mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
1064 lines
58 KiB
Python
1064 lines
58 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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import numpy as np
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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 mmgp.offload import get_cache, clear_caches
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from .modules.t5 import T5EncoderModel
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from .modules.vae import WanVAE
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from .modules.vae2_2 import Wan2_2_VAE
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from .modules.clip import CLIPModel
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from shared.utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
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get_sampling_sigmas, retrieve_timesteps)
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from shared.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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from .modules.posemb_layers import get_rotary_pos_embed, get_nd_rotary_pos_embed
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from shared.utils.vace_preprocessor import VaceVideoProcessor
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from shared.utils.basic_flowmatch import FlowMatchScheduler
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from shared.utils.utils import get_outpainting_frame_location, resize_lanczos, calculate_new_dimensions, convert_image_to_tensor
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from .multitalk.multitalk_utils import MomentumBuffer, adaptive_projected_guidance, match_and_blend_colors, match_and_blend_colors_with_mask
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from mmgp import safetensors2
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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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def timestep_transform(t, shift=5.0, num_timesteps=1000 ):
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t = t / num_timesteps
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# shift the timestep based on ratio
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new_t = shift * t / (1 + (shift - 1) * t)
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new_t = new_t * num_timesteps
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return new_t
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class WanAny2V:
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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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model_filename = None,
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model_type = None,
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model_def = None,
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base_model_type = None,
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text_encoder_filename = None,
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quantizeTransformer = False,
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save_quantized = False,
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dtype = torch.bfloat16,
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VAE_dtype = torch.float32,
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mixed_precision_transformer = False
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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.VAE_dtype = VAE_dtype
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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.model_def = model_def
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self.model2 = None
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self.transformer_switch = model_def.get("URLs2", None) is not None
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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, "umt5-xxl"),
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shard_fn= None)
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# base_model_type = "i2v2_2"
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if hasattr(config, "clip_checkpoint") and not base_model_type in ["i2v_2_2", "i2v_2_2_multitalk"]:
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self.clip = CLIPModel(
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dtype=config.clip_dtype,
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device=self.device,
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checkpoint_path=os.path.join(checkpoint_dir ,
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config.clip_checkpoint),
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tokenizer_path=os.path.join(checkpoint_dir , "xlm-roberta-large"))
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if base_model_type in ["ti2v_2_2"]:
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self.vae_stride = (4, 16, 16)
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vae_checkpoint = "Wan2.2_VAE.safetensors"
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vae = Wan2_2_VAE
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else:
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self.vae_stride = config.vae_stride
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vae_checkpoint = "Wan2.1_VAE.safetensors"
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vae = WanVAE
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self.patch_size = config.patch_size
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self.vae = vae(
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vae_pth=os.path.join(checkpoint_dir, vae_checkpoint), dtype= VAE_dtype,
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device="cpu")
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self.vae.device = self.device
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# config_filename= "configs/t2v_1.3B.json"
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# import json
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# with open(config_filename, 'r', encoding='utf-8') as f:
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# config = json.load(f)
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# sd = safetensors2.torch_load_file(xmodel_filename)
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# model_filename = "c:/temp/wan2.2i2v/low/diffusion_pytorch_model-00001-of-00006.safetensors"
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base_config_file = f"configs/{base_model_type}.json"
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forcedConfigPath = base_config_file if len(model_filename) > 1 else None
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# forcedConfigPath = base_config_file = f"configs/flf2v_720p.json"
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# model_filename[1] = xmodel_filename
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source = model_def.get("source", None)
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module_source = model_def.get("module_source", None)
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if module_source is not None:
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model_filename = [] + model_filename
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model_filename[1] = module_source
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self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath)
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elif source is not None:
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self.model = offload.fast_load_transformers_model(source, modelClass=WanModel, writable_tensors= False, forcedConfigPath= base_config_file)
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elif self.transformer_switch:
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shared_modules= {}
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self.model = offload.fast_load_transformers_model(model_filename[:1], modules = model_filename[2:], modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, return_shared_modules= shared_modules)
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self.model2 = offload.fast_load_transformers_model(model_filename[1:2], modules = shared_modules, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath)
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shared_modules = None
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else:
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self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath)
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# self.model = offload.load_model_data(self.model, xmodel_filename )
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# offload.load_model_data(self.model, "c:/temp/Phantom-Wan-1.3B.pth")
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self.model.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
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offload.change_dtype(self.model, dtype, True)
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if self.model2 is not None:
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self.model2.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
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offload.change_dtype(self.model2, dtype, True)
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# offload.save_model(self.model, "wan2.1_text2video_1.3B_mbf16.safetensors", do_quantize= False, config_file_path=base_config_file, filter_sd=sd)
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# offload.save_model(self.model, "wan2.2_image2video_14B_low_mbf16.safetensors", config_file_path=base_config_file)
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# offload.save_model(self.model, "wan2.2_image2video_14B_low_quanto_mbf16_int8.safetensors", do_quantize=True, config_file_path=base_config_file)
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self.model.eval().requires_grad_(False)
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if self.model2 is not None:
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self.model2.eval().requires_grad_(False)
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if module_source is not None:
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from wgp import save_model
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from mmgp.safetensors2 import torch_load_file
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filter = list(torch_load_file(module_source))
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save_model(self.model, model_type, dtype, None, is_module=True, filter=filter)
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elif not source is None:
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from wgp import save_model
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save_model(self.model, model_type, dtype, None)
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if save_quantized:
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from wgp import save_quantized_model
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save_quantized_model(self.model, model_type, model_filename[0], dtype, base_config_file)
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if self.model2 is not None:
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save_quantized_model(self.model2, model_type, model_filename[1], dtype, base_config_file, submodel_no=2)
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self.sample_neg_prompt = config.sample_neg_prompt
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if self.model.config.get("vace_in_dim", None) != None:
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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(self.model)
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if self.model2 is not None: self.adapt_vace_model(self.model2)
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self.num_timesteps = 1000
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self.use_timestep_transform = True
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def vace_encode_frames(self, frames, ref_images, masks=None, tile_size = 0, overlapped_latents = None):
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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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if overlapped_latents != None and False : # disabled as quality seems worse
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# inactive[0][:, 0:1] = self.vae.encode([frames[0][:, 0:1]], tile_size = tile_size)[0] # redundant
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for t in inactive:
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t[:, 1:overlapped_latents.shape[1] + 1] = overlapped_latents
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overlapped_latents[: 0:1] = inactive[0][: 0:1]
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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]) # nb latents token without (ref tokens not included)
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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(mask.shape[0], length, *mask.shape[-2:], dtype=mask.dtype, device=mask.device)
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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 fit_image_into_canvas(self, ref_img, image_size, canvas_tf_bg, device, full_frame = False, outpainting_dims = None, return_mask = False):
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from shared.utils.utils import save_image
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ref_width, ref_height = ref_img.size
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if (ref_height, ref_width) == image_size and outpainting_dims == None:
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ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
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canvas = torch.zeros_like(ref_img) if return_mask else None
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else:
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if outpainting_dims != None:
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final_height, final_width = image_size
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canvas_height, canvas_width, margin_top, margin_left = get_outpainting_frame_location(final_height, final_width, outpainting_dims, 1)
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else:
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canvas_height, canvas_width = image_size
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if full_frame:
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new_height = canvas_height
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new_width = canvas_width
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top = left = 0
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else:
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# if fill_max and (canvas_height - new_height) < 16:
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# new_height = canvas_height
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# if fill_max and (canvas_width - new_width) < 16:
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# new_width = canvas_width
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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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top = (canvas_height - new_height) // 2
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left = (canvas_width - new_width) // 2
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ref_img = ref_img.resize((new_width, new_height), resample=Image.Resampling.LANCZOS)
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ref_img = TF.to_tensor(ref_img).sub_(0.5).div_(0.5).unsqueeze(1)
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if outpainting_dims != None:
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canvas = torch.full((3, 1, final_height, final_width), canvas_tf_bg, dtype= torch.float, device=device) # [-1, 1]
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canvas[:, :, margin_top + top:margin_top + top + new_height, margin_left + left:margin_left + left + new_width] = ref_img
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else:
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canvas = torch.full((3, 1, canvas_height, canvas_width), canvas_tf_bg, dtype= torch.float, device=device) # [-1, 1]
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canvas[:, :, top:top + new_height, left:left + new_width] = ref_img
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ref_img = canvas
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canvas = None
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if return_mask:
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if outpainting_dims != None:
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canvas = torch.ones((3, 1, final_height, final_width), dtype= torch.float, device=device) # [-1, 1]
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canvas[:, :, margin_top + top:margin_top + top + new_height, margin_left + left:margin_left + left + new_width] = 0
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else:
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canvas = torch.ones((3, 1, canvas_height, canvas_width), dtype= torch.float, device=device) # [-1, 1]
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canvas[:, :, top:top + new_height, left:left + new_width] = 0
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canvas = canvas.to(device)
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return ref_img.to(device), canvas
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def prepare_source(self, src_video, src_mask, src_ref_images, total_frames, image_size, device, keep_video_guide_frames= [], start_frame = 0, pre_src_video = None, inject_frames = [], outpainting_dims = None, any_background_ref = False):
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image_sizes = []
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trim_video_guide = len(keep_video_guide_frames)
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def conv_tensor(t, device):
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return t.float().div_(127.5).add_(-1).permute(3, 0, 1, 2).to(device)
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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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num_frames = min(num_frames, trim_video_guide) if trim_video_guide > 0 and sub_src_video != None else num_frames
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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] = conv_tensor(sub_src_video[:num_frames], device)
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src_mask[i] = conv_tensor(sub_src_mask[:num_frames], device)
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# src_video is [-1, 1] (at this function output), 0 = inpainting area (in fact 127 in [0, 255])
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# src_mask is [-1, 1] (at this function output), 0 = preserve original video (in fact 127 in [0, 255]) and 1 = Inpainting (in fact 255 in [0, 255])
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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.full_like(sub_pre_src_video, -1.0), 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) / 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, total_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] = conv_tensor(sub_src_video[:num_frames], device)
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src_mask[i] = 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:])
|
|
for k, keep in enumerate(keep_video_guide_frames):
|
|
if not keep:
|
|
pos = prepend_count + k
|
|
src_video[i][:, pos:pos+1] = 0
|
|
src_mask[i][:, pos:pos+1] = 1
|
|
|
|
for k, frame in enumerate(inject_frames):
|
|
if frame != None:
|
|
pos = prepend_count + k
|
|
src_video[i][:, pos:pos+1], src_mask[i][:, pos:pos+1] = self.fit_image_into_canvas(frame, image_size, 0, device, True, outpainting_dims, return_mask= True)
|
|
|
|
|
|
self.background_mask = None
|
|
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 and not torch.is_tensor(ref_img):
|
|
if j==0 and any_background_ref:
|
|
if self.background_mask == None: self.background_mask = [None] * len(src_ref_images)
|
|
src_ref_images[i][j], self.background_mask[i] = self.fit_image_into_canvas(ref_img, image_size, 0, device, True, outpainting_dims, return_mask= True)
|
|
else:
|
|
src_ref_images[i][j], _ = self.fit_image_into_canvas(ref_img, image_size, 1, device)
|
|
if self.background_mask != None:
|
|
self.background_mask = [ item if item != None else self.background_mask[0] for item in self.background_mask ] # deplicate background mask with double control net since first controlnet image ref modifed by ref
|
|
return src_video, src_mask, src_ref_images
|
|
|
|
def get_vae_latents(self, ref_images, device, tile_size= 0):
|
|
ref_vae_latents = []
|
|
for ref_image in ref_images:
|
|
ref_image = TF.to_tensor(ref_image).sub_(0.5).div_(0.5).to(self.device)
|
|
img_vae_latent = self.vae.encode([ref_image.unsqueeze(1)], tile_size= tile_size)
|
|
ref_vae_latents.append(img_vae_latent[0])
|
|
|
|
return torch.cat(ref_vae_latents, dim=1)
|
|
|
|
|
|
def generate(self,
|
|
input_prompt,
|
|
input_frames= None,
|
|
input_masks = None,
|
|
input_ref_images = None,
|
|
input_video = None,
|
|
image_start = None,
|
|
image_end = None,
|
|
denoising_strength = 1.0,
|
|
target_camera=None,
|
|
context_scale=None,
|
|
width = 1280,
|
|
height = 720,
|
|
fit_into_canvas = True,
|
|
frame_num=81,
|
|
batch_size = 1,
|
|
shift=5.0,
|
|
sample_solver='unipc',
|
|
sampling_steps=50,
|
|
guide_scale=5.0,
|
|
guide2_scale = 5.0,
|
|
guide3_scale = 5.0,
|
|
switch_threshold = 0,
|
|
switch2_threshold = 0,
|
|
guide_phases= 1 ,
|
|
model_switch_phase = 1,
|
|
n_prompt="",
|
|
seed=-1,
|
|
callback = None,
|
|
enable_RIFLEx = None,
|
|
VAE_tile_size = 0,
|
|
joint_pass = False,
|
|
slg_layers = None,
|
|
slg_start = 0.0,
|
|
slg_end = 1.0,
|
|
cfg_star_switch = True,
|
|
cfg_zero_step = 5,
|
|
audio_scale=None,
|
|
audio_cfg_scale=None,
|
|
audio_proj=None,
|
|
audio_context_lens=None,
|
|
overlapped_latents = None,
|
|
return_latent_slice = None,
|
|
overlap_noise = 0,
|
|
conditioning_latents_size = 0,
|
|
keep_frames_parsed = [],
|
|
model_type = None,
|
|
model_mode = None,
|
|
loras_slists = None,
|
|
NAG_scale = 0,
|
|
NAG_tau = 3.5,
|
|
NAG_alpha = 0.5,
|
|
offloadobj = None,
|
|
apg_switch = False,
|
|
speakers_bboxes = None,
|
|
color_correction_strength = 1,
|
|
prefix_frames_count = 0,
|
|
image_mode = 0,
|
|
window_no = 0,
|
|
set_header_text = None,
|
|
pre_video_frame = None,
|
|
video_prompt_type= "",
|
|
original_input_ref_images = [],
|
|
**bbargs
|
|
):
|
|
|
|
if sample_solver =="euler":
|
|
# prepare timesteps
|
|
timesteps = list(np.linspace(self.num_timesteps, 1, sampling_steps, dtype=np.float32))
|
|
timesteps.append(0.)
|
|
timesteps = [torch.tensor([t], device=self.device) for t in timesteps]
|
|
if self.use_timestep_transform:
|
|
timesteps = [timestep_transform(t, shift=shift, num_timesteps=self.num_timesteps) for t in timesteps][:-1]
|
|
timesteps = torch.tensor(timesteps)
|
|
sample_scheduler = None
|
|
elif sample_solver == 'causvid':
|
|
sample_scheduler = FlowMatchScheduler(num_inference_steps=sampling_steps, shift=shift, sigma_min=0, extra_one_step=True)
|
|
timesteps = torch.tensor([1000, 934, 862, 756, 603, 410, 250, 140, 74])[:sampling_steps].to(self.device)
|
|
sample_scheduler.timesteps =timesteps
|
|
sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.], device=self.device)])
|
|
elif sample_solver == 'unipc' or sample_solver == "":
|
|
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(f"Unsupported Scheduler {sample_solver}")
|
|
original_timesteps = timesteps
|
|
|
|
seed_g = torch.Generator(device=self.device)
|
|
seed_g.manual_seed(seed)
|
|
image_outputs = image_mode == 1
|
|
kwargs = {'pipeline': self, 'callback': callback}
|
|
color_reference_frame = None
|
|
if self._interrupt:
|
|
return None
|
|
# Text Encoder
|
|
if n_prompt == "":
|
|
n_prompt = self.sample_neg_prompt
|
|
context = self.text_encoder([input_prompt], self.device)[0]
|
|
context_null = self.text_encoder([n_prompt], self.device)[0]
|
|
context = context.to(self.dtype)
|
|
context_null = context_null.to(self.dtype)
|
|
text_len = self.model.text_len
|
|
context = torch.cat([context, context.new_zeros(text_len -context.size(0), context.size(1)) ]).unsqueeze(0)
|
|
context_null = torch.cat([context_null, context_null.new_zeros(text_len -context_null.size(0), context_null.size(1)) ]).unsqueeze(0)
|
|
if input_video is not None: height, width = input_video.shape[-2:]
|
|
|
|
# NAG_prompt = "static, low resolution, blurry"
|
|
# context_NAG = self.text_encoder([NAG_prompt], self.device)[0]
|
|
# context_NAG = context_NAG.to(self.dtype)
|
|
# context_NAG = torch.cat([context_NAG, context_NAG.new_zeros(text_len -context_NAG.size(0), context_NAG.size(1)) ]).unsqueeze(0)
|
|
|
|
# from mmgp import offload
|
|
# offloadobj.unload_all()
|
|
|
|
offload.shared_state.update({"_nag_scale" : NAG_scale, "_nag_tau" : NAG_tau, "_nag_alpha": NAG_alpha })
|
|
if NAG_scale > 1: context = torch.cat([context, context_null], dim=0)
|
|
# if NAG_scale > 1: context = torch.cat([context, context_NAG], dim=0)
|
|
if self._interrupt: return None
|
|
|
|
vace = model_type in ["vace_1.3B","vace_14B", "vace_multitalk_14B", "vace_standin_14B"]
|
|
phantom = model_type in ["phantom_1.3B", "phantom_14B"]
|
|
fantasy = model_type in ["fantasy"]
|
|
multitalk = model_type in ["multitalk", "infinitetalk", "vace_multitalk_14B", "i2v_2_2_multitalk"]
|
|
infinitetalk = model_type in ["infinitetalk"]
|
|
standin = model_type in ["standin", "vace_standin_14B"]
|
|
recam = model_type in ["recam_1.3B"]
|
|
ti2v = model_type in ["ti2v_2_2"]
|
|
start_step_no = 0
|
|
ref_images_count = 0
|
|
trim_frames = 0
|
|
extended_overlapped_latents = None
|
|
no_noise_latents_injection = infinitetalk
|
|
timestep_injection = False
|
|
lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
|
|
# image2video
|
|
if model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "fantasy", "multitalk", "infinitetalk", "i2v_2_2_multitalk", "flf2v_720p"]:
|
|
any_end_frame = False
|
|
if image_start is None:
|
|
if infinitetalk:
|
|
new_shot = "Q" in video_prompt_type
|
|
if input_frames is not None:
|
|
image_ref = input_frames[:, 0]
|
|
else:
|
|
if input_ref_images is None:
|
|
if pre_video_frame is None: raise Exception("Missing Reference Image")
|
|
input_ref_images, new_shot = [pre_video_frame], False
|
|
new_shot = new_shot and window_no <= len(input_ref_images)
|
|
image_ref = convert_image_to_tensor(input_ref_images[ min(window_no, len(input_ref_images))-1 ])
|
|
if new_shot or input_video is None:
|
|
input_video = image_ref.unsqueeze(1)
|
|
else:
|
|
color_correction_strength = 0 #disable color correction as transition frames between shots may have a complete different color level than the colors of the new shot
|
|
_ , preframes_count, height, width = input_video.shape
|
|
input_video = input_video.to(device=self.device).to(dtype= self.VAE_dtype)
|
|
if infinitetalk:
|
|
image_start = image_ref.to(input_video)
|
|
control_pre_frames_count = 1
|
|
control_video = image_start.unsqueeze(1)
|
|
else:
|
|
image_start = input_video[:, -1]
|
|
control_pre_frames_count = preframes_count
|
|
control_video = input_video
|
|
|
|
color_reference_frame = image_start.unsqueeze(1).clone()
|
|
else:
|
|
preframes_count = control_pre_frames_count = 1
|
|
height, width = image_start.shape[1:]
|
|
control_video = image_start.unsqueeze(1).to(self.device)
|
|
color_reference_frame = control_video.clone()
|
|
|
|
any_end_frame = image_end is not None
|
|
add_frames_for_end_image = any_end_frame and model_type == "i2v"
|
|
if any_end_frame:
|
|
color_correction_strength = 0 #disable color correction as transition frames between shots may have a complete different color level than the colors of the new shot
|
|
if add_frames_for_end_image:
|
|
frame_num +=1
|
|
lat_frames = int((frame_num - 2) // self.vae_stride[0] + 2)
|
|
trim_frames = 1
|
|
|
|
lat_h, lat_w = height // self.vae_stride[1], width // self.vae_stride[2]
|
|
|
|
if image_end is not None:
|
|
img_end_frame = image_end.unsqueeze(1).to(self.device)
|
|
|
|
if hasattr(self, "clip"):
|
|
clip_image_size = self.clip.model.image_size
|
|
image_start = resize_lanczos(image_start, clip_image_size, clip_image_size)
|
|
image_end = resize_lanczos(image_end, clip_image_size, clip_image_size) if image_end is not None else image_start
|
|
if model_type == "flf2v_720p":
|
|
clip_context = self.clip.visual([image_start[:, None, :, :], image_end[:, None, :, :] if image_end is not None else image_start[:, None, :, :]])
|
|
else:
|
|
clip_context = self.clip.visual([image_start[:, None, :, :]])
|
|
else:
|
|
clip_context = None
|
|
|
|
if any_end_frame:
|
|
enc= torch.concat([
|
|
control_video,
|
|
torch.zeros( (3, frame_num-control_pre_frames_count-1, height, width), device=self.device, dtype= self.VAE_dtype),
|
|
img_end_frame,
|
|
], dim=1).to(self.device)
|
|
else:
|
|
enc= torch.concat([
|
|
control_video,
|
|
torch.zeros( (3, frame_num-control_pre_frames_count, height, width), device=self.device, dtype= self.VAE_dtype)
|
|
], dim=1).to(self.device)
|
|
|
|
image_start = image_end = img_end_frame = image_ref = control_video = None
|
|
|
|
msk = torch.ones(1, frame_num, lat_h, lat_w, device=self.device)
|
|
if any_end_frame:
|
|
msk[:, control_pre_frames_count: -1] = 0
|
|
if add_frames_for_end_image:
|
|
msk = torch.concat([ torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:-1], torch.repeat_interleave(msk[:, -1:], repeats=4, dim=1) ], dim=1)
|
|
else:
|
|
msk = torch.concat([ torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:] ], dim=1)
|
|
else:
|
|
msk[:, control_pre_frames_count:] = 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, lat_h, lat_w)
|
|
msk = msk.transpose(1, 2)[0]
|
|
|
|
lat_y = self.vae.encode([enc], VAE_tile_size, any_end_frame= any_end_frame and add_frames_for_end_image)[0]
|
|
y = torch.concat([msk, lat_y])
|
|
overlapped_latents_frames_num = int(1 + (preframes_count-1) // 4)
|
|
# if overlapped_latents != None:
|
|
if overlapped_latents_frames_num > 0:
|
|
# disabled because looks worse
|
|
if False and overlapped_latents_frames_num > 1: lat_y[:, :, 1:overlapped_latents_frames_num] = overlapped_latents[:, 1:]
|
|
if infinitetalk:
|
|
lat_y = self.vae.encode([input_video], VAE_tile_size)[0]
|
|
extended_overlapped_latents = lat_y[:, :overlapped_latents_frames_num].clone().unsqueeze(0)
|
|
# if control_pre_frames_count != pre_frames_count:
|
|
|
|
lat_y = input_video = None
|
|
kwargs.update({ 'y': y})
|
|
if not clip_context is None:
|
|
kwargs.update({'clip_fea': clip_context})
|
|
|
|
# Recam Master
|
|
if recam:
|
|
target_camera = model_mode
|
|
height,width = input_frames.shape[-2:]
|
|
input_frames = input_frames.to(dtype=self.dtype , device=self.device)
|
|
source_latents = self.vae.encode([input_frames])[0].unsqueeze(0) #.to(dtype=self.dtype, device=self.device)
|
|
del input_frames
|
|
# Process target camera (recammaster)
|
|
from shared.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)
|
|
kwargs['cam_emb'] = cam_emb
|
|
|
|
# Video 2 Video
|
|
if denoising_strength < 1. and input_frames != None:
|
|
height, width = input_frames.shape[-2:]
|
|
source_latents = self.vae.encode([input_frames])[0].unsqueeze(0)
|
|
injection_denoising_step = 0
|
|
inject_from_start = False
|
|
if input_frames != None and denoising_strength < 1 :
|
|
color_reference_frame = input_frames[:, -1:].clone()
|
|
if overlapped_latents != None:
|
|
overlapped_latents_frames_num = overlapped_latents.shape[2]
|
|
overlapped_frames_num = (overlapped_latents_frames_num-1) * 4 + 1
|
|
else:
|
|
overlapped_latents_frames_num = overlapped_frames_num = 0
|
|
if len(keep_frames_parsed) == 0 or image_outputs or (overlapped_frames_num + len(keep_frames_parsed)) == input_frames.shape[1] and all(keep_frames_parsed) : keep_frames_parsed = []
|
|
injection_denoising_step = int(sampling_steps * (1. - denoising_strength) )
|
|
latent_keep_frames = []
|
|
if source_latents.shape[2] < lat_frames or len(keep_frames_parsed) > 0:
|
|
inject_from_start = True
|
|
if len(keep_frames_parsed) >0 :
|
|
if overlapped_frames_num > 0: keep_frames_parsed = [True] * overlapped_frames_num + keep_frames_parsed
|
|
latent_keep_frames =[keep_frames_parsed[0]]
|
|
for i in range(1, len(keep_frames_parsed), 4):
|
|
latent_keep_frames.append(all(keep_frames_parsed[i:i+4]))
|
|
else:
|
|
timesteps = timesteps[injection_denoising_step:]
|
|
start_step_no = injection_denoising_step
|
|
if hasattr(sample_scheduler, "timesteps"): sample_scheduler.timesteps = timesteps
|
|
if hasattr(sample_scheduler, "sigmas"): sample_scheduler.sigmas= sample_scheduler.sigmas[injection_denoising_step:]
|
|
injection_denoising_step = 0
|
|
|
|
# Phantom
|
|
if phantom:
|
|
input_ref_images_neg = None
|
|
if input_ref_images != None: # Phantom Ref images
|
|
input_ref_images = self.get_vae_latents(input_ref_images, self.device)
|
|
input_ref_images_neg = torch.zeros_like(input_ref_images)
|
|
ref_images_count = input_ref_images.shape[1] if input_ref_images != None else 0
|
|
trim_frames = input_ref_images.shape[1]
|
|
|
|
if ti2v:
|
|
if input_video is None:
|
|
height, width = (height // 32) * 32, (width // 32) * 32
|
|
else:
|
|
height, width = input_video.shape[-2:]
|
|
source_latents = self.vae.encode([input_video], tile_size = VAE_tile_size)[0].unsqueeze(0)
|
|
timestep_injection = True
|
|
|
|
# Vace
|
|
if vace :
|
|
# 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]
|
|
if self.background_mask != None: self.background_mask = [m.to(self.device) for m in self.background_mask]
|
|
z0 = self.vace_encode_frames(input_frames, input_ref_images, masks=input_masks, tile_size = VAE_tile_size, overlapped_latents = overlapped_latents )
|
|
m0 = self.vace_encode_masks(input_masks, input_ref_images)
|
|
if self.background_mask != None:
|
|
color_reference_frame = input_ref_images[0][0].clone()
|
|
zbg = self.vace_encode_frames([ref_img[0] for ref_img in input_ref_images], None, masks=self.background_mask, tile_size = VAE_tile_size )
|
|
mbg = self.vace_encode_masks(self.background_mask, None)
|
|
for zz0, mm0, zzbg, mmbg in zip(z0, m0, zbg, mbg):
|
|
zz0[:, 0:1] = zzbg
|
|
mm0[:, 0:1] = mmbg
|
|
|
|
self.background_mask = zz0 = mm0 = zzbg = mmbg = None
|
|
z = self.vace_latent(z0, m0)
|
|
|
|
ref_images_count = len(input_ref_images[0]) if input_ref_images != None and input_ref_images[0] != None else 0
|
|
context_scale = context_scale if context_scale != None else [1.0] * len(z)
|
|
kwargs.update({'vace_context' : z, 'vace_context_scale' : context_scale, "ref_images_count": ref_images_count })
|
|
if overlapped_latents != None :
|
|
overlapped_latents_size = overlapped_latents.shape[2]
|
|
extended_overlapped_latents = z[0][:16, :overlapped_latents_size + ref_images_count].clone().unsqueeze(0)
|
|
if prefix_frames_count > 0:
|
|
color_reference_frame = input_frames[0][:, prefix_frames_count -1:prefix_frames_count].clone()
|
|
|
|
target_shape = list(z0[0].shape)
|
|
target_shape[0] = int(target_shape[0] / 2)
|
|
lat_h, lat_w = target_shape[-2:]
|
|
height = self.vae_stride[1] * lat_h
|
|
width = self.vae_stride[2] * lat_w
|
|
|
|
else:
|
|
target_shape = (self.vae.model.z_dim, lat_frames + ref_images_count, height // self.vae_stride[1], width // self.vae_stride[2])
|
|
|
|
if multitalk:
|
|
if audio_proj is None:
|
|
audio_proj = [ torch.zeros( (1, 1, 5, 12, 768 ), dtype=self.dtype, device=self.device), torch.zeros( (1, (frame_num - 1) // 4, 8, 12, 768 ), dtype=self.dtype, device=self.device) ]
|
|
from .multitalk.multitalk import get_target_masks
|
|
audio_proj = [audio.to(self.dtype) for audio in audio_proj]
|
|
human_no = len(audio_proj[0])
|
|
token_ref_target_masks = get_target_masks(human_no, lat_h, lat_w, height, width, face_scale = 0.05, bbox = speakers_bboxes).to(self.dtype) if human_no > 1 else None
|
|
|
|
if fantasy and audio_proj != None:
|
|
kwargs.update({ "audio_proj": audio_proj.to(self.dtype), "audio_context_lens": audio_context_lens, })
|
|
|
|
|
|
if self._interrupt:
|
|
return None
|
|
|
|
expand_shape = [batch_size] + [-1] * len(target_shape)
|
|
# Ropes
|
|
if target_camera != None:
|
|
shape = list(target_shape[1:])
|
|
shape[0] *= 2
|
|
freqs = get_rotary_pos_embed(shape, enable_RIFLEx= False)
|
|
else:
|
|
freqs = get_rotary_pos_embed(target_shape[1:], enable_RIFLEx= enable_RIFLEx)
|
|
|
|
kwargs["freqs"] = freqs
|
|
|
|
#Standin
|
|
if standin:
|
|
from preprocessing.face_preprocessor import FaceProcessor
|
|
standin_ref_pos = 1 if "K" in video_prompt_type else 0
|
|
if len(original_input_ref_images) < standin_ref_pos + 1:
|
|
if "I" in video_prompt_type and model_type in ["vace_standin_14B"]:
|
|
print("Warning: Missing Standin ref image, make sure 'Inject only People / Objets' is selected or if there is 'Landscape and then People or Objects' there are at least two ref images.")
|
|
else:
|
|
standin_ref_pos = -1
|
|
image_ref = original_input_ref_images[standin_ref_pos]
|
|
face_processor = FaceProcessor()
|
|
standin_ref = face_processor.process(image_ref, remove_bg = model_type in ["vace_standin_14B"])
|
|
face_processor = None
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
standin_freqs = get_nd_rotary_pos_embed((-1, int(target_shape[-2]/2), int(target_shape[-1]/2) ), (-1, int(target_shape[-2]/2 + standin_ref.height/16), int(target_shape[-1]/2 + standin_ref.width/16) ))
|
|
standin_ref = self.vae.encode([ convert_image_to_tensor(standin_ref).unsqueeze(1) ], VAE_tile_size)[0].unsqueeze(0)
|
|
kwargs.update({ "standin_freqs": standin_freqs, "standin_ref": standin_ref, })
|
|
|
|
|
|
# Steps Skipping
|
|
skip_steps_cache = self.model.cache
|
|
if skip_steps_cache != None:
|
|
cache_type = skip_steps_cache.cache_type
|
|
x_count = 3 if phantom or fantasy or multitalk else 2
|
|
skip_steps_cache.previous_residual = [None] * x_count
|
|
if cache_type == "tea":
|
|
self.model.compute_teacache_threshold(max(skip_steps_cache.start_step, start_step_no), original_timesteps, skip_steps_cache.multiplier)
|
|
else:
|
|
self.model.compute_magcache_threshold(max(skip_steps_cache.start_step, start_step_no), original_timesteps, skip_steps_cache.multiplier)
|
|
skip_steps_cache.accumulated_err, skip_steps_cache.accumulated_steps, skip_steps_cache.accumulated_ratio = [0.0] * x_count, [0] * x_count, [1.0] * x_count
|
|
skip_steps_cache.one_for_all = x_count > 2
|
|
|
|
if callback != None:
|
|
callback(-1, None, True)
|
|
|
|
offload.shared_state["_chipmunk"] = False
|
|
chipmunk = offload.shared_state.get("_chipmunk", False)
|
|
if chipmunk:
|
|
self.model.setup_chipmunk()
|
|
|
|
# init denoising
|
|
updated_num_steps= len(timesteps)
|
|
|
|
denoising_extra = ""
|
|
from shared.utils.loras_mutipliers import update_loras_slists, get_model_switch_steps
|
|
|
|
phase_switch_step, phase_switch_step2, phases_description = get_model_switch_steps(timesteps, updated_num_steps, guide_phases, 0 if self.model2 is None else model_switch_phase, switch_threshold, switch2_threshold )
|
|
if len(phases_description) > 0: set_header_text(phases_description)
|
|
guidance_switch_done = guidance_switch2_done = False
|
|
if guide_phases > 1: denoising_extra = f"Phase 1/{guide_phases} High Noise" if self.model2 is not None else f"Phase 1/{guide_phases}"
|
|
def update_guidance(step_no, t, guide_scale, new_guide_scale, guidance_switch_done, switch_threshold, trans, phase_no, denoising_extra):
|
|
if guide_phases >= phase_no and not guidance_switch_done and t <= switch_threshold:
|
|
if model_switch_phase == phase_no-1 and self.model2 is not None: trans = self.model2
|
|
guide_scale, guidance_switch_done = new_guide_scale, True
|
|
denoising_extra = f"Phase {phase_no}/{guide_phases} {'Low Noise' if trans == self.model2 else 'High Noise'}" if self.model2 is not None else f"Phase {phase_no}/{guide_phases}"
|
|
callback(step_no-1, denoising_extra = denoising_extra)
|
|
return guide_scale, guidance_switch_done, trans, denoising_extra
|
|
update_loras_slists(self.model, loras_slists, updated_num_steps, phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
|
|
if self.model2 is not None: update_loras_slists(self.model2, loras_slists, updated_num_steps, phase_switch_step= phase_switch_step, phase_switch_step2= phase_switch_step2)
|
|
callback(-1, None, True, override_num_inference_steps = updated_num_steps, denoising_extra = denoising_extra)
|
|
|
|
def clear():
|
|
clear_caches()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
return None
|
|
|
|
if sample_scheduler != None:
|
|
scheduler_kwargs = {} if isinstance(sample_scheduler, FlowMatchScheduler) else {"generator": seed_g}
|
|
# b, c, lat_f, lat_h, lat_w
|
|
latents = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
|
|
if apg_switch != 0:
|
|
apg_momentum = -0.75
|
|
apg_norm_threshold = 55
|
|
text_momentumbuffer = MomentumBuffer(apg_momentum)
|
|
audio_momentumbuffer = MomentumBuffer(apg_momentum)
|
|
|
|
|
|
# denoising
|
|
trans = self.model
|
|
for i, t in enumerate(tqdm(timesteps)):
|
|
guide_scale, guidance_switch_done, trans, denoising_extra = update_guidance(i, t, guide_scale, guide2_scale, guidance_switch_done, switch_threshold, trans, 2, denoising_extra)
|
|
guide_scale, guidance_switch2_done, trans, denoising_extra = update_guidance(i, t, guide_scale, guide3_scale, guidance_switch2_done, switch2_threshold, trans, 3, denoising_extra)
|
|
offload.set_step_no_for_lora(trans, start_step_no + i)
|
|
timestep = torch.stack([t])
|
|
|
|
if timestep_injection:
|
|
latents[:, :, :source_latents.shape[2]] = source_latents
|
|
timestep = torch.full((target_shape[-3],), t, dtype=torch.int64, device=latents.device)
|
|
timestep[:source_latents.shape[2]] = 0
|
|
|
|
kwargs.update({"t": timestep, "current_step": start_step_no + i})
|
|
kwargs["slg_layers"] = slg_layers if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps) else None
|
|
|
|
if denoising_strength < 1 and input_frames != None and i <= injection_denoising_step:
|
|
sigma = t / 1000
|
|
noise = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
|
|
if inject_from_start:
|
|
new_latents = latents.clone()
|
|
new_latents[:,:, :source_latents.shape[2] ] = noise[:, :, :source_latents.shape[2] ] * sigma + (1 - sigma) * source_latents
|
|
for latent_no, keep_latent in enumerate(latent_keep_frames):
|
|
if not keep_latent:
|
|
new_latents[:, :, latent_no:latent_no+1 ] = latents[:, :, latent_no:latent_no+1]
|
|
latents = new_latents
|
|
new_latents = None
|
|
else:
|
|
latents = noise * sigma + (1 - sigma) * source_latents
|
|
noise = None
|
|
|
|
if extended_overlapped_latents != None:
|
|
if no_noise_latents_injection:
|
|
latents[:, :, :extended_overlapped_latents.shape[2]] = extended_overlapped_latents
|
|
else:
|
|
latent_noise_factor = t / 1000
|
|
latents[:, :, :extended_overlapped_latents.shape[2]] = extended_overlapped_latents * (1.0 - latent_noise_factor) + torch.randn_like(extended_overlapped_latents ) * latent_noise_factor
|
|
if vace:
|
|
overlap_noise_factor = overlap_noise / 1000
|
|
for zz in z:
|
|
zz[0:16, ref_images_count:extended_overlapped_latents.shape[2] ] = extended_overlapped_latents[0, :, ref_images_count:] * (1.0 - overlap_noise_factor) + torch.randn_like(extended_overlapped_latents[0, :, ref_images_count:] ) * overlap_noise_factor
|
|
|
|
if target_camera != None:
|
|
latent_model_input = torch.cat([latents, source_latents.expand(*expand_shape)], dim=2)
|
|
else:
|
|
latent_model_input = latents
|
|
|
|
any_guidance = guide_scale != 1
|
|
if phantom:
|
|
gen_args = {
|
|
"x" : ([ torch.cat([latent_model_input[:,:, :-ref_images_count], input_ref_images.unsqueeze(0).expand(*expand_shape)], dim=2) ] * 2 +
|
|
[ torch.cat([latent_model_input[:,:, :-ref_images_count], input_ref_images_neg.unsqueeze(0).expand(*expand_shape)], dim=2)]),
|
|
"context": [context, context_null, context_null] ,
|
|
}
|
|
elif fantasy:
|
|
gen_args = {
|
|
"x" : [latent_model_input, latent_model_input, latent_model_input],
|
|
"context" : [context, context_null, context_null],
|
|
"audio_scale": [audio_scale, None, None ]
|
|
}
|
|
elif multitalk and audio_proj != None:
|
|
if guide_scale == 1:
|
|
gen_args = {
|
|
"x" : [latent_model_input, latent_model_input],
|
|
"context" : [context, context],
|
|
"multitalk_audio": [audio_proj, [torch.zeros_like(audio_proj[0][-1:]), torch.zeros_like(audio_proj[1][-1:])]],
|
|
"multitalk_masks": [token_ref_target_masks, None]
|
|
}
|
|
any_guidance = audio_cfg_scale != 1
|
|
else:
|
|
gen_args = {
|
|
"x" : [latent_model_input, latent_model_input, latent_model_input],
|
|
"context" : [context, context_null, context_null],
|
|
"multitalk_audio": [audio_proj, audio_proj, [torch.zeros_like(audio_proj[0][-1:]), torch.zeros_like(audio_proj[1][-1:])]],
|
|
"multitalk_masks": [token_ref_target_masks, token_ref_target_masks, None]
|
|
}
|
|
else:
|
|
gen_args = {
|
|
"x" : [latent_model_input, latent_model_input],
|
|
"context": [context, context_null]
|
|
}
|
|
|
|
if joint_pass and any_guidance:
|
|
ret_values = trans( **gen_args , **kwargs)
|
|
if self._interrupt:
|
|
return clear()
|
|
else:
|
|
size = len(gen_args["x"]) if any_guidance else 1
|
|
ret_values = [None] * size
|
|
for x_id in range(size):
|
|
sub_gen_args = {k : [v[x_id]] for k, v in gen_args.items() }
|
|
ret_values[x_id] = trans( **sub_gen_args, x_id= x_id , **kwargs)[0]
|
|
if self._interrupt:
|
|
return clear()
|
|
sub_gen_args = None
|
|
if not any_guidance:
|
|
noise_pred = ret_values[0]
|
|
elif phantom:
|
|
guide_scale_img= 5.0
|
|
guide_scale_text= guide_scale #7.5
|
|
pos_it, pos_i, neg = ret_values
|
|
noise_pred = neg + guide_scale_img * (pos_i - neg) + guide_scale_text * (pos_it - pos_i)
|
|
pos_it = pos_i = neg = None
|
|
elif fantasy:
|
|
noise_pred_cond, noise_pred_noaudio, noise_pred_uncond = ret_values
|
|
noise_pred = noise_pred_uncond + guide_scale * (noise_pred_noaudio - noise_pred_uncond) + audio_cfg_scale * (noise_pred_cond - noise_pred_noaudio)
|
|
noise_pred_noaudio = None
|
|
elif multitalk and audio_proj != None:
|
|
if apg_switch != 0:
|
|
if guide_scale == 1:
|
|
noise_pred_cond, noise_pred_drop_audio = ret_values
|
|
noise_pred = noise_pred_cond + (audio_cfg_scale - 1)* adaptive_projected_guidance(noise_pred_cond - noise_pred_drop_audio,
|
|
noise_pred_cond,
|
|
momentum_buffer=audio_momentumbuffer,
|
|
norm_threshold=apg_norm_threshold)
|
|
|
|
else:
|
|
noise_pred_cond, noise_pred_drop_text, noise_pred_uncond = ret_values
|
|
noise_pred = noise_pred_cond + (guide_scale - 1) * adaptive_projected_guidance(noise_pred_cond - noise_pred_drop_text,
|
|
noise_pred_cond,
|
|
momentum_buffer=text_momentumbuffer,
|
|
norm_threshold=apg_norm_threshold) \
|
|
+ (audio_cfg_scale - 1) * adaptive_projected_guidance(noise_pred_drop_text - noise_pred_uncond,
|
|
noise_pred_cond,
|
|
momentum_buffer=audio_momentumbuffer,
|
|
norm_threshold=apg_norm_threshold)
|
|
else:
|
|
if guide_scale == 1:
|
|
noise_pred_cond, noise_pred_drop_audio = ret_values
|
|
noise_pred = noise_pred_drop_audio + audio_cfg_scale* (noise_pred_cond - noise_pred_drop_audio)
|
|
else:
|
|
noise_pred_cond, noise_pred_drop_text, noise_pred_uncond = ret_values
|
|
noise_pred = noise_pred_uncond + guide_scale * (noise_pred_cond - noise_pred_drop_text) + audio_cfg_scale * (noise_pred_drop_text - noise_pred_uncond)
|
|
noise_pred_uncond = noise_pred_cond = noise_pred_drop_text = noise_pred_drop_audio = None
|
|
else:
|
|
noise_pred_cond, noise_pred_uncond = ret_values
|
|
if apg_switch != 0:
|
|
noise_pred = noise_pred_cond + (guide_scale - 1) * adaptive_projected_guidance(noise_pred_cond - noise_pred_uncond,
|
|
noise_pred_cond,
|
|
momentum_buffer=text_momentumbuffer,
|
|
norm_threshold=apg_norm_threshold)
|
|
else:
|
|
noise_pred_text = noise_pred_cond
|
|
if cfg_star_switch:
|
|
# CFG Zero *. Thanks to https://github.com/WeichenFan/CFG-Zero-star/
|
|
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)
|
|
ret_values = noise_pred_uncond = noise_pred_cond = noise_pred_text = neg = None
|
|
|
|
if sample_solver == "euler":
|
|
dt = timesteps[i] if i == len(timesteps)-1 else (timesteps[i] - timesteps[i + 1])
|
|
dt = dt.item() / self.num_timesteps
|
|
latents = latents - noise_pred * dt
|
|
else:
|
|
latents = sample_scheduler.step(
|
|
noise_pred[:, :, :target_shape[1]],
|
|
t,
|
|
latents,
|
|
**scheduler_kwargs)[0]
|
|
|
|
if callback is not None:
|
|
latents_preview = latents
|
|
if vace and ref_images_count > 0: latents_preview = latents_preview[:, :, ref_images_count: ]
|
|
if trim_frames > 0: latents_preview= latents_preview[:, :,:-trim_frames]
|
|
if image_outputs: latents_preview= latents_preview[:, :,:1]
|
|
if len(latents_preview) > 1: latents_preview = latents_preview.transpose(0,2)
|
|
callback(i, latents_preview[0], False, denoising_extra =denoising_extra )
|
|
latents_preview = None
|
|
|
|
clear()
|
|
if timestep_injection:
|
|
latents[:, :, :source_latents.shape[2]] = source_latents
|
|
|
|
if vace and ref_images_count > 0: latents = latents[:, :, ref_images_count:]
|
|
if trim_frames > 0: latents= latents[:, :,:-trim_frames]
|
|
if return_latent_slice != None:
|
|
latent_slice = latents[:, :, return_latent_slice].clone()
|
|
|
|
x0 =latents.unbind(dim=0)
|
|
|
|
if chipmunk:
|
|
self.model.release_chipmunk() # need to add it at every exit when in prod
|
|
|
|
videos = self.vae.decode(x0, VAE_tile_size)
|
|
|
|
if image_outputs:
|
|
videos = torch.cat([video[:,:1] for video in videos], dim=1) if len(videos) > 1 else videos[0][:,:1]
|
|
else:
|
|
videos = videos[0] # return only first video
|
|
if color_correction_strength > 0 and (prefix_frames_count > 0 and window_no > 1 or prefix_frames_count > 1 and window_no == 1):
|
|
if vace and False:
|
|
# videos = match_and_blend_colors_with_mask(videos.unsqueeze(0), input_frames[0].unsqueeze(0), input_masks[0][:1].unsqueeze(0), color_correction_strength,copy_mode= "progressive_blend").squeeze(0)
|
|
videos = match_and_blend_colors_with_mask(videos.unsqueeze(0), input_frames[0].unsqueeze(0), input_masks[0][:1].unsqueeze(0), color_correction_strength,copy_mode= "reference").squeeze(0)
|
|
# videos = match_and_blend_colors_with_mask(videos.unsqueeze(0), videos.unsqueeze(0), input_masks[0][:1].unsqueeze(0), color_correction_strength,copy_mode= "reference").squeeze(0)
|
|
elif color_reference_frame is not None:
|
|
videos = match_and_blend_colors(videos.unsqueeze(0), color_reference_frame.unsqueeze(0), color_correction_strength).squeeze(0)
|
|
|
|
if return_latent_slice != None:
|
|
return { "x" : videos, "latent_slice" : latent_slice }
|
|
return videos
|
|
|
|
def adapt_vace_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")
|
|
|
|
|
|
|