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wuwukaka-ComfyUI-WanAnimate…/nodes_sampler.py
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wuwukasi 76173c0e66 feat: improve SCAIL-2 prefix conditioning 
Changes since the previous commit:

- Add bg_image support for SCAIL-2 animation mode with an internal white mask and four-slot limits for user prefix frames/masks when bg_image is connected.

- Add single_frame_prefix_encoding so prefix frames can be encoded as full-resolution reference latents without expanding the output canvas, while keeping the legacy 37-frame canvas prefix path available.

- Add preserve_main_ref_background to optionally crop the main reference image background in animation mode using reference_image_mask.

- Route single-frame prefix masks through the reference-mask latent path, keep legacy prefix masks on the canvas/SAM mask path, and avoid duplicate prefix alpha cropping.

- Preserve SCAIL-2 prefix/transition context across context-window sampling and remove prepended predictions before overlap fusion.

- Add a local SCAIL-2 fast path in the sampler/model, including multi-reference prefix handling, RoPE/cache key updates, block-swap support, clip/text/time embedding handling, and dtype guards.

- Update README documentation for the new SCAIL-2 inputs, prefix modes, bg_image behavior, and transition trimming rules.
2026-06-13 00:35:43 +08:00

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# Copyright (c) 2025 kijai
# Modified from nodes_sampler.py in ComfyUI-WanVideoWrapper.
# Original project: https://github.com/kijai/ComfyUI-WanVideoWrapper
# Modified portions Copyright (c) 2026 wuwukasi/wuwukaka.
# - Added APG, APG-chain, and Bernini CFG-chain guidance modes.
# - Added sampler graph-detach protection to prevent denoising graph accumulation.
# - Added Bernini context_latents/context_roles propagation, RoPE override, window slicing, and simple T2V/Bernini fast-path routing.
# - Added prefix frame support in context windows, looping, face/pose slices, image conditions, and noise predictions.
# - Added transition_video hard conditioning and canvas_expansion_px-aware Uni3C/render/output handling.
# - Added EverAnimate segmented sampling with anchors, generated/random/user-first anchors, repeat-anchor padding, bg/mask conditioning, motion latents, and internal context-option blocking.
# - Added EverAnimate VAE re-encode clamp/uint8 preprocessing to match the reference path.
# - Added variable WanAnimate anchor-count forwarding for pose/face alignment.
# - Added SCAIL-2 sampler-side freeze_mask handling for prefix/transition latents.
# Licensed under the Apache License, Version 2.0
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)
# --- APG (Adaptive Projected Guidance) helpers, adapted from Bernini ---
class MomentumBuffer:
"""EMA buffer for smoothing guidance differences across timesteps."""
def __init__(self, momentum: float = -0.5):
self.momentum = momentum
self.running_average = 0
def update(self, value: torch.Tensor):
self.running_average = value + self.momentum * self.running_average
def _normalize_diff(diff: torch.Tensor, base_pred: torch.Tensor,
momentum_buffer: MomentumBuffer | None = None,
eta: float = 1.0, norm_threshold: float = 0.0) -> torch.Tensor:
"""Project diff onto/off base_pred and recombine with weight eta.
Operates on 4-D tensors [C, F, H, W] (spatial latent format).
Norm is computed over the spatio-temporal dims [-1, -2, -3] = [F, H, W].
"""
# Momentum
if momentum_buffer is not None:
momentum_buffer.update(diff)
diff = momentum_buffer.running_average
# Norm clipping
if norm_threshold > 0:
diff_n = diff.norm(p=2, dim=[-1, -2, -3], keepdim=True)
scale = torch.minimum(torch.ones_like(diff_n), norm_threshold / diff_n)
diff = diff * scale
# Parallel / orthogonal projection in double precision
v0 = diff.double()
v1 = base_pred.double()
v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
v0_orthogonal = v0 - v0_parallel
return (v0_orthogonal + eta * v0_parallel).to(diff.dtype)
def _normalized_guidance(pred_cond: torch.Tensor, pred_uncond: torch.Tensor,
guidance_scale: float,
momentum_buffer: MomentumBuffer | None = None,
eta: float = 1.0, norm_threshold: float = 0.0) -> torch.Tensor:
"""Single-condition APG: project (cond - uncond) onto cond with eta weight."""
nd = _normalize_diff(pred_cond - pred_uncond, pred_cond,
momentum_buffer, eta, norm_threshold)
return pred_uncond + guidance_scale * nd
def _normalized_guidance_chain(pred_uncond, preds, scales,
momentum_buffers, eta, norm_threshold):
"""Chained APG. Each condition's diff is taken against the previous prediction.
preds = [eps_I, eps_TI], scales = [omega_I, omega_TI]
Returns: uncond + sum_i scale_i * normalize_diff(cond_i - base_i)"""
bases = [pred_uncond] + list(preds)
result = pred_uncond
for i, cond in enumerate(preds):
nd = _normalize_diff(cond - bases[i], cond,
momentum_buffers[i], eta, norm_threshold)
result = result + scales[i] * nd
return result
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"}),
"guidance_mode": (["cfg", "apg", "apg_chain", "cfg_chain"], {"default": "cfg", "tooltip": "Guidance mode: cfg (standard CFG), apg (single-condition APG), apg_chain (image-reference APG chain), or cfg_chain (Bernini chained CFG)"}),
"apg_eta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "APG: parallel/orthogonal balance (0=orthogonal only, 1=full)"}),
"apg_momentum": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01, "tooltip": "APG: EMA momentum for smoothing guidance differences"}),
"apg_norm_threshold": ("FLOAT", {"default": 50.0, "min": 0.0, "max": 1000.0, "step": 1.0, "tooltip": "APG: L2 norm clipping threshold (0=disabled)"}),
"apg_omega": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG: guidance strength (equivalent to CFG scale, replaces cfg when APG is active)"}),
"apg_omega_I": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG chain: image-only guidance strength"}),
"apg_omega_TI": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG chain: text+image guidance strength"}),
"chain_omega_V": ("FLOAT", {"default": 1.25, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: video-only guidance strength"}),
"chain_omega_I": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: extra reference context strength (VI - V; reference video/images)"}),
"chain_omega_TI": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: text+image guidance strength"}),
}
}
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,
guidance_mode="cfg", apg_eta=0.5, apg_momentum=0.0, apg_norm_threshold=50.0,
apg_omega=4.0, apg_omega_I=4.5, apg_omega_TI=4.0,
chain_omega_V=1.25, chain_omega_I=4.5, chain_omega_TI=4.0):
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)
everanimate_sampling = image_embeds.get("everanimate", 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 everanimate_sampling and context_options is not None:
raise Exception("context_options are not compatible with EverAnimate sampling, since it creates the video in internal segments.")
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()
if scheduler != "multitalk":
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)
image_cond_mask = 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)
# ============ Global transition_video read ============
transition_latent = image_embeds.get("transition_latent", None)
transition_mask_values = image_embeds.get("transition_mask_values", None)
transition_len = transition_latent.shape[1] if transition_latent is not None else 0
has_transition = transition_latent is not None
scail_prefix_prepend_latents = int(image_embeds.get("scail_prefix_prepend_latents", 0))
if has_transition:
log.info(f"Transition video: {transition_len} latent frames")
if transition_mask_values is not None:
log.info(f"Linear decay mask values: {transition_mask_values.tolist()}")
# ================================================================
has_prefix = image_embeds.get("has_prefix", False)
canvas_expansion_px = image_embeds.get("canvas_expansion_px", 0)
prefix_prepend_latents = image_embeds.get("prefix_prepend_latents", 6 if has_prefix else 0)
context_latents = image_embeds.get("context_latents", None)
context_roles = image_embeds.get("context_roles", None)
if has_prefix:
log.info(f"Prefix frames: detected, will prepend {prefix_prepend_latents} latent frames to each non-first context window")
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_audio_stride = 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)
base_patches_per_frame = math.ceil((noise.shape[2] * noise.shape[3]) / 4)
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 = scail_freeze_mask = None
if samples is not None and not multitalk_sampling and not wananimate_loop and not everanimate_sampling:
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)
scail_freeze_latents = image_embeds.get("scail_freeze_latents", None)
if scail_freeze_latents is not None and not multitalk_sampling and not wananimate_loop and not everanimate_sampling:
freeze_latents = scail_freeze_latents
if freeze_latents.ndim == 5:
freeze_latents = freeze_latents.squeeze(0)
if freeze_latents.shape[0] != noise.shape[0]:
raise ValueError(f"SCAIL-2 freeze latent channels {freeze_latents.shape[0]} do not match target channels {noise.shape[0]}")
freeze_latents = freeze_latents.to(noise)
if freeze_latents.shape[1] < noise.shape[1]:
pad = torch.zeros(
freeze_latents.shape[0], noise.shape[1] - freeze_latents.shape[1],
freeze_latents.shape[2], freeze_latents.shape[3],
device=freeze_latents.device, dtype=freeze_latents.dtype,
)
freeze_latents = torch.cat([freeze_latents, pad], dim=1)
elif freeze_latents.shape[1] > noise.shape[1]:
freeze_latents = freeze_latents[:, :noise.shape[1]]
if freeze_latents.shape[2:] != noise.shape[2:]:
freeze_latents = torch.nn.functional.interpolate(
freeze_latents.unsqueeze(0),
size=(noise.shape[1], noise.shape[2], noise.shape[3]),
mode="trilinear",
align_corners=False,
)[0]
raw_freeze_mask = image_embeds.get("scail_freeze_mask", None)
if raw_freeze_mask is None:
raw_freeze_mask = torch.ones(
freeze_latents.shape[1], freeze_latents.shape[2], freeze_latents.shape[3],
device=freeze_latents.device, dtype=freeze_latents.dtype,
)
if raw_freeze_mask.ndim == 5:
raw_freeze_mask = raw_freeze_mask.squeeze(0).squeeze(0)
elif raw_freeze_mask.ndim == 4:
raw_freeze_mask = raw_freeze_mask.squeeze(0) if raw_freeze_mask.shape[0] == 1 else raw_freeze_mask[0]
raw_freeze_mask = raw_freeze_mask.to(noise)
if raw_freeze_mask.shape[0] < noise.shape[1]:
pad = torch.zeros(
noise.shape[1] - raw_freeze_mask.shape[0], raw_freeze_mask.shape[1], raw_freeze_mask.shape[2],
device=raw_freeze_mask.device, dtype=raw_freeze_mask.dtype,
)
raw_freeze_mask = torch.cat([raw_freeze_mask, pad], dim=0)
elif raw_freeze_mask.shape[0] > noise.shape[1]:
raw_freeze_mask = raw_freeze_mask[:noise.shape[1]]
scail_freeze_mask = torch.nn.functional.interpolate(
raw_freeze_mask.unsqueeze(0).unsqueeze(0),
size=(noise.shape[1], noise.shape[2], noise.shape[3]),
mode="trilinear",
align_corners=False,
).repeat(1, noise.shape[0], 1, 1, 1).to(device)
base_noise = noise.to(device)
original_image = freeze_latents.to(device)
freeze = scail_freeze_mask[0].to(base_noise)
noise = (original_image * freeze + base_noise * (1 - freeze)).detach()
log.info(f"SCAIL-2 freeze_mask active: protecting {int((raw_freeze_mask > 0).any(dim=(1, 2)).sum().item())} latent frames")
# 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}")
# v1.5 (Whisper) embeds set audio_stride=1; v1.0 (wav2vec2) uses 2 for LongCat
if multitalk_audio_stride is not None:
audio_stride = multitalk_audio_stride
else:
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:
# If temporal is shorter (prefix/transition expansion), pad with first frame at beginning
if has_prefix and render_latent.shape[2] < noise.shape[1]:
pad_len = noise.shape[1] - render_latent.shape[2]
first_frame = render_latent[:, :, :1].repeat(1, 1, pad_len, 1, 1)
render_latent = torch.cat([first_frame, render_latent], dim=2)
log.info(f"Uni3C: padded render_latent by {pad_len} latent frames for prefix canvas expansion")
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)
scail2_fast_path_blocked_by_experimental_sampling = (
use_cfg_zero_star
or use_tangential
or raag_alpha > 0.0
or use_fresca
or use_tsr
or bool(transformer.video_attention_split_steps)
)
# 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
if context_latents is not None and "comfy" not in rope_function:
log.info("Bernini context requires comfy RoPE; overriding rope_function to comfy")
rope_function = "comfy"
scail_embeds_for_rope = image_embeds.get("scail_embeds", None)
if scail_embeds_for_rope is not None and "comfy" not in rope_function:
log.info("SCAIL-2 requires comfy RoPE; overriding rope_function to comfy")
rope_function = "comfy"
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 and context_latents is 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 context_latents is None and ("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)
scail2_fast_path_fallback_logged = False
# 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)
dual_control_input = 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)
# APG momentum buffers (persist across sampling steps)
_momentum_buf = MomentumBuffer(apg_momentum) if guidance_mode == "apg" else None
_momentum_buf_I = MomentumBuffer(apg_momentum) if guidance_mode == "apg_chain" else None
_momentum_buf_TI = MomentumBuffer(apg_momentum) if guidance_mode == "apg_chain" else None
#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, wananim_num_anchor_latents=1, uni3c_data=None, latent_model_input_ovi=None, flashvsr_LQ_latent=None,
context_latents=None, context_roles=None, context_window_start=0, scail_context_prepend_latents=0,):
nonlocal transformer
nonlocal audio_cfg_scale
nonlocal scail2_fast_path_fallback_logged
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])
if scail_freeze_mask is not None:
history_mask = scail_freeze_mask[0, :4].to(z)
if context_window is not None:
history_parts = []
if scail_context_prepend_latents > 0:
history_parts.append(history_mask[:, :scail_context_prepend_latents])
history_parts.append(history_mask[:, context_window])
history_mask = torch.cat(history_parts, dim=1)
else:
history_mask = history_mask[:, :z.shape[1]]
if history_mask.shape[1] < z.shape[1]:
pad = torch.zeros(
history_mask.shape[0], z.shape[1] - history_mask.shape[1],
history_mask.shape[2], history_mask.shape[3],
device=history_mask.device, dtype=history_mask.dtype,
)
history_mask = torch.cat([history_mask, pad], dim=1)
elif history_mask.shape[1] > z.shape[1]:
history_mask = history_mask[:, :z.shape[1]]
if history_mask.shape[2:] != z.shape[2:]:
history_mask = torch.nn.functional.interpolate(
history_mask.unsqueeze(0),
size=(z.shape[1], z.shape[2], z.shape[3]),
mode="trilinear",
align_corners=False,
)[0]
ref_concat_mask = history_mask
z = torch.cat([z, ref_concat_mask])
if context_window is not None:
scail_data_in = scail_data.copy()
if scail_data.get("pose_latent", None) is not None:
pose_parts = []
if scail_context_prepend_latents > 0:
pose_parts.append(scail_data["pose_latent"][:, :scail_context_prepend_latents])
pose_parts.append(scail_data["pose_latent"][:, context_window])
scail_data_in["pose_latent"] = torch.cat(pose_parts, dim=1)
if scail_data.get("sam_latents", None) is not None:
sam_parts = []
if scail_context_prepend_latents > 0:
sam_parts.append(scail_data["sam_latents"][:, :scail_context_prepend_latents])
sam_parts.append(scail_data["sam_latents"][:, context_window])
scail_data_in["sam_latents"] = torch.cat(sam_parts, dim=1)
ref_mask_latents = scail_data.get("ref_mask_latents", None)
if ref_mask_latents is not None:
ref_latent = scail_data.get("ref_latent_pos", scail_data.get("ref_latent_neg", None))
ref_count = min(ref_latent.shape[1], ref_mask_latents.shape[1]) if ref_latent is not None else 0
ref_mask_prefix = ref_mask_latents[:, :ref_count]
ref_mask_target = ref_mask_latents[:, ref_count:]
if ref_mask_target.shape[1] > 0:
target_parts = []
if scail_context_prepend_latents > 0:
target_parts.append(ref_mask_target[:, :scail_context_prepend_latents])
target_parts.append(ref_mask_target[:, context_window])
ref_mask_target = torch.cat(target_parts, dim=1)
scail_data_in["ref_mask_latents"] = torch.cat([ref_mask_prefix, ref_mask_target], dim=1)
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,
"wananim_num_anchor_latents": wananim_num_anchor_latents,
"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
"context_latents": context_latents, # Bernini in-context reference
"context_window_start": context_window_start, # Context window start frame index for temporal RoPE alignment
"simple_t2v": False, # enabled below for Bernini context or strict plain T2V
"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
}
fast_path_no_extra_conditions = (
base_params['y'] is None
and base_params['clip_fea'] is None
and not reverse_time
and not enhance_enabled
and cache_args is None
and slg_args is None
and not batched_cfg
and not bidirectional_sampling
and control_latents is None
and vace_data is None
and unianim_data is None
and audio_proj is None
and control_camera_latents is None
and add_cond is None
and multitalk_audio_embeds is None
and fantasy_portrait_input is None
and mtv_motion_tokens is None
and s2v_audio_input is None
and s2v_ref_motion is None
and s2v_pose is None
and humo_image_cond is None
and humo_audio is None
and wananim_pose_latents is None
and wananim_face_pixels is None
and uni3c_data is None
and latent_model_input_ovi is None
and flashvsr_LQ_latent is None
and base_params['fun_ref'] is None
and base_params['fun_camera'] is None
and base_params['audio_proj'] is None
and base_params['uni3c_data'] is None
and base_params['controlnet'] is None
and base_params['add_cond'] is None
and not base_params['nag_params']
and base_params['nag_context'] is None
and base_params['multitalk_audio'] is None
and base_params['ref_target_masks'] is None
and base_params['inner_t'] is None
and base_params['standin_input'] is None
and base_params['fantasy_portrait_input'] is None
and base_params['phantom_ref'] is None
and base_params['mtv_motion_tokens'] is None
and base_params['mtv_motion_rotary_emb'] is None
and base_params['s2v_audio_input'] is None
and base_params['s2v_ref_latent'] is None
and base_params['s2v_ref_motion'] is None
and base_params['s2v_pose'] is None
and base_params['humo_audio'] is None
and base_params['wananim_pose_latents'] is None
and base_params['wananim_face_pixel_values'] is None
and base_params['lynx_embeds'] is None
and base_params['x_ovi'] is None
and base_params['flashvsr_LQ_latent'] is None
and base_params['longcat_num_cond_latents'] == 0
and base_params['longcat_num_ref_latents'] == 0
and base_params['longcat_avatar_options'] is None
and base_params['sdancer_input'] is None
and base_params['one_to_all_input'] is None
and base_params['scail_input'] is None
and base_params['dual_control_input'] is None
and base_params['rope_negative_offset'] == 0
and base_params['num_memory_frames'] == 0
)
strict_plain_t2v_fast_path = context_latents is None and context_window is None and fast_path_no_extra_conditions
base_params["simple_t2v"] = (context_latents is not None and base_params['scail_input'] is None) or strict_plain_t2v_fast_path
scail2_fast_path = (
base_params['scail_input'] is not None
and guidance_mode == "cfg"
and context_latents is None
and base_params['y'] is None
and image_cond_neg is None
and attn_cond is None
and attn_cond_neg is None
and not reverse_time
and not enhance_enabled
and cache_args is None
and freeinit_args is None
and not scail2_fast_path_blocked_by_experimental_sampling
and slg_args is None
and getattr(transformer, "slg_blocks", None) is None
and not getattr(transformer, "is_longcat", False)
and getattr(transformer, "audio_model", None) is None
and getattr(transformer, "vace_layers", None) is None
and not batched_cfg
and not bidirectional_sampling
and loop_args is None
and qwenvl_embeds_pos is None
and qwenvl_embeds_neg is None
and control_latents is None
and vace_data is None
and unianim_data is None
and audio_proj is None
and control_camera_latents is None
and add_cond is None
and multitalk_audio_embeds is None
and fantasy_portrait_input is None
and mtv_motion_tokens is None
and s2v_audio_input is None
and s2v_ref_motion is None
and s2v_pose is None
and humo_image_cond is None
and humo_audio is None
and wananim_pose_latents is None
and wananim_face_pixels is None
and uni3c_data is None
and latent_model_input_ovi is None
and flashvsr_LQ_latent is None
and base_params['fun_ref'] is None
and base_params['fun_camera'] is None
and base_params['audio_proj'] is None
and base_params['uni3c_data'] is None
and base_params['controlnet'] is None
and base_params['add_cond'] is None
and not base_params['nag_params']
and base_params['nag_context'] is None
and base_params['multitalk_audio'] is None
and base_params['ref_target_masks'] is None
and base_params['inner_t'] is None
and base_params['standin_input'] is None
and base_params['fantasy_portrait_input'] is None
and base_params['phantom_ref'] is None
and base_params['mtv_motion_tokens'] is None
and base_params['mtv_motion_rotary_emb'] is None
and base_params['s2v_audio_input'] is None
and base_params['s2v_ref_latent'] is None
and base_params['s2v_ref_motion'] is None
and base_params['s2v_pose'] is None
and base_params['humo_audio'] is None
and base_params['wananim_pose_latents'] is None
and base_params['wananim_face_pixel_values'] is None
and base_params['lynx_embeds'] is None
and base_params['x_ovi'] is None
and base_params['flashvsr_LQ_latent'] is None
and base_params['longcat_num_cond_latents'] == 0
and base_params['longcat_num_ref_latents'] == 0
and base_params['longcat_avatar_options'] is None
and base_params['sdancer_input'] is None
and base_params['one_to_all_input'] is None
and base_params['dual_control_input'] is None
and base_params['rope_negative_offset'] == 0
and base_params['num_memory_frames'] == 0
and "comfy" in getattr(transformer, "rope_func", "")
)
if base_params['scail_input'] is not None and not scail2_fast_path:
if not scail2_fast_path_fallback_logged:
scail2_fast_path_fallback_logged = True
fallback_reasons = []
if guidance_mode != "cfg":
fallback_reasons.append(f"guidance_mode={guidance_mode!r}")
if context_latents is not None:
fallback_reasons.append("Bernini context_latents connected")
if base_params['y'] is not None:
fallback_reasons.append("image condition active")
if image_cond_neg is not None:
fallback_reasons.append("negative image condition active")
if attn_cond is not None or attn_cond_neg is not None:
fallback_reasons.append("attention condition active")
if reverse_time:
fallback_reasons.append("reverse_time enabled")
if enhance_enabled:
fallback_reasons.append("enhance enabled")
if cache_args is not None:
fallback_reasons.append("cache enabled")
if freeinit_args is not None:
fallback_reasons.append("FreeInit enabled")
if scail2_fast_path_blocked_by_experimental_sampling:
fallback_reasons.append("experimental sampling enabled")
if slg_args is not None or getattr(transformer, "slg_blocks", None) is not None:
fallback_reasons.append("SLG enabled")
if getattr(transformer, "is_longcat", False):
fallback_reasons.append("LongCat model active")
if getattr(transformer, "audio_model", None) is not None:
fallback_reasons.append("OVI audio model active")
if getattr(transformer, "vace_layers", None) is not None:
fallback_reasons.append("VACE layers active")
if batched_cfg:
fallback_reasons.append("batched CFG enabled")
if bidirectional_sampling:
fallback_reasons.append("bidirectional sampling enabled")
if loop_args is not None:
fallback_reasons.append("loop enabled")
if qwenvl_embeds_pos is not None or qwenvl_embeds_neg is not None:
fallback_reasons.append("QwenVL embeddings connected")
if control_latents is not None or control_camera_latents is not None:
fallback_reasons.append("control latents connected")
if vace_data is not None:
fallback_reasons.append("VACE input connected")
if unianim_data is not None:
fallback_reasons.append("UniAnimate input connected")
if audio_proj is not None:
fallback_reasons.append("audio projection connected")
if add_cond is not None:
fallback_reasons.append("additional condition connected")
if multitalk_audio_embeds is not None:
fallback_reasons.append("MultiTalk input connected")
if fantasy_portrait_input is not None:
fallback_reasons.append("FantasyPortrait input connected")
if mtv_motion_tokens is not None:
fallback_reasons.append("MTV motion tokens connected")
if s2v_audio_input is not None or s2v_ref_motion is not None or s2v_pose is not None:
fallback_reasons.append("S2V input connected")
if humo_image_cond is not None or humo_audio is not None:
fallback_reasons.append("HuMo input connected")
if wananim_pose_latents is not None or wananim_face_pixels is not None:
fallback_reasons.append("WanAnimate input connected")
if uni3c_data is not None:
fallback_reasons.append("Uni3C input connected")
if latent_model_input_ovi is not None:
fallback_reasons.append("OVI latent input connected")
if flashvsr_LQ_latent is not None:
fallback_reasons.append("FlashVSR input connected")
if base_params['fun_ref'] is not None or base_params['fun_camera'] is not None:
fallback_reasons.append("Fun reference/camera active")
if base_params['controlnet'] is not None:
fallback_reasons.append("ControlNet active")
if base_params['nag_params'] or base_params['nag_context'] is not None:
fallback_reasons.append("NAG active")
if base_params['ref_target_masks'] is not None:
fallback_reasons.append("reference target masks active")
if base_params['inner_t'] is not None:
fallback_reasons.append("EchoShot active")
if base_params['standin_input'] is not None:
fallback_reasons.append("Stand-In input connected")
if base_params['phantom_ref'] is not None:
fallback_reasons.append("Phantom reference connected")
if base_params['lynx_embeds'] is not None:
fallback_reasons.append("Lynx input connected")
if base_params['longcat_num_cond_latents'] != 0 or base_params['longcat_num_ref_latents'] != 0 or base_params['longcat_avatar_options'] is not None:
fallback_reasons.append("LongCat options active")
if base_params['sdancer_input'] is not None:
fallback_reasons.append("SteadyDancer input connected")
if base_params['one_to_all_input'] is not None:
fallback_reasons.append("One-to-All input connected")
if base_params['dual_control_input'] is not None:
fallback_reasons.append("Dual Control input connected")
if base_params['rope_negative_offset'] != 0 or base_params['num_memory_frames'] != 0:
fallback_reasons.append("StoryMem/memory RoPE active")
if "comfy" not in getattr(transformer, "rope_func", ""):
fallback_reasons.append(f"rope_func={getattr(transformer, 'rope_func', None)!r}")
if not fallback_reasons:
fallback_reasons.append("unsupported sampler/model option")
log.info("SCAIL-2 fast path disabled; using normal path. Reason(s): " + "; ".join(fallback_reasons))
base_params["simple_scail2"] = scail2_fast_path
if scail2_fast_path:
base_params["simple_t2v"] = False
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 and guidance_mode not in ("cfg_chain", "apg_chain"):
#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
elif guidance_mode not in ("cfg_chain", "apg_chain"):
base_params['z'] = [z] * 2
base_params['y'] = [image_cond_input] * 2 if image_cond_input is not None else None
if clip_fea is not None:
uncond_clip_fea = clip_fea_neg if clip_fea_neg is not None else clip_fea
base_params['clip_fea'] = torch.cat([clip_fea, uncond_clip_fea], dim=0)
else:
base_params['clip_fea'] = None
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
# Post-processing (standard CFG only; chain modes use their own formulas)
if guidance_mode not in ("cfg_chain", "apg_chain"):
#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 and guidance_mode not in ("cfg_chain", "apg_chain"):
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
elif guidance_mode == "apg":
sigma = sample_scheduler.sigmas[idx]
x_cond = z - sigma * noise_pred_cond
x_uncond = z - sigma * noise_pred_uncond_text
x_guided = _normalized_guidance(x_cond, x_uncond, apg_omega, _momentum_buf, apg_eta, apg_norm_threshold)
noise_pred = (z - x_guided) / sigma
elif guidance_mode == "cfg_chain":
saved_ctx = base_params.pop("context_latents", None)
noise_pred_ovi_uncond = None # not used in chain modes; prevents NameError
saved_clip_fea = base_params.get('clip_fea', None)
saved_add_text_emb = base_params.get("add_text_emb", None)
saved_y = base_params.get('y', None)
saved_x = base_params.get('x', None)
saved_wananim_face = base_params.get('wananim_face_pixel_values', None)
saved_humo_audio = base_params.get('humo_audio', None)
saved_ctx_list = list(saved_ctx or [])
if context_roles is not None and len(context_roles) == len(saved_ctx_list):
role_pairs = list(zip(saved_ctx_list, context_roles))
V_ctx = [lat for lat, role in role_pairs if role == "source_video"]
I_ctx = [lat for lat, role in role_pairs if role == "reference_image"]
VI_ctx = saved_ctx_list
else:
# Backward compatibility for old Bernini outputs without roles.
V_ctx = [lat for lat in saved_ctx_list if lat.shape[1] > 1]
I_ctx = [lat for lat in saved_ctx_list if lat.shape[1] == 1]
VI_ctx = saved_ctx_list
has_extra_context = len(VI_ctx) > len(V_ctx)
# Forward 1: ∅ + uncond text
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
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)]
base_params["context_latents"] = None
eps_uncond, _, cache_state_uncond = transformer(
context=negative_embeds,
pred_id=cache_state[1] if cache_state else None, **base_params)
eps_uncond = eps_uncond[0]
# Forward 2: V + uncond text. If V is empty, this equals eps_uncond.
if V_ctx:
base_params["context_latents"] = V_ctx
eps_V, _, _ = transformer(
context=negative_embeds, pred_id=None, **base_params)
eps_V = eps_V[0]
else:
eps_V = eps_uncond
if has_extra_context:
# VI combo: source video + reference video/images, uncond text
base_params["context_latents"] = VI_ctx if VI_ctx else None
eps_VI, _, _ = transformer(
context=negative_embeds, pred_id=None, **base_params)
eps_VI = eps_VI[0]
# Final forward: VI + cond text
base_params["context_latents"] = VI_ctx if VI_ctx else None
base_params['is_uncond'] = False
base_params['clip_fea'] = saved_clip_fea
base_params["add_text_emb"] = saved_add_text_emb
base_params['y'] = saved_y
base_params['x'] = saved_x
base_params['wananim_face_pixel_values'] = saved_wananim_face
base_params['humo_audio'] = saved_humo_audio
eps_VTI, npo, cache_state_cond = transformer(
context=positive_embeds, pred_id=None, **base_params)
eps_VTI = eps_VTI[0]
noise_pred_ovi = npo[0] if npo is not None else None
base_params["context_latents"] = saved_ctx
noise_pred_cond = eps_VTI
noise_pred_uncond_text = eps_uncond
if has_extra_context:
noise_pred = (eps_uncond
+ chain_omega_V * (eps_V - eps_uncond)
+ chain_omega_I * (eps_VI - eps_V)
+ chain_omega_TI * (eps_VTI - eps_VI))
else:
noise_pred = (eps_uncond
+ chain_omega_V * (eps_V - eps_uncond)
+ chain_omega_TI * (eps_VTI - eps_V))
elif guidance_mode == "apg_chain":
sigma = sample_scheduler.sigmas[idx]
# ∅ combo: no context, uncond text
saved_ctx = base_params.pop("context_latents", None)
noise_pred_ovi_uncond = None # not used in chain modes; prevents NameError
saved_clip_fea = base_params.get('clip_fea', None)
saved_add_text_emb = base_params.get("add_text_emb", None)
saved_y = base_params.get('y', None)
saved_x = base_params.get('x', None)
saved_wananim_face = base_params.get('wananim_face_pixel_values', None)
saved_humo_audio = base_params.get('humo_audio', None)
saved_ctx_list = list(saved_ctx or [])
if context_roles is not None and len(context_roles) == len(saved_ctx_list):
I_ctx = [lat for lat, role in zip(saved_ctx_list, context_roles) if role == "reference_image"]
else:
# Backward compatibility for old Bernini outputs without roles.
I_ctx = [lat for lat in saved_ctx_list if lat.shape[1] == 1]
if not I_ctx:
I_ctx = saved_ctx_list
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
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)]
base_params["context_latents"] = None
noise_pred_0, _, cache_state_uncond = transformer(
context=negative_embeds,
pred_id=cache_state[1] if cache_state else None,
**base_params)
noise_pred_0 = noise_pred_0[0]
# I combo: reference images only, uncond text
base_params["context_latents"] = I_ctx if I_ctx else None
noise_pred_I, _, _ = transformer(
context=negative_embeds, pred_id=None, **base_params)
noise_pred_I = noise_pred_I[0]
# I+T combo: reference images only, cond text
base_params["context_latents"] = I_ctx if I_ctx else None
base_params['is_uncond'] = False
base_params['clip_fea'] = saved_clip_fea
base_params["add_text_emb"] = saved_add_text_emb
base_params['y'] = saved_y
base_params['x'] = saved_x
base_params['wananim_face_pixel_values'] = saved_wananim_face
base_params['humo_audio'] = saved_humo_audio
noise_pred_TI, npo, cache_state_cond = transformer(
context=positive_embeds, pred_id=None, **base_params)
noise_pred_TI = noise_pred_TI[0]
noise_pred_ovi = npo[0] if npo is not None else None
# Restore context_latents
base_params["context_latents"] = saved_ctx
noise_pred_cond = noise_pred_TI
noise_pred_uncond_text = noise_pred_0
# x-pred space → chain APG → v-pred space
x_0 = z - sigma * noise_pred_0
x_I = z - sigma * noise_pred_I
x_TI = z - sigma * noise_pred_TI
x_guided = _normalized_guidance_chain(
x_0, [x_I, x_TI], [apg_omega_I, apg_omega_TI],
[_momentum_buf_I, _momentum_buf_TI],
apg_eta, apg_norm_threshold)
noise_pred = (z - x_guided) / sigma
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 and guidance_mode not in ("cfg_chain", "apg_chain"):
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 and not everanimate_sampling:
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 scail_freeze_mask is not None:
masks = scail_freeze_mask.repeat(len(timesteps), 1, 1, 1, 1).to(device) > 0.5
elif 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 Exception:
pass
if everanimate_sampling:
if freeinit_args is not None:
raise ValueError("FreeInit is not supported with EverAnimate sampling")
if samples is not None:
raise ValueError("Input latent samples are not supported with EverAnimate sampling yet")
if vae is None:
raise ValueError("EverAnimate sampling requires a VAE in image_embeds")
segment_frames = int(image_embeds.get("frame_window_size", 77))
overlap_frames = int(image_embeds.get("num_overlap_frame", 0))
num_anchor_latents = int(image_embeds.get("num_video_anchor_latents", 4))
num_motion_latents_ea = int(image_embeds.get("num_motion_latents", 1))
total_output_frames = int(num_frames)
if (segment_frames - 1) % 4 != 0:
raise ValueError("frame_window_size must be 1 mod 4 for EverAnimate, e.g. 77, 81, 85")
if overlap_frames >= segment_frames:
raise ValueError("num_overlap_frame must be smaller than frame_window_size")
if num_anchor_latents <= 0:
raise ValueError("num_video_anchor_latents must be positive")
stride_frames = segment_frames - overlap_frames
num_segments = int(image_embeds.get(
"everanimate_num_segments",
1 if total_output_frames <= segment_frames else math.ceil((total_output_frames - segment_frames) / stride_frames) + 1,
))
segment_latent_frames = int(image_embeds.get(
"everanimate_segment_latent_frames",
((segment_frames + 4 * num_anchor_latents - 1) // 4) + 1,
))
target_latent_frames = segment_latent_frames - num_anchor_latents
if target_latent_frames <= 0:
raise ValueError("EverAnimate segment latent length must be larger than the anchor latent count")
# EverAnimate does not use CLIP Vision conditioning; "segment" is temporal-window terminology only.
clip_fea = None
clip_fea_neg = None
outer_freqs = freqs
outer_rope_num_frames = getattr(transformer.rope_embedder, "num_frames", None)
outer_cached_freqs = getattr(transformer, "cached_freqs", None)
outer_cached_key = getattr(transformer, "cached_key", None)
def _ea_restore_rope_state():
nonlocal freqs
freqs = outer_freqs
transformer.rope_embedder.num_frames = outer_rope_num_frames
transformer.cached_freqs = outer_cached_freqs
if hasattr(transformer, "cached_key"):
transformer.cached_key = outer_cached_key
if context_latents is None and ("default" in rope_function or bidirectional_sampling):
freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6), L_test=segment_latent_frames, k=riflex_freq_index),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
], dim=1)
elif "comfy" in rope_function:
transformer.rope_embedder.num_frames = segment_latent_frames
transformer.cached_freqs = None
if hasattr(transformer, "cached_key"):
transformer.cached_key = None
try:
lat_h = int(image_embeds.get("lat_h", noise.shape[2]))
lat_w = int(image_embeds.get("lat_w", noise.shape[3]))
pose_pixels = image_embeds.get("pose_images", None)
face_pixels = wananim_face_pixels
bg_pixels = image_embeds.get("bg_images", None)
mask_pixels = image_embeds.get("mask", None)
if pose_pixels is None or face_pixels is None:
raise ValueError("EverAnimate sampling requires pose_images and face_images")
anchor_latents = image_embeds.get("anchor_latents", None)
if anchor_latents is None:
raise ValueError("EverAnimate sampling requires anchor_latents")
manual_anchor_latents = image_embeds.get("manual_anchor_latents", anchor_latents)
user_first_anchor_latent = image_embeds.get("user_first_anchor_latent", manual_anchor_latents[:, :1])
use_image_anchor = bool(image_embeds.get("use_image_anchor", True))
use_random_frame_anchor = bool(image_embeds.get("use_random_frame_anchor", True))
random_anchor_with_user_first = bool(image_embeds.get("random_anchor_with_user_first", True))
use_repeat_anchor = bool(image_embeds.get("use_repeat_anchor", False))
def _ea_slice(tensor, start, length, dim):
if tensor is None:
return None
if tensor.shape[dim] <= 0:
raise ValueError("EverAnimate input sequence contains no frames")
take = max(min(length, tensor.shape[dim] - start), 0)
if take > 0:
out = tensor.narrow(dim, start, take)
else:
out = tensor.narrow(dim, tensor.shape[dim] - 1, 1).narrow(dim, 0, 0)
if out.shape[dim] < length:
pad_shape = [1] * tensor.ndim
pad_shape[dim] = length - out.shape[dim]
last = tensor.narrow(dim, tensor.shape[dim] - 1, 1).repeat(*pad_shape)
out = torch.cat([out, last], dim=dim)
return out
def _ea_fit_latents(lat, count, repeat_sequence=False):
if count <= 0:
return lat[:, :0]
if lat.shape[1] > count:
return lat[:, :count]
if lat.shape[1] < count:
if repeat_sequence:
repeats = math.ceil(count / lat.shape[1])
lat = lat.repeat(1, repeats, 1, 1)[:, :count]
else:
lat = torch.cat([lat, lat[:, -1:].repeat(1, count - lat.shape[1], 1, 1)], dim=1)
return lat
def _ea_encode_video_latents(video_cthw):
vae.to(device)
return vae.encode([video_cthw.to(device, vae.dtype)], device, tiled=tiled_vae, pbar=False)[0].to(device, dtype)
def _ea_encode_pose_latents(pose_cthw):
vae.to(device)
return vae.encode([pose_cthw.to(device, vae.dtype)], device, tiled=tiled_vae, pbar=False).to(device, dtype)
def _ea_mask_latents(mask_thw):
mask_thw = (1.0 - mask_thw.to(device, vae.dtype)).clamp(0.0, 1.0)
mask_thw = torch.nn.functional.interpolate(
mask_thw.unsqueeze(0).unsqueeze(0),
size=(mask_thw.shape[0], lat_h, lat_w),
mode="nearest",
).squeeze(0).squeeze(0)
expanded = torch.cat([mask_thw[:1].repeat(4, 1, 1), mask_thw[1:]], dim=0)
latent_count = expanded.shape[0] // 4
expanded = expanded[:latent_count * 4]
mask_lat = expanded.view(latent_count, 4, lat_h, lat_w).permute(1, 0, 2, 3).contiguous()
if mask_lat.shape[1] < target_latent_frames:
mask_lat = torch.cat([mask_lat, mask_lat[:, -1:].repeat(1, target_latent_frames - mask_lat.shape[1], 1, 1)], dim=1)
return mask_lat[:, :target_latent_frames].to(device, dtype)
def _ea_make_condition(current_anchor_latents, motion_latents, bg_latents=None, mask_latents=None):
cond_mask = torch.zeros(4, segment_latent_frames, lat_h, lat_w, device=device, dtype=dtype)
cond_latents = torch.zeros(16, segment_latent_frames, lat_h, lat_w, device=device, dtype=dtype)
current_anchor_latents = _ea_fit_latents(current_anchor_latents.to(device, dtype), num_anchor_latents)
cond_mask[:, :num_anchor_latents] = 1
cond_latents[:, :num_anchor_latents] = current_anchor_latents
target_start = num_anchor_latents
if bg_latents is not None:
bg_latents = _ea_fit_latents(bg_latents.to(device, dtype), target_latent_frames)
cond_latents[:, target_start:target_start + bg_latents.shape[1]] = bg_latents
if mask_latents is not None:
cond_mask[:, target_start:target_start + mask_latents.shape[1]] = mask_latents[:, :bg_latents.shape[1]]
if motion_latents is not None and motion_latents.shape[1] > 0:
motion_latents = _ea_fit_latents(motion_latents.to(device, dtype), min(num_motion_latents_ea, target_latent_frames))
cond_latents[:, target_start:target_start + motion_latents.shape[1]] = motion_latents
return torch.cat([cond_mask, cond_latents], dim=0)
def _ea_pick_random_frame_indices(frame_count, count):
if count <= 0:
return []
if frame_count <= 1:
return [0] * count
if random_anchor_with_user_first:
candidates = torch.arange(1, frame_count, dtype=torch.long)
sample_count = min(count, candidates.numel())
if sample_count > 0:
perm = torch.randperm(candidates.numel(), generator=seed_g)[:sample_count]
indices = sorted(int(candidates[i]) for i in perm)
else:
indices = []
else:
candidates = torch.arange(1, frame_count, dtype=torch.long)
sample_count = min(max(count - 1, 0), candidates.numel())
if sample_count > 0:
perm = torch.randperm(candidates.numel(), generator=seed_g)[:sample_count]
indices = [0] + sorted(int(candidates[i]) for i in perm)
else:
indices = [0]
while len(indices) < count:
indices.append(indices[-1] if indices else 0)
return indices[:count]
def _ea_pose_difference_score(pose_01, masks, i, j):
union = masks[i] | masks[j]
if not bool(union.any()):
return 0.0
xor_ratio = float((masks[i] ^ masks[j]).sum().item()) / float(union.sum().item())
color_diff = (pose_01[i] - pose_01[j]).abs().mean(dim=0)
fg_color_diff = float(color_diff[union].mean().item())
return xor_ratio + 0.25 * fg_color_diff
def _ea_select_pose_triangle_indices(pose_cthw):
frame_count = int(pose_cthw.shape[1])
if frame_count <= 0:
return []
if frame_count == 1:
return [0, 0, 0]
if frame_count == 2:
return [0, 1, 1]
pose = pose_cthw[:, :frame_count].detach().to(torch.float32).clamp(-1, 1).cpu().movedim(0, 1)
max_side = max(int(pose.shape[-2]), int(pose.shape[-1]))
if max_side > 128:
scale = 128.0 / max_side
new_h = max(1, int(round(pose.shape[-2] * scale)))
new_w = max(1, int(round(pose.shape[-1] * scale)))
pose = torch.nn.functional.interpolate(pose, size=(new_h, new_w), mode="area")
pose_01 = (pose + 1.0) * 0.5
masks = torch.any(pose_01 > (20.0 / 255.0), dim=1)
best_pair = None
best_score = -1.0
diff_cache = {}
def diff(i, j):
key = (min(i, j), max(i, j))
if key not in diff_cache:
diff_cache[key] = _ea_pose_difference_score(pose_01, masks, key[0], key[1])
return diff_cache[key]
for i in range(1, frame_count):
diff_0_i = diff(0, i)
for j in range(i + 1, frame_count):
pairwise_sum = diff_0_i + diff(0, j) + diff(i, j)
if pairwise_sum > best_score:
best_score = pairwise_sum
best_pair = (i, j)
if best_pair is None:
fallback = min(2, frame_count - 1)
return [0, 1, fallback]
return [0, best_pair[0], best_pair[1]]
def _ea_preprocess_decoded_anchor_frame(frame):
# Match the reference path: VAE decode -> RGB uint8/PIL -> preprocess_image [-1, 1].
frame = frame.to(device=device, dtype=torch.float32).clamp(-1.0, 1.0)
frame_u8 = ((frame + 1.0) * 127.5).clamp(0.0, 255.0).to(torch.uint8)
return frame_u8.to(device=device, dtype=torch.float32).div(127.5).sub(1.0).to(vae.dtype)
def _ea_encode_anchor_frames(video_cthw, frame_indices):
encoded = []
vae.to(device)
for frame_idx in frame_indices:
frame = _ea_preprocess_decoded_anchor_frame(video_cthw[:, frame_idx:frame_idx + 1])
encoded.append(vae.encode([frame], device, tiled=tiled_vae, pbar=False)[0][:, :1].to(device, dtype))
return encoded
def _ea_build_generated_anchor(video_cthw, pose_cthw):
if use_random_frame_anchor:
random_slots = num_anchor_latents - 1 if random_anchor_with_user_first else num_anchor_latents
frame_indices = _ea_pick_random_frame_indices(video_cthw.shape[1], random_slots)
encoded = _ea_encode_anchor_frames(video_cthw, frame_indices)
if random_anchor_with_user_first:
encoded.append(user_first_anchor_latent.to(device, dtype))
if not encoded:
encoded.append(user_first_anchor_latent.to(device, dtype))
return _ea_fit_latents(torch.cat(encoded, dim=1), num_anchor_latents, repeat_sequence=use_repeat_anchor)
num_user_slots = min(2, num_anchor_latents)
num_sample_slots = max(num_anchor_latents - num_user_slots, 0)
selected_triplet = _ea_select_pose_triangle_indices(pose_cthw)
sample_indices = selected_triplet[1:1 + num_sample_slots]
encoded = _ea_encode_anchor_frames(video_cthw, sample_indices)
if num_sample_slots > 0 and encoded:
sampled = _ea_fit_latents(torch.cat(encoded, dim=1), num_sample_slots)
anchor_latents_out = [sampled]
else:
anchor_latents_out = []
if num_user_slots > 0:
anchor_latents_out.append(user_first_anchor_latent.to(device, dtype).repeat(1, num_user_slots, 1, 1))
if not anchor_latents_out:
anchor_latents_out.append(user_first_anchor_latent.to(device, dtype))
return _ea_fit_latents(torch.cat(anchor_latents_out, dim=1), num_anchor_latents, repeat_sequence=use_repeat_anchor)
log.info(
f"EverAnimate sampling: {total_output_frames} requested frames, {num_segments} segments, "
f"{segment_frames} frames/segment, {overlap_frames} overlap frames, {num_anchor_latents} anchor latents"
)
callback = prepare_callback(patcher, num_segments * len(timesteps))
generated_segments = []
video_anchor_latent = None
prev_segment_latents = None
step_iteration_count = 0
for segment_idx in range(num_segments):
segment_start = segment_idx * stride_frames
current_anchor = anchor_latents if segment_idx == 0 or video_anchor_latent is None else video_anchor_latent
if segment_idx == 0 or prev_segment_latents is None or num_motion_latents_ea <= 0:
motion_latents = torch.zeros(16, max(num_motion_latents_ea, 0), lat_h, lat_w, device=device, dtype=dtype)
else:
motion_latents = _ea_fit_latents(prev_segment_latents[:, -num_motion_latents_ea:], num_motion_latents_ea)
pose_window = _ea_slice(pose_pixels, segment_start, segment_frames, 1)
face_window = _ea_slice(face_pixels, segment_start, segment_frames, 2).to(device, torch.float32)
pose_latents = _ea_encode_pose_latents(pose_window)
bg_latents = mask_latents = None
if bg_pixels is not None and mask_pixels is not None:
bg_window = _ea_slice(bg_pixels, segment_start, segment_frames, 1)
bg_latents = _ea_fit_latents(_ea_encode_video_latents(bg_window), target_latent_frames)
mask_window = _ea_slice(mask_pixels, segment_start, segment_frames, 0)
mask_latents = _ea_mask_latents(mask_window)
image_cond_in = _ea_make_condition(current_anchor, motion_latents, bg_latents, mask_latents)
latent = torch.randn(
16,
segment_latent_frames,
lat_h,
lat_w,
dtype=torch.float32,
generator=seed_g,
device=torch.device("cpu"),
).to(device)
seq_len = math.ceil((latent.shape[2] * latent.shape[3]) / 4 * latent.shape[1])
if isinstance(scheduler, dict):
sample_scheduler = copy.deepcopy(scheduler["sample_scheduler"])
segment_timesteps = scheduler["timesteps"]
else:
sample_scheduler, segment_timesteps, _, _ = get_scheduler(
scheduler, total_steps, start_step, end_step, shift, device,
transformer.dim, denoise_strength, sigmas=sigmas,
)
if hasattr(sample_scheduler, "timesteps"):
sample_scheduler.timesteps = segment_timesteps
self.cache_state = [None, None]
sampling_pbar = tqdm(total=len(segment_timesteps), desc=f"EverAnimate segment {segment_idx + 1}/{num_segments}", position=0, leave=True)
for i, t in enumerate(segment_timesteps):
timestep = torch.tensor([t]).to(device)
latent_model_input = latent.to(device)
noise_pred, _, self.cache_state = predict_with_cfg(
latent_model_input,
cfg[min(i, len(cfg) - 1)],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep,
i,
image_cond_in,
clip_fea,
cache_state=self.cache_state,
wananim_face_pixels=face_window,
wananim_pose_latents=pose_latents,
wananim_num_anchor_latents=num_anchor_latents,
)
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).detach()
if callback is not None:
callback_latent = (latent_model_input - noise_pred.to(device) * timestep.to(device) / 1000).detach()
callback(step_iteration_count, callback_latent.permute(1, 0, 2, 3), None, num_segments * len(segment_timesteps))
del callback_latent
sampling_pbar.update(1)
step_iteration_count += 1
del noise_pred, latent_model_input, timestep
sampling_pbar.close()
segment_latents = latent[:, num_anchor_latents:].detach()
prev_segment_latents = segment_latents
vae.to(device)
segment_video = vae.decode(
segment_latents.unsqueeze(0).to(device, vae.dtype),
device=device,
tiled=tiled_vae,
pbar=False,
)[0].detach().cpu()
if segment_video.shape[1] > segment_frames:
segment_video = segment_video[:, :segment_frames]
if segment_idx == 0:
generated_segments.append(segment_video)
if use_image_anchor:
video_anchor_latent = _ea_build_generated_anchor(segment_video, pose_window)
else:
video_anchor_latent = segment_latents.to(device, dtype)
else:
generated_segments.append(segment_video[:, overlap_frames:])
del latent, segment_video, segment_latents, pose_latents, face_window, image_cond_in
mm.soft_empty_cache()
gc.collect()
gen_video_samples = torch.cat(generated_segments, dim=1)
if gen_video_samples.shape[1] > total_output_frames:
gen_video_samples = gen_video_samples[:, :total_output_frames]
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 Exception:
pass
return {"video": gen_video_samples.permute(1, 2, 3, 0)},
finally:
_ea_restore_rope_state()
# 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:
# ============ Get latent spatial dimensions ============
lat_h = latent.shape[-2]
lat_w = latent.shape[-1]
# ============================================
counter = torch.zeros_like(latent_model_input, device=device)
noise_pred = torch.zeros_like(latent_model_input, device=device)
if image_cond is not None and image_cond.ndim > 1:
latent_video_length = min(latent_video_length, image_cond.shape[1])
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)
# ============ Build msk channels ============
window_frames = len(c)
window_msk = partial_img_emb[:4].clone()
# ========================================
# ============ Transition replacement (1st window only) ============
if has_transition and c[0] == 0 and not has_prefix:
partial_img_emb[4:20, 1:1+transition_len] = transition_latent.to(device, dtype=transition_latent.dtype)
log.info(f"Replaced first {transition_len} latent frames with transition_video (context_options mode)")
# ============================================================
# ============ Set transition mask ============
if has_transition and c[0] == 0 and not has_prefix:
window_trans_start = 1
window_trans_end = 1 + transition_len
if window_trans_start < window_trans_end:
mask_slice = transition_mask_values[0:transition_len]
window_msk[:, window_trans_start:window_trans_end] = mask_slice.view(1, -1, 1, 1)
# ================================================================
# ============ Prefix: prepend cached prefix context to non-first windows ============
if has_prefix and c[0] != 0 and prefix_prepend_latents > 0:
prefix_ctx = image_cond[:, :prefix_prepend_latents].to(device, dtype)
partial_img_emb = torch.cat([prefix_ctx, partial_img_emb], dim=1)
prefix_msk = image_cond[:4, :prefix_prepend_latents].to(device, dtype)
window_msk = torch.cat([prefix_msk, window_msk], dim=1)
# ======================================================================
# ============ ref_latent replacement (skip when prefix active) ============
if not has_prefix:
if c[0] != 0 and context_reference_latent is not None:
if context_reference_latent.shape[0] == 1:
new_init_image = context_reference_latent[0, :, 0].to(device)
partial_img_emb[:, 0] = torch.cat([image_cond[:4, 0].to(device), new_init_image], dim=0)
window_msk[:, 0] = image_cond[:4, 0].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}")
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)
window_msk[:, 0] = image_cond[:4, 0].to(device)
else:
new_init_image = image_cond[:, 0].to(device)
partial_img_emb[:, 0] = new_init_image
window_msk[:, 0] = image_cond[:4, 0].to(device)
# ==========================================================================
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
scail_window_prepend_latents = scail_prefix_prepend_latents if c[0] != 0 else 0
if scail_window_prepend_latents > 0:
prefix_noise = latent_model_input[:, :scail_window_prepend_latents].to(device)
partial_latent_model_input = torch.cat([prefix_noise, partial_latent_model_input], dim=1)
# ============ Prefix: prepend noise for cached prefix context ============
if has_prefix and c[0] != 0 and prefix_prepend_latents > 0:
prefix_noise = latent_model_input[:, :prefix_prepend_latents].to(device)
partial_latent_model_input = torch.cat([prefix_noise, partial_latent_model_input], dim=1)
# =============================================================
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)
pose_limit = context_frames - 1
partial_wananim_pose_latents = wananim_pose_latents[:, :, center_indices][:, :, :pose_limit].to(device, dtype)
# ============ Prefix: prepend face/pose for cached prefix context ============
if has_prefix and c[0] != 0 and prefix_prepend_latents > 0:
if partial_wananim_face_pixels is not None:
prefix_face = wananim_face_pixels[:, :, :prefix_prepend_latents * 4].to(device, dtype)
partial_wananim_face_pixels = torch.cat([prefix_face, partial_wananim_face_pixels], dim=2)
if partial_wananim_pose_latents is not None:
prefix_pose = wananim_pose_latents[:, :, :prefix_prepend_latents].to(device, dtype)
partial_wananim_pose_latents = torch.cat([prefix_pose, partial_wananim_pose_latents], dim=2)
# ================================================================
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]
# ============ Replace msk placeholder channels ============
# image_cond is optional in this path. Only build image_cond_in
# when partial_img_emb is available.
if partial_img_emb is not None:
# image_cond structure: [image_cond_mask (4) + image_embeds (32)] = 36 channels
# Replace the first 4 channels of image_cond_mask with window_msk
image_cond_in = partial_img_emb.clone()
image_cond_in[:4] = window_msk # Replace the first 4 channels
else:
image_cond_in = None
# ========================================
original_seq_len = seq_len
prepend_seq_latents = 0
if has_prefix and c[0] != 0 and prefix_prepend_latents > 0:
prepend_seq_latents += prefix_prepend_latents
if scail_window_prepend_latents > 0:
prepend_seq_latents += scail_window_prepend_latents
if prepend_seq_latents > 0:
seq_len = original_seq_len + prepend_seq_latents * base_patches_per_frame
# Slice context_latents for current context window
sliced_context_latents = None
if context_latents is not None:
sliced_context_latents = []
for lat in context_latents:
if lat.shape[1] > 1 and lat.shape[1] == noise.shape[1]:
sliced_context_latents.append(lat[:, c].to(device))
else:
sliced_context_latents.append(lat.to(device))
# Context latents are already sliced to this local window. The target
# window RoPE also starts at 0, so keep Bernini context RoPE local.
noise_pred_context, _, new_teacache = predict_with_cfg(
partial_latent_model_input,
cfg[idx], positive,
text_embeds["negative_prompt_embeds"],
partial_timestep, idx, image_cond_in, 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, context_latents=sliced_context_latents,
context_roles=context_roles, context_window_start=0, scail_context_prepend_latents=scail_window_prepend_latents)
seq_len = original_seq_len # restore seq_len
# ============ Prefix: slice off prepended predictions ============
if has_prefix and c[0] != 0 and prefix_prepend_latents > 0:
noise_pred_context = noise_pred_context[:, prefix_prepend_latents:]
# ==============================================================
if scail_window_prepend_latents > 0:
noise_pred_context = noise_pred_context[:, scail_window_prepend_latents:]
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,
context_latents=context_latents, context_roles=context_roles,
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).detach()
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 Exception:
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)
prefix_ctx = image_embeds.get("prefix_ctx", None)
prefix_T = image_embeds.get("prefix_T", 0)
current_ref_images = image_embeds.get("start_ref_image", None)
if current_ref_images is not None:
log.info(
"WanAnimate: Detected manual start reference image, enabling continuous generation across windows.")
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()
if current_ref_images is not None:
mask_reft_len = refert_num
else:
mask_reft_len = 0 if start == 0 else refert_num
self.cache_state = [None, None]
noise = torch.randn(16, latent_window_size + 1 + prefix_T, 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 or has_transition or prefix_ctx 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 and not has_transition:
temporal_ref_latents = vae.encode([bg_image_slice], device,tiled=tiled_vae)[0]
else:
# Build concatenated image
concat_parts = []
# 1. Add start_ref_image (if any)
if current_ref_images is not None:
concat_parts.append(current_ref_images.to(device, dtype=vae.dtype))
# 2. Add the remaining part of bg_image_slice
bg_start_idx = mask_reft_len if current_ref_images is not None else 0
if bg_image_slice.shape[1] > bg_start_idx:
concat_parts.append(bg_image_slice[:, bg_start_idx:])
# 3. VAE encode the image part
if len(concat_parts) > 0:
image_concat = torch.cat(concat_parts, dim=1)
temporal_ref_latents = vae.encode([image_concat.to(device, vae.dtype)], device, tiled=tiled_vae, pbar=False)[0]
else:
temporal_ref_latents = None
# 4. Initialize mask
msk = torch.zeros(4, latent_window_size, lat_h, lat_w, device=device, dtype=dtype)
# 4a. Hard mask for start_ref_image
if current_ref_images is not None:
msk[:, :mask_reft_len] = 1
# 4b. Hard mask for transition_latent (replaces first 8 target latent frames ON THE FIRST CHUNK ONLY)
if has_transition and start == 0:
if temporal_ref_latents is not None:
# ============ Mod: Replace first transition_len frames of target latent ============
# Keep target latent after transition_len
remaining_target = temporal_ref_latents[:, transition_len:]
# transition_latent replaces the first transition_len target latent frames
temporal_ref_latents = torch.cat([transition_latent.to(device, dtype=transition_latent.dtype), remaining_target], dim=1)
log.info(f"Replaced first {transition_len} target latent frames with transition_video in Chunk 0")
# ========================================================================
else:
# Only transition_latent, no image part
temporal_ref_latents = transition_latent.to(device, dtype=dtype)
# Set hard mask.
transition_start = 0
msk[:, transition_start:transition_start+transition_len] = transition_mask_values.to(device).view(1, -1, 1, 1)
# [FIX] Unified shape alignment for temporal_ref_latents and msk
# Handles VAE time downsampling offset: (T-1)//4 + 1 vs latent_window_size
# Moved outside branches to ensure execution for both transition and non-transition modes
if temporal_ref_latents is not None and 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 or prefix_ctx is not None:
ref_part = prefix_ctx if prefix_ctx is not None else ref_latent
temporal_ref_latents = torch.cat([msk, temporal_ref_latents], dim=0) # 4+C T H W
image_cond_in = torch.cat([ref_part.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
# ============ Prefix prepend for looping: copy first frame of current chunk ============
if prefix_T > 0:
if pose_input_slice is not None:
first_pose = pose_input_slice[:, :, :1].repeat(1, 1, prefix_T, 1, 1)
pose_input_slice = torch.cat([first_pose, pose_input_slice], dim=2)
if face_images_in is not None:
first_face = face_images_in[:, :, :1].repeat(1, 1, prefix_T * 4, 1, 1)
face_images_in = torch.cat([first_face, face_images_in], dim=2)
# ========================================================================================
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,
context_latents=context_latents, context_roles=context_roles,
)
if prefix_T > 0:
noise_pred = noise_pred[:, prefix_T:]
latent = latent[:, prefix_T:]
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).detach()
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 or current_ref_images is not None:
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)
if canvas_expansion_px:
if gen_video_samples.shape[1] > canvas_expansion_px:
gen_video_samples = gen_video_samples[:, canvas_expansion_px:]
log.info(f"WanAnimate loop: trimmed {canvas_expansion_px} canvas expansion pixel frames from output")
else:
log.warning(
f"WanAnimate loop: canvas_expansion_px={canvas_expansion_px} is not smaller than output length {gen_video_samples.shape[1]}, skipping trim"
)
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 Exception:
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,
context_latents=context_latents, context_roles=context_roles,
)
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,
context_latents=context_latents, context_roles=context_roles,)
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).detach()
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).detach()
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).detach()
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)
latent = latent.detach()
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).detach()
#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:
image_latent = None
if scail_freeze_mask is not None:
image_latent = original_image.to(device)
elif 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)
)
if image_latent is not None:
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)
# Release per-step tensor references after all scheduler/callback work is done.
noise_pred = noise_pred_in = noise_pred_ovi = None
noise_pred_flipped = None
latent_model_input = latent_model_input_ovi = None
latent_model_input_flipped = None
latent_flipped = latent_backwards = raw_latent = None
timestep = orig_timestep = None
noise_for_cond = timestep_cond = None
counter = partial_latent_model_input = prefix_noise = None
partial_img_emb = partial_control_latents = partial_control_camera_latents = None
partial_vace_context = partial_audio_proj = partial_s2v_audio_input = None
partial_s2v_pose = partial_add_cond = None
partial_wananim_face_pixels = partial_wananim_pose_latents = None
except Exception as e:
log.error(f"Error during sampling: {e}")
raise
finally:
if force_offload and not model["auto_cpu_offload"]:
offload_transformer(transformer)
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,
}
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except Exception:
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,
"has_prefix": has_prefix,
"canvas_expansion_px": canvas_expansion_px,
"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"}),
"guidance_mode": (["cfg", "apg", "apg_chain", "cfg_chain"], {"default": "cfg", "tooltip": "Guidance mode: cfg (standard CFG), apg (single-condition APG), apg_chain (image-reference APG chain), or cfg_chain (Bernini chained CFG)"}),
"apg_eta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "APG: parallel/orthogonal balance (0=orthogonal only, 1=full)"}),
"apg_momentum": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01, "tooltip": "APG: EMA momentum for smoothing guidance differences"}),
"apg_norm_threshold": ("FLOAT", {"default": 50.0, "min": 0.0, "max": 1000.0, "step": 1.0, "tooltip": "APG: L2 norm clipping threshold (0=disabled)"}),
"apg_omega": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG: guidance strength"}),
"apg_omega_I": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG chain: image-only guidance strength"}),
"apg_omega_TI": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "APG chain: text+image guidance strength"}),
"chain_omega_V": ("FLOAT", {"default": 1.25, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: video-only guidance strength"}),
"chain_omega_I": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: extra reference context strength (VI - V; reference video/images)"}),
"chain_omega_TI": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 30.0, "step": 0.01, "tooltip": "Chain CFG: text+image guidance strength"}),
"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 Exception:
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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