|
|
| import gc
|
| import logging
|
| import math
|
| import os
|
| import random
|
| import time
|
| import sys
|
| import types
|
| import math
|
| 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,
|
| set_rope_freqs_dtype,
|
| set_use_fp32_rope_freqs,
|
| )
|
| from shared.utils.vace_preprocessor import VaceVideoProcessor
|
| from shared.utils.basic_flowmatch import FlowMatchScheduler
|
| from shared.utils.euler_scheduler import EulerScheduler
|
| 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, convert_tensor_to_image, fit_image_into_canvas
|
| from .multitalk.multitalk_utils import MomentumBuffer, adaptive_projected_guidance, match_and_blend_colors, match_and_blend_colors_with_mask
|
| from .wanmove.trajectory import replace_feature, create_pos_feature_map
|
| from .alpha.utils import load_gauss_mask, apply_alpha_shift
|
| from shared.utils.audio_video import save_video
|
| from shared.utils.text_encoder_cache import TextEncoderCache
|
| from shared.utils.self_refiner import PnPHandler, create_self_refiner_handler
|
| from mmgp import safetensors2
|
| from shared.utils import files_locator as fl
|
|
|
| WAN_USE_FP32_ROPE_FREQS = True
|
|
|
| def optimized_scale(positive_flat, negative_flat):
|
|
|
|
|
| dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
|
|
|
|
|
| squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
|
|
|
|
|
| st_star = dot_product / squared_norm
|
|
|
| return st_star
|
|
|
| def timestep_transform(t, shift=5.0, num_timesteps=1000 ):
|
| t = t / num_timesteps
|
|
|
| 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,
|
| VAE_upsampling = None,
|
| ):
|
| 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.is_mocha = model_def.get("mocha_mode", False)
|
| self.text_encoder = None
|
| self.text_encoder_cache = None
|
| if base_model_type != "kiwi_edit":
|
| text_encoder_folder = model_def.get("text_encoder_folder")
|
| if text_encoder_folder:
|
| tokenizer_path = os.path.dirname(fl.locate_file(os.path.join(text_encoder_folder, "tokenizer_config.json")))
|
| else:
|
| tokenizer_path = os.path.dirname(text_encoder_filename)
|
| self.text_encoder = T5EncoderModel(text_len=config.text_len, dtype=config.t5_dtype, device=torch.device('cpu'), checkpoint_path=text_encoder_filename, tokenizer_path=tokenizer_path, shard_fn=None)
|
| self.text_encoder_cache = TextEncoderCache()
|
| if hasattr(config, "clip_checkpoint") and not model_def.get("i2v_2_2", False) 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)
|
| vae_upsampler_factor = 1
|
| vae_checkpoint2 = None
|
| vae_checkpoint = model_def.get("VAE_URLs", None )
|
| vae = WanVAE
|
| self.vae_stride = config.vae_stride
|
| if isinstance(vae_checkpoint, str):
|
| pass
|
| elif isinstance(vae_checkpoint, list) and len(vae_checkpoint):
|
| vae_checkpoint = fl.locate_file(vae_checkpoint[0])
|
| elif model_def.get("wan_5B_class", False):
|
| self.vae_stride = (4, 16, 16)
|
| vae_checkpoint = "Wan2.2_VAE.safetensors"
|
| vae = Wan2_2_VAE
|
| else:
|
| if VAE_upsampling is not None:
|
| vae_upsampler_factor = 2
|
| vae_checkpoint ="Wan2.1_VAE_upscale2x_imageonly_real_v1.safetensors"
|
| elif model_def.get("alpha_class", False):
|
| if base_model_type == "alpha2":
|
| vae_checkpoint = "wan_alpha_2.1_vae_rgb_channel_v2.safetensors"
|
| vae_checkpoint2 = "wan_alpha_2.1_vae_alpha_channel_v2.safetensors"
|
| else:
|
| vae_checkpoint ="wan_alpha_2.1_vae_rgb_channel.safetensors"
|
| vae_checkpoint2 ="wan_alpha_2.1_vae_alpha_channel.safetensors"
|
| else:
|
| vae_checkpoint = "Wan2.1_VAE.safetensors"
|
| self.patch_size = config.patch_size
|
|
|
| self.vae = vae( vae_pth=fl.locate_file(vae_checkpoint), dtype= VAE_dtype, upsampler_factor = vae_upsampler_factor, device="cpu")
|
| self.vae.upsampling_set = VAE_upsampling
|
| self.vae.device = self.device
|
| self.vae2 = None
|
| if vae_checkpoint2 is not None:
|
| self.vae2 = vae( vae_pth=fl.locate_file(vae_checkpoint2), dtype= VAE_dtype, device="cpu")
|
| self.vae2.device = self.device
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| base_config_file = model_def.get("config_file", f"models/wan/configs/{base_model_type}.json")
|
| forcedConfigPath = base_config_file if len(model_filename) > 1 else None
|
|
|
|
|
| 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)
|
| def preprocess_sd(sd):
|
| return WanModel.preprocess_sd_with_dtype(dtype, sd)
|
| kwargs= { "modelClass": WanModel,"do_quantize": quantizeTransformer and not save_quantized, "defaultConfigPath": base_config_file , "ignore_unused_weights": ignore_unused_weights, "writable_tensors": False, "default_dtype": dtype, "preprocess_sd": preprocess_sd, "forcedConfigPath": forcedConfigPath, }
|
| kwargs_light= { "modelClass": WanModel,"writable_tensors": False, "preprocess_sd": preprocess_sd , "forcedConfigPath" : base_config_file}
|
| if module_source is not None:
|
| self.model = offload.fast_load_transformers_model(model_filename[:1] + [fl.locate_file(module_source)], **kwargs)
|
| if module_source2 is not None:
|
| self.model2 = offload.fast_load_transformers_model(model_filename[1:2] + [fl.locate_file(module_source2)], **kwargs)
|
| if source is not None:
|
| self.model = offload.fast_load_transformers_model(fl.locate_file(source), **kwargs_light)
|
| if source2 is not None:
|
| self.model2 = offload.fast_load_transformers_model(fl.locate_file(source2), **kwargs_light)
|
|
|
| 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:], return_shared_modules= shared_modules, **kwargs)
|
| self.model2 = offload.fast_load_transformers_model(model_filename[1:2], modules = shared_modules, **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, **kwargs)
|
| self.model2 = offload.fast_load_transformers_model(model_filename[1:2], modules = modules_for_2, **kwargs)
|
|
|
| else:
|
| self.model = offload.fast_load_transformers_model(model_filename, **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
|
|
|
| self.use_fp32_rope_freqs = bool(model_def.get("wan_rope_freqs_fp32", WAN_USE_FP32_ROPE_FREQS))
|
| set_use_fp32_rope_freqs(self.use_fp32_rope_freqs)
|
| set_rope_freqs_dtype(self.dtype)
|
|
|
| self.model.apply_post_init_changes()
|
| if self.model2 is not None: self.model2.apply_post_init_changes()
|
|
|
| self.kiwi_mllm = None
|
| self.kiwi_source_embedder_file = None
|
| self.kiwi_ref_embedder_file = None
|
| if base_model_type == "kiwi_edit":
|
| from .kiwi.mllm import KiwiMLLMContextEncoder
|
| self.kiwi_mllm = KiwiMLLMContextEncoder(
|
| mllm_root_folder=model_def.get("kiwi_mllm_folder", "kiwi_mllm_encoder_instruct_reference"),
|
| qwen_weights_path=text_encoder_filename,
|
| any_ref=model_def.get("any_kiwi_ref", True),
|
| device=self.device,
|
| dtype=self.dtype,
|
| offload_after_encode=True,
|
| )
|
| self.kiwi_source_embedder_file = model_def.get("kiwi_source_embedder_file", None)
|
| self.kiwi_ref_embedder_file = model_def.get("kiwi_ref_embedder_file", None)
|
|
|
| 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 :
|
|
|
| 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])
|
| height = 2 * (int(height) // (self.vae_stride[1] * 2))
|
| width = 2 * (int(width) // (self.vae_stride[2] * 2))
|
|
|
|
|
| mask = mask[0, :, :, :]
|
| mask = mask.view(
|
| depth, height, self.vae_stride[1], width, self.vae_stride[1]
|
| )
|
| mask = mask.permute(2, 4, 0, 1, 3)
|
| mask = mask.reshape(
|
| self.vae_stride[1] * self.vae_stride[2], depth, height, width
|
| )
|
|
|
|
|
| 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, enable_loras = True):
|
| 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 ))
|
|
|
| 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)
|
| encode_fn = lambda prompts: self.text_encoder(prompts, self.device)
|
| context = self.text_encoder_cache.encode(encode_fn, [ref_prompt], device=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: {} })
|
| _loras_active_adapters = None
|
| if not enable_loras:
|
| if hasattr(self.model, "_loras_active_adapters"):
|
| _loras_active_adapters = self.model._loras_active_adapters
|
| self.model._loras_active_adapters = []
|
| 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,
|
| )
|
| if _loras_active_adapters is not None:
|
| self.model._loras_active_adapters = _loras_active_adapters
|
|
|
| clear_caches()
|
| return ref_buffer[0], (ref_buffer[1] if any_guidance else None)
|
|
|
| def _build_mocha_latents(self, source_video, mask_tensor, ref_images, frame_num, lat_frames, lat_h, lat_w, tile_size):
|
| video = source_video.to(device=self.device, dtype=self.VAE_dtype)
|
| source_latents = self.vae.encode([video], tile_size=tile_size)[0].unsqueeze(0).to(self.dtype)
|
| mask = mask_tensor[:, :1].to(device=self.device, dtype=self.dtype)
|
| mask_latents = F.interpolate(mask, size=(lat_h, lat_w), mode="nearest").unsqueeze(2).repeat(1, self.vae.model.z_dim, 1, 1, 1)
|
|
|
| ref_latents = [self.vae.encode([convert_image_to_tensor(img).unsqueeze(1).to(device=self.device, dtype=self.VAE_dtype)], tile_size=tile_size)[0].unsqueeze(0).to(self.dtype) for img in ref_images[:2]]
|
| ref_latents = torch.cat(ref_latents, dim=2)
|
|
|
| mocha_latents = torch.cat([source_latents, mask_latents, ref_latents], dim=2)
|
|
|
| base_len, source_len, mask_len = lat_frames, source_latents.shape[2], mask_latents.shape[2]
|
| cos_parts, sin_parts = [], []
|
|
|
| def append_freq(start_t, length, h_offset=1, w_offset=1):
|
| cos, sin = get_nd_rotary_pos_embed( (start_t, h_offset, w_offset), (start_t + length, h_offset + lat_h // 2, w_offset + lat_w // 2))
|
| cos_parts.append(cos)
|
| sin_parts.append(sin)
|
|
|
| append_freq(1, base_len)
|
| append_freq(1, source_len)
|
| append_freq(1, mask_len)
|
| append_freq(0, 1)
|
| if ref_latents.shape[2] > 1: append_freq(0, 1, 1 + lat_h // 2, 1 + lat_w // 2)
|
|
|
| return mocha_latents, (torch.cat(cos_parts, dim=0), torch.cat(sin_parts, dim=0))
|
|
|
| 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,
|
| input_custom = None,
|
| denoising_strength = 1.0,
|
| masking_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,
|
| perturbation_layers = None,
|
| perturbation_start = 0.0,
|
| perturbation_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,
|
| alt_guide_scale = 1.0,
|
| overlapped_latents = None,
|
| return_latent_slice = None,
|
| overlap_noise = 0,
|
| overlap_size = 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,
|
| prefix_video = None,
|
| video_prompt_type= "",
|
| original_input_ref_images = [],
|
| face_arc_embeds = None,
|
| control_scale_alt = 1.,
|
| motion_amplitude = 1.,
|
| window_start_frame_no = 0,
|
| self_refiner_setting=0,
|
| self_refiner_plan="",
|
| self_refiner_f_uncertainty = 0.0,
|
| self_refiner_certain_percentage = 0.999,
|
| **bbargs
|
| ):
|
|
|
| model_def = self.model_def
|
|
|
| if sample_solver =="euler":
|
| sample_scheduler = EulerScheduler(
|
| num_train_timesteps=self.num_timesteps,
|
| use_timestep_transform=self.use_timestep_transform,
|
| )
|
| sample_scheduler.set_timesteps(sampling_steps, device=self.device, shift=shift)
|
| timesteps = sample_scheduler.timesteps
|
| 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':
|
|
|
|
|
| effective_steps = min(sampling_steps, 8)
|
| 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
|
|
|
| kiwi_edit = model_type in ["kiwi_edit"]
|
| 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_null = context = None
|
| if input_video is not None: height, width = input_video.shape[-2:]
|
|
|
| if kiwi_edit:
|
| from .kiwi.embedders import build_kiwi_conditions
|
| kiwi_ref_images = original_input_ref_images[0] if original_input_ref_images is not None and len(original_input_ref_images) else None
|
| kiwi_state = build_kiwi_conditions(vae=self.vae, source_frames=input_frames, ref_images=kiwi_ref_images, width=width, height=height, batch_size=batch_size, device=self.device, dtype=self.dtype, source_embedder_file=self.kiwi_source_embedder_file, ref_embedder_file=self.kiwi_ref_embedder_file, vae_tile_size=VAE_tile_size)
|
| context = self.kiwi_mllm.encode_from_inputs(input_prompt, input_frames, kiwi_ref_images, use_ref_image=self.kiwi_ref_embedder_file is not None, max_frames=16)
|
| context = [context]
|
| if any_guidance_at_all or NAG_scale > 1:
|
| context_null = self.kiwi_mllm.encode_from_inputs(n_prompt, input_frames, kiwi_ref_images, use_ref_image=self.kiwi_ref_embedder_file is not None, max_frames=16)
|
| context_null = [context_null]
|
| else:
|
| text_len = self.model.text_len
|
| encode_fn = lambda prompts: self.text_encoder(prompts, self.device)
|
| context = self.text_encoder_cache.encode(encode_fn, [input_prompt], device=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_cache.encode(encode_fn, [n_prompt], device=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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 self._interrupt: return None
|
| vace = model_def.get("vace_class", False)
|
| svi_dance = model_def.get("svi_dance", False)
|
| phantom = model_type in ["phantom_1.3B", "phantom_14B"]
|
| fantasy = model_type in ["fantasy"]
|
| multitalk = model_def.get("multitalk_class", False)
|
| infinitetalk = model_type in ["infinitetalk"]
|
| standin = model_def.get("standin_class", False)
|
| lynx = model_def.get("lynx_class", False)
|
| recam = model_type in ["recam_1.3B"]
|
| ti2v = model_def.get("wan_5B_class", False)
|
| alpha_class = model_def.get("alpha_class", False)
|
| alpha2 = model_type in ["alpha2"]
|
| lucy_edit= model_type in ["lucy_edit"]
|
| animate= model_type in ["animate"]
|
| chrono_edit = model_type in ["chrono_edit"]
|
| mocha = model_type in ["mocha"]
|
| steadydancer = model_type in ["steadydancer"]
|
| wanmove = model_type in ["wanmove"]
|
| scail = model_type in ["scail"]
|
| svi_pro = model_def.get("svi2pro", False)
|
| svi_mode = 2 if svi_pro else 0
|
| svi_ref_pad_num = 0
|
| start_step_no = 0
|
| ref_images_count = inner_latent_frames = 0
|
| trim_frames = 0
|
| post_decode_pre_trim = 0
|
| last_latent_preview = False
|
| extended_overlapped_latents = clip_image_start = clip_image_end = image_mask_latents = latent_slice = freqs = post_freqs = None
|
| use_extended_overlapped_latents = True
|
|
|
| no_noise_latents_injection = infinitetalk or scail
|
| timestep_injection = False
|
| ps_t, ps_h, ps_w = self.model.patch_size
|
|
|
| lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
|
| extended_input_dim = 0
|
| ref_images_before = False
|
|
|
| if model_def.get("i2v_class", False) and not (animate or scail):
|
| 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
|
| if input_video is None:
|
| input_video = torch.full((3, 1, height, width), -1)
|
| color_correction_strength = 0
|
|
|
| _ , 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
|
| 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
|
|
|
| remaining_frames = frame_num - control_pre_frames_count
|
| if any_end_frame and not svi_pro:
|
| 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:
|
| if svi_pro or svi_mode and svi_ref_pad_num != 0:
|
| use_extended_overlapped_latents = False
|
| if input_ref_images is None or len(input_ref_images)==0:
|
| if pre_video_frame is None: raise Exception("Missing Reference Image")
|
| image_ref = pre_video_frame
|
| else:
|
| image_ref = input_ref_images[ min(window_no, len(input_ref_images))-1 ]
|
| image_ref = convert_image_to_tensor(image_ref).unsqueeze(1).to(device=self.device, dtype=self.VAE_dtype)
|
| if svi_pro:
|
| if overlapped_latents is not None:
|
| post_decode_pre_trim = 1
|
| elif prefix_video is not None and prefix_video.shape[1] >= (5 + overlap_size):
|
| overlapped_latents = self.vae.encode([torch.cat( [prefix_video[:, -(5 + overlap_size):]], dim=1)], VAE_tile_size)[0][:, -overlap_size//4: ].unsqueeze(0)
|
| post_decode_pre_trim = 1
|
|
|
| image_ref_latents = self.vae.encode([image_ref], VAE_tile_size)[0]
|
| pad_len = lat_frames + ref_images_count - image_ref_latents.shape[1] - (overlapped_latents.shape[2] if overlapped_latents is not None else 0)
|
| pad_latents = torch.zeros(image_ref_latents.shape[0], pad_len, lat_h, lat_w, device=image_ref_latents.device, dtype=image_ref_latents.dtype)
|
| if overlapped_latents is None:
|
| lat_y = torch.concat([image_ref_latents, pad_latents], dim=1).to(self.device)
|
| else:
|
| lat_y = torch.concat([image_ref_latents, overlapped_latents.squeeze(0), pad_latents], dim=1).to(self.device)
|
| if any_end_frame:
|
| lat_y[:, -1:] = self.vae.encode([img_end_frame], VAE_tile_size)[0][:, -1:]
|
| image_ref_latents = None
|
| else:
|
| svi_ref_pad_num = remaining_frames if svi_ref_pad_num == -1 else min(svi_ref_pad_num, remaining_frames)
|
| padded_frames = image_ref.expand(-1, svi_ref_pad_num, -1, -1)
|
| if remaining_frames > svi_ref_pad_num:
|
| padded_frames = torch.cat([padded_frames, torch.zeros((3, remaining_frames - svi_ref_pad_num, height, width), device=self.device, dtype=self.VAE_dtype)], dim=1)
|
| enc = torch.concat([control_video, padded_frames], dim=1).to(self.device)
|
| else:
|
| enc= torch.concat([ control_video, torch.zeros( (3, remaining_frames, height, width), device=self.device, dtype= self.VAE_dtype) ], dim=1).to(self.device)
|
| padded_frames = None
|
|
|
| if not svi_pro:
|
| lat_y = self.vae.encode([enc], VAE_tile_size, any_end_frame= any_end_frame and add_frames_for_end_image)[0]
|
|
|
|
|
| msk = torch.ones(1, frame_num + ref_images_count * 4, lat_h, lat_w, device=self.device)
|
| if any_end_frame:
|
| msk[:, (1 if svi_mode else 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[:, 1 if svi_mode else 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]
|
|
|
| image_start = image_end = img_end_frame = image_ref = control_video = None
|
|
|
| if motion_amplitude > 1:
|
| base_latent = lat_y[:, :1]
|
| diff = lat_y[:, control_pre_frames_count:] - base_latent
|
| diff_mean = diff.mean(dim=(0, 2, 3), keepdim=True)
|
| diff_centered = diff - diff_mean
|
| scaled_latent = base_latent + diff_centered * motion_amplitude + diff_mean
|
| scaled_latent = torch.clamp(scaled_latent, -6, 6)
|
| if any_end_frame:
|
| lat_y = torch.cat([lat_y[:, :control_pre_frames_count], scaled_latent[:, :-1], lat_y[:, -1:]], dim=1)
|
| else:
|
| lat_y = torch.cat([lat_y[:, :control_pre_frames_count], scaled_latent], dim=1)
|
| base_latent = scaled_latent = diff_mean = diff = diff_centered = None
|
|
|
| y = torch.concat([msk, lat_y])
|
| overlapped_latents_frames_num = int(1 + (preframes_count-1) // 4)
|
|
|
| if overlapped_latents_frames_num > 0 and use_extended_overlapped_latents:
|
|
|
| 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 = None
|
| kwargs.update({ 'y': y})
|
|
|
|
|
| if wanmove:
|
| track = np.load(input_custom)
|
| if track.ndim == 4: track = track.squeeze(0)
|
| if track.max() <= 1:
|
| track = np.round(track * [width, height]).astype(np.int64)
|
| control_video_pos= 0 if "T" in video_prompt_type else window_start_frame_no
|
| track = torch.from_numpy(track[control_video_pos:control_video_pos+frame_num]).to(self.device)
|
| track_feats, track_pos = create_pos_feature_map(track, None, [4, 8, 8], height, width, 16, device=y.device)
|
| track_feats = None
|
| y_cond = kwargs.pop("y")
|
| y_uncond = y_cond.clone()
|
| y_cond[4:20] = replace_feature(y[4:20].unsqueeze(0), track_pos.unsqueeze(0))[0]
|
|
|
|
|
| if steadydancer:
|
| condition_guide_scale = alt_guide_scale
|
|
|
| ref_x = self.vae.encode([input_video[:, :1]], VAE_tile_size)[0]
|
| msk_ref = torch.ones(4, 1, lat_h, lat_w, device=self.device)
|
| ref_x = torch.concat([ref_x, msk_ref, ref_x])
|
|
|
| ref_c = self.vae.encode([input_frames[:, :1]], VAE_tile_size)[0]
|
| msk_c = torch.zeros(4, 1, lat_h, lat_w, device=self.device)
|
| ref_c = torch.concat([ref_c, msk_c, ref_c])
|
| kwargs.update({ 'steadydancer_ref_x': ref_x, 'steadydancer_ref_c': ref_c})
|
|
|
| conditions = self.vae.encode([input_frames])[0].unsqueeze(0)
|
|
|
| conditions_null = self.vae.encode([input_frames2])[0].unsqueeze(0)
|
| inner_latent_frames = 2
|
|
|
|
|
| if chrono_edit:
|
| if frame_num == 5:
|
| freq0, freq7 = get_nd_rotary_pos_embed( (0, 0, 0), (1, lat_h // 2, lat_w // 2)), get_nd_rotary_pos_embed( (7, 0, 0), (8, lat_h // 2, lat_w // 2))
|
| freqs = ( torch.cat([freq0[0], freq7[0]]), torch.cat([freq0[1],freq7[1]]))
|
| freq0 = freq7 = None
|
| last_latent_preview = image_outputs
|
|
|
|
|
| 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
|
|
|
| if prefix_frames_count > 0:
|
| input_frames[:, :prefix_frames_count] = input_video
|
| input_masks[:, :prefix_frames_count] = 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
|
|
|
|
|
| if scail:
|
| pose_pixels = input_frames
|
| image_ref = input_ref_images[0].to(self.device) if input_ref_images is not None else convert_image_to_tensor(pre_video_frame).unsqueeze(1).to(self.device)
|
| insert_start_frames = window_start_frame_no + prefix_frames_count > 1
|
| if insert_start_frames:
|
| ref_latents = self.vae.encode([image_ref], VAE_tile_size)[0].unsqueeze(0)
|
| start_frames = input_video.to(self.device)
|
| color_reference_frame = input_video[:, :1].to(self.device)
|
| start_latents = self.vae.encode([start_frames], VAE_tile_size)[0].unsqueeze(0)
|
| extended_overlapped_latents = torch.cat([ref_latents, start_latents], dim=2)
|
| start_latents = None
|
| else:
|
|
|
| sigma = torch.exp(torch.normal(mean=-5.0, std=0.5, size=(1,), device=self.device)).to(image_ref.dtype)
|
| noisy_ref = image_ref + torch.randn_like(image_ref) * sigma
|
| ref_latents = self.vae.encode([noisy_ref], VAE_tile_size)[0].unsqueeze(0)
|
| extended_overlapped_latents = ref_latents
|
|
|
| lat_h, lat_w = height // self.vae_stride[1], width // self.vae_stride[2]
|
| pose_frames = pose_pixels.shape[1]
|
| lat_t = int((pose_frames - 1) // self.vae_stride[0]) + 1
|
| 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=prefix_frames_count if insert_start_frames else 0, lat_t=lat_t, device=self.device)
|
| y = torch.concat([msk_ref, msk_control], dim=1)
|
|
|
| pose_pixels_ds = pose_pixels.permute(1, 0, 2, 3)
|
| pose_pixels_ds = F.interpolate( pose_pixels_ds, size=(max(1, pose_pixels.shape[-2] // 2), max(1, pose_pixels.shape[-1] // 2)), mode="bilinear", align_corners=False, ).permute(1, 0, 2, 3)
|
| pose_latents = self.vae.encode([pose_pixels_ds], VAE_tile_size)[0].unsqueeze(0)
|
|
|
| clip_image_start = image_ref.squeeze(1)
|
| kwargs.update({"y": y, "scail_pose_latents": pose_latents, "ref_images_count": 1})
|
|
|
| pose_grid_t = pose_latents.shape[2] // ps_t
|
| pose_rope_h = lat_h // ps_h
|
| pose_rope_w = lat_w // ps_w
|
| pose_freqs_cos, pose_freqs_sin = get_nd_rotary_pos_embed( (ref_images_count, 0, 120), (ref_images_count + pose_grid_t, pose_rope_h, 120 + pose_rope_w), (pose_grid_t, pose_rope_h, pose_rope_w), L_test = lat_t, enable_riflex = enable_RIFLEx)
|
|
|
| head_dim = pose_freqs_cos.shape[1]
|
| pose_freqs_cos = pose_freqs_cos.view(pose_grid_t, pose_rope_h, pose_rope_w, head_dim).permute(0, 3, 1, 2)
|
| pose_freqs_sin = pose_freqs_sin.view(pose_grid_t, pose_rope_h, pose_rope_w, head_dim).permute(0, 3, 1, 2)
|
|
|
| pose_freqs_cos = F.avg_pool2d(pose_freqs_cos, kernel_size=2, stride=2).permute(0, 2, 3, 1).reshape(-1, head_dim)
|
| pose_freqs_sin = F.avg_pool2d(pose_freqs_sin, kernel_size=2, stride=2).permute(0, 2, 3, 1).reshape(-1, head_dim)
|
| post_freqs = (pose_freqs_cos, pose_freqs_sin)
|
|
|
| pose_pixels = pose_pixels_ds = pose_freqs_cos_full = None
|
| ref_images_before = True
|
| ref_images_count = 1
|
| lat_frames = lat_t
|
|
|
|
|
| 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})
|
| if steadydancer:
|
| kwargs['steadydancer_clip_fea_c'] = self.clip.visual([input_frames[:, :1]])
|
|
|
|
|
| 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)
|
| extended_input_dim = 2 if recam else 1
|
| del input_frames
|
|
|
| if recam:
|
|
|
| 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
|
|
|
|
|
| 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
|
|
|
|
|
| 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)
|
|
|
|
|
| masked_steps = math.ceil(sampling_steps * masking_strength)
|
| else:
|
| denoising_strength = 1
|
|
|
| if phantom:
|
| lat_input_ref_images_neg = None
|
| if input_ref_images is not None:
|
| 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 kiwi_edit:
|
| if kiwi_state["source_condition"] is not None:
|
| kwargs["kiwi_source_condition"] = kiwi_state["source_condition"]
|
| if kiwi_state["ref_condition"] is not None:
|
| kwargs["kiwi_ref_condition"] = kiwi_state["ref_condition"]
|
| kwargs["kiwi_ref_pad_first"] = self.model_def.get("kiwi_ref_pad_first", False)
|
| inner_latent_frames = 1
|
| input_video = None
|
|
|
| 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
|
|
|
|
|
| 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"), writable_tensors=False)
|
| 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, enable_loras = False)
|
| lynx_ref = None
|
| gc.collect()
|
| torch.cuda.empty_cache()
|
| kwargs["lynx_ip_scale"] = control_scale_alt
|
| kwargs["lynx_ref_scale"] = control_scale_alt
|
|
|
|
|
| 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 vace:
|
| 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 = vace)
|
| 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, })
|
|
|
|
|
|
|
| if vace :
|
|
|
| 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 lynx 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]
|
|
|
|
|
| if mocha:
|
| extended_latents, freqs = self._build_mocha_latents( input_frames, input_masks, input_ref_images[:2], frame_num, lat_frames, lat_h, lat_w, VAE_tile_size )
|
| extended_input_dim = 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)
|
|
|
| if freqs is not None:
|
| pass
|
| elif 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]+ inner_latent_frames ,) + target_shape[2:] , enable_RIFLEx= enable_RIFLEx)
|
|
|
| if post_freqs is not None:
|
| freqs = ( torch.cat([freqs[0], post_freqs[0]]), torch.cat([freqs[1], post_freqs[1]]) )
|
|
|
| kwargs["freqs"] = freqs
|
|
|
|
|
|
|
| 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:])
|
|
|
|
|
| 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:
|
| if isinstance(sample_scheduler, FlowMatchScheduler) or sample_solver == 'unipc_hf':
|
| scheduler_kwargs = {}
|
| else:
|
| scheduler_kwargs = {"generator": seed_g}
|
|
|
| latents = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
|
| if alpha_class and alpha2:
|
| gauss_mask = load_gauss_mask(fl.locate_file("gauss_mask"))
|
| latents = apply_alpha_shift(latents, gauss_mask, 0.03)
|
| 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()
|
|
|
| trans = self.model
|
| if self_refiner_setting > 0:
|
| self_refiner_handler = create_self_refiner_handler(self_refiner_plan, self_refiner_f_uncertainty, self_refiner_setting, self_refiner_certain_percentage)
|
| else:
|
| self_refiner_handler = None
|
|
|
| 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["perturbation_layers"] = perturbation_layers if int(perturbation_start * sampling_steps) <= i < int(perturbation_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
|
|
|
| def denoise_with_cfg_fn(latents):
|
|
|
| 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, face_pixel_values]
|
| }
|
| elif wanmove:
|
| gen_args = {
|
| "x" : [latent_model_input, latent_model_input],
|
| "context" : [context, context_null],
|
| "y" : [y_cond, y_uncond],
|
| }
|
| 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 steadydancer:
|
|
|
| apply_cond_cfg = 0.1 <= i / sampling_steps < 0.5 and condition_guide_scale != 1
|
| x_list, ctx_list, cond_list = [latent_model_input], [context], [conditions]
|
| if guide_scale != 1:
|
| x_list.append(latent_model_input); ctx_list.append(context_null); cond_list.append(conditions)
|
| if apply_cond_cfg:
|
| x_list.append(latent_model_input); ctx_list.append(context); cond_list.append(conditions_null)
|
| gen_args = {"x": x_list, "context": ctx_list, "steadydancer_condition": cond_list}
|
| any_guidance = len(x_list) > 1
|
| 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]
|
| }
|
| elif kiwi_edit:
|
| if guide_scale == 1:
|
| any_guidance = False
|
| gen_args = {"x": [latent_model_input], "context": context}
|
| else:
|
| gen_args = {"x": [latent_model_input, latent_model_input], "context": context + context_null}
|
| 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
|
| 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 steadydancer:
|
| noise_pred_cond = ret_values[0]
|
| if guide_scale == 1:
|
| noise_pred = ret_values[1] + condition_guide_scale * (noise_pred_cond - ret_values[1])
|
| else:
|
| noise_pred = ret_values[1] + guide_scale * (noise_pred_cond - ret_values[1])
|
| if apply_cond_cfg:
|
| noise_pred = noise_pred + condition_guide_scale * (noise_pred_cond - ret_values[2])
|
| noise_pred_cond = 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:
|
|
|
| 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.
|
| 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
|
| return noise_pred
|
|
|
| noise_pred = denoise_with_cfg_fn(latents)
|
| if noise_pred is None: return clear()
|
| if self_refiner_handler:
|
| latents, sample_scheduler = self_refiner_handler.step(i, latents, noise_pred, t, timesteps, target_shape, seed_g, sample_scheduler, scheduler_kwargs, denoise_with_cfg_fn)
|
| if latents is None: return clear()
|
| else:
|
| latents = sample_scheduler.step( noise_pred[:, :, :target_shape[1]], t, latents, **scheduler_kwargs)[0]
|
|
|
|
|
| if image_mask_latents is not None and i< masked_steps:
|
| sigma = 0 if i == len(timesteps)-1 else timesteps[i+1]/1000
|
| noisy_image = randn[:, :, :source_latents.shape[2]] * sigma + (1 - sigma) * source_latents
|
| latents[:, :, :source_latents.shape[2]] = noisy_image * (1-image_mask_latents) + image_mask_latents * latents[:, :, :source_latents.shape[2]]
|
|
|
|
|
| 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 last_latent_preview else 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 extended_overlapped_latents != None:
|
| latents[:, :, :extended_overlapped_latents.shape[2]] = extended_overlapped_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()
|
|
|
| if chrono_edit:
|
| if frame_num == 5 :
|
| videos = self.vae.decode(x0, VAE_tile_size)
|
| else:
|
| videos_edit = self.vae.decode([x[:, [0,-1]] for x in x0 ], VAE_tile_size)
|
| videos = self.vae.decode([x[:, :-1] for x in x0 ], VAE_tile_size)
|
| videos = [ torch.cat([video, video_edit[:, 1:]], dim=1) for video, video_edit in zip(videos, videos_edit)]
|
| if image_outputs:
|
| return torch.cat([video[:,-1:] for video in videos], dim=1) if len(videos) > 1 else videos[0][:,-1:]
|
| else:
|
| return videos[0]
|
| if image_outputs :
|
| x0 = [x[:,:1] for x in x0 ]
|
|
|
| videos = self.vae.decode(x0, VAE_tile_size)
|
| any_vae2= self.vae2 is not None
|
| if any_vae2:
|
| videos2 = self.vae2.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]
|
| if any_vae2: videos2 = torch.cat([video[:,:1] for video in videos2], dim=1) if len(videos2) > 1 else videos2[0][:,:1]
|
| else:
|
| videos = videos[0]
|
| if any_vae2: videos2 = videos2[0]
|
| if color_correction_strength > 0 and (window_start_frame_no + prefix_frames_count) >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= "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)
|
|
|
| ret = { "x" : videos, "latent_slice" : latent_slice}
|
| if post_decode_pre_trim > 0:
|
| ret["post_decode_pre_trim"] = post_decode_pre_trim
|
|
|
| if alpha_class:
|
| BGRA_frames = None
|
| from .alpha.utils import render_video, from_BRGA_numpy_to_RGBA_torch
|
| videos, BGRA_frames = render_video(videos[None], videos2[None])
|
| if image_outputs:
|
| videos = from_BRGA_numpy_to_RGBA_torch(BGRA_frames)
|
| BGRA_frames = None
|
| if BGRA_frames is not None: ret["BGRA_frames"] = BGRA_frames
|
| return ret
|
|
|
| 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]
|
| elif base_model_type == "vace_ditto_14B":
|
| preloadURLs = get_model_recursive_prop(model_type, "preload_URLs")
|
| model_mode = int(model_mode)
|
| if len(preloadURLs) > model_mode:
|
| return [fl.locate_file(os.path.basename(preloadURLs[model_mode]))] , [1]
|
| return [], []
|
|
|
