AI/ComfyUI-WanVideoWrapper
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ComfyUI-WanVideoWrapper/nodes_sampler.py
kijai 2c2a6e1889 RoPE frequency offset option for storymem 
I'm not 100% sure on this, I initially tested this when I noticed the original code doesn't, but it's described in the paper... now I see the original code has added it too so it seems to be the intended way to use it.
2026-01-23 14:31:04 +02:00

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import os, gc, math, copy
import torch
import numpy as np
from tqdm import tqdm
import inspect
from .wanvideo.modules.model import rope_params
from .custom_linear import remove_lora_from_module, set_lora_params, _replace_linear
from .wanvideo.schedulers import get_scheduler, scheduler_list
from .gguf.gguf import set_lora_params_gguf
from .multitalk.multitalk import add_noise
from .utils import(log, print_memory, apply_lora, fourier_filter, optimized_scale, setup_radial_attention,
compile_model, dict_to_device, tangential_projection, get_raag_guidance, temporal_score_rescaling, offload_transformer, init_blockswap)
from .multitalk.multitalk_loop import multitalk_loop
from .cache_methods.cache_methods import cache_report
from .nodes_model_loading import load_weights
from .enhance_a_video.globals import set_enhance_weight, set_num_frames
from .WanMove.trajectory import replace_feature
from contextlib import nullcontext
from comfy import model_management as mm
from comfy.utils import ProgressBar
from comfy.cli_args import args, LatentPreviewMethod
script_directory = os.path.dirname(os.path.abspath(__file__))
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
rope_functions = ["default", "comfy", "comfy_chunked"]
VAE_STRIDE = (4, 8, 8)
PATCH_SIZE = (1, 2, 2)
class WanVideoSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("WANVIDEOMODEL",),
"image_embeds": ("WANVIDIMAGE_EMBEDS", ),
"steps": ("INT", {"default": 30, "min": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"shift": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"force_offload": ("BOOLEAN", {"default": True, "tooltip": "Moves the model to the offload device after sampling"}),
"scheduler": (scheduler_list, {"default": "unipc",}),
"riflex_freq_index": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping"}),
},
"optional": {
"text_embeds": ("WANVIDEOTEXTEMBEDS", ),
"samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
"denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"feta_args": ("FETAARGS", ),
"context_options": ("WANVIDCONTEXT", ),
"cache_args": ("CACHEARGS", ),
"flowedit_args": ("FLOWEDITARGS", {"tooltip": "FlowEdit support has been deprecated"}),
"batched_cfg": ("BOOLEAN", {"default": False, "tooltip": "Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory"}),
"slg_args": ("SLGARGS", ),
"rope_function": (rope_functions, {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile"}),
"loop_args": ("LOOPARGS", ),
"experimental_args": ("EXPERIMENTALARGS", ),
"sigmas": ("SIGMAS", ),
"unianimate_poses": ("UNIANIMATE_POSE", ),
"fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ),
"uni3c_embeds": ("UNI3C_EMBEDS", ),
"multitalk_embeds": ("MULTITALK_EMBEDS", ),
"freeinit_args": ("FREEINITARGS", ),
"start_step": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "tooltip": "Start step for the sampling, 0 means full sampling, otherwise samples only from this step"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 10000, "step": 1, "tooltip": "End step for the sampling, -1 means full sampling, otherwise samples only until this step"}),
"add_noise_to_samples": ("BOOLEAN", {"default": False, "tooltip": "Add noise to the samples before sampling, needed for video2video sampling when starting from clean video"}),
}
}
RETURN_TYPES = ("LATENT", "LATENT",)
RETURN_NAMES = ("samples", "denoised_samples",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, model, image_embeds, shift, steps, cfg, seed, scheduler, riflex_freq_index, text_embeds=None,
force_offload=True, samples=None, feta_args=None, denoise_strength=1.0, context_options=None,
cache_args=None, teacache_args=None, flowedit_args=None, batched_cfg=False, slg_args=None, rope_function="default", loop_args=None,
experimental_args=None, sigmas=None, unianimate_poses=None, fantasytalking_embeds=None, uni3c_embeds=None, multitalk_embeds=None, freeinit_args=None, start_step=0, end_step=-1, add_noise_to_samples=False):
if flowedit_args is not None:
raise Exception("FlowEdit support has been deprecated and removed due to lack of use and code maintainability")
patcher = model
model = model.model
transformer = model.diffusion_model
dtype = model["base_dtype"]
weight_dtype = model["weight_dtype"]
fp8_matmul = model["fp8_matmul"]
gguf_reader = model["gguf_reader"]
control_lora = model["control_lora"]
vae = image_embeds.get("vae", None)
tiled_vae = image_embeds.get("tiled_vae", False)
transformer_options = copy.deepcopy(patcher.model_options.get("transformer_options", None))
merge_loras = transformer_options["merge_loras"]
block_swap_args = transformer_options.get("block_swap_args", None)
if block_swap_args is not None:
transformer.use_non_blocking = block_swap_args.get("use_non_blocking", False)
transformer.blocks_to_swap = block_swap_args.get("blocks_to_swap", 0)
transformer.vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", 0)
transformer.prefetch_blocks = block_swap_args.get("prefetch_blocks", 0)
transformer.block_swap_debug = block_swap_args.get("block_swap_debug", False)
transformer.offload_img_emb = block_swap_args.get("offload_img_emb", False)
transformer.offload_txt_emb = block_swap_args.get("offload_txt_emb", False)
is_5b = transformer.out_dim == 48
vae_upscale_factor = 16 if is_5b else 8
# Load weights
if transformer.audio_model is not None:
for block in transformer.blocks:
if hasattr(block, 'audio_block'):
block.audio_block = None
if not transformer.patched_linear and patcher.model["sd"] is not None and len(patcher.patches) != 0 and gguf_reader is None:
transformer = _replace_linear(transformer, dtype, patcher.model["sd"], compile_args=model["compile_args"])
transformer.patched_linear = True
if patcher.model["sd"] is not None and gguf_reader is None:
load_weights(patcher.model.diffusion_model, patcher.model["sd"], weight_dtype, base_dtype=dtype, transformer_load_device=device,
block_swap_args=block_swap_args, compile_args=model["compile_args"])
if gguf_reader is not None: #handle GGUF
load_weights(transformer, patcher.model["sd"], base_dtype=dtype, transformer_load_device=device, patcher=patcher, gguf=True,
reader=gguf_reader, block_swap_args=block_swap_args, compile_args=model["compile_args"])
set_lora_params_gguf(transformer, patcher.patches)
transformer.patched_linear = True
elif len(patcher.patches) != 0: #handle patched linear layers (unmerged loras, fp8 scaled)
log.info(f"Using {len(patcher.patches)} LoRA weight patches for WanVideo model")
if not merge_loras and fp8_matmul:
raise NotImplementedError("FP8 matmul with unmerged LoRAs is not supported")
set_lora_params(transformer, patcher.patches)
else:
remove_lora_from_module(transformer) #clear possible unmerged lora weights
transformer.lora_scheduling_enabled = transformer_options.get("lora_scheduling_enabled", False)
#torch.compile
if model["auto_cpu_offload"] is False:
transformer = compile_model(transformer, model["compile_args"])
multitalk_sampling = image_embeds.get("multitalk_sampling", False)
if multitalk_sampling and context_options is not None:
raise Exception("context_options are not compatible or necessary with 'WanVideoImageToVideoMultiTalk' node, since it's already an alternative method that creates the video in a loop.")
if not multitalk_sampling and scheduler == "multitalk":
raise Exception("multitalk scheduler is only for multitalk sampling when using ImagetoVideoMultiTalk -node")
if text_embeds == None:
text_embeds = {
"prompt_embeds": [],
"negative_prompt_embeds": [],
}
else:
text_embeds = dict_to_device(text_embeds, device)
seed_g = torch.Generator(device=torch.device("cpu"))
seed_g.manual_seed(seed)
#region Scheduler
if denoise_strength < 1.0:
if start_step != 0:
raise ValueError("start_step must be 0 when denoise_strength is used")
start_step = steps - int(steps * denoise_strength) - 1
add_noise_to_samples = True #for now to not break old workflows
sample_scheduler = None
if isinstance(scheduler, dict):
sample_scheduler = copy.deepcopy(scheduler["sample_scheduler"])
timesteps = scheduler["timesteps"]
start_step = scheduler.get("start_step", start_step)
elif scheduler != "multitalk":
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, steps, start_step, end_step, shift, device, transformer.dim, denoise_strength, sigmas=sigmas, log_timesteps=True)
else:
timesteps = torch.tensor([1000, 750, 500, 250], device=device)
total_steps = steps
steps = len(timesteps)
is_pusa = "pusa" in sample_scheduler.__class__.__name__.lower()
scheduler_step_args = {"generator": seed_g}
step_sig = inspect.signature(sample_scheduler.step)
for arg in list(scheduler_step_args.keys()):
if arg not in step_sig.parameters:
scheduler_step_args.pop(arg)
# Ovi
if transformer.audio_model is not None: # temporary workaround (...nothing more permanent)
for i, block in enumerate(transformer.blocks):
block.audio_block = transformer.audio_model.blocks[i]
sample_scheduler_ovi = copy.deepcopy(sample_scheduler)
rope_function = "default" # comfy rope not implemented for ovi model yet
ovi_negative_text_embeds = text_embeds.get("ovi_negative_prompt_embeds", None)
ovi_audio_cfg = text_embeds.get("ovi_audio_cfg", None)
if ovi_audio_cfg is not None:
if not isinstance(ovi_audio_cfg, list):
ovi_audio_cfg = [ovi_audio_cfg] * (steps + 1)
if isinstance(cfg, list):
if steps < len(cfg):
log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Slicing cfg list to match steps.")
cfg = cfg[:steps]
elif steps > len(cfg):
log.info(f"Received only {len(cfg)} cfg values, but {steps} steps. Extending cfg list to match steps.")
cfg.extend([cfg[-1]] * (steps - len(cfg)))
log.info(f"Using per-step cfg list: {cfg}")
else:
cfg = [cfg] * (steps + 1)
control_latents = control_camera_latents = clip_fea = clip_fea_neg = end_image = recammaster = camera_embed = unianim_data = mocha_embeds = image_cond_neg =None
vace_data = vace_context = vace_scale = None
fun_or_fl2v_model = drop_last = False
phantom_latents = fun_ref_image = ATI_tracks = None
add_cond = attn_cond = attn_cond_neg = noise_pred_flipped = None
humo_audio = humo_audio_neg = None
has_ref = image_embeds.get("has_ref", False)
#I2V
story_mem_latents = image_embeds.get("story_mem_latents", None)
image_cond = image_embeds.get("image_embeds", None)
if image_cond is not None:
if transformer.in_dim == 16:
raise ValueError("T2V (text to video) model detected, encoded images only work with I2V (Image to video) models")
elif transformer.in_dim not in [48, 32]: # fun 2.1 models don't use the mask
image_cond_mask = image_embeds.get("mask", None)
# StoryMem
if story_mem_latents is not None:
image_cond = torch.cat([story_mem_latents.to(image_cond), image_cond], dim=1)
image_cond_mask = torch.cat([torch.ones_like(story_mem_latents)[:4], image_cond_mask], dim=1) if image_cond_mask is not None else None
if image_cond_mask is not None:
image_cond = torch.cat([image_cond_mask, image_cond])
else:
image_cond[:, 1:] = 0
#ATI tracks
if transformer_options is not None:
ATI_tracks = transformer_options.get("ati_tracks", None)
if ATI_tracks is not None:
from .ATI.motion_patch import patch_motion
topk = transformer_options.get("ati_topk", 2)
temperature = transformer_options.get("ati_temperature", 220.0)
ati_start_percent = transformer_options.get("ati_start_percent", 0.0)
ati_end_percent = transformer_options.get("ati_end_percent", 1.0)
image_cond_ati = patch_motion(ATI_tracks.to(image_cond.device, image_cond.dtype), image_cond, topk=topk, temperature=temperature)
log.info(f"ATI tracks shape: {ATI_tracks.shape}")
add_cond_latents = image_embeds.get("add_cond_latents", None)
if add_cond_latents is not None:
add_cond = add_cond_latents["pose_latent"]
attn_cond = add_cond_latents["ref_latent"]
attn_cond_neg = add_cond_latents["ref_latent_neg"]
add_cond_start_percent = add_cond_latents["pose_cond_start_percent"]
add_cond_end_percent = add_cond_latents["pose_cond_end_percent"]
end_image = image_embeds.get("end_image", None)
fun_or_fl2v_model = image_embeds.get("fun_or_fl2v_model", False)
latent_frames = (image_embeds["num_frames"] - 1) // 4
latent_frames = latent_frames + (2 if end_image is not None and not fun_or_fl2v_model else 1)
latent_frames = latent_frames + story_mem_latents.shape[1] if story_mem_latents is not None else latent_frames
noise = torch.randn( #C, T, H, W
48 if is_5b else 16,
latent_frames,
image_embeds["lat_h"],
image_embeds["lat_w"],
dtype=torch.float32,
generator=seed_g,
device=torch.device("cpu"))
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
control_embeds = image_embeds.get("control_embeds", None)
if control_embeds is not None:
if transformer.in_dim not in [148, 52, 48, 36, 32]:
raise ValueError("Control signal only works with Fun-Control model")
control_latents = control_embeds.get("control_images", None)
control_start_percent = control_embeds.get("start_percent", 0.0)
control_end_percent = control_embeds.get("end_percent", 1.0)
control_camera_latents = control_embeds.get("control_camera_latents", None)
if control_camera_latents is not None:
if transformer.control_adapter is None:
raise ValueError("Control camera latents are only supported with Fun-Control-Camera model")
control_camera_start_percent = control_embeds.get("control_camera_start_percent", 0.0)
control_camera_end_percent = control_embeds.get("control_camera_end_percent", 1.0)
drop_last = image_embeds.get("drop_last", False)
else: #t2v
target_shape = image_embeds.get("target_shape", None)
if target_shape is None:
raise ValueError("Empty image embeds must be provided for T2V models")
# VACE
vace_context = image_embeds.get("vace_context", None)
vace_scale = image_embeds.get("vace_scale", None)
if not isinstance(vace_scale, list):
vace_scale = [vace_scale] * (steps+1)
vace_start_percent = image_embeds.get("vace_start_percent", 0.0)
vace_end_percent = image_embeds.get("vace_end_percent", 1.0)
vace_seqlen = image_embeds.get("vace_seq_len", None)
vace_additional_embeds = image_embeds.get("additional_vace_inputs", [])
if vace_context is not None:
vace_data = [
{"context": vace_context,
"scale": vace_scale,
"start": vace_start_percent,
"end": vace_end_percent,
"seq_len": vace_seqlen
}
]
if len(vace_additional_embeds) > 0:
for i in range(len(vace_additional_embeds)):
if vace_additional_embeds[i].get("has_ref", False):
has_ref = True
vace_scale = vace_additional_embeds[i]["vace_scale"]
if not isinstance(vace_scale, list):
vace_scale = [vace_scale] * (steps+1)
vace_data.append({
"context": vace_additional_embeds[i]["vace_context"],
"scale": vace_scale,
"start": vace_additional_embeds[i]["vace_start_percent"],
"end": vace_additional_embeds[i]["vace_end_percent"],
"seq_len": vace_additional_embeds[i]["vace_seq_len"]
})
noise = torch.randn(
48 if is_5b else 16,
target_shape[1] + 1 if has_ref else target_shape[1],
target_shape[2] // 2 if is_5b else target_shape[2], #todo make this smarter
target_shape[3] // 2 if is_5b else target_shape[3], #todo make this smarter
dtype=torch.float32,
device=torch.device("cpu"),
generator=seed_g)
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
recammaster = image_embeds.get("recammaster", None)
if recammaster is not None:
camera_embed = recammaster.get("camera_embed", None)
recam_latents = recammaster.get("source_latents", None)
orig_noise_len = noise.shape[1]
log.info(f"RecamMaster camera embed shape: {camera_embed.shape}")
log.info(f"RecamMaster source video shape: {recam_latents.shape}")
seq_len *= 2
if image_embeds.get("mocha_embeds", None) is not None:
mocha_embeds = image_embeds.get("mocha_embeds", None)
mocha_num_refs = image_embeds.get("mocha_num_refs", 0)
orig_noise_len = noise.shape[1]
seq_len = image_embeds.get("seq_len", seq_len)
log.info(f"MoCha embeds shape: {mocha_embeds.shape}")
# Fun control and control lora
control_embeds = image_embeds.get("control_embeds", None)
if control_embeds is not None:
control_latents = control_embeds.get("control_images", None)
if control_latents is not None:
control_latents = control_latents.to(device)
control_camera_latents = control_embeds.get("control_camera_latents", None)
if control_camera_latents is not None:
if transformer.control_adapter is None:
raise ValueError("Control camera latents are only supported with Fun-Control-Camera model")
control_camera_start_percent = control_embeds.get("control_camera_start_percent", 0.0)
control_camera_end_percent = control_embeds.get("control_camera_end_percent", 1.0)
if control_lora:
image_cond = control_latents.to(device)
if not patcher.model.is_patched:
log.info("Re-loading control LoRA...")
patcher = apply_lora(patcher, device, device, low_mem_load=False, control_lora=True)
patcher.model.is_patched = True
else:
if transformer.in_dim not in [148, 48, 36, 32, 52]:
raise ValueError("Control signal only works with Fun-Control model")
image_cond = torch.zeros_like(noise).to(device) #fun control
if transformer.in_dim in [148, 52] or transformer.control_adapter is not None: #fun 2.2 control
mask_latents = torch.tile(
torch.zeros_like(noise[:1]), [4, 1, 1, 1]
)
masked_video_latents_input = torch.zeros_like(noise)
image_cond = torch.cat([mask_latents, masked_video_latents_input], dim=0).to(device)
clip_fea = None
fun_ref_image = control_embeds.get("fun_ref_image", None)
if fun_ref_image is not None:
if transformer.ref_conv.weight.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
raise ValueError("Fun-Control reference image won't work with this specific fp8_scaled model, it's been fixed in latest version of the model")
control_start_percent = control_embeds.get("start_percent", 0.0)
control_end_percent = control_embeds.get("end_percent", 1.0)
else:
if transformer.in_dim in [148, 52]: #fun inp
mask_latents = torch.tile(
torch.zeros_like(noise[:1]), [4, 1, 1, 1]
)
masked_video_latents_input = torch.zeros_like(noise)
image_cond = torch.cat([mask_latents, masked_video_latents_input], dim=0).to(device)
# Phantom inputs
phantom_latents = image_embeds.get("phantom_latents", None)
phantom_cfg_scale = image_embeds.get("phantom_cfg_scale", None)
if not isinstance(phantom_cfg_scale, list):
phantom_cfg_scale = [phantom_cfg_scale] * (steps +1)
phantom_start_percent = image_embeds.get("phantom_start_percent", 0.0)
phantom_end_percent = image_embeds.get("phantom_end_percent", 1.0)
# CLIP image features
clip_fea = image_embeds.get("clip_context", None)
if clip_fea is not None:
clip_fea = clip_fea.to(dtype)
clip_fea_neg = image_embeds.get("negative_clip_context", None)
if clip_fea_neg is not None:
clip_fea_neg = clip_fea_neg.to(dtype)
num_frames = image_embeds.get("num_frames", 0)
#HuMo inputs
humo_audio = image_embeds.get("humo_audio_emb", None)
humo_audio_neg = image_embeds.get("humo_audio_emb_neg", None)
humo_reference_count = image_embeds.get("humo_reference_count", 0)
if humo_audio is not None:
from .HuMo.nodes import get_audio_emb_window
if not multitalk_sampling:
humo_audio, _ = get_audio_emb_window(humo_audio, num_frames, frame0_idx=0)
zero_audio_pad = torch.zeros(humo_reference_count, *humo_audio.shape[1:]).to(humo_audio.device)
humo_audio = torch.cat([humo_audio, zero_audio_pad], dim=0)
humo_audio_neg = torch.zeros_like(humo_audio, dtype=humo_audio.dtype, device=humo_audio.device)
humo_audio = humo_audio.to(device, dtype)
if humo_audio_neg is not None:
humo_audio_neg = humo_audio_neg.to(device, dtype)
humo_audio_scale = image_embeds.get("humo_audio_scale", 1.0)
humo_image_cond = image_embeds.get("humo_image_cond", None)
humo_image_cond_neg = image_embeds.get("humo_image_cond_neg", None)
pos_latent = neg_latent = None
# Ovi
noise_audio = latent_ovi = seq_len_ovi = None
if transformer.audio_model is not None:
noise_audio = samples.get("latent_ovi_audio", None) if samples is not None else None
if noise_audio is not None:
if not torch.any(noise_audio):
noise_audio = torch.randn(noise_audio.shape, device=torch.device("cpu"), dtype=torch.float32, generator=seed_g)
else:
noise_audio = noise_audio.squeeze().movedim(0, 1).to(device, dtype)
else:
noise_audio = torch.randn((157, 20), device=torch.device("cpu"), dtype=torch.float32, generator=seed_g) # T C
log.info(f"Ovi audio latent shape: {noise_audio.shape}")
latent_ovi = noise_audio
seq_len_ovi = noise_audio.shape[0]
if transformer.dim == 1536 and humo_image_cond is not None: #small humo model
#noise = torch.cat([noise[:, :-humo_reference_count], humo_image_cond[4:, -humo_reference_count:]], dim=1)
pos_latent = humo_image_cond[4:, -humo_reference_count:].to(device, dtype)
neg_latent = torch.zeros_like(pos_latent)
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
humo_image_cond = humo_image_cond_neg = None
humo_audio_cfg_scale = image_embeds.get("humo_audio_cfg_scale", 1.0)
humo_start_percent = image_embeds.get("humo_start_percent", 0.0)
humo_end_percent = image_embeds.get("humo_end_percent", 1.0)
if not isinstance(humo_audio_cfg_scale, list):
humo_audio_cfg_scale = [humo_audio_cfg_scale] * (steps + 1)
# region WanAnim inputs
frame_window_size = image_embeds.get("frame_window_size", 77)
wananimate_loop = image_embeds.get("looping", False)
if wananimate_loop and context_options is not None:
raise Exception("context_options are not compatible or necessary with WanAnim looping, since it creates the video in a loop.")
wananim_pose_latents = image_embeds.get("pose_latents", None)
wananim_pose_strength = image_embeds.get("pose_strength", 1.0)
wananim_face_strength = image_embeds.get("face_strength", 1.0)
wananim_face_pixels = image_embeds.get("face_pixels", None)
wananim_ref_masks = image_embeds.get("ref_masks", None)
wananim_is_masked = image_embeds.get("is_masked", False)
if not wananimate_loop: # create zero face pixels if mask is provided without face pixels, as masking seems to require face input to work properly
if wananim_face_pixels is None and wananim_is_masked:
if context_options is None:
wananim_face_pixels = torch.zeros(1, 3, num_frames-1, 512, 512, dtype=torch.float32, device=offload_device)
else:
wananim_face_pixels = torch.zeros(1, 3, context_options["context_frames"]-1, 512, 512, dtype=torch.float32, device=device)
if image_cond is None:
image_cond = image_embeds.get("ref_latent", None)
has_ref = image_cond is not None or has_ref
latent_video_length = noise.shape[1]
# Initialize FreeInit filter if enabled
freq_filter = None
if freeinit_args is not None:
from .freeinit.freeinit_utils import get_freq_filter, freq_mix_3d
filter_shape = list(noise.shape) # [batch, C, T, H, W]
freq_filter = get_freq_filter(
filter_shape,
device=device,
filter_type=freeinit_args.get("freeinit_method", "butterworth"),
n=freeinit_args.get("freeinit_n", 4) if freeinit_args.get("freeinit_method", "butterworth") == "butterworth" else None,
d_s=freeinit_args.get("freeinit_s", 1.0),
d_t=freeinit_args.get("freeinit_t", 1.0)
)
if samples is not None:
saved_generator_state = samples.get("generator_state", None)
if saved_generator_state is not None:
seed_g.set_state(saved_generator_state)
# UniAnimate
if unianimate_poses is not None:
transformer.dwpose_embedding.to(device, dtype)
dwpose_data = unianimate_poses["pose"].to(device, dtype)
dwpose_data = torch.cat([dwpose_data[:,:,:1].repeat(1,1,3,1,1), dwpose_data], dim=2)
dwpose_data = transformer.dwpose_embedding(dwpose_data)
log.info(f"UniAnimate pose embed shape: {dwpose_data.shape}")
if not multitalk_sampling:
if dwpose_data.shape[2] > latent_video_length:
log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is longer than the video length {latent_video_length}, truncating")
dwpose_data = dwpose_data[:,:, :latent_video_length]
elif dwpose_data.shape[2] < latent_video_length:
log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is shorter than the video length {latent_video_length}, padding with last pose")
pad_len = latent_video_length - dwpose_data.shape[2]
pad = dwpose_data[:,:,:1].repeat(1,1,pad_len,1,1)
dwpose_data = torch.cat([dwpose_data, pad], dim=2)
random_ref_dwpose_data = None
if image_cond is not None:
transformer.randomref_embedding_pose.to(device, dtype)
random_ref_dwpose = unianimate_poses.get("ref", None)
if random_ref_dwpose is not None:
random_ref_dwpose_data = transformer.randomref_embedding_pose(
random_ref_dwpose.to(device, dtype)
).unsqueeze(2).to(dtype) # [1, 20, 104, 60]
del random_ref_dwpose
unianim_data = {
"dwpose": dwpose_data,
"random_ref": random_ref_dwpose_data.squeeze(0) if random_ref_dwpose_data is not None else None,
"strength": unianimate_poses["strength"],
"start_percent": unianimate_poses["start_percent"],
"end_percent": unianimate_poses["end_percent"]
}
# FantasyTalking
audio_proj = multitalk_audio_embeds = None
audio_scale = 1.0
if fantasytalking_embeds is not None:
audio_proj = fantasytalking_embeds["audio_proj"].to(device)
audio_scale = fantasytalking_embeds["audio_scale"]
audio_cfg_scale = fantasytalking_embeds["audio_cfg_scale"]
if not isinstance(audio_cfg_scale, list):
audio_cfg_scale = [audio_cfg_scale] * (steps +1)
log.info(f"Audio proj shape: {audio_proj.shape}")
# MultiTalk
multitalk_audio_embeds = audio_emb_slice = audio_features_in = None
multitalk_embeds = image_embeds.get("multitalk_embeds", multitalk_embeds)
if multitalk_embeds is not None:
audio_emb_slice = multitalk_embeds.get("audio_emb_slice", None) # if already sliced
# Handle single or multiple speaker embeddings
if audio_emb_slice is None:
audio_features_in = multitalk_embeds.get("audio_features", None)
if audio_features_in is not None:
if isinstance(audio_features_in, list):
multitalk_audio_embeds = [emb.to(device, dtype) for emb in audio_features_in]
else:
# keep backward-compatibility with single tensor input
multitalk_audio_embeds = [audio_features_in.to(device, dtype)]
shapes = [tuple(e.shape) for e in multitalk_audio_embeds]
log.info(f"Multitalk audio features shapes (per speaker): {shapes}")
audio_scale = multitalk_embeds.get("audio_scale", 1.0)
audio_cfg_scale = multitalk_embeds.get("audio_cfg_scale", 1.0)
ref_target_masks = multitalk_embeds.get("ref_target_masks", None)
if not isinstance(audio_cfg_scale, list):
audio_cfg_scale = [audio_cfg_scale] * (steps + 1)
# FantasyPortrait
fantasy_portrait_input = None
fantasy_portrait_embeds = image_embeds.get("portrait_embeds", None)
if fantasy_portrait_embeds is not None:
log.info("Using FantasyPortrait embeddings")
fantasy_portrait_input = fantasy_portrait_embeds.copy()
portrait_cfg = fantasy_portrait_input.get("cfg_scale", 1.0)
if not isinstance(portrait_cfg, list):
portrait_cfg = [portrait_cfg] * (steps + 1)
# MiniMax Remover
minimax_latents = image_embeds.get("minimax_latents", None)
minimax_mask_latents = image_embeds.get("minimax_mask_latents", None)
if minimax_latents is not None:
log.info(f"minimax_latents: {minimax_latents.shape}, minimax_mask_latents: {minimax_mask_latents.shape}")
minimax_latents = minimax_latents.to(device, dtype)
minimax_mask_latents = minimax_mask_latents.to(device, dtype)
# Context windows
is_looped = False
context_reference_latent = None
if context_options is not None:
if context_options["context_frames"] <= num_frames:
context_schedule = context_options["context_schedule"]
context_frames = (context_options["context_frames"] - 1) // 4 + 1
context_stride = context_options["context_stride"] // 4
context_overlap = context_options["context_overlap"] // 4
context_reference_latent = context_options.get("reference_latent", None)
# Get total number of prompts
num_prompts = len(text_embeds["prompt_embeds"])
log.info(f"Number of prompts: {num_prompts}")
# Calculate which section this context window belongs to
section_size = (latent_video_length / num_prompts) if num_prompts != 0 else 1
log.info(f"Section size: {section_size}")
is_looped = context_schedule == "uniform_looped"
if mocha_embeds is not None:
seq_len = (context_frames * 2 + 1 + mocha_num_refs) * (noise.shape[2] * noise.shape[3] // 4)
else:
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * context_frames)
log.info(f"context window seq len: {seq_len}")
if context_options["freenoise"]:
log.info("Applying FreeNoise")
# code from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
delta = context_frames - context_overlap
for start_idx in range(0, latent_video_length-context_frames, delta):
place_idx = start_idx + context_frames
if place_idx >= latent_video_length:
break
end_idx = place_idx - 1
if end_idx + delta >= latent_video_length:
final_delta = latent_video_length - place_idx
list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long)
list_idx = list_idx[torch.randperm(final_delta, generator=seed_g)]
noise[:, place_idx:place_idx + final_delta, :, :] = noise[:, list_idx, :, :]
break
list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long)
list_idx = list_idx[torch.randperm(delta, generator=seed_g)]
noise[:, place_idx:place_idx + delta, :, :] = noise[:, list_idx, :, :]
log.info(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
from .context_windows.context import get_context_scheduler, create_window_mask, WindowTracker
self.window_tracker = WindowTracker(verbose=context_options["verbose"])
context = get_context_scheduler(context_schedule)
else:
log.info("Context frames is larger than total num_frames, disabling context windows")
context_options = None
#MTV Crafter
mtv_input = image_embeds.get("mtv_crafter_motion", None)
mtv_motion_tokens = None
if mtv_input is not None:
from .MTV.mtv import prepare_motion_embeddings
log.info("Using MTV Crafter embeddings")
mtv_start_percent = mtv_input.get("start_percent", 0.0)
mtv_end_percent = mtv_input.get("end_percent", 1.0)
mtv_strength = mtv_input.get("strength", 1.0)
mtv_motion_tokens = mtv_input.get("mtv_motion_tokens", None)
if not isinstance(mtv_strength, list):
mtv_strength = [mtv_strength] * (steps + 1)
d = transformer.dim // transformer.num_heads
mtv_freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6)),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
],
dim=1)
motion_rotary_emb = prepare_motion_embeddings(
latent_video_length if context_options is None else context_frames,
24, mtv_input["global_mean"], [mtv_input["global_std"]], device=device)
log.info(f"mtv_motion_rotary_emb: {motion_rotary_emb[0].shape}")
mtv_freqs = mtv_freqs.to(device, dtype)
#region S2V
s2v_audio_input = s2v_ref_latent = s2v_pose = s2v_ref_motion = None
framepack = False
s2v_audio_embeds = image_embeds.get("audio_embeds", None)
if s2v_audio_embeds is not None:
log.info("Using S2V audio embeddings")
framepack = s2v_audio_embeds.get("enable_framepack", False)
if framepack and context_options is not None:
raise ValueError("S2V framepack and context windows cannot be used at the same time")
s2v_audio_input = s2v_audio_embeds.get("audio_embed_bucket", None)
if s2v_audio_input is not None:
#s2v_audio_input = s2v_audio_input[..., 0:image_embeds["num_frames"]]
s2v_audio_input = s2v_audio_input.to(device, dtype)
s2v_audio_scale = s2v_audio_embeds["audio_scale"]
s2v_ref_latent = s2v_audio_embeds.get("ref_latent", None)
if s2v_ref_latent is not None:
s2v_ref_latent = s2v_ref_latent.to(device, dtype)
s2v_ref_motion = s2v_audio_embeds.get("ref_motion", None)
if s2v_ref_motion is not None:
s2v_ref_motion = s2v_ref_motion.to(device, dtype)
s2v_pose = s2v_audio_embeds.get("pose_latent", None)
if s2v_pose is not None:
s2v_pose = s2v_pose.to(device, dtype)
s2v_pose_start_percent = s2v_audio_embeds.get("pose_start_percent", 0.0)
s2v_pose_end_percent = s2v_audio_embeds.get("pose_end_percent", 1.0)
s2v_num_repeat = s2v_audio_embeds.get("num_repeat", 1)
vae = s2v_audio_embeds.get("vae", None)
# vid2vid
noise_mask=original_image=None
if samples is not None and not multitalk_sampling and not wananimate_loop:
saved_generator_state = samples.get("generator_state", None)
if saved_generator_state is not None:
seed_g.set_state(saved_generator_state)
input_samples = samples.get("samples", None)
if input_samples is not None:
input_samples = input_samples.squeeze(0).to(noise)
if input_samples.shape[1] != noise.shape[1]:
input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1)
if add_noise_to_samples:
latent_timestep = timesteps[:1].to(noise)
noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples
else:
noise = input_samples
noise_mask = samples.get("noise_mask", None)
if noise_mask is not None:
log.info(f"Latent noise_mask shape: {noise_mask.shape}")
original_image = samples.get("original_image", None)
if original_image is None:
original_image = input_samples
if len(noise_mask.shape) == 4:
noise_mask = noise_mask.squeeze(1)
if noise_mask.shape[0] < noise.shape[1]:
noise_mask = noise_mask.repeat(noise.shape[1] // noise_mask.shape[0], 1, 1)
noise_mask = torch.nn.functional.interpolate(
noise_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W]
size=(noise.shape[1], noise.shape[2], noise.shape[3]),
mode='trilinear',
align_corners=False
).repeat(1, noise.shape[0], 1, 1, 1)
# extra latents (Pusa) and 5b
latents_to_insert = add_index = noise_multipliers = None
extra_latents = image_embeds.get("extra_latents", None)
clean_latent_indices = []
noise_multiplier_list = image_embeds.get("pusa_noise_multipliers", None)
if noise_multiplier_list is not None:
if len(noise_multiplier_list) != latent_video_length:
noise_multipliers = torch.zeros(latent_video_length)
else:
noise_multipliers = torch.tensor(noise_multiplier_list)
log.info(f"Using Pusa noise multipliers: {noise_multipliers}")
if extra_latents is not None and transformer.multitalk_model_type.lower() != "infinitetalk":
if noise_multiplier_list is not None:
noise_multiplier_list = list(noise_multiplier_list) + [1.0] * (len(clean_latent_indices) - len(noise_multiplier_list))
for i, entry in enumerate(extra_latents):
add_index = entry["index"]
num_extra_frames = entry["samples"].shape[2]
# Handle negative indices
if add_index < 0:
add_index = noise.shape[1] + add_index
add_index = max(0, min(add_index, noise.shape[1] - num_extra_frames))
if start_step == 0:
noise[:, add_index:add_index+num_extra_frames] = entry["samples"].to(noise)
log.info(f"Adding extra samples to latent indices {add_index} to {add_index+num_extra_frames-1}")
clean_latent_indices.extend(range(add_index, add_index+num_extra_frames))
if noise_multipliers is not None and len(noise_multiplier_list) != latent_video_length:
for i, idx in enumerate(clean_latent_indices):
noise_multipliers[idx] = noise_multiplier_list[i]
log.info(f"Using Pusa noise multipliers: {noise_multipliers}")
# lucy edit
extra_channel_latents = image_embeds.get("extra_channel_latents", None)
if extra_channel_latents is not None:
extra_channel_latents = extra_channel_latents[0].to(noise)
# FlashVSR
flashvsr_LQ_latent = LQ_images = None
flashvsr_LQ_images = image_embeds.get("flashvsr_LQ_images", None)
flashvsr_strength = image_embeds.get("flashvsr_strength", 1.0)
if flashvsr_LQ_images is not None:
flashvsr_LQ_images = flashvsr_LQ_images[:num_frames]
first_frame = flashvsr_LQ_images[:1]
last_frame = flashvsr_LQ_images[-1:].repeat(3, 1, 1, 1)
flashvsr_LQ_images = torch.cat([first_frame, flashvsr_LQ_images, last_frame], dim=0)
LQ_images = flashvsr_LQ_images.unsqueeze(0).movedim(-1, 1).to(dtype) * 2 - 1
if context_options is None:
flashvsr_LQ_latent = transformer.LQ_proj_in(LQ_images.to(device))
log.info(f"flashvsr_LQ_latent: {flashvsr_LQ_latent[0].shape}")
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
latent = noise
# LongCat-Avatar
longcat_ref_latent = None
longcat_num_ref_latents = longcat_num_cond_latents = 0
longcat_avatar_options = image_embeds.get("longcat_avatar_options", None)
if longcat_avatar_options is not None:
longcat_ref_latent = longcat_avatar_options.get("longcat_ref_latent", None)
if longcat_ref_latent is not None:
log.info(f"LongCat-Avatar reference latent shape: {longcat_ref_latent.shape}")
latent = torch.cat([longcat_ref_latent.to(latent), latent], dim=1)
seq_len = math.ceil((latent.shape[2] * latent.shape[3]) / 4 * latent.shape[1])
insert_len = longcat_ref_latent.shape[1]
clean_latent_indices = list(range(0, insert_len)) + [i + insert_len for i in clean_latent_indices]
longcat_num_ref_latents = longcat_ref_latent.shape[1]
latent_video_length += insert_len
longcat_num_cond_latents = len(clean_latent_indices)
log.info(f"LongCat num_cond_latents: {longcat_num_cond_latents} num_ref_latents: {longcat_num_ref_latents}")
audio_stride = 2 if transformer.is_longcat else 1
#controlnet
controlnet_latents = controlnet = None
if transformer_options is not None:
controlnet = transformer_options.get("controlnet", None)
if controlnet is not None:
self.controlnet = controlnet["controlnet"]
controlnet_start = controlnet["controlnet_start"]
controlnet_end = controlnet["controlnet_end"]
controlnet_latents = controlnet["control_latents"]
controlnet["controlnet_weight"] = controlnet["controlnet_strength"]
controlnet["controlnet_stride"] = controlnet["control_stride"]
#uni3c
uni3c_data = uni3c_data_input = None
if uni3c_embeds is not None:
transformer.uni3c_controlnet = uni3c_embeds["controlnet"]
render_latent = uni3c_embeds["render_latent"].to(device)
uni3c_data = uni3c_embeds.copy()
if render_latent.shape != noise.shape:
render_latent = torch.nn.functional.interpolate(render_latent, size=(noise.shape[1], noise.shape[2], noise.shape[3]), mode='trilinear', align_corners=False)
uni3c_data["render_latent"] = render_latent
# Enhance-a-video (feta)
if feta_args is not None and latent_video_length > 1:
set_enhance_weight(feta_args["weight"])
feta_start_percent = feta_args["start_percent"]
feta_end_percent = feta_args["end_percent"]
set_num_frames(latent_video_length) if context_options is None else set_num_frames(context_frames)
enhance_enabled = True
else:
feta_args = None
enhance_enabled = False
# EchoShot https://github.com/D2I-ai/EchoShot
echoshot = False
shot_len = None
if text_embeds is not None:
echoshot = text_embeds.get("echoshot", False)
if echoshot:
shot_num = len(text_embeds["prompt_embeds"])
shot_len = [latent_video_length//shot_num] * (shot_num-1)
shot_len.append(latent_video_length-sum(shot_len))
rope_function = "default" #echoshot does not support comfy rope function
log.info(f"Number of shots in prompt: {shot_num}, Shot token lengths: {shot_len}")
# Bindweave
qwenvl_embeds_pos = image_embeds.get("qwenvl_embeds_pos", None)
qwenvl_embeds_neg = image_embeds.get("qwenvl_embeds_neg", None)
mm.unload_all_models()
mm.soft_empty_cache()
gc.collect()
#blockswap init
init_blockswap(transformer, block_swap_args, model)
# Initialize Cache if enabled
previous_cache_states = None
transformer.enable_teacache = transformer.enable_magcache = transformer.enable_easycache = False
cache_args = teacache_args if teacache_args is not None else cache_args #for backward compatibility on old workflows
if cache_args is not None:
from .cache_methods.cache_methods import set_transformer_cache_method
transformer = set_transformer_cache_method(transformer, timesteps, cache_args)
# Initialize cache state
if samples is not None:
previous_cache_states = samples.get("cache_states", None)
if previous_cache_states is not None:
log.info("Using cache states from previous sampler")
self.cache_state = previous_cache_states["cache_state"]
transformer.easycache_state = previous_cache_states["easycache_state"]
transformer.magcache_state = previous_cache_states["magcache_state"]
transformer.teacache_state = previous_cache_states["teacache_state"]
if previous_cache_states is None:
self.cache_state = [None, None]
if phantom_latents is not None:
log.info(f"Phantom latents shape: {phantom_latents.shape}")
self.cache_state = [None, None, None]
self.cache_state_source = [None, None]
self.cache_states_context = []
# Skip layer guidance (SLG)
if slg_args is not None:
assert batched_cfg is not None, "Batched cfg is not supported with SLG"
transformer.slg_blocks = slg_args["blocks"]
transformer.slg_start_percent = slg_args["start_percent"]
transformer.slg_end_percent = slg_args["end_percent"]
else:
transformer.slg_blocks = None
# Setup radial attention
if transformer.attention_mode == "radial_sage_attention":
setup_radial_attention(transformer, transformer_options, latent, seq_len, latent_video_length, context_options=context_options)
# Experimental args
use_cfg_zero_star = use_tangential = use_fresca = bidirectional_sampling = use_tsr = False
raag_alpha = 0.0
transformer.video_attention_split_steps = []
if experimental_args is not None:
video_attention_split_steps = experimental_args.get("video_attention_split_steps", [])
if video_attention_split_steps:
transformer.video_attention_split_steps = [int(x.strip()) for x in video_attention_split_steps.split(",")]
use_zero_init = experimental_args.get("use_zero_init", True)
use_cfg_zero_star = experimental_args.get("cfg_zero_star", False)
use_tangential = experimental_args.get("use_tcfg", False)
zero_star_steps = experimental_args.get("zero_star_steps", 0)
raag_alpha = experimental_args.get("raag_alpha", 0.0)
use_fresca = experimental_args.get("use_fresca", False)
if use_fresca:
fresca_scale_low = experimental_args.get("fresca_scale_low", 1.0)
fresca_scale_high = experimental_args.get("fresca_scale_high", 1.25)
fresca_freq_cutoff = experimental_args.get("fresca_freq_cutoff", 20)
bidirectional_sampling = experimental_args.get("bidirectional_sampling", False)
if bidirectional_sampling:
sample_scheduler_flipped = copy.deepcopy(sample_scheduler)
use_tsr = experimental_args.get("temporal_score_rescaling", False)
tsr_k = experimental_args.get("tsr_k", 1.0)
tsr_sigma = experimental_args.get("tsr_sigma", 1.0)
# Rotary positional embeddings (RoPE)
# RoPE base freq scaling as used with CineScale
ntk_alphas = [1.0, 1.0, 1.0]
if isinstance(rope_function, dict):
ntk_alphas = rope_function["ntk_scale_f"], rope_function["ntk_scale_h"], rope_function["ntk_scale_w"]
rope_function = rope_function["rope_function"]
# Stand-In
standin_input = image_embeds.get("standin_input", None)
if standin_input is not None:
rope_function = "comfy" # only works with this currently
freqs = None
log.info(f"Rope function: {rope_function}")
riflex_freq_index = 0 if riflex_freq_index is None else riflex_freq_index
transformer.rope_embedder.k = None
transformer.rope_embedder.num_frames = None
d = transformer.dim // transformer.num_heads
if mocha_embeds is not None:
from .mocha.nodes import rope_params_mocha
log.info("Using Mocha RoPE")
rope_function = 'mocha'
freqs = torch.cat([
rope_params_mocha(1024, d - 4 * (d // 6), L_test=latent_video_length, k=riflex_freq_index, start=-1),
rope_params_mocha(1024, 2 * (d // 6), start=-1),
rope_params_mocha(1024, 2 * (d // 6), start=-1)
],
dim=1)
elif "default" in rope_function or bidirectional_sampling: # original RoPE
freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6), L_test=latent_video_length, k=riflex_freq_index),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
],
dim=1)
elif "comfy" in rope_function: # comfy's rope
transformer.rope_embedder.k = riflex_freq_index
transformer.rope_embedder.num_frames = latent_video_length
transformer.rope_func = rope_function
for block in transformer.blocks:
block.rope_func = rope_function
if transformer.vace_layers is not None:
for block in transformer.vace_blocks:
block.rope_func = rope_function
# Lynx
lynx_ref_buffer = None
lynx_embeds = image_embeds.get("lynx_embeds", None)
if lynx_embeds is not None:
if lynx_embeds.get("ip_x", None) is not None:
if transformer.blocks[0].cross_attn.ip_adapter is None:
raise ValueError("Lynx IP embeds provided, but the no lynx ip adapter layers found in the model.")
lynx_embeds = lynx_embeds.copy()
log.info("Using Lynx embeddings", lynx_embeds)
lynx_ref_latent = lynx_embeds.get("ref_latent", None)
lynx_ref_latent_uncond = lynx_embeds.get("ref_latent_uncond", None)
lynx_ref_text_embed = lynx_embeds.get("ref_text_embed", None)
lynx_ref_text_embed = dict_to_device(lynx_ref_text_embed, device)
lynx_cfg_scale = lynx_embeds.get("cfg_scale", 1.0)
if not isinstance(lynx_cfg_scale, list):
lynx_cfg_scale = [lynx_cfg_scale] * (steps + 1)
if lynx_ref_latent is not None:
if transformer.blocks[0].self_attn.ref_adapter is None:
raise ValueError("Lynx reference provided, but the no lynx reference adapter layers found in the model.")
lynx_ref_latent = lynx_ref_latent[0]
lynx_ref_latent_uncond = lynx_ref_latent_uncond[0]
lynx_embeds["ref_feature_extractor"] = True
log.info(f"Lynx ref latent shape: {lynx_ref_latent.shape}")
log.info("Extracting Lynx ref cond buffer...")
if transformer.in_dim == 36:
mask_latents = torch.tile(torch.zeros_like(lynx_ref_latent[:1]), [4, 1, 1, 1])
empty_image_cond = torch.cat([mask_latents, torch.zeros_like(lynx_ref_latent)], dim=0).to(device)
lynx_ref_input = torch.cat([lynx_ref_latent, empty_image_cond], dim=0)
else:
lynx_ref_input = lynx_ref_latent
lynx_ref_buffer = transformer(
[lynx_ref_input.to(device, dtype)],
torch.tensor([0], device=device),
lynx_ref_text_embed["prompt_embeds"],
seq_len=math.ceil((lynx_ref_latent.shape[2] * lynx_ref_latent.shape[3]) / 4 * lynx_ref_latent.shape[1]),
lynx_embeds=lynx_embeds
)
log.info(f"Extracted {len(lynx_ref_buffer)} cond ref buffers")
if any(not math.isclose(c, 1.0) for c in cfg):
log.info("Extracting Lynx ref uncond buffer...")
if transformer.in_dim == 36:
lynx_ref_input_uncond = torch.cat([lynx_ref_latent_uncond, empty_image_cond], dim=0)
else:
lynx_ref_input_uncond = lynx_ref_latent_uncond
lynx_ref_buffer_uncond = transformer(
[lynx_ref_input_uncond.to(device, dtype)],
torch.tensor([0], device=device),
lynx_ref_text_embed["prompt_embeds"],
seq_len=math.ceil((lynx_ref_latent.shape[2] * lynx_ref_latent.shape[3]) / 4 * lynx_ref_latent.shape[1]),
lynx_embeds=lynx_embeds,
is_uncond=True
)
log.info(f"Extracted {len(lynx_ref_buffer_uncond)} uncond ref buffers")
if lynx_embeds.get("ip_x", None) is not None:
lynx_embeds["ip_x"] = lynx_embeds["ip_x"].to(device, dtype)
lynx_embeds["ip_x_uncond"] = lynx_embeds["ip_x_uncond"].to(device, dtype)
lynx_embeds["ref_feature_extractor"] = False
lynx_embeds["ref_latent"] = lynx_embeds["ref_text_embed"] = None
lynx_embeds["ref_buffer"] = lynx_ref_buffer
lynx_embeds["ref_buffer_uncond"] = lynx_ref_buffer_uncond if not math.isclose(cfg[0], 1.0) else None
mm.soft_empty_cache()
# UniLumos
foreground_latents = image_embeds.get("foreground_latents", None)
if foreground_latents is not None:
log.info(f"UniLumos foreground latent input shape: {foreground_latents.shape}")
foreground_latents = foreground_latents.to(device, dtype)
background_latents = image_embeds.get("background_latents", None)
if background_latents is not None:
log.info(f"UniLumos background latent input shape: {background_latents.shape}")
background_latents = background_latents.to(device, dtype)
#Time-to-move (TTM)
ttm_start_step = 0
ttm_reference_latents = image_embeds.get("ttm_reference_latents", None)
if ttm_reference_latents is not None:
motion_mask = image_embeds["ttm_mask"].to(device, dtype)
ttm_start_step = max(image_embeds["ttm_start_step"] - start_step, 0)
ttm_end_step = image_embeds["ttm_end_step"] - start_step
if ttm_start_step > steps:
raise ValueError("TTM start step is beyond the total number of steps")
if ttm_end_step > ttm_start_step:
log.info("Using Time-to-move (TTM)")
log.info(f"TTM reference latents shape: {ttm_reference_latents.shape}")
log.info(f"TTM motion mask shape: {motion_mask.shape}")
log.info(f"Applying TTM from step {ttm_start_step} to {ttm_end_step}")
latent = add_noise(ttm_reference_latents, noise, timesteps[ttm_start_step].to(noise.device)).to(latent)
# SteadyDancer
sdancer_embeds = image_embeds.get("sdancer_embeds", None)
sdancer_data = sdancer_input = None
if sdancer_embeds is not None:
log.info("Using SteadyDancer embeddings:")
for k, v in sdancer_embeds.items():
log.info(f" {k}: {v.shape if isinstance(v, torch.Tensor) else v}")
sdancer_data = sdancer_embeds.copy()
sdancer_data = dict_to_device(sdancer_data, device, dtype)
# One-to-all-Animation
one_to_all_embeds = image_embeds.get("one_to_all_embeds", None)
one_to_all_data = prev_latents = None
latents_to_not_step = 0
if one_to_all_embeds is not None:
log.info("Using One-to-All embeddings:")
for k, v in one_to_all_embeds.items():
log.info(f" {k}: {v.shape if isinstance(v, torch.Tensor) else v}")
one_to_all_data = one_to_all_embeds.copy()
one_to_all_data = dict_to_device(one_to_all_data, device, dtype)
if one_to_all_embeds.get("pose_images") is not None:
transformer.input_hint_block.to(device)
pose_images_in = one_to_all_data.pop("pose_images")
pose_images = transformer.input_hint_block(pose_images_in)
if one_to_all_embeds.get("ref_latent_pos") is not None:
pose_prefix_image = transformer.input_hint_block(one_to_all_data.pop("pose_prefix_image"))
pose_images = torch.cat([pose_prefix_image, pose_images],dim=2)
one_to_all_data["controlnet_tokens"] = pose_images.flatten(2).transpose(1, 2)
transformer.input_hint_block.to(offload_device)
one_to_all_pose_cfg_scale = one_to_all_embeds.get("pose_cfg_scale", 1.0)
if not isinstance(one_to_all_pose_cfg_scale, list):
one_to_all_pose_cfg_scale = [one_to_all_pose_cfg_scale] * (steps + 1)
prev_latents = one_to_all_data.get("prev_latents", None)
if prev_latents is not None:
log.info(f"Using previous latents for One-to-All Animation with shape: {prev_latents.shape}")
latent[:, :prev_latents.shape[1]] = prev_latents.to(latent)
one_to_all_data["token_replace"] = True
latents_to_not_step = prev_latents.shape[1]
one_to_all_data["num_latent_frames_to_replace"] = latents_to_not_step
# SCAIL
scail_embeds = image_embeds.get("scail_embeds", None)
scail_data = None
if scail_embeds is not None:
log.info("Using SCAIL embeddings:")
for k, v in scail_embeds.items():
log.info(f" {k}: {v.shape if isinstance(v, torch.Tensor) else v}")
scail_data = scail_embeds.copy()
scail_data = dict_to_device(scail_data, device, dtype)
# WanMove
wanmove_embeds = None
if image_cond is not None:
wanmove_embeds = image_embeds.get("wanmove_embeds", None)
if wanmove_embeds is not None:
track_pos = wanmove_embeds["track_pos"]
if any(not math.isclose(c, 1.0) for c in cfg):
image_cond_neg = torch.cat([image_embeds["mask"], image_cond])
if context_options is None:
image_cond = replace_feature(image_cond.unsqueeze(0).clone(), track_pos.unsqueeze(0), wanmove_embeds.get("strength", 1.0))[0]
# LongVie2 dual control
dual_control_embeds = image_embeds.get("dual_control", None)
if dual_control_embeds is not None and context_options is None:
dual_control_input = dict_to_device(dual_control_embeds.copy(), device, dtype) if dual_control_embeds is not None else None
prev_latents = dual_control_input.get("prev_latent", None)
if prev_latents is not None:
_sigma = dual_control_embeds.get("first_frame_noise_level", 0.925926)
log.info(f"Using dual control previous latents with first frame noise level: {_sigma}")
latent[:, :1] = (1 - _sigma) * prev_latents[:, -1:].to(latent) + _sigma * noise[:, :1]
prev_ones = torch.ones(20, *prev_latents.shape[1:], device=device, dtype=dtype)
dual_control_input["prev_latent"] = torch.cat([prev_ones, prev_latents]).unsqueeze(0)
#region model pred
def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None,
control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None,
add_cond=None, cache_state=None, context_window=None, multitalk_audio_embeds=None, fantasy_portrait_input=None, reverse_time=False,
mtv_motion_tokens=None, s2v_audio_input=None, s2v_ref_motion=None, s2v_motion_frames=[1, 0], s2v_pose=None,
humo_image_cond=None, humo_image_cond_neg=None, humo_audio=None, humo_audio_neg=None, wananim_pose_latents=None,
wananim_face_pixels=None, uni3c_data=None, latent_model_input_ovi=None, flashvsr_LQ_latent=None,):
nonlocal transformer
nonlocal audio_cfg_scale
autocast_enabled = ("fp8" in model["quantization"] and not transformer.patched_linear)
with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype) if autocast_enabled else nullcontext():
if use_cfg_zero_star and (idx <= zero_star_steps) and use_zero_init:
return z*0, None
nonlocal patcher
current_step_percentage = idx / len(timesteps)
control_lora_enabled = False
image_cond_input = None
if control_embeds is not None and control_camera_latents is None:
if control_lora:
control_lora_enabled = True
else:
if ((control_start_percent <= current_step_percentage <= control_end_percent) or \
(control_end_percent > 0 and idx == 0 and current_step_percentage >= control_start_percent)) and \
(control_latents is not None):
image_cond_input = torch.cat([control_latents.to(z), image_cond.to(z)])
else:
image_cond_input = torch.cat([torch.zeros_like(noise, device=device, dtype=dtype), image_cond.to(z)])
if fun_ref_image is not None:
fun_ref_input = fun_ref_image.to(z)
else:
fun_ref_input = torch.zeros_like(z, dtype=z.dtype)[:, 0].unsqueeze(1)
if control_lora:
if not control_start_percent <= current_step_percentage <= control_end_percent:
control_lora_enabled = False
if patcher.model.is_patched:
log.info("Unloading LoRA...")
patcher.unpatch_model(device)
patcher.model.is_patched = False
else:
image_cond_input = control_latents.to(z)
if not patcher.model.is_patched:
log.info("Loading LoRA...")
patcher = apply_lora(patcher, device, device, low_mem_load=False, control_lora=True)
patcher.model.is_patched = True
elif ATI_tracks is not None and ((ati_start_percent <= current_step_percentage <= ati_end_percent) or
(ati_end_percent > 0 and idx == 0 and current_step_percentage >= ati_start_percent)):
image_cond_input = image_cond_ati.to(z)
elif humo_image_cond is not None:
humo_image_cond_neg_input = None
if context_window is not None:
image_cond_input = humo_image_cond[:, context_window].to(z)
humo_image_cond_neg_input = humo_image_cond_neg[:, context_window].to(z)
if humo_reference_count > 0:
image_cond_input[:, -humo_reference_count:] = humo_image_cond[:, -humo_reference_count:]
humo_image_cond_neg_input[:, -humo_reference_count:] = humo_image_cond_neg[:, -humo_reference_count:]
else:
if image_cond is not None:
image_cond_input = image_cond.to(z)
if humo_reference_count > 0:
image_cond_input = torch.cat([image_cond_input, humo_image_cond[:, -humo_reference_count:].to(z)], dim=1)
humo_image_cond_neg_input = torch.cat([image_cond_input, humo_image_cond_neg[:, -humo_reference_count:].to(z)], dim=1)
else:
image_cond_input = humo_image_cond.to(z)
humo_image_cond_neg_input = humo_image_cond_neg.to(z)
elif image_cond is not None:
if reverse_time: # Flip the image condition
image_cond_input = torch.cat([
torch.flip(image_cond[:4], dims=[1]),
torch.flip(image_cond[4:], dims=[1])
]).to(z)
else:
image_cond_input = image_cond.to(z)
if control_camera_latents is not None:
if (control_camera_start_percent <= current_step_percentage <= control_camera_end_percent) or \
(control_end_percent > 0 and idx == 0 and current_step_percentage >= control_camera_start_percent):
control_camera_input = control_camera_latents.to(device, dtype)
else:
control_camera_input = None
if recammaster is not None:
z = torch.cat([z, recam_latents.to(z)], dim=1)
if mocha_embeds is not None:
if context_window is not None and mocha_embeds.shape[2] != context_frames:
latent_frames = len(context_window)
# [latent_frames, 1 mask frame, mocha_num_refs]
latent_end = latent_frames
mask_end = latent_end + 1
partial_latents = mocha_embeds[:, context_window] # windowed latents
mask_frame = mocha_embeds[:, latent_end:mask_end] # single mask frame
ref_frames = mocha_embeds[:, -mocha_num_refs:] # reference frames
partial_mocha_embeds = torch.cat([partial_latents, mask_frame, ref_frames], dim=1)
z = torch.cat([z, partial_mocha_embeds.to(z)], dim=1)
else:
z = torch.cat([z, mocha_embeds.to(z)], dim=1)
if mtv_input is not None:
if ((mtv_start_percent <= current_step_percentage <= mtv_end_percent) or \
(mtv_end_percent > 0 and idx == 0 and current_step_percentage >= mtv_start_percent)):
mtv_motion_tokens = mtv_motion_tokens.to(z)
mtv_motion_rotary_emb = motion_rotary_emb
use_phantom = False
phantom_ref = None
if phantom_latents is not None:
if (phantom_start_percent <= current_step_percentage <= phantom_end_percent) or \
(phantom_end_percent > 0 and idx == 0 and current_step_percentage >= phantom_start_percent):
phantom_ref = phantom_latents.to(z)
use_phantom = True
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
if controlnet_latents is not None:
if (controlnet_start <= current_step_percentage < controlnet_end):
self.controlnet.to(device)
controlnet_states = self.controlnet(
hidden_states=z.unsqueeze(0).to(device, self.controlnet.dtype),
timestep=timestep,
encoder_hidden_states=positive_embeds[0].unsqueeze(0).to(device, self.controlnet.dtype),
attention_kwargs=None,
controlnet_states=controlnet_latents.to(device, self.controlnet.dtype),
return_dict=False,
)[0]
if isinstance(controlnet_states, (tuple, list)):
controlnet["controlnet_states"] = [x.to(z) for x in controlnet_states]
else:
controlnet["controlnet_states"] = controlnet_states.to(z)
add_cond_input = None
if add_cond is not None:
if (add_cond_start_percent <= current_step_percentage <= add_cond_end_percent) or \
(add_cond_end_percent > 0 and idx == 0 and current_step_percentage >= add_cond_start_percent):
add_cond_input = add_cond
if minimax_latents is not None:
if context_window is not None:
z = torch.cat([z, minimax_latents[:, context_window], minimax_mask_latents[:, context_window]], dim=0)
else:
z = torch.cat([z, minimax_latents, minimax_mask_latents], dim=0)
multitalk_audio_input = None
if audio_emb_slice is not None:
multitalk_audio_input = audio_emb_slice.to(z)
elif not multitalk_sampling and multitalk_audio_embeds is not None:
audio_embedding = multitalk_audio_embeds
audio_embs = []
indices = (torch.arange(4 + 1) - 2) * 1
human_num = len(audio_embedding)
# split audio with window size
audio_end_idx = latent_video_length * 4 + 1 if add_cond is not None else (latent_video_length-1) * 4 + 1
audio_end_idx = audio_end_idx * audio_stride
if context_window is None:
for human_idx in range(human_num):
center_indices = torch.arange(0, audio_end_idx, audio_stride).unsqueeze(1) + indices.unsqueeze(0)
center_indices = torch.clamp(center_indices, min=0, max=audio_embedding[human_idx].shape[0] - 1)
audio_emb = audio_embedding[human_idx][center_indices].unsqueeze(0).to(device)
audio_embs.append(audio_emb)
else:
for human_idx in range(human_num):
audio_start = (context_window[0] * 4) * audio_stride
audio_end = (context_window[-1] * 4 + 1) * audio_stride
#print("audio_start: ", audio_start, "audio_end: ", audio_end)
center_indices = torch.arange(audio_start, audio_end, audio_stride).unsqueeze(1) + indices.unsqueeze(0)
center_indices = torch.clamp(center_indices, min=0, max=audio_embedding[human_idx].shape[0] - 1)
audio_emb = audio_embedding[human_idx][center_indices].unsqueeze(0).to(device)
audio_embs.append(audio_emb)
multitalk_audio_input = torch.concat(audio_embs, dim=0).to(dtype)
elif multitalk_sampling and multitalk_audio_embeds is not None:
multitalk_audio_input = multitalk_audio_embeds
if context_window is not None and uni3c_data is not None and uni3c_data["render_latent"].shape[2] != context_frames:
uni3c_data_input = {"render_latent": uni3c_data["render_latent"][:, :, context_window]}
for k in uni3c_data:
if k != "render_latent":
uni3c_data_input[k] = uni3c_data[k]
else:
uni3c_data_input = uni3c_data
if context_window is not None and sdancer_data is not None and sdancer_data["cond_pos"].shape[1] != context_frames:
sdancer_input = sdancer_data.copy()
sdancer_input["cond_pos"] = sdancer_data["cond_pos"][:, context_window]
sdancer_input["cond_neg"] = sdancer_data["cond_neg"][:, context_window] if sdancer_data.get("cond_neg", None) is not None else None
else:
sdancer_input = sdancer_data
if s2v_pose is not None:
if not ((s2v_pose_start_percent <= current_step_percentage <= s2v_pose_end_percent) or \
(s2v_pose_end_percent > 0 and idx == 0 and current_step_percentage >= s2v_pose_start_percent)):
s2v_pose = None
if humo_audio is not None and ((humo_start_percent <= current_step_percentage <= humo_end_percent) or \
(humo_end_percent > 0 and idx == 0 and current_step_percentage >= humo_start_percent)):
if context_window is None:
humo_audio_input = humo_audio
humo_audio_input_neg = humo_audio_neg if humo_audio_neg is not None else None
else:
humo_audio_input = humo_audio[context_window].to(z)
if humo_audio_neg is not None:
humo_audio_input_neg = humo_audio_neg[context_window].to(z)
else:
humo_audio_input_neg = None
else:
humo_audio_input = humo_audio_input_neg = None
if extra_channel_latents is not None:
if context_window is not None:
extra_channel_latents_input = extra_channel_latents[:, context_window].to(z)
else:
extra_channel_latents_input = extra_channel_latents.to(z)
z = torch.cat([z, extra_channel_latents_input])
if "rcm" in sample_scheduler.__class__.__name__.lower():
c_in = 1 / (torch.cos(timestep) + torch.sin(timestep))
c_noise = (torch.sin(timestep) / (torch.cos(timestep) + torch.sin(timestep))) * 1000
z = z * c_in
timestep = c_noise
if image_cond is not None:
self.noise_front_pad_num = image_cond_input.shape[1] - z.shape[1]
if self.noise_front_pad_num > 0:
pad = torch.zeros((z.shape[0], self.noise_front_pad_num, z.shape[2], z.shape[3]), dtype=z.dtype, device=z.device)
z = torch.cat([pad, z], dim=1)
nonlocal seq_len
seq_len = math.ceil((z.shape[2] * z.shape[3]) / 4 * z.shape[1])
if background_latents is not None or foreground_latents is not None:
z = torch.cat([z, foreground_latents.to(z), background_latents.to(z)], dim=0)
scail_data_in = None
if scail_data is not None:
ref_concat_mask = torch.zeros_like(z[:4])
z = torch.cat([z, ref_concat_mask])
if context_window is not None:
scail_data_in = scail_data.copy()
scail_data_in["pose_latent"] = scail_data["pose_latent"][:, context_window]
else:
scail_data_in = scail_data
if wanmove_embeds is not None and context_window is not None:
image_cond_input = replace_feature(image_cond_input.unsqueeze(0), track_pos[:, context_window].unsqueeze(0), wanmove_embeds.get("strength", 1.0))[0]
dual_control_in = None
if dual_control_embeds is not None:
if context_window is not None:
dual_control_in = dual_control_embeds.copy()
dense_input_latent = dual_control_embeds.get("dense_input_latent", None)
if dense_input_latent is not None:
dual_control_in["dense_input_latent"] = dual_control_embeds["dense_input_latent"][:, :, context_window]
sparse_input_latent = dual_control_embeds.get("sparse_input_latent", None)
if sparse_input_latent is not None:
dual_control_in["sparse_input_latent"] = dual_control_embeds["sparse_input_latent"][:, :, context_window]
else:
dual_control_in = dual_control_input
base_params = {
'x': [z], # latent
'y': [image_cond_input] if image_cond_input is not None else None, # image cond
'clip_fea': clip_fea, # clip features
'seq_len': seq_len, # sequence length
'device': device, # main device
'freqs': freqs, # rope freqs
't': timestep, # current timestep
'is_uncond': False, # is unconditional
'current_step': idx, # current step
'current_step_percentage': current_step_percentage, # current step percentage
'last_step': len(timesteps) - 1 == idx, # is last step
'control_lora_enabled': control_lora_enabled, # control lora toggle for patch embed selection
'enhance_enabled': enhance_enabled, # enhance-a-video toggle
'camera_embed': camera_embed, # recammaster embedding
'unianim_data': unianim_data, # unianimate input
'fun_ref': fun_ref_input if fun_ref_image is not None else None, # Fun model reference latent
'fun_camera': control_camera_input if control_camera_latents is not None else None, # Fun model camera embed
'audio_proj': audio_proj if fantasytalking_embeds is not None else None, # FantasyTalking audio projection
'audio_scale': audio_scale, # FantasyTalking audio scale
"uni3c_data": uni3c_data_input, # Uni3C input
"controlnet": controlnet, # TheDenk's controlnet input
"add_cond": add_cond_input, # additional conditioning input
"nag_params": text_embeds.get("nag_params", {}), # normalized attention guidance
"nag_context": text_embeds.get("nag_prompt_embeds", None), # normalized attention guidance context
"multitalk_audio": multitalk_audio_input, # Multi/InfiniteTalk audio input
"ref_target_masks": ref_target_masks if multitalk_audio_embeds is not None else None, # Multi/InfiniteTalk reference target masks
"inner_t": [shot_len] if shot_len else None, # inner timestep for EchoShot
"standin_input": standin_input, # Stand-in reference input
"fantasy_portrait_input": fantasy_portrait_input, # Fantasy portrait input
"phantom_ref": phantom_ref, # Phantom reference input
"reverse_time": reverse_time, # Reverse RoPE toggle
"ntk_alphas": ntk_alphas, # RoPE freq scaling values
"mtv_motion_tokens": mtv_motion_tokens if mtv_input is not None else None, # MTV-Crafter motion tokens
"mtv_motion_rotary_emb": mtv_motion_rotary_emb if mtv_input is not None else None, # MTV-Crafter RoPE
"mtv_strength": mtv_strength[idx] if mtv_input is not None else 1.0, # MTV-Crafter scaling
"mtv_freqs": mtv_freqs if mtv_input is not None else None, # MTV-Crafter extra RoPE freqs
"s2v_audio_input": s2v_audio_input, # official speech-to-video audio input
"s2v_ref_latent": s2v_ref_latent, # speech-to-video reference latent
"s2v_ref_motion": s2v_ref_motion, # speech-to-video reference motion latent
"s2v_audio_scale": s2v_audio_scale if s2v_audio_input is not None else 1.0, # speech-to-video audio scale
"s2v_pose": s2v_pose if s2v_pose is not None else None, # speech-to-video pose control
"s2v_motion_frames": s2v_motion_frames, # speech-to-video motion frames,
"humo_audio": humo_audio, # humo audio input
"humo_audio_scale": humo_audio_scale if humo_audio is not None else 1,
"wananim_pose_latents": wananim_pose_latents.to(device) if wananim_pose_latents is not None else None, # WanAnimate pose latents
"wananim_face_pixel_values": wananim_face_pixels.to(device, torch.float32) if wananim_face_pixels is not None else None, # WanAnimate face images
"wananim_pose_strength": wananim_pose_strength,
"wananim_face_strength": wananim_face_strength,
"lynx_embeds": lynx_embeds, # Lynx face and reference embeddings
"x_ovi": [latent_model_input_ovi.to(z)] if latent_model_input_ovi is not None else None, # Audio latent model input for Ovi
"seq_len_ovi": seq_len_ovi, # Audio latent model sequence length for Ovi
"ovi_negative_text_embeds": ovi_negative_text_embeds, # Audio latent model negative text embeds for Ovi
"flashvsr_LQ_latent": flashvsr_LQ_latent, # FlashVSR LQ latent for upsampling
"flashvsr_strength": flashvsr_strength, # FlashVSR strength
"longcat_num_cond_latents": longcat_num_cond_latents,
"longcat_num_ref_latents": longcat_num_ref_latents,
"longcat_avatar_options": longcat_avatar_options, # LongCat avatar attention options
"sdancer_input": sdancer_input, # SteadyDancer input
"one_to_all_input": one_to_all_data, # One-to-All input
"one_to_all_controlnet_strength": one_to_all_data["controlnet_strength"] if one_to_all_data is not None else 0.0,
"scail_input": scail_data_in, # SCAIL input
"dual_control_input": dual_control_in, # LongVie2 dual control input
"transformer_options": transformer_options,
"rope_negative_offset": image_embeds.get("rope_negative_offset_frames", 0), # StoryMem rope negative offset
"num_memory_frames": story_mem_latents.shape[1] if story_mem_latents is not None else 0, # StoryMem memory frames
}
batch_size = 1
if not math.isclose(cfg_scale, 1.0):
if negative_embeds is None:
raise ValueError("Negative embeddings must be provided for CFG scale > 1.0")
if len(positive_embeds) > 1:
negative_embeds = negative_embeds * len(positive_embeds)
try:
if not batched_cfg:
#conditional (positive) pass
if pos_latent is not None: # for humo
base_params['x'] = [torch.cat([z[:, :-humo_reference_count], pos_latent], dim=1)]
base_params["add_text_emb"] = qwenvl_embeds_pos.to(device) if qwenvl_embeds_pos is not None else None # QwenVL embeddings for Bindweave
noise_pred_cond, noise_pred_ovi, cache_state_cond = transformer(
context=positive_embeds,
pred_id=cache_state[0] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond,
**base_params
)
noise_pred_cond = noise_pred_cond[0]
noise_pred_ovi = noise_pred_ovi[0] if noise_pred_ovi is not None else None
if math.isclose(cfg_scale, 1.0):
if use_fresca:
noise_pred_cond = fourier_filter(noise_pred_cond, fresca_scale_low, fresca_scale_high, fresca_freq_cutoff)
if fantasy_portrait_input is not None and not math.isclose(portrait_cfg[idx], 1.0):
base_params["fantasy_portrait_input"] = None
noise_pred_no_portrait, noise_pred_ovi, cache_state_uncond = transformer(context=positive_embeds, pred_id=cache_state[0] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond, **base_params)
return noise_pred_no_portrait[0] + portrait_cfg[idx] * (noise_pred_cond - noise_pred_no_portrait[0]), noise_pred_ovi, [cache_state_cond, cache_state_uncond]
elif multitalk_audio_input is not None and not math.isclose(audio_cfg_scale[idx], 1.0):
base_params['multitalk_audio'] = torch.zeros_like(multitalk_audio_input)[-1:]
noise_pred_uncond_audio, _, cache_state_uncond = transformer(
context=positive_embeds, pred_id=cache_state[0] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond, **base_params)
return noise_pred_uncond_audio[0] + audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_uncond_audio[0]), noise_pred_ovi, [cache_state_cond, cache_state_uncond]
else:
return noise_pred_cond, noise_pred_ovi, [cache_state_cond]
#unconditional (negative) pass
base_params['is_uncond'] = True
base_params['clip_fea'] = clip_fea_neg if clip_fea_neg is not None else clip_fea
base_params["add_text_emb"] = qwenvl_embeds_neg.to(device) if qwenvl_embeds_neg is not None else None # QwenVL embeddings for Bindweave
base_params['y'] = [image_cond_neg.to(z)] if image_cond_neg is not None else base_params['y']
if wananim_face_pixels is not None:
base_params['wananim_face_pixel_values'] = torch.zeros_like(wananim_face_pixels).to(device, torch.float32) - 1
if humo_audio_input_neg is not None:
base_params['humo_audio'] = humo_audio_input_neg
if neg_latent is not None:
base_params['x'] = [torch.cat([z[:, :-humo_reference_count], neg_latent], dim=1)]
noise_pred_uncond_text, noise_pred_ovi_uncond, cache_state_uncond = transformer(
context=negative_embeds if humo_audio_input_neg is None else positive_embeds, #ti #t
pred_id=cache_state[1] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond_neg,
**base_params)
noise_pred_uncond_text = noise_pred_uncond_text[0]
noise_pred_ovi_uncond = noise_pred_ovi_uncond[0] if noise_pred_ovi_uncond is not None else None
# HuMo
if not math.isclose(humo_audio_cfg_scale[idx], 1.0):
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
if humo_image_cond is not None and humo_audio_input_neg is not None:
if t > 980 and humo_image_cond_neg_input is not None: # use image cond for first timesteps
base_params['y'] = [humo_image_cond_neg_input]
noise_pred_humo_audio_uncond, _, cache_state_humo = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (noise_pred_uncond_text + humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio_uncond[0])
+ (cfg_scale - 2.0) * (noise_pred_humo_audio_uncond[0] - noise_pred_uncond_text))
return noise_pred, None, [cache_state_cond, cache_state_uncond, cache_state_humo]
elif humo_audio_input is not None:
if cache_state is not None and len(cache_state) != 4:
cache_state.append(None)
# audio
noise_pred_humo_null, _, cache_state_humo = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
# negative
if humo_audio_input is not None:
base_params['humo_audio'] = humo_audio_input
noise_pred_humo_audio, _, cache_state_humo2 = transformer(
context=positive_embeds, pred_id=cache_state[3] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (humo_audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_humo_audio[0])
+ cfg_scale * (noise_pred_humo_audio[0] - noise_pred_uncond_text)
+ cfg_scale * (noise_pred_uncond_text - noise_pred_humo_null[0])
+ noise_pred_humo_null[0])
return noise_pred, None, [cache_state_cond, cache_state_uncond, cache_state_humo, cache_state_humo2]
#phantom
if use_phantom and not math.isclose(phantom_cfg_scale[idx], 1.0):
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
noise_pred_phantom, _, cache_state_phantom = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (noise_pred_uncond_text + phantom_cfg_scale[idx] * (noise_pred_phantom[0] - noise_pred_uncond_text)
+ cfg_scale * (noise_pred_cond - noise_pred_phantom[0]))
return noise_pred, None,[cache_state_cond, cache_state_uncond, cache_state_phantom]
# audio cfg (fantasytalking and multitalk)
if (fantasytalking_embeds is not None or multitalk_audio_input is not None):
if not math.isclose(audio_cfg_scale[idx], 1.0):
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
base_params['audio_proj'] = None
base_params['multitalk_audio'] = torch.zeros_like(multitalk_audio_input)[-1:] if multitalk_audio_input is not None else None
base_params['is_uncond'] = False
noise_pred_uncond_audio, _, cache_state_audio = transformer(
context=negative_embeds,
pred_id=cache_state[2] if cache_state else None,
vace_data=vace_data,
**base_params)
noise_pred_uncond_audio = noise_pred_uncond_audio[0]
noise_pred = noise_pred_uncond_audio + cfg_scale * (
(noise_pred_cond - noise_pred_uncond_text)
+ audio_cfg_scale[idx] * (noise_pred_uncond_text - noise_pred_uncond_audio))
return noise_pred, None,[cache_state_cond, cache_state_uncond, cache_state_audio]
# lynx
if lynx_embeds is not None and not math.isclose(lynx_cfg_scale[idx], 1.0):
base_params['is_uncond'] = False
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
noise_pred_lynx, _, cache_state_lynx = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (noise_pred_uncond_text + lynx_cfg_scale[idx] * (noise_pred_lynx[0] - noise_pred_uncond_text)
+ cfg_scale * (noise_pred_cond - noise_pred_lynx[0]))
return noise_pred, None, [cache_state_cond, cache_state_uncond, cache_state_lynx]
# one-to-all
if one_to_all_data is not None and not math.isclose(one_to_all_pose_cfg_scale[idx], 1.0):
tqdm.write("One-to-All pose CFG pass...")
base_params['is_uncond'] = False
base_params['one_to_all_controlnet_strength'] = 0.0
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
noise_pred_pose_uncond, _, cache_state_ref = transformer(
context=negative_embeds, pred_id=cache_state[2] if cache_state else None, vace_data=None,
**base_params)
noise_pred = (noise_pred_uncond_text + one_to_all_pose_cfg_scale[idx] * (noise_pred_pose_uncond[0] - noise_pred_uncond_text)
+ cfg_scale * (noise_pred_cond - noise_pred_pose_uncond[0]))
return noise_pred, None, [cache_state_cond, cache_state_uncond, cache_state_ref]
#batched
else:
base_params['z'] = [z] * 2
base_params['y'] = [image_cond_input] * 2 if image_cond_input is not None else None
base_params['clip_fea'] = torch.cat([clip_fea, clip_fea], dim=0)
cache_state_uncond = None
[noise_pred_cond, noise_pred_uncond_text], _, cache_state_cond = transformer(
context=positive_embeds + negative_embeds, is_uncond=False,
pred_id=cache_state[0] if cache_state else None,
**base_params
)
except Exception as e:
log.error(f"Error during model prediction: {e}")
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
raise e
#https://github.com/WeichenFan/CFG-Zero-star/
alpha = 1.0
if use_cfg_zero_star:
alpha = optimized_scale(
noise_pred_cond.view(batch_size, -1),
noise_pred_uncond_text.view(batch_size, -1)
).view(batch_size, 1, 1, 1)
noise_pred_uncond_text = noise_pred_uncond_text * alpha
if use_tangential:
noise_pred_uncond_text = tangential_projection(noise_pred_cond, noise_pred_uncond_text)
# RAAG (RATIO-aware Adaptive Guidance)
if raag_alpha > 0.0:
cfg_scale = get_raag_guidance(noise_pred_cond, noise_pred_uncond_text, cfg_scale, raag_alpha)
log.info(f"RAAG modified cfg: {cfg_scale}")
#https://github.com/WikiChao/FreSca
if use_fresca:
filtered_cond = fourier_filter(noise_pred_cond - noise_pred_uncond_text, fresca_scale_low, fresca_scale_high, fresca_freq_cutoff)
noise_pred = noise_pred_uncond_text + cfg_scale * filtered_cond * alpha
else:
noise_pred = noise_pred_uncond_text + cfg_scale * (noise_pred_cond - noise_pred_uncond_text)
del noise_pred_uncond_text, noise_pred_cond
if latent_model_input_ovi is not None:
if ovi_audio_cfg is None:
audio_cfg_scale = cfg_scale - 1.0 if cfg_scale > 4.0 else cfg_scale
else:
audio_cfg_scale = ovi_audio_cfg[idx]
noise_pred_ovi = noise_pred_ovi_uncond + audio_cfg_scale * (noise_pred_ovi - noise_pred_ovi_uncond)
return noise_pred, noise_pred_ovi, [cache_state_cond, cache_state_uncond]
if args.preview_method in [LatentPreviewMethod.Auto, LatentPreviewMethod.Latent2RGB]: #default for latent2rgb
from latent_preview import prepare_callback
else:
from .latent_preview import prepare_callback #custom for tiny VAE previews
callback = prepare_callback(patcher, len(timesteps))
if not multitalk_sampling and not framepack and not wananimate_loop:
log.info("-" * 10 + " Sampling start " + "-" * 10)
log.info(f"{(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*vae_upscale_factor}x{latent.shape[2]*vae_upscale_factor} (Input sequence length: {seq_len}) with {steps-ttm_start_step} steps")
# Differential diffusion prep
masks = None
if not multitalk_sampling and samples is not None and noise_mask is not None:
thresholds = torch.arange(len(timesteps), dtype=original_image.dtype) / len(timesteps)
thresholds = thresholds.reshape(-1, 1, 1, 1, 1).to(device)
masks = (1-noise_mask.repeat(len(timesteps), 1, 1, 1, 1).to(device)) > thresholds
latent_shift_loop = False
if loop_args is not None:
latent_shift_loop = is_looped = True
latent_skip = loop_args["shift_skip"]
latent_shift_start_percent = loop_args["start_percent"]
latent_shift_end_percent = loop_args["end_percent"]
shift_idx = 0
#clear memory before sampling
mm.soft_empty_cache()
gc.collect()
try:
torch.cuda.reset_peak_memory_stats(device)
except:
pass
# Main sampling loop with FreeInit iterations
iterations = freeinit_args.get("freeinit_num_iters", 3) if freeinit_args is not None else 1
current_latent = latent
initial_noise_saved = None
for iter_idx in range(iterations):
# FreeInit noise reinitialization (after first iteration)
if freeinit_args is not None and iter_idx > 0:
# restart scheduler for each iteration
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, steps, start_step, end_step, shift, device, transformer.dim, denoise_strength, sigmas=sigmas)
# Re-apply start_step and end_step logic to timesteps and sigmas
if end_step != -1:
timesteps = timesteps[:end_step]
sample_scheduler.sigmas = sample_scheduler.sigmas[:end_step+1]
if start_step > 0:
timesteps = timesteps[start_step:]
sample_scheduler.sigmas = sample_scheduler.sigmas[start_step:]
if hasattr(sample_scheduler, 'timesteps'):
sample_scheduler.timesteps = timesteps
# Diffuse current latent to t=999
diffuse_timesteps = torch.full((noise.shape[0],), 999, device=device, dtype=torch.long)
z_T = add_noise(
current_latent.to(device),
initial_noise_saved.to(device),
diffuse_timesteps
)
# Generate new random noise
z_rand = torch.randn(z_T.shape, dtype=torch.float32, generator=seed_g, device=torch.device("cpu"))
# Apply frequency mixing
current_latent = (freq_mix_3d(z_T.to(torch.float32), z_rand.to(device), LPF=freq_filter)).to(dtype)
# Store initial noise for first iteration
if freeinit_args is not None and iter_idx == 0:
initial_noise_saved = current_latent.detach().clone()
if input_samples is not None:
current_latent = input_samples.to(device)
continue
# Reset per-iteration states
self.cache_state = [None, None]
self.cache_state_source = [None, None]
self.cache_states_context = []
if context_options is not None:
self.window_tracker = WindowTracker(verbose=context_options["verbose"])
# Set latent for denoising
latent = current_latent
if is_pusa and clean_latent_indices:
pusa_noisy_steps = image_embeds.get("pusa_noisy_steps", -1)
if pusa_noisy_steps == -1:
pusa_noisy_steps = len(timesteps)
try:
pbar = ProgressBar(len(timesteps) - ttm_start_step)
#region main loop start
for idx, t in enumerate(tqdm(timesteps[ttm_start_step:], disable=multitalk_sampling or wananimate_loop)):
if bidirectional_sampling:
latent_flipped = torch.flip(latent, dims=[1])
latent_model_input_flipped = latent_flipped.to(device)
self.noise_front_pad_num = 0
#InfiniteTalk first frame handling
if (extra_latents is not None
and not multitalk_sampling
and transformer.multitalk_model_type=="InfiniteTalk"):
for entry in extra_latents:
add_index = entry["index"]
num_extra_frames = entry["samples"].shape[2]
latent[:, add_index:add_index+num_extra_frames] = entry["samples"].to(latent)
latent_model_input = latent.to(device)
latent_model_input_ovi = latent_ovi.to(device) if latent_ovi is not None else None
current_step_percentage = idx / len(timesteps)
timestep = torch.tensor([t]).to(device)
if is_pusa or ((is_5b or transformer.is_longcat) and clean_latent_indices):
orig_timestep = timestep
timestep = timestep.unsqueeze(1).repeat(1, latent_video_length)
if extra_latents is not None:
if clean_latent_indices and noise_multipliers is not None:
if is_pusa:
scheduler_step_args["cond_frame_latent_indices"] = clean_latent_indices
scheduler_step_args["noise_multipliers"] = noise_multipliers
for latent_idx in clean_latent_indices:
timestep[:, latent_idx] = timestep[:, latent_idx] * noise_multipliers[latent_idx]
# add noise for conditioning frames if multiplier > 0
if idx < pusa_noisy_steps and noise_multipliers[latent_idx] > 0:
latent_size = (1, latent.shape[0], latent.shape[1], latent.shape[2], latent.shape[3])
noise_for_cond = torch.randn(latent_size, generator=seed_g, device=torch.device("cpu"))
timestep_cond = torch.ones_like(timestep) * timestep.max()
if is_pusa:
latent[:, latent_idx:latent_idx+1] = sample_scheduler.add_noise_for_conditioning_frames(
latent[:, latent_idx:latent_idx+1].to(device),
noise_for_cond[:, :, latent_idx:latent_idx+1].to(device),
timestep_cond[:, latent_idx:latent_idx+1].to(device),
noise_multiplier=noise_multipliers[latent_idx])
else:
timestep[:, clean_latent_indices] = 0
#print("timestep: ", timestep)
### latent shift
if latent_shift_loop:
if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent:
latent_model_input = torch.cat([latent_model_input[:, shift_idx:]] + [latent_model_input[:, :shift_idx]], dim=1)
#enhance-a-video
enhance_enabled = False
if feta_args is not None and feta_start_percent <= current_step_percentage <= feta_end_percent:
enhance_enabled = True
#region context windowing
if context_options is not None:
counter = torch.zeros_like(latent_model_input, device=device)
noise_pred = torch.zeros_like(latent_model_input, device=device)
context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap))
fraction_per_context = 1.0 / len(context_queue)
context_pbar = ProgressBar(steps)
step_start_progress = idx
# Validate all context windows before processing
max_idx = latent_model_input.shape[1] if latent_model_input.ndim > 1 else 0
for window_indices in context_queue:
if not all(0 <= idx < max_idx for idx in window_indices):
raise ValueError(f"Invalid context window indices {window_indices} for latent_model_input with shape {latent_model_input.shape}")
for i, c in enumerate(context_queue):
window_id = self.window_tracker.get_window_id(c)
if cache_args is not None:
current_teacache = self.window_tracker.get_teacache(window_id, self.cache_state)
else:
current_teacache = None
prompt_index = min(int(max(c) / section_size), num_prompts - 1)
if context_options["verbose"]:
log.info(f"Prompt index: {prompt_index}")
# Use the appropriate prompt for this section
if len(text_embeds["prompt_embeds"]) > 1:
positive = [text_embeds["prompt_embeds"][prompt_index]]
else:
positive = text_embeds["prompt_embeds"]
partial_img_emb = partial_control_latents = None
if image_cond is not None:
partial_img_emb = image_cond[:, c].to(device)
if c[0] != 0 and context_reference_latent is not None:
if context_reference_latent.shape[0] == 1: #only single extra init latent
new_init_image = context_reference_latent[0, :, 0].to(device)
# Concatenate the first 4 channels of partial_img_emb with new_init_image to match the required shape
partial_img_emb[:, 0] = torch.cat([image_cond[:4, 0].to(device), new_init_image], dim=0)
elif context_reference_latent.shape[0] > 1:
num_extra_inits = context_reference_latent.shape[0]
section_size = (latent_video_length / num_extra_inits)
extra_init_index = min(int(max(c) / section_size), num_extra_inits - 1)
if context_options["verbose"]:
log.info(f"extra init image index: {extra_init_index}")
new_init_image = context_reference_latent[extra_init_index, :, 0].to(device)
partial_img_emb[:, 0] = torch.cat([image_cond[:4, 0].to(device), new_init_image], dim=0)
else:
new_init_image = image_cond[:, 0].to(device)
partial_img_emb[:, 0] = new_init_image
if control_latents is not None:
partial_control_latents = control_latents[:, c]
partial_control_camera_latents = None
if control_camera_latents is not None:
partial_control_camera_latents = control_camera_latents[:, :, c]
partial_vace_context = None
if vace_data is not None:
window_vace_data = []
for vace_entry in vace_data:
partial_context = vace_entry["context"][0][:, c]
if has_ref:
if c[0] != 0 and context_reference_latent is not None:
if context_reference_latent.shape[0] == 1: #only single extra init latent
partial_context[16:32, :1] = context_reference_latent[0, :, :1].to(device)
elif context_reference_latent.shape[0] > 1:
num_extra_inits = context_reference_latent.shape[0]
section_size = (latent_video_length / num_extra_inits)
extra_init_index = min(int(max(c) / section_size), num_extra_inits - 1)
if context_options["verbose"]:
log.info(f"extra init image index: {extra_init_index}")
partial_context[16:32, :1] = context_reference_latent[extra_init_index, :, :1].to(device)
else:
partial_context[:, 0] = vace_entry["context"][0][:, 0]
window_vace_data.append({
"context": [partial_context],
"scale": vace_entry["scale"],
"start": vace_entry["start"],
"end": vace_entry["end"],
"seq_len": vace_entry["seq_len"]
})
partial_vace_context = window_vace_data
partial_audio_proj = None
if fantasytalking_embeds is not None:
partial_audio_proj = audio_proj[:, c]
partial_fantasy_portrait_input = None
if fantasy_portrait_input is not None:
partial_fantasy_portrait_input = fantasy_portrait_input.copy()
partial_fantasy_portrait_input["adapter_proj"] = fantasy_portrait_input["adapter_proj"][:, c]
partial_latent_model_input = latent_model_input[:, c]
if latents_to_insert is not None and c[0] != 0:
partial_latent_model_input[:, :1] = latents_to_insert
partial_unianim_data = None
if unianim_data is not None:
partial_dwpose = dwpose_data[:, :, c]
partial_unianim_data = {
"dwpose": partial_dwpose,
"random_ref": unianim_data["random_ref"],
"strength": unianimate_poses["strength"],
"start_percent": unianimate_poses["start_percent"],
"end_percent": unianimate_poses["end_percent"]
}
partial_mtv_motion_tokens = None
if mtv_input is not None:
start_token_index = c[0] * 24
end_token_index = (c[-1] + 1) * 24
partial_mtv_motion_tokens = mtv_motion_tokens[:, start_token_index:end_token_index, :]
if context_options["verbose"]:
log.info(f"context window: {c}")
log.info(f"motion_token_indices: {start_token_index}-{end_token_index}")
partial_s2v_audio_input = None
if s2v_audio_input is not None:
audio_start = c[0] * 4
audio_end = c[-1] * 4 + 1
center_indices = torch.arange(audio_start, audio_end, 1)
center_indices = torch.clamp(center_indices, min=0, max=s2v_audio_input.shape[-1] - 1)
partial_s2v_audio_input = s2v_audio_input[..., center_indices]
partial_s2v_pose = None
if s2v_pose is not None:
partial_s2v_pose = s2v_pose[:, :, c].to(device, dtype)
partial_add_cond = None
if add_cond is not None:
partial_add_cond = add_cond[:, :, c].to(device, dtype)
partial_wananim_face_pixels = partial_wananim_pose_latents = None
if wananim_face_pixels is not None and partial_wananim_face_pixels is None:
start = c[0] * 4
end = c[-1] * 4
center_indices = torch.arange(start, end, 1)
center_indices = torch.clamp(center_indices, min=0, max=wananim_face_pixels.shape[2] - 1)
partial_wananim_face_pixels = wananim_face_pixels[:, :, center_indices].to(device, dtype)
if wananim_pose_latents is not None:
start = c[0]
end = c[-1]
center_indices = torch.arange(start, end, 1)
center_indices = torch.clamp(center_indices, min=0, max=wananim_pose_latents.shape[2] - 1)
partial_wananim_pose_latents = wananim_pose_latents[:, :, center_indices][:, :, :context_frames-1].to(device, dtype)
partial_flashvsr_LQ_latent = None
if LQ_images is not None:
start = c[0] * 4
end = c[-1] * 4 + 1 + 4
center_indices = torch.arange(start, end, 1)
center_indices = torch.clamp(center_indices, min=0, max=LQ_images.shape[2] - 1)
partial_flashvsr_LQ_images = LQ_images[:, :, center_indices].to(device)
partial_flashvsr_LQ_latent = transformer.LQ_proj_in(partial_flashvsr_LQ_images)
if len(timestep.shape) != 1:
partial_timestep = timestep[:, c]
partial_timestep[:, :1] = 0
else:
partial_timestep = timestep
orig_model_input_frames = partial_latent_model_input.shape[1]
noise_pred_context, _, new_teacache = predict_with_cfg(
partial_latent_model_input,
cfg[idx], positive,
text_embeds["negative_prompt_embeds"],
partial_timestep, idx, partial_img_emb, clip_fea, partial_control_latents, partial_vace_context, partial_unianim_data,partial_audio_proj,
partial_control_camera_latents, partial_add_cond, current_teacache, context_window=c, fantasy_portrait_input=partial_fantasy_portrait_input,
mtv_motion_tokens=partial_mtv_motion_tokens, s2v_audio_input=partial_s2v_audio_input, s2v_motion_frames=[1, 0], s2v_pose=partial_s2v_pose,
humo_image_cond=humo_image_cond, humo_image_cond_neg=humo_image_cond_neg, humo_audio=humo_audio, humo_audio_neg=humo_audio_neg,
wananim_face_pixels=partial_wananim_face_pixels, wananim_pose_latents=partial_wananim_pose_latents, multitalk_audio_embeds=multitalk_audio_embeds,
uni3c_data=uni3c_data, flashvsr_LQ_latent=partial_flashvsr_LQ_latent)
if cache_args is not None:
self.window_tracker.cache_states[window_id] = new_teacache
if mocha_embeds is not None:
noise_pred_context = noise_pred_context[:, :orig_model_input_frames]
window_mask = create_window_mask(noise_pred_context, c, noise.shape[1], context_overlap, looped=is_looped, window_type=context_options["fuse_method"])
noise_pred[:, c] += noise_pred_context * window_mask
counter[:, c] += window_mask
context_pbar.update_absolute(step_start_progress + (i + 1) * fraction_per_context, len(timesteps))
noise_pred /= counter
#region multitalk
elif multitalk_sampling:
return multitalk_loop(**locals())
# region framepack loop
elif framepack:
framepack_out = []
ref_motion_image = None
#infer_frames = image_embeds["num_frames"]
infer_frames = s2v_audio_embeds.get("frame_window_size", 80)
motion_frames = infer_frames - 7 #73 default
lat_motion_frames = (motion_frames + 3) // 4
lat_target_frames = (infer_frames + 3 + motion_frames) // 4 - lat_motion_frames
step_iteration_count = 0
total_frames = s2v_audio_input.shape[-1]
s2v_motion_frames = [motion_frames, lat_motion_frames]
noise = torch.randn( #C, T, H, W
48 if is_5b else 16,
lat_target_frames,
target_shape[2],
target_shape[3],
dtype=torch.float32,
generator=seed_g,
device=torch.device("cpu"))
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
if ref_motion_image is None:
ref_motion_image = torch.zeros(
[1, 3, motion_frames, latent.shape[2]*vae_upscale_factor, latent.shape[3]*vae_upscale_factor],
dtype=vae.dtype,
device=device)
videos_last_frames = ref_motion_image
if s2v_pose is not None:
pose_cond_list = []
for r in range(s2v_num_repeat):
pose_start = r * (infer_frames // 4)
pose_end = pose_start + (infer_frames // 4)
cond_lat = s2v_pose[:, :, pose_start:pose_end]
pad_len = (infer_frames // 4) - cond_lat.shape[2]
if pad_len > 0:
pad = -torch.ones(cond_lat.shape[0], cond_lat.shape[1], pad_len, cond_lat.shape[3], cond_lat.shape[4], device=cond_lat.device, dtype=cond_lat.dtype)
cond_lat = torch.cat([cond_lat, pad], dim=2)
pose_cond_list.append(cond_lat.cpu())
log.info(f"Sampling {total_frames} frames in {s2v_num_repeat} windows, at {latent.shape[3]*vae_upscale_factor}x{latent.shape[2]*vae_upscale_factor} with {steps} steps")
# sample
for r in range(s2v_num_repeat):
mm.soft_empty_cache()
gc.collect()
if ref_motion_image is not None:
vae.to(device)
ref_motion = vae.encode(ref_motion_image.to(vae.dtype), device=device, pbar=False).to(dtype)[0]
vae.to(offload_device)
left_idx = r * infer_frames
right_idx = r * infer_frames + infer_frames
s2v_audio_input_slice = s2v_audio_input[..., left_idx:right_idx]
if s2v_audio_input_slice.shape[-1] < (right_idx - left_idx):
pad_len = (right_idx - left_idx) - s2v_audio_input_slice.shape[-1]
pad_shape = list(s2v_audio_input_slice.shape)
pad_shape[-1] = pad_len
pad = torch.zeros(pad_shape, device=s2v_audio_input_slice.device, dtype=s2v_audio_input_slice.dtype)
log.info(f"Padding s2v_audio_input_slice from {s2v_audio_input_slice.shape[-1]} to {right_idx - left_idx}")
s2v_audio_input_slice = torch.cat([s2v_audio_input_slice, pad], dim=-1)
if ref_motion_image is not None:
input_motion_latents = ref_motion.clone().unsqueeze(0)
else:
input_motion_latents = None
s2v_pose_slice = None
if s2v_pose is not None:
s2v_pose_slice = pose_cond_list[r].to(device)
if isinstance(scheduler, dict):
sample_scheduler = copy.deepcopy(scheduler["sample_scheduler"])
timesteps = scheduler["timesteps"]
else:
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, total_steps, start_step, end_step, shift, device, transformer.dim, denoise_strength, sigmas=sigmas)
latent = noise.to(device)
for i, t in enumerate(tqdm(timesteps, desc=f"Sampling audio indices {left_idx}-{right_idx}", position=0)):
latent_model_input = latent.to(device)
timestep = torch.tensor([t]).to(device)
noise_pred, _, self.cache_state = predict_with_cfg(
latent_model_input,
cfg[idx],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, fantasy_portrait_input=fantasy_portrait_input, mtv_motion_tokens=mtv_motion_tokens,
s2v_audio_input=s2v_audio_input_slice, s2v_ref_motion=input_motion_latents, s2v_motion_frames=s2v_motion_frames, s2v_pose=s2v_pose_slice)
latent = sample_scheduler.step(
noise_pred.unsqueeze(0), timestep, latent.unsqueeze(0),
**scheduler_step_args)[0].squeeze(0)
if callback is not None:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3)
callback(step_iteration_count, callback_latent, None, s2v_num_repeat*(len(timesteps)))
del callback_latent
step_iteration_count += 1
del latent_model_input, noise_pred
vae.to(device)
decode_latents = torch.cat([ref_motion.unsqueeze(0), latent.unsqueeze(0)], dim=2)
image = vae.decode(decode_latents.to(device, vae.dtype), device=device, pbar=False)[0]
del decode_latents
image = image.unsqueeze(0)[:, :, -infer_frames:]
if r == 0:
image = image[:, :, 3:]
framepack_out.append(image.cpu())
overlap_frames_num = min(motion_frames, image.shape[2])
videos_last_frames = torch.cat([
videos_last_frames[:, :, overlap_frames_num:],
image[:, :, -overlap_frames_num:]], dim=2).to(device, vae.dtype)
ref_motion_image = videos_last_frames
vae.to(offload_device)
mm.soft_empty_cache()
gen_video_samples = torch.cat(framepack_out, dim=2).squeeze(0).permute(1, 2, 3, 0)
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return {"video": gen_video_samples},
# region wananimate loop
elif wananimate_loop:
# calculate frame counts
total_frames = num_frames
refert_num = 1
real_clip_len = frame_window_size - refert_num
last_clip_num = (total_frames - refert_num) % real_clip_len
extra = 0 if last_clip_num == 0 else real_clip_len - last_clip_num
target_len = total_frames + extra
estimated_iterations = target_len // real_clip_len
target_latent_len = (target_len - 1) // 4 + estimated_iterations
latent_window_size = (frame_window_size - 1) // 4 + 1
from .utils import tensor_pingpong_pad
ref_latent = image_embeds.get("ref_latent", None)
ref_images = image_embeds.get("ref_image", None)
bg_images = image_embeds.get("bg_images", None)
pose_images = image_embeds.get("pose_images", None)
current_ref_images = face_images = face_images_in = None
if wananim_face_pixels is not None:
face_images = tensor_pingpong_pad(wananim_face_pixels, target_len)
log.info(f"WanAnimate: Face input {wananim_face_pixels.shape} padded to shape {face_images.shape}")
if wananim_ref_masks is not None:
ref_masks_in = tensor_pingpong_pad(wananim_ref_masks, target_latent_len)
log.info(f"WanAnimate: Ref masks {wananim_ref_masks.shape} padded to shape {ref_masks_in.shape}")
if bg_images is not None:
bg_images_in = tensor_pingpong_pad(bg_images, target_len)
log.info(f"WanAnimate: BG images {bg_images.shape} padded to shape {bg_images.shape}")
if pose_images is not None:
pose_images_in = tensor_pingpong_pad(pose_images, target_len)
log.info(f"WanAnimate: Pose images {pose_images.shape} padded to shape {pose_images_in.shape}")
# init variables
offloaded = False
colormatch = image_embeds.get("colormatch", "disabled")
output_path = image_embeds.get("output_path", "")
offload = image_embeds.get("force_offload", False)
lat_h, lat_w = noise.shape[2], noise.shape[3]
start = start_latent = img_counter = step_iteration_count = iteration_count = 0
end = frame_window_size
end_latent = latent_window_size
callback = prepare_callback(patcher, estimated_iterations)
log.info(f"Sampling {total_frames} frames in {estimated_iterations} windows, at {latent.shape[3]*vae_upscale_factor}x{latent.shape[2]*vae_upscale_factor} with {steps} steps")
# outer WanAnimate loop
gen_video_list = []
while True:
if start + refert_num >= total_frames:
break
mm.soft_empty_cache()
mask_reft_len = 0 if start == 0 else refert_num
self.cache_state = [None, None]
noise = torch.randn(16, latent_window_size + 1, lat_h, lat_w, dtype=torch.float32, device=torch.device("cpu"), generator=seed_g).to(device)
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
if current_ref_images is not None or bg_images is not None or ref_latent is not None:
if offload:
offload_transformer(transformer, remove_lora=False)
offloaded = True
vae.to(device)
if wananim_ref_masks is not None:
msk = ref_masks_in[:, start_latent:end_latent].to(device, dtype)
else:
msk = torch.zeros(4, latent_window_size, lat_h, lat_w, device=device, dtype=dtype)
if bg_images is not None:
bg_image_slice = bg_images_in[:, start:end].to(device)
else:
bg_image_slice = torch.zeros(3, frame_window_size-refert_num, lat_h * 8, lat_w * 8, device=device, dtype=vae.dtype)
if mask_reft_len == 0:
temporal_ref_latents = vae.encode([bg_image_slice], device,tiled=tiled_vae)[0]
else:
concatenated = torch.cat([current_ref_images.to(device, dtype=vae.dtype), bg_image_slice[:, mask_reft_len:]], dim=1)
temporal_ref_latents = vae.encode([concatenated.to(device, vae.dtype)], device,tiled=tiled_vae, pbar=False)[0]
msk[:, :mask_reft_len] = 1
if msk.shape[1] != temporal_ref_latents.shape[1]:
if temporal_ref_latents.shape[1] < msk.shape[1]:
pad_len = msk.shape[1] - temporal_ref_latents.shape[1]
pad_tensor = temporal_ref_latents[:, -1:].repeat(1, pad_len, 1, 1)
temporal_ref_latents = torch.cat([temporal_ref_latents, pad_tensor], dim=1)
else:
temporal_ref_latents = temporal_ref_latents[:, :msk.shape[1]]
if ref_latent is not None:
temporal_ref_latents = torch.cat([msk, temporal_ref_latents], dim=0) # 4+C T H W
image_cond_in = torch.cat([ref_latent.to(device), temporal_ref_latents], dim=1) # 4+C T+trefs H W
del temporal_ref_latents, msk, bg_image_slice
else:
image_cond_in = torch.cat([torch.tile(torch.zeros_like(noise[:1]), [4, 1, 1, 1]), torch.zeros_like(noise)], dim=0).to(device)
else:
image_cond_in = torch.cat([torch.tile(torch.zeros_like(noise[:1]), [4, 1, 1, 1]), torch.zeros_like(noise)], dim=0).to(device)
pose_input_slice = None
if pose_images is not None:
vae.to(device)
pose_image_slice = pose_images_in[:, start:end].to(device)
pose_input_slice = vae.encode([pose_image_slice], device,tiled=tiled_vae, pbar=False).to(dtype)
vae.to(offload_device)
if wananim_face_pixels is None and wananim_ref_masks is not None:
face_images_in = torch.zeros(1, 3, frame_window_size, 512, 512, device=device, dtype=torch.float32)
elif wananim_face_pixels is not None:
face_images_in = face_images[:, :, start:end].to(device, torch.float32) if face_images is not None else None
if samples is not None:
input_samples = samples["samples"]
if input_samples is not None:
input_samples = input_samples.squeeze(0).to(noise)
# Check if we have enough frames in input_samples
# if latent_end_idx > input_samples.shape[1]:
# # We need more frames than available - pad the input_samples at the end
# pad_length = latent_end_idx - input_samples.shape[1]
# last_frame = input_samples[:, -1:].repeat(1, pad_length, 1, 1)
# input_samples = torch.cat([input_samples, last_frame], dim=1)
# input_samples = input_samples[:, latent_start_idx:latent_end_idx]
if noise_mask is not None:
original_image = input_samples.to(device)
assert input_samples.shape[1] == noise.shape[1], f"Slice mismatch: {input_samples.shape[1]} vs {noise.shape[1]}"
if add_noise_to_samples:
latent_timestep = timesteps[0]
noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples
else:
noise = input_samples
# diff diff prep
noise_mask = samples.get("noise_mask", None)
if noise_mask is not None:
if len(noise_mask.shape) == 4:
noise_mask = noise_mask.squeeze(1)
if noise_mask.shape[0] < noise.shape[1]:
noise_mask = noise_mask.repeat(noise.shape[1] // noise_mask.shape[0], 1, 1)
else:
noise_mask = noise_mask[start_latent:end_latent]
noise_mask = torch.nn.functional.interpolate(
noise_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W]
size=(noise.shape[1], noise.shape[2], noise.shape[3]),
mode='trilinear',
align_corners=False
).repeat(1, noise.shape[0], 1, 1, 1)
thresholds = torch.arange(len(timesteps), dtype=original_image.dtype) / len(timesteps)
thresholds = thresholds.reshape(-1, 1, 1, 1, 1).to(device)
masks = (1-noise_mask.repeat(len(timesteps), 1, 1, 1, 1).to(device)) > thresholds
if isinstance(scheduler, dict):
sample_scheduler = copy.deepcopy(scheduler["sample_scheduler"])
timesteps = scheduler["timesteps"]
else:
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, total_steps, start_step, end_step, shift, device, transformer.dim, denoise_strength, sigmas=sigmas)
# sample videos
latent = noise
if offloaded:
# Load weights
if transformer.patched_linear and gguf_reader is None:
load_weights(patcher.model.diffusion_model, patcher.model["sd"], weight_dtype, base_dtype=dtype, transformer_load_device=device, block_swap_args=block_swap_args)
elif gguf_reader is not None: #handle GGUF
load_weights(transformer, patcher.model["sd"], base_dtype=dtype, transformer_load_device=device, patcher=patcher, gguf=True, reader=gguf_reader, block_swap_args=block_swap_args)
#blockswap init
init_blockswap(transformer, block_swap_args, model)
# Use the appropriate prompt for this section
if len(text_embeds["prompt_embeds"]) > 1:
prompt_index = min(iteration_count, len(text_embeds["prompt_embeds"]) - 1)
positive = [text_embeds["prompt_embeds"][prompt_index]]
log.info(f"Using prompt index: {prompt_index}")
else:
positive = text_embeds["prompt_embeds"]
# uni3c slices
uni3c_data_input = None
if uni3c_embeds is not None:
render_latent = uni3c_embeds["render_latent"][:,:,start_latent:end_latent].to(device)
if render_latent.shape[2] < noise.shape[1]:
render_latent = torch.nn.functional.interpolate(render_latent, size=(noise.shape[1], noise.shape[2], noise.shape[3]), mode='trilinear', align_corners=False)
uni3c_data_input = {"render_latent": render_latent}
for k in uni3c_data:
if k != "render_latent":
uni3c_data_input[k] = uni3c_data[k]
mm.soft_empty_cache()
gc.collect()
# inner WanAnimate sampling loop
sampling_pbar = tqdm(total=len(timesteps), desc=f"Frames {start}-{end}", position=0, leave=True)
for i in range(len(timesteps)):
timestep = timesteps[i]
latent_model_input = latent.to(device)
noise_pred, _, self.cache_state = predict_with_cfg(
latent_model_input, cfg[min(i, len(timesteps)-1)], positive, text_embeds["negative_prompt_embeds"],
timestep, i, cache_state=self.cache_state, image_cond=image_cond_in, clip_fea=clip_fea, wananim_face_pixels=face_images_in,
wananim_pose_latents=pose_input_slice, uni3c_data=uni3c_data_input,
)
if callback is not None:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3)
callback(step_iteration_count, callback_latent, None, estimated_iterations*(len(timesteps)))
del callback_latent
sampling_pbar.update(1)
step_iteration_count += 1
if use_tsr:
noise_pred = temporal_score_rescaling(noise_pred, latent, timestep, tsr_k, tsr_sigma)
latent = sample_scheduler.step(noise_pred.unsqueeze(0), timestep, latent.unsqueeze(0).to(noise_pred.device), **scheduler_step_args)[0].squeeze(0)
del noise_pred, latent_model_input, timestep
# differential diffusion inpaint
if masks is not None:
if i < len(timesteps) - 1:
image_latent = add_noise(original_image.to(device), noise.to(device), timesteps[i+1])
mask = masks[i].to(latent)
latent = image_latent * mask + latent * (1-mask)
del noise
if offload:
offload_transformer(transformer, remove_lora=False)
offloaded = True
vae.to(device)
videos = vae.decode(latent[:, 1:].unsqueeze(0).to(device, vae.dtype), device=device, tiled=tiled_vae, pbar=False)[0].cpu()
del latent
if start != 0:
videos = videos[:, refert_num:]
sampling_pbar.close()
# optional color correction
if colormatch != "disabled":
videos = videos.permute(1, 2, 3, 0).float().numpy()
from color_matcher import ColorMatcher
cm = ColorMatcher()
cm_result_list = []
for img in videos:
cm_result = cm.transfer(src=img, ref=ref_images.permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
cm_result_list.append(torch.from_numpy(cm_result).to(vae.dtype))
videos = torch.stack(cm_result_list, dim=0).permute(3, 0, 1, 2)
del cm_result_list
current_ref_images = videos[:, -refert_num:].clone().detach()
# optionally save generated samples to disk
# if output_path:
# video_np = videos.clamp(-1.0, 1.0).add(1.0).div(2.0).mul(255).cpu().float().numpy().transpose(1, 2, 3, 0).astype('uint8')
# num_frames_to_save = video_np.shape[0] if is_first_clip else video_np.shape[0] - cur_motion_frames_num
# log.info(f"Saving {num_frames_to_save} generated frames to {output_path}")
# start_idx = 0 if is_first_clip else cur_motion_frames_num
# for i in range(start_idx, video_np.shape[0]):
# im = Image.fromarray(video_np[i])
# im.save(os.path.join(output_path, f"frame_{img_counter:05d}.png"))
# img_counter += 1
# else:
gen_video_list.append(videos)
del videos
iteration_count += 1
start += frame_window_size - refert_num
end += frame_window_size - refert_num
start_latent += latent_window_size - ((refert_num - 1)// 4 + 1)
end_latent += latent_window_size - ((refert_num - 1)// 4 + 1)
if not output_path:
gen_video_samples = torch.cat(gen_video_list, dim=1)
else:
gen_video_samples = torch.zeros(3, 1, 64, 64) # dummy output
if force_offload:
vae.to(offload_device)
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return {"video": gen_video_samples.permute(1, 2, 3, 0), "output_path": output_path},
#region normal inference
else:
noise_pred, noise_pred_ovi, self.cache_state = predict_with_cfg(
latent_model_input,
cfg[idx], text_embeds["prompt_embeds"], text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, fantasy_portrait_input=fantasy_portrait_input, multitalk_audio_embeds=multitalk_audio_embeds, mtv_motion_tokens=mtv_motion_tokens, s2v_audio_input=s2v_audio_input,
humo_image_cond=humo_image_cond, humo_image_cond_neg=humo_image_cond_neg, humo_audio=humo_audio, humo_audio_neg=humo_audio_neg,
wananim_face_pixels=wananim_face_pixels, wananim_pose_latents=wananim_pose_latents, uni3c_data = uni3c_data, latent_model_input_ovi=latent_model_input_ovi, flashvsr_LQ_latent=flashvsr_LQ_latent,
)
if bidirectional_sampling:
noise_pred_flipped, _,self.cache_state = predict_with_cfg(
latent_model_input_flipped,
cfg[idx], text_embeds["prompt_embeds"], text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, fantasy_portrait_input=fantasy_portrait_input, mtv_motion_tokens=mtv_motion_tokens,reverse_time=True)
if latent_shift_loop:
#reverse latent shift
if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent:
noise_pred = torch.cat([noise_pred[:, latent_video_length - shift_idx:]] + [noise_pred[:, :latent_video_length - shift_idx]], dim=1)
shift_idx = (shift_idx + latent_skip) % latent_video_length
latent = latent.to(device)
if self.noise_front_pad_num > 0:
noise_pred = noise_pred[:, self.noise_front_pad_num:]
if use_tsr:
noise_pred = temporal_score_rescaling(noise_pred, latent, timestep, tsr_k, tsr_sigma)
if transformer.is_longcat:
noise_pred = -noise_pred
if len(timestep.shape) != 1 and clean_latent_indices and not is_pusa: #5b and longcat, skip clean latents for scheduler step
step_process_indices = [i for i in range(latent.shape[1]) if i not in clean_latent_indices]
latent[:, step_process_indices] = sample_scheduler.step(noise_pred[:, step_process_indices].unsqueeze(0), orig_timestep,
latent[:, step_process_indices].unsqueeze(0), **scheduler_step_args)[0].squeeze(0)
else:
if latents_to_not_step > 0:
raw_latent = latent[:, :latents_to_not_step]
noise_pred_in = noise_pred[:, latents_to_not_step:]
latent = latent[:, latents_to_not_step:]
elif recammaster is not None or mocha_embeds is not None:
noise_pred_in = noise_pred[:, :orig_noise_len]
latent = latent[:, :orig_noise_len]
else:
noise_pred_in = noise_pred
latent = sample_scheduler.step(noise_pred_in.unsqueeze(0), timestep, latent.unsqueeze(0), **scheduler_step_args)[0].squeeze(0)
if noise_pred_flipped is not None:
latent_backwards = sample_scheduler_flipped.step(noise_pred_flipped.unsqueeze(0), timestep, latent_flipped.unsqueeze(0), **scheduler_step_args)[0].squeeze(0)
latent_backwards = torch.flip(latent_backwards, dims=[1])
latent = latent * 0.5 + latent_backwards * 0.5
if latents_to_not_step > 0:
latent = torch.cat([raw_latent, latent], dim=1)
if latent_ovi is not None:
latent_ovi = sample_scheduler_ovi.step(noise_pred_ovi.unsqueeze(0), t, latent_ovi.to(device).unsqueeze(0), **scheduler_step_args)[0].squeeze(0)
#InfiniteTalk first frame handling
if (extra_latents is not None
and not multitalk_sampling
and transformer.multitalk_model_type=="InfiniteTalk"):
for entry in extra_latents:
add_index = entry["index"]
num_extra_frames = entry["samples"].shape[2]
latent[:, add_index:add_index+num_extra_frames] = entry["samples"].to(latent)
# differential diffusion inpaint
if masks is not None:
if idx < len(timesteps) - 1:
noise_timestep = timesteps[idx+1]
image_latent = sample_scheduler.scale_noise(
original_image.to(device), torch.tensor([noise_timestep]), noise.to(device)
)
mask = masks[idx].to(latent)
latent = image_latent * mask + latent * (1-mask)
# TTM
if ttm_reference_latents is not None and (idx + ttm_start_step) < ttm_end_step:
if idx + ttm_start_step + 1 < len(sample_scheduler.all_timesteps):
noisy_latents = add_noise(ttm_reference_latents, noise, sample_scheduler.all_timesteps[idx + ttm_start_step + 1].to(noise.device)).to(latent)
latent = latent * (1 - motion_mask) + noisy_latents * motion_mask
else:
latent = latent * (1 - motion_mask) + ttm_reference_latents.to(latent) * motion_mask
if freeinit_args is not None:
current_latent = latent.clone()
if callback is not None:
if recammaster is not None or mocha_embeds is not None:
callback_latent = (latent_model_input[:, :orig_noise_len].to(device) - noise_pred[:, :orig_noise_len].to(device) * t.to(device) / 1000).detach()
#elif phantom_latents is not None:
# callback_latent = (latent_model_input[:,:-phantom_latents.shape[1]].to(device) - noise_pred[:,:-phantom_latents.shape[1]].to(device) * t.to(device) / 1000).detach()
elif humo_reference_count > 0:
callback_latent = (latent_model_input[:,:-humo_reference_count].to(device) - noise_pred[:,:-humo_reference_count].to(device) * t.to(device) / 1000).detach()
elif "rcm" in sample_scheduler.__class__.__name__.lower():
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device)).detach()
else:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach()
callback(idx, callback_latent.permute(1,0,2,3), None, len(timesteps))
else:
pbar.update(1)
except Exception as e:
log.error(f"Error during sampling: {e}")
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
raise e
if phantom_latents is not None:
latent = latent[:,:-phantom_latents.shape[1]]
if humo_reference_count > 0:
latent = latent[:,:-humo_reference_count]
if longcat_ref_latent is not None:
latent = latent[:, longcat_ref_latent.shape[1]:]
if story_mem_latents is not None:
latent = latent[:, story_mem_latents.shape[1]:]
log.info("-" * 10 + " Sampling end " + "-" * 12)
cache_states = None
if cache_args is not None:
cache_report(transformer, cache_args)
if end_step != -1 and end_step < total_steps:
cache_states = {
"cache_state": self.cache_state,
"easycache_state": transformer.easycache_state,
"teacache_state": transformer.teacache_state,
"magcache_state": transformer.magcache_state,
}
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return ({
"samples": latent.unsqueeze(0).cpu(),
"looped": is_looped,
"end_image": end_image if not fun_or_fl2v_model else None,
"has_ref": has_ref,
"drop_last": drop_last,
"generator_state": seed_g.get_state(),
"original_image": original_image.cpu() if original_image is not None else None,
"cache_states": cache_states,
"latent_ovi_audio": latent_ovi.unsqueeze(0).transpose(1, 2).cpu() if latent_ovi is not None else None,
"flashvsr_LQ_images": LQ_images,
},{
"samples": callback_latent.unsqueeze(0).cpu() if callback is not None else None,
})
class WanVideoSamplerSettings(WanVideoSampler):
RETURN_TYPES = ("SAMPLER_ARGS",)
RETURN_NAMES = ("sampler_inputs", )
DESCRIPTION = "Node to output all settings and inputs for the WanVideoSamplerFromSettings -node"
def process(self, *args, **kwargs):
import inspect
params = inspect.signature(WanVideoSampler.process).parameters
args_dict = {name: kwargs.get(name, param.default if param.default is not inspect.Parameter.empty else None)
for name, param in params.items() if name != "self"}
return args_dict,
class WanVideoSamplerFromSettings(WanVideoSampler):
DESCRIPTION = "Utility node with no other functionality than to look cleaner, useful for the live preview as the main sampler node has become a messy monster"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"sampler_inputs": ("SAMPLER_ARGS",),},
}
def process(self, sampler_inputs):
return super().process(**sampler_inputs)
class WanVideoSamplerExtraArgs():
@classmethod
def INPUT_TYPES(s):
return {
"required": {
},
"optional": {
"riflex_freq_index": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping"}),
"feta_args": ("FETAARGS", ),
"context_options": ("WANVIDCONTEXT", ),
"cache_args": ("CACHEARGS", ),
"slg_args": ("SLGARGS", ),
"rope_function": (rope_functions, {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile"}),
"loop_args": ("LOOPARGS", ),
"experimental_args": ("EXPERIMENTALARGS", ),
"unianimate_poses": ("UNIANIMATE_POSE", ),
"fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ),
"uni3c_embeds": ("UNI3C_EMBEDS", ),
"multitalk_embeds": ("MULTITALK_EMBEDS", ),
}
}
RETURN_TYPES = ("WANVIDSAMPLEREXTRAARGS",)
RETURN_NAMES = ("extra_args", )
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, *args, **kwargs):
return kwargs,
class WanVideoSamplerv2(WanVideoSampler):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("WANVIDEOMODEL",),
"image_embeds": ("WANVIDIMAGE_EMBEDS", ),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"force_offload": ("BOOLEAN", {"default": True, "tooltip": "Moves the model to the offload device after sampling"}),
"scheduler": ("WANVIDEOSCHEDULER",),
},
"optional": {
"text_embeds": ("WANVIDEOTEXTEMBEDS", ),
"samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
"add_noise_to_samples": ("BOOLEAN", {"default": False, "tooltip": "Add noise to the samples before sampling, needed for video2video sampling when starting from clean video"}),
"extra_args": ("WANVIDSAMPLEREXTRAARGS", ),
}
}
def process(self, *args, extra_args=None, **kwargs):
import inspect
params = inspect.signature(WanVideoSampler.process).parameters
args_dict = {name: kwargs.get(name, param.default if param.default is not inspect.Parameter.empty else None)
for name, param in params.items() if name != "self"}
if extra_args is not None:
args_dict.update(extra_args)
else:
args_dict["rope_function"] = "comfy"
return super().process(**args_dict)
class WanVideoScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"scheduler": (scheduler_list, {"default": "unipc"}),
"steps": ("INT", {"default": 30, "min": 1, "tooltip": "Number of steps for the scheduler"}),
"shift": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"start_step": ("INT", {"default": 0, "min": 0, "tooltip": "Starting step for the scheduler"}),
"end_step": ("INT", {"default": -1, "min": -1, "tooltip": "Ending step for the scheduler"})
},
"optional": {
"sigmas": ("SIGMAS", ),
"enhance_hf": ("BOOLEAN", {"default": False, "tooltip": "Enhanced high-frequency denoising schedule"}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = ("SIGMAS", "INT", "FLOAT", scheduler_list, "INT", "INT",)
RETURN_NAMES = ("sigmas", "steps", "shift", "scheduler", "start_step", "end_step")
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
EXPERIMENTAL = True
def process(self, scheduler, steps, start_step, end_step, shift, unique_id, sigmas=None, enhance_hf=False):
sample_scheduler, timesteps, start_idx, end_idx = get_scheduler(
scheduler, steps, start_step, end_step, shift, device, sigmas=sigmas, log_timesteps=True, enhance_hf=enhance_hf)
scheduler_dict = {
"sample_scheduler": sample_scheduler,
"timesteps": timesteps,
}
try:
from server import PromptServer
import io
import base64
import matplotlib.pyplot as plt
except:
PromptServer = None
if unique_id and PromptServer is not None:
try:
# Plot sigmas and save to a buffer
sigmas_np = sample_scheduler.full_sigmas.cpu().numpy()
if not np.isclose(sigmas_np[-1], 0.0, atol=1e-6):
sigmas_np = np.append(sigmas_np, 0.0)
buf = io.BytesIO()
fig = plt.figure(facecolor='#353535')
ax = fig.add_subplot(111)
ax.set_facecolor('#353535') # Set axes background color
x_values = range(0, len(sigmas_np))
ax.plot(x_values, sigmas_np)
# Annotate each sigma value
ax.scatter(x_values, sigmas_np, color='white', s=20, zorder=3) # Small dots at each sigma
for x, y in zip(x_values, sigmas_np):
# Show all annotations if few steps, or just show split step annotations
show_annotation = len(sigmas_np) <= 10
is_split_step = (start_idx > 0 and x == start_idx) or (end_idx != -1 and x == end_idx + 1)
if show_annotation or is_split_step:
color = 'orange'
if is_split_step:
color = 'yellow'
ax.annotate(f"{y:.3f}", (x, y), textcoords="offset points", xytext=(10, 1), ha='center', color=color, fontsize=12)
ax.set_xticks(x_values)
ax.set_title("Sigmas", color='white') # Title font color
ax.set_xlabel("Step", color='white') # X label font color
ax.set_ylabel("Sigma Value", color='white') # Y label font color
ax.tick_params(axis='x', colors='white', labelsize=10) # X tick color
ax.tick_params(axis='y', colors='white', labelsize=10) # Y tick color
# Add split point if end_step is defined
end_idx += 1
if end_idx != -1 and 0 <= end_idx < len(sigmas_np) - 1:
ax.axvline(end_idx, color='red', linestyle='--', linewidth=2, label='end_step split')
# Add split point if start_step is defined
if start_idx > 0 and 0 <= start_idx < len(sigmas_np):
ax.axvline(start_idx, color='green', linestyle='--', linewidth=2, label='start_step split')
if (end_idx != -1 and 0 <= end_idx < len(sigmas_np)) or (start_idx > 0 and 0 <= start_idx < len(sigmas_np)):
handles, labels = ax.get_legend_handles_labels()
if labels:
ax.legend()
# Draw shaded range
range_start_idx = start_idx if start_idx > 0 else 0
range_end_idx = end_idx if end_idx > 0 and end_idx < len(sigmas_np) else len(sigmas_np) - 1
if range_start_idx < range_end_idx:
ax.axvspan(range_start_idx, range_end_idx, color='lightblue', alpha=0.1, label='Sampled Range')
plt.tight_layout()
plt.savefig(buf, format='png')
plt.close(fig)
buf.seek(0)
img_base64 = base64.b64encode(buf.read()).decode('utf-8')
buf.close()
# Send as HTML img tag with base64 data
html_img = f"<img src='data:image/png;base64,{img_base64}' alt='Sigmas Plot' style='max-width:100%; height:100%; overflow:hidden; display:block;'>"
PromptServer.instance.send_progress_text(html_img, unique_id)
except Exception as e:
log.error(f"Failed to send sigmas plot: {e}")
pass
return (sigmas, steps, shift, scheduler_dict, start_step, end_step)
class WanVideoSchedulerv2(WanVideoScheduler):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"scheduler": (scheduler_list, {"default": "unipc"}),
"steps": ("INT", {"default": 30, "min": 1, "tooltip": "Number of steps for the scheduler"}),
"shift": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"start_step": ("INT", {"default": 0, "min": 0, "tooltip": "Starting step for the scheduler"}),
"end_step": ("INT", {"default": -1, "min": -1, "tooltip": "Ending step for the scheduler"})
},
"optional": {
"sigmas": ("SIGMAS", ),
"enhance_hf": ("BOOLEAN", {"default": False, "tooltip": "Enhanced high-frequency denoising schedule"}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = ("WANVIDEOSCHEDULER",)
RETURN_NAMES = ("scheduler",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
EXPERIMENTAL = True
def process(self, *args, **kwargs):
sigmas, steps, shift, scheduler_dict, start_step, end_step = super().process(*args, **kwargs)
return scheduler_dict,
NODE_CLASS_MAPPINGS = {
"WanVideoSampler": WanVideoSampler,
"WanVideoSamplerSettings": WanVideoSamplerSettings,
"WanVideoSamplerFromSettings": WanVideoSamplerFromSettings,
"WanVideoSamplerv2": WanVideoSamplerv2,
"WanVideoSamplerExtraArgs": WanVideoSamplerExtraArgs,
"WanVideoScheduler": WanVideoScheduler,
"WanVideoSchedulerv2": WanVideoSchedulerv2,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"WanVideoSampler": "WanVideo Sampler",
"WanVideoSamplerSettings": "WanVideo Sampler Settings",
"WanVideoSamplerFromSettings": "WanVideo Sampler From Settings",
"WanVideoSamplerv2": "WanVideo Sampler v2",
"WanVideoSamplerExtraArgs": "WanVideoSampler v2 Extra Args",
"WanVideoScheduler": "WanVideo Scheduler",
"WanVideoSchedulerv2": "WanVideo Scheduler v2",
}
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