custom_nodes/ComfyUI_WanVideoWrapper/nodes.py

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
import numpy as np
import math
from tqdm import tqdm

from .wanvideo.modules.clip import CLIPModel
from .wanvideo.modules.model import WanModel, rope_params
from .wanvideo.modules.t5 import T5EncoderModel
from .wanvideo.utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
                               get_sampling_sigmas, retrieve_timesteps)
from .wanvideo.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
from .wanvideo.utils.basic_flowmatch import FlowMatchScheduler
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, DEISMultistepScheduler
from .wanvideo.utils.scheduling_flow_match_lcm import FlowMatchLCMScheduler

from .enhance_a_video.globals import enable_enhance, disable_enhance, set_enhance_weight, set_num_frames
from .taehv import TAEHV

from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from einops import rearrange

import folder_paths
import comfy.model_management as mm
from comfy.utils import load_torch_file, ProgressBar, common_upscale
import comfy.model_base
import comfy.latent_formats
from comfy.clip_vision import clip_preprocess, ClipVisionModel
from comfy.sd import load_lora_for_models
from comfy.cli_args import args, LatentPreviewMethod

script_directory = os.path.dirname(os.path.abspath(__file__))

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> 
"""
    EXPERIMENTAL = True

    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 WanVideoModel(comfy.model_base.BaseModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.pipeline = {}

    def __getitem__(self, k):
        return self.pipeline[k]

    def __setitem__(self, k, v):
        self.pipeline[k] = v

try:
    from comfy.latent_formats import Wan21
    latent_format = Wan21
except: #for backwards compatibility
    log.warning("Wan21 latent format not found, update ComfyUI for better livepreview")
    from comfy.latent_formats import HunyuanVideo
    latent_format = HunyuanVideo

class WanVideoModelConfig:
    def __init__(self, dtype):
        self.unet_config = {}
        self.unet_extra_config = {}
        self.latent_format = latent_format
        self.latent_format.latent_channels = 16
        self.manual_cast_dtype = dtype
        self.sampling_settings = {"multiplier": 1.0}
        self.memory_usage_factor = 2.0
        self.unet_config["disable_unet_model_creation"] = True

def filter_state_dict_by_blocks(state_dict, blocks_mapping, layer_filter=[]):
    filtered_dict = {}

    if isinstance(layer_filter, str):
        layer_filters = [layer_filter] if layer_filter else []
    else:
        # Filter out empty strings
        layer_filters = [f for f in layer_filter if f] if layer_filter else []

    #print("layer_filter: ", layer_filters)

    for key in state_dict:
        if not any(filter_str in key for filter_str in layer_filters):
            if 'blocks.' in key:
                
                block_pattern = key.split('diffusion_model.')[1].split('.', 2)[0:2]
                block_key = f'{block_pattern[0]}.{block_pattern[1]}.'

                if block_key in blocks_mapping:
                    filtered_dict[key] = state_dict[key]
            else:
                filtered_dict[key] = state_dict[key]
    
    for key in filtered_dict:
        print(key)

    #from safetensors.torch import save_file
    #save_file(filtered_dict, "filtered_state_dict_2.safetensors")

    return filtered_dict

def standardize_lora_key_format(lora_sd):
    new_sd = {}
    for k, v in lora_sd.items():
        # Diffusers format
        if k.startswith('transformer.'):
            k = k.replace('transformer.', 'diffusion_model.')
        if k.startswith('pipe.dit.'): #unianimate-dit/diffsynth
            k = k.replace('pipe.dit.', 'diffusion_model.')

        # Fun LoRA format
        if k.startswith('lora_unet__'):
            # Split into main path and weight type parts
            parts = k.split('.')
            main_part = parts[0]  # e.g. lora_unet__blocks_0_cross_attn_k
            weight_type = '.'.join(parts[1:]) if len(parts) > 1 else None  # e.g. lora_down.weight
            
            # Process the main part - convert from underscore to dot format
            if 'blocks_' in main_part:
                # Extract components
                components = main_part[len('lora_unet__'):].split('_')
                
                # Start with diffusion_model
                new_key = "diffusion_model"
                
                # Add blocks.N
                if components[0] == 'blocks':
                    new_key += f".blocks.{components[1]}"
                    
                    # Handle different module types
                    idx = 2
                    if idx < len(components):
                        if components[idx] == 'self' and idx+1 < len(components) and components[idx+1] == 'attn':
                            new_key += ".self_attn"
                            idx += 2
                        elif components[idx] == 'cross' and idx+1 < len(components) and components[idx+1] == 'attn':
                            new_key += ".cross_attn"
                            idx += 2
                        elif components[idx] == 'ffn':
                            new_key += ".ffn"
                            idx += 1
                    
                    # Add the component (k, q, v, o) and handle img suffix
                    if idx < len(components):
                        component = components[idx]
                        idx += 1
                        
                        # Check for img suffix
                        if idx < len(components) and components[idx] == 'img':
                            component += '_img'
                            idx += 1
                            
                        new_key += f".{component}"
                
                # Handle weight type - this is the critical fix
                if weight_type:
                    if weight_type == 'alpha':
                        new_key += '.alpha'
                    elif weight_type == 'lora_down.weight' or weight_type == 'lora_down':
                        new_key += '.lora_A.weight'
                    elif weight_type == 'lora_up.weight' or weight_type == 'lora_up':
                        new_key += '.lora_B.weight'
                    else:
                        # Keep original weight type if not matching our patterns
                        new_key += f'.{weight_type}'
                        # Add .weight suffix if missing
                        if not new_key.endswith('.weight'):
                            new_key += '.weight'
                
                k = new_key
            else:
                # For other lora_unet__ formats (head, embeddings, etc.)
                new_key = main_part.replace('lora_unet__', 'diffusion_model.')
                
                # Fix specific component naming patterns
                new_key = new_key.replace('_self_attn', '.self_attn')
                new_key = new_key.replace('_cross_attn', '.cross_attn')
                new_key = new_key.replace('_ffn', '.ffn')
                new_key = new_key.replace('blocks_', 'blocks.')
                new_key = new_key.replace('head_head', 'head.head')
                new_key = new_key.replace('img_emb', 'img_emb')
                new_key = new_key.replace('text_embedding', 'text.embedding')
                new_key = new_key.replace('time_embedding', 'time.embedding')
                new_key = new_key.replace('time_projection', 'time.projection')
                
                # Replace remaining underscores with dots, carefully
                parts = new_key.split('.')
                final_parts = []
                for part in parts:
                    if part in ['img_emb', 'self_attn', 'cross_attn']:
                        final_parts.append(part)  # Keep these intact
                    else:
                        final_parts.append(part.replace('_', '.'))
                new_key = '.'.join(final_parts)
                
                # Handle weight type
                if weight_type:
                    if weight_type == 'alpha':
                        new_key += '.alpha'
                    elif weight_type == 'lora_down.weight' or weight_type == 'lora_down':
                        new_key += '.lora_A.weight'
                    elif weight_type == 'lora_up.weight' or weight_type == 'lora_up':
                        new_key += '.lora_B.weight'
                    else:
                        new_key += f'.{weight_type}'
                        if not new_key.endswith('.weight'):
                            new_key += '.weight'
                
                k = new_key
                
            # Handle special embedded components
            special_components = {
                'time.projection': 'time_projection',
                'img.emb': 'img_emb',
                'text.emb': 'text_emb',
                'time.emb': 'time_emb',
            }
            for old, new in special_components.items():
                if old in k:
                    k = k.replace(old, new)

        # Fix diffusion.model -> diffusion_model
        if k.startswith('diffusion.model.'):
            k = k.replace('diffusion.model.', 'diffusion_model.')
            
        # Finetrainer format
        if '.attn1.' in k:
            k = k.replace('.attn1.', '.cross_attn.')
            k = k.replace('.to_k.', '.k.')
            k = k.replace('.to_q.', '.q.')
            k = k.replace('.to_v.', '.v.')
            k = k.replace('.to_out.0.', '.o.')
        elif '.attn2.' in k:
            k = k.replace('.attn2.', '.cross_attn.')
            k = k.replace('.to_k.', '.k.')
            k = k.replace('.to_q.', '.q.')
            k = k.replace('.to_v.', '.v.')
            k = k.replace('.to_out.0.', '.o.')
            
        if "img_attn.proj" in k:
            k = k.replace("img_attn.proj", "img_attn_proj")
        if "img_attn.qkv" in k:
            k = k.replace("img_attn.qkv", "img_attn_qkv")
        if "txt_attn.proj" in k:
            k = k.replace("txt_attn.proj", "txt_attn_proj")
        if "txt_attn.qkv" in k:
            k = k.replace("txt_attn.qkv", "txt_attn_qkv")
        new_sd[k] = v
    return new_sd

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 WanVideoLoraSelect:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
               "lora": (folder_paths.get_filename_list("loras"),
                {"tooltip": "LORA models are expected to be in ComfyUI/models/loras with .safetensors extension"}),
                "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
            },
            "optional": {
                "prev_lora":("WANVIDLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
                "blocks":("SELECTEDBLOCKS", ),
                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}),
            }
        }

    RETURN_TYPES = ("WANVIDLORA",)
    RETURN_NAMES = ("lora", )
    FUNCTION = "getlorapath"
    CATEGORY = "WanVideoWrapper"
    DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras"

    def getlorapath(self, lora, strength, blocks={}, prev_lora=None, low_mem_load=False):
        loras_list = []

        lora = {
            "path": folder_paths.get_full_path("loras", lora),
            "strength": strength,
            "name": lora.split(".")[0],
            "blocks": blocks.get("selected_blocks", {}),
            "layer_filter": blocks.get("layer_filter", ""),
            "low_mem_load": low_mem_load,
        }
        if prev_lora is not None:
            loras_list.extend(prev_lora)

        loras_list.append(lora)
        return (loras_list,)
    
class WanVideoLoraSelectMulti:
    @classmethod
    def INPUT_TYPES(s):
        lora_files = folder_paths.get_filename_list("loras")
        lora_files = ["none"] + lora_files  # Add "none" as the first option
        return {
            "required": {
               "lora_0": (lora_files, {"default": "none"}),
                "strength_0": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
                "lora_1": (lora_files, {"default": "none"}),
                "strength_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
                "lora_2": (lora_files, {"default": "none"}),
                "strength_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
                "lora_3": (lora_files, {"default": "none"}),
                "strength_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
                "lora_4": (lora_files, {"default": "none"}),
                "strength_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
            },
            "optional": {
                "prev_lora":("WANVIDLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
                "blocks":("SELECTEDBLOCKS", ),
                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}),
            }
        }

    RETURN_TYPES = ("WANVIDLORA",)
    RETURN_NAMES = ("lora", )
    FUNCTION = "getlorapath"
    CATEGORY = "WanVideoWrapper"
    DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras"

    def getlorapath(self, lora_0, strength_0, lora_1, strength_1, lora_2, strength_2, 
                lora_3, strength_3, lora_4, strength_4, blocks={}, prev_lora=None, 
                low_mem_load=False):
        loras_list = []
        
        if prev_lora is not None:
            loras_list.extend(prev_lora)
        
        # Process each LoRA
        lora_inputs = [
            (lora_0, strength_0), 
            (lora_1, strength_1), 
            (lora_2, strength_2), 
            (lora_3, strength_3), 
            (lora_4, strength_4)
        ]
        
        for lora_name, strength in lora_inputs:
            # Skip if the LoRA is empty
            if not lora_name or lora_name == "none":
                continue
                
            lora = {
                "path": folder_paths.get_full_path("loras", lora_name),
                "strength": strength,
                "name": lora_name.split(".")[0],
                "blocks": blocks.get("selected_blocks", {}),
                "layer_filter": blocks.get("layer_filter", ""),
                "low_mem_load": low_mem_load,
            }
            
            loras_list.append(lora)
        
        return (loras_list,)
    
class WanVideoVACEModelSelect:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "vace_model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' VACE model to use when not using model that has it included"}),
            },
        }

    RETURN_TYPES = ("VACEPATH",)
    RETURN_NAMES = ("vace_model", )
    FUNCTION = "getvacepath"
    CATEGORY = "WanVideoWrapper"
    DESCRIPTION = "VACE model to use when not using model that has it included, loaded from 'ComfyUI/models/diffusion_models'"

    def getvacepath(self, vace_model):
        vace_model = {
            "path": folder_paths.get_full_path("diffusion_models", vace_model),
        }
        return (vace_model,)

class WanVideoLoraBlockEdit:
    def __init__(self):
        self.loaded_lora = None

    @classmethod
    def INPUT_TYPES(s):
        arg_dict = {}
        argument = ("BOOLEAN", {"default": True})

        for i in range(40):
            arg_dict["blocks.{}.".format(i)] = argument

        return {"required": arg_dict, "optional": {"layer_filter": ("STRING", {"default": "", "multiline": True})}}

    RETURN_TYPES = ("SELECTEDBLOCKS", )
    RETURN_NAMES = ("blocks", )
    OUTPUT_TOOLTIPS = ("The modified lora model",)
    FUNCTION = "select"

    CATEGORY = "WanVideoWrapper"

    def select(self, layer_filter=[], **kwargs):
        selected_blocks = {k: v for k, v in kwargs.items() if v is True and isinstance(v, bool)}
        print("Selected blocks LoRA: ", selected_blocks)
        selected = {
            "selected_blocks": selected_blocks,
            "layer_filter": [x.strip() for x in layer_filter.split(",")]
        }
        return (selected,)

#region Model loading
class WanVideoModelLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),

            "base_precision": (["fp32", "bf16", "fp16", "fp16_fast"], {"default": "bf16"}),
            "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2', 'fp8_e4m3fn_fast_no_ffn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
            "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
            },
            "optional": {
                "attention_mode": ([
                    "sdpa",
                    "flash_attn_2",
                    "flash_attn_3",
                    "sageattn",
                    "flex_attention",
                    #"spargeattn", needs tuning
                    #"spargeattn_tune",
                    ], {"default": "sdpa"}),
                "compile_args": ("WANCOMPILEARGS", ),
                "block_swap_args": ("BLOCKSWAPARGS", ),
                "lora": ("WANVIDLORA", {"default": None}),
                "vram_management_args": ("VRAM_MANAGEMENTARGS", {"default": None, "tooltip": "Alternative offloading method from DiffSynth-Studio, more aggressive in reducing memory use than block swapping, but can be slower"}),
                "vace_model": ("VACEPATH", {"default": None, "tooltip": "VACE model to use when not using model that has it included"}),
                "fantasytalking_model": ("FANTASYTALKINGMODEL", {"default": None, "tooltip": "FantasyTalking model https://github.com/Fantasy-AMAP"}),
            }
        }

    RETURN_TYPES = ("WANVIDEOMODEL",)
    RETURN_NAMES = ("model", )
    FUNCTION = "loadmodel"
    CATEGORY = "WanVideoWrapper"

    def loadmodel(self, model, base_precision, load_device,  quantization,
                  compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, vram_management_args=None, vace_model=None, fantasytalking_model=None):
        assert not (vram_management_args is not None and block_swap_args is not None), "Can't use both block_swap_args and vram_management_args at the same time"
        lora_low_mem_load = False
        if lora is not None:
            for l in lora:
                lora_low_mem_load = l.get("low_mem_load") if lora is not None else False

        transformer = None
        mm.unload_all_models()
        mm.cleanup_models()
        mm.soft_empty_cache()
        manual_offloading = True
        if "sage" in attention_mode:
            try:
                from sageattention import sageattn
            except Exception as e:
                raise ValueError(f"Can't import SageAttention: {str(e)}")

        device = mm.get_torch_device()
        offload_device = mm.unet_offload_device()

                
        manual_offloading = True
        transformer_load_device = device if load_device == "main_device" else offload_device
        
        base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
        
        if base_precision == "fp16_fast":
            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
                torch.backends.cuda.matmul.allow_fp16_accumulation = True
            else:
                raise ValueError("torch.backends.cuda.matmul.allow_fp16_accumulation is not available in this version of torch, requires torch 2.7.0.dev2025 02 26 nightly minimum currently")
        else:
            try:
                if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
                    torch.backends.cuda.matmul.allow_fp16_accumulation = False
            except:
                pass

        model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
      
        sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)

        
        if "vace_blocks.0.after_proj.weight" in sd and not "patch_embedding.weight" in sd:
            raise ValueError("You are attempting to load a VACE module as a WanVideo model, instead you should use the vace_model input and matching T2V base model")

        if vace_model is not None:
            vace_sd = load_torch_file(vace_model["path"], device=transformer_load_device, safe_load=True)
            sd.update(vace_sd)

        first_key = next(iter(sd))
        if first_key.startswith("model.diffusion_model."):
            new_sd = {}
            for key, value in sd.items():
                new_key = key.replace("model.diffusion_model.", "", 1)
                new_sd[new_key] = value
            sd = new_sd
        elif first_key.startswith("model."):
            new_sd = {}
            for key, value in sd.items():
                new_key = key.replace("model.", "", 1)
                new_sd[new_key] = value
            sd = new_sd
        if not "patch_embedding.weight" in sd:
            raise ValueError("Invalid WanVideo model selected")
        dim = sd["patch_embedding.weight"].shape[0]
        in_channels = sd["patch_embedding.weight"].shape[1]
        log.info(f"Detected model in_channels: {in_channels}")
        ffn_dim = sd["blocks.0.ffn.0.bias"].shape[0]

        if not "text_embedding.0.weight" in sd:
            model_type = "no_cross_attn" #minimaxremover
        elif "model_type.Wan2_1-FLF2V-14B-720P" in sd or "img_emb.emb_pos" in sd or "flf2v" in model.lower():
            model_type = "fl2v"
        elif in_channels in [36, 48]:
            model_type = "i2v"
        elif in_channels == 16:
            model_type = "t2v"
        elif "control_adapter.conv.weight" in sd:
            model_type = "t2v"

        num_heads = 40 if dim == 5120 else 12
        num_layers = 40 if dim == 5120 else 30

        vace_layers, vace_in_dim = None, None
        if "vace_blocks.0.after_proj.weight" in sd:
            if in_channels != 16:
                raise ValueError("VACE only works properly with T2V models.")
            model_type = "t2v"
            if dim == 5120:
                vace_layers = [0, 5, 10, 15, 20, 25, 30, 35]
            else:
                vace_layers = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28]
            vace_in_dim = 96

        log.info(f"Model type: {model_type}, num_heads: {num_heads}, num_layers: {num_layers}")

        teacache_coefficients_map = {
            "1_3B": {
                "e": [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01],
                "e0": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02],
            },
            "14B": {
                "e": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404],
                "e0": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01],
            },
            "i2v_480": {
                "e": [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01],
                "e0": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01],
            },
            "i2v_720":{
                "e": [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683],
                "e0": [8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02],
            },
        }

        magcache_ratios_map = {
            "1_3B": np.array([1.0]*2+[1.0124, 1.02213, 1.00166, 1.0041, 0.99791, 1.00061, 0.99682, 0.99762, 0.99634, 0.99685, 0.99567, 0.99586, 0.99416, 0.99422, 0.99578, 0.99575, 0.9957, 0.99563, 0.99511, 0.99506, 0.99535, 0.99531, 0.99552, 0.99549, 0.99541, 0.99539, 0.9954, 0.99536, 0.99489, 0.99485, 0.99518, 0.99514, 0.99484, 0.99478, 0.99481, 0.99479, 0.99415, 0.99413, 0.99419, 0.99416, 0.99396, 0.99393, 0.99388, 0.99386, 0.99349, 0.99349, 0.99309, 0.99304, 0.9927, 0.9927, 0.99228, 0.99226, 0.99171, 0.9917, 0.99137, 0.99135, 0.99068, 0.99063, 0.99005, 0.99003, 0.98944, 0.98942, 0.98849, 0.98849, 0.98758, 0.98757, 0.98644, 0.98643, 0.98504, 0.98503, 0.9836, 0.98359, 0.98202, 0.98201, 0.97977, 0.97978, 0.97717, 0.97718, 0.9741, 0.97411, 0.97003, 0.97002, 0.96538, 0.96541, 0.9593, 0.95933, 0.95086, 0.95089, 0.94013, 0.94019, 0.92402, 0.92414, 0.90241, 0.9026, 0.86821, 0.86868, 0.81838, 0.81939]),
            "14B": np.array([1.0]*2+[1.02504, 1.03017, 1.00025, 1.00251, 0.9985, 0.99962, 0.99779, 0.99771, 0.9966, 0.99658, 0.99482, 0.99476, 0.99467, 0.99451, 0.99664, 0.99656, 0.99434, 0.99431, 0.99533, 0.99545, 0.99468, 0.99465, 0.99438, 0.99434, 0.99516, 0.99517, 0.99384, 0.9938, 0.99404, 0.99401, 0.99517, 0.99516, 0.99409, 0.99408, 0.99428, 0.99426, 0.99347, 0.99343, 0.99418, 0.99416, 0.99271, 0.99269, 0.99313, 0.99311, 0.99215, 0.99215, 0.99218, 0.99215, 0.99216, 0.99217, 0.99163, 0.99161, 0.99138, 0.99135, 0.98982, 0.9898, 0.98996, 0.98995, 0.9887, 0.98866, 0.98772, 0.9877, 0.98767, 0.98765, 0.98573, 0.9857, 0.98501, 0.98498, 0.9838, 0.98376, 0.98177, 0.98173, 0.98037, 0.98035, 0.97678, 0.97677, 0.97546, 0.97543, 0.97184, 0.97183, 0.96711, 0.96708, 0.96349, 0.96345, 0.95629, 0.95625, 0.94926, 0.94929, 0.93964, 0.93961, 0.92511, 0.92504, 0.90693, 0.90678, 0.8796, 0.87945, 0.86111, 0.86189]),
            "i2v_480": np.array([1.0]*2+[0.98783, 0.98993, 0.97559, 0.97593, 0.98311, 0.98319, 0.98202, 0.98225, 0.9888, 0.98878, 0.98762, 0.98759, 0.98957, 0.98971, 0.99052, 0.99043, 0.99383, 0.99384, 0.98857, 0.9886, 0.99065, 0.99068, 0.98845, 0.98847, 0.99057, 0.99057, 0.98957, 0.98961, 0.98601, 0.9861, 0.98823, 0.98823, 0.98756, 0.98759, 0.98808, 0.98814, 0.98721, 0.98724, 0.98571, 0.98572, 0.98543, 0.98544, 0.98157, 0.98165, 0.98411, 0.98413, 0.97952, 0.97953, 0.98149, 0.9815, 0.9774, 0.97742, 0.97825, 0.97826, 0.97355, 0.97361, 0.97085, 0.97087, 0.97056, 0.97055, 0.96588, 0.96587, 0.96113, 0.96124, 0.9567, 0.95681, 0.94961, 0.94969, 0.93973, 0.93988, 0.93217, 0.93224, 0.91878, 0.91896, 0.90955, 0.90954, 0.92617, 0.92616]),
            "i2v_720": np.array([1.0]*2+[0.99428, 0.99498, 0.98588, 0.98621, 0.98273, 0.98281, 0.99018, 0.99023, 0.98911, 0.98917, 0.98646, 0.98652, 0.99454, 0.99456, 0.9891, 0.98909, 0.99124, 0.99127, 0.99102, 0.99103, 0.99215, 0.99212, 0.99515, 0.99515, 0.99576, 0.99572, 0.99068, 0.99072, 0.99097, 0.99097, 0.99166, 0.99169, 0.99041, 0.99042, 0.99201, 0.99198, 0.99101, 0.99101, 0.98599, 0.98603, 0.98845, 0.98844, 0.98848, 0.98851, 0.98862, 0.98857, 0.98718, 0.98719, 0.98497, 0.98497, 0.98264, 0.98263, 0.98389, 0.98393, 0.97938, 0.9794, 0.97535, 0.97536, 0.97498, 0.97499, 0.973, 0.97301, 0.96827, 0.96828, 0.96261, 0.96263, 0.95335, 0.9534, 0.94649, 0.94655, 0.93397, 0.93414, 0.91636, 0.9165, 0.89088, 0.89109, 0.8679, 0.86768]),
        }

        model_variant = "14B" #default to this
        if model_type == "i2v" or model_type == "fl2v":
            if "480" in model or "fun" in model.lower() or "a2" in model.lower() or "540" in model: #just a guess for the Fun model for now...
                model_variant = "i2v_480"
            elif "720" in model:
                model_variant = "i2v_720"
        elif model_type == "t2v":
            model_variant = "14B"
            
        if dim == 1536:
            model_variant = "1_3B"
        log.info(f"Model variant detected: {model_variant}")
        
        TRANSFORMER_CONFIG= {
            "dim": dim,
            "ffn_dim": ffn_dim,
            "eps": 1e-06,
            "freq_dim": 256,
            "in_dim": in_channels,
            "model_type": model_type,
            "out_dim": 16,
            "text_len": 512,
            "num_heads": num_heads,
            "num_layers": num_layers,
            "attention_mode": attention_mode,
            "main_device": device,
            "offload_device": offload_device,
            "teacache_coefficients": teacache_coefficients_map[model_variant],
            "magcache_ratios": magcache_ratios_map[model_variant],
            "vace_layers": vace_layers,
            "vace_in_dim": vace_in_dim,
            "inject_sample_info": True if "fps_embedding.weight" in sd else False,
            "add_ref_conv": True if "ref_conv.weight" in sd else False,
            "in_dim_ref_conv": sd["ref_conv.weight"].shape[1] if "ref_conv.weight" in sd else None,
            "add_control_adapter": True if "control_adapter.conv.weight" in sd else False,
        }

        with init_empty_weights():
            transformer = WanModel(**TRANSFORMER_CONFIG)
        transformer.eval()

        #ReCamMaster
        if "blocks.0.cam_encoder.weight" in sd:
            log.info("ReCamMaster model detected, patching model...")
            import torch.nn as nn
            for block in transformer.blocks:
                block.cam_encoder = nn.Linear(12, dim)
                block.projector = nn.Linear(dim, dim)
                block.cam_encoder.weight.data.zero_()
                block.cam_encoder.bias.data.zero_()
                block.projector.weight = nn.Parameter(torch.eye(dim))
                block.projector.bias = nn.Parameter(torch.zeros(dim))

        # FantasyTalking https://github.com/Fantasy-AMAP
        if fantasytalking_model is not None:
            log.info("FantasyTalking model detected, patching model...")
            context_dim = fantasytalking_model["sd"]["proj_model.proj.weight"].shape[0]
            import torch.nn as nn
            for block in transformer.blocks:
                block.cross_attn.k_proj = nn.Linear(context_dim, dim, bias=False)
                block.cross_attn.v_proj = nn.Linear(context_dim, dim, bias=False)
            sd.update(fantasytalking_model["sd"])
        
        # RealisDance-DiT
        if "add_conv_in.weight" in sd:
            def zero_module(module):
                for p in module.parameters():
                    torch.nn.init.zeros_(p)
                return module
            inner_dim = sd["add_conv_in.weight"].shape[0]
            add_cond_in_dim = sd["add_conv_in.weight"].shape[1]
            attn_cond_in_dim = sd["attn_conv_in.weight"].shape[1]
            transformer.add_conv_in = torch.nn.Conv3d(add_cond_in_dim, inner_dim, kernel_size=transformer.patch_size, stride=transformer.patch_size)
            transformer.add_proj = zero_module(torch.nn.Linear(inner_dim, inner_dim))
            transformer.attn_conv_in = torch.nn.Conv3d(attn_cond_in_dim, inner_dim, kernel_size=transformer.patch_size, stride=transformer.patch_size)
        
        comfy_model = WanVideoModel(
            WanVideoModelConfig(base_dtype),
            model_type=comfy.model_base.ModelType.FLOW,
            device=device,
        )
        
        if quantization == "disabled":
            for k, v in sd.items():
                if isinstance(v, torch.Tensor):
                    if v.dtype == torch.float8_e4m3fn:
                        quantization = "fp8_e4m3fn"
                        break
                    elif v.dtype == torch.float8_e5m2:
                        quantization = "fp8_e5m2"
                        break

        if "fp8_e4m3fn" in quantization:
            dtype = torch.float8_e4m3fn
        elif quantization == "fp8_e5m2":
            dtype = torch.float8_e5m2
        else:
            dtype = base_dtype
        params_to_keep = {"norm", "head", "bias", "time_in", "vector_in", "patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter", "add"}
        #if lora is not None:
        #    transformer_load_device = device
        if not lora_low_mem_load:
            log.info("Using accelerate to load and assign model weights to device...")
            param_count = sum(1 for _ in transformer.named_parameters())
            for name, param in tqdm(transformer.named_parameters(), 
                    desc=f"Loading transformer parameters to {transformer_load_device}", 
                    total=param_count,
                    leave=True):
                dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
                if "patch_embedding" in name:
                    dtype_to_use = torch.float32
                set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
        comfy_model.diffusion_model = transformer
        comfy_model.load_device = transformer_load_device
        
        patcher = comfy.model_patcher.ModelPatcher(comfy_model, device, offload_device)
        patcher.model.is_patched = False

        control_lora = False
        
        if lora is not None:
            for l in lora:
                log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}")
                lora_path = l["path"]
                lora_strength = l["strength"]
                lora_sd = load_torch_file(lora_path, safe_load=True)
                if "dwpose_embedding.0.weight" in lora_sd: #unianimate
                    from .unianimate.nodes import update_transformer
                    log.info("Unianimate LoRA detected, patching model...")
                    transformer = update_transformer(transformer, lora_sd)

                lora_sd = standardize_lora_key_format(lora_sd)
                if l["blocks"]:
                    lora_sd = filter_state_dict_by_blocks(lora_sd, l["blocks"], l.get("layer_filter", []))

                #spacepxl's control LoRA patch
                # for key in lora_sd.keys():
                #     print(key)
                
                if "diffusion_model.patch_embedding.lora_A.weight" in lora_sd:
                    log.info("Control-LoRA detected, patching model...")
                    control_lora = True

                    in_cls = transformer.patch_embedding.__class__ # nn.Conv3d
                    old_in_dim = transformer.in_dim # 16
                    new_in_dim = lora_sd["diffusion_model.patch_embedding.lora_A.weight"].shape[1]
                    assert new_in_dim == 32
                    
                    new_in = in_cls(
                        new_in_dim,
                        transformer.patch_embedding.out_channels,
                        transformer.patch_embedding.kernel_size,
                        transformer.patch_embedding.stride,
                        transformer.patch_embedding.padding,
                    ).to(device=device, dtype=torch.float32)
                    
                    new_in.weight.zero_()
                    new_in.bias.zero_()
                    
                    new_in.weight[:, :old_in_dim].copy_(transformer.patch_embedding.weight)
                    new_in.bias.copy_(transformer.patch_embedding.bias)
                    
                    transformer.patch_embedding = new_in
                    transformer.expanded_patch_embedding = new_in
                    transformer.register_to_config(in_dim=new_in_dim)

                patcher, _ = load_lora_for_models(patcher, None, lora_sd, lora_strength, 0)
                
                del lora_sd
            
            patcher = apply_lora(patcher, device, transformer_load_device, params_to_keep=params_to_keep, dtype=dtype, base_dtype=base_dtype, state_dict=sd, low_mem_load=lora_low_mem_load)
            #patcher.load(device, full_load=True)
            patcher.model.is_patched = True

        
        
        if "fast" in quantization:
            from .fp8_optimization import convert_fp8_linear
            if quantization == "fp8_e4m3fn_fast_no_ffn":
                params_to_keep.update({"ffn"})
            print(params_to_keep)
            convert_fp8_linear(patcher.model.diffusion_model, base_dtype, params_to_keep=params_to_keep)

        del sd

        if vram_management_args is not None:
            from .diffsynth.vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
            from .wanvideo.modules.model import WanLayerNorm, WanRMSNorm

            total_params_in_model = sum(p.numel() for p in patcher.model.diffusion_model.parameters())
            log.info(f"Total number of parameters in the loaded model: {total_params_in_model}")

            offload_percent = vram_management_args["offload_percent"]
            offload_params = int(total_params_in_model * offload_percent)
            params_to_keep = total_params_in_model - offload_params
            log.info(f"Selected params to offload: {offload_params}")
        
            enable_vram_management(
                patcher.model.diffusion_model,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv3d: AutoWrappedModule,
                    torch.nn.LayerNorm: AutoWrappedModule,
                    WanLayerNorm: AutoWrappedModule,
                    WanRMSNorm: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device=offload_device,
                    onload_dtype=dtype,
                    onload_device=device,
                    computation_dtype=base_dtype,
                    computation_device=device,
                ),
                max_num_param=params_to_keep,
                overflow_module_config = dict(
                    offload_dtype=dtype,
                    offload_device=offload_device,
                    onload_dtype=dtype,
                    onload_device=offload_device,
                    computation_dtype=base_dtype,
                    computation_device=device,
                ),
                compile_args = compile_args,
            )

        #compile
        if compile_args is not None and vram_management_args is None:
            torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
            try:
                if hasattr(torch, '_dynamo') and hasattr(torch._dynamo, 'config'):
                    torch._dynamo.config.recompile_limit = compile_args["dynamo_recompile_limit"]
            except Exception as e:
                log.warning(f"Could not set recompile_limit: {e}")
            if compile_args["compile_transformer_blocks_only"]:
                for i, block in enumerate(patcher.model.diffusion_model.blocks):
                    patcher.model.diffusion_model.blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
                if vace_layers is not None:
                    for i, block in enumerate(patcher.model.diffusion_model.vace_blocks):
                        patcher.model.diffusion_model.vace_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
            else:
                patcher.model.diffusion_model = torch.compile(patcher.model.diffusion_model, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])        
        
        if load_device == "offload_device" and patcher.model.diffusion_model.device != offload_device:
            log.info(f"Moving diffusion model from {patcher.model.diffusion_model.device} to {offload_device}")
            patcher.model.diffusion_model.to(offload_device)
            gc.collect()
            mm.soft_empty_cache()

        patcher.model["dtype"] = base_dtype
        patcher.model["base_path"] = model_path
        patcher.model["model_name"] = model
        patcher.model["manual_offloading"] = manual_offloading
        patcher.model["quantization"] = quantization
        patcher.model["auto_cpu_offload"] = True if vram_management_args is not None else False
        patcher.model["control_lora"] = control_lora

        if 'transformer_options' not in patcher.model_options:
            patcher.model_options['transformer_options'] = {}
        patcher.model_options["transformer_options"]["block_swap_args"] = block_swap_args   

        for model in mm.current_loaded_models:
            if model._model() == patcher:
                mm.current_loaded_models.remove(model)            

        return (patcher,)

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,)

#region load VAE

class WanVideoVAELoader:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
            },
            "optional": {
                "precision": (["fp16", "fp32", "bf16"],
                    {"default": "bf16"}
                ),
            }
        }

    RETURN_TYPES = ("WANVAE",)
    RETURN_NAMES = ("vae", )
    FUNCTION = "loadmodel"
    CATEGORY = "WanVideoWrapper"
    DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'"

    def loadmodel(self, model_name, precision):
        from .wanvideo.wan_video_vae import WanVideoVAE

        device = mm.get_torch_device()
        offload_device = mm.unet_offload_device()

        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
        #with open(os.path.join(script_directory, 'configs', 'hy_vae_config.json')) as f:
        #    vae_config = json.load(f)
        model_path = folder_paths.get_full_path("vae", model_name)
        vae_sd = load_torch_file(model_path, safe_load=True)

        has_model_prefix = any(k.startswith("model.") for k in vae_sd.keys())
        if not has_model_prefix:
            vae_sd = {f"model.{k}": v for k, v in vae_sd.items()}
        
        vae = WanVideoVAE(dtype=dtype)
        vae.load_state_dict(vae_sd)
        vae.eval()
        vae.to(device = offload_device, dtype = dtype)
            

        return (vae,)

class WanVideoTinyVAELoader:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model_name": (folder_paths.get_filename_list("vae_approx"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae_approx'"}),
            },
            "optional": {
                "precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}), 
                "parallel": ("BOOLEAN", {"default": False, "tooltip": "uses more memory but is faster"}),
            }
        }

    RETURN_TYPES = ("WANVAE",)
    RETURN_NAMES = ("vae", )
    FUNCTION = "loadmodel"
    CATEGORY = "WanVideoWrapper"
    DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'"

    def loadmodel(self, model_name, precision, parallel=False):
        from .taehv import TAEHV

        device = mm.get_torch_device()
        offload_device = mm.unet_offload_device()

        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
        model_path = folder_paths.get_full_path("vae_approx", model_name)
        vae_sd = load_torch_file(model_path, safe_load=True)
        
        vae = TAEHV(vae_sd, parallel=parallel)
       
        vae.to(device = offload_device, dtype = dtype)

        return (vae,)



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": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "generation_height": ("INT", {"default": 480, "min": 64, "max": 29048, "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]
        patch_size = (1, 2, 2)
        vae_stride = (4, 8, 8)
    
        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": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "generation_height": ("INT", {"default": 480, "min": 64, "max": 29048, "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 ):

        patch_size = (1, 2, 2)
        vae_stride = (4, 8, 8)
    
        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"}),
            "smpl_latent": ("LATENT", {"tooltip": "SMPL pose 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": {
                "hamer_latent": ("LATENT", {"tooltip": "Hamer hand pose image to encode"}),
            },
        }

    RETURN_TYPES = ("REALISDANCELATENTS",)
    RETURN_NAMES = ("realisdance_latents",)
    FUNCTION = "process"
    CATEGORY = "WanVideoWrapper"

    def process(self, ref_latent, smpl_latent, pose_cond_start_percent, pose_cond_end_percent, hamer_latent=None):
        if hamer_latent is None:
            hamer = torch.zeros_like(smpl_latent["samples"])
        else:
            hamer = hamer_latent["samples"]

        pose_latent = torch.cat((smpl_latent["samples"], hamer), dim=1)
        
        realisdance_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 (realisdance_latents,)

class WanVideoImageToVideoEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "vae": ("WANVAE",),
            "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "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"}),
                "realisdance_latents": ("REALISDANCELATENTS", {"tooltip": "RealisDance latents"}),
            }
        }

    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, realisdance_latents=None):

        device = mm.get_torch_device()
        offload_device = mm.unet_offload_device()

        patch_size = (1, 2, 2)

        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 realisdance_latents is not None:
            realisdance_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,
            "realisdance_latents": realisdance_latents
        }

        return (image_embeds,)
    
class WanVideoEmptyEmbeds:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "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"}),
            }
        }

    RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
    RETURN_NAMES = ("image_embeds",)
    FUNCTION = "process"
    CATEGORY = "WanVideoWrapper"

    def process(self, num_frames, width, height, control_embeds=None):
        vae_stride = (4, 8, 8)

        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,
        }
    
        return (embeds,)

class WanVideoMiniMaxRemoverEmbeds:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "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):
        vae_stride = (4, 8, 8)

        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):
        vae_stride = (4, 8, 8)
        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": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "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)
        self.vae_stride = (4, 8, 8)

        width = (width // 16) * 16
        height = (height // 16) * 16

        target_shape = (16, (num_frames - 1) // self.vae_stride[0] + 1,
                        height // self.vae_stride[1],
                        width // self.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) // self.vae_stride[0])
            height = 2 * (int(height) // (self.vae_stride[1] * 2))
            width = 2 * (int(width) // (self.vae_stride[2] * 2))

            # reshape
            mask = mask[0, :, :, :]
            mask = mask.view(
                depth, height, self.vae_stride[1], width, self.vae_stride[1]
            )  # depth, height, 8, width, 8
            mask = mask.permute(2, 4, 0, 1, 3)  # 8, 8, depth, height, width
            mask = mask.reshape(
                self.vae_stride[1] * self.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 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"}),
            },
        }

    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):
        
        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

        masks = torch.ones((num_frames, H, W), device=device)

        if control_images is not None:
            control_images = common_upscale(control_images.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1)
        
        if start_image is not None and end_image is not None:
            if start_image.shape != end_image.shape:
                end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1)
            if control_images is None:
                empty_frames = torch.ones((num_frames - start_image.shape[0] - end_image.shape[0], H, W, 3), device=device) * empty_frame_level
            else:
                empty_frames = control_images[start_image.shape[0]:num_frames - end_image.shape[0]]
            out_batch = torch.cat([start_image, empty_frames, end_image], dim=0)
            masks[0:start_image.shape[0]] = 0
            masks[-end_image.shape[0]:] = 0
        elif start_image is not None:
            if control_images is None:
                empty_frames = torch.ones((num_frames - start_image.shape[0], H, W, 3), device=device) * empty_frame_level
            else:
                empty_frames = control_images[start_image.shape[0]:num_frames]
            out_batch = torch.cat([start_image, empty_frames], dim=0)
            masks[0:start_image.shape[0]] = 0
        elif end_image is not None:
            if control_images is None:
                empty_frames = torch.ones((num_frames - end_image.shape[0], H, W, 3), device=device) * empty_frame_level
            else:
                empty_frames = control_images[:num_frames - end_image.shape[0]]
            out_batch = torch.cat([empty_frames, end_image], dim=0)
            masks[-end_image.shape[0]:] = 0

        if inpaint_mask is not None:
            inpaint_mask = common_upscale(inpaint_mask.unsqueeze(1), W, H, "nearest-exact", "disabled").squeeze(1).to(device)
            if inpaint_mask.shape[0] > num_frames:
                inpaint_mask = inpaint_mask[:num_frames]
            elif inpaint_mask.shape[0] < num_frames:
                inpaint_mask = inpaint_mask.repeat(num_frames // inpaint_mask.shape[0] + 1, 1, 1)[:num_frames]

            empty_mask = torch.ones_like(masks, device=device)
            masks = inpaint_mask * empty_mask
    
        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": {
               "vae": ("WANVAE",),
            }
        }

    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):
        context_options = {
            "context_schedule":context_schedule,
            "context_frames":context_frames,
            "context_stride":context_stride,
            "context_overlap":context_overlap,
            "freenoise":freenoise,
            "verbose":verbose,
            "vae": vae,
        }

        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,)
    
#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"],
                    {
                        "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"], {"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"}),
                "loop_args": ("LOOPARGS", ),
                "experimental_args": ("EXPERIMENTALARGS", ),
                "sigmas": ("SIGMAS", ),
                "unianimate_poses": ("UNIANIMATE_POSE", ),
                "fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ),
                "uni3c_embeds": ("UNI3C_EMBEDS", ),
            }
        }

    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):
        
        patcher = model
        model = model.model
        transformer = model.diffusion_model
        dtype = model["dtype"]
        control_lora = model["control_lora"]

        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)

        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)
            

            #denoising_step_list = [999, 750, 500, 250]
            if steps != 4:
                raise ValueError("This scheduler is only for 4 steps")
            #sample_scheduler = FlowMatchScheduler(num_inference_steps=steps, shift=shift, sigma_min=0, extra_one_step=True)
            sample_scheduler.timesteps = torch.tensor(denoising_step_list)[:steps].to(device)
            sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.0], device=device)])
        
        if timesteps is None:
            timesteps = sample_scheduler.timesteps
        log.info(f"timesteps: {timesteps}")
        
        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}")
            
            realisdance_latents = image_embeds.get("realisdance_latents", None)
            if realisdance_latents is not None:
                add_cond = realisdance_latents["pose_latent"]
                attn_cond = realisdance_latents["ref_latent"]
                attn_cond_neg = realisdance_latents["ref_latent_neg"]
                add_cond_start_percent = realisdance_latents["pose_cond_start_percent"]
                add_cond_end_percent = realisdance_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]
        
        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 = None
        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}")
        
        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_mask = torch.ones_like(noise_pred_context)
                
                # 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)            

        latent = noise.to(device)

        freqs = None
        transformer.rope_embedder.k = None
        transformer.rope_embedder.num_frames = None
        if rope_function=="comfy":
            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)

        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 = None
        if uni3c_embeds is not None:
            transformer.controlnet = uni3c_embeds["controlnet"]
            pcd_data = {
                "render_latent": uni3c_embeds["render_latent"],
                "render_mask": uni3c_embeds["render_mask"],
                "camera_embedding": uni3c_embeds["camera_embedding"],
                "controlnet_weight": uni3c_embeds["controlnet_weight"],
                "start": uni3c_embeds["start"],
                "end": uni3c_embeds["end"],
            }

        #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)
            enable_enhance()
        else:
            feta_args = None
            disable_enhance()

        # 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"]

        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

        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):
            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)
                 
                base_params = {
                    'seq_len': seq_len,
                    'device': device,
                    'freqs': freqs,
                    't': timestep,
                    'current_step': idx,
                    'control_lora_enabled': control_lora_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 if fantasytalking_embeds is not None else None,
                    "pcd_data": pcd_data,
                    "controlnet": controlnet,
                    "add_cond": add_cond_input,
                    "nag_params": text_embeds.get("nag_params", {}),
                    "nag_context": text_embeds.get("nag_prompt_embeds", 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]

                #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)
        except:
            pass

        #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)
            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
            if feta_args is not None and feta_start_percent <= current_step_percentage <= feta_end_percent:
                enable_enhance()
            else:
                disable_enhance()

            #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
            #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))
                
                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}")
                    
                    # 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)

                    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)                    
                    noise_pred[:, c] += noise_pred_context * window_mask
                    counter[:, c] += window_mask
                noise_pred /= counter
            #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),
                    t,
                    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 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
            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()
            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.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,
            }, )
    
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."}),
                    },
                }

    @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):
        device = mm.get_torch_device()
        offload_device = mm.unet_offload_device()
        mm.soft_empty_cache()
        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 - images.min()) / (images.max() - images.min())      
        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())
            out = temp_images[:, 9:]
            out = torch.cat([out, images[:, 5:]], dim=1)
            images = out

        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).cpu().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
        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},)

NODE_CLASS_MAPPINGS = {
    "WanVideoSampler": WanVideoSampler,
    "WanVideoDecode": WanVideoDecode,
    "WanVideoTextEncode": WanVideoTextEncode,
    "WanVideoTextEncodeSingle": WanVideoTextEncodeSingle,
    "WanVideoModelLoader": WanVideoModelLoader,
    "WanVideoVAELoader": WanVideoVAELoader,
    "LoadWanVideoT5TextEncoder": LoadWanVideoT5TextEncoder,
    "WanVideoImageClipEncode": WanVideoImageClipEncode,#deprecated
    "WanVideoClipVisionEncode": WanVideoClipVisionEncode,
    "WanVideoImageToVideoEncode": WanVideoImageToVideoEncode,
    "LoadWanVideoClipTextEncoder": LoadWanVideoClipTextEncoder,
    "WanVideoEncode": WanVideoEncode,
    "WanVideoBlockSwap": WanVideoBlockSwap,
    "WanVideoTorchCompileSettings": WanVideoTorchCompileSettings,
    "WanVideoEmptyEmbeds": WanVideoEmptyEmbeds,
    "WanVideoLoraSelect": WanVideoLoraSelect,
    "WanVideoLoraBlockEdit": WanVideoLoraBlockEdit,
    "WanVideoEnhanceAVideo": WanVideoEnhanceAVideo,
    "WanVideoContextOptions": WanVideoContextOptions,
    "WanVideoTeaCache": WanVideoTeaCache,
    "WanVideoMagCache": WanVideoMagCache,
    "WanVideoVRAMManagement": WanVideoVRAMManagement,
    "WanVideoTextEmbedBridge": WanVideoTextEmbedBridge,
    "WanVideoFlowEdit": WanVideoFlowEdit,
    "WanVideoControlEmbeds": WanVideoControlEmbeds,
    "WanVideoSLG": WanVideoSLG,
    "WanVideoTinyVAELoader": WanVideoTinyVAELoader,
    "WanVideoLoopArgs": WanVideoLoopArgs,
    "WanVideoImageResizeToClosest": WanVideoImageResizeToClosest,
    "WanVideoSetBlockSwap": WanVideoSetBlockSwap,
    "WanVideoExperimentalArgs": WanVideoExperimentalArgs,
    "WanVideoVACEEncode": WanVideoVACEEncode,
    "WanVideoVACEStartToEndFrame": WanVideoVACEStartToEndFrame,
    "WanVideoVACEModelSelect": WanVideoVACEModelSelect,
    "WanVideoPhantomEmbeds": WanVideoPhantomEmbeds,
    "CreateCFGScheduleFloatList": CreateCFGScheduleFloatList,
    "WanVideoRealisDanceLatents": WanVideoRealisDanceLatents,
    "WanVideoApplyNAG": WanVideoApplyNAG,
    "WanVideoMiniMaxRemoverEmbeds": WanVideoMiniMaxRemoverEmbeds,
    "WanVideoLoraSelectMulti": WanVideoLoraSelectMulti
    }
NODE_DISPLAY_NAME_MAPPINGS = {
    "WanVideoSampler": "WanVideo Sampler",
    "WanVideoDecode": "WanVideo Decode",
    "WanVideoTextEncode": "WanVideo TextEncode",
    "WanVideoTextEncodeSingle": "WanVideo TextEncodeSingle",
    "WanVideoTextImageEncode": "WanVideo TextImageEncode (IP2V)",
    "WanVideoModelLoader": "WanVideo Model Loader",
    "WanVideoVAELoader": "WanVideo VAE Loader",
    "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",
    "WanVideoLoraSelect": "WanVideo Lora Select",
    "WanVideoLoraBlockEdit": "WanVideo Lora Block Edit",
    "WanVideoEnhanceAVideo": "WanVideo Enhance-A-Video",
    "WanVideoContextOptions": "WanVideo Context Options",
    "WanVideoTeaCache": "WanVideo TeaCache",
    "WanVideoMagCache": "WanVideo MagCache",
    "WanVideoVRAMManagement": "WanVideo VRAM Management",
    "WanVideoTextEmbedBridge": "WanVideo TextEmbed Bridge",
    "WanVideoFlowEdit": "WanVideo FlowEdit",
    "WanVideoControlEmbeds": "WanVideo Control Embeds",
    "WanVideoSLG": "WanVideo SLG",
    "WanVideoTinyVAELoader": "WanVideo Tiny VAE Loader",
    "WanVideoLoopArgs": "WanVideo Loop Args",
    "WanVideoImageResizeToClosest": "WanVideo Image Resize To Closest",
    "WanVideoSetBlockSwap": "WanVideo Set BlockSwap",
    "WanVideoExperimentalArgs": "WanVideo Experimental Args",
    "WanVideoVACEEncode": "WanVideo VACE Encode",
    "WanVideoVACEStartToEndFrame": "WanVideo VACE Start To End Frame",
    "WanVideoVACEModelSelect": "WanVideo VACE Model Select",
    "WanVideoPhantomEmbeds": "WanVideo Phantom Embeds",
    "CreateCFGScheduleFloatList": "WanVideo CFG Schedule Float List",
    "WanVideoRealisDanceLatents": "WanVideo RealisDance Latents",
    "WanVideoApplyNAG": "WanVideo Apply NAG",
    "WanVideoMiniMaxRemoverEmbeds": "WanVideo MiniMax Remover Embeds",
    "WanVideoLoraSelectMulti": "WanVideo Lora Select Multi"
    }