nodes/pollockjj-ComfyUI-MultiGPU
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pollockjj-ComfyUI-MultiGPU/wanvideo.py
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John Pollock 2f605e0db3 Add WanVideoVACEEncode and enhance WanVideoEncode for multi-GPU support 
- Implemented WanVideoVACEEncode class for encoding with VACE, including input parameters for width, height, number of frames, and strength.
- Enhanced WanVideoEncode class to support multi-GPU encoding, with additional parameters for noise augmentation and latent strength.
- Updated device management to ensure proper device context during encoding processes.
2025-10-09 14:41:53 -05:00

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import logging
import torch
import sys
import inspect
import copy
import folder_paths
import comfy.model_management as mm
from nodes import NODE_CLASS_MAPPINGS
from .device_utils import get_device_list
from .model_management_mgpu import multigpu_memory_log
from comfy.utils import load_torch_file, ProgressBar
import gc
import numpy as np
from accelerate import init_empty_weights
import os
import importlib.util
logger = logging.getLogger("MultiGPU")
scheduler_list = [
"unipc", "unipc/beta",
"dpm++", "dpm++/beta",
"dpm++_sde", "dpm++_sde/beta",
"euler", "euler/beta",
"deis",
"lcm", "lcm/beta",
"res_multistep",
"flowmatch_causvid",
"flowmatch_distill",
"flowmatch_pusa",
"multitalk",
"sa_ode_stable"
]
rope_functions = ["default", "comfy", "comfy_chunked"]
class WanVideoModelLoader:
@classmethod
def INPUT_TYPES(s):
devices = get_device_list()
default_device = devices[1] if len(devices) > 1 else devices[0]
return {
"required": {
"model": (folder_paths.get_filename_list("unet_gguf") + 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_e4m3fn_scaled", "fp8_e4m3fn_scaled_fast", "fp8_e5m2", "fp8_e5m2_fast", "fp8_e5m2_scaled", "fp8_e5m2_scaled_fast"], {"default": "disabled",
"tooltip": "Optional quantization method, 'disabled' acts as autoselect based by weights. Scaled modes only work with matching weights, _fast modes (fp8 matmul) require CUDA compute capability >= 8.9 (NVIDIA 4000 series and up), e4m3fn generally can not be torch.compiled on compute capability < 8.9 (3000 series and under)"}),
"load_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
"compute_device": (devices, {"default": default_device}),
},
"optional": {
"attention_mode": ([
"sdpa",
"flash_attn_2",
"flash_attn_3",
"sageattn",
"sageattn_3",
"radial_sage_attention",
], {"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"}),
"extra_model": ("VACEPATH", {"default": None, "tooltip": "Extra model to add to the main model, ie. VACE or MTV Crafter"}),
"fantasytalking_model": ("FANTASYTALKINGMODEL", {"default": None, "tooltip": "FantasyTalking model https://github.com/Fantasy-AMAP"}),
"multitalk_model": ("MULTITALKMODEL", {"default": None, "tooltip": "Multitalk model"}),
"fantasyportrait_model": ("FANTASYPORTRAITMODEL", {"default": None, "tooltip": "FantasyPortrait model"}),
"rms_norm_function": (["default", "pytorch"], {"default": "default", "tooltip": "RMSNorm function to use, 'pytorch' is the new native torch RMSNorm, which is faster (when not using torch.compile mostly) but changes results slightly. 'default' is the original WanRMSNorm"}),
}
}
RETURN_TYPES = ("WANVIDEOMODEL", "MULTIGPUDEVICE",)
RETURN_NAMES = ("model", "compute_device",)
FUNCTION = "loadmodel"
CATEGORY = "multigpu/WanVideoWrapper"
def loadmodel(self, model, base_precision, compute_device, quantization, load_device, **kwargs):
from . import set_current_device
original_loader = NODE_CLASS_MAPPINGS["WanVideoModelLoader"]()
loader_module = inspect.getmodule(original_loader)
original_module_device = loader_module.device
set_current_device(compute_device)
compute_device_to_be_patched = mm.get_torch_device()
loader_module.device = compute_device_to_be_patched
result = original_loader.loadmodel(model, base_precision, load_device, quantization, **kwargs,)
patcher = result[0]
try:
return (patcher, compute_device)
finally:
loader_module.device = original_module_device
class WanVideoSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("WANVIDEOMODEL",),
"compute_device": ("MULTIGPUDEVICE",),
"image_embeds": ("WANVIDIMAGE_EMBEDS", ),
"steps": ("INT", {"default": 30, "min": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"shift": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"force_offload": ("BOOLEAN", {"default": True, "tooltip": "Moves the model to the offload device after sampling"}),
"scheduler": (scheduler_list, {"default": "unipc",}),
"riflex_freq_index": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping"}),
},
"optional": {
"text_embeds": ("WANVIDEOTEXTEMBEDS", ),
"samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
"denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"feta_args": ("FETAARGS", ),
"context_options": ("WANVIDCONTEXT", ),
"cache_args": ("CACHEARGS", ),
"flowedit_args": ("FLOWEDITARGS", ),
"batched_cfg": ("BOOLEAN", {"default": False, "tooltip": "Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory"}),
"slg_args": ("SLGARGS", ),
"rope_function": (rope_functions, {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile"}),
"loop_args": ("LOOPARGS", ),
"experimental_args": ("EXPERIMENTALARGS", ),
"sigmas": ("SIGMAS", ),
"unianimate_poses": ("UNIANIMATE_POSE", ),
"fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ),
"uni3c_embeds": ("UNI3C_EMBEDS", ),
"multitalk_embeds": ("MULTITALK_EMBEDS", ),
"freeinit_args": ("FREEINITARGS", ),
"start_step": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "tooltip": "Start step for the sampling, 0 means full sampling, otherwise samples only from this step"}),
"end_step": ("INT", {"default": -1, "min": -1, "max": 10000, "step": 1, "tooltip": "End step for the sampling, -1 means full sampling, otherwise samples only until this step"}),
"add_noise_to_samples": ("BOOLEAN", {"default": False, "tooltip": "Add noise to the samples before sampling, needed for video2video sampling when starting from clean video"}),
}
}
RETURN_TYPES = ("LATENT", "LATENT",)
RETURN_NAMES = ("samples", "denoised_samples",)
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "MultiGPU-aware sampler that ensures correct device for each model"
def process(self, model, compute_device, **kwargs):
from . import set_current_device
original_sampler = NODE_CLASS_MAPPINGS["WanVideoSampler"]()
sampler_module = inspect.getmodule(original_sampler)
original_module_device = sampler_module.device
original_module_offload_device = sampler_module.offload_device
set_current_device(compute_device)
compute_device_to_be_patched = mm.get_torch_device()
sampler_module.device = compute_device_to_be_patched
transformer = model.model.diffusion_model
transformer_options = model.model_options.get("transformer_options", {})
block_swap_args = transformer_options.get("block_swap_args")
multi_gpu_block_swap = block_swap_args is not None and "swap_device" in block_swap_args
offload_device_to_be_patched = None
if multi_gpu_block_swap:
swap_label = block_swap_args.get("swap_device")
logger.info(f"[MultiGPU WanVideoWrapper][WanVideoSamplerMultiGPU] block swap enabled, swap device: {swap_label}")
offload_device_to_be_patched = torch.device(str(swap_label))
sampler_module.offload_device = offload_device_to_be_patched
if transformer is not None and offload_device_to_be_patched is not None:
transformer.offload_device = offload_device_to_be_patched
transformer.cache_device = offload_device_to_be_patched
try:
return original_sampler.process(model, **kwargs)
finally:
sampler_module.device = original_module_device
sampler_module.offload_device = original_module_offload_device
class WanVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"t5": ("WANTEXTENCODER",),
"load_device": ("MULTIGPUDEVICE",),
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Encodes text prompts into text embeddings. For rudimentary prompt travel you can input multiple prompts separated by '|', they will be equally spread over the video length"
def process(self, positive_prompt, negative_prompt, t5=None, load_device=None,force_offload=True, model_to_offload=None, use_disk_cache=False):
from . import set_current_device
set_current_device(load_device)
if load_device == "cpu":
device = "cpu"
else:
device = "gpu"
text_encoder = t5[0]
original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncode"]()
prompt_embeds_dict = original_encoder.process(positive_prompt, negative_prompt, text_encoder, force_offload, model_to_offload, use_disk_cache, device)
return (prompt_embeds_dict)
def parse_prompt_weights(self, prompt):
original_parser = NODE_CLASS_MAPPINGS["WanVideoTextEncode"]()
return original_parser.parse_prompt_weights(prompt)
class LoadWanVideoT5TextEncoder:
@classmethod
def INPUT_TYPES(s):
devices = get_device_list()
default_device = devices[1] if len(devices) > 1 else devices[0]
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": {
"device": (devices, {"default": default_device}),
"quantization": (['disabled', 'fp8_e4m3fn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
}
}
RETURN_TYPES = ("WANTEXTENCODER", "MULTIGPUDEVICE")
RETURN_NAMES = ("wan_t5_model", "load_device")
FUNCTION = "loadmodel"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Loads Wan text_encoder model from 'ComfyUI/models/LLM'"
def loadmodel(self, model_name, precision, device=None, quantization="disabled"):
from . import set_current_device
set_current_device(device)
if device == "cpu":
load_device = "offload_device"
else:
load_device = "main_device"
original_loader = NODE_CLASS_MAPPINGS["LoadWanVideoT5TextEncoder"]()
text_encoder = original_loader.loadmodel(model_name, precision, load_device, quantization)
return text_encoder, device
class WanVideoTextEncodeCached:
@classmethod
def INPUT_TYPES(s):
devices = get_device_list()
default_device = devices[1] if len(devices) > 1 else devices[0]
return {
"required": {
"model_name": (folder_paths.get_filename_list("text_encoders"), {"tooltip": "These models are loaded from 'ComfyUI/models/text_encoders'"}),
"precision": (["fp32", "bf16"], {"default": "bf16"}),
"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
"quantization": (['disabled', 'fp8_e4m3fn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
"use_disk_cache": ("BOOLEAN", {"default": True, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
"load_device": (devices, {"default": default_device}
),
},
"optional": {
"extender_args": ("WANVIDEOPROMPTEXTENDER_ARGS", {"tooltip": "Use this node to extend the prompt with additional text."}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", "WANVIDEOTEXTEMBEDS", "STRING")
RETURN_NAMES = ("text_embeds", "negative_text_embeds", "positive_prompt")
OUTPUT_TOOLTIPS = ("The text embeddings for both prompts", "The text embeddings for the negative prompt only (for NAG)", "Positive prompt to display prompt extender results")
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = """Encodes text prompts into text embeddings. This node loads and completely unloads the T5 after done, leaving no VRAM or RAM imprint."""
def process(self, model_name, precision, positive_prompt, negative_prompt, quantization='disabled', use_disk_cache=True, load_device=None, extender_args=None):
from . import set_current_device
set_current_device(load_device)
if load_device == "cpu":
device = "cpu"
else:
device = "gpu"
original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncodeCached"]()
prompt_embeds_dict, negative_text_embeds, positive_prompt_out = original_encoder.process(model_name, precision, positive_prompt, negative_prompt, quantization, use_disk_cache, device, extender_args)
return prompt_embeds_dict, negative_text_embeds, positive_prompt_out
class WanVideoTextEncodeSingle:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"t5": ("WANTEXTENCODER",),
"load_device": ("MULTIGPUDEVICE",),
"force_offload": ("BOOLEAN", {"default": True}),
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory"}),
}
}
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
RETURN_NAMES = ("text_embeds",)
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Encodes text prompt into text embedding."
def process(self, prompt, t5=None, load_device=None, force_offload=True, model_to_offload=None, use_disk_cache=False):
from . import set_current_device
set_current_device(load_device)
if load_device == "cpu":
device = "cpu"
else:
device = "gpu"
text_encoder = t5[0]
original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncodeSingle"]()
prompt_embeds_dict = original_encoder.process(prompt, text_encoder, force_offload, model_to_offload, use_disk_cache, device)
return (prompt_embeds_dict)
class WanVideoVAELoader:
@classmethod
def INPUT_TYPES(s):
devices = get_device_list()
default_device = devices[1] if len(devices) > 1 else devices[0]
return {
"required": {
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
},
"optional": {
"load_device": (devices, {"default": default_device}),
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16"}
),
"compile_args": ("WANCOMPILEARGS", ),
}
}
RETURN_TYPES = ("WANVAE", "MULTIGPUDEVICE",)
RETURN_NAMES = ("vae", "load_device",)
FUNCTION = "loadmodel"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'"
def loadmodel(self, model_name, load_device=None, precision="fp16", compile_args=None):
from . import set_current_device
set_current_device(load_device)
original_loader = NODE_CLASS_MAPPINGS["WanVideoVAELoader"]()
vae_model = original_loader.loadmodel(model_name, precision, compile_args)
return vae_model, load_device
class WanVideoTinyVAELoader:
@classmethod
def INPUT_TYPES(s):
devices = get_device_list()
default_device = devices[1] if len(devices) > 1 else devices[0]
return {
"required": {
"model_name": (folder_paths.get_filename_list("vae_approx"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae_approx'"}),
},
"optional": {
"load_device": (devices, {"default": default_device}),
"precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
"parallel": ("BOOLEAN", {"default": False, "tooltip": "uses more memory but is faster"}),
}
}
RETURN_TYPES = ("WANVAE","MULTIGPUDEVICE")
RETURN_NAMES = ("vae", "load_device")
FUNCTION = "loadmodel"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae_approx'"
def loadmodel(self, model_name, load_device=None, precision="fp16", parallel=False):
from . import set_current_device
set_current_device(load_device)
original_loader = NODE_CLASS_MAPPINGS["WanVideoTinyVAELoader"]()
vae_model = original_loader.loadmodel(model_name, precision, parallel)
return vae_model, load_device
class WanVideoBlockSwap:
@classmethod
def INPUT_TYPES(s):
base_inputs = copy.deepcopy(NODE_CLASS_MAPPINGS["WanVideoBlockSwap"].INPUT_TYPES())
devices = get_device_list()
default_device = "cpu" if "cpu" in devices else devices[0]
base_inputs.setdefault("optional", {})
base_inputs["optional"]["swap_device"] = (
devices,
{
"default": default_device,
"tooltip": "Device that receives swapped transformer blocks",
},
)
return base_inputs
RETURN_TYPES = ("BLOCKSWAPARGS",)
RETURN_NAMES = ("block_swap_args",)
FUNCTION = "setargs"
CATEGORY = "multigpu/WanVideoWrapper"
DESCRIPTION = "Extends Wan block swap with explicit device selection"
def setargs(self, swap_device=None, **kwargs):
block_swap_config = dict(kwargs)
block_swap_config["swap_device"] = str(swap_device)
return (block_swap_config,)
class WanVideoImageToVideoEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for I2V where some noise can add motion and give sharper results"}),
"start_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}),
"end_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}),
"force_offload": ("BOOLEAN", {"default": True}),
},
"optional": {
"vae": ("WANVAE",),
"load_device": ("MULTIGPUDEVICE",),
"clip_embeds": ("WANVIDIMAGE_CLIPEMBEDS", {"tooltip": "Clip vision encoded image"}),
"start_image": ("IMAGE", {"tooltip": "Image to encode"}),
"end_image": ("IMAGE", {"tooltip": "end frame"}),
"control_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "Control signal for the Fun -model"}),
"fun_or_fl2v_model": ("BOOLEAN", {"default": True, "tooltip": "Enable when using official FLF2V or Fun model"}),
"temporal_mask": ("MASK", {"tooltip": "mask"}),
"extra_latents": ("LATENT", {"tooltip": "Extra latents to add to the input front, used for Skyreels A2 reference images"}),
"tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
"add_cond_latents": ("ADD_COND_LATENTS", {"advanced": True, "tooltip": "Additional cond latents WIP"}),
}
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
RETURN_NAMES = ("image_embeds",)
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
def process(self, width, height, num_frames, force_offload, noise_aug_strength,
start_latent_strength, end_latent_strength, start_image=None, end_image=None, control_embeds=None, fun_or_fl2v_model=False,
temporal_mask=None, extra_latents=None, clip_embeds=None, tiled_vae=False, add_cond_latents=None, vae=None, load_device=None):
from . import set_current_device
original_encoder = NODE_CLASS_MAPPINGS["WanVideoImageToVideoEncode"]()
encoder_module = inspect.getmodule(original_encoder)
original_module_device = encoder_module.device
original_module_offload = encoder_module.offload_device
set_current_device(load_device)
compute_device_to_be_patched = mm.get_torch_device()
encoder_module.device = compute_device_to_be_patched
encoder_module.offload_device = mm.unet_offload_device()
inner_vae = vae[0]
try:
return original_encoder.process(width, height, num_frames, force_offload, noise_aug_strength, start_latent_strength, end_latent_strength, start_image,
end_image, control_embeds, fun_or_fl2v_model, temporal_mask, extra_latents, clip_embeds, tiled_vae, add_cond_latents, inner_vae,)
finally:
encoder_module.device = original_module_device
encoder_module.offload_device = original_module_offload
class WanVideoDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"load_device": ("MULTIGPUDEVICE",),
"samples": ("LATENT",),
"enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": (
"Drastically reduces memory use but will introduce seams at tile stride boundaries. "
"The location and number of seams is dictated by the tile stride size. "
"The visibility of seams can be controlled by increasing the tile size. "
"Seams become less obvious at 1.5x stride and are barely noticeable at 2x stride size. "
"Which is to say if you use a stride width of 160, the seams are barely noticeable with a tile width of 320."
)}),
"tile_x": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile width in pixels. Smaller values use less VRAM but will make seams more obvious."}),
"tile_y": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile height in pixels. Smaller values use less VRAM but will make seams more obvious."}),
"tile_stride_x": ("INT", {"default": 144, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams."}),
"tile_stride_y": ("INT", {"default": 128, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams."}),
},
"optional": {
"normalization": (["default", "minmax"], {"advanced": True}),
}
}
@classmethod
def VALIDATE_INPUTS(s, tile_x, tile_y, tile_stride_x, tile_stride_y):
if tile_x <= tile_stride_x:
return "Tile width must be larger than the tile stride width."
if tile_y <= tile_stride_y:
return "Tile height must be larger than the tile stride height."
return True
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "decode"
CATEGORY = "multigpu/WanVideoWrapper"
def decode(self, vae, load_device, samples, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, normalization="default"):
from . import set_current_device
original_decode = NODE_CLASS_MAPPINGS["WanVideoDecode"]()
decode_module = inspect.getmodule(original_decode)
original_module_device = decode_module.device
set_current_device(load_device)
compute_device_to_be_patched = mm.get_torch_device()
decode_module.device = compute_device_to_be_patched
try:
return original_decode.decode(vae[0], samples, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, normalization)
finally:
decode_module.device = original_module_device
class WanVideoVACEEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"load_device": ("MULTIGPUDEVICE",),
"width": ("INT", {"default": 832, "min": 64, "max": 8096, "step": 8, "tooltip": "Width of the image to encode"}),
"height": ("INT", {"default": 480, "min": 64, "max": 8096, "step": 8, "tooltip": "Height of the image to encode"}),
"num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
"vace_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply VACE"}),
"vace_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply VACE"}),
},
"optional": {
"input_frames": ("IMAGE",),
"ref_images": ("IMAGE",),
"input_masks": ("MASK",),
"prev_vace_embeds": ("WANVIDIMAGE_EMBEDS",),
"tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
},
}
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
RETURN_NAMES = ("vace_embeds",)
FUNCTION = "process"
CATEGORY = "multigpu/WanVideoWrapper"
def process(self, vae, load_device, 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):
from . import set_current_device
original_encode = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"]()
encode_module = inspect.getmodule(original_encode)
original_module_device = encode_module.device
set_current_device(load_device)
compute_device_to_be_patched = mm.get_torch_device()
encode_module.device = compute_device_to_be_patched
try:
return original_encode.process(vae[0], width, height, num_frames, strength, vace_start_percent, vace_end_percent, input_frames, ref_images, input_masks, prev_vace_embeds, tiled_vae)
finally:
encode_module.device = original_module_device
class WanVideoEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"vae": ("WANVAE",),
"load_device": ("MULTIGPUDEVICE",),
"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 height 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 = "multigpu/WanVideoWrapper"
def encode(self, vae, load_device, 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):
from . import set_current_device
original_encode = NODE_CLASS_MAPPINGS["WanVideoEncode"]()
encode_module = inspect.getmodule(original_encode)
original_module_device = encode_module.device
set_current_device(load_device)
compute_device_to_be_patched = mm.get_torch_device()
encode_module.device = compute_device_to_be_patched
try:
return original_encode.encode(vae[0], image, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, noise_aug_strength, latent_strength, mask)
finally:
encode_module.device = original_module_device
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