nodes/kijai-ComfyUI-WanVideoWrapper
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kijai-ComfyUI-WanVideoWrapper/nodes.py
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kijai 712eecf64b more compile friendly
2025-07-18 00:53:12 +03:00

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import os
import torch
import torch.nn.functional as F
import gc
from .utils import log, print_memory, apply_lora, clip_encode_image_tiled, fourier_filter, is_image_black
import numpy as np
import math
from tqdm import tqdm
from .wanvideo.modules.clip import CLIPModel
from .wanvideo.modules.model import rope_params
from .wanvideo.modules.t5 import T5EncoderModel
from .wanvideo.schedulers import (
FlowDPMSolverMultistepScheduler, FlowUniPCMultistepScheduler,
FlowMatchScheduler, FlowMatchSchedulerPusa, FlowMatchLCMScheduler,
get_sampling_sigmas, retrieve_timesteps
)
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, DEISMultistepScheduler
from .multitalk.multitalk import timestep_transform, add_noise
from .enhance_a_video.globals import set_enhance_weight, set_num_frames
from .taehv import TAEHV
from einops import rearrange
import folder_paths
import comfy.model_management as mm
from comfy.utils import load_torch_file, ProgressBar, common_upscale
from comfy.clip_vision import clip_preprocess, ClipVisionModel
from comfy.cli_args import args, LatentPreviewMethod
script_directory = os.path.dirname(os.path.abspath(__file__))
VAE_STRIDE = (4, 8, 8)
PATCH_SIZE = (1, 2, 2)
def add_noise_to_reference_video(image, ratio=None):
sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
image_noise = torch.randn_like(image) * sigma[:, None, None, None]
image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
image = image + image_noise
return image
def optimized_scale(positive_flat, negative_flat):
# Calculate dot production
dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
# Squared norm of uncondition
squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
# st_star = v_cond^T * v_uncond / ||v_uncond||^2
st_star = dot_product / squared_norm
return st_star
class WanVideoBlockSwap:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"blocks_to_swap": ("INT", {"default": 20, "min": 0, "max": 40, "step": 1, "tooltip": "Number of transformer blocks to swap, the 14B model has 40, while the 1.3B model has 30 blocks"}),
"offload_img_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload img_emb to offload_device"}),
"offload_txt_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload time_emb to offload_device"}),
},
"optional": {
"use_non_blocking": ("BOOLEAN", {"default": True, "tooltip": "Use non-blocking memory transfer for offloading, reserves more RAM but is faster"}),
"vace_blocks_to_swap": ("INT", {"default": 0, "min": 0, "max": 15, "step": 1, "tooltip": "Number of VACE blocks to swap, the VACE model has 15 blocks"}),
},
}
RETURN_TYPES = ("BLOCKSWAPARGS",)
RETURN_NAMES = ("block_swap_args",)
FUNCTION = "setargs"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Settings for block swapping, reduces VRAM use by swapping blocks to CPU memory"
def setargs(self, **kwargs):
return (kwargs, )
class WanVideoVRAMManagement:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"offload_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Percentage of parameters to offload"}),
},
}
RETURN_TYPES = ("VRAM_MANAGEMENTARGS",)
RETURN_NAMES = ("vram_management_args",)
FUNCTION = "setargs"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Alternative offloading method from DiffSynth-Studio, more aggressive in reducing memory use than block swapping, but can be slower"
def setargs(self, **kwargs):
return (kwargs, )
class WanVideoTeaCache:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"rel_l1_thresh": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.001,
"tooltip": "Higher values will make TeaCache more aggressive, faster, but may cause artifacts. Good value range for 1.3B: 0.05 - 0.08, for other models 0.15-0.30"}),
"start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Start percentage of the steps to apply TeaCache"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "End steps to apply TeaCache"}),
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
"use_coefficients": ("BOOLEAN", {"default": True, "tooltip": "Use calculated coefficients for more accuracy. When enabled therel_l1_thresh should be about 10 times higher than without"}),
},
"optional": {
"mode": (["e", "e0"], {"default": "e", "tooltip": "Choice between using e (time embeds, default) or e0 (modulated time embeds)"}),
},
}
RETURN_TYPES = ("CACHEARGS",)
RETURN_NAMES = ("cache_args",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = """
Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and
applying it instead of doing the step. Best results are achieved by choosing the
appropriate coefficients for the model. Early steps should never be skipped, with too
aggressive values this can happen and the motion suffers. Starting later can help with that too.
When NOT using coefficients, the threshold value should be
about 10 times smaller than the value used with coefficients.
Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1:
<pre style='font-family:monospace'>
+-------------------+--------+---------+--------+
| Model | Low | Medium | High |
+-------------------+--------+---------+--------+
| Wan2.1 t2v 1.3B | 0.05 | 0.07 | 0.08 |
| Wan2.1 t2v 14B | 0.14 | 0.15 | 0.20 |
| Wan2.1 i2v 480P | 0.13 | 0.19 | 0.26 |
| Wan2.1 i2v 720P | 0.18 | 0.20 | 0.30 |
+-------------------+--------+---------+--------+
</pre>
"""
def process(self, rel_l1_thresh, start_step, end_step, cache_device, use_coefficients, mode="e"):
if cache_device == "main_device":
cache_device = mm.get_torch_device()
else:
cache_device = mm.unet_offload_device()
cache_args = {
"cache_type": "TeaCache",
"rel_l1_thresh": rel_l1_thresh,
"start_step": start_step,
"end_step": end_step,
"cache_device": cache_device,
"use_coefficients": use_coefficients,
"mode": mode,
}
return (cache_args,)
class WanVideoMagCache:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"magcache_thresh": ("FLOAT", {"default": 0.02, "min": 0.0, "max": 0.3, "step": 0.001, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}),
"magcache_K": ("INT", {"default": 4, "min": 0, "max": 6, "step": 1, "tooltip": "The maxium skip steps of MagCache."}),
"start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Step to start applying MagCache"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "Step to end applying MagCache"}),
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
},
}
RETURN_TYPES = ("CACHEARGS",)
RETURN_NAMES = ("cache_args",)
FUNCTION = "setargs"
CATEGORY = "WanVideoWrapper"
EXPERIMENTAL = True
DESCRIPTION = "MagCache for WanVideoWrapper, source https://github.com/Zehong-Ma/MagCache"
def setargs(self, magcache_thresh, magcache_K, start_step, end_step, cache_device):
if cache_device == "main_device":
cache_device = mm.get_torch_device()
else:
cache_device = mm.unet_offload_device()
cache_args = {
"cache_type": "MagCache",
"magcache_thresh": magcache_thresh,
"magcache_K": magcache_K,
"start_step": start_step,
"end_step": end_step,
"cache_device": cache_device,
}
return (cache_args,)
class WanVideoEasyCache:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"easycache_thresh": ("FLOAT", {"default": 0.015, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}),
"start_step": ("INT", {"default": 10, "min": 1, "max": 9999, "step": 1, "tooltip": "Step to start applying EasyCache"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "Step to end applying EasyCache"}),
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
},
}
RETURN_TYPES = ("CACHEARGS",)
RETURN_NAMES = ("cache_args",)
FUNCTION = "setargs"
CATEGORY = "WanVideoWrapper"
EXPERIMENTAL = True
DESCRIPTION = "EasyCache for WanVideoWrapper, source https://github.com/H-EmbodVis/EasyCache"
def setargs(self, easycache_thresh, start_step, end_step, cache_device):
if cache_device == "main_device":
cache_device = mm.get_torch_device()
else:
cache_device = mm.unet_offload_device()
cache_args = {
"cache_type": "EasyCache",
"easycache_thresh": easycache_thresh,
"start_step": start_step,
"end_step": end_step,
"cache_device": cache_device,
}
return (cache_args,)
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", ),
"block_swap_args": ("BLOCKSWAPARGS", ),
}
}
RETURN_TYPES = ("WANVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
def loadmodel(self, model, block_swap_args):
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_block": ("INT", {"default": 1, "min": 0, "max": 8, "step": 1, "tooltip": "Number of blocks to apply normal attention to"}),
"dense_timestep": ("INT", {"default": 10, "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."}),
}
}
RETURN_TYPES = ("WANVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
def loadmodel(self, model, dense_attention_mode, dense_block, dense_timestep, decay_factor):
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_block"] = dense_block
patcher.model_options["transformer_options"]["dense_timestep"] = dense_timestep
patcher.model_options["transformer_options"]["decay_factor"] = decay_factor
return (patcher,)
class WanVideoTorchCompileSettings:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"backend": (["inductor","cudagraphs"], {"default": "inductor"}),
"fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
"mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
"dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
"dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
"compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only the transformer blocks, usually enough and can make compilation faster and less error prone"}),
},
"optional": {
"dynamo_recompile_limit": ("INT", {"default": 128, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.recompile_limit"}),
},
}
RETURN_TYPES = ("WANCOMPILEARGS",)
RETURN_NAMES = ("torch_compile_args",)
FUNCTION = "set_args"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "torch.compile settings, when connected to the model loader, torch.compile of the selected layers is attempted. Requires Triton and torch 2.5.0 is recommended"
def set_args(self, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only, dynamo_recompile_limit=128):
compile_args = {
"backend": backend,
"fullgraph": fullgraph,
"mode": mode,
"dynamic": dynamic,
"dynamo_cache_size_limit": dynamo_cache_size_limit,
"dynamo_recompile_limit": dynamo_recompile_limit,
"compile_transformer_blocks_only": compile_transformer_blocks_only,
}
return (compile_args, )
#region TextEncode
class LoadWanVideoT5TextEncoder:
@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"}
),
},
"optional": {
"load_device": (["main_device", "offload_device"], {"default": "offload_device"}),
"quantization": (['disabled', 'fp8_e4m3fn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
}
}
RETURN_TYPES = ("WANTEXTENCODER",)
RETURN_NAMES = ("wan_t5_model", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Loads Wan text_encoder model from 'ComfyUI/models/LLM'"
def loadmodel(self, model_name, precision, load_device="offload_device", quantization="disabled"):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
text_encoder_load_device = device if load_device == "main_device" else offload_device
tokenizer_path = os.path.join(script_directory, "configs", "T5_tokenizer")
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("text_encoders", model_name)
sd = load_torch_file(model_path, safe_load=True)
if "token_embedding.weight" not in sd and "shared.weight" not in sd:
raise ValueError("Invalid T5 text encoder model, this node expects the 'umt5-xxl' model")
if "scaled_fp8" in sd:
raise ValueError("Invalid T5 text encoder model, fp8 scaled is not supported by this node")
# Convert state dict keys from T5 format to the expected format
if "shared.weight" in sd:
log.info("Converting T5 text encoder model to the expected format...")
converted_sd = {}
for key, value in sd.items():
# Handle encoder block patterns
if key.startswith('encoder.block.'):
parts = key.split('.')
block_num = parts[2]
# Self-attention components
if 'layer.0.SelfAttention' in key:
if key.endswith('.k.weight'):
new_key = f"blocks.{block_num}.attn.k.weight"
elif key.endswith('.o.weight'):
new_key = f"blocks.{block_num}.attn.o.weight"
elif key.endswith('.q.weight'):
new_key = f"blocks.{block_num}.attn.q.weight"
elif key.endswith('.v.weight'):
new_key = f"blocks.{block_num}.attn.v.weight"
elif 'relative_attention_bias' in key:
new_key = f"blocks.{block_num}.pos_embedding.embedding.weight"
else:
new_key = key
# Layer norms
elif 'layer.0.layer_norm' in key:
new_key = f"blocks.{block_num}.norm1.weight"
elif 'layer.1.layer_norm' in key:
new_key = f"blocks.{block_num}.norm2.weight"
# Feed-forward components
elif 'layer.1.DenseReluDense' in key:
if 'wi_0' in key:
new_key = f"blocks.{block_num}.ffn.gate.0.weight"
elif 'wi_1' in key:
new_key = f"blocks.{block_num}.ffn.fc1.weight"
elif 'wo' in key:
new_key = f"blocks.{block_num}.ffn.fc2.weight"
else:
new_key = key
else:
new_key = key
elif key == "shared.weight":
new_key = "token_embedding.weight"
elif key == "encoder.final_layer_norm.weight":
new_key = "norm.weight"
else:
new_key = key
converted_sd[new_key] = value
sd = converted_sd
T5_text_encoder = T5EncoderModel(
text_len=512,
dtype=dtype,
device=text_encoder_load_device,
state_dict=sd,
tokenizer_path=tokenizer_path,
quantization=quantization
)
text_encoder = {
"model": T5_text_encoder,
"dtype": dtype,
}
return (text_encoder,)
class LoadWanVideoClipTextEncoder:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model_name": (folder_paths.get_filename_list("clip_vision") + folder_paths.get_filename_list("text_encoders"), {"tooltip": "These models are loaded from 'ComfyUI/models/clip_vision'"}),
"precision": (["fp16", "fp32", "bf16"],
{"default": "fp16"}
),
},
"optional": {
"load_device": (["main_device", "offload_device"], {"default": "offload_device"}),
}
}
RETURN_TYPES = ("CLIP_VISION",)
RETURN_NAMES = ("wan_clip_vision", )
FUNCTION = "loadmodel"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Loads Wan clip_vision model from 'ComfyUI/models/clip_vision'"
def loadmodel(self, model_name, precision, load_device="offload_device"):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
text_encoder_load_device = device if load_device == "main_device" else offload_device
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("clip_vision", model_name)
# We also support legacy setups where the model is in the text_encoders folder
if model_path is None:
model_path = folder_paths.get_full_path("text_encoders", model_name)
sd = load_torch_file(model_path, safe_load=True)
if "log_scale" not in sd:
raise ValueError("Invalid CLIP model, this node expectes the 'open-clip-xlm-roberta-large-vit-huge-14' model")
clip_model = CLIPModel(dtype=dtype, device=device, state_dict=sd)
clip_model.model.to(text_encoder_load_device)
del sd
return (clip_model,)
class WanVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"t5": ("WANTEXTENCODER",),
"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
}
}
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, t5, positive_prompt, negative_prompt,force_offload=True, model_to_offload=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
if model_to_offload is not None:
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"]
# Split positive prompts and process each with weights
positive_prompts_raw = [p.strip() for p in positive_prompt.split('|')]
positive_prompts = []
all_weights = []
for p in positive_prompts_raw:
cleaned_prompt, weights = self.parse_prompt_weights(p)
positive_prompts.append(cleaned_prompt)
all_weights.append(weights)
encoder.model.to(device)
with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=True):
context = encoder(positive_prompts, device)
context_null = encoder([negative_prompt], device)
# 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
if force_offload:
encoder.model.to(offload_device)
mm.soft_empty_cache()
prompt_embeds_dict = {
"prompt_embeds": context,
"negative_prompt_embeds": context_null,
}
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": {
"t5": ("WANTEXTENCODER",),
"prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Encodes text prompt into text embedding."
def process(self, t5, prompt, force_offload=True, model_to_offload=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
if model_to_offload is not None:
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"]
encoder.model.to(device)
with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=True):
encoded = encoder([prompt], device)
if force_offload:
encoder.model.to(offload_device)
mm.soft_empty_cache()
prompt_embeds_dict = {
"prompt_embeds": encoded,
"negative_prompt_embeds": None,
}
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):
device=mm.get_torch_device()
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 image encode
class WanVideoImageClipEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip_vision": ("CLIP_VISION",),
"image": ("IMAGE", {"tooltip": "Image to encode"}),
"vae": ("WANVAE",),
"generation_width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"generation_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": {
"force_offload": ("BOOLEAN", {"default": True}),
"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"}),
"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"}),
"clip_embed_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}),
"adjust_resolution": ("BOOLEAN", {"default": True, "tooltip": "Performs the same resolution adjustment as in the original code"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DEPRECATED = True
def process(self, clip_vision, vae, image, num_frames, generation_width, generation_height, force_offload=True, noise_aug_strength=0.0,
latent_strength=1.0, clip_embed_strength=1.0, adjust_resolution=True):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
self.image_mean = [0.48145466, 0.4578275, 0.40821073]
self.image_std = [0.26862954, 0.26130258, 0.27577711]
H, W = image.shape[1], image.shape[2]
max_area = generation_width * generation_height
print(clip_vision)
clip_vision.model.to(device)
if isinstance(clip_vision, ClipVisionModel):
clip_context = clip_vision.encode_image(image).last_hidden_state.to(device)
else:
pixel_values = clip_preprocess(image.to(device), size=224, mean=self.image_mean, std=self.image_std, crop=True).float()
clip_context = clip_vision.visual(pixel_values)
if clip_embed_strength != 1.0:
clip_context *= clip_embed_strength
if force_offload:
clip_vision.model.to(offload_device)
mm.soft_empty_cache()
if adjust_resolution:
aspect_ratio = H / W
lat_h = round(
np.sqrt(max_area * aspect_ratio) // VAE_STRIDE[1] //
PATCH_SIZE[1] * PATCH_SIZE[1])
lat_w = round(
np.sqrt(max_area / aspect_ratio) // VAE_STRIDE[2] //
PATCH_SIZE[2] * PATCH_SIZE[2])
h = lat_h * VAE_STRIDE[1]
w = lat_w * VAE_STRIDE[2]
else:
h = generation_height
w = generation_width
lat_h = h // 8
lat_w = w // 8
# Step 1: Create initial mask with ones for first frame, zeros for others
mask = torch.ones(1, num_frames, lat_h, lat_w, device=device)
mask[:, 1:] = 0
# Step 2: Repeat first frame 4 times and concatenate with remaining frames
first_frame_repeated = torch.repeat_interleave(mask[:, 0:1], repeats=4, dim=1)
mask = torch.concat([first_frame_repeated, mask[:, 1:]], dim=1)
# Step 3: Reshape mask into groups of 4 frames
mask = mask.view(1, mask.shape[1] // 4, 4, lat_h, lat_w)
# Step 4: Transpose dimensions and select first batch
mask = mask.transpose(1, 2)[0]
# Calculate maximum sequence length
frames_per_stride = (num_frames - 1) // VAE_STRIDE[0] + 1
patches_per_frame = lat_h * lat_w // (PATCH_SIZE[1] * PATCH_SIZE[2])
max_seq_len = frames_per_stride * patches_per_frame
vae.to(device)
# Step 1: Resize and rearrange the input image dimensions
#resized_image = image.permute(0, 3, 1, 2) # Rearrange dimensions to (B, C, H, W)
#resized_image = torch.nn.functional.interpolate(resized_image, size=(h, w), mode='bicubic')
resized_image = common_upscale(image.movedim(-1, 1), w, h, "lanczos", "disabled")
resized_image = resized_image.transpose(0, 1) # Transpose to match required format
resized_image = resized_image * 2 - 1
if noise_aug_strength > 0.0:
resized_image = add_noise_to_reference_video(resized_image, ratio=noise_aug_strength)
# Step 2: Create zero padding frames
zero_frames = torch.zeros(3, num_frames-1, h, w, device=device)
# Step 3: Concatenate image with zero frames
concatenated = torch.concat([resized_image.to(device), zero_frames, resized_image.to(device)], dim=1).to(device = device, dtype = vae.dtype)
concatenated *= latent_strength
y = vae.encode([concatenated], device)[0]
y = torch.concat([mask, y])
vae.model.clear_cache()
vae.to(offload_device)
image_embeds = {
"image_embeds": y,
"clip_context": clip_context,
"max_seq_len": max_seq_len,
"num_frames": num_frames,
"lat_h": lat_h,
"lat_w": lat_w,
}
return (image_embeds,)
class WanVideoImageResizeToClosest:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", {"tooltip": "Image to resize"}),
"generation_width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"generation_height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"aspect_ratio_preservation": (["keep_input", "stretch_to_new", "crop_to_new"],),
},
}
RETURN_TYPES = ("IMAGE", "INT", "INT", )
RETURN_NAMES = ("image","width","height",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Resizes image to the closest supported resolution based on aspect ratio and max pixels, according to the original code"
def process(self, image, generation_width, generation_height, aspect_ratio_preservation ):
H, W = image.shape[1], image.shape[2]
max_area = generation_width * generation_height
crop = "disabled"
if aspect_ratio_preservation == "keep_input":
aspect_ratio = H / W
elif aspect_ratio_preservation == "stretch_to_new" or aspect_ratio_preservation == "crop_to_new":
aspect_ratio = generation_height / generation_width
if aspect_ratio_preservation == "crop_to_new":
crop = "center"
lat_h = round(
np.sqrt(max_area * aspect_ratio) // VAE_STRIDE[1] //
PATCH_SIZE[1] * PATCH_SIZE[1])
lat_w = round(
np.sqrt(max_area / aspect_ratio) // VAE_STRIDE[2] //
PATCH_SIZE[2] * PATCH_SIZE[2])
h = lat_h * VAE_STRIDE[1]
w = lat_w * VAE_STRIDE[2]
resized_image = common_upscale(image.movedim(-1, 1), w, h, "lanczos", crop).movedim(1, -1)
return (resized_image, w, h)
#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):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
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 WanVideoImageToVideoEncode:
@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"}),
"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": {
"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, vae, 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):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
H = height
W = width
lat_h = H // 8
lat_w = W // 8
num_frames = ((num_frames - 1) // 4) * 4 + 1
two_ref_images = start_image is not None and end_image is not None
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)
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)
# 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:
resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
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:
resized_end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
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
vae.to(device)
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)
concatenated = torch.cat([resized_start_image.to(device), zero_frames], dim=1)
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)
concatenated = torch.cat([zero_frames, resized_end_image.to(device)], dim=1)
elif start_image is None and end_image is None:
concatenated = torch.zeros(3, num_frames, H, W, device=device)
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)
else:
zero_frames = torch.zeros(3, num_frames-1, H, W, device=device)
concatenated = torch.cat([resized_start_image.to(device), zero_frames, resized_end_image.to(device)], dim=1)
else:
temporal_mask = common_upscale(temporal_mask.unsqueeze(1), W, H, "nearest", "disabled").squeeze(1)
concatenated = resized_start_image[:,:num_frames] * temporal_mask[:num_frames].unsqueeze(0)
y = vae.encode([concatenated.to(device=device, dtype=vae.dtype)], device, end_=(end_image is not None and not fun_or_fl2v_model),tiled=tiled_vae)[0]
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
if control_embeds is None:
y = torch.cat([mask, y])
else:
if end_image is None:
y[:, 1:] = 0
elif start_image is None:
y[:, -1:] = 0
else:
y[:, 1:-1] = 0 # doesn't seem to work anyway though...
# 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)
vae.model.clear_cache()
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
}
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,
"extra_latents": extra_latents
}
return (embeds,)
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 + T,
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": {
"latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}),
"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": {
"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 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):
self.device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
self.vae = vae.to(self.device)
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=self.device, dtype=self.vae.dtype)
else:
input_frames = input_frames[:num_frames]
input_frames = common_upscale(input_frames.clone().movedim(-1, 1), width, height, "lanczos", "disabled").movedim(1, -1)
input_frames = input_frames.to(self.vae.dtype).to(self.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=self.device)
else:
print("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(self.vae.dtype).to(self.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:
# 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(self.vae.dtype).to(self.device).unsqueeze(0).permute(0, 4, 1, 2, 3).unsqueeze(0)
ref_images = ref_images * 2 - 1
z0 = self.vace_encode_frames(input_frames, ref_images, masks=input_masks, tiled_vae=tiled_vae)
self.vae.model.clear_cache()
m0 = self.vace_encode_masks(input_masks, ref_images)
z = self.vace_latent(z0, m0)
self.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, 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)
if masks is None:
latents = self.vae.encode(frames, device=self.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)]
inactive = self.vae.encode(inactive, device=self.device, tiled=tiled_vae)
reactive = self.vae.encode(reactive, device=self.device, tiled=tiled_vae)
latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)]
self.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 = self.vae.encode(refs, device=self.device, tiled=tiled_vae)
else:
print("refs shape", refs.shape)#torch.Size([3, 1, 512, 512])
ref_latent = self.vae.encode(refs, device=self.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)
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 = []
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)
return result_masks
def vace_latent(self, z, m):
return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)]
class ExtractStartFramesForContinuations:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input_video_frames": ("IMAGE", {"tooltip": "Input video frames to extract the start frames from."}),
"num_frames": ("INT", {"default": 10, "min": 1, "max": 1024, "step": 1, "tooltip": "Number of frames to get from the start of the video."}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("start_frames",)
FUNCTION = "get_start_frames"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Extracts the first N frames from a video sequence for continuations."
def get_start_frames(self, input_video_frames, num_frames):
if input_video_frames is None or input_video_frames.shape[0] == 0:
log.warning("Input video frames are empty. Returning an empty tensor.")
if input_video_frames is not None:
return (torch.empty((0,) + input_video_frames.shape[1:], dtype=input_video_frames.dtype),)
else:
# Return a tensor with 4 dimensions, as expected for an IMAGE type.
return (torch.empty((0, 64, 64, 3), dtype=torch.float32),)
total_frames = input_video_frames.shape[0]
num_to_get = min(num_frames, total_frames)
if num_to_get < num_frames:
log.warning(f"Requested {num_frames} frames, but input video only has {total_frames} frames. Returning first {num_to_get} frames.")
start_frames = input_video_frames[:num_to_get]
return (start_frames.cpu().float(),)
class WanVideoVACEStartToEndFrame:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"empty_frame_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "White level of empty frame to use"}),
},
"optional": {
"start_image": ("IMAGE",),
"end_image": ("IMAGE",),
"control_images": ("IMAGE",),
"inpaint_mask": ("MASK", {"tooltip": "Inpaint mask to use for the empty frames"}),
"start_index": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "tooltip": "Index to start from"}),
"end_index": ("INT", {"default": -1, "min": -10000, "max": 10000, "step": 1, "tooltip": "Index to end at"}),
},
}
RETURN_TYPES = ("IMAGE", "MASK", )
RETURN_NAMES = ("images", "masks",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Helper node to create start/end frame batch and masks for VACE"
def process(self, num_frames, empty_frame_level, start_image=None, end_image=None, control_images=None, inpaint_mask=None, start_index=0, end_index=-1):
B, H, W, C = start_image.shape if start_image is not None else end_image.shape
device = start_image.device if start_image is not None else end_image.device
# Convert negative end_index to positive
if end_index < 0:
end_index = num_frames + end_index
# Create output batch with empty frames
out_batch = torch.ones((num_frames, H, W, 3), device=device) * empty_frame_level
# Create mask tensor with proper dimensions
masks = torch.ones((num_frames, H, W), device=device)
# Pre-process all images at once to avoid redundant work
if end_image is not None and (end_image.shape[1] != H or end_image.shape[2] != W):
end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1)
if control_images is not None and (control_images.shape[1] != H or control_images.shape[2] != W):
control_images = common_upscale(control_images.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1)
# Place start image at start_index
if start_image is not None:
frames_to_copy = min(start_image.shape[0], num_frames - start_index)
if frames_to_copy > 0:
out_batch[start_index:start_index + frames_to_copy] = start_image[:frames_to_copy]
masks[start_index:start_index + frames_to_copy] = 0
# Place end image at end_index
if end_image is not None:
# Calculate where to start placing end images
end_start = end_index - end_image.shape[0] + 1
if end_start < 0: # Handle case where end images won't all fit
end_image = end_image[abs(end_start):]
end_start = 0
frames_to_copy = min(end_image.shape[0], num_frames - end_start)
if frames_to_copy > 0:
out_batch[end_start:end_start + frames_to_copy] = end_image[:frames_to_copy]
masks[end_start:end_start + frames_to_copy] = 0
# Apply control images to remaining frames that don't have start or end images
if control_images is not None:
# Create a mask of frames that are still empty (mask == 1)
empty_frames = masks.sum(dim=(1, 2)) > 0.5 * H * W
if empty_frames.any():
# Only apply control images where they exist
control_length = control_images.shape[0]
for frame_idx in range(num_frames):
if empty_frames[frame_idx] and frame_idx < control_length:
out_batch[frame_idx] = control_images[frame_idx]
# Apply inpaint mask if provided
if inpaint_mask is not None:
inpaint_mask = common_upscale(inpaint_mask.unsqueeze(1), W, H, "nearest-exact", "disabled").squeeze(1).to(device)
# Handle different mask lengths efficiently
if inpaint_mask.shape[0] > num_frames:
inpaint_mask = inpaint_mask[:num_frames]
elif inpaint_mask.shape[0] < num_frames:
repeat_factor = (num_frames + inpaint_mask.shape[0] - 1) // inpaint_mask.shape[0] # Ceiling division
inpaint_mask = inpaint_mask.repeat(repeat_factor, 1, 1)[:num_frames]
# Apply mask in one operation
masks = inpaint_mask * masks
return (out_batch.cpu().float(), masks.cpu().float())
#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"}),
}
}
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"):
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
}
return (context_options,)
class CreateCFGScheduleFloatList:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"steps": ("INT", {"default": 30, "min": 2, "max": 1000, "step": 1, "tooltip": "Number of steps to schedule cfg for"} ),
"cfg_scale_start": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}),
"cfg_scale_end": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}),
"interpolation": (["linear", "ease_in", "ease_out"], {"default": "linear", "tooltip": "Interpolation method to use for the cfg scale"}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "Start percent of the steps to apply cfg"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "End percent of the steps to apply cfg"}),
}
}
RETURN_TYPES = ("FLOAT", )
RETURN_NAMES = ("float_list",)
FUNCTION = "process"
CATEGORY = "WanVideoWrapper"
DESCRIPTION = "Helper node to generate a list of floats that can be used to schedule cfg scale for the steps, outside the set range cfg is set to 1.0"
def process(self, steps, cfg_scale_start, cfg_scale_end, interpolation, start_percent, end_percent):
# Create a list of floats for the cfg schedule
cfg_list = [1.0] * steps
start_idx = min(int(steps * start_percent), steps - 1)
end_idx = min(int(steps * end_percent), steps - 1)
for i in range(start_idx, end_idx + 1):
if i >= steps:
break
if end_idx == start_idx:
t = 0
else:
t = (i - start_idx) / (end_idx - start_idx)
if interpolation == "linear":
factor = t
elif interpolation == "ease_in":
factor = t * t
elif interpolation == "ease_out":
factor = t * (2 - t)
cfg_list[i] = round(cfg_scale_start + factor * (cfg_scale_end - cfg_scale_start), 2)
# If start_percent > 0, always include the first step
if start_percent > 0:
cfg_list[0] = 1.0
return (cfg_list,)
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}),
},
}
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,)
#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": (["unipc", "unipc/beta", "dpm++", "dpm++/beta","dpm++_sde", "dpm++_sde/beta", "euler", "euler/beta", "euler/accvideo", "deis", "lcm", "lcm/beta", "flowmatch_causvid", "flowmatch_distill", "flowmatch_pusa", "multitalk"],
{
"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": (["default", "comfy", "comfy_chunked"], {"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", ),
}
}
RETURN_TYPES = ("LATENT", )
RETURN_NAMES = ("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):
patcher = model
model = model.model
transformer = model.diffusion_model
dtype = model["dtype"]
control_lora = model["control_lora"]
multitalk_sampling = image_embeds.get("multitalk_sampling", False)
if not multitalk_sampling and scheduler == "multitalk":
raise Exception("multitalk scheduler is only for multitalk sampling when using ImagetoVideoMultiTalk -node")
transformer_options = patcher.model_options.get("transformer_options", None)
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
steps = int(steps/denoise_strength)
if text_embeds == None:
text_embeds = {
"prompt_embeds": [],
"negative_prompt_embeds": [],
}
if isinstance(cfg, list):
if steps != len(cfg):
log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Setting step count to match.")
steps = len(cfg)
def get_scheduler(scheduler, steps, shift, device, sigmas=None):
timesteps = None
if 'unipc' in scheduler:
sample_scheduler = FlowUniPCMultistepScheduler(shift=shift)
if sigmas is None:
sample_scheduler.set_timesteps(steps, device=device, shift=shift, use_beta_sigmas=('beta' in scheduler))
else:
sample_scheduler.sigmas = sigmas.to(device)
sample_scheduler.timesteps = (sample_scheduler.sigmas[:-1] * 1000).to(torch.int64).to(device)
sample_scheduler.num_inference_steps = len(sample_scheduler.timesteps)
elif scheduler in ['euler/beta', 'euler']:
sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift, use_beta_sigmas=(scheduler == 'euler/beta'))
if flowedit_args: #seems to work better
timesteps, _ = retrieve_timesteps(sample_scheduler, device=device, sigmas=get_sampling_sigmas(steps, shift))
else:
sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None)
elif scheduler in ['euler/accvideo']:
if steps != 50:
raise Exception("Steps must be set to 50 for accvideo scheduler, 10 actual steps are used")
sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift, use_beta_sigmas=(scheduler == 'euler/beta'))
sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None)
start_latent_list = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
sample_scheduler.sigmas = sample_scheduler.sigmas[start_latent_list]
steps = len(start_latent_list) - 1
sample_scheduler.timesteps = timesteps = sample_scheduler.timesteps[start_latent_list[:steps]]
elif 'dpm++' in scheduler:
if 'sde' in scheduler:
algorithm_type = "sde-dpmsolver++"
else:
algorithm_type = "dpmsolver++"
sample_scheduler = FlowDPMSolverMultistepScheduler(shift=shift, algorithm_type=algorithm_type)
if sigmas is None:
sample_scheduler.set_timesteps(steps, device=device, use_beta_sigmas=('beta' in scheduler))
else:
sample_scheduler.sigmas = sigmas.to(device)
sample_scheduler.timesteps = (sample_scheduler.sigmas[:-1] * 1000).to(torch.int64).to(device)
sample_scheduler.num_inference_steps = len(sample_scheduler.timesteps)
elif scheduler == 'deis':
sample_scheduler = DEISMultistepScheduler(use_flow_sigmas=True, prediction_type="flow_prediction", flow_shift=shift)
sample_scheduler.set_timesteps(steps, device=device)
sample_scheduler.sigmas[-1] = 1e-6
elif 'lcm' in scheduler:
sample_scheduler = FlowMatchLCMScheduler(shift=shift, use_beta_sigmas=(scheduler == 'lcm/beta'))
sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None)
elif 'flowmatch_causvid' in scheduler:
if transformer.dim == 5120:
denoising_list = [999, 934, 862, 756, 603, 410, 250, 140, 74]
else:
if steps != 4:
raise ValueError("CausVid 1.3B schedule is only for 4 steps")
denoising_list = [1000, 750, 500, 250]
sample_scheduler = FlowMatchScheduler(num_inference_steps=steps, shift=shift, sigma_min=0, extra_one_step=True)
sample_scheduler.timesteps = torch.tensor(denoising_list)[:steps].to(device)
sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.0], device=device)])
elif 'flowmatch_distill' in scheduler:
sample_scheduler = FlowMatchScheduler(
shift=shift, sigma_min=0.0, extra_one_step=True
)
sample_scheduler.set_timesteps(1000, training=True)
denoising_step_list = torch.tensor([999, 750, 500, 250] , dtype=torch.long)
temp_timesteps = torch.cat((sample_scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32)))
denoising_step_list = temp_timesteps[1000 - denoising_step_list]
#print("denoising_step_list: ", denoising_step_list)
if steps != 4:
raise ValueError("This scheduler is only for 4 steps")
sample_scheduler.timesteps = denoising_step_list[:steps].clone().detach().to(device)
sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.0], device=device)])
elif 'flowmatch_pusa' in scheduler:
sample_scheduler = FlowMatchSchedulerPusa(
shift=shift, sigma_min=0.0, extra_one_step=True
)
sample_scheduler.set_timesteps(steps, denoising_strength=denoise_strength, shift=shift)
return sample_scheduler, timesteps
if scheduler != "multitalk":
sample_scheduler, timesteps = get_scheduler(scheduler, steps, shift, device, sigmas=sigmas)
if timesteps is None:
timesteps = sample_scheduler.timesteps
log.info(f"timesteps: {timesteps}")
else:
timesteps = torch.tensor([1000, 750, 500, 250], device=device)
if denoise_strength < 1.0:
steps = int(steps * denoise_strength)
timesteps = timesteps[-(steps + 1):]
seed_g = torch.Generator(device=torch.device("cpu"))
seed_g.manual_seed(seed)
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 = None
fun_ref_image = None
image_cond = image_embeds.get("image_embeds", None)
ATI_tracks = None
add_cond = attn_cond = attn_cond_neg = None
if image_cond is not None:
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)
lat_h = image_embeds.get("lat_h", None)
lat_w = image_embeds.get("lat_w", None)
if lat_h is None or lat_w is None:
raise ValueError("Clip encoded image embeds must be provided for I2V (Image to Video) model")
fun_or_fl2v_model = image_embeds.get("fun_or_fl2v_model", False)
noise = torch.randn(
16,
(image_embeds["num_frames"] - 1) // 4 + (2 if end_image is not None and not fun_or_fl2v_model else 1),
lat_h,
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 [48, 32]:
raise ValueError("Control signal only works with Fun-Control model")
control_latents = control_embeds.get("control_images", None)
control_camera_latents = control_embeds.get("control_camera_latents", None)
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)
control_start_percent = control_embeds.get("start_percent", 0.0)
control_end_percent = control_embeds.get("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 (Text to Video")
has_ref = image_embeds.get("has_ref", False)
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(
target_shape[0],
target_shape[1] + 1 if has_ref else target_shape[1],
target_shape[2],
target_shape[3],
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
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)
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_camera_latents is not None:
control_camera_latents = control_camera_latents.to(device)
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)
patcher.model.is_patched = True
else:
if transformer.in_dim not in [48, 32]:
raise ValueError("Control signal only works with Fun-Control model")
image_cond = torch.zeros_like(noise).to(device) #fun control
clip_fea = None
fun_ref_image = control_embeds.get("fun_ref_image", None)
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 == 36: #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_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)
if phantom_latents is not None:
phantom_latents = phantom_latents.to(device)
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)
if unianimate_poses is not None:
transformer.dwpose_embedding.to(device, model["dtype"])
dwpose_data = unianimate_poses["pose"].to(device, model["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 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)
dwpose_data_flat = rearrange(dwpose_data, 'b c f h w -> b (f h w) c').contiguous()
random_ref_dwpose_data = None
if image_cond is not None:
transformer.randomref_embedding_pose.to(device)
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)
).unsqueeze(2).to(model["dtype"]) # [1, 20, 104, 60]
unianim_data = {
"dwpose": dwpose_data_flat,
"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"]
}
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_context_lens = fantasytalking_embeds["audio_context_lens"]
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}, audio context lens: {audio_context_lens}")
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}")
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)
is_looped = False
if context_options is not None:
def create_window_mask(noise_pred_context, c, latent_video_length, context_overlap, looped=False, window_type="linear"):
window_mask = torch.ones_like(noise_pred_context)
if window_type == "pyramid":
# Create pyramid weights that peak in the middle
length = noise_pred_context.shape[1]
if length % 2 == 0:
max_weight = length // 2
weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))
else:
max_weight = (length + 1) // 2
weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
# Normalize weights to range from 0 to 1
max_val = max(weight_sequence)
weight_sequence = [w / max_val for w in weight_sequence]
# Apply the weights to create the mask
weights_tensor = torch.tensor(weight_sequence, device=noise_pred_context.device)
weights_tensor = weights_tensor.view(1, -1, 1, 1)
window_mask = weights_tensor.expand_as(window_mask).clone()
# Adjust for position in sequence if needed
if not looped:
if min(c) == 0: # First chunk
left_ramp = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device).view(1, -1, 1, 1)
# Clone to avoid in-place memory conflict
left_section = window_mask[:, :context_overlap].clone()
window_mask[:, :context_overlap] = torch.maximum(left_section, left_ramp)
if max(c) == latent_video_length - 1: # Last chunk
right_ramp = torch.linspace(1, 0, context_overlap, device=noise_pred_context.device).view(1, -1, 1, 1)
# Clone to avoid in-place memory conflict
right_section = window_mask[:, -context_overlap:].clone()
window_mask[:, -context_overlap:] = torch.maximum(right_section, right_ramp)
else: # Original "linear" window masking
# Apply left-side blending for all except first chunk (or always in loop mode)
if min(c) > 0 or (looped and max(c) == latent_video_length - 1):
ramp_up = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device)
ramp_up = ramp_up.view(1, -1, 1, 1)
window_mask[:, :context_overlap] = ramp_up
# Apply right-side blending for all except last chunk (or always in loop mode)
if max(c) < latent_video_length - 1 or (looped and min(c) == 0):
ramp_down = torch.linspace(1, 0, context_overlap, device=noise_pred_context.device)
ramp_down = ramp_down.view(1, -1, 1, 1)
window_mask[:, -context_overlap:] = ramp_down
return window_mask
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_vae = context_options.get("vae", None)
if context_vae is not None:
context_vae.to(device)
self.window_tracker = WindowTracker(verbose=context_options["verbose"])
# 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
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 import get_context_scheduler
context = get_context_scheduler(context_schedule)
if samples is not None:
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)
original_image = input_samples.to(device)
if denoise_strength < 1.0:
latent_timestep = timesteps[:1].to(noise)
noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples
mask = samples.get("mask", None)
if mask is not None:
if mask.shape[2] != noise.shape[1]:
mask = torch.cat([torch.zeros(1, noise.shape[0], noise.shape[1] - mask.shape[2], noise.shape[2], noise.shape[3]), mask], dim=2)
if (extra_latents := image_embeds.get("extra_latents", None)) is not None:
encoded_image_latents = extra_latents["samples"].squeeze(0).to(noise)
if (empty_latent_indices := extra_latents.get("empty_latent_indices", None)) is not None and len(empty_latent_indices) > 0:
noise_out = encoded_image_latents.clone()
for idx in empty_latent_indices:
#print(f"Adding noise to Empty latent index: {idx}")
noise_out[:, idx] = noise[:, idx]
noise = noise_out
else:
noise[:,0:encoded_image_latents.shape[1]] = encoded_image_latents
latent = noise.to(device)
freqs = None
transformer.rope_embedder.k = None
transformer.rope_embedder.num_frames = None
if "comfy" in rope_function:
transformer.rope_embedder.k = riflex_freq_index
transformer.rope_embedder.num_frames = latent_video_length
else:
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)
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
if not isinstance(cfg, list):
cfg = [cfg] * (steps +1)
log.info(f"Seq len: {seq_len}")
pbar = ProgressBar(steps)
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, steps)
#blockswap init
if transformer_options is not None:
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", True)
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, non_blocking=transformer.use_non_blocking)
elif block_swap_args["offload_img_emb"] and "img_emb" in name:
param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking)
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()
elif model["manual_offloading"]:
transformer.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"].to(dtype),
"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"],
}
#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"]
if context_options is not None:
set_num_frames(context_frames)
else:
set_num_frames(latent_video_length)
enhance_enabled = True
else:
feta_args = None
enhance_enabled = False
# Initialize Cache if enabled
transformer.enable_teacache = transformer.enable_magcache = False
if teacache_args is not None: #for backward compatibility on old workflows
cache_args = teacache_args
if cache_args is not None:
transformer.cache_device = cache_args["cache_device"]
if cache_args["cache_type"] == "TeaCache":
log.info(f"TeaCache: Using cache device: {transformer.cache_device}")
transformer.teacache_state.clear_all()
transformer.enable_teacache = True
transformer.rel_l1_thresh = cache_args["rel_l1_thresh"]
transformer.teacache_start_step = cache_args["start_step"]
transformer.teacache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
transformer.teacache_use_coefficients = cache_args["use_coefficients"]
transformer.teacache_mode = cache_args["mode"]
elif cache_args["cache_type"] == "MagCache":
log.info(f"MagCache: Using cache device: {transformer.cache_device}")
transformer.magcache_state.clear_all()
transformer.enable_magcache = True
transformer.magcache_start_step = cache_args["start_step"]
transformer.magcache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
transformer.magcache_thresh = cache_args["magcache_thresh"]
transformer.magcache_K = cache_args["magcache_K"]
elif cache_args["cache_type"] == "EasyCache":
log.info(f"EasyCache: Using cache device: {transformer.cache_device}")
transformer.easycache_state.clear_all()
transformer.enable_easycache = True
transformer.easycache_start_step = cache_args["start_step"]
transformer.easycache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
transformer.easycache_thresh = cache_args["easycache_thresh"]
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
# Radial attention setup
if transformer.attention_mode == "radial_sage_attention":
if transformer_options is not None:
dense_timestep = transformer_options.get("dense_timestep", 10)
dense_block = transformer_options.get("dense_block", 1)
decay_factor = transformer_options.get("decay_factor", 0.2)
dense_attention_mode = transformer_options.get("dense_attention_mode", "sageattn")
from .wanvideo.radial_attention.attn_mask import MaskMap
for i, block in enumerate(transformer.blocks):
block.self_attn.mask_map = block.dense_attention_mode = block.dense_timestep = block.self_attn.decay_factor = None
block.dense_block = True if i < dense_block else False
block.self_attn.mask_map = MaskMap(video_token_num=seq_len, num_frame=latent_video_length)
block.dense_attention_mode = dense_attention_mode
block.dense_timestep = dense_timestep
block.self_attn.decay_factor = decay_factor
log.info(f"Radial attention mode enabled. dense_attention_mode: {dense_attention_mode}, dense_timestep: {dense_timestep}, dense_block: {dense_block}, decay_factor: {decay_factor}")
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 = []
if flowedit_args is not None:
source_embeds = flowedit_args["source_embeds"]
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:]
use_cfg_zero_star = use_fresca = False
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)
zero_star_steps = experimental_args.get("zero_star_steps", 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)
#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):
z = z.to(dtype)
with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=("fp8" in model["quantization"])):
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_latents is not 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):
image_cond_input = torch.cat([control_latents.to(z), image_cond.to(z)])
else:
image_cond_input = torch.cat([torch.zeros_like(image_cond, 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)
#fun_ref_input = None
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)
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)
else:
image_cond_input = image_cond.to(z) if image_cond is not None else None
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)
use_phantom = False
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):
z_pos = torch.cat([z[:,:-phantom_latents.shape[1]], phantom_latents.to(z)], dim=1)
z_phantom_img = torch.cat([z[:,:-phantom_latents.shape[1]], phantom_latents.to(z)], dim=1)
z_neg = torch.cat([z[:,:-phantom_latents.shape[1]], torch.zeros_like(phantom_latents).to(z)], dim=1)
use_phantom = True
if cache_state is not None and len(cache_state) != 3:
cache_state.append(None)
if not use_phantom:
z_pos = z_neg = z
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:
z_pos = z_neg = 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,
'device': device,
'freqs': freqs,
't': timestep,
'current_step': idx,
'control_lora_enabled': control_lora_enabled,
'enhance_enabled': enhance_enabled,
'camera_embed': camera_embed,
'unianim_data': unianim_data,
'fun_ref': fun_ref_input if fun_ref_image is not None else None,
'fun_camera': control_camera_input if control_camera_latents is not None else None,
'audio_proj': audio_proj if fantasytalking_embeds is not None else None,
'audio_context_lens': audio_context_lens if fantasytalking_embeds is not None else None,
'audio_scale': audio_scale,
"pcd_data": pcd_data_input,
"controlnet": controlnet,
"add_cond": add_cond_input,
"nag_params": text_embeds.get("nag_params", {}),
"nag_context": text_embeds.get("nag_prompt_embeds", None),
"multitalk_audio": multitalk_audio_input if multitalk_audio_embedding is not None else None,
"ref_target_masks": ref_target_masks if multitalk_audio_embedding is not None else None,
}
batch_size = 1
if not math.isclose(cfg_scale, 1.0) and len(positive_embeds) > 1:
negative_embeds = negative_embeds * len(positive_embeds)
if not batched_cfg:
#cond
noise_pred_cond, cache_state_cond = transformer(
[z_pos], 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_neg], 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_phantom_img], 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_pos], 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_pos], 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
)
#cfg
#https://github.com/WeichenFan/CFG-Zero-star/
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)
else:
alpha = 1.0
#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 * alpha + cfg_scale * filtered_cond * alpha
else:
noise_pred = noise_pred_uncond * alpha + cfg_scale * (noise_pred_cond - noise_pred_uncond * alpha)
return noise_pred, [cache_state_cond, cache_state_uncond]
log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*8}x{latent.shape[2]*8} with {steps} steps")
intermediate_device = device
# diff diff prep
masks = None
if samples is not None and mask is not None:
mask = 1 - mask
thresholds = torch.arange(len(timesteps), dtype=original_image.dtype) / len(timesteps)
thresholds = thresholds.unsqueeze(1).unsqueeze(1).unsqueeze(1).unsqueeze(1).to(device)
masks = mask.repeat(len(timesteps), 1, 1, 1, 1).to(device)
masks = masks > thresholds
latent_shift_loop = False
if loop_args is not None:
latent_shift_loop = True
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.unload_all_models()
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, shift, device, sigmas=sigmas)
if timesteps is None:
timesteps = sample_scheduler.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)
current_latent = current_latent.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
#region main loop start
for idx, t in enumerate(tqdm(timesteps)):
if flowedit_args is not None:
if idx < skip_steps:
continue
# diff diff
if masks is not None:
if idx < len(timesteps) - 1:
noise_timestep = timesteps[idx+1]
image_latent = sample_scheduler.scale_noise(
original_image, torch.tensor([noise_timestep]), noise.to(device)
)
mask = masks[idx]
mask = mask.to(latent)
latent = image_latent * mask + latent * (1-mask)
# end diff diff
latent_model_input = latent.to(device)
timestep = torch.tensor([t]).to(device)
if scheduler == "flowmatch_pusa":
timestep = timestep.unsqueeze(1).repeat(1, latent_video_length)
if extra_latents is not None:
if empty_latent_indices is not None and len(empty_latent_indices) > 0:
# Set timestep to zero for all non-noise (non-empty) indices
non_noise_indices = [i for i in range(timestep.shape[1]) if i not in empty_latent_indices]
timestep[:, non_noise_indices] = 0
else:
timestep[:,0:encoded_image_latents.shape[1]] = 0
#print(f"timestep: {timestep}")
current_step_percentage = idx / len(timesteps)
### 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
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]
partial_img_emb[:, 0] = image_cond[:, 0].to(intermediate_device)
if control_latents is not None:
partial_control_latents = control_latents[:, c]
partial_control_camera_latents = None
if control_camera_latents is not None:
partial_control_camera_latents = control_camera_latents[:, :, c]
partial_vace_context = None
if vace_data is not None:
window_vace_data = []
for vace_entry in vace_data:
partial_context = vace_entry["context"][0][:, c]
if has_ref:
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_latent_model_input = latent_model_input[:, c]
partial_unianim_data = None
if unianim_data is not None:
partial_dwpose = dwpose_data[:, :, c]
partial_dwpose_flat=rearrange(partial_dwpose, 'b c f h w -> b (f h w) c')
partial_unianim_data = {
"dwpose": partial_dwpose_flat,
"random_ref": unianim_data["random_ref"],
"strength": unianimate_poses["strength"],
"start_percent": unianimate_poses["start_percent"],
"end_percent": unianimate_poses["end_percent"]
}
partial_add_cond = None
if add_cond is not None:
partial_add_cond = add_cond[:, :, c].to(device, dtype)
noise_pred_context, new_teacache = predict_with_cfg(
partial_latent_model_input,
cfg[idx], positive,
text_embeds["negative_prompt_embeds"],
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)
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:
original_image = cond_image = image_embeds.get("multitalk_start_image", 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)
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)
gen_video_list = []
is_first_clip = True
arrive_last_frame = False
cur_motion_frames_num = 1
audio_start_idx = iteration_count = 0
audio_end_idx = audio_start_idx + clip_length
indices = (torch.arange(4 + 1) - 2) * 1
if multitalk_embeds is not None:
total_frames = len(multitalk_audio_embedding)
estimated_iterations = total_frames // (frame_num - motion_frame) + 1
loop_pbar = tqdm(total=estimated_iterations, desc="Generating video clips")
callback = prepare_callback(patcher, estimated_iterations)
audio_embedding = multitalk_audio_embedding
human_num = len(audio_embedding)
audio_embs = None
while True: # start video generation iteratively
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]
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])
noise = torch.randn(
16, (frame_num - 1) // 4 + 1,
lat_h, lat_w, dtype=torch.float32, device=torch.device("cpu"), generator=seed_g).to(device)
# get mask
msk = torch.ones(1, frame_num, lat_h, lat_w, device=device)
msk[:, cur_motion_frames_num:] = 0
msk = torch.concat([
torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]
], dim=1)
msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
msk = msk.transpose(1, 2).to(dtype) # B 4 T H W
mm.soft_empty_cache()
# zero padding and vae encode
video_frames = torch.zeros(1, cond_image.shape[1], 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)
vae.to(device)
y = vae.encode(padding_frames_pixels_values, device=device, tiled=tiled_vae).to(dtype)
vae.to(offload_device)
cur_motion_frames_latent_num = int(1 + (cur_motion_frames_num-1) // 4)
latent_motion_frames = y[:, :, :cur_motion_frames_latent_num][0] # C T H W
y = torch.concat([msk, y], dim=1) # B 4+C T H W
mm.soft_empty_cache()
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, steps, shift, device, sigmas=sigmas)
if timesteps is None:
timesteps = sample_scheduler.timesteps
transformed_timesteps = []
for t in timesteps:
t_tensor = torch.tensor([t.item()], device=device)
transformed_timesteps.append(t_tensor)
transformed_timesteps.append(torch.tensor([0.], device=device))
timesteps = transformed_timesteps
# sample videos
latent = noise
# injecting motion frames
if not is_first_clip:
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])
_, T_m, _, _ = add_latent.shape
latent[:, :T_m] = add_latent
if offload:
#blockswap init
if transformer_options is not None:
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", True)
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, non_blocking=transformer.use_non_blocking)
elif block_swap_args["offload_img_emb"] and "img_emb" in name:
param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking)
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()
elif model["manual_offloading"]:
transformer.to(device)
comfy_pbar = ProgressBar(len(timesteps)-1)
for i in tqdm(range(len(timesteps)-1)):
timestep = timesteps[i]
latent_model_input = latent.to(device)
noise_pred, self.cache_state = predict_with_cfg(
latent_model_input,
cfg[idx],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep, idx, y.squeeze(0), clip_embeds.to(dtype), control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
cache_state=self.cache_state, multitalk_audio_embeds=audio_embs)
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(iteration_count, callback_latent, None, estimated_iterations)
# update latent
if scheduler == "multitalk":
noise_pred = -noise_pred
dt = timesteps[i] - timesteps[i + 1]
dt = dt / 1000
latent = latent + noise_pred * dt[:, None, None, None]
else:
latent = latent.to(intermediate_device)
step_args = {
"generator": seed_g,
}
if isinstance(sample_scheduler, DEISMultistepScheduler) or isinstance(sample_scheduler, FlowMatchScheduler):
step_args.pop("generator", None)
temp_x0 = sample_scheduler.step(
noise_pred.unsqueeze(0),
timestep,
latent.unsqueeze(0),
#return_dict=False,
**step_args)[0]
latent = temp_x0.squeeze(0)
# injecting motion frames
if not is_first_clip:
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])
_, T_m, _, _ = add_latent.shape
latent[:, :T_m] = add_latent
x0 = latent.to(device)
del latent_model_input, timestep
comfy_pbar.update(1)
if offload:
transformer.to(offload_device)
vae.to(device)
videos = vae.decode(x0.unsqueeze(0).to(vae.dtype), device=device, tiled=tiled_vae)
vae.to(offload_device)
# cache generated samples
videos = torch.stack(videos).cpu() # B C T H W
if colormatch != "disabled":
videos = videos[0].permute(1, 2, 3, 0).cpu().numpy()
from color_matcher import ColorMatcher
cm = ColorMatcher()
cm_result_list = []
for img in videos:
cm_result = cm.transfer(src=img, ref=original_image[0].permute(1, 2, 3, 0).squeeze(0).cpu().numpy(), method=colormatch)
cm_result_list.append(torch.from_numpy(cm_result))
videos = torch.stack(cm_result_list, dim=0).to(torch.float32).permute(3, 0, 1, 2).unsqueeze(0)
if is_first_clip:
gen_video_list.append(videos)
else:
gen_video_list.append(videos[:, :, cur_motion_frames_num:])
# decide whether is done
if arrive_last_frame:
loop_pbar.update(estimated_iterations - iteration_count)
loop_pbar.close()
break
# update next condition frames
is_first_clip = False
cur_motion_frames_num = motion_frame
cond_image = videos[:, :, -cur_motion_frames_num:].to(torch.float32).to(device)
# Update progress bar
iteration_count += 1
loop_pbar.update(1)
# 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)
gen_video_samples = torch.cat(gen_video_list, dim=2).to(torch.float32)
del noise, latent
if force_offload:
if model["manual_offloading"]:
transformer.to(offload_device)
mm.soft_empty_cache()
gc.collect()
try:
print_memory(device)
torch.cuda.reset_peak_memory_stats(device)
except:
pass
return {"video": gen_video_samples[0].permute(1, 2, 3, 0).cpu()},
#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)
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)
step_args = {
"generator": seed_g,
}
if isinstance(sample_scheduler, DEISMultistepScheduler) or isinstance(sample_scheduler, FlowMatchScheduler):
step_args.pop("generator", None)
temp_x0 = 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),
#return_dict=False,
**step_args)[0]
latent = temp_x0.squeeze(0)
x0 = latent.to(device)
if freeinit_args is not None:
current_latent = x0.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().permute(1,0,2,3)
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().permute(1,0,2,3)
else:
callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3)
callback(idx, callback_latent, None, steps)
else:
pbar.update(1)
del latent_model_input, timestep
else:
if callback is not None:
callback_latent = (zt_tgt.to(device) - vt_tgt.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3)
callback(idx, callback_latent, None, steps)
else:
pbar.update(1)
if phantom_latents is not None:
x0 = x0[:,:-phantom_latents.shape[1]]
if cache_args is not None:
cache_type = cache_args["cache_type"]
states = (
transformer.teacache_state.states if cache_type == "TeaCache" else
transformer.magcache_state.states if cache_type == "MagCache" else
transformer.easycache_state.states if cache_type == "EasyCache" else
None
)
state_names = {
0: "conditional",
1: "unconditional"
}
for pred_id, state in states.items():
name = state_names.get(pred_id, f"prediction_{pred_id}")
if 'skipped_steps' in state:
log.info(f"{cache_type} skipped: {len(state['skipped_steps'])} {name} steps: {state['skipped_steps']}")
transformer.teacache_state.clear_all()
transformer.magcache_state.clear_all()
transformer.easycache_state.clear_all()
del states
if force_offload:
if model["manual_offloading"]:
transformer.to(offload_device)
mm.soft_empty_cache()
gc.collect()
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": x0.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(),
}, )
class WindowTracker:
def __init__(self, verbose=False):
self.window_map = {} # Maps frame sequence to persistent ID
self.next_id = 0
self.cache_states = {} # Maps persistent ID to teacache state
self.verbose = verbose
def get_window_id(self, frames):
key = tuple(sorted(frames)) # Order-independent frame sequence
if key not in self.window_map:
self.window_map[key] = self.next_id
if self.verbose:
log.info(f"New window pattern {key} -> ID {self.next_id}")
self.next_id += 1
return self.window_map[key]
def get_teacache(self, window_id, base_state):
if window_id not in self.cache_states:
if self.verbose:
log.info(f"Initializing persistent teacache for window {window_id}")
self.cache_states[window_id] = base_state.copy()
return self.cache_states[window_id]
#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"):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
video = samples.get("video", None)
if video is not None:
video = torch.clamp(video, -1.0, 1.0)
video = (video + 1.0) / 2.0
return video.cpu(),
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 is_looped:
# latents = torch.cat([latents[:, :, :warmup_latent_count],latents], dim=2)
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()
if normalization == "minmax":
images = (images - images.min()) / (images.max() - images.min())
else:
images = torch.clamp(images, -1.0, 1.0)
images = (images + 1.0) / 2.0
if is_looped:
#images = images[:, warmup_latent_count * 4:]
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//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
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:
#end_image = (end_image - end_image.min()) / (end_image.max() - end_image.min())
#image[:, -1] = end_image[:, 0].to(image) #not sure about this
images = images[:, 0:-1]
vae.model.clear_cache()
vae.to(offload_device)
mm.soft_empty_cache()
images = torch.clamp(images, 0.0, 1.0)
images = images.permute(1, 2, 3, 0).float()
return (images,)
#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):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
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)
image = image.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
empty_frame_indices = []
for i in range(image.shape[2]):
if is_image_black(image[:, :, i]):
empty_frame_indices.append(i)
empty_frame_indices = []
for i in range(image.shape[2]):
if is_image_black(image[:, :, i]):
empty_frame_indices.append(i)
empty_latent_indices = []
if empty_frame_indices:
frames_per_latent = 4
num_frames = image.shape[2]
# Special mapping: latent 0 = [0], latent 1 = [1,2,3,4], latent 2 = [5,6,7,8], ...
latent_frame_ranges = []
latent_frame_ranges.append([0])
for i in range(1, math.ceil((num_frames - 1) / frames_per_latent) + 1):
start = 1 + (i - 1) * frames_per_latent
end = min(start + frames_per_latent, num_frames)
latent_frame_ranges.append(list(range(start, end)))
for latent_idx, latent_frames in enumerate(latent_frame_ranges):
print(f"latent {latent_idx}: frames {latent_frames}")
if latent_frames and set(latent_frames).issubset(empty_frame_indices):
empty_latent_indices.append(latent_idx)
if empty_latent_indices:
log.info(f"Empty frames {empty_frame_indices} map to latents {empty_latent_indices}")
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//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))
vae.model.clear_cache()
if latent_strength != 1.0:
latents *= latent_strength
log.info(f"encoded latents shape {latents.shape}")
latent_mask = None
if mask is None:
vae.to(offload_device)
else:
#latent_mask = mask.clone().to(vae.dtype).to(device) * 2.0 - 1.0
#latent_mask = latent_mask.unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1, 1)
#latent_mask = vae.encode(latent_mask, device=device, tiled=enable_vae_tiling, tile_size=(tile_x, tile_y), tile_stride=(tile_stride_x, tile_stride_y))
target_h, target_w = latents.shape[3:]
mask = torch.nn.functional.interpolate(
mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W]
size=(latents.shape[2], target_h, target_w),
mode='trilinear',
align_corners=False
).squeeze(0) # Remove batch dim, keep channel dim
# Add batch & channel dims for final output
latent_mask = mask.unsqueeze(0).repeat(1, latents.shape[1], 1, 1, 1)
log.info(f"latent mask shape {latent_mask.shape}")
vae.to(offload_device)
mm.soft_empty_cache()
return ({"samples": latents, "mask": latent_mask, "empty_latent_indices": empty_latent_indices},)
NODE_CLASS_MAPPINGS = {
"WanVideoSampler": WanVideoSampler,
"WanVideoDecode": WanVideoDecode,
"WanVideoTextEncode": WanVideoTextEncode,
"WanVideoTextEncodeSingle": WanVideoTextEncodeSingle,
"LoadWanVideoT5TextEncoder": LoadWanVideoT5TextEncoder,
"WanVideoImageClipEncode": WanVideoImageClipEncode,#deprecated
"WanVideoClipVisionEncode": WanVideoClipVisionEncode,
"WanVideoImageToVideoEncode": WanVideoImageToVideoEncode,
"LoadWanVideoClipTextEncoder": LoadWanVideoClipTextEncoder,
"WanVideoEncode": WanVideoEncode,
"WanVideoBlockSwap": WanVideoBlockSwap,
"WanVideoTorchCompileSettings": WanVideoTorchCompileSettings,
"WanVideoEmptyEmbeds": WanVideoEmptyEmbeds,
"WanVideoEnhanceAVideo": WanVideoEnhanceAVideo,
"WanVideoContextOptions": WanVideoContextOptions,
"WanVideoTeaCache": WanVideoTeaCache,
"WanVideoMagCache": WanVideoMagCache,
"WanVideoEasyCache": WanVideoEasyCache,
"WanVideoVRAMManagement": WanVideoVRAMManagement,
"WanVideoTextEmbedBridge": WanVideoTextEmbedBridge,
"WanVideoFlowEdit": WanVideoFlowEdit,
"WanVideoControlEmbeds": WanVideoControlEmbeds,
"WanVideoSLG": WanVideoSLG,
"WanVideoLoopArgs": WanVideoLoopArgs,
"WanVideoImageResizeToClosest": WanVideoImageResizeToClosest,
"WanVideoSetBlockSwap": WanVideoSetBlockSwap,
"WanVideoExperimentalArgs": WanVideoExperimentalArgs,
"WanVideoVACEEncode": WanVideoVACEEncode,
"ExtractStartFramesForContinuations": ExtractStartFramesForContinuations,
"WanVideoVACEStartToEndFrame": WanVideoVACEStartToEndFrame,
"WanVideoPhantomEmbeds": WanVideoPhantomEmbeds,
"CreateCFGScheduleFloatList": CreateCFGScheduleFloatList,
"WanVideoRealisDanceLatents": WanVideoRealisDanceLatents,
"WanVideoApplyNAG": WanVideoApplyNAG,
"WanVideoMiniMaxRemoverEmbeds": WanVideoMiniMaxRemoverEmbeds,
"WanVideoFreeInitArgs": WanVideoFreeInitArgs,
"WanVideoSetRadialAttention": WanVideoSetRadialAttention
}
NODE_DISPLAY_NAME_MAPPINGS = {
"WanVideoSampler": "WanVideo Sampler",
"WanVideoDecode": "WanVideo Decode",
"WanVideoTextEncode": "WanVideo TextEncode",
"WanVideoTextEncodeSingle": "WanVideo TextEncodeSingle",
"WanVideoTextImageEncode": "WanVideo TextImageEncode (IP2V)",
"LoadWanVideoT5TextEncoder": "Load WanVideo T5 TextEncoder",
"WanVideoImageClipEncode": "WanVideo ImageClip Encode (Deprecated)",
"WanVideoClipVisionEncode": "WanVideo ClipVision Encode",
"WanVideoImageToVideoEncode": "WanVideo ImageToVideo Encode",
"LoadWanVideoClipTextEncoder": "Load WanVideo Clip Encoder",
"WanVideoEncode": "WanVideo Encode",
"WanVideoBlockSwap": "WanVideo BlockSwap",
"WanVideoTorchCompileSettings": "WanVideo Torch Compile Settings",
"WanVideoEmptyEmbeds": "WanVideo Empty Embeds",
"WanVideoEnhanceAVideo": "WanVideo Enhance-A-Video",
"WanVideoContextOptions": "WanVideo Context Options",
"WanVideoTeaCache": "WanVideo TeaCache",
"WanVideoMagCache": "WanVideo MagCache",
"WanVideoEasyCache": "WanVideo EasyCache",
"WanVideoVRAMManagement": "WanVideo VRAM Management",
"WanVideoTextEmbedBridge": "WanVideo TextEmbed Bridge",
"WanVideoFlowEdit": "WanVideo FlowEdit",
"WanVideoControlEmbeds": "WanVideo Control Embeds",
"WanVideoSLG": "WanVideo SLG",
"WanVideoLoopArgs": "WanVideo Loop Args",
"WanVideoImageResizeToClosest": "WanVideo Image Resize To Closest",
"WanVideoSetBlockSwap": "WanVideo Set BlockSwap",
"WanVideoExperimentalArgs": "WanVideo Experimental Args",
"WanVideoVACEEncode": "WanVideo VACE Encode",
"ExtractStartFramesForContinuations": "Extract Start Frames For Continuations",
"WanVideoVACEStartToEndFrame": "WanVideo VACE Start To End Frame",
"WanVideoPhantomEmbeds": "WanVideo Phantom Embeds",
"CreateCFGScheduleFloatList": "WanVideo CFG Schedule Float List",
"WanVideoRealisDanceLatents": "WanVideo RealisDance Latents",
"WanVideoApplyNAG": "WanVideo Apply NAG",
"WanVideoMiniMaxRemoverEmbeds": "WanVideo MiniMax Remover Embeds",
"WanVideoFreeInitArgs": "WanVideo Free Init Args",
"WanVideoSetRadialAttention": "WanVideo Set Radial Attention"
}
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