912 lines
46 KiB
Python
912 lines
46 KiB
Python
import logging
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import torch
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import sys
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import inspect
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import folder_paths
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import comfy.model_management as mm
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from .device_utils import get_device_list
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from .model_management_mgpu import multigpu_memory_log
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logger = logging.getLogger("MultiGPU")
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class WanVideoModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"model": (folder_paths.get_filename_list("unet_gguf") + folder_paths.get_filename_list("diffusion_models"),
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{"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' folder",}),
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"base_precision": (["fp32", "bf16", "fp16", "fp16_fast"], {"default": "bf16"}),
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"quantization": (
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["disabled", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp8_e4m3fn_fast_no_ffn", "fp8_e4m3fn_scaled", "fp8_e5m2_scaled"],
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{"default": "disabled", "tooltip": "optional quantization method"}
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),
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0], "tooltip": "Device to load the model to"}),
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},
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"optional": {
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"attention_mode": ([
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"sdpa",
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"flash_attn_2",
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"flash_attn_3",
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"sageattn",
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"sageattn_3",
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"flex_attention",
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"radial_sage_attention",
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], {"default": "sdpa"}),
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"compile_args": ("WANCOMPILEARGS", ),
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"block_swap_args": ("BLOCKSWAPARGS", ),
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"lora": ("WANVIDLORA", {"default": None}),
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"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"}),
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"extra_model": ("VACEPATH", {"default": None, "tooltip": "Extra model to add to the main model, ie. VACE or MTV Crafter"}),
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"fantasytalking_model": ("FANTASYTALKMODEL", {"default": None, "tooltip": "FantasyTalking model https://github.com/Fantasy-AMAP"}),
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"multitalk_model": ("MULTITALKMODEL", {"default": None, "tooltip": "Multitalk model"}),
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"fantasyportrait_model": ("FANTASYPORTRAITMODEL", {"default": None, "tooltip": "FantasyPortrait model"}),
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"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"}),
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}
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}
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RETURN_TYPES = ("WANVIDEOMODEL",)
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RETURN_NAMES = ("model", )
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FUNCTION = "loadmodel"
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CATEGORY = "WanVideoWrapper"
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def loadmodel(self, model, base_precision, device, quantization,
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compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, vram_management_args=None, extra_model=None, fantasytalking_model=None, multitalk_model=None, fantasyportrait_model=None, rms_norm_function="default"):
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from . import set_current_device
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logging.debug(f"[MultiGPU] WanVideoModelLoader: User selected device: {device}")
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selected_device = torch.device(device)
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# UPDATE GLOBAL DEVICE CONTEXT
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set_current_device(selected_device)
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load_device = "offload_device" if device == "cpu" else "main_device"
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["WanVideoModelLoader"]()
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loader_module = inspect.getmodule(original_loader)
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if loader_module:
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logging.debug(f"[MultiGPU] Patching WanVideo modules to use {selected_device}")
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original_device = getattr(loader_module, 'device', None)
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original_offload = getattr(loader_module, 'offload_device', None)
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model_offload_override = getattr(loader_module, '_model_offload_device_override', None)
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setattr(loader_module, 'device', selected_device)
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if model_offload_override:
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setattr(loader_module, 'offload_device', model_offload_override)
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logging.debug(f"[MultiGPU] Using model offload override: {model_offload_override}")
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elif device == "cpu":
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setattr(loader_module, 'offload_device', selected_device)
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nodes_module_name = loader_module.__name__.replace('.nodes_model_loading', '.nodes')
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if nodes_module_name in sys.modules:
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nodes_module = sys.modules[nodes_module_name]
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setattr(nodes_module, 'device', selected_device)
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nodes_model_offload_override = getattr(nodes_module, '_model_offload_device_override', None)
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if nodes_model_offload_override:
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setattr(nodes_module, 'offload_device', nodes_model_offload_override)
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elif device == "cpu":
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setattr(nodes_module, 'offload_device', selected_device)
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logging.debug(f"[MultiGPU] Both WanVideo modules patched successfully")
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logger.info(f"[MultiGPU WanVideo] Device patching complete. Calling original loader...")
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logger.info(f"[MultiGPU WanVideo] Module variables: device={loader_module.device}, offload_device={getattr(loader_module, 'offload_device', 'NOT SET')}")
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multigpu_memory_log("wanvideo_model_load", "pre-load")
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result = original_loader.loadmodel(model, base_precision, load_device, quantization,
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compile_args, attention_mode, block_swap_args, lora, vram_management_args, extra_model=extra_model, fantasytalking_model=fantasytalking_model, multitalk_model=multitalk_model, fantasyportrait_model=fantasyportrait_model, rms_norm_function=rms_norm_function)
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multigpu_memory_log("wanvideo_model_load", "post-load")
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if result and len(result) > 0 and hasattr(result[0], 'model'):
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model_obj = result[0]
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if hasattr(model_obj.model, 'diffusion_model'):
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transformer = model_obj.model.diffusion_model
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block_swap_override = getattr(loader_module, '_block_swap_device_override', None)
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if block_swap_override:
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transformer.offload_device = block_swap_override
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logging.debug(f"[MultiGPU] Patched WanVideo transformer for block swap to use: {block_swap_override}")
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logging.info(f"[MultiGPU] WanVideo model loaded on {selected_device}")
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return result
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else:
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logging.error(f"[MultiGPU] Could not patch WanVideo modules, falling back")
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return original_loader.loadmodel(model, base_precision, load_device, quantization,
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compile_args, attention_mode, block_swap_args, lora, vram_management_args, extra_model=extra_model, fantasytalking_model=fantasytalking_model, multitalk_model=multitalk_model, fantasyportrait_model=fantasyportrait_model, rms_norm_function=rms_norm_function)
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class WanVideoVAELoader:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"model_name": (folder_paths.get_filename_list("vae"),
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{"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
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"tooltip": "Device to load the VAE to"}),
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},
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"optional": {
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"precision": (["fp16", "fp32", "bf16"], {"default": "bf16"}),
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"compile_args": ("WANCOMPILEARGS", ),
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}
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}
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RETURN_TYPES = ("WANVAE",)
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RETURN_NAMES = ("vae", )
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FUNCTION = "loadmodel"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Loads Wan VAE model with explicit device selection"
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def loadmodel(self, model_name, device, precision="bf16", compile_args=None):
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from . import set_current_device
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logging.debug(f"[MultiGPU] WanVideoVAELoader: User selected device: {device}")
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selected_device = torch.device(device)
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# UPDATE GLOBAL DEVICE CONTEXT
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set_current_device(selected_device)
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["WanVideoVAELoader"]()
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loader_module = inspect.getmodule(original_loader)
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if loader_module:
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logging.debug(f"[MultiGPU] Patching WanVideo VAE modules to use {selected_device}")
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setattr(loader_module, 'offload_device', selected_device)
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setattr(loader_module, 'device', selected_device)
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nodes_module_name = loader_module.__name__.replace('.nodes_model_loading', '.nodes')
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if nodes_module_name in sys.modules:
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nodes_module = sys.modules[nodes_module_name]
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setattr(nodes_module, 'device', selected_device)
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setattr(nodes_module, 'offload_device', selected_device)
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multigpu_memory_log("wanvideo_vae_load", "pre-load")
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result = original_loader.loadmodel(model_name, precision, compile_args)
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multigpu_memory_log("wanvideo_vae_load", "post-load")
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# Attach device info to VAE object for downstream nodes
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if result and len(result) > 0:
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result[0].load_device = selected_device
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logger.info(f"[MultiGPU WanVideo VAE] VAE loaded successfully on {selected_device}")
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return result
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else:
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logging.error(f"[MultiGPU] Could not patch WanVideo VAE modules")
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return original_loader.loadmodel(model_name, precision, compile_args)
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class LoadWanVideoT5TextEncoder:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"model_name": (folder_paths.get_filename_list("text_encoders"),
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{"tooltip": "These models are loaded from 'ComfyUI/models/text_encoders'"}),
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"precision": (["fp32", "bf16"], {"default": "bf16"}),
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
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"tooltip": "Device to load the text encoder to"}),
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},
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"optional": {
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"quantization": (['disabled', 'fp8_e4m3fn'],
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{"default": 'disabled', "tooltip": "optional quantization method"}),
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}
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}
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RETURN_TYPES = ("WANTEXTENCODER",)
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RETURN_NAMES = ("wan_t5_model", )
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FUNCTION = "loadmodel"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Loads Wan text_encoder model from 'ComfyUI/models/text_encoders'"
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def loadmodel(self, model_name, precision, device, quantization="disabled"):
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import traceback
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from . import set_current_device
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logger.info(f"[T5 INSTRUMENT] ====== START LoadWanVideoT5TextEncoder ======")
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logger.info(f"[T5 INSTRUMENT] User selected device: {device}")
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logger.info(f"[T5 INSTRUMENT] Model name: {model_name}, Precision: {precision}, Quantization: {quantization}")
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selected_device = torch.device(device)
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load_device = "offload_device" if device == "cpu" else "main_device"
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LoadWanVideoT5TextEncoder"]()
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loader_module = inspect.getmodule(original_loader)
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if loader_module:
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# PRE-PATCH STATE
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logger.info(f"[T5 INSTRUMENT] PRE-PATCH loader_module.device = {getattr(loader_module, 'device', 'NOT SET')}")
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logger.info(f"[T5 INSTRUMENT] PRE-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
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# UPDATE GLOBAL DEVICE CONTEXT
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logger.info(f"[T5 INSTRUMENT] Calling set_current_device({selected_device})")
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set_current_device(selected_device)
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# WRAP mm.get_torch_device() TO LOG ALL CALLS
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original_mm_get_device = mm.get_torch_device
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call_count = [0]
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def logged_get_torch_device():
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call_count[0] += 1
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result = original_mm_get_device()
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stack = traceback.extract_stack()
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# Get caller info (skip this function and get the actual caller)
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caller = stack[-2] if len(stack) >= 2 else stack[-1]
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logger.info(f"[DEVICE CALL #{call_count[0]}] mm.get_torch_device() called from {caller.filename}:{caller.lineno} in {caller.name}() → returning {result}")
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return result
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mm.get_torch_device = logged_get_torch_device
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# WRAP MODULE DEVICE ACCESS
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original_device = getattr(loader_module, 'device', None)
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access_count = [0]
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class DeviceAccessLogger:
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def __init__(self, actual_device):
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self._actual_device = actual_device
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def __str__(self):
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access_count[0] += 1
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stack = traceback.extract_stack()
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caller = stack[-2] if len(stack) >= 2 else stack[-1]
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logger.info(f"[MODULE ACCESS #{access_count[0]}] loader_module.device accessed from {caller.filename}:{caller.lineno} → returning {self._actual_device}")
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return str(self._actual_device)
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def __repr__(self):
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return str(self)
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# PATCH MODULE VARIABLES
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logger.info(f"[T5 INSTRUMENT] PATCHING loader_module.device to {selected_device}")
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setattr(loader_module, 'device', selected_device)
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if device == "cpu":
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setattr(loader_module, 'offload_device', selected_device)
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nodes_module_name = loader_module.__name__.replace('.nodes_model_loading', '.nodes')
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if nodes_module_name in sys.modules:
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nodes_module = sys.modules[nodes_module_name]
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logger.info(f"[T5 INSTRUMENT] PATCHING nodes_module.device to {selected_device}")
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setattr(nodes_module, 'device', selected_device)
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if device == "cpu":
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setattr(nodes_module, 'offload_device', selected_device)
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# POST-PATCH STATE
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logger.info(f"[T5 INSTRUMENT] POST-PATCH loader_module.device = {loader_module.device}")
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logger.info(f"[T5 INSTRUMENT] POST-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
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multigpu_memory_log("wanvideo_t5_load", "pre-load")
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logger.info(f"[T5 INSTRUMENT] ===== CALLING ORIGINAL LOADER =====")
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logger.info(f"[T5 INSTRUMENT] Watch for DEVICE CALL and MODULE ACCESS logs below:")
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result = original_loader.loadmodel(model_name, precision, load_device, quantization)
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logger.info(f"[T5 INSTRUMENT] ===== ORIGINAL LOADER RETURNED =====")
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logger.info(f"[T5 INSTRUMENT] Total mm.get_torch_device() calls: {call_count[0]}")
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logger.info(f"[T5 INSTRUMENT] Total module.device accesses: {access_count[0]}")
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# RESTORE ORIGINAL
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mm.get_torch_device = original_mm_get_device
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multigpu_memory_log("wanvideo_t5_load", "post-load")
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# POST-LOAD STATE
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if result and len(result) > 0:
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t5_encoder = result[0]
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if isinstance(t5_encoder, dict) and 'model' in t5_encoder:
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try:
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actual_device = next(t5_encoder['model'].parameters()).device
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logger.info(f"[T5 INSTRUMENT] ACTUAL model device after load = {actual_device}")
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except Exception as e:
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logger.info(f"[T5 INSTRUMENT] Could not determine actual model device: {e}")
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logger.info(f"[T5 INSTRUMENT] ====== END LoadWanVideoT5TextEncoder ======")
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return result
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else:
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logger.error(f"[T5 INSTRUMENT] Could not get loader module - falling back")
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return original_loader.loadmodel(model_name, precision, load_device, quantization)
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class WanVideoTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {"required": {
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"positive_prompt": ("STRING", {"default": "", "multiline": True} ),
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"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
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"tooltip": "Device to run the text encoding on"}),
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},
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"optional": {
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"t5": ("WANTEXTENCODER",),
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"force_offload": ("BOOLEAN", {"default": True}),
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"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
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"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use"}),
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}
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}
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RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
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RETURN_NAMES = ("text_embeds",)
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FUNCTION = "process"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Encodes text prompts with explicit device selection"
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def process(self, positive_prompt, negative_prompt, device, t5=None, force_offload=True,
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model_to_offload=None, use_disk_cache=False):
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import traceback
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from . import set_current_device
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logger.info(f"[TEXTENCODE INSTRUMENT] ====== START WanVideoTextEncode ======")
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logger.info(f"[TEXTENCODE INSTRUMENT] User selected device: {device}")
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selected_device = torch.device(device)
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original_device = "gpu" if device != "cpu" else "cpu"
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from nodes import NODE_CLASS_MAPPINGS
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original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncode"]()
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encoder_module = inspect.getmodule(original_encoder)
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if encoder_module:
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# PRE-PATCH STATE
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logger.info(f"[TEXTENCODE INSTRUMENT] PRE-PATCH encoder_module.device = {getattr(encoder_module, 'device', 'NOT SET')}")
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logger.info(f"[TEXTENCODE INSTRUMENT] PRE-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
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# UPDATE GLOBAL DEVICE CONTEXT
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logger.info(f"[TEXTENCODE INSTRUMENT] Calling set_current_device({selected_device})")
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set_current_device(selected_device)
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# WRAP mm.get_torch_device() TO LOG ALL CALLS
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original_mm_get_device = mm.get_torch_device
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call_count = [0]
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def logged_get_torch_device():
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call_count[0] += 1
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result = original_mm_get_device()
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stack = traceback.extract_stack()
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caller = stack[-2] if len(stack) >= 2 else stack[-1]
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logger.info(f"[DEVICE CALL #{call_count[0]}] mm.get_torch_device() called from {caller.filename}:{caller.lineno} in {caller.name}() → returning {result}")
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return result
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mm.get_torch_device = logged_get_torch_device
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# PATCH MODULE VARIABLES
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logger.info(f"[TEXTENCODE INSTRUMENT] PATCHING encoder_module.device to {selected_device}")
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setattr(encoder_module, 'device', selected_device)
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model_loading_name = encoder_module.__name__.replace('.nodes', '.nodes_model_loading')
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if model_loading_name in sys.modules:
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model_loading_module = sys.modules[model_loading_name]
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logger.info(f"[TEXTENCODE INSTRUMENT] PATCHING model_loading_module.device to {selected_device}")
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setattr(model_loading_module, 'device', selected_device)
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# POST-PATCH STATE
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logger.info(f"[TEXTENCODE INSTRUMENT] POST-PATCH encoder_module.device = {encoder_module.device}")
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logger.info(f"[TEXTENCODE INSTRUMENT] POST-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
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multigpu_memory_log("wanvideo_textencode", "pre-encode")
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logger.info(f"[TEXTENCODE INSTRUMENT] ===== CALLING ORIGINAL ENCODER =====")
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result = original_encoder.process(positive_prompt, negative_prompt, t5=t5,
|
|
force_offload=force_offload, model_to_offload=model_to_offload,
|
|
use_disk_cache=use_disk_cache, device=original_device)
|
|
|
|
multigpu_memory_log("wanvideo_textencode", "post-encode")
|
|
|
|
logger.info(f"[TEXTENCODE INSTRUMENT] ===== ORIGINAL ENCODER RETURNED =====")
|
|
logger.info(f"[TEXTENCODE INSTRUMENT] Total mm.get_torch_device() calls: {call_count[0]}")
|
|
|
|
# RESTORE ORIGINAL
|
|
mm.get_torch_device = original_mm_get_device
|
|
|
|
logger.info(f"[TEXTENCODE INSTRUMENT] ====== END WanVideoTextEncode ======")
|
|
return result
|
|
else:
|
|
logger.error(f"[TEXTENCODE INSTRUMENT] Could not get encoder module - falling back")
|
|
return original_encoder.process(positive_prompt, negative_prompt, t5=t5,
|
|
force_offload=force_offload, model_to_offload=model_to_offload,
|
|
use_disk_cache=use_disk_cache, device=original_device)
|
|
|
|
class WanVideoTextEncodeSingle:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
devices = get_device_list()
|
|
|
|
return {"required": {
|
|
"prompt": ("STRING", {"default": "", "multiline": True}),
|
|
"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
|
|
"tooltip": "Device to run the text encoding on"}),
|
|
},
|
|
"optional": {
|
|
"t5": ("WANTEXTENCODER",),
|
|
"force_offload": ("BOOLEAN", {"default": True}),
|
|
"model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}),
|
|
"use_disk_cache": ("BOOLEAN", {"default": False, "tooltip": "Cache the text embeddings to disk for faster re-use"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS",)
|
|
RETURN_NAMES = ("text_embeds",)
|
|
FUNCTION = "process"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "Encodes single text prompt with explicit device selection"
|
|
|
|
def process(self, prompt, device, t5=None, force_offload=True,
|
|
model_to_offload=None, use_disk_cache=False):
|
|
import traceback
|
|
from . import set_current_device
|
|
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] ====== START WanVideoTextEncodeSingle ======")
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] User selected device: {device}")
|
|
|
|
selected_device = torch.device(device)
|
|
original_device = "gpu" if device != "cpu" else "cpu"
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncodeSingle"]()
|
|
|
|
encoder_module = inspect.getmodule(original_encoder)
|
|
|
|
if encoder_module:
|
|
# PRE-PATCH STATE
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] PRE-PATCH encoder_module.device = {getattr(encoder_module, 'device', 'NOT SET')}")
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] PRE-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
# UPDATE GLOBAL DEVICE CONTEXT
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] Calling set_current_device({selected_device})")
|
|
set_current_device(selected_device)
|
|
|
|
# WRAP mm.get_torch_device() TO LOG ALL CALLS
|
|
original_mm_get_device = mm.get_torch_device
|
|
call_count = [0]
|
|
|
|
def logged_get_torch_device():
|
|
call_count[0] += 1
|
|
result = original_mm_get_device()
|
|
stack = traceback.extract_stack()
|
|
caller = stack[-2] if len(stack) >= 2 else stack[-1]
|
|
logger.info(f"[DEVICE CALL #{call_count[0]}] mm.get_torch_device() called from {caller.filename}:{caller.lineno} in {caller.name}() → returning {result}")
|
|
return result
|
|
|
|
mm.get_torch_device = logged_get_torch_device
|
|
|
|
# PATCH MODULE VARIABLES
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] PATCHING encoder_module.device to {selected_device}")
|
|
setattr(encoder_module, 'device', selected_device)
|
|
|
|
model_loading_name = encoder_module.__name__.replace('.nodes', '.nodes_model_loading')
|
|
if model_loading_name in sys.modules:
|
|
model_loading_module = sys.modules[model_loading_name]
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] PATCHING model_loading_module.device to {selected_device}")
|
|
setattr(model_loading_module, 'device', selected_device)
|
|
|
|
# POST-PATCH STATE
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] POST-PATCH encoder_module.device = {encoder_module.device}")
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] POST-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
multigpu_memory_log("wanvideo_textencodesingle", "pre-encode")
|
|
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] ===== CALLING ORIGINAL ENCODER =====")
|
|
|
|
result = original_encoder.process(prompt, t5=t5,
|
|
force_offload=force_offload, model_to_offload=model_to_offload,
|
|
use_disk_cache=use_disk_cache, device=original_device)
|
|
|
|
multigpu_memory_log("wanvideo_textencodesingle", "post-encode")
|
|
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] ===== ORIGINAL ENCODER RETURNED =====")
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] Total mm.get_torch_device() calls: {call_count[0]}")
|
|
|
|
# RESTORE ORIGINAL
|
|
mm.get_torch_device = original_mm_get_device
|
|
|
|
logger.info(f"[TEXTENCODESINGLE INSTRUMENT] ====== END WanVideoTextEncodeSingle ======")
|
|
return result
|
|
else:
|
|
logger.error(f"[TEXTENCODESINGLE INSTRUMENT] Could not get encoder module - falling back")
|
|
return original_encoder.process(prompt, t5=t5,
|
|
force_offload=force_offload, model_to_offload=model_to_offload,
|
|
use_disk_cache=use_disk_cache, device=original_device)
|
|
|
|
|
|
class WanVideoTextEncodeCached:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
devices = get_device_list()
|
|
|
|
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"}),
|
|
"use_disk_cache": ("BOOLEAN", {"default": True}),
|
|
"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
|
|
"tooltip": "Device to run the text encoding on"}),
|
|
},
|
|
"optional": {
|
|
"extender_args": ("WANVIDEOPROMPTEXTENDER_ARGS",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", "WANVIDEOTEXTEMBEDS", "STRING")
|
|
RETURN_NAMES = ("text_embeds", "negative_text_embeds", "positive_prompt")
|
|
FUNCTION = "process"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "Cached text encoding with explicit device selection"
|
|
|
|
def process(self, model_name, precision, positive_prompt, negative_prompt,
|
|
quantization, use_disk_cache, device, extender_args=None):
|
|
import traceback
|
|
from . import set_current_device
|
|
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] ====== START WanVideoTextEncodeCached ======")
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] User selected device: {device}")
|
|
|
|
selected_device = torch.device(device)
|
|
original_device = "gpu" if device != "cpu" else "cpu"
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_encoder = NODE_CLASS_MAPPINGS["WanVideoTextEncodeCached"]()
|
|
|
|
encoder_module = inspect.getmodule(original_encoder)
|
|
|
|
if encoder_module:
|
|
# PRE-PATCH STATE
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] PRE-PATCH encoder_module.device = {getattr(encoder_module, 'device', 'NOT SET')}")
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] PRE-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
# UPDATE GLOBAL DEVICE CONTEXT
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] Calling set_current_device({selected_device})")
|
|
set_current_device(selected_device)
|
|
|
|
# WRAP mm.get_torch_device() TO LOG ALL CALLS
|
|
original_mm_get_device = mm.get_torch_device
|
|
call_count = [0]
|
|
|
|
def logged_get_torch_device():
|
|
call_count[0] += 1
|
|
result = original_mm_get_device()
|
|
stack = traceback.extract_stack()
|
|
caller = stack[-2] if len(stack) >= 2 else stack[-1]
|
|
logger.info(f"[DEVICE CALL #{call_count[0]}] mm.get_torch_device() called from {caller.filename}:{caller.lineno} in {caller.name}() → returning {result}")
|
|
return result
|
|
|
|
mm.get_torch_device = logged_get_torch_device
|
|
|
|
# PATCH MODULE VARIABLES
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] PATCHING encoder_module.device to {selected_device}")
|
|
setattr(encoder_module, 'device', selected_device)
|
|
|
|
model_loading_name = encoder_module.__name__.replace('.nodes', '.nodes_model_loading')
|
|
if model_loading_name in sys.modules:
|
|
model_loading_module = sys.modules[model_loading_name]
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] PATCHING model_loading_module.device to {selected_device}")
|
|
setattr(model_loading_module, 'device', selected_device)
|
|
|
|
# POST-PATCH STATE
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] POST-PATCH encoder_module.device = {encoder_module.device}")
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] POST-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
multigpu_memory_log("wanvideo_textencodecached", "pre-encode")
|
|
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] ===== CALLING ORIGINAL ENCODER =====")
|
|
|
|
result = original_encoder.process(model_name, precision, positive_prompt, negative_prompt,
|
|
quantization, use_disk_cache, device=original_device,
|
|
extender_args=extender_args)
|
|
|
|
multigpu_memory_log("wanvideo_textencodecached", "post-encode")
|
|
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] ===== ORIGINAL ENCODER RETURNED =====")
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] Total mm.get_torch_device() calls: {call_count[0]}")
|
|
|
|
# RESTORE ORIGINAL
|
|
mm.get_torch_device = original_mm_get_device
|
|
|
|
logger.info(f"[TEXTENCODECACHED INSTRUMENT] ====== END WanVideoTextEncodeCached ======")
|
|
return result
|
|
else:
|
|
logger.error(f"[TEXTENCODECACHED INSTRUMENT] Could not get encoder module - falling back")
|
|
return original_encoder.process(model_name, precision, positive_prompt, negative_prompt,
|
|
quantization, use_disk_cache, device=original_device,
|
|
extender_args=extender_args)
|
|
|
|
|
|
class LoadWanVideoClipTextEncoder:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
devices = get_device_list()
|
|
|
|
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"}),
|
|
"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0],
|
|
"tooltip": "Device to load the CLIP encoder to"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CLIP_VISION",)
|
|
RETURN_NAMES = ("clip_vision", )
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "Loads Wan CLIP text encoder model from 'ComfyUI/models/clip_vision'"
|
|
|
|
def loadmodel(self, model_name, precision, device):
|
|
import traceback
|
|
from . import set_current_device
|
|
|
|
logger.info(f"[CLIP INSTRUMENT] ====== START LoadWanVideoClipTextEncoder ======")
|
|
logger.info(f"[CLIP INSTRUMENT] User selected device: {device}")
|
|
logger.info(f"[CLIP INSTRUMENT] Model name: {model_name}, Precision: {precision}")
|
|
|
|
selected_device = torch.device(device)
|
|
load_device = "offload_device" if device == "cpu" else "main_device"
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["LoadWanVideoClipTextEncoder"]()
|
|
|
|
loader_module = inspect.getmodule(original_loader)
|
|
|
|
if loader_module:
|
|
# PRE-PATCH STATE
|
|
logger.info(f"[CLIP INSTRUMENT] PRE-PATCH loader_module.device = {getattr(loader_module, 'device', 'NOT SET')}")
|
|
logger.info(f"[CLIP INSTRUMENT] PRE-PATCH loader_module.offload_device = {getattr(loader_module, 'offload_device', 'NOT SET')}")
|
|
logger.info(f"[CLIP INSTRUMENT] PRE-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
# UPDATE GLOBAL DEVICE CONTEXT
|
|
logger.info(f"[CLIP INSTRUMENT] Calling set_current_device({selected_device})")
|
|
set_current_device(selected_device)
|
|
|
|
# WRAP mm.get_torch_device() TO LOG ALL CALLS
|
|
original_mm_get_device = mm.get_torch_device
|
|
call_count = [0]
|
|
|
|
def logged_get_torch_device():
|
|
call_count[0] += 1
|
|
result = original_mm_get_device()
|
|
stack = traceback.extract_stack()
|
|
caller = stack[-2] if len(stack) >= 2 else stack[-1]
|
|
logger.info(f"[DEVICE CALL #{call_count[0]}] mm.get_torch_device() called from {caller.filename}:{caller.lineno} in {caller.name}() → returning {result}")
|
|
return result
|
|
|
|
mm.get_torch_device = logged_get_torch_device
|
|
|
|
# WRAP MODULE DEVICE ACCESS
|
|
original_device = getattr(loader_module, 'device', None)
|
|
access_count = [0]
|
|
|
|
class DeviceAccessLogger:
|
|
def __init__(self, actual_device):
|
|
self._actual_device = actual_device
|
|
|
|
def __str__(self):
|
|
access_count[0] += 1
|
|
stack = traceback.extract_stack()
|
|
caller = stack[-2] if len(stack) >= 2 else stack[-1]
|
|
logger.info(f"[MODULE ACCESS #{access_count[0]}] loader_module.device accessed from {caller.filename}:{caller.lineno} → returning {self._actual_device}")
|
|
return str(self._actual_device)
|
|
|
|
def __repr__(self):
|
|
return str(self)
|
|
|
|
# PATCH MODULE VARIABLES
|
|
logger.info(f"[CLIP INSTRUMENT] PATCHING loader_module.device to {selected_device}")
|
|
setattr(loader_module, 'device', selected_device)
|
|
if device == "cpu":
|
|
setattr(loader_module, 'offload_device', selected_device)
|
|
|
|
nodes_module_name = loader_module.__name__.replace('.nodes_model_loading', '.nodes')
|
|
if nodes_module_name in sys.modules:
|
|
nodes_module = sys.modules[nodes_module_name]
|
|
logger.info(f"[CLIP INSTRUMENT] PRE-PATCH nodes_module.device = {getattr(nodes_module, 'device', 'NOT SET')}")
|
|
logger.info(f"[CLIP INSTRUMENT] PATCHING nodes_module.device to {selected_device}")
|
|
setattr(nodes_module, 'device', selected_device)
|
|
if device == "cpu":
|
|
setattr(nodes_module, 'offload_device', selected_device)
|
|
|
|
# POST-PATCH STATE
|
|
logger.info(f"[CLIP INSTRUMENT] POST-PATCH loader_module.device = {loader_module.device}")
|
|
logger.info(f"[CLIP INSTRUMENT] POST-PATCH loader_module.offload_device = {getattr(loader_module, 'offload_device', 'NOT SET')}")
|
|
logger.info(f"[CLIP INSTRUMENT] POST-PATCH mm.get_torch_device() = {mm.get_torch_device()}")
|
|
|
|
multigpu_memory_log("wanvideo_clip_load", "pre-load")
|
|
|
|
logger.info(f"[CLIP INSTRUMENT] ===== CALLING ORIGINAL LOADER =====")
|
|
logger.info(f"[CLIP INSTRUMENT] Watch for DEVICE CALL and MODULE ACCESS logs below:")
|
|
|
|
result = original_loader.loadmodel(model_name, precision, load_device)
|
|
|
|
logger.info(f"[CLIP INSTRUMENT] ===== ORIGINAL LOADER RETURNED =====")
|
|
logger.info(f"[CLIP INSTRUMENT] Total mm.get_torch_device() calls: {call_count[0]}")
|
|
logger.info(f"[CLIP INSTRUMENT] Total module.device accesses: {access_count[0]}")
|
|
|
|
# RESTORE ORIGINAL
|
|
mm.get_torch_device = original_mm_get_device
|
|
|
|
multigpu_memory_log("wanvideo_clip_load", "post-load")
|
|
|
|
# POST-LOAD STATE
|
|
if result and len(result) > 0:
|
|
clip_model = result[0]
|
|
if hasattr(clip_model, 'model'):
|
|
try:
|
|
actual_device = next(clip_model.model.parameters()).device
|
|
logger.info(f"[CLIP INSTRUMENT] ACTUAL model device after load = {actual_device}")
|
|
except Exception as e:
|
|
logger.info(f"[CLIP INSTRUMENT] Could not determine actual model device: {e}")
|
|
logger.info(f"[CLIP INSTRUMENT] Result type: {type(clip_model)}")
|
|
|
|
logger.info(f"[CLIP INSTRUMENT] ====== END LoadWanVideoClipTextEncoder ======")
|
|
|
|
return result
|
|
else:
|
|
logger.error(f"[CLIP INSTRUMENT] Could not get loader module - falling back")
|
|
return original_loader.loadmodel(model_name, precision, load_device)
|
|
|
|
class WanVideoModelLoader_2:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return WanVideoModelLoader.INPUT_TYPES()
|
|
|
|
RETURN_TYPES = WanVideoModelLoader.RETURN_TYPES
|
|
RETURN_NAMES = WanVideoModelLoader.RETURN_NAMES
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "Second model loader instance for workflows using multiple models on different devices"
|
|
|
|
def loadmodel(self, model, base_precision, device, quantization,
|
|
compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None,
|
|
vram_management_args=None, vace_model=None, fantasytalking_model=None, multitalk_model=None, fantasyportrait_model=None, rms_norm_function="default"):
|
|
loader = WanVideoModelLoader()
|
|
return loader.loadmodel(model, base_precision, device, quantization,
|
|
compile_args, attention_mode, block_swap_args, lora,
|
|
vram_management_args, vace_model, fantasytalking_model, multitalk_model, fantasyportrait_model, rms_norm_function)
|
|
|
|
class WanVideoSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_types = NODE_CLASS_MAPPINGS["WanVideoSampler"].INPUT_TYPES()
|
|
return original_types
|
|
|
|
RETURN_TYPES = ("LATENT", "LATENT",)
|
|
RETURN_NAMES = ("samples", "denoised_samples",)
|
|
FUNCTION = "process"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "MultiGPU-aware sampler that ensures correct device for each model"
|
|
|
|
def process(self, model, **kwargs):
|
|
model_device = model.load_device
|
|
logging.info(f"[MultiGPU] WanVideoSampler: Processing on device: {model_device}")
|
|
|
|
for module_name in sys.modules.keys():
|
|
if 'WanVideoWrapper' in module_name and hasattr(sys.modules[module_name], 'device'):
|
|
sys.modules[module_name].device = model_device
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_sampler = NODE_CLASS_MAPPINGS["WanVideoSampler"]()
|
|
return original_sampler.process(model, **kwargs)
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|
|
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class WanVideoVACEEncode:
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@classmethod
|
|
def INPUT_TYPES(s):
|
|
from nodes import NODE_CLASS_MAPPINGS
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original_types = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"].INPUT_TYPES()
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|
return original_types
|
|
|
|
RETURN_TYPES = ("LATENT",)
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|
RETURN_NAMES = ("latent",)
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|
FUNCTION = "process"
|
|
CATEGORY = "WanVideoWrapper"
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|
DESCRIPTION = "MultiGPU-aware VACE encoder that uses device from input VAE"
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|
|
|
def process(self, vae, **kwargs):
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|
# Get device from VAE object
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|
vae_device = vae.load_device
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|
logging.info(f"[MultiGPU] WanVideoVACEEncode: Processing on device: {vae_device}")
|
|
|
|
# Patch all WanVideo modules to use the VAE's device
|
|
for module_name in sys.modules.keys():
|
|
if 'WanVideoWrapper' in module_name and hasattr(sys.modules[module_name], 'device'):
|
|
sys.modules[module_name].device = vae_device
|
|
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_encoder = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"]()
|
|
return original_encoder.process(vae, **kwargs)
|
|
|
|
class WanVideoBlockSwap:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
devices = get_device_list()
|
|
|
|
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"}),
|
|
"swap_device": (devices, {"default": "cpu",
|
|
"tooltip": "Device to swap blocks to during sampling (default: cpu for standard behavior)"}),
|
|
"model_offload_device": (devices, {"default": "cpu",
|
|
"tooltip": "Device to offload entire model to when done (default: cpu)"}),
|
|
"offload_img_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload img_emb to swap_device"}),
|
|
"offload_txt_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload time_emb to swap_device"}),
|
|
},
|
|
"optional": {
|
|
"use_non_blocking": ("BOOLEAN", {"default": False,
|
|
"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"}),
|
|
"prefetch_blocks": ("INT", {"default": 0, "min": 0, "max": 40, "step": 1, "tooltip": "Number of blocks to prefetch ahead, can speed up processing but increases memory usage. 1 is usually enough to offset speed loss from block swapping, use the debug option to confirm it for your system"}),
|
|
"block_swap_debug": ("BOOLEAN", {"default": False, "tooltip": "Enable debug logging for block swapping"}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("BLOCKSWAPARGS",)
|
|
RETURN_NAMES = ("block_swap_args",)
|
|
FUNCTION = "setargs"
|
|
CATEGORY = "WanVideoWrapper"
|
|
DESCRIPTION = "Block swap settings with explicit device selection for memory management across GPUs"
|
|
|
|
def setargs(self, blocks_to_swap, swap_device, model_offload_device, offload_img_emb, offload_txt_emb,
|
|
use_non_blocking=False, vace_blocks_to_swap=0, prefetch_blocks=0, block_swap_debug=False):
|
|
logging.debug(f"[MultiGPU] WanVideoBlockSwap: swap_device={swap_device}, model_offload_device={model_offload_device}, blocks_to_swap={blocks_to_swap}")
|
|
|
|
selected_swap_device = torch.device(swap_device)
|
|
selected_offload_device = torch.device(model_offload_device)
|
|
|
|
for module_name in sys.modules.keys():
|
|
if 'WanVideoWrapper' in module_name and 'nodes_model_loading' in module_name:
|
|
module = sys.modules[module_name]
|
|
setattr(module, 'offload_device', selected_offload_device)
|
|
setattr(module, '_block_swap_device_override', selected_swap_device)
|
|
setattr(module, '_model_offload_device_override', selected_offload_device)
|
|
logging.debug(f"[MultiGPU] Patched {module_name} for offload to {selected_offload_device} and swap to {selected_swap_device}")
|
|
|
|
if 'WanVideoWrapper' in module_name and module_name.endswith('.nodes'):
|
|
module = sys.modules[module_name]
|
|
setattr(module, 'offload_device', selected_offload_device)
|
|
setattr(module, '_block_swap_device_override', selected_swap_device)
|
|
setattr(module, '_model_offload_device_override', selected_offload_device)
|
|
|
|
block_swap_args = {
|
|
"blocks_to_swap": blocks_to_swap,
|
|
"offload_img_emb": offload_img_emb,
|
|
"offload_txt_emb": offload_txt_emb,
|
|
"use_non_blocking": use_non_blocking,
|
|
"vace_blocks_to_swap": vace_blocks_to_swap,
|
|
"prefetch_blocks": prefetch_blocks,
|
|
"block_swap_debug": block_swap_debug,
|
|
"swap_device": swap_device,
|
|
"model_offload_device": model_offload_device,
|
|
}
|
|
|
|
logging.info(f"[MultiGPU] WanVideoBlockSwap configuration complete")
|
|
|
|
return (block_swap_args,)
|