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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import gc
import logging
import math
import os
import random
import sys
import types
from contextlib import contextmanager
from functools import partial
from mmgp import offload
import torch
import torch.nn as nn
import torch.cuda.amp as amp
import torch.distributed as dist
import numpy as np
from tqdm import tqdm
from PIL import Image
import torchvision.transforms.functional as TF
import torch.nn.functional as F
from .distributed.fsdp import shard_model
from .modules.model import WanModel
from mmgp.offload import get_cache, clear_caches
from .modules.t5 import T5EncoderModel
from .modules.vae import WanVAE
from .modules.vae2_2 import Wan2_2_VAE
from .modules.clip import CLIPModel
from shared.utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
get_sampling_sigmas, retrieve_timesteps)
from shared.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
from .modules.posemb_layers import get_rotary_pos_embed, get_nd_rotary_pos_embed
from shared.utils.vace_preprocessor import VaceVideoProcessor
from shared.utils.basic_flowmatch import FlowMatchScheduler
from shared.utils.lcm_scheduler import LCMScheduler
from shared.utils.utils import get_outpainting_frame_location, resize_lanczos, calculate_new_dimensions, convert_image_to_tensor, fit_image_into_canvas
from .multitalk.multitalk_utils import MomentumBuffer, adaptive_projected_guidance, match_and_blend_colors, match_and_blend_colors_with_mask
from shared.utils.audio_video import save_video
from mmgp import safetensors2
from shared.utils import files_locator as fl
def optimized_scale(positive_flat, negative_flat):
# Calculate dot production
dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
# Squared norm of uncondition
squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
# st_star = v_cond^T * v_uncond / ||v_uncond||^2
st_star = dot_product / squared_norm
return st_star
def timestep_transform(t, shift=5.0, num_timesteps=1000 ):
t = t / num_timesteps
# shift the timestep based on ratio
new_t = shift * t / (1 + (shift - 1) * t)
new_t = new_t * num_timesteps
return new_t
class WanAny2V:
def __init__(
self,
config,
checkpoint_dir,
model_filename = None,
submodel_no_list = None,
model_type = None,
model_def = None,
base_model_type = None,
text_encoder_filename = None,
quantizeTransformer = False,
save_quantized = False,
dtype = torch.bfloat16,
VAE_dtype = torch.float32,
mixed_precision_transformer = False
):
self.device = torch.device(f"cuda")
self.config = config
self.VAE_dtype = VAE_dtype
self.dtype = dtype
self.num_train_timesteps = config.num_train_timesteps
self.param_dtype = config.param_dtype
self.model_def = model_def
self.model2 = None
self.transformer_switch = model_def.get("URLs2", None) is not None
self.text_encoder = T5EncoderModel(
text_len=config.text_len,
dtype=config.t5_dtype,
device=torch.device('cpu'),
checkpoint_path=text_encoder_filename,
tokenizer_path=fl.locate_folder("umt5-xxl"),
shard_fn= None)
# base_model_type = "i2v2_2"
if hasattr(config, "clip_checkpoint") and not base_model_type in ["i2v_2_2", "i2v_2_2_multitalk"] or base_model_type in ["animate"]:
self.clip = CLIPModel(
dtype=config.clip_dtype,
device=self.device,
checkpoint_path=fl.locate_file(config.clip_checkpoint),
tokenizer_path=fl.locate_folder("xlm-roberta-large"))
ignore_unused_weights = model_def.get("ignore_unused_weights", False)
if base_model_type in ["ti2v_2_2", "lucy_edit"]:
self.vae_stride = (4, 16, 16)
vae_checkpoint = "Wan2.2_VAE.safetensors"
vae = Wan2_2_VAE
else:
self.vae_stride = config.vae_stride
vae_checkpoint = "Wan2.1_VAE.safetensors"
vae = WanVAE
self.patch_size = config.patch_size
self.vae = vae( vae_pth=fl.locate_file(vae_checkpoint), dtype= VAE_dtype, device="cpu")
self.vae.device = self.device
# config_filename= "configs/t2v_1.3B.json"
# import json
# with open(config_filename, 'r', encoding='utf-8') as f:
# config = json.load(f)
# sd = safetensors2.torch_load_file(xmodel_filename)
# model_filename = "c:/temp/wan2.2i2v/low/diffusion_pytorch_model-00001-of-00006.safetensors"
base_config_file = f"models/wan/configs/{base_model_type}.json"
forcedConfigPath = base_config_file if len(model_filename) > 1 else None
# forcedConfigPath = base_config_file = f"configs/flf2v_720p.json"
# model_filename[1] = xmodel_filename
self.model = self.model2 = None
source = model_def.get("source", None)
source2 = model_def.get("source2", None)
module_source = model_def.get("module_source", None)
module_source2 = model_def.get("module_source2", None)
kwargs= { "ignore_unused_weights": ignore_unused_weights, "writable_tensors": False, "default_dtype": dtype }
if module_source is not None:
self.model = offload.fast_load_transformers_model(model_filename[:1] + [module_source], modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
if module_source2 is not None:
self.model2 = offload.fast_load_transformers_model(model_filename[1:2] + [module_source2], modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
if source is not None:
self.model = offload.fast_load_transformers_model(source, modelClass=WanModel, writable_tensors= False, forcedConfigPath= base_config_file)
if source2 is not None:
self.model2 = offload.fast_load_transformers_model(source2, modelClass=WanModel, writable_tensors= False, forcedConfigPath= base_config_file)
if self.model is not None or self.model2 is not None:
from wgp import save_model
from mmgp.safetensors2 import torch_load_file
else:
if self.transformer_switch:
if 0 in submodel_no_list[2:] and 1 in submodel_no_list[2:]:
raise Exception("Shared and non shared modules at the same time across multipe models is not supported")
if 0 in submodel_no_list[2:]:
shared_modules= {}
self.model = offload.fast_load_transformers_model(model_filename[:1], modules = model_filename[2:], modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, return_shared_modules= shared_modules, **kwargs)
self.model2 = offload.fast_load_transformers_model(model_filename[1:2], modules = shared_modules, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
shared_modules = None
else:
modules_for_1 =[ file_name for file_name, submodel_no in zip(model_filename[2:],submodel_no_list[2:] ) if submodel_no ==1 ]
modules_for_2 =[ file_name for file_name, submodel_no in zip(model_filename[2:],submodel_no_list[2:] ) if submodel_no ==2 ]
self.model = offload.fast_load_transformers_model(model_filename[:1], modules = modules_for_1, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
self.model2 = offload.fast_load_transformers_model(model_filename[1:2], modules = modules_for_2, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
else:
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath, **kwargs)
if self.model is not None:
self.model.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
offload.change_dtype(self.model, dtype, True)
self.model.eval().requires_grad_(False)
if self.model2 is not None:
self.model2.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
offload.change_dtype(self.model2, dtype, True)
self.model2.eval().requires_grad_(False)
if module_source is not None:
save_model(self.model, model_type, dtype, None, is_module=True, filter=list(torch_load_file(module_source)), module_source_no=1)
if module_source2 is not None:
save_model(self.model2, model_type, dtype, None, is_module=True, filter=list(torch_load_file(module_source2)), module_source_no=2)
if not source is None:
save_model(self.model, model_type, dtype, None, submodel_no= 1)
if not source2 is None:
save_model(self.model2, model_type, dtype, None, submodel_no= 2)
if save_quantized:
from wgp import save_quantized_model
if self.model is not None:
save_quantized_model(self.model, model_type, model_filename[0], dtype, base_config_file)
if self.model2 is not None:
save_quantized_model(self.model2, model_type, model_filename[1], dtype, base_config_file, submodel_no=2)
self.sample_neg_prompt = config.sample_neg_prompt
if hasattr(self.model, "vace_blocks"):
self.adapt_vace_model(self.model)
if self.model2 is not None: self.adapt_vace_model(self.model2)
if hasattr(self.model, "face_adapter"):
self.adapt_animate_model(self.model)
if self.model2 is not None: self.adapt_animate_model(self.model2)
self.num_timesteps = 1000
self.use_timestep_transform = True
def vace_encode_frames(self, frames, ref_images, masks=None, tile_size = 0, overlapped_latents = None):
ref_images = [ref_images] * len(frames)
if masks is None:
latents = self.vae.encode(frames, tile_size = tile_size)
else:
inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
inactive = self.vae.encode(inactive, tile_size = tile_size)
if overlapped_latents != None and False : # disabled as quality seems worse
# inactive[0][:, 0:1] = self.vae.encode([frames[0][:, 0:1]], tile_size = tile_size)[0] # redundant
for t in inactive:
t[:, 1:overlapped_latents.shape[1] + 1] = overlapped_latents
overlapped_latents[: 0:1] = inactive[0][: 0:1]
reactive = self.vae.encode(reactive, tile_size = tile_size)
latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]
cat_latents = []
for latent, refs in zip(latents, ref_images):
if refs is not None:
if masks is None:
ref_latent = self.vae.encode(refs, tile_size = tile_size)
else:
ref_latent = self.vae.encode(refs, tile_size = tile_size)
ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
assert all([x.shape[1] == 1 for x in ref_latent])
latent = torch.cat([*ref_latent, latent], dim=1)
cat_latents.append(latent)
return cat_latents
def vace_encode_masks(self, masks, ref_images=None):
ref_images = [ref_images] * len(masks)
result_masks = []
for mask, refs in zip(masks, ref_images):
c, depth, height, width = mask.shape
new_depth = int((depth + 3) // self.vae_stride[0]) # nb latents token without (ref tokens not included)
height = 2 * (int(height) // (self.vae_stride[1] * 2))
width = 2 * (int(width) // (self.vae_stride[2] * 2))
# reshape
mask = mask[0, :, :, :]
mask = mask.view(
depth, height, self.vae_stride[1], width, self.vae_stride[1]
) # depth, height, 8, width, 8
mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width
mask = mask.reshape(
self.vae_stride[1] * self.vae_stride[2], depth, height, width
) # 8*8, depth, height, width
# interpolation
mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0)
if refs is not None:
length = len(refs)
mask_pad = torch.zeros(mask.shape[0], length, *mask.shape[-2:], dtype=mask.dtype, device=mask.device)
mask = torch.cat((mask_pad, mask), dim=1)
result_masks.append(mask)
return result_masks
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 get_i2v_mask(self, lat_h, lat_w, nb_frames_unchanged=0, mask_pixel_values=None, lat_t =0, device="cuda"):
if mask_pixel_values is None:
msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device)
else:
msk = F.interpolate(mask_pixel_values.to(device), size=(lat_h, lat_w), mode='nearest')
if nb_frames_unchanged >0:
msk[:, :nb_frames_unchanged] = 1
msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
msk = msk.transpose(1,2)[0]
return msk
def encode_reference_images(self, ref_images, ref_prompt="image of a face", any_guidance= False, tile_size = None):
ref_images = [convert_image_to_tensor(img).unsqueeze(1).to(device=self.device, dtype=self.dtype) for img in ref_images]
shape = ref_images[0].shape
freqs = get_rotary_pos_embed( (len(ref_images) , shape[-2] // 8, shape[-1] // 8 ))
# batch_ref_image: [B, C, F, H, W]
vae_feat = self.vae.encode(ref_images, tile_size = tile_size)
vae_feat = torch.cat( vae_feat, dim=1).unsqueeze(0)
if any_guidance:
vae_feat_uncond = self.vae.encode([ref_images[0] * 0], tile_size = tile_size) * len(ref_images)
vae_feat_uncond = torch.cat( vae_feat_uncond, dim=1).unsqueeze(0)
context = self.text_encoder([ref_prompt], self.device)[0].to(self.dtype)
context = torch.cat([context, context.new_zeros(self.model.text_len -context.size(0), context.size(1)) ]).unsqueeze(0)
clear_caches()
get_cache("lynx_ref_buffer").update({ 0: {}, 1: {} })
ref_buffer = self.model(
pipeline =self,
x = [vae_feat, vae_feat_uncond] if any_guidance else [vae_feat],
context = [context, context] if any_guidance else [context],
freqs= freqs,
t=torch.stack([torch.tensor(0, dtype=torch.float)]).to(self.device),
lynx_feature_extractor = True,
)
clear_caches()
return ref_buffer[0], (ref_buffer[1] if any_guidance else None)
def generate(self,
input_prompt,
input_frames= None,
input_frames2= None,
input_masks = None,
input_masks2 = None,
input_ref_images = None,
input_ref_masks = None,
input_faces = 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 = [],
face_arc_embeds = None,
control_scale_alt = 1.,
**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)
elif sample_solver == 'lcm':
# LCM + LTX scheduler: Latent Consistency Model with RectifiedFlow
# Optimized for Lightning LoRAs with ultra-fast 2-8 step inference
effective_steps = min(sampling_steps, 8) # LCM works best with few steps
sample_scheduler = LCMScheduler(
num_train_timesteps=self.num_train_timesteps,
num_inference_steps=effective_steps,
shift=shift
)
sample_scheduler.set_timesteps(effective_steps, device=self.device, shift=shift)
timesteps = sample_scheduler.timesteps
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
text_len = self.model.text_len
any_guidance_at_all = guide_scale > 1 or guide2_scale > 1 and guide_phases >=2 or guide3_scale > 1 and guide_phases >=3
context = self.text_encoder([input_prompt], self.device)[0].to(self.dtype)
context = torch.cat([context, context.new_zeros(text_len -context.size(0), context.size(1)) ]).unsqueeze(0)
if NAG_scale > 1 or any_guidance_at_all:
context_null = self.text_encoder([n_prompt], self.device)[0].to(self.dtype)
context_null = torch.cat([context_null, context_null.new_zeros(text_len -context_null.size(0), context_null.size(1)) ]).unsqueeze(0)
else:
context_null = None
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_14B_2_2", "vace_multitalk_14B", "vace_standin_14B", "vace_lynx_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"]
lynx = model_type in ["lynx_lite", "lynx", "vace_lynx_lite_14B", "vace_lynx_14B"]
recam = model_type in ["recam_1.3B"]
ti2v = model_type in ["ti2v_2_2", "lucy_edit"]
lucy_edit= model_type in ["lucy_edit"]
animate= model_type in ["animate"]
start_step_no = 0
ref_images_count = 0
trim_frames = 0
extended_overlapped_latents = clip_image_start = clip_image_end = image_mask_latents = None
no_noise_latents_injection = infinitetalk
timestep_injection = False
lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
extended_input_dim = 0
ref_images_before = False
# 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 infinitetalk:
new_shot = "0" 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()
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)
clip_image_start, clip_image_end = image_start, image_end
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)
lat_y = input_video = None
kwargs.update({ 'y': y})
# Animate
if animate:
pose_pixels = input_frames * input_masks
input_masks = 1. - input_masks
pose_pixels -= input_masks
pose_latents = self.vae.encode([pose_pixels], VAE_tile_size)[0].unsqueeze(0)
input_frames = input_frames * input_masks
if not "X" in video_prompt_type: input_frames += input_masks - 1 # masked area should black (-1) in background frames
# input_frames = input_frames[:, :1].expand(-1, input_frames.shape[1], -1, -1)
if prefix_frames_count > 0:
input_frames[:, :prefix_frames_count] = input_video
input_masks[:, :prefix_frames_count] = 1
# save_video(pose_pixels, "pose.mp4")
# save_video(input_frames, "input_frames.mp4")
# save_video(input_masks, "input_masks.mp4", value_range=(0,1))
lat_h, lat_w = height // self.vae_stride[1], width // self.vae_stride[2]
msk_ref = self.get_i2v_mask(lat_h, lat_w, nb_frames_unchanged=1,lat_t=1, device=self.device)
msk_control = self.get_i2v_mask(lat_h, lat_w, nb_frames_unchanged=0, mask_pixel_values=input_masks, device=self.device)
msk = torch.concat([msk_ref, msk_control], dim=1)
image_ref = input_ref_images[0].to(self.device)
clip_image_start = image_ref.squeeze(1)
lat_y = torch.concat(self.vae.encode([image_ref, input_frames.to(self.device)], VAE_tile_size), dim=1)
y = torch.concat([msk, lat_y])
kwargs.update({ 'y': y, 'pose_latents': pose_latents})
face_pixel_values = input_faces.unsqueeze(0)
lat_y = msk = msk_control = msk_ref = pose_pixels = None
ref_images_before = True
ref_images_count = 1
lat_frames = int((input_frames.shape[1] - 1) // self.vae_stride[0]) + 1
# Clip image
if hasattr(self, "clip") and clip_image_start is not None:
clip_image_size = self.clip.model.image_size
clip_image_start = resize_lanczos(clip_image_start, clip_image_size, clip_image_size)
clip_image_end = resize_lanczos(clip_image_end, clip_image_size, clip_image_size) if clip_image_end is not None else clip_image_start
if model_type == "flf2v_720p":
clip_context = self.clip.visual([clip_image_start[:, None, :, :], clip_image_end[:, None, :, :] if clip_image_end is not None else clip_image_start[:, None, :, :]])
else:
clip_context = self.clip.visual([clip_image_start[:, None, :, :]])
clip_image_start = clip_image_end = None
kwargs.update({'clip_fea': clip_context})
# Recam Master & Lucy Edit
if recam or lucy_edit:
frame_num, height,width = input_frames.shape[-3:]
lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
frame_num = (lat_frames -1) * self.vae_stride[0] + 1
input_frames = input_frames[:, :frame_num].to(dtype=self.dtype , device=self.device)
extended_latents = self.vae.encode([input_frames])[0].unsqueeze(0) #.to(dtype=self.dtype, device=self.device)
extended_input_dim = 2 if recam else 1
del input_frames
if recam:
# Process target camera (recammaster)
target_camera = model_mode
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 "G" in video_prompt_type 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 prefix_frames_count > 0:
overlapped_frames_num = prefix_frames_count
overlapped_latents_frames_num = (overlapped_frames_num -1 // 4) + 1
# 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( round(sampling_steps * (1. - denoising_strength),4) )
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
if input_masks is not None and not "U" in video_prompt_type:
image_mask_latents = torch.nn.functional.interpolate(input_masks, size= source_latents.shape[-2:], mode="nearest").unsqueeze(0)
if image_mask_latents.shape[2] !=1:
image_mask_latents = torch.cat([ image_mask_latents[:,:, :1], torch.nn.functional.interpolate(image_mask_latents, size= (source_latents.shape[-3]-1, *source_latents.shape[-2:]), mode="nearest") ], dim=2)
image_mask_latents = torch.where(image_mask_latents>=0.5, 1., 0. )[:1].to(self.device)
# save_video(image_mask_latents.squeeze(0), "mama.mp4", value_range=(0,1) )
# image_mask_rebuilt = image_mask_latents.repeat_interleave(8, dim=-1).repeat_interleave(8, dim=-2).unsqueeze(0)
# Phantom
if phantom:
lat_input_ref_images_neg = None
if input_ref_images is not None: # Phantom Ref images
lat_input_ref_images = self.get_vae_latents(input_ref_images, self.device)
lat_input_ref_images_neg = torch.zeros_like(lat_input_ref_images)
ref_images_count = trim_frames = lat_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
if extended_input_dim > 0:
extended_latents[:, :, :source_latents.shape[2]] = source_latents
# Lynx
if lynx :
if original_input_ref_images is None or len(original_input_ref_images) == 0:
lynx = False
elif "K" in video_prompt_type and len(input_ref_images) <= 1:
print("Warning: Missing Lynx 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 (one Landscape image followed by face).")
lynx = False
else:
from .lynx.resampler import Resampler
from accelerate import init_empty_weights
lynx_lite = model_type in ["lynx_lite", "vace_lynx_lite_14B"]
ip_hidden_states = ip_hidden_states_uncond = None
if True:
with init_empty_weights():
arc_resampler = Resampler( depth=4, dim=1280, dim_head=64, embedding_dim=512, ff_mult=4, heads=20, num_queries=16, output_dim=2048 if lynx_lite else 5120 )
offload.load_model_data(arc_resampler, fl.locate_file("wan2.1_lynx_lite_arc_resampler.safetensors" if lynx_lite else "wan2.1_lynx_full_arc_resampler.safetensors"))
arc_resampler.to(self.device)
arcface_embed = face_arc_embeds[None,None,:].to(device=self.device, dtype=torch.float)
ip_hidden_states = arc_resampler(arcface_embed).to(self.dtype)
ip_hidden_states_uncond = arc_resampler(torch.zeros_like(arcface_embed)).to(self.dtype)
arc_resampler = None
if not lynx_lite:
image_ref = original_input_ref_images[-1]
from preprocessing.face_preprocessor import FaceProcessor
face_processor = FaceProcessor()
lynx_ref = face_processor.process(image_ref, resize_to = 256 )
lynx_ref_buffer, lynx_ref_buffer_uncond = self.encode_reference_images([lynx_ref], tile_size=VAE_tile_size, any_guidance= any_guidance_at_all)
lynx_ref = None
gc.collect()
torch.cuda.empty_cache()
vace_lynx = model_type in ["vace_lynx_14B"]
kwargs["lynx_ip_scale"] = control_scale_alt
kwargs["lynx_ref_scale"] = control_scale_alt
#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(height/16), int(width/16) ), (-1, int(height/16 + standin_ref.height/16), int(width/16 + 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, })
# Vace
if vace :
# vace context encode
input_frames = [input_frames.to(self.device)] +([] if input_frames2 is None else [input_frames2.to(self.device)])
input_masks = [input_masks.to(self.device)] + ([] if input_masks2 is None else [input_masks2.to(self.device)])
if model_type in ["vace_lynx_14B"] and input_ref_images is not None:
input_ref_images,input_ref_masks = input_ref_images[:-1], input_ref_masks[:-1]
input_ref_images = None if input_ref_images is None else [ u.to(self.device) for u in input_ref_images]
input_ref_masks = None if input_ref_masks is None else [ None if u is None else u.to(self.device) for u in input_ref_masks]
ref_images_before = True
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 input_ref_masks is not None and len(input_ref_masks) > 0 and input_ref_masks[0] is not None:
color_reference_frame = input_ref_images[0].clone()
zbg = self.vace_encode_frames( input_ref_images[:1] * len(input_frames), None, masks=input_ref_masks[0], tile_size = VAE_tile_size )
mbg = self.vace_encode_masks(input_ref_masks[:1] * len(input_frames), None)
for zz0, mm0, zzbg, mmbg in zip(z0, m0, zbg, mbg):
zz0[:, 0:1] = zzbg
mm0[:, 0:1] = mmbg
zz0 = mm0 = zzbg = mmbg = None
z = [torch.cat([zz, mm], dim=0) for zz, mm in zip(z0, m0)]
ref_images_count = len(input_ref_images) if input_ref_images is not None and input_ref_images is not 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()
lat_h, lat_w = height // self.vae_stride[1], width // self.vae_stride[2]
target_shape = (self.vae.model.z_dim, lat_frames + ref_images_count, lat_h, lat_w)
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 extended_input_dim>=2:
shape = list(target_shape[1:])
shape[extended_input_dim-2] *= 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
# 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)
clear_caches()
offload.shared_state["_chipmunk"] = False
chipmunk = offload.shared_state.get("_chipmunk", False)
if chipmunk:
self.model.setup_chipmunk()
offload.shared_state["_radial"] = offload.shared_state["_attention"]=="radial"
radial = offload.shared_state.get("_radial", False)
if radial:
radial_cache = get_cache("radial")
from shared.radial_attention.attention import fill_radial_cache
fill_radial_cache(radial_cache, len(self.model.blocks), *target_shape[1:])
# 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(original_timesteps,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, len(original_timesteps), 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, len(original_timesteps), 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 "G" in video_prompt_type: randn = latents
if apg_switch != 0:
apg_momentum = -0.75
apg_norm_threshold = 55
text_momentumbuffer = MomentumBuffer(apg_momentum)
audio_momentumbuffer = MomentumBuffer(apg_momentum)
input_frames = input_frames2 = input_masks =input_masks2 = input_video = input_ref_images = input_ref_masks = pre_video_frame = None
gc.collect()
torch.cuda.empty_cache()
# 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_no": i, "real_step_no": 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 i <= injection_denoising_step:
sigma = t / 1000
if inject_from_start:
noisy_image = latents.clone()
noisy_image[:,:, :source_latents.shape[2] ] = randn[:, :, :source_latents.shape[2] ] * sigma + (1 - sigma) * source_latents
for latent_no, keep_latent in enumerate(latent_keep_frames):
if not keep_latent:
noisy_image[:, :, latent_no:latent_no+1 ] = latents[:, :, latent_no:latent_no+1]
latents = noisy_image
noisy_image = None
else:
latents = randn * sigma + (1 - sigma) * source_latents
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 extended_input_dim > 0:
latent_model_input = torch.cat([latents, extended_latents.expand(*expand_shape)], dim=extended_input_dim)
else:
latent_model_input = latents
any_guidance = guide_scale != 1
if phantom:
gen_args = {
"x" : ([ torch.cat([latent_model_input[:,:, :-ref_images_count], lat_input_ref_images.unsqueeze(0).expand(*expand_shape)], dim=2) ] * 2 +
[ torch.cat([latent_model_input[:,:, :-ref_images_count], lat_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 animate:
gen_args = {
"x" : [latent_model_input, latent_model_input],
"context" : [context, context_null],
# "face_pixel_values": [face_pixel_values, None]
"face_pixel_values": [face_pixel_values, face_pixel_values] # seems to look better this way
}
elif lynx:
gen_args = {
"x" : [latent_model_input, latent_model_input],
"context" : [context, context_null],
"lynx_ip_embeds": [ip_hidden_states, ip_hidden_states_uncond]
}
if model_type in ["lynx", "vace_lynx_14B"]:
gen_args["lynx_ref_buffer"] = [lynx_ref_buffer, lynx_ref_buffer_uncond]
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 image_mask_latents is not None:
sigma = 0 if i == len(timesteps)-1 else timesteps[i+1]/1000
noisy_image = randn * sigma + (1 - sigma) * source_latents
latents = noisy_image * (1-image_mask_latents) + image_mask_latents * latents
if callback is not None:
latents_preview = latents
if ref_images_before 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 ref_images_before 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")
def adapt_animate_model(self, model):
modules_dict= { k: m for k, m in model.named_modules()}
for animate_layer in range(8):
module = modules_dict[f"face_adapter.fuser_blocks.{animate_layer}"]
model_layer = animate_layer * 5
target = modules_dict[f"blocks.{model_layer}"]
setattr(target, "face_adapter_fuser_blocks", module )
delattr(model, "face_adapter")
def get_loras_transformer(self, get_model_recursive_prop, base_model_type, model_type, video_prompt_type, model_mode, **kwargs):
if base_model_type == "animate":
if "#" in video_prompt_type and "1" in video_prompt_type:
preloadURLs = get_model_recursive_prop(model_type, "preload_URLs")
if len(preloadURLs) > 0:
return [fl.locate_file(os.path.basename(preloadURLs[0]))] , [1]
return [], []
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