str`, *optional*, defaults to 'unipc'):
                Solver used to sample the video.
            sampling_steps (`int`, *optional*, defaults to 40):
                Number of diffusion sampling steps. Higher values improve quality but slow generation
            guide_scale (`float`, *optional*, defaults 5.0):
                Classifier-free guidance scale. Controls prompt adherence vs. creativity
            n_prompt (`str`, *optional*, defaults to ""):
                Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
            seed (`int`, *optional*, defaults to -1):
                Random seed for noise generation. If -1, use random seed.
            offload_model (`bool`, *optional*, defaults to True):
                If True, offloads models to CPU during generation to save VRAM

        Returns:
            torch.Tensor:
                Generated video frames tensor. Dimensions: (C, N H, W) where:
                - C: Color channels (3 for RGB)
                - N: Number of frames (81)
                - H: Frame height (from size)
                - W: Frame width from size)
        """
        # preprocess
        # F = frame_num
        # target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
        #                 size[1] // self.vae_stride[1],
        #                 size[0] // self.vae_stride[2])
        #
        # seq_len = math.ceil((target_shape[2] * target_shape[3]) /
        #                     (self.patch_size[1] * self.patch_size[2]) *
        #                     target_shape[1] / self.sp_size) * self.sp_size

        if n_prompt == "":
            n_prompt = self.sample_neg_prompt
        seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
        seed_g = torch.Generator(device=self.device)
        seed_g.manual_seed(seed)

        if not self.t5_cpu:
            self.text_encoder.model.to(self.device)
            context = self.text_encoder([input_prompt], self.device)
            context_null = self.text_encoder([n_prompt], self.device)
            if offload_model:
                self.text_encoder.model.cpu()
        else:
            context = self.text_encoder([input_prompt], torch.device('cpu'))
            context_null = self.text_encoder([n_prompt], torch.device('cpu'))
            context = [t.to(self.device) for t in context]
            context_null = [t.to(self.device) for t in context_null]

        # vace context encode
        z0 = self.vace_encode_frames(input_frames, input_ref_images, masks=input_masks)
        m0 = self.vace_encode_masks(input_masks, input_ref_images)
        z = self.vace_latent(z0, m0)

        target_shape = list(z0[0].shape)
        target_shape[0] = int(target_shape[0] / 2)
        noise = [
            torch.randn(
                target_shape[0],
                target_shape[1],
                target_shape[2],
                target_shape[3],
                dtype=torch.float32,
                device=self.device,
                generator=seed_g)
        ]
        seq_len = math.ceil((target_shape[2] * target_shape[3]) /
                            (self.patch_size[1] * self.patch_size[2]) *
                            target_shape[1] / self.sp_size) * self.sp_size

        @contextmanager
        def noop_no_sync():
            yield

        no_sync = getattr(self.model, 'no_sync', noop_no_sync)

        # evaluation mode
        with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync():

            if sample_solver == 'unipc':
                sample_scheduler = FlowUniPCMultistepScheduler(
                    num_train_timesteps=self.num_train_timesteps,
                    shift=1,
                    use_dynamic_shifting=False)
                sample_scheduler.set_timesteps(
                    sampling_steps, device=self.device, shift=shift)
                timesteps = sample_scheduler.timesteps
            elif sample_solver == 'dpm++':
                sample_scheduler = FlowDPMSolverMultistepScheduler(
                    num_train_timesteps=self.num_train_timesteps,
                    shift=1,
                    use_dynamic_shifting=False)
                sampling_sigmas = get_sampling_sigmas(sampling_steps, shift)
                timesteps, _ = retrieve_timesteps(
                    sample_scheduler,
                    device=self.device,
                    sigmas=sampling_sigmas)
            else:
                raise NotImplementedError("Unsupported solver.")

            # sample videos
            latents = noise

            arg_c = {'context': context, 'seq_len': seq_len}
            arg_null = {'context': context_null, 'seq_len': seq_len}

            for _, t in enumerate(tqdm(timesteps)):
                latent_model_input = latents
                timestep = [t]

                timestep = torch.stack(timestep)

                self.model.to(self.device)
                noise_pred_cond = self.model(
                    latent_model_input, t=timestep, vace_context=z, vace_context_scale=context_scale, **arg_c)[0]
                noise_pred_uncond = self.model(
                    latent_model_input, t=timestep, vace_context=z, vace_context_scale=context_scale,**arg_null)[0]

                noise_pred = noise_pred_uncond + guide_scale * (
                    noise_pred_cond - noise_pred_uncond)

                temp_x0 = sample_scheduler.step(
                    noise_pred.unsqueeze(0),
                    t,
                    latents[0].unsqueeze(0),
                    return_dict=False,
                    generator=seed_g)[0]
                latents = [temp_x0.squeeze(0)]

            x0 = latents
            if offload_model:
                self.model.cpu()
                torch.cuda.empty_cache()
            if self.rank == 0:
                videos = self.decode_latent(x0, input_ref_images)

        del noise, latents
        del sample_scheduler
        if offload_model:
            gc.collect()
            torch.cuda.synchronize()
        if dist.is_initialized():
            dist.barrier()

        return videos[0] if self.rank == 0 else None