482 lines
23 KiB
Python
482 lines
23 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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class WanVideoModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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from . import get_device_list
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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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"vace_model": ("VACEPATH", {"default": None, "tooltip": "VACE model to use when not using model that has it included"}),
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"fantasytalking_model": ("FANTASYTALKINGMODEL", {"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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}
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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, vace_model=None, fantasytalking_model=None, multitalk_model=None):
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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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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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logging.debug(f"[MultiGPU] Calling original WanVideo loader")
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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, vace_model, fantasytalking_model, multitalk_model)
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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, vace_model, fantasytalking_model, multitalk_model)
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class WanVideoVAELoader:
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@classmethod
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def INPUT_TYPES(s):
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from . import get_device_list
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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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logging.debug(f"[MultiGPU] WanVideoVAELoader: User selected device: {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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selected_device = torch.device(device)
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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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result = original_loader.loadmodel(model_name, precision, compile_args)
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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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logging.info(f"[MultiGPU] WanVideo VAE 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 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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from . import get_device_list
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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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logging.debug(f"[MultiGPU] LoadWanVideoT5TextEncoder: User selected device: {device}")
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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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logging.debug(f"[MultiGPU] Patching WanVideo T5 modules to use {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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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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result = original_loader.loadmodel(model_name, precision, load_device, quantization)
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logging.info(f"[MultiGPU] WanVideo T5 Text encoder 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 T5 modules, 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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from . import get_device_list
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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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logging.debug(f"[MultiGPU] WanVideoTextEncode: User selected 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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selected_device = torch.device(device)
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logging.debug(f"[MultiGPU] Patching WanVideo TextEncode module to use {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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setattr(model_loading_module, 'device', selected_device)
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result = original_encoder.process(positive_prompt, negative_prompt, t5=t5,
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force_offload=force_offload, model_to_offload=model_to_offload,
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use_disk_cache=use_disk_cache, device=original_device)
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logging.info(f"[MultiGPU] WanVideo TextEncode completed on {selected_device}")
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return result
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else:
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return original_encoder.process(positive_prompt, negative_prompt, t5=t5,
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force_offload=force_offload, model_to_offload=model_to_offload,
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use_disk_cache=use_disk_cache, device=original_device)
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class LoadWanVideoClipTextEncoder:
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@classmethod
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def INPUT_TYPES(s):
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from . import get_device_list
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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("clip_vision") + folder_paths.get_filename_list("text_encoders"),
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{"tooltip": "These models are loaded from 'ComfyUI/models/clip_vision'"}),
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"precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
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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 CLIP encoder to"}),
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}
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}
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RETURN_TYPES = ("CLIP_VISION",)
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RETURN_NAMES = ("clip_vision", )
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FUNCTION = "loadmodel"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Loads Wan CLIP text encoder model from 'ComfyUI/models/clip_vision'"
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def loadmodel(self, model_name, precision, device):
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logging.debug(f"[MultiGPU] LoadWanVideoClipTextEncoder: User selected device: {device}")
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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["LoadWanVideoClipTextEncoder"]()
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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 CLIP modules to use {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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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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result = original_loader.loadmodel(model_name, precision, load_device)
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logging.info(f"[MultiGPU] WanVideo CLIP encoder 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 CLIP modules, falling back")
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return original_loader.loadmodel(model_name, precision, load_device)
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class WanVideoModelLoader_2:
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@classmethod
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def INPUT_TYPES(s):
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return WanVideoModelLoader.INPUT_TYPES()
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RETURN_TYPES = WanVideoModelLoader.RETURN_TYPES
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RETURN_NAMES = WanVideoModelLoader.RETURN_NAMES
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FUNCTION = "loadmodel"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Second model loader instance for workflows using multiple models on different devices"
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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,
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vram_management_args=None, vace_model=None, fantasytalking_model=None, multitalk_model=None):
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loader = WanVideoModelLoader()
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return loader.loadmodel(model, base_precision, device, quantization,
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compile_args, attention_mode, block_swap_args, lora,
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vram_management_args, vace_model, fantasytalking_model, multitalk_model)
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class WanVideoSampler:
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@classmethod
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def INPUT_TYPES(s):
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from nodes import NODE_CLASS_MAPPINGS
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original_types = NODE_CLASS_MAPPINGS["WanVideoSampler"].INPUT_TYPES()
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return original_types
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RETURN_TYPES = ("LATENT", "LATENT",)
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RETURN_NAMES = ("samples", "denoised_samples",)
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FUNCTION = "process"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "MultiGPU-aware sampler that ensures correct device for each model"
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def process(self, model, **kwargs):
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model_device = model.load_device
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logging.info(f"[MultiGPU] WanVideoSampler: Processing on device: {model_device}")
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for module_name in sys.modules.keys():
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if 'WanVideoWrapper' in module_name and hasattr(sys.modules[module_name], 'device'):
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sys.modules[module_name].device = model_device
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from nodes import NODE_CLASS_MAPPINGS
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original_sampler = NODE_CLASS_MAPPINGS["WanVideoSampler"]()
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return original_sampler.process(model, **kwargs)
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class WanVideoVACEEncode:
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@classmethod
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def INPUT_TYPES(s):
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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
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("latent",)
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FUNCTION = "process"
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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}")
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# Patch all WanVideo modules to use the VAE's device
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for module_name in sys.modules.keys():
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if 'WanVideoWrapper' in module_name and hasattr(sys.modules[module_name], 'device'):
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sys.modules[module_name].device = vae_device
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from nodes import NODE_CLASS_MAPPINGS
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original_encoder = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"]()
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return original_encoder.process(vae, **kwargs)
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class WanVideoBlockSwap:
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@classmethod
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def INPUT_TYPES(s):
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from . import get_device_list
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devices = get_device_list()
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return {
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"required": {
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"blocks_to_swap": ("INT", {"default": 20, "min": 0, "max": 40, "step": 1,
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"tooltip": "Number of transformer blocks to swap, the 14B model has 40, while the 1.3B model has 30 blocks"}),
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"swap_device": (devices, {"default": "cpu",
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"tooltip": "Device to swap blocks to during sampling (default: cpu for standard behavior)"}),
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"model_offload_device": (devices, {"default": "cpu",
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"tooltip": "Device to offload entire model to when done (default: cpu)"}),
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"offload_img_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload img_emb to swap_device"}),
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"offload_txt_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload time_emb to swap_device"}),
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},
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"optional": {
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"use_non_blocking": ("BOOLEAN", {"default": False,
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"tooltip": "Use non-blocking memory transfer for offloading, reserves more RAM but is faster"}),
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|
"vace_blocks_to_swap": ("INT", {"default": 0, "min": 0, "max": 15, "step": 1,
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"tooltip": "Number of VACE blocks to swap, the VACE model has 15 blocks"}),
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|
},
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|
}
|
|
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RETURN_TYPES = ("BLOCKSWAPARGS",)
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RETURN_NAMES = ("block_swap_args",)
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FUNCTION = "setargs"
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CATEGORY = "WanVideoWrapper"
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DESCRIPTION = "Block swap settings with explicit device selection for memory management across GPUs"
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|
|
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def setargs(self, blocks_to_swap, swap_device, model_offload_device, offload_img_emb, offload_txt_emb,
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use_non_blocking=False, vace_blocks_to_swap=0):
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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)
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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]
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|
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,
|
|
"swap_device": swap_device,
|
|
"model_offload_device": model_offload_device,
|
|
}
|
|
|
|
logging.info(f"[MultiGPU] WanVideoBlockSwap configuration complete")
|
|
|
|
return (block_swap_args,)
|