nodes/kijai-ComfyUI-WanVideoWrapper
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kijai-ComfyUI-WanVideoWrapper/nodes.py
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kijai 740157bd61 Fix MultiTalk when using the Multi/InfiniteTalk node 
InfiniteTalk was also mistakenly using only first frame mask... but honestly after trying the reference method, the result was worse, so I'll default to single frame mask in all cases for now.
2025-08-29 14:48:12 +03:00

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import os, gc, math
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
import inspect
import hashlib
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from .wanvideo.modules.model import rope_params
from .custom_linear import remove_lora_from_module, set_lora_params
from .wanvideo.schedulers import get_scheduler, get_sampling_sigmas, retrieve_timesteps, scheduler_list
from .gguf.gguf import set_lora_params_gguf
from .multitalk.multitalk import timestep_transform, add_noise
from .utils import(log, print_memory, apply_lora, clip_encode_image_tiled, fourier_filter,
add_noise_to_reference_video, optimized_scale, setup_radial_attention,
compile_model, dict_to_device, tangential_projection, set_module_tensor_to_device, get_raag_guidance)
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 .taehv import TAEHV
from contextlib import nullcontext
from einops import rearrange
from comfy import model_management as mm
from comfy.utils import ProgressBar, common_upscale
from comfy.clip_vision import clip_preprocess, ClipVisionModel
from comfy.cli_args import args, LatentPreviewMethod
import folder_paths
script_directory = os.path.dirname(os.path.abspath(__file__))
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
VAE_STRIDE = (4, 8, 8)
PATCH_SIZE = (1, 2, 2)
try:
from .gguf.gguf import GGUFParameter
except:
pass
class MetaParameter(torch.nn.Parameter):
def __new__(cls, dtype, quant_type=None):
data = torch.empty(0, dtype=dtype)
self = torch.nn.Parameter(data, requires_grad=False)
self.quant_type = quant_type
return self
def offload_transformer(transformer):
for block in transformer.blocks:
block.kv_cache = None
transformer.teacache_state.clear_all()
transformer.magcache_state.clear_all()
transformer.easycache_state.clear_all()
if transformer.patched_linear:
for name, param in transformer.named_parameters():
if "controlnet" in name:
continue
module = transformer
subnames = name.split('.')
for subname in subnames[:-1]:
module = getattr(module, subname)
attr_name = subnames[-1]
if param.data.is_floating_point():
meta_param = torch.nn.Parameter(torch.empty_like(param.data, device='meta'), requires_grad=False)
setattr(module, attr_name, meta_param)
elif isinstance(param.data, GGUFParameter):
quant_type = getattr(param, 'quant_type', None)
setattr(module, attr_name, MetaParameter(param.data.dtype, quant_type))
else:
pass
else:
transformer.to(offload_device)
mm.soft_empty_cache()
gc.collect()
class WanVideoEnhanceAVideo:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight": ("FLOAT", {"default": 2.0, "min": 0, "max": 100, "step": 0.01, "tooltip": "The feta Weight of the Enhance-A-Video"}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply Enhance-A-Video"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the steps to apply Enhance-A-Video"}),
},
}
RETURN_TYPES = ("FETAARGS",)
RETURN_NAMES = ("feta_args",)
FUNCTION = "setargs"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video"
def setargs(self, **kwargs):
return (kwargs, )
class WanVideoSetBlockSwap:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("WANVIDEOMODEL", ),
},
"optional": {
"block_swap_args": ("BLOCKSWAPARGS", ),
}
}
RETURN_TYPES = ("WANVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
def loadmodel(self, model, block_swap_args=None):
if block_swap_args is None:
return (model,)
patcher = model.clone()
if 'transformer_options' not in patcher.model_options:
patcher.model_options['transformer_options'] = {}
patcher.model_options["transformer_options"]["block_swap_args"] = block_swap_args
return (patcher,)
class WanVideoSetRadialAttention:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("WANVIDEOMODEL", ),
"dense_attention_mode": ([
"sdpa",
"flash_attn_2",
"flash_attn_3",
"sageattn",
"sparse_sage_attention",
], {"default": "sageattn", "tooltip": "The attention mode for dense attention"}),
"dense_blocks": ("INT", {"default": 1, "min": 0, "max": 40, "step": 1, "tooltip": "Number of blocks to apply normal attention to"}),
"dense_vace_blocks": ("INT", {"default": 1, "min": 0, "max": 15, "step": 1, "tooltip": "Number of vace blocks to apply normal attention to"}),
"dense_timesteps": ("INT", {"default": 2, "min": 0, "max": 100, "step": 1, "tooltip": "The step to start applying sparse attention"}),
"decay_factor": ("FLOAT", {"default": 0.2, "min": 0, "max": 1, "step": 0.01, "tooltip": "Controls how quickly the attention window shrinks as the distance between frames increases in the sparse attention mask."}),
"block_size":([128, 64], {"default": 128, "tooltip": "Radial attention block size, larger blocks are faster but restricts usable dimensions more."}),
}
}
RETURN_TYPES = ("WANVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Sets radial attention parameters, dense attention refers to normal attention"
def loadmodel(self, model, dense_attention_mode, dense_blocks, dense_vace_blocks, dense_timesteps, decay_factor, block_size):
if "radial" not in model.model.diffusion_model.attention_mode:
raise Exception("Enable radial attention first in the model loader.")
patcher = model.clone()
if 'transformer_options' not in patcher.model_options:
patcher.model_options['transformer_options'] = {}
patcher.model_options["transformer_options"]["dense_attention_mode"] = dense_attention_mode
patcher.model_options["transformer_options"]["dense_blocks"] = dense_blocks
patcher.model_options["transformer_options"]["dense_vace_blocks"] = dense_vace_blocks
patcher.model_options["transformer_options"]["dense_timesteps"] = dense_timesteps
patcher.model_options["transformer_options"]["decay_factor"] = decay_factor
patcher.model_options["transformer_options"]["block_size"] = block_size
return (patcher,)
class WanVideoBlockList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"blocks": ("STRING", {"default": "1", "multiline":True}),
}
}
RETURN_TYPES = ("INT",)
RETURN_NAMES = ("block_list", )
FUNCTION = "create_list"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Comma separated list of blocks to apply block swap to, can also use ranges like '0-5' or '0,2,3-5' etc., can be connected to the dense_blocks input of 'WanVideoSetRadialAttention' node"
def create_list(self, blocks):
block_list = []
for line in blocks.splitlines():
for part in line.split(","):
part = part.strip()
if not part:
continue
if "-" in part:
try:
start, end = map(int, part.split("-", 1))
block_list.extend(range(start, end + 1))
except Exception:
raise ValueError(f"Invalid range: '{part}'")
else:
try:
block_list.append(int(part))
except Exception:
raise ValueError(f"Invalid integer: '{part}'")
return (block_list,)
# In-memory cache for prompt extender output
_extender_cache = {}
cache_dir = os.path.join(script_directory, 'text_embed_cache')
def get_cache_path(prompt):
cache_key = prompt.strip()
cache_hash = hashlib.sha256(cache_key.encode('utf-8')).hexdigest()
return os.path.join(cache_dir, f"{cache_hash}.pt")
def get_cached_text_embeds(positive_prompt, negative_prompt):
os.makedirs(cache_dir, exist_ok=True)
context = None
context_null = None
pos_cache_path = get_cache_path(positive_prompt)
neg_cache_path = get_cache_path(negative_prompt)
# Try to load positive prompt embeds
if os.path.exists(pos_cache_path):
try:
log.info(f"Loading prompt embeds from cache: {pos_cache_path}")
context = torch.load(pos_cache_path)
except Exception as e:
log.warning(f"Failed to load cache: {e}, will re-encode.")
# Try to load negative prompt embeds
if os.path.exists(neg_cache_path):
try:
log.info(f"Loading prompt embeds from cache: {neg_cache_path}")
context_null = torch.load(neg_cache_path)
except Exception as e:
log.warning(f"Failed to load cache: {e}, will re-encode.")
return context, context_null
class WanVideoTextEncodeCached:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model_name": (folder_paths.get_filename_list("text_encoders"), {"tooltip": "These models are loaded from 'ComfyUI/models/text_encoders'"}),
"precision": (["fp32", "bf16"],
{"default": "bf16"}
),
"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
"quantization": (['disabled', 'fp8_e4m3fn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
"use_disk_cache": ("BOOLEAN", {"default": True, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
"device": (["gpu", "cpu"], {"default": "gpu", "tooltip": "Device to run the text encoding on."}),
},
"optional": {
"extender_args": ("WANVIDEOPROMPTEXTENDER_ARGS", {"tooltip": "Use this node to extend the prompt with additional text."}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", "WANVIDEOTEXTEMBEDS", "STRING")
RETURN_NAMES = ("text_embeds", "negative_text_embeds", "positive_prompt")
OUTPUT_TOOLTIPS = ("The text embeddings for both prompts", "The text embeddings for the negative prompt only (for NAG)", "Positive prompt to display prompt extender results")
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = """Encodes text prompts into text embeddings. This node loads and completely unloads the T5 after done,
leaving no VRAM or RAM imprint. If prompts have been cached before T5 is not loaded at all.
negative output is meant to be used with NAG, it contains only negative prompt embeddings.
Additionally you can provide a Qwen LLM model to extend the positive prompt with either one
of the original Wan templates or a custom system prompt.
"""
def process(self, model_name, precision, positive_prompt, negative_prompt, quantization='disabled', use_disk_cache=True, device="gpu", extender_args=None):
from .nodes_model_loading import LoadWanVideoT5TextEncoder
pbar = ProgressBar(3)
echoshot = True if "[1]" in positive_prompt else False
# Handle prompt extension with in-memory cache
orig_prompt = positive_prompt
if extender_args is not None:
extender_key = (orig_prompt, str(extender_args))
if extender_key in _extender_cache:
positive_prompt = _extender_cache[extender_key]
log.info(f"Loaded extended prompt from in-memory cache: {positive_prompt}")
else:
from .qwen.qwen import QwenLoader, WanVideoPromptExtender
log.info("Using WanVideoPromptExtender to process prompts")
qwen, = QwenLoader().load(
extender_args["model"],
load_device="main_device" if device == "gpu" else "cpu",
precision=precision)
positive_prompt, = WanVideoPromptExtender().generate(
qwen=qwen,
max_new_tokens=extender_args["max_new_tokens"],
prompt=orig_prompt,
device=device,
force_offload=False,
custom_system_prompt=extender_args["system_prompt"],
seed=extender_args["seed"]
)
log.info(f"Extended positive prompt: {positive_prompt}")
_extender_cache[extender_key] = positive_prompt
del qwen
pbar.update(1)
# Now check disk cache using the (possibly extended) prompt
if use_disk_cache:
context, context_null = get_cached_text_embeds(positive_prompt, negative_prompt)
if context is not None and context_null is not None:
return{
"prompt_embeds": context,
"negative_prompt_embeds": context_null,
"echoshot": echoshot,
},{"prompt_embeds": context_null}, positive_prompt
t5, = LoadWanVideoT5TextEncoder().loadmodel(model_name, precision, "main_device", quantization)
pbar.update(1)
prompt_embeds_dict, = WanVideoTextEncode().process(
positive_prompt=positive_prompt,
negative_prompt=negative_prompt,
t5=t5,
force_offload=False,
model_to_offload=None,
use_disk_cache=use_disk_cache,
device=device
)
pbar.update(1)
del t5
mm.soft_empty_cache()
gc.collect()
return (prompt_embeds_dict, {"prompt_embeds": prompt_embeds_dict["negative_prompt_embeds"]}, positive_prompt)
#region TextEncode
class WanVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"t5": ("WANTEXTENCODER",),
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
"device": (["gpu", "cpu"], {"default": "gpu", "tooltip": "Device to run the text encoding on."}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Encodes text prompts into text embeddings. For rudimentary prompt travel you can input multiple prompts separated by '|', they will be equally spread over the video length"
def process(self, positive_prompt, negative_prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False, device="gpu"):
if t5 is None and not use_disk_cache:
raise ValueError("T5 encoder is required for text encoding. Please provide a valid T5 encoder or enable disk cache.")
echoshot = True if "[1]" in positive_prompt else False
if use_disk_cache:
context, context_null = get_cached_text_embeds(positive_prompt, negative_prompt)
if context is not None and context_null is not None:
return{
"prompt_embeds": context,
"negative_prompt_embeds": context_null,
"echoshot": echoshot,
},
if t5 is None:
raise ValueError("No cached text embeds found for prompts, please provide a T5 encoder.")
if model_to_offload is not None and device == "gpu":
try:
log.info(f"Moving video model to {offload_device}")
model_to_offload.model.to(offload_device)
except:
pass
encoder = t5["model"]
dtype = t5["dtype"]
positive_prompts = []
all_weights = []
# Split positive prompts and process each with weights
if "|" in positive_prompt:
log.info("Multiple positive prompts detected, splitting by '|'")
positive_prompts_raw = [p.strip() for p in positive_prompt.split('|')]
elif "[1]" in positive_prompt:
log.info("Multiple positive prompts detected, splitting by [#] and enabling EchoShot")
import re
segments = re.split(r'\[\d+\]', positive_prompt)
positive_prompts_raw = [segment.strip() for segment in segments if segment.strip()]
assert len(positive_prompts_raw) > 1 and len(positive_prompts_raw) < 7, 'Input shot num must between 2~6 !'
else:
positive_prompts_raw = [positive_prompt.strip()]
for p in positive_prompts_raw:
cleaned_prompt, weights = self.parse_prompt_weights(p)
positive_prompts.append(cleaned_prompt)
all_weights.append(weights)
mm.soft_empty_cache()
if device == "gpu":
device_to = mm.get_torch_device()
else:
device_to = torch.device("cpu")
if encoder.quantization == "fp8_e4m3fn":
cast_dtype = torch.float8_e4m3fn
else:
cast_dtype = encoder.dtype
params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
for name, param in encoder.model.named_parameters():
dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
value = encoder.state_dict[name] if hasattr(encoder, 'state_dict') else encoder.model.state_dict()[name]
set_module_tensor_to_device(encoder.model, name, device=device_to, dtype=dtype_to_use, value=value)
if hasattr(encoder, 'state_dict'):
del encoder.state_dict
mm.soft_empty_cache()
gc.collect()
with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=encoder.dtype, enabled=encoder.quantization != 'disabled'):
# Encode positive if not loaded from cache
if use_disk_cache and context is not None:
pass
else:
context = encoder(positive_prompts, device_to)
# Apply weights to embeddings if any were extracted
for i, weights in enumerate(all_weights):
for text, weight in weights.items():
log.info(f"Applying weight {weight} to prompt: {text}")
if len(weights) > 0:
context[i] = context[i] * weight
# Encode negative if not loaded from cache
if use_disk_cache and context_null is not None:
pass
else:
context_null = encoder([negative_prompt], device_to)
if force_offload:
encoder.model.to(offload_device)
mm.soft_empty_cache()
gc.collect()
prompt_embeds_dict = {
"prompt_embeds": context,
"negative_prompt_embeds": context_null,
"echoshot": echoshot,
}
# Save each part to its own cache file if needed
if use_disk_cache:
pos_cache_path = get_cache_path(positive_prompt)
neg_cache_path = get_cache_path(negative_prompt)
try:
if not os.path.exists(pos_cache_path):
torch.save(context, pos_cache_path)
log.info(f"Saved prompt embeds to cache: {pos_cache_path}")
except Exception as e:
log.warning(f"Failed to save cache: {e}")
try:
if not os.path.exists(neg_cache_path):
torch.save(context_null, neg_cache_path)
log.info(f"Saved prompt embeds to cache: {neg_cache_path}")
except Exception as e:
log.warning(f"Failed to save cache: {e}")
return (prompt_embeds_dict,)
def parse_prompt_weights(self, prompt):
"""Extract text and weights from prompts with (text:weight) format"""
import re
# Parse all instances of (text:weight) in the prompt
pattern = r'\((.*?):([\d\.]+)\)'
matches = re.findall(pattern, prompt)
# Replace each match with just the text part
cleaned_prompt = prompt
weights = {}
for match in matches:
text, weight = match
orig_text = f"({text}:{weight})"
cleaned_prompt = cleaned_prompt.replace(orig_text, text)
weights[text] = float(weight)
return cleaned_prompt, weights
class WanVideoTextEncodeSingle:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"t5": ("WANTEXTENCODER",),
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
"device": (["gpu", "cpu"], {"default": "gpu", "tooltip": "Device to run the text encoding on."}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Encodes text prompt into text embedding."
def process(self, prompt, t5=None, force_offload=True, model_to_offload=None, use_disk_cache=False, device="gpu"):
# Unified cache logic: use a single cache file per unique prompt
encoded = None
echoshot = True if "[1]" in prompt else False
if use_disk_cache:
cache_dir = os.path.join(script_directory, 'text_embed_cache')
os.makedirs(cache_dir, exist_ok=True)
def get_cache_path(prompt):
cache_key = prompt.strip()
cache_hash = hashlib.sha256(cache_key.encode('utf-8')).hexdigest()
return os.path.join(cache_dir, f"{cache_hash}.pt")
cache_path = get_cache_path(prompt)
if os.path.exists(cache_path):
try:
log.info(f"Loading prompt embeds from cache: {cache_path}")
encoded = torch.load(cache_path)
except Exception as e:
log.warning(f"Failed to load cache: {e}, will re-encode.")
if t5 is None and encoded is None:
raise ValueError("No cached text embeds found for prompts, please provide a T5 encoder.")
if encoded is None:
if model_to_offload is not None and device == "gpu":
log.info(f"Moving video model to {offload_device}")
model_to_offload.model.to(offload_device)
mm.soft_empty_cache()
encoder = t5["model"]
dtype = t5["dtype"]
if device == "gpu":
device_to = mm.get_torch_device()
else:
device_to = torch.device("cpu")
if encoder.quantization == "fp8_e4m3fn":
cast_dtype = torch.float8_e4m3fn
else:
cast_dtype = encoder.dtype
params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
for name, param in encoder.model.named_parameters():
dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
value = encoder.state_dict[name] if hasattr(encoder, 'state_dict') else encoder.model.state_dict()[name]
set_module_tensor_to_device(encoder.model, name, device=device_to, dtype=dtype_to_use, value=value)
if hasattr(encoder, 'state_dict'):
del encoder.state_dict
mm.soft_empty_cache()
gc.collect()
with torch.autocast(device_type=mm.get_autocast_device(device_to), dtype=encoder.dtype, enabled=encoder.quantization != 'disabled'):
encoded = encoder([prompt], device_to)
if force_offload:
encoder.model.to(offload_device)
mm.soft_empty_cache()
# Save to cache if enabled
if use_disk_cache:
try:
if not os.path.exists(cache_path):
torch.save(encoded, cache_path)
log.info(f"Saved prompt embeds to cache: {cache_path}")
except Exception as e:
log.warning(f"Failed to save cache: {e}")
prompt_embeds_dict = {
"prompt_embeds": encoded,
"negative_prompt_embeds": None,
"echoshot": echoshot
}
return (prompt_embeds_dict,)
class WanVideoApplyNAG:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"original_text_embeds": ("WANVIDEOTEXTEMBEDS",),
"nag_text_embeds": ("WANVIDEOTEXTEMBEDS",),
"nag_scale": ("FLOAT", {"default": 11.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.1}),
"nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Adds NAG prompt embeds to original prompt embeds: 'https://github.com/ChenDarYen/Normalized-Attention-Guidance'"
def process(self, original_text_embeds, nag_text_embeds, nag_scale, nag_tau, nag_alpha):
prompt_embeds_dict_copy = original_text_embeds.copy()
prompt_embeds_dict_copy.update({
"nag_prompt_embeds": nag_text_embeds["prompt_embeds"],
"nag_params": {
"nag_scale": nag_scale,
"nag_tau": nag_tau,
"nag_alpha": nag_alpha,
}
})
return (prompt_embeds_dict_copy,)
class WanVideoTextEmbedBridge:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"positive": ("CONDITIONING",),
},
"optional": {
"negative": ("CONDITIONING",),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Bridge between ComfyUI native text embedding and WanVideoWrapper text embedding"
def process(self, positive, negative=None):
prompt_embeds_dict = {
"prompt_embeds": positive[0][0].to(device),
"negative_prompt_embeds": negative[0][0].to(device) if negative is not None else None,
}
return (prompt_embeds_dict,)
#region clip vision
class WanVideoClipVisionEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip_vision": ("CLIP_VISION",),
"image_1": ("IMAGE", {"tooltip": "Image to encode"}),
"strength_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}),
"strength_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}),
"crop": (["center", "disabled"], {"default": "center", "tooltip": "Crop image to 224x224 before encoding"}),
"combine_embeds": (["average", "sum", "concat", "batch"], {"default": "average", "tooltip": "Method to combine multiple clip embeds"}),
"force_offload": ("BOOLEAN", {"default": True}),
},
"optional": {
"image_2": ("IMAGE", ),
"negative_image": ("IMAGE", {"tooltip": "image to use for uncond"}),
"tiles": ("INT", {"default": 0, "min": 0, "max": 16, "step": 2, "tooltip": "Use matteo's tiled image encoding for improved accuracy"}),
"ratio": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Ratio of the tile average"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_CLIPEMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, clip_vision, image_1, strength_1, strength_2, force_offload, crop, combine_embeds, image_2=None, negative_image=None, tiles=0, ratio=1.0):
image_mean = [0.48145466, 0.4578275, 0.40821073]
image_std = [0.26862954, 0.26130258, 0.27577711]
if image_2 is not None:
image = torch.cat([image_1, image_2], dim=0)
else:
image = image_1
clip_vision.model.to(device)
negative_clip_embeds = None
if tiles > 0:
log.info("Using tiled image encoding")
clip_embeds = clip_encode_image_tiled(clip_vision, image.to(device), tiles=tiles, ratio=ratio)
if negative_image is not None:
negative_clip_embeds = clip_encode_image_tiled(clip_vision, negative_image.to(device), tiles=tiles, ratio=ratio)
else:
if isinstance(clip_vision, ClipVisionModel):
clip_embeds = clip_vision.encode_image(image).penultimate_hidden_states.to(device)
if negative_image is not None:
negative_clip_embeds = clip_vision.encode_image(negative_image).penultimate_hidden_states.to(device)
else:
pixel_values = clip_preprocess(image.to(device), size=224, mean=image_mean, std=image_std, crop=(not crop == "disabled")).float()
clip_embeds = clip_vision.visual(pixel_values)
if negative_image is not None:
pixel_values = clip_preprocess(negative_image.to(device), size=224, mean=image_mean, std=image_std, crop=(not crop == "disabled")).float()
negative_clip_embeds = clip_vision.visual(pixel_values)
log.info(f"Clip embeds shape: {clip_embeds.shape}, dtype: {clip_embeds.dtype}")
weighted_embeds = []
weighted_embeds.append(clip_embeds[0:1] * strength_1)
# Handle all additional embeddings
if clip_embeds.shape[0] > 1:
weighted_embeds.append(clip_embeds[1:2] * strength_2)
if clip_embeds.shape[0] > 2:
for i in range(2, clip_embeds.shape[0]):
weighted_embeds.append(clip_embeds[i:i+1]) # Add as-is without strength modifier
# Combine all weighted embeddings
if combine_embeds == "average":
clip_embeds = torch.mean(torch.stack(weighted_embeds), dim=0)
elif combine_embeds == "sum":
clip_embeds = torch.sum(torch.stack(weighted_embeds), dim=0)
elif combine_embeds == "concat":
clip_embeds = torch.cat(weighted_embeds, dim=1)
elif combine_embeds == "batch":
clip_embeds = torch.cat(weighted_embeds, dim=0)
else:
clip_embeds = weighted_embeds[0]
log.info(f"Combined clip embeds shape: {clip_embeds.shape}")
if force_offload:
clip_vision.model.to(offload_device)
mm.soft_empty_cache()
clip_embeds_dict = {
"clip_embeds": clip_embeds,
"negative_clip_embeds": negative_clip_embeds
}
return (clip_embeds_dict,)
class WanVideoRealisDanceLatents:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"ref_latent": ("LATENT", {"tooltip": "Reference image to encode"}),
"pose_cond_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the SMPL model"}),
"pose_cond_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the SMPL model"}),
},
"optional": {
"smpl_latent": ("LATENT", {"tooltip": "SMPL pose image to encode"}),
"hamer_latent": ("LATENT", {"tooltip": "Hamer hand pose image to encode"}),
},
}
RETURN_TYPES = ("ADD_COND_LATENTS",)
RETURN_NAMES = ("add_cond_latents",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, ref_latent, pose_cond_start_percent, pose_cond_end_percent, hamer_latent=None, smpl_latent=None):
if smpl_latent is None and hamer_latent is None:
raise Exception("At least one of smpl_latent or hamer_latent must be provided")
if smpl_latent is None:
smpl = torch.zeros_like(hamer_latent["samples"])
else:
smpl = smpl_latent["samples"]
if hamer_latent is None:
hamer = torch.zeros_like(smpl_latent["samples"])
else:
hamer = hamer_latent["samples"]
pose_latent = torch.cat((smpl, hamer), dim=1)
add_cond_latents = {
"ref_latent": ref_latent["samples"],
"pose_latent": pose_latent,
"pose_cond_start_percent": pose_cond_start_percent,
"pose_cond_end_percent": pose_cond_end_percent,
}
return (add_cond_latents,)
class WanVideoAddStandInLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"embeds": ("WANVIDIMAGE_EMBEDS",),
"ip_image_latent": ("LATENT", {"tooltip": "Reference image to encode"}),
"freq_offset": ("INT", {"default": 1, "min": 0, "max": 100, "step": 1, "tooltip": "EXPERIMENTAL: RoPE frequency offset between the reference and rest of the sequence"}),
#"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent to apply the ref "}),
#"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent to apply the ref "}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "add"
CATEGORY = "WanVideoWrapper"
def add(self, embeds, ip_image_latent, freq_offset):
# Prepare the new extra latent entry
new_entry = {
"ip_image_latent": ip_image_latent["samples"],
"freq_offset": freq_offset,
#"ip_start_percent": start_percent,
#"ip_end_percent": end_percent,
}
# Return a new dict with updated extra_latents
updated = dict(embeds)
updated["standin_input"] = new_entry
return (updated,)
class WanVideoAddMTVMotion:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"embeds": ("WANVIDIMAGE_EMBEDS",),
"mtv_crafter_motion": ("MTVCRAFTERMOTION",),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the MTV motion"}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent to apply the ref "}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent to apply the ref "}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "add"
CATEGORY = "WanVideoWrapper"
def add(self, embeds, mtv_crafter_motion, strength, start_percent, end_percent):
# Prepare the new extra latent entry
new_entry = {
"mtv_motion_tokens": mtv_crafter_motion["mtv_motion_tokens"],
"strength": strength,
"start_percent": start_percent,
"end_percent": end_percent,
"global_mean": mtv_crafter_motion["global_mean"],
"global_std": mtv_crafter_motion["global_std"]
}
# Return a new dict with updated extra_latents
updated = dict(embeds)
updated["mtv_crafter_motion"] = new_entry
return (updated,)
class WanVideoImageToVideoEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for I2V where some noise can add motion and give sharper results"}),
"start_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}),
"end_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}),
"force_offload": ("BOOLEAN", {"default": True}),
},
"optional": {
"vae": ("WANVAE",),
"clip_embeds": ("WANVIDIMAGE_CLIPEMBEDS", {"tooltip": "Clip vision encoded image"}),
"start_image": ("IMAGE", {"tooltip": "Image to encode"}),
"end_image": ("IMAGE", {"tooltip": "end frame"}),
"control_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "Control signal for the Fun -model"}),
"fun_or_fl2v_model": ("BOOLEAN", {"default": True, "tooltip": "Enable when using official FLF2V or Fun model"}),
"temporal_mask": ("MASK", {"tooltip": "mask"}),
"extra_latents": ("LATENT", {"tooltip": "Extra latents to add to the input front, used for Skyreels A2 reference images"}),
"tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
"add_cond_latents": ("ADD_COND_LATENTS", {"advanced": True, "tooltip": "Additional cond latents WIP"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, width, height, num_frames, force_offload, noise_aug_strength,
start_latent_strength, end_latent_strength, start_image=None, end_image=None, control_embeds=None, fun_or_fl2v_model=False,
temporal_mask=None, extra_latents=None, clip_embeds=None, tiled_vae=False, add_cond_latents=None, vae=None):
if start_image is None and end_image is None:
return WanVideoEmptyEmbeds().process(
num_frames, width, height, control_embeds=control_embeds, extra_latents=extra_latents,
)
if vae is None:
raise ValueError("VAE is required for image encoding.")
H = height
W = width
lat_h = H // vae.upsampling_factor
lat_w = W // vae.upsampling_factor
num_frames = ((num_frames - 1) // 4) * 4 + 1
two_ref_images = start_image is not None and end_image is not None
if start_image is None and end_image is not None:
fun_or_fl2v_model = True # end image alone only works with this option
base_frames = num_frames + (1 if two_ref_images and not fun_or_fl2v_model else 0)
if temporal_mask is None:
mask = torch.zeros(1, base_frames, lat_h, lat_w, device=device, dtype=vae.dtype)
if start_image is not None:
mask[:, 0:start_image.shape[0]] = 1 # First frame
if end_image is not None:
mask[:, -end_image.shape[0]:] = 1 # End frame if exists
else:
mask = common_upscale(temporal_mask.unsqueeze(1).to(device), lat_w, lat_h, "nearest", "disabled").squeeze(1)
if mask.shape[0] > base_frames:
mask = mask[:base_frames]
elif mask.shape[0] < base_frames:
mask = torch.cat([mask, torch.zeros(base_frames - mask.shape[0], lat_h, lat_w, device=device)])
mask = mask.unsqueeze(0).to(device, vae.dtype)
# Repeat first frame and optionally end frame
start_mask_repeated = torch.repeat_interleave(mask[:, 0:1], repeats=4, dim=1) # T, C, H, W
if end_image is not None and not fun_or_fl2v_model:
end_mask_repeated = torch.repeat_interleave(mask[:, -1:], repeats=4, dim=1) # T, C, H, W
mask = torch.cat([start_mask_repeated, mask[:, 1:-1], end_mask_repeated], dim=1)
else:
mask = torch.cat([start_mask_repeated, mask[:, 1:]], dim=1)
# Reshape mask into groups of 4 frames
mask = mask.view(1, mask.shape[1] // 4, 4, lat_h, lat_w) # 1, T, C, H, W
mask = mask.movedim(1, 2)[0]# C, T, H, W
# Resize and rearrange the input image dimensions
if start_image is not None:
start_image = start_image[..., :3]
if start_image.shape[1] != H or start_image.shape[2] != W:
resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
else:
resized_start_image = start_image.permute(3, 0, 1, 2) # C, T, H, W
resized_start_image = resized_start_image * 2 - 1
if noise_aug_strength > 0.0:
resized_start_image = add_noise_to_reference_video(resized_start_image, ratio=noise_aug_strength)
if end_image is not None:
end_image = end_image[..., :3]
if end_image.shape[1] != H or end_image.shape[2] != W:
resized_end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
else:
resized_end_image = end_image.permute(3, 0, 1, 2) # C, T, H, W
resized_end_image = resized_end_image * 2 - 1
if noise_aug_strength > 0.0:
resized_end_image = add_noise_to_reference_video(resized_end_image, ratio=noise_aug_strength)
# Concatenate image with zero frames and encode
if temporal_mask is None:
if start_image is not None and end_image is None:
zero_frames = torch.zeros(3, num_frames-start_image.shape[0], H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([resized_start_image.to(device, dtype=vae.dtype), zero_frames], dim=1)
del resized_start_image, zero_frames
elif start_image is None and end_image is not None:
zero_frames = torch.zeros(3, num_frames-end_image.shape[0], H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([zero_frames, resized_end_image.to(device, dtype=vae.dtype)], dim=1)
del zero_frames
elif start_image is None and end_image is None:
concatenated = torch.zeros(3, num_frames, H, W, device=device, dtype=vae.dtype)
else:
if fun_or_fl2v_model:
zero_frames = torch.zeros(3, num_frames-(start_image.shape[0]+end_image.shape[0]), H, W, device=device, dtype=vae.dtype)
else:
zero_frames = torch.zeros(3, num_frames-1, H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([resized_start_image.to(device, dtype=vae.dtype), zero_frames, resized_end_image.to(device, dtype=vae.dtype)], dim=1)
del resized_start_image, zero_frames
else:
temporal_mask = common_upscale(temporal_mask.unsqueeze(1), W, H, "nearest", "disabled").squeeze(1)
concatenated = resized_start_image[:,:num_frames].to(vae.dtype) * temporal_mask[:num_frames].unsqueeze(0).to(vae.dtype)
del resized_start_image, temporal_mask
mm.soft_empty_cache()
gc.collect()
vae.to(device)
y = vae.encode([concatenated], device, end_=(end_image is not None and not fun_or_fl2v_model),tiled=tiled_vae)[0]
vae.model.clear_cache()
del concatenated
has_ref = False
if extra_latents is not None:
samples = extra_latents["samples"].squeeze(0)
y = torch.cat([samples, y], dim=1)
mask = torch.cat([torch.ones_like(mask[:, 0:samples.shape[1]]), mask], dim=1)
num_frames += samples.shape[1] * 4
has_ref = True
y[:, :1] *= start_latent_strength
y[:, -1:] *= end_latent_strength
# Calculate maximum sequence length
patches_per_frame = lat_h * lat_w // (PATCH_SIZE[1] * PATCH_SIZE[2])
frames_per_stride = (num_frames - 1) // 4 + (2 if end_image is not None and not fun_or_fl2v_model else 1)
max_seq_len = frames_per_stride * patches_per_frame
if add_cond_latents is not None:
add_cond_latents["ref_latent_neg"] = vae.encode(torch.zeros(1, 3, 1, H, W, device=device, dtype=vae.dtype), device)
if force_offload:
vae.model.to(offload_device)
mm.soft_empty_cache()
gc.collect()
image_embeds = {
"image_embeds": y,
"clip_context": clip_embeds.get("clip_embeds", None) if clip_embeds is not None else None,
"negative_clip_context": clip_embeds.get("negative_clip_embeds", None) if clip_embeds is not None else None,
"max_seq_len": max_seq_len,
"num_frames": num_frames,
"lat_h": lat_h,
"lat_w": lat_w,
"control_embeds": control_embeds["control_embeds"] if control_embeds is not None else None,
"end_image": resized_end_image if end_image is not None else None,
"fun_or_fl2v_model": fun_or_fl2v_model,
"has_ref": has_ref,
"add_cond_latents": add_cond_latents,
"mask": mask
}
return (image_embeds,)
class WanVideoEmptyEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
},
"optional": {
"control_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "control signal for the Fun -model"}),
"extra_latents": ("LATENT", {"tooltip": "First latent to use for the Pusa -model"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, num_frames, width, height, control_embeds=None, extra_latents=None):
target_shape = (16, (num_frames - 1) // VAE_STRIDE[0] + 1,
height // VAE_STRIDE[1],
width // VAE_STRIDE[2])
embeds = {
"target_shape": target_shape,
"num_frames": num_frames,
"control_embeds": control_embeds["control_embeds"] if control_embeds is not None else None,
}
if extra_latents is not None:
embeds["extra_latents"] = [{
"samples": extra_latents["samples"],
"index": 0,
}]
return (embeds,)
class WanVideoAddExtraLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"embeds": ("WANVIDIMAGE_EMBEDS",),
"extra_latents": ("LATENT",),
"latent_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1, "tooltip": "Index to insert the extra latents at in latent space"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "add"
CATEGORY = "WanVideoWrapper"
def add(self, embeds, extra_latents, latent_index):
# Prepare the new extra latent entry
new_entry = {
"samples": extra_latents["samples"],
"index": latent_index,
}
# Get previous extra_latents list, or start a new one
prev_extra_latents = embeds.get("extra_latents", None)
if prev_extra_latents is None:
extra_latents_list = [new_entry]
elif isinstance(prev_extra_latents, list):
extra_latents_list = prev_extra_latents + [new_entry]
else:
extra_latents_list = [prev_extra_latents, new_entry]
# Return a new dict with updated extra_latents
updated = dict(embeds)
updated["extra_latents"] = extra_latents_list
return (updated,)
class WanVideoMiniMaxRemoverEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}),
"mask_latents": ("LATENT", {"tooltip": "Encoded latents to use as mask"}),
},
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, num_frames, width, height, latents, mask_latents):
target_shape = (16, (num_frames - 1) // VAE_STRIDE[0] + 1,
height // VAE_STRIDE[1],
width // VAE_STRIDE[2])
embeds = {
"target_shape": target_shape,
"num_frames": num_frames,
"minimax_latents": latents["samples"].squeeze(0),
"minimax_mask_latents": mask_latents["samples"].squeeze(0),
}
return (embeds,)
# region phantom
class WanVideoPhantomEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"phantom_latent_1": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_cfg_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "CFG scale for the extra phantom cond pass"}),
"phantom_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the phantom model"}),
"phantom_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the phantom model"}),
},
"optional": {
"phantom_latent_2": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_latent_3": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"phantom_latent_4": ("LATENT", {"tooltip": "reference latents for the phantom model"}),
"vace_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "VACE embeds"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, num_frames, phantom_cfg_scale, phantom_start_percent, phantom_end_percent, phantom_latent_1, phantom_latent_2=None, phantom_latent_3=None, phantom_latent_4=None, vace_embeds=None):
samples = phantom_latent_1["samples"].squeeze(0)
if phantom_latent_2 is not None:
samples = torch.cat([samples, phantom_latent_2["samples"].squeeze(0)], dim=1)
if phantom_latent_3 is not None:
samples = torch.cat([samples, phantom_latent_3["samples"].squeeze(0)], dim=1)
if phantom_latent_4 is not None:
samples = torch.cat([samples, phantom_latent_4["samples"].squeeze(0)], dim=1)
C, T, H, W = samples.shape
log.info(f"Phantom latents shape: {samples.shape}")
target_shape = (16, (num_frames - 1) // VAE_STRIDE[0] + 1,
H * 8 // VAE_STRIDE[1],
W * 8 // VAE_STRIDE[2])
embeds = {
"target_shape": target_shape,
"num_frames": num_frames,
"phantom_latents": samples,
"phantom_cfg_scale": phantom_cfg_scale,
"phantom_start_percent": phantom_start_percent,
"phantom_end_percent": phantom_end_percent,
}
if vace_embeds is not None:
vace_input = {
"vace_context": vace_embeds["vace_context"],
"vace_scale": vace_embeds["vace_scale"],
"has_ref": vace_embeds["has_ref"],
"vace_start_percent": vace_embeds["vace_start_percent"],
"vace_end_percent": vace_embeds["vace_end_percent"],
"vace_seq_len": vace_embeds["vace_seq_len"],
"additional_vace_inputs": vace_embeds["additional_vace_inputs"],
}
embeds.update(vace_input)
return (embeds,)
class WanVideoControlEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the control signal"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the control signal"}),
"latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}),
},
"optional": {
"fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, latents, start_percent, end_percent, fun_ref_image=None):
samples = latents["samples"].squeeze(0)
C, T, H, W = samples.shape
num_frames = (T - 1) * 4 + 1
seq_len = math.ceil((H * W) / 4 * ((num_frames - 1) // 4 + 1))
embeds = {
"max_seq_len": seq_len,
"target_shape": samples.shape,
"num_frames": num_frames,
"control_embeds": {
"control_images": samples,
"start_percent": start_percent,
"end_percent": end_percent,
"fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None,
}
}
return (embeds,)
class WanVideoAddControlEmbeds:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"embeds": ("WANVIDIMAGE_EMBEDS",),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the control signal"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the control signal"}),
},
"optional": {
"latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}),
"fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, embeds, start_percent, end_percent, fun_ref_image=None, latents=None):
new_entry = {
"control_images": latents["samples"].squeeze(0) if latents is not None else None,
"start_percent": start_percent,
"end_percent": end_percent,
"fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None,
}
updated = dict(embeds)
updated["control_embeds"] = new_entry
return (updated,)
class WanVideoSLG:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"blocks": ("STRING", {"default": "10", "tooltip": "Blocks to skip uncond on, separated by comma, index starts from 0"}),
"start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the control signal"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the control signal"}),
},
}
RETURN_TYPES = ("SLGARGS", )
RETURN_NAMES = ("slg_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Skips uncond on the selected blocks"
def process(self, blocks, start_percent, end_percent):
slg_block_list = [int(x.strip()) for x in blocks.split(",")]
slg_args = {
"blocks": slg_block_list,
"start_percent": start_percent,
"end_percent": end_percent,
}
return (slg_args,)
#region VACE
class WanVideoVACEEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
"vace_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply VACE"}),
"vace_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply VACE"}),
},
"optional": {
"input_frames": ("IMAGE",),
"ref_images": ("IMAGE",),
"input_masks": ("MASK",),
"prev_vace_embeds": ("WANVIDIMAGE_EMBEDS",),
"tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
},
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("vace_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, vae, width, height, num_frames, strength, vace_start_percent, vace_end_percent, input_frames=None, ref_images=None, input_masks=None, prev_vace_embeds=None, tiled_vae=False):
width = (width // 16) * 16
height = (height // 16) * 16
target_shape = (16, (num_frames - 1) // VAE_STRIDE[0] + 1,
height // VAE_STRIDE[1],
width // VAE_STRIDE[2])
# vace context encode
if input_frames is None:
input_frames = torch.zeros((1, 3, num_frames, height, width), device=device, dtype=vae.dtype)
else:
input_frames = input_frames.clone()[:num_frames, :, :, :3]
input_frames = common_upscale(input_frames.movedim(-1, 1), width, height, "lanczos", "disabled").movedim(1, -1)
input_frames = input_frames.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
input_frames = input_frames * 2 - 1
if input_masks is None:
input_masks = torch.ones_like(input_frames, device=device)
else:
log.info(f"input_masks shape: {input_masks.shape}")
input_masks = input_masks[:num_frames]
input_masks = common_upscale(input_masks.clone().unsqueeze(1), width, height, "nearest-exact", "disabled").squeeze(1)
input_masks = input_masks.to(vae.dtype).to(device)
input_masks = input_masks.unsqueeze(-1).unsqueeze(0).permute(0, 4, 1, 2, 3).repeat(1, 3, 1, 1, 1) # B, C, T, H, W
if ref_images is not None:
ref_images = ref_images.clone()[..., :3]
# Create padded image
if ref_images.shape[0] > 1:
ref_images = torch.cat([ref_images[i] for i in range(ref_images.shape[0])], dim=1).unsqueeze(0)
B, H, W, C = ref_images.shape
current_aspect = W / H
target_aspect = width / height
if current_aspect > target_aspect:
# Image is wider than target, pad height
new_h = int(W / target_aspect)
pad_h = (new_h - H) // 2
padded = torch.ones(ref_images.shape[0], new_h, W, ref_images.shape[3], device=ref_images.device, dtype=ref_images.dtype)
padded[:, pad_h:pad_h+H, :, :] = ref_images
ref_images = padded
elif current_aspect < target_aspect:
# Image is taller than target, pad width
new_w = int(H * target_aspect)
pad_w = (new_w - W) // 2
padded = torch.ones(ref_images.shape[0], H, new_w, ref_images.shape[3], device=ref_images.device, dtype=ref_images.dtype)
padded[:, :, pad_w:pad_w+W, :] = ref_images
ref_images = padded
ref_images = common_upscale(ref_images.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
ref_images = ref_images.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3).unsqueeze(0)
ref_images = ref_images * 2 - 1
vae = vae.to(device)
z0 = self.vace_encode_frames(vae, input_frames, ref_images, masks=input_masks, tiled_vae=tiled_vae)
vae.model.clear_cache()
m0 = self.vace_encode_masks(input_masks, ref_images)
z = self.vace_latent(z0, m0)
vae.to(offload_device)
vace_input = {
"vace_context": z,
"vace_scale": strength,
"has_ref": ref_images is not None,
"num_frames": num_frames,
"target_shape": target_shape,
"vace_start_percent": vace_start_percent,
"vace_end_percent": vace_end_percent,
"vace_seq_len": math.ceil((z[0].shape[2] * z[0].shape[3]) / 4 * z[0].shape[1]),
"additional_vace_inputs": [],
}
if prev_vace_embeds is not None:
if "additional_vace_inputs" in prev_vace_embeds and prev_vace_embeds["additional_vace_inputs"]:
vace_input["additional_vace_inputs"] = prev_vace_embeds["additional_vace_inputs"].copy()
vace_input["additional_vace_inputs"].append(prev_vace_embeds)
return (vace_input,)
def vace_encode_frames(self, vae, frames, ref_images, masks=None, tiled_vae=False):
if ref_images is None:
ref_images = [None] * len(frames)
else:
assert len(frames) == len(ref_images)
pbar = ProgressBar(len(frames))
if masks is None:
latents = vae.encode(frames, device=device, tiled=tiled_vae)
else:
inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)]
reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)]
del frames
inactive = vae.encode(inactive, device=device, tiled=tiled_vae)
reactive = vae.encode(reactive, device=device, tiled=tiled_vae)
latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]
del inactive, reactive
vae.model.clear_cache()
cat_latents = []
for latent, refs in zip(latents, ref_images):
if refs is not None:
if masks is None:
ref_latent = vae.encode(refs, device=device, tiled=tiled_vae)
else:
ref_latent = vae.encode(refs, device=device, tiled=tiled_vae)
ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
assert all([x.shape[1] == 1 for x in ref_latent])
latent = torch.cat([*ref_latent, latent], dim=1)
cat_latents.append(latent)
pbar.update(1)
return cat_latents
def vace_encode_masks(self, masks, ref_images=None):
if ref_images is None:
ref_images = [None] * len(masks)
else:
assert len(masks) == len(ref_images)
result_masks = []
pbar = ProgressBar(len(masks))
for mask, refs in zip(masks, ref_images):
_c, depth, height, width = mask.shape
new_depth = int((depth + 3) // VAE_STRIDE[0])
height = 2 * (int(height) // (VAE_STRIDE[1] * 2))
width = 2 * (int(width) // (VAE_STRIDE[2] * 2))
# reshape
mask = mask[0, :, :, :]
mask = mask.view(
depth, height, VAE_STRIDE[1], width, VAE_STRIDE[1]
) # depth, height, 8, width, 8
mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width
mask = mask.reshape(
VAE_STRIDE[1] * VAE_STRIDE[2], depth, height, width
) # 8*8, depth, height, width
# interpolation
mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0)
if refs is not None:
length = len(refs)
mask_pad = torch.zeros_like(mask[:, :length, :, :])
mask = torch.cat((mask_pad, mask), dim=1)
result_masks.append(mask)
pbar.update(1)
return result_masks
def vace_latent(self, z, m):
return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)]
#region context options
class WanVideoContextOptions:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"context_schedule": (["uniform_standard", "uniform_looped", "static_standard"],),
"context_frames": ("INT", {"default": 81, "min": 2, "max": 1000, "step": 1, "tooltip": "Number of pixel frames in the context, NOTE: the latent space has 4 frames in 1"} ),
"context_stride": ("INT", {"default": 4, "min": 4, "max": 100, "step": 1, "tooltip": "Context stride as pixel frames, NOTE: the latent space has 4 frames in 1"} ),
"context_overlap": ("INT", {"default": 16, "min": 4, "max": 100, "step": 1, "tooltip": "Context overlap as pixel frames, NOTE: the latent space has 4 frames in 1"} ),
"freenoise": ("BOOLEAN", {"default": True, "tooltip": "Shuffle the noise"}),
"verbose": ("BOOLEAN", {"default": False, "tooltip": "Print debug output"}),
},
"optional": {
"fuse_method": (["linear", "pyramid"], {"default": "linear", "tooltip": "Window weight function: linear=ramps at edges only, pyramid=triangular weights peaking in middle"}),
"reference_latent": ("LATENT", {"tooltip": "Image to be used as init for I2V models for windows where first frame is not the actual first frame. Mostly useful with MAGREF model"}),
}
}
RETURN_TYPES = ("WANVIDCONTEXT", )
RETURN_NAMES = ("context_options",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Context options for WanVideo, allows splitting the video into context windows and attemps blending them for longer generations than the model and memory otherwise would allow."
def process(self, context_schedule, context_frames, context_stride, context_overlap, freenoise, verbose, image_cond_start_step=6, image_cond_window_count=2, vae=None, fuse_method="linear", reference_latent=None):
context_options = {
"context_schedule":context_schedule,
"context_frames":context_frames,
"context_stride":context_stride,
"context_overlap":context_overlap,
"freenoise":freenoise,
"verbose":verbose,
"fuse_method":fuse_method,
"reference_latent":reference_latent["samples"][0] if reference_latent is not None else None,
}
return (context_options,)
class WanVideoFlowEdit:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"source_embeds": ("WANVIDEOTEXTEMBEDS", ),
"skip_steps": ("INT", {"default": 4, "min": 0}),
"drift_steps": ("INT", {"default": 0, "min": 0}),
"drift_flow_shift": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 30.0, "step": 0.01}),
"source_cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"drift_cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
},
"optional": {
"source_image_embeds": ("WANVIDIMAGE_EMBEDS", ),
}
}
RETURN_TYPES = ("FLOWEDITARGS", )
RETURN_NAMES = ("flowedit_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Flowedit options for WanVideo"
def process(self, **kwargs):
return (kwargs,)
class WanVideoLoopArgs:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"shift_skip": ("INT", {"default": 6, "min": 0, "tooltip": "Skip step of latent shift"}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the looping effect"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the looping effect"}),
},
}
RETURN_TYPES = ("LOOPARGS", )
RETURN_NAMES = ("loop_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Looping through latent shift as shown in https://github.com/YisuiTT/Mobius/"
def process(self, **kwargs):
return (kwargs,)
class WanVideoExperimentalArgs:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"video_attention_split_steps": ("STRING", {"default": "", "tooltip": "Steps to split self attention when using multiple prompts"}),
"cfg_zero_star": ("BOOLEAN", {"default": False, "tooltip": "https://github.com/WeichenFan/CFG-Zero-star"}),
"use_zero_init": ("BOOLEAN", {"default": False}),
"zero_star_steps": ("INT", {"default": 0, "min": 0, "tooltip": "Steps to split self attention when using multiple prompts"}),
"use_fresca": ("BOOLEAN", {"default": False, "tooltip": "https://github.com/WikiChao/FreSca"}),
"fresca_scale_low": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"fresca_scale_high": ("FLOAT", {"default": 1.25, "min": 0.0, "max": 10.0, "step": 0.01}),
"fresca_freq_cutoff": ("INT", {"default": 20, "min": 0, "max": 10000, "step": 1}),
"use_tcfg": ("BOOLEAN", {"default": False, "tooltip": "https://arxiv.org/abs/2503.18137 TCFG: Tangential Damping Classifier-free Guidance. CFG artifacts reduction."}),
"raag_alpha": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Alpha value for RAAG, 1.0 is default, 0.0 is disabled."}),
"bidirectional_sampling": ("BOOLEAN", {"default": False, "tooltip": "Enable bidirectional sampling, based on https://github.com/ff2416/WanFM"})
},
}
RETURN_TYPES = ("EXPERIMENTALARGS", )
RETURN_NAMES = ("exp_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Experimental stuff"
EXPERIMENTAL = True
def process(self, **kwargs):
return (kwargs,)
class WanVideoFreeInitArgs:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"freeinit_num_iters": ("INT", {"default": 3, "min": 1, "max": 10, "tooltip": "Number of FreeInit iterations"}),
"freeinit_method": (["butterworth", "ideal", "gaussian", "none"], {"default": "ideal", "tooltip": "Frequency filter type"}),
"freeinit_n": ("INT", {"default": 4, "min": 1, "max": 10, "tooltip": "Butterworth filter order (only for butterworth)"}),
"freeinit_d_s": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Spatial filter cutoff"}),
"freeinit_d_t": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Temporal filter cutoff"}),
},
}
RETURN_TYPES = ("FREEINITARGS", )
RETURN_NAMES = ("freeinit_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "https://github.com/TianxingWu/FreeInit; FreeInit, a concise yet effective method to improve temporal consistency of videos generated by diffusion models"
EXPERIMENTAL = True
def process(self, **kwargs):
return (kwargs,)
class WanVideoScheduler: #WIP
@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", ),
},
"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):
sample_scheduler, timesteps, start_idx, end_idx = get_scheduler(
scheduler,
steps,
start_step, end_step, shift,
device,
sigmas=sigmas)
scheduler_dict = {
"sample_scheduler": sample_scheduler,
"timesteps": timesteps,
}
try:
from server import PromptServer
import io
import base64
import matplotlib.pyplot as plt
except:
PromptServer = None
if unique_id and PromptServer is not None:
try:
# Plot sigmas and save to a buffer
sigmas_np = sample_scheduler.full_sigmas[:-1].cpu().numpy()
buf = io.BytesIO()
fig = plt.figure(facecolor='#353535')
ax = fig.add_subplot(111)
ax.set_facecolor('#353535') # Set axes background color
ax.plot(sigmas_np)
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') # X tick color
ax.tick_params(axis='y', colors='white') # Y tick color
# Add split point if end_step is defined
if end_idx != -1 and 0 <= end_idx < len(sigmas_np):
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)):
ax.legend()
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:
print("Failed to send sigmas plot:", e)
pass
return (sigmas, steps, shift, scheduler_dict, start_step, end_step)
rope_functions = ["default", "comfy", "comfy_chunked"]
class WanVideoRoPEFunction:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"rope_function": (rope_functions, {"default": "comfy"}),
"ntk_scale_f": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"ntk_scale_h": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"ntk_scale_w": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
},
}
RETURN_TYPES = (rope_functions, )
RETURN_NAMES = ("rope_function",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
EXPERIMENTAL = True
def process(self, rope_function, ntk_scale_f, ntk_scale_h, ntk_scale_w):
if ntk_scale_f != 1.0 or ntk_scale_h != 1.0 or ntk_scale_w != 1.0:
rope_func_dict = {
"rope_function": rope_function,
"ntk_scale_f": ntk_scale_f,
"ntk_scale_h": ntk_scale_h,
"ntk_scale_w": ntk_scale_w,
}
return (rope_func_dict,)
return (rope_function,)
#region Sampler
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", ),
"batched_cfg": ("BOOLEAN", {"default": False, "tooltip": "Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory"}),
"slg_args": ("SLGARGS", ),
"rope_function": (rope_functions, {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile"}),
"loop_args": ("LOOPARGS", ),
"experimental_args": ("EXPERIMENTALARGS", ),
"sigmas": ("SIGMAS", ),
"unianimate_poses": ("UNIANIMATE_POSE", ),
"fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ),
"uni3c_embeds": ("UNI3C_EMBEDS", ),
"multitalk_embeds": ("MULTITALK_EMBEDS", ),
"freeinit_args": ("FREEINITARGS", ),
"start_step": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "tooltip": "Start step for the sampling, 0 means full sampling, otherwise samples only from this step"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 10000, "step": 1, "tooltip": "End step for the sampling, -1 means full sampling, otherwise samples only until this step"}),
"add_noise_to_samples": ("BOOLEAN", {"default": False, "tooltip": "Add noise to the samples before sampling, needed for video2video sampling when starting from clean video"}),
}
}
RETURN_TYPES = ("LATENT", "LATENT",)
RETURN_NAMES = ("samples", "denoised_samples",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
def process(self, model, image_embeds, shift, steps, cfg, seed, scheduler, riflex_freq_index, text_embeds=None,
force_offload=True, samples=None, feta_args=None, denoise_strength=1.0, context_options=None,
cache_args=None, teacache_args=None, flowedit_args=None, batched_cfg=False, slg_args=None, rope_function="default", loop_args=None,
experimental_args=None, sigmas=None, unianimate_poses=None, fantasytalking_embeds=None, uni3c_embeds=None, multitalk_embeds=None, freeinit_args=None, start_step=0, end_step=-1, add_noise_to_samples=False):
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"]
transformer_options = 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.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)
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)
set_lora_params_gguf(transformer, patcher.patches)
transformer.patched_linear = True
elif len(patcher.patches) != 0 and transformer.patched_linear: #handle patched linear layers (unmerged loras, fp8 scaled)
log.info(f"Using {len(patcher.patches)} LoRA weight patches for WanVideo model")
if not merge_loras and fp8_matmul:
raise NotImplementedError("FP8 matmul with unmerged LoRAs is not supported")
set_lora_params(transformer, patcher.patches)
else:
remove_lora_from_module(transformer) #clear possible unmerged lora weights
transformer.lora_scheduling_enabled = transformer_options.get("lora_scheduling_enabled", False)
#torch.compile
if model["auto_cpu_offload"] is False:
transformer = compile_model(transformer, model["compile_args"])
multitalk_sampling = image_embeds.get("multitalk_sampling", False)
if multitalk_sampling and context_options is not None:
raise Exception("context_options are not compatible or necessary with 'WanVideoImageToVideoMultiTalk' node, since it's already an alternative method that creates the video in a loop.")
if not multitalk_sampling and scheduler == "multitalk":
raise Exception("multitalk scheduler is only for multitalk sampling when using ImagetoVideoMultiTalk -node")
if text_embeds == None:
text_embeds = {
"prompt_embeds": [],
"negative_prompt_embeds": [],
}
else:
text_embeds = dict_to_device(text_embeds, device)
seed_g = torch.Generator(device=torch.device("cpu"))
seed_g.manual_seed(seed)
#region Scheduler
sample_scheduler = None
if isinstance(scheduler, dict):
sample_scheduler = scheduler["sample_scheduler"]
timesteps = scheduler["timesteps"]
elif scheduler != "multitalk":
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, steps, start_step, end_step, shift, device, transformer.dim, flowedit_args, denoise_strength, sigmas=sigmas)
log.info(f"sigmas: {sample_scheduler.sigmas}")
else:
timesteps = torch.tensor([1000, 750, 500, 250], device=device)
total_steps = steps
steps = len(timesteps)
if end_step != -1 and start_step >= end_step:
raise ValueError("start_step must be less than end_step")
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
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)
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 = None
vace_data = vace_context = vace_scale = None
fun_or_fl2v_model = has_ref = drop_last = False
phantom_latents = fun_ref_image = ATI_tracks = None
add_cond = attn_cond = attn_cond_neg = noise_pred_flipped = None
#I2V
image_cond = image_embeds.get("image_embeds", None)
if image_cond is not None:
if transformer.in_dim == 16:
raise ValueError("T2V (text to video) model detected, encoded images only work with I2V (Image to video) models")
if transformer.in_dim not in [48, 32]: # fun 2.1 models don't use the mask
image_cond_mask = image_embeds.get("mask", None)
if image_cond_mask is not None:
image_cond = torch.cat([image_cond_mask, image_cond])
else:
image_cond[:, 1:] = 0
log.info(f"image_cond shape: {image_cond.shape}")
#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)
noise = torch.randn( #C, T, H, W
48 if is_5b else 16,
(image_embeds["num_frames"] - 1) // 4 + (2 if end_image is not None and not fun_or_fl2v_model else 1),
image_embeds["lat_h"],
image_embeds["lat_w"],
dtype=torch.float32,
generator=seed_g,
device=torch.device("cpu"))
seq_len = image_embeds["max_seq_len"]
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)
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)
has_ref = image_embeds.get("has_ref", 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")
has_ref = image_embeds.get("has_ref", False)
# 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
# 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)
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(model["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_embedding = 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}")
elif multitalk_embeds is not None:
# Handle single or multiple speaker embeddings
audio_features_in = multitalk_embeds.get("audio_features", None)
if audio_features_in is None:
multitalk_audio_embedding = None
else:
if isinstance(audio_features_in, list):
multitalk_audio_embedding = [emb.to(device, dtype) for emb in audio_features_in]
else:
# keep backward-compatibility with single tensor input
multitalk_audio_embedding = [audio_features_in.to(device, dtype)]
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)
shapes = [tuple(e.shape) for e in multitalk_audio_embedding]
log.info(f"Multitalk audio features shapes (per speaker): {shapes}")
# 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 = {
"adapter_proj": fantasy_portrait_embeds.get("adapter_proj", None),
"strength": fantasy_portrait_embeds.get("strength", 1.0),
"start_percent": fantasy_portrait_embeds.get("start_percent", 0.0),
"end_percent": fantasy_portrait_embeds.get("end_percent", 1.0),
}
# MiniMax Remover
minimax_latents = minimax_mask_latents = None
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}")
log.info(f"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:
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"
seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * context_frames)
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)
#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)
# vid2vid
noise_mask=original_image=None
if samples is not None and not multitalk_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["samples"].squeeze(0).to(noise)
if input_samples.shape[1] != noise.shape[1]:
input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1)
if add_noise_to_samples:
latent_timestep = timesteps[:1].to(noise)
noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples
else:
noise = input_samples
noise_mask = samples.get("noise_mask", None)
if noise_mask is not None:
log.info(f"Latent noise_mask shape: {noise_mask.shape}")
original_image = samples.get("original_image", None)
if original_image is None:
original_image = input_samples
if len(noise_mask.shape) == 4:
noise_mask = noise_mask.squeeze(1)
if noise_mask.shape[0] < noise.shape[1]:
noise_mask = noise_mask.repeat(noise.shape[1] // noise_mask.shape[0], 1, 1)
noise_mask = torch.nn.functional.interpolate(
noise_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W]
size=(noise.shape[1], noise.shape[2], noise.shape[3]),
mode='trilinear',
align_corners=False
).repeat(1, noise.shape[0], 1, 1, 1)
# extra latents (Pusa) and 5b
latents_to_insert = add_index = None
if (extra_latents := image_embeds.get("extra_latents", None)) is not None and transformer.multitalk_model_type.lower() != "infinitetalk":
all_indices = []
for entry in extra_latents:
add_index = entry["index"]
num_extra_frames = entry["samples"].shape[2]
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}")
all_indices.extend(range(add_index, add_index+num_extra_frames))
latent = noise.to(device)
#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
pcd_data = pcd_data_input = None
if uni3c_embeds is not None:
transformer.controlnet = uni3c_embeds["controlnet"]
pcd_data = {
"render_latent": uni3c_embeds["render_latent"],
"render_mask": uni3c_embeds["render_mask"],
"camera_embedding": uni3c_embeds["camera_embedding"],
"controlnet_weight": uni3c_embeds["controlnet_weight"],
"start": uni3c_embeds["start"],
"end": uni3c_embeds["end"],
}
# 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}")
mm.unload_all_models()
mm.soft_empty_cache()
gc.collect()
#blockswap init
if not transformer.patched_linear:
if block_swap_args is not None:
transformer.use_non_blocking = block_swap_args.get("use_non_blocking", False)
for name, param in transformer.named_parameters():
if "block" not in name:
param.data = param.data.to(device)
if "control_adapter" in name:
param.data = param.data.to(device)
elif block_swap_args["offload_txt_emb"] and "txt_emb" in name:
param.data = param.data.to(offload_device)
elif block_swap_args["offload_img_emb"] and "img_emb" in name:
param.data = param.data.to(offload_device)
transformer.block_swap(
block_swap_args["blocks_to_swap"] - 1 ,
block_swap_args["offload_txt_emb"],
block_swap_args["offload_img_emb"],
vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", None),
prefetch_blocks = block_swap_args.get("prefetch_blocks", 0),
block_swap_debug = block_swap_args.get("block_swap_debug", False),
)
elif model["auto_cpu_offload"]:
for module in transformer.modules():
if hasattr(module, "offload"):
module.offload()
if hasattr(module, "onload"):
module.onload()
for block in transformer.blocks:
block.modulation = torch.nn.Parameter(block.modulation.to(device))
transformer.head.modulation = torch.nn.Parameter(transformer.head.modulation.to(device))
else:
transformer.to(device)
# 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)
print("Using previous cache states", previous_cache_states)
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)
# FlowEdit setup
if flowedit_args is not None:
source_embeds = flowedit_args["source_embeds"]
source_embeds = dict_to_device(source_embeds, device)
source_image_embeds = flowedit_args.get("source_image_embeds", image_embeds)
source_image_cond = source_image_embeds.get("image_embeds", None)
source_clip_fea = source_image_embeds.get("clip_fea", clip_fea)
if source_image_cond is not None:
source_image_cond = source_image_cond.to(dtype)
skip_steps = flowedit_args["skip_steps"]
drift_steps = flowedit_args["drift_steps"]
source_cfg = flowedit_args["source_cfg"]
if not isinstance(source_cfg, list):
source_cfg = [source_cfg] * (steps +1)
drift_cfg = flowedit_args["drift_cfg"]
if not isinstance(drift_cfg, list):
drift_cfg = [drift_cfg] * (steps +1)
x_init = samples["samples"].clone().squeeze(0).to(device)
x_tgt = samples["samples"].squeeze(0).to(device)
sample_scheduler = FlowMatchEulerDiscreteScheduler(
num_train_timesteps=1000,
shift=flowedit_args["drift_flow_shift"],
use_dynamic_shifting=False)
sampling_sigmas = get_sampling_sigmas(steps, flowedit_args["drift_flow_shift"])
drift_timesteps, _ = retrieve_timesteps(
sample_scheduler,
device=device,
sigmas=sampling_sigmas)
if drift_steps > 0:
drift_timesteps = torch.cat([drift_timesteps, torch.tensor([0]).to(drift_timesteps.device)]).to(drift_timesteps.device)
timesteps[-drift_steps:] = drift_timesteps[-drift_steps:]
# Experimental args
use_cfg_zero_star = use_tangential = use_fresca = bidirectional_sampling =False
raag_alpha = 0.0
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(",")]
else:
transformer.video_attention_split_steps = []
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:
import copy
sample_scheduler_flipped = copy.deepcopy(sample_scheduler)
# Rotary positional embeddings (RoPE)
# RoPE base freq scaling as used with CineScale
ntk_alphas = [1.0, 1.0, 1.0]
if isinstance(rope_function, dict):
ntk_alphas = rope_function["ntk_scale_f"], rope_function["ntk_scale_h"], rope_function["ntk_scale_w"]
rope_function = rope_function["rope_function"]
# Stand-In
standin_input = image_embeds.get("standin_input", None)
if standin_input is not None:
rope_function = "comfy" # only works with this currently
freqs = None
transformer.rope_embedder.k = None
transformer.rope_embedder.num_frames = None
if "default" in rope_function or bidirectional_sampling: # original RoPE
d = transformer.dim // transformer.num_heads
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
#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):
nonlocal transformer
z = z.to(dtype)
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 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(z)
else:
control_camera_input = None
if recammaster is not None:
z = torch.cat([z, recam_latents.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)
if not multitalk_sampling and multitalk_audio_embedding is not None:
audio_embedding = multitalk_audio_embedding
audio_embs = []
indices = (torch.arange(4 + 1) - 2) * 1
human_num = len(audio_embedding)
# split audio with window size
if context_window is None:
for human_idx in range(human_num):
center_indices = torch.arange(
0,
latent_video_length * 4 + 1 if add_cond is not None else (latent_video_length-1) * 4 + 1,
1).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_end = context_window[-1] * 4 + 1
#print("audio_start: ", audio_start, "audio_end: ", audio_end)
center_indices = torch.arange(audio_start, audio_end, 1).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 pcd_data is not None and pcd_data["render_latent"].shape[2] != context_frames:
pcd_data_input = {"render_latent": pcd_data["render_latent"][:, :, context_window]}
for k in pcd_data:
if k != "render_latent":
pcd_data_input[k] = pcd_data[k]
else:
pcd_data_input = pcd_data
base_params = {
'seq_len': seq_len, # sequence length
'device': device, # main device
'freqs': freqs, # rope freqs
't': timestep, # current timestep
'current_step': idx, # current step
'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
"pcd_data": pcd_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 if multitalk_audio_embedding is not None else None, # Multi/InfiniteTalk audio input
"ref_target_masks": ref_target_masks if multitalk_audio_embedding 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
}
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:
#cond
noise_pred_cond, cache_state_cond = transformer(
[z], context=positive_embeds, y=[image_cond_input] if image_cond_input is not None else None,
clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage,
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].to(intermediate_device)
if math.isclose(cfg_scale, 1.0):
if use_fresca:
noise_pred_cond = fourier_filter(
noise_pred_cond,
scale_low=fresca_scale_low,
scale_high=fresca_scale_high,
freq_cutoff=fresca_freq_cutoff,
)
return noise_pred_cond, [cache_state_cond]
#uncond
if fantasytalking_embeds is not None:
if not math.isclose(audio_cfg_scale[idx], 1.0):
base_params['audio_proj'] = None
noise_pred_uncond, cache_state_uncond = transformer(
[z], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea,
y=[image_cond_input] if image_cond_input is not None else None,
is_uncond=True, current_step_percentage=current_step_percentage,
pred_id=cache_state[1] if cache_state else None,
vace_data=vace_data, attn_cond=attn_cond_neg,
**base_params
)
noise_pred_uncond = noise_pred_uncond[0].to(intermediate_device)
#phantom
if use_phantom and not math.isclose(phantom_cfg_scale[idx], 1.0):
noise_pred_phantom, cache_state_phantom = transformer(
[z], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea,
y=[image_cond_input] if image_cond_input is not None else None,
is_uncond=True, current_step_percentage=current_step_percentage,
pred_id=cache_state[2] if cache_state else None,
vace_data=None,
**base_params
)
noise_pred_phantom = noise_pred_phantom[0].to(intermediate_device)
noise_pred = noise_pred_uncond + phantom_cfg_scale[idx] * (noise_pred_phantom - noise_pred_uncond) + cfg_scale * (noise_pred_cond - noise_pred_phantom)
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_phantom]
#fantasytalking
if fantasytalking_embeds 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
noise_pred_no_audio, cache_state_audio = transformer(
[z], context=positive_embeds, y=[image_cond_input] if image_cond_input is not None else None,
clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage,
pred_id=cache_state[2] if cache_state else None,
vace_data=vace_data,
**base_params
)
noise_pred_no_audio = noise_pred_no_audio[0].to(intermediate_device)
noise_pred = (
noise_pred_uncond
+ cfg_scale * (noise_pred_no_audio - noise_pred_uncond)
+ audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_no_audio)
)
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_audio]
elif multitalk_audio_embedding 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['multitalk_audio'] = torch.zeros_like(multitalk_audio_input)[-1:]
noise_pred_no_audio, cache_state_audio = transformer(
[z], context=negative_embeds, y=[image_cond_input] if image_cond_input is not None else None,
clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage,
pred_id=cache_state[2] if cache_state else None,
vace_data=vace_data,
**base_params
)
noise_pred_no_audio = noise_pred_no_audio[0].to(intermediate_device)
noise_pred = (
noise_pred_no_audio
+ cfg_scale * (noise_pred_cond - noise_pred_uncond)
+ audio_cfg_scale[idx] * (noise_pred_uncond - noise_pred_no_audio)
)
return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_audio]
#batched
else:
cache_state_uncond = None
[noise_pred_cond, noise_pred_uncond], cache_state_cond = transformer(
[z] + [z], context=positive_embeds + negative_embeds,
y=[image_cond_input] + [image_cond_input] if image_cond_input is not None else None,
clip_fea=clip_fea.repeat(2,1,1), is_uncond=False, current_step_percentage=current_step_percentage,
pred_id=cache_state[0] if cache_state else None,
**base_params
)
except Exception as e:
log.error(f"Error during model prediction: {e}")
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
raise e
#https://github.com/WeichenFan/CFG-Zero-star/
alpha = 1.0
if use_cfg_zero_star:
alpha = optimized_scale(
noise_pred_cond.view(batch_size, -1),
noise_pred_uncond.view(batch_size, -1)
).view(batch_size, 1, 1, 1)
noise_pred_uncond_scaled = noise_pred_uncond * alpha
if use_tangential:
noise_pred_uncond_scaled = tangential_projection(noise_pred_cond, noise_pred_uncond_scaled)
# RAAG (RATIO-aware Adaptive Guidance)
if raag_alpha > 0.0:
cfg_scale = get_raag_guidance(noise_pred_cond, noise_pred_uncond_scaled, cfg_scale, raag_alpha)
log.info(f"RAAG modified cfg: {cfg_scale}")
#https://github.com/WikiChao/FreSca
if use_fresca:
filtered_cond = fourier_filter(
noise_pred_cond - noise_pred_uncond,
scale_low=fresca_scale_low,
scale_high=fresca_scale_high,
freq_cutoff=fresca_freq_cutoff,
)
noise_pred = noise_pred_uncond_scaled + cfg_scale * filtered_cond * alpha
else:
noise_pred = noise_pred_uncond_scaled + cfg_scale * (noise_pred_cond - noise_pred_uncond_scaled)
return noise_pred, [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:
log.info(f"Input sequence length: {seq_len}")
log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*vae_upscale_factor}x{latent.shape[2]*vae_upscale_factor} with {steps} steps")
intermediate_device = device
# Differential diffusion prep
masks = None
if not multitalk_sampling and samples is not None and noise_mask is not None:
thresholds = torch.arange(len(timesteps), dtype=original_image.dtype) / len(timesteps)
thresholds = thresholds.reshape(-1, 1, 1, 1, 1).to(device)
masks = (1-noise_mask.repeat(len(timesteps), 1, 1, 1, 1).to(device)) > thresholds
latent_shift_loop = False
if loop_args is not None:
latent_shift_loop = is_looped = True
latent_skip = loop_args["shift_skip"]
latent_shift_start_percent = loop_args["start_percent"]
latent_shift_end_percent = loop_args["end_percent"]
shift_idx = 0
#clear memory before sampling
mm.soft_empty_cache()
gc.collect()
try:
torch.cuda.reset_peak_memory_stats(device)
#torch.cuda.memory._record_memory_history(max_entries=100000)
except:
pass
# Main sampling loop with FreeInit iterations
iterations = freeinit_args.get("freeinit_num_iters", 3) if freeinit_args is not None else 1
current_latent = latent
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, flowedit_args, 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 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
try:
pbar = ProgressBar(len(timesteps))
#region main loop start
for idx, t in enumerate(tqdm(timesteps, disable=multitalk_sampling)):
if flowedit_args is not None:
if idx < skip_steps:
continue
if bidirectional_sampling:
latent_flipped = torch.flip(latent, dims=[1])
latent_model_input_flipped = latent_flipped.to(device)
#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)
current_step_percentage = idx / len(timesteps)
timestep = torch.tensor([t]).to(device)
if scheduler == "flowmatch_pusa" or (is_5b and 'all_indices' in locals()):
orig_timestep = timestep
timestep = timestep.unsqueeze(1).repeat(1, latent_video_length)
if extra_latents is not None:
if 'all_indices' in locals() and all_indices:
timestep[:, all_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
#flow-edit
if flowedit_args is not None:
sigma = t / 1000.0
sigma_prev = (timesteps[idx + 1] if idx < len(timesteps) - 1 else timesteps[-1]) / 1000.0
noise = torch.randn(x_init.shape, generator=seed_g, device=torch.device("cpu"))
if idx < len(timesteps) - drift_steps:
cfg = drift_cfg
zt_src = (1-sigma) * x_init + sigma * noise.to(t)
zt_tgt = x_tgt + zt_src - x_init
#source
if idx < len(timesteps) - drift_steps:
if context_options is not None:
counter = torch.zeros_like(zt_src, device=intermediate_device)
vt_src = torch.zeros_like(zt_src, device=intermediate_device)
context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap))
for c in 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}")
if len(source_embeds["prompt_embeds"]) > 1:
positive = source_embeds["prompt_embeds"][prompt_index]
else:
positive = source_embeds["prompt_embeds"]
partial_img_emb = None
if source_image_cond is not None:
partial_img_emb = source_image_cond[:, c, :, :]
partial_img_emb[:, 0, :, :] = source_image_cond[:, 0, :, :].to(intermediate_device)
partial_zt_src = zt_src[:, c, :, :]
vt_src_context, new_teacache = predict_with_cfg(
partial_zt_src, cfg[idx],
positive, source_embeds["negative_prompt_embeds"],
timestep, idx, partial_img_emb, control_latents,
source_clip_fea, current_teacache)
if cache_args is not None:
self.window_tracker.cache_states[window_id] = new_teacache
window_mask = create_window_mask(vt_src_context, c, latent_video_length, context_overlap)
vt_src[:, c, :, :] += vt_src_context * window_mask
counter[:, c, :, :] += window_mask
vt_src /= counter
else:
vt_src, self.cache_state_source = predict_with_cfg(
zt_src, cfg[idx],
source_embeds["prompt_embeds"],
source_embeds["negative_prompt_embeds"],
timestep, idx, source_image_cond,
source_clip_fea, control_latents,
cache_state=self.cache_state_source)
else:
if idx == len(timesteps) - drift_steps:
x_tgt = zt_tgt
zt_tgt = x_tgt
vt_src = 0
#target
if context_options is not None:
counter = torch.zeros_like(zt_tgt, device=intermediate_device)
vt_tgt = torch.zeros_like(zt_tgt, device=intermediate_device)
context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap))
for c in 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}")
if len(text_embeds["prompt_embeds"]) > 1:
positive = text_embeds["prompt_embeds"][prompt_index]
else:
positive = text_embeds["prompt_embeds"]
partial_img_emb = None
partial_control_latents = None
if image_cond is not None:
partial_img_emb = image_cond[:, c, :, :]
partial_img_emb[:, 0, :, :] = image_cond[:, 0, :, :].to(intermediate_device)
if control_latents is not None:
partial_control_latents = control_latents[:, c, :, :]
partial_zt_tgt = zt_tgt[:, c, :, :]
vt_tgt_context, new_teacache = predict_with_cfg(
partial_zt_tgt, cfg[idx],
positive, text_embeds["negative_prompt_embeds"],
timestep, idx, partial_img_emb, partial_control_latents,
clip_fea, current_teacache)
if cache_args is not None:
self.window_tracker.cache_states[window_id] = new_teacache
window_mask = create_window_mask(vt_tgt_context, c, latent_video_length, context_overlap)
vt_tgt[:, c, :, :] += vt_tgt_context * window_mask
counter[:, c, :, :] += window_mask
vt_tgt /= counter
else:
vt_tgt, self.cache_state = predict_with_cfg(
zt_tgt, cfg[idx],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents,
cache_state=self.cache_state)
v_delta = vt_tgt - vt_src
x_tgt = x_tgt.to(torch.float32)
v_delta = v_delta.to(torch.float32)
x_tgt = x_tgt + (sigma_prev - sigma) * v_delta
x0 = x_tgt
#region context windowing
elif context_options is not None:
counter = torch.zeros_like(latent_model_input, device=intermediate_device)
noise_pred = torch.zeros_like(latent_model_input, device=intermediate_device)
context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap))
fraction_per_context = 1.0 / len(context_queue)
context_pbar = ProgressBar(steps)
step_start_progress = idx
# Validate all context windows before processing
max_idx = latent_model_input.shape[1] if latent_model_input.ndim > 1 else 0
for window_indices in context_queue:
if not all(0 <= idx < max_idx for idx in window_indices):
raise ValueError(f"Invalid context window indices {window_indices} for latent_model_input with shape {latent_model_input.shape}")
for i, c in enumerate(context_queue):
window_id = self.window_tracker.get_window_id(c)
if cache_args is not None:
current_teacache = self.window_tracker.get_teacache(window_id, self.cache_state)
else:
current_teacache = None
prompt_index = min(int(max(c) / section_size), num_prompts - 1)
if context_options["verbose"]:
log.info(f"Prompt index: {prompt_index}")
# Use the appropriate prompt for this section
if len(text_embeds["prompt_embeds"]) > 1:
positive = [text_embeds["prompt_embeds"][prompt_index]]
else:
positive = text_embeds["prompt_embeds"]
partial_img_emb = None
partial_control_latents = None
if image_cond is not None:
partial_img_emb = image_cond[:, c]
if c[0] != 0 and context_reference_latent is not None:
new_init_image = context_reference_latent[:, 0].to(intermediate_device)
# Concatenate the first 4 channels of partial_img_emb with new_init_image to match the required shape
if new_init_image.shape[0] + 4 == partial_img_emb.shape[0]:
partial_img_emb[:, 0] = torch.cat([
image_cond[:4, 0],
new_init_image
], dim=0)
else:
# fallback to original assignment if shape matches
partial_img_emb[:, 0] = new_init_image
else:
new_init_image = image_cond[:, 0].to(intermediate_device)
partial_img_emb[:, 0] = new_init_image
if control_latents is not None:
partial_control_latents = control_latents[:, c]
partial_control_camera_latents = None
if control_camera_latents is not None:
partial_control_camera_latents = control_camera_latents[:, :, c]
partial_vace_context = None
if vace_data is not None:
window_vace_data = []
for vace_entry in vace_data:
partial_context = vace_entry["context"][0][:, c]
if has_ref:
partial_context[:, 0] = vace_entry["context"][0][:, 0]
window_vace_data.append({
"context": [partial_context],
"scale": vace_entry["scale"],
"start": vace_entry["start"],
"end": vace_entry["end"],
"seq_len": vace_entry["seq_len"]
})
partial_vace_context = window_vace_data
partial_audio_proj = None
if fantasytalking_embeds is not None:
partial_audio_proj = audio_proj[:, c]
partial_fantasy_portrait_input = None
if fantasy_portrait_input is not None:
partial_fantasy_portrait_input = fantasy_portrait_input.copy()
partial_fantasy_portrait_input["adapter_proj"] = fantasy_portrait_input["adapter_proj"][:, c]
partial_latent_model_input = latent_model_input[:, c]
if latents_to_insert is not None and c[0] != 0:
partial_latent_model_input[:, :1] = latents_to_insert
partial_unianim_data = None
if unianim_data is not None:
partial_dwpose = dwpose_data[:, :, c]
partial_unianim_data = {
"dwpose": partial_dwpose,
"random_ref": unianim_data["random_ref"],
"strength": unianimate_poses["strength"],
"start_percent": unianimate_poses["start_percent"],
"end_percent": unianimate_poses["end_percent"]
}
partial_mtv_motion_tokens = None
if mtv_input is not None:
start_token_index = c[0] * 24
end_token_index = (c[-1] + 1) * 24
partial_mtv_motion_tokens = mtv_motion_tokens[:, start_token_index:end_token_index, :]
if context_options["verbose"]:
log.info(f"context window: {c}")
log.info(f"motion_token_indices: {start_token_index}-{end_token_index}")
partial_add_cond = None
if add_cond is not None:
partial_add_cond = add_cond[:, :, c].to(device, dtype)
if len(timestep.shape) != 1:
partial_timestep = timestep[:, c]
partial_timestep[:, :1] = 0
else:
partial_timestep = timestep
#print("Partial timestep:", partial_timestep)
noise_pred_context, new_teacache = predict_with_cfg(
partial_latent_model_input,
cfg[idx], positive,
text_embeds["negative_prompt_embeds"],
partial_timestep, idx, partial_img_emb, clip_fea, partial_control_latents, partial_vace_context, partial_unianim_data,partial_audio_proj,
partial_control_camera_latents, partial_add_cond, current_teacache, context_window=c, fantasy_portrait_input=partial_fantasy_portrait_input,
mtv_motion_tokens=partial_mtv_motion_tokens)
if cache_args is not None:
self.window_tracker.cache_states[window_id] = new_teacache
window_mask = create_window_mask(noise_pred_context, c, latent_video_length, 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, steps)
noise_pred /= counter
#region multitalk
elif multitalk_sampling:
mode = image_embeds.get("multitalk_mode", "multitalk")
if mode == "auto":
mode = transformer.multitalk_model_type.lower()
log.info(f"Multitalk mode: {mode}")
cond_frame = None
offload = image_embeds.get("force_offload", False)
tiled_vae = image_embeds.get("tiled_vae", False)
frame_num = clip_length = image_embeds.get("num_frames", 81)
vae = image_embeds.get("vae", None)
clip_embeds = image_embeds.get("clip_context", None)
if clip_embeds is not None:
clip_embeds = clip_embeds.to(dtype)
colormatch = image_embeds.get("colormatch", "disabled")
motion_frame = image_embeds.get("motion_frame", 25)
target_w = image_embeds.get("target_w", None)
target_h = image_embeds.get("target_h", None)
original_images = cond_image = image_embeds.get("multitalk_start_image", None)
if original_images is None:
original_images = torch.zeros([noise.shape[0], 1, target_h, target_w], device=device)
if len(multitalk_embeds['audio_features'])==2 and (multitalk_embeds['ref_target_masks'] is None):
face_scale = 0.1
x_min, x_max = int(target_h * face_scale), int(target_h * (1 - face_scale))
lefty_min, lefty_max = int((target_w//2) * face_scale), int((target_w//2) * (1 - face_scale))
righty_min, righty_max = int((target_w//2) * face_scale + (target_w//2)), int((target_w//2) * (1 - face_scale) + (target_w//2))
human_mask1, human_mask2 = (torch.zeros([target_h, target_w]) for _ in range(2))
human_mask1[x_min:x_max, lefty_min:lefty_max] = 1
human_mask2[x_min:x_max, righty_min:righty_max] = 1
background_mask = torch.where((human_mask1 + human_mask2) > 0, torch.tensor(0), torch.tensor(1))
human_masks = [human_mask1, human_mask2, background_mask]
ref_target_masks = torch.stack(human_masks, dim=0)
multitalk_embeds['ref_target_masks'] = ref_target_masks
gen_video_list = []
is_first_clip = True
arrive_last_frame = False
cur_motion_frames_num = 1
audio_start_idx = iteration_count = step_iteration_count= 0
audio_end_idx = audio_start_idx + clip_length
indices = (torch.arange(4 + 1) - 2) * 1
current_condframe_index = 0
audio_embedding = multitalk_audio_embedding
human_num = len(audio_embedding)
audio_embs = None
pcd_data = pcd_data_input = None
if uni3c_embeds is not None:
transformer.controlnet = uni3c_embeds["controlnet"]
pcd_data = {
"render_latent": uni3c_embeds["render_latent"],
"render_mask": uni3c_embeds["render_mask"],
"camera_embedding": uni3c_embeds["camera_embedding"],
"controlnet_weight": uni3c_embeds["controlnet_weight"],
"start": uni3c_embeds["start"],
"end": uni3c_embeds["end"],
}
total_frames = len(audio_embedding[0])
estimated_iterations = total_frames // (frame_num - motion_frame) + 1
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")
while True: # start video generation iteratively
cur_motion_frames_latent_num = int(1 + (cur_motion_frames_num-1) // 4)
if mode == "infinitetalk":
cond_image = original_images[:, :, current_condframe_index:current_condframe_index+1] if cond_image is not None else None
if multitalk_embeds is not None:
audio_embs = []
# split audio with window size
for human_idx in range(human_num):
center_indices = torch.arange(audio_start_idx, audio_end_idx, 1).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)
audio_embs = torch.concat(audio_embs, dim=0).to(dtype)
h, w = (cond_image.shape[-2], cond_image.shape[-1]) if cond_image is not None else (target_h, target_w)
lat_h, lat_w = h // VAE_STRIDE[1], w // VAE_STRIDE[2]
seq_len = ((frame_num - 1) // VAE_STRIDE[0] + 1) * lat_h * lat_w // (PATCH_SIZE[1] * PATCH_SIZE[2])
latent_frame_num = (frame_num - 1) // 4 + 1
noise = torch.randn(
16, latent_frame_num,
lat_h, lat_w, dtype=torch.float32, device=torch.device("cpu"), generator=seed_g).to(device)
# Calculate the correct latent slice based on current iteration
if is_first_clip:
latent_start_idx = 0
latent_end_idx = noise.shape[1]
else:
new_frames_per_iteration = frame_num - motion_frame
new_latent_frames_per_iteration = ((new_frames_per_iteration - 1) // 4 + 1)
latent_start_idx = iteration_count * new_latent_frames_per_iteration
latent_end_idx = latent_start_idx + noise.shape[1]
if samples is not None:
input_samples = samples["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[latent_start_idx:latent_end_idx]
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
# zero padding and vae encode for img cond
if cond_image is not None:
video_frames = torch.zeros(1, 3, frame_num-cond_image.shape[2], target_h, target_w, device=device, dtype=vae.dtype)
padding_frames_pixels_values = torch.concat([cond_image.to(device, vae.dtype), video_frames], dim=2)
# encode
vae.to(device)
y = vae.encode(padding_frames_pixels_values, device=device, tiled=tiled_vae, pbar=False).to(dtype)[0]
if mode == "multitalk":
latent_motion_frames = y[:, :cur_motion_frames_latent_num] # C T H W
else:
cond_ = cond_image if is_first_clip else cond_frame
latent_motion_frames = vae.encode(cond_.to(device, vae.dtype), device=device, tiled=tiled_vae, pbar=False).to(dtype)[0]
vae.model.clear_cache()
vae.to(offload_device)
#motion_frame_index = cur_motion_frames_latent_num if mode == "infinitetalk" else 1
msk = torch.zeros(4, latent_frame_num, lat_h, lat_w, device=device, dtype=dtype)
msk[:, :1] = 1
y = torch.cat([msk, y]) # 4+C T H W
mm.soft_empty_cache()
else:
y = None
latent_motion_frames = noise[:, :1]
if scheduler == "multitalk":
timesteps = list(np.linspace(1000, 1, steps, dtype=np.float32))
timesteps.append(0.)
timesteps = [torch.tensor([t], device=device) for t in timesteps]
timesteps = [timestep_transform(t, shift=shift, num_timesteps=1000) for t in timesteps]
else:
sample_scheduler, timesteps,_,_ = get_scheduler(scheduler, total_steps, start_step, end_step, shift, device, transformer.dim, flowedit_args, denoise_strength, sigmas=sigmas)
timesteps = [torch.tensor([float(t)], device=device) for t in timesteps] + [torch.tensor([0.], device=device)]
# sample videos
latent = noise
# injecting motion frames
if not is_first_clip and mode == "multitalk":
latent_motion_frames = latent_motion_frames.to(latent.dtype).to(device)
motion_add_noise = torch.randn(latent_motion_frames.shape, device=torch.device("cpu"), generator=seed_g).to(device).contiguous()
add_latent = add_noise(latent_motion_frames, motion_add_noise, timesteps[0])
latent[:, :add_latent.shape[1]] = add_latent
if offload:
#blockswap init
if not transformer.patched_linear:
if block_swap_args is not None:
transformer.use_non_blocking = block_swap_args.get("use_non_blocking", False)
for name, param in transformer.named_parameters():
if "block" not in name:
param.data = param.data.to(device)
if "control_adapter" in name:
param.data = param.data.to(device)
elif block_swap_args["offload_txt_emb"] and "txt_emb" in name:
param.data = param.data.to(offload_device)
elif block_swap_args["offload_img_emb"] and "img_emb" in name:
param.data = param.data.to(offload_device)
transformer.block_swap(
block_swap_args["blocks_to_swap"] - 1 ,
block_swap_args["offload_txt_emb"],
block_swap_args["offload_img_emb"],
vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", None),
)
elif model["auto_cpu_offload"]:
for module in transformer.modules():
if hasattr(module, "offload"):
module.offload()
if hasattr(module, "onload"):
module.onload()
for block in transformer.blocks:
block.modulation = torch.nn.Parameter(block.modulation.to(device))
transformer.head.modulation = torch.nn.Parameter(transformer.head.modulation.to(device))
else:
transformer.to(device)
# 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"]
window_vace_data = None
# if vace_data is not None:
# window_vace_data = []
# for vace_entry in vace_data:
# partial_context = vace_entry["context"][0][:, latent_start_idx:latent_end_idx]
# if has_ref:
# 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"]
# })
# uni3c slices
if uni3c_embeds is not None:
vae.to(device)
# Pad original_images if needed
num_frames = original_images.shape[2]
required_frames = audio_end_idx - audio_start_idx
if audio_end_idx > num_frames:
pad_len = audio_end_idx - num_frames
last_frame = original_images[:, :, -1:].repeat(1, 1, pad_len, 1, 1)
padded_images = torch.cat([original_images, last_frame], dim=2)
else:
padded_images = original_images
render_latent = vae.encode(
padded_images[:, :, audio_start_idx:audio_end_idx].to(device, vae.dtype),
device=device, tiled=tiled_vae
).to(dtype)
vae.model.clear_cache()
vae.to(offload_device)
pcd_data['render_latent'] = render_latent
# unianimate slices
partial_unianim_data = None
if unianim_data is not None:
partial_dwpose = dwpose_data[:, :, latent_start_idx:latent_end_idx]
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"]
}
# fantasy portrait slices
partial_fantasy_portrait_input = None
if fantasy_portrait_input is not None:
adapter_proj = fantasy_portrait_input["adapter_proj"]
if latent_end_idx > adapter_proj.shape[1]:
pad_len = latent_end_idx - adapter_proj.shape[1]
last_frame = adapter_proj[:, -1:, :, :].repeat(1, pad_len, 1, 1)
padded_proj = torch.cat([adapter_proj, last_frame], dim=1)
else:
padded_proj = adapter_proj
partial_fantasy_portrait_input = fantasy_portrait_input.copy()
partial_fantasy_portrait_input["adapter_proj"] = padded_proj[:, latent_start_idx:latent_end_idx]
mm.soft_empty_cache()
gc.collect()
# sampling loop
sampling_pbar = tqdm(total=len(timesteps)-1, desc=f"Sampling audio indices {audio_start_idx}-{audio_end_idx}", position=0, leave=True)
for i in range(len(timesteps)-1):
timestep = timesteps[i]
latent_model_input = latent.to(device)
if mode == "infinitetalk":
latent_model_input[:, :cur_motion_frames_latent_num] = latent_motion_frames
noise_pred, self.cache_state = predict_with_cfg(
latent_model_input, cfg[i], positive, text_embeds["negative_prompt_embeds"],
timestep, i, y, clip_embeds, control_latents, window_vace_data, partial_unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, multitalk_audio_embeds=audio_embs, fantasy_portrait_input=partial_fantasy_portrait_input)
if callback is not None:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3)
callback(step_iteration_count, callback_latent, None, estimated_iterations*(len(timesteps)-1))
del callback_latent
sampling_pbar.update(1)
step_iteration_count += 1
# update latent
if scheduler == "multitalk":
noise_pred = -noise_pred
dt = (timesteps[i] - timesteps[i + 1]) / 1000
latent = latent + noise_pred * dt[:, None, None, None]
else:
latent = sample_scheduler.step(noise_pred.unsqueeze(0), timestep, latent.unsqueeze(0).to(noise_pred.device), **scheduler_step_args)[0].squeeze(0)
del noise_pred, latent_model_input, timestep
# differential diffusion inpaint
if masks is not None:
if i < len(timesteps) - 1:
image_latent = add_noise(original_image.to(device), noise.to(device), timesteps[i+1])
mask = masks[i].to(latent)
latent = image_latent * mask + latent * (1-mask)
# injecting motion frames
if not is_first_clip and mode == "multitalk":
latent_motion_frames = latent_motion_frames.to(latent.dtype).to(device)
motion_add_noise = torch.randn(latent_motion_frames.shape, device=torch.device("cpu"), generator=seed_g).to(device).contiguous()
add_latent = add_noise(latent_motion_frames, motion_add_noise, timesteps[i+1])
latent[:, :add_latent.shape[1]] = add_latent
else:
latent[:, :cur_motion_frames_latent_num] = latent_motion_frames
del noise, latent_motion_frames
if offload:
offload_transformer(transformer)
vae.to(device)
videos = vae.decode(latent.unsqueeze(0).to(device, vae.dtype), device=device, tiled=tiled_vae, pbar=False)[0].cpu()
vae.model.clear_cache()
vae.to(offload_device)
sampling_pbar.close()
# optional color correction (less relevant for InfiniteTalk)
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:
if mode == "multitalk":
cm_result = cm.transfer(src=img, ref=original_images[0].permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
else:
cm_result = cm.transfer(src=img, ref=cond_image[0].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)
# cache generated samples
gen_video_list.append(videos if is_first_clip else videos[:, cur_motion_frames_num:])
current_condframe_index += 1
iteration_count += 1
# decide whether is done
if arrive_last_frame:
break
# update next condition frames
is_first_clip = False
cur_motion_frames_num = motion_frame
cond_ = videos[:, -cur_motion_frames_num:].unsqueeze(0)
if mode == "infinitetalk":
cond_frame = cond_
else:
cond_image = cond_
del videos, latent
# Repeat audio emb
if multitalk_embeds is not None:
audio_start_idx += (frame_num - cur_motion_frames_num)
audio_end_idx = audio_start_idx + clip_length
if audio_end_idx >= len(audio_embedding[0]):
arrive_last_frame = True
miss_lengths = []
source_frames = []
for human_inx in range(human_num):
source_frame = len(audio_embedding[human_inx])
source_frames.append(source_frame)
if audio_end_idx >= len(audio_embedding[human_inx]):
miss_length = audio_end_idx - len(audio_embedding[human_inx]) + 3
add_audio_emb = torch.flip(audio_embedding[human_inx][-1*miss_length:], dims=[0])
audio_embedding[human_inx] = torch.cat([audio_embedding[human_inx], add_audio_emb], dim=0)
miss_lengths.append(miss_length)
else:
miss_lengths.append(0)
if mode == "infinitetalk" and current_condframe_index >= original_images.shape[2]:
last_frame = original_images[:, :, -1:, :, :]
miss_length = 1
original_images = torch.cat([original_images, last_frame.repeat(1, 1, miss_length, 1, 1)], dim=2)
gen_video_samples = torch.cat(gen_video_list, dim=1)
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return {"video": gen_video_samples.permute(1, 2, 3, 0)},
#region normal inference
else:
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)
if bidirectional_sampling:
noise_pred_flipped, self.cache_state = predict_with_cfg(
latent_model_input_flipped,
cfg[idx],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, fantasy_portrait_input=fantasy_portrait_input, mtv_motion_tokens=mtv_motion_tokens,reverse_time=True)
if latent_shift_loop:
#reverse latent shift
if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent:
noise_pred = torch.cat([noise_pred[:, latent_video_length - shift_idx:]] + [noise_pred[:, :latent_video_length - shift_idx]], dim=1)
shift_idx = (shift_idx + latent_skip) % latent_video_length
if flowedit_args is None:
latent = latent.to(intermediate_device)
if len(timestep.shape) != 1 and scheduler != "flowmatch_pusa": #5b
# all_indices is a list of indices to skip
total_indices = list(range(latent.shape[1]))
process_indices = [i for i in total_indices if i not in all_indices]
if process_indices:
latent_to_process = latent[:, process_indices]
noise_pred_to_process = noise_pred[:, process_indices]
latent_slice = sample_scheduler.step(
noise_pred_to_process.unsqueeze(0),
orig_timestep,
latent_to_process.unsqueeze(0),
**scheduler_step_args
)[0].squeeze(0)
# Reconstruct the latent tensor: keep skipped indices as-is, update others
new_latent = []
for i in total_indices:
if i in all_indices:
new_latent.append(latent[:, i:i+1])
else:
j = process_indices.index(i)
new_latent.append(latent_slice[:, j:j+1])
latent = torch.cat(new_latent, dim=1)
else:
latent = sample_scheduler.step(
noise_pred[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else noise_pred.unsqueeze(0),
timestep,
latent[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else latent.unsqueeze(0),
**scheduler_step_args)[0].squeeze(0)
if noise_pred_flipped is not None:
latent_backwards = sample_scheduler_flipped.step(
noise_pred_flipped.unsqueeze(0),
timestep,
latent_flipped.unsqueeze(0),
**scheduler_step_args)[0].squeeze(0)
latent_backwards = torch.flip(latent_backwards, dims=[1])
latent = latent * 0.5 + latent_backwards * 0.5
#InfiniteTalk first frame handling
if (extra_latents is not None
and not multitalk_sampling
and transformer.multitalk_model_type=="InfiniteTalk"):
for entry in extra_latents:
add_index = entry["index"]
num_extra_frames = entry["samples"].shape[2]
latent[:, add_index:add_index+num_extra_frames] = entry["samples"].to(latent)
# differential diffusion inpaint
if masks is not None:
if idx < len(timesteps) - 1:
noise_timestep = timesteps[idx+1]
image_latent = sample_scheduler.scale_noise(
original_image.to(device), torch.tensor([noise_timestep]), noise.to(device)
)
mask = masks[idx].to(latent)
latent = image_latent * mask + latent * (1-mask)
if freeinit_args is not None:
current_latent = latent.clone()
if callback is not None:
if recammaster 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()
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)
else:
if callback is not None:
callback_latent = (zt_tgt.to(device) - vt_tgt.to(device) * t.to(device) / 1000).detach()
callback(idx, callback_latent.permute(1,0,2,3), None, len(timesteps))
else:
pbar.update(1)
except Exception as e:
log.error(f"Error during sampling: {e}")
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
raise e
if phantom_latents is not None:
latent = latent[:,:-phantom_latents.shape[1]]
cache_states = None
if cache_args is not None:
cache_report(transformer, cache_args)
if end_step != -1 and end_step < total_steps:
cache_states = {
"cache_state": self.cache_state,
"easycache_state": transformer.easycache_state,
"teacache_state": transformer.teacache_state,
"magcache_state": transformer.magcache_state,
}
if force_offload:
if not model["auto_cpu_offload"]:
offload_transformer(transformer)
try:
print_memory(device)
#torch.cuda.memory._dump_snapshot("wanvideowrapper_memory_dump.pt")
#torch.cuda.memory._record_memory_history(enabled=None)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return ({
"samples": latent.unsqueeze(0).cpu(),
"looped": is_looped,
"end_image": end_image if not fun_or_fl2v_model else None,
"has_ref": has_ref,
"drop_last": drop_last,
"generator_state": seed_g.get_state(),
"original_image": original_image.cpu() if original_image is not None else None,
"cache_states": cache_states
},{
"samples": callback_latent.unsqueeze(0).cpu() if callback is not None else None,
})
#region VideoDecode
class WanVideoDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"samples": ("LATENT",),
"enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": (
"Drastically reduces memory use but will introduce seams at tile stride boundaries. "
"The location and number of seams is dictated by the tile stride size. "
"The visibility of seams can be controlled by increasing the tile size. "
"Seams become less obvious at 1.5x stride and are barely noticeable at 2x stride size. "
"Which is to say if you use a stride width of 160, the seams are barely noticeable with a tile width of 320."
)}),
"tile_x": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile width in pixels. Smaller values use less VRAM but will make seams more obvious."}),
"tile_y": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile height in pixels. Smaller values use less VRAM but will make seams more obvious."}),
"tile_stride_x": ("INT", {"default": 144, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams."}),
"tile_stride_y": ("INT", {"default": 128, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams."}),
},
"optional": {
"normalization": (["default", "minmax"], {"advanced": True}),
}
}
@classmethod
def VALIDATE_INPUTS(s, tile_x, tile_y, tile_stride_x, tile_stride_y):
if tile_x <= tile_stride_x:
return "Tile width must be larger than the tile stride width."
if tile_y <= tile_stride_y:
return "Tile height must be larger than the tile stride height."
return True
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "decode"
CATEGORY = "WanVideoWrapper"
def decode(self, vae, samples, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, normalization="default"):
mm.soft_empty_cache()
video = samples.get("video", None)
if video is not None:
video.clamp_(-1.0, 1.0)
video.add_(1.0).div_(2.0)
return video.cpu().float(),
latents = samples["samples"]
end_image = samples.get("end_image", None)
has_ref = samples.get("has_ref", False)
drop_last = samples.get("drop_last", False)
is_looped = samples.get("looped", False)
vae.to(device)
latents = latents.to(device = device, dtype = vae.dtype)
mm.soft_empty_cache()
if has_ref:
latents = latents[:, :, 1:]
if drop_last:
latents = latents[:, :, :-1]
if type(vae).__name__ == "TAEHV":
images = vae.decode_video(latents.permute(0, 2, 1, 3, 4))[0].permute(1, 0, 2, 3)
images = torch.clamp(images, 0.0, 1.0)
images = images.permute(1, 2, 3, 0).cpu().float()
return (images,)
else:
if end_image is not None:
enable_vae_tiling = False
images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
vae.model.clear_cache()
images = images.cpu().float()
if normalization == "minmax":
images.sub_(images.min()).div_(images.max() - images.min())
else:
images.clamp_(-1.0, 1.0)
images.add_(1.0).div_(2.0)
if is_looped:
temp_latents = torch.cat([latents[:, :, -3:]] + [latents[:, :, :2]], dim=2)
temp_images = vae.decode(temp_latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//vae.upsampling_factor, tile_y//vae.upsampling_factor), tile_stride=(tile_stride_x//vae.upsampling_factor, tile_stride_y//vae.upsampling_factor))[0]
temp_images = temp_images.cpu().float()
temp_images = (temp_images - temp_images.min()) / (temp_images.max() - temp_images.min())
images = torch.cat([temp_images[:, 9:].to(images), images[:, 5:]], dim=1)
if end_image is not None:
images = images[:, 0:-1]
vae.model.clear_cache()
vae.to(offload_device)
mm.soft_empty_cache()
images.clamp_(0.0, 1.0)
return (images.permute(1, 2, 3, 0),)
#region VideoEncode
class WanVideoEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"image": ("IMAGE",),
"enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": "Drastically reduces memory use but may introduce seams"}),
"tile_x": ("INT", {"default": 272, "min": 64, "max": 2048, "step": 1, "tooltip": "Tile size in pixels, smaller values use less VRAM, may introduce more seams"}),
"tile_y": ("INT", {"default": 272, "min": 64, "max": 2048, "step": 1, "tooltip": "Tile size in pixels, smaller values use less VRAM, may introduce more seams"}),
"tile_stride_x": ("INT", {"default": 144, "min": 32, "max": 2048, "step": 32, "tooltip": "Tile stride in pixels, smaller values use less VRAM, may introduce more seams"}),
"tile_stride_y": ("INT", {"default": 128, "min": 32, "max": 2048, "step": 32, "tooltip": "Tile stride in pixels, smaller values use less VRAM, may introduce more seams"}),
},
"optional": {
"noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for leapfusion I2V where some noise can add motion and give sharper results"}),
"latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for leapfusion I2V where lower values allow for more motion"}),
"mask": ("MASK", ),
}
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("samples",)
FUNCTION = "encode"
CATEGORY = "WanVideoWrapper"
def encode(self, vae, image, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, noise_aug_strength=0.0, latent_strength=1.0, mask=None):
vae.to(device)
image = image.clone()
B, H, W, C = image.shape
if W % 16 != 0 or H % 16 != 0:
new_height = (H // 16) * 16
new_width = (W // 16) * 16
log.warning(f"Image size {W}x{H} is not divisible by 16, resizing to {new_width}x{new_height}")
image = common_upscale(image.movedim(-1, 1), new_width, new_height, "lanczos", "disabled").movedim(1, -1)
if image.shape[-1] == 4:
image = image[..., :3]
image = image.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
if noise_aug_strength > 0.0:
image = add_noise_to_reference_video(image, ratio=noise_aug_strength)
if isinstance(vae, TAEHV):
latents = vae.encode_video(image.permute(0, 2, 1, 3, 4), parallel=False)# B, T, C, H, W
latents = latents.permute(0, 2, 1, 3, 4)
else:
latents = vae.encode(image * 2.0 - 1.0, device=device, tiled=enable_vae_tiling, tile_size=(tile_x//vae.upsampling_factor, tile_y//vae.upsampling_factor), tile_stride=(tile_stride_x//vae.upsampling_factor, tile_stride_y//vae.upsampling_factor))
vae.model.clear_cache()
if latent_strength != 1.0:
latents *= latent_strength
log.info(f"WanVideoEncode: Encoded latents shape {latents.shape}")
mm.soft_empty_cache()
return ({"samples": latents, "noise_mask": mask},)
NODE_CLASS_MAPPINGS = {
"WanVideoSampler": WanVideoSampler,
"WanVideoDecode": WanVideoDecode,
"WanVideoTextEncode": WanVideoTextEncode,
"WanVideoTextEncodeSingle": WanVideoTextEncodeSingle,
"WanVideoClipVisionEncode": WanVideoClipVisionEncode,
"WanVideoImageToVideoEncode": WanVideoImageToVideoEncode,
"WanVideoEncode": WanVideoEncode,
"WanVideoEmptyEmbeds": WanVideoEmptyEmbeds,
"WanVideoEnhanceAVideo": WanVideoEnhanceAVideo,
"WanVideoContextOptions": WanVideoContextOptions,
"WanVideoTextEmbedBridge": WanVideoTextEmbedBridge,
"WanVideoFlowEdit": WanVideoFlowEdit,
"WanVideoControlEmbeds": WanVideoControlEmbeds,
"WanVideoSLG": WanVideoSLG,
"WanVideoLoopArgs": WanVideoLoopArgs,
"WanVideoSetBlockSwap": WanVideoSetBlockSwap,
"WanVideoExperimentalArgs": WanVideoExperimentalArgs,
"WanVideoVACEEncode": WanVideoVACEEncode,
"WanVideoPhantomEmbeds": WanVideoPhantomEmbeds,
"WanVideoRealisDanceLatents": WanVideoRealisDanceLatents,
"WanVideoApplyNAG": WanVideoApplyNAG,
"WanVideoMiniMaxRemoverEmbeds": WanVideoMiniMaxRemoverEmbeds,
"WanVideoFreeInitArgs": WanVideoFreeInitArgs,
"WanVideoSetRadialAttention": WanVideoSetRadialAttention,
"WanVideoBlockList": WanVideoBlockList,
"WanVideoTextEncodeCached": WanVideoTextEncodeCached,
"WanVideoAddExtraLatent": WanVideoAddExtraLatent,
"WanVideoScheduler": WanVideoScheduler,
"WanVideoAddStandInLatent": WanVideoAddStandInLatent,
"WanVideoAddControlEmbeds": WanVideoAddControlEmbeds,
"WanVideoAddMTVMotion": WanVideoAddMTVMotion,
"WanVideoRoPEFunction": WanVideoRoPEFunction,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"WanVideoSampler": "WanVideo Sampler",
"WanVideoDecode": "WanVideo Decode",
"WanVideoTextEncode": "WanVideo TextEncode",
"WanVideoTextEncodeSingle": "WanVideo TextEncodeSingle",
"WanVideoTextImageEncode": "WanVideo TextImageEncode (IP2V)",
"WanVideoClipVisionEncode": "WanVideo ClipVision Encode",
"WanVideoImageToVideoEncode": "WanVideo ImageToVideo Encode",
"WanVideoEncode": "WanVideo Encode",
"WanVideoEmptyEmbeds": "WanVideo Empty Embeds",
"WanVideoEnhanceAVideo": "WanVideo Enhance-A-Video",
"WanVideoContextOptions": "WanVideo Context Options",
"WanVideoTextEmbedBridge": "WanVideo TextEmbed Bridge",
"WanVideoFlowEdit": "WanVideo FlowEdit",
"WanVideoControlEmbeds": "WanVideo Control Embeds",
"WanVideoSLG": "WanVideo SLG",
"WanVideoLoopArgs": "WanVideo Loop Args",
"WanVideoSetBlockSwap": "WanVideo Set BlockSwap",
"WanVideoExperimentalArgs": "WanVideo Experimental Args",
"WanVideoVACEEncode": "WanVideo VACE Encode",
"WanVideoPhantomEmbeds": "WanVideo Phantom Embeds",
"WanVideoRealisDanceLatents": "WanVideo RealisDance Latents",
"WanVideoApplyNAG": "WanVideo Apply NAG",
"WanVideoMiniMaxRemoverEmbeds": "WanVideo MiniMax Remover Embeds",
"WanVideoFreeInitArgs": "WanVideo Free Init Args",
"WanVideoSetRadialAttention": "WanVideo Set Radial Attention",
"WanVideoBlockList": "WanVideo Block List",
"WanVideoTextEncodeCached": "WanVideo TextEncode Cached",
"WanVideoAddExtraLatent": "WanVideo Add Extra Latent",
"WanVideoAddStandInLatent": "WanVideo Add StandIn Latent",
"WanVideoAddControlEmbeds": "WanVideo Add Control Embeds",
"WanVideoAddMTVMotion": "WanVideo MTV Crafter Motion",
"WanVideoRoPEFunction": "WanVideo RoPE Function",
}
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