mirror of
https://github.com/Wan-Video/Wan2.1.git
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698 lines
32 KiB
Python
698 lines
32 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import gc
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import logging
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import math
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import os
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import random
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import sys
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import types
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from contextlib import contextmanager
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from functools import partial
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from mmgp import offload
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import torch
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import torch.nn as nn
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import torch.cuda.amp as amp
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import torch.distributed as dist
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from tqdm import tqdm
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from PIL import Image
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import torchvision.transforms.functional as TF
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import torch.nn.functional as F
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from .distributed.fsdp import shard_model
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from .modules.model import WanModel
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from .modules.t5 import T5EncoderModel
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from .modules.vae import WanVAE
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from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
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get_sampling_sigmas, retrieve_timesteps)
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from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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from wan.modules.posemb_layers import get_rotary_pos_embed
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from .utils.vace_preprocessor import VaceVideoProcessor
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def optimized_scale(positive_flat, negative_flat):
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star
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class WanT2V:
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def __init__(
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self,
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config,
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checkpoint_dir,
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rank=0,
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model_filename = None,
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text_encoder_filename = None,
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quantizeTransformer = False,
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dtype = torch.bfloat16
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):
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self.device = torch.device(f"cuda")
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self.config = config
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self.rank = rank
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self.dtype = dtype
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self.num_train_timesteps = config.num_train_timesteps
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self.param_dtype = config.param_dtype
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self.text_encoder = T5EncoderModel(
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text_len=config.text_len,
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dtype=config.t5_dtype,
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device=torch.device('cpu'),
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checkpoint_path=text_encoder_filename,
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tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
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shard_fn= None)
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self.vae_stride = config.vae_stride
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self.patch_size = config.patch_size
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self.vae = WanVAE(
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vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
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device=self.device)
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logging.info(f"Creating WanModel from {model_filename}")
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from mmgp import offload
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self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer, writable_tensors= False)
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# offload.load_model_data(self.model, "recam.ckpt")
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# self.model.cpu()
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# offload.save_model(self.model, "recam.safetensors")
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if self.dtype == torch.float16 and not "fp16" in model_filename:
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self.model.to(self.dtype)
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# offload.save_model(self.model, "t2v_fp16.safetensors",do_quantize=True)
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if self.dtype == torch.float16:
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self.vae.model.to(self.dtype)
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self.model.eval().requires_grad_(False)
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self.sample_neg_prompt = config.sample_neg_prompt
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if "Vace" in model_filename:
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self.vid_proc = VaceVideoProcessor(downsample=tuple([x * y for x, y in zip(config.vae_stride, self.patch_size)]),
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min_area=480*832,
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max_area=480*832,
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min_fps=config.sample_fps,
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max_fps=config.sample_fps,
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zero_start=True,
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seq_len=32760,
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keep_last=True)
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self.adapt_vace_model()
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self.scheduler = FlowUniPCMultistepScheduler()
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def vace_encode_frames(self, frames, ref_images, masks=None, tile_size = 0):
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if ref_images is None:
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ref_images = [None] * len(frames)
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else:
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assert len(frames) == len(ref_images)
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if masks is None:
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latents = self.vae.encode(frames, tile_size = tile_size)
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else:
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inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
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reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
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inactive = self.vae.encode(inactive, tile_size = tile_size)
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reactive = self.vae.encode(reactive, tile_size = tile_size)
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latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]
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cat_latents = []
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for latent, refs in zip(latents, ref_images):
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if refs is not None:
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if masks is None:
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ref_latent = self.vae.encode(refs, tile_size = tile_size)
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else:
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ref_latent = self.vae.encode(refs, tile_size = tile_size)
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ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
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assert all([x.shape[1] == 1 for x in ref_latent])
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latent = torch.cat([*ref_latent, latent], dim=1)
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cat_latents.append(latent)
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return cat_latents
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def vace_encode_masks(self, masks, ref_images=None):
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if ref_images is None:
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ref_images = [None] * len(masks)
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else:
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assert len(masks) == len(ref_images)
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result_masks = []
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for mask, refs in zip(masks, ref_images):
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c, depth, height, width = mask.shape
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new_depth = int((depth + 3) // self.vae_stride[0])
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height = 2 * (int(height) // (self.vae_stride[1] * 2))
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width = 2 * (int(width) // (self.vae_stride[2] * 2))
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# reshape
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mask = mask[0, :, :, :]
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mask = mask.view(
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depth, height, self.vae_stride[1], width, self.vae_stride[1]
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) # depth, height, 8, width, 8
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mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width
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mask = mask.reshape(
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self.vae_stride[1] * self.vae_stride[2], depth, height, width
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) # 8*8, depth, height, width
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# interpolation
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mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0)
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if refs is not None:
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length = len(refs)
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mask_pad = torch.zeros_like(mask[:, :length, :, :])
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mask = torch.cat((mask_pad, mask), dim=1)
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result_masks.append(mask)
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return result_masks
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def vace_latent(self, z, m):
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return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)]
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def prepare_source(self, src_video, src_mask, src_ref_images, total_frames, image_size, device, original_video = False, keep_frames= [], start_frame = 0, pre_src_video = None):
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image_sizes = []
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trim_video = len(keep_frames)
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for i, (sub_src_video, sub_src_mask, sub_pre_src_video) in enumerate(zip(src_video, src_mask,pre_src_video)):
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prepend_count = 0 if sub_pre_src_video == None else sub_pre_src_video.shape[1]
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num_frames = total_frames - prepend_count
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if sub_src_mask is not None and sub_src_video is not None:
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src_video[i], src_mask[i], _, _, _ = self.vid_proc.load_video_pair(sub_src_video, sub_src_mask, max_frames= num_frames, trim_video = trim_video - prepend_count, start_frame = start_frame)
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# src_video is [-1, 1], 0 = inpainting area (in fact 127 in [0, 255])
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# src_mask is [-1, 1], 0 = preserve original video (in fact 127 in [0, 255]) and 1 = Inpainting (in fact 255 in [0, 255])
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src_video[i] = src_video[i].to(device)
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src_mask[i] = src_mask[i].to(device)
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if prepend_count > 0:
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src_video[i] = torch.cat( [sub_pre_src_video, src_video[i]], dim=1)
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src_mask[i] = torch.cat( [torch.zeros_like(sub_pre_src_video), src_mask[i]] ,1)
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src_video_shape = src_video[i].shape
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if src_video_shape[1] != total_frames:
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src_video[i] = torch.cat( [src_video[i], src_video[i].new_zeros(src_video_shape[0], total_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.cat( [src_mask[i], src_mask[i].new_ones(src_video_shape[0], total_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.clamp((src_mask[i][:1, :, :, :] + 1) / 2, min=0, max=1)
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image_sizes.append(src_video[i].shape[2:])
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elif sub_src_video is None:
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if prepend_count > 0:
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src_video[i] = torch.cat( [sub_pre_src_video, torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)], dim=1)
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src_mask[i] = torch.cat( [torch.zeros_like(sub_pre_src_video), torch.ones((3, num_frames, image_size[0], image_size[1]), device=device)] ,1)
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else:
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src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)
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src_mask[i] = torch.ones_like(src_video[i], device=device)
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image_sizes.append(image_size)
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else:
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src_video[i], _, _, _ = self.vid_proc.load_video(sub_src_video, max_frames= num_frames, trim_video = trim_video - prepend_count, start_frame = start_frame)
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src_video[i] = src_video[i].to(device)
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src_mask[i] = torch.zeros_like(src_video[i], device=device) if original_video else torch.ones_like(src_video[i], device=device)
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if prepend_count > 0:
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src_video[i] = torch.cat( [sub_pre_src_video, src_video[i]], dim=1)
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src_mask[i] = torch.cat( [torch.zeros_like(sub_pre_src_video), src_mask[i]] ,1)
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src_video_shape = src_video[i].shape
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if src_video_shape[1] != total_frames:
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src_video[i] = torch.cat( [src_video[i], src_video[i].new_zeros(src_video_shape[0], total_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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src_mask[i] = torch.cat( [src_mask[i], src_mask[i].new_ones(src_video_shape[0], total_frames -src_video_shape[1], *src_video_shape[-2:])], dim=1)
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image_sizes.append(src_video[i].shape[2:])
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for k, keep in enumerate(keep_frames):
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if not keep:
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src_video[i][:, k:k+1] = 0
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src_mask[i][:, k:k+1] = 1
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for i, ref_images in enumerate(src_ref_images):
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if ref_images is not None:
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image_size = image_sizes[i]
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for j, ref_img in enumerate(ref_images):
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if ref_img is not None:
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ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
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if ref_img.shape[-2:] != image_size:
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canvas_height, canvas_width = image_size
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ref_height, ref_width = ref_img.shape[-2:]
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white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1]
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scale = min(canvas_height / ref_height, canvas_width / ref_width)
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new_height = int(ref_height * scale)
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new_width = int(ref_width * scale)
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resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1)
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top = (canvas_height - new_height) // 2
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left = (canvas_width - new_width) // 2
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white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image
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ref_img = white_canvas
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src_ref_images[i][j] = ref_img.to(device)
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return src_video, src_mask, src_ref_images
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def decode_latent(self, zs, ref_images=None, tile_size= 0 ):
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if ref_images is None:
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ref_images = [None] * len(zs)
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else:
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assert len(zs) == len(ref_images)
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trimed_zs = []
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for z, refs in zip(zs, ref_images):
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if refs is not None:
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z = z[:, len(refs):, :, :]
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trimed_zs.append(z)
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return self.vae.decode(trimed_zs, tile_size= tile_size)
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def generate_timestep_matrix(
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self,
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num_frames,
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step_template,
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base_num_frames,
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ar_step=5,
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num_pre_ready=0,
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casual_block_size=1,
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shrink_interval_with_mask=False,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[tuple]]:
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step_matrix, step_index = [], []
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update_mask, valid_interval = [], []
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num_iterations = len(step_template) + 1
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num_frames_block = num_frames // casual_block_size
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base_num_frames_block = base_num_frames // casual_block_size
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if base_num_frames_block < num_frames_block:
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infer_step_num = len(step_template)
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gen_block = base_num_frames_block
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min_ar_step = infer_step_num / gen_block
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assert ar_step >= min_ar_step, f"ar_step should be at least {math.ceil(min_ar_step)} in your setting"
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# print(num_frames, step_template, base_num_frames, ar_step, num_pre_ready, casual_block_size, num_frames_block, base_num_frames_block)
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step_template = torch.cat(
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[
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torch.tensor([999], dtype=torch.int64, device=step_template.device),
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step_template.long(),
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torch.tensor([0], dtype=torch.int64, device=step_template.device),
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]
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) # to handle the counter in row works starting from 1
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pre_row = torch.zeros(num_frames_block, dtype=torch.long)
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if num_pre_ready > 0:
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pre_row[: num_pre_ready // casual_block_size] = num_iterations
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while torch.all(pre_row >= (num_iterations - 1)) == False:
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new_row = torch.zeros(num_frames_block, dtype=torch.long)
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for i in range(num_frames_block):
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if i == 0 or pre_row[i - 1] >= (
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num_iterations - 1
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): # the first frame or the last frame is completely denoised
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new_row[i] = pre_row[i] + 1
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else:
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new_row[i] = new_row[i - 1] - ar_step
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new_row = new_row.clamp(0, num_iterations)
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update_mask.append(
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(new_row != pre_row) & (new_row != num_iterations)
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) # False: no need to update， True: need to update
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step_index.append(new_row)
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step_matrix.append(step_template[new_row])
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pre_row = new_row
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# for long video we split into several sequences, base_num_frames is set to the model max length (for training)
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terminal_flag = base_num_frames_block
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if shrink_interval_with_mask:
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idx_sequence = torch.arange(num_frames_block, dtype=torch.int64)
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update_mask = update_mask[0]
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update_mask_idx = idx_sequence[update_mask]
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last_update_idx = update_mask_idx[-1].item()
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terminal_flag = last_update_idx + 1
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# for i in range(0, len(update_mask)):
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for curr_mask in update_mask:
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if terminal_flag < num_frames_block and curr_mask[terminal_flag]:
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terminal_flag += 1
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valid_interval.append((max(terminal_flag - base_num_frames_block, 0), terminal_flag))
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step_update_mask = torch.stack(update_mask, dim=0)
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step_index = torch.stack(step_index, dim=0)
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step_matrix = torch.stack(step_matrix, dim=0)
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if casual_block_size > 1:
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step_update_mask = step_update_mask.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous()
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step_index = step_index.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous()
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step_matrix = step_matrix.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous()
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valid_interval = [(s * casual_block_size, e * casual_block_size) for s, e in valid_interval]
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return step_matrix, step_index, step_update_mask, valid_interval
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def generate(self,
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input_prompt,
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input_frames= None,
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input_masks = None,
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input_ref_images = None,
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source_video=None,
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target_camera=None,
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context_scale=1.0,
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size=(1280, 720),
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frame_num=81,
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shift=5.0,
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sample_solver='unipc',
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sampling_steps=50,
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guide_scale=5.0,
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n_prompt="",
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seed=-1,
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offload_model=True,
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callback = None,
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enable_RIFLEx = None,
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VAE_tile_size = 0,
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joint_pass = False,
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slg_layers = None,
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slg_start = 0.0,
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slg_end = 1.0,
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cfg_star_switch = True,
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cfg_zero_step = 5,
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):
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r"""
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Generates video frames from text prompt using diffusion process.
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Args:
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input_prompt (`str`):
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Text prompt for content generation
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size (tupele[`int`], *optional*, defaults to (1280,720)):
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Controls video resolution, (width,height).
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frame_num (`int`, *optional*, defaults to 81):
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How many frames to sample from a video. The number should be 4n+1
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shift (`float`, *optional*, defaults to 5.0):
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Noise schedule shift parameter. Affects temporal dynamics
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sample_solver (`str`, *optional*, defaults to 'unipc'):
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Solver used to sample the video.
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sampling_steps (`int`, *optional*, defaults to 40):
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Number of diffusion sampling steps. Higher values improve quality but slow generation
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guide_scale (`float`, *optional*, defaults 5.0):
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Classifier-free guidance scale. Controls prompt adherence vs. creativity
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n_prompt (`str`, *optional*, defaults to ""):
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Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
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seed (`int`, *optional*, defaults to -1):
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Random seed for noise generation. If -1, use random seed.
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offload_model (`bool`, *optional*, defaults to True):
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If True, offloads models to CPU during generation to save VRAM
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Returns:
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torch.Tensor:
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Generated video frames tensor. Dimensions: (C, N H, W) where:
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- C: Color channels (3 for RGB)
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- N: Number of frames (81)
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- H: Frame height (from size)
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- W: Frame width from size)
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"""
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# preprocess
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if n_prompt == "":
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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)
|
||
|
||
frame_num = max(17, frame_num) # must match causal_block_size for value of 5
|
||
frame_num = int( round( (frame_num - 17) / 20)* 20 + 17 )
|
||
num_frames = frame_num
|
||
addnoise_condition = 20
|
||
causal_attention = True
|
||
fps = 16
|
||
ar_step = 5
|
||
|
||
|
||
|
||
context = self.text_encoder([input_prompt], self.device)
|
||
context_null = self.text_encoder([n_prompt], self.device)
|
||
if target_camera != None:
|
||
size = (source_video.shape[2], source_video.shape[1])
|
||
source_video = source_video.to(dtype=self.dtype , device=self.device)
|
||
source_video = source_video.permute(3, 0, 1, 2).div_(127.5).sub_(1.)
|
||
source_latents = self.vae.encode([source_video]) #.to(dtype=self.dtype, device=self.device)
|
||
del source_video
|
||
# Process target camera (recammaster)
|
||
from wan.utils.cammmaster_tools import get_camera_embedding
|
||
cam_emb = get_camera_embedding(target_camera)
|
||
cam_emb = cam_emb.to(dtype=self.dtype, device=self.device)
|
||
|
||
if input_frames != None:
|
||
# vace context encode
|
||
input_frames = [u.to(self.device) for u in input_frames]
|
||
input_ref_images = [ None if u == None else [v.to(self.device) for v in u] for u in input_ref_images]
|
||
input_masks = [u.to(self.device) for u in input_masks]
|
||
|
||
z0 = self.vace_encode_frames(input_frames, input_ref_images, masks=input_masks, tile_size = VAE_tile_size)
|
||
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)
|
||
else:
|
||
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])
|
||
|
||
context = [u.to(self.dtype) for u in context]
|
||
context_null = [u.to(self.dtype) for u in context_null]
|
||
|
||
noise = [ torch.randn( *target_shape, dtype=torch.float32, device=self.device, generator=seed_g) ]
|
||
|
||
# evaluation mode
|
||
|
||
# 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
|
||
del noise
|
||
batch_size =len(latents)
|
||
if target_camera != None:
|
||
shape = list(latents[0].shape[1:])
|
||
shape[0] *= 2
|
||
freqs = get_rotary_pos_embed(shape, enable_RIFLEx= False)
|
||
else:
|
||
freqs = get_rotary_pos_embed(latents[0].shape[1:], enable_RIFLEx= enable_RIFLEx)
|
||
# arg_c = {'context': context, 'freqs': freqs, 'pipeline': self, 'callback': callback}
|
||
# arg_null = {'context': context_null, 'freqs': freqs, 'pipeline': self, 'callback': callback}
|
||
# arg_both = {'context': context, 'context2': context_null, 'freqs': freqs, 'pipeline': self, 'callback': callback}
|
||
|
||
i2v_extra_kwrags = {}
|
||
|
||
if target_camera != None:
|
||
recam_dict = {'cam_emb': cam_emb}
|
||
i2v_extra_kwrags.update(recam_dict)
|
||
|
||
if input_frames != None:
|
||
vace_dict = {'vace_context' : z, 'vace_context_scale' : context_scale}
|
||
i2v_extra_kwrags.update(vace_dict)
|
||
|
||
|
||
latent_length = (num_frames - 1) // 4 + 1
|
||
latent_height = height // 8
|
||
latent_width = width // 8
|
||
if ar_step == 0:
|
||
causal_block_size = 1
|
||
fps_embeds = [fps] #* prompt_embeds[0].shape[0]
|
||
fps_embeds = [0 if i == 16 else 1 for i in fps_embeds]
|
||
|
||
self.scheduler.set_timesteps(sampling_steps, device=self.device, shift=shift)
|
||
init_timesteps = self.scheduler.timesteps
|
||
base_num_frames_iter = latent_length
|
||
latent_shape = [16, base_num_frames_iter, latent_height, latent_width]
|
||
|
||
prefix_video = None
|
||
predix_video_latent_length = 0
|
||
|
||
if prefix_video is not None:
|
||
latents[0][:, :predix_video_latent_length] = prefix_video[0].to(torch.float32)
|
||
step_matrix, _, step_update_mask, valid_interval = self.generate_timestep_matrix(
|
||
base_num_frames_iter,
|
||
init_timesteps,
|
||
base_num_frames_iter,
|
||
ar_step,
|
||
predix_video_latent_length,
|
||
causal_block_size,
|
||
)
|
||
sample_schedulers = []
|
||
for _ in range(base_num_frames_iter):
|
||
sample_scheduler = FlowUniPCMultistepScheduler(
|
||
num_train_timesteps=1000, shift=1, use_dynamic_shifting=False
|
||
)
|
||
sample_scheduler.set_timesteps(sampling_steps, device=self.device, shift=shift)
|
||
sample_schedulers.append(sample_scheduler)
|
||
sample_schedulers_counter = [0] * base_num_frames_iter
|
||
|
||
updated_num_steps= len(step_matrix)
|
||
|
||
if callback != None:
|
||
callback(-1, None, True, override_num_inference_steps = updated_num_steps)
|
||
if self.model.enable_teacache:
|
||
self.model.compute_teacache_threshold(self.model.teacache_start_step, timesteps, self.model.teacache_multiplier)
|
||
# if callback != None:
|
||
# callback(-1, None, True)
|
||
|
||
for i, timestep_i in enumerate(tqdm(step_matrix)):
|
||
update_mask_i = step_update_mask[i]
|
||
valid_interval_i = valid_interval[i]
|
||
valid_interval_start, valid_interval_end = valid_interval_i
|
||
timestep = timestep_i[None, valid_interval_start:valid_interval_end].clone()
|
||
latent_model_input = [latents[0][:, valid_interval_start:valid_interval_end, :, :].clone()]
|
||
if addnoise_condition > 0 and valid_interval_start < predix_video_latent_length:
|
||
noise_factor = 0.001 * addnoise_condition
|
||
timestep_for_noised_condition = addnoise_condition
|
||
latent_model_input[0][:, valid_interval_start:predix_video_latent_length] = (
|
||
latent_model_input[0][:, valid_interval_start:predix_video_latent_length]
|
||
* (1.0 - noise_factor)
|
||
+ torch.randn_like(
|
||
latent_model_input[0][:, valid_interval_start:predix_video_latent_length]
|
||
)
|
||
* noise_factor
|
||
)
|
||
timestep[:, valid_interval_start:predix_video_latent_length] = timestep_for_noised_condition
|
||
kwrags = {
|
||
"x" : torch.stack([latent_model_input[0]]),
|
||
"t" : timestep,
|
||
"freqs" :freqs,
|
||
"fps" : fps_embeds,
|
||
"causal_block_size" : causal_block_size,
|
||
"causal_attention" : causal_attention,
|
||
"callback" : callback,
|
||
"pipeline" : self,
|
||
"current_step" : i,
|
||
}
|
||
kwrags.update(i2v_extra_kwrags)
|
||
|
||
if not self.do_classifier_free_guidance:
|
||
noise_pred = self.model(
|
||
context=context,
|
||
**kwrags,
|
||
)[0]
|
||
if self._interrupt:
|
||
return None
|
||
noise_pred= noise_pred.to(torch.float32)
|
||
else:
|
||
if joint_pass:
|
||
noise_pred_cond, noise_pred_uncond = self.model(
|
||
context=context,
|
||
context2=context_null,
|
||
**kwrags,
|
||
)
|
||
if self._interrupt:
|
||
return None
|
||
else:
|
||
noise_pred_cond = self.model(
|
||
context=context,
|
||
**kwrags,
|
||
)[0]
|
||
if self._interrupt:
|
||
return None
|
||
noise_pred_uncond = self.model(
|
||
context=context_null,
|
||
)[0]
|
||
if self._interrupt:
|
||
return None
|
||
noise_pred_cond= noise_pred_cond.to(torch.float32)
|
||
noise_pred_uncond= noise_pred_uncond.to(torch.float32)
|
||
noise_pred = noise_pred_uncond + guide_scale * (noise_pred_cond - noise_pred_uncond)
|
||
del noise_pred_cond, noise_pred_uncond
|
||
for idx in range(valid_interval_start, valid_interval_end):
|
||
if update_mask_i[idx].item():
|
||
latents[0][:, idx] = sample_schedulers[idx].step(
|
||
noise_pred[:, idx - valid_interval_start],
|
||
timestep_i[idx],
|
||
latents[0][:, idx],
|
||
return_dict=False,
|
||
generator=seed_g,
|
||
)[0]
|
||
sample_schedulers_counter[idx] += 1
|
||
if callback is not None:
|
||
callback(i, latents[0].squeeze(0), False)
|
||
|
||
# for i, t in enumerate(tqdm(timesteps)):
|
||
# if target_camera != None:
|
||
# latent_model_input = [torch.cat([u,v], dim=1) for u,v in zip(latents,source_latents )]
|
||
# else:
|
||
# latent_model_input = latents
|
||
# slg_layers_local = None
|
||
# if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps):
|
||
# slg_layers_local = slg_layers
|
||
# timestep = [t]
|
||
# offload.set_step_no_for_lora(self.model, i)
|
||
# timestep = torch.stack(timestep)
|
||
|
||
# if joint_pass:
|
||
# noise_pred_cond, noise_pred_uncond = self.model(
|
||
# latent_model_input, t=timestep, current_step=i, slg_layers=slg_layers_local, **arg_both)
|
||
# if self._interrupt:
|
||
# return None
|
||
# else:
|
||
# noise_pred_cond = self.model(
|
||
# latent_model_input, t=timestep,current_step=i, is_uncond = False, **arg_c)[0]
|
||
# if self._interrupt:
|
||
# return None
|
||
# noise_pred_uncond = self.model(
|
||
# latent_model_input, t=timestep,current_step=i, is_uncond = True, slg_layers=slg_layers_local, **arg_null)[0]
|
||
# if self._interrupt:
|
||
# return None
|
||
|
||
# # del latent_model_input
|
||
|
||
# # CFG Zero *. Thanks to https://github.com/WeichenFan/CFG-Zero-star/
|
||
# noise_pred_text = noise_pred_cond
|
||
# if cfg_star_switch:
|
||
# positive_flat = noise_pred_text.view(batch_size, -1)
|
||
# negative_flat = noise_pred_uncond.view(batch_size, -1)
|
||
|
||
# alpha = optimized_scale(positive_flat,negative_flat)
|
||
# alpha = alpha.view(batch_size, 1, 1, 1)
|
||
|
||
# if (i <= cfg_zero_step):
|
||
# noise_pred = noise_pred_text*0. # it would be faster not to compute noise_pred...
|
||
# else:
|
||
# noise_pred_uncond *= alpha
|
||
# noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)
|
||
# del noise_pred_uncond
|
||
|
||
# temp_x0 = sample_scheduler.step(
|
||
# noise_pred[:, :target_shape[1]].unsqueeze(0),
|
||
# t,
|
||
# latents[0].unsqueeze(0),
|
||
# return_dict=False,
|
||
# generator=seed_g)[0]
|
||
# latents = [temp_x0.squeeze(0)]
|
||
# del temp_x0
|
||
|
||
# if callback is not None:
|
||
# callback(i, latents[0], False)
|
||
|
||
x0 = latents
|
||
|
||
if input_frames == None:
|
||
videos = self.vae.decode(x0, VAE_tile_size)
|
||
else:
|
||
videos = self.decode_latent(x0, input_ref_images, VAE_tile_size)
|
||
|
||
del latents
|
||
del sample_scheduler
|
||
|
||
return videos[0] if self.rank == 0 else None
|
||
|
||
def adapt_vace_model(self):
|
||
model = self.model
|
||
modules_dict= { k: m for k, m in model.named_modules()}
|
||
for model_layer, vace_layer in model.vace_layers_mapping.items():
|
||
module = modules_dict[f"vace_blocks.{vace_layer}"]
|
||
target = modules_dict[f"blocks.{model_layer}"]
|
||
setattr(target, "vace", module )
|
||
delattr(model, "vace_blocks")
|
||
|
||
|