fix: hard coded all supported nodes. If the parent custom_node is installed then it will inherit the needed functionality at run-time, fully eliminating any load depenencies. No outside custom_nodes need to be pre-loaded and no python is inspected. New MultiGPU variants are created in a self-contained manner.
This commit is contained in:
John Pollock
+460
-46
@@ -3,8 +3,10 @@ import copy
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import torch
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import comfy.model_management
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import os
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from pathlib import Path # Add this import
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import importlib.util
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import logging
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import folder_paths
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.info("MultiGPU: Initialization started")
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@@ -59,56 +61,468 @@ def register_module(module_path, target_nodes):
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logging.info(f"MultiGPU: Core node {node} not found - this shouldn't happen!")
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return
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# For custom nodes, try to load the module first
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full_path = os.path.join("custom_nodes", module_path, "__init__.py")
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logging.info(f"MultiGPU: Checking for module at {full_path}")
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if not os.path.exists(full_path):
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logging.info(f"MultiGPU: Module {module_path} not found - skipping")
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return
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logging.info(f"MultiGPU: Found {module_path}, attempting to load")
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spec = importlib.util.spec_from_file_location(module_path, full_path)
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module = importlib.util.module_from_spec(spec)
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try:
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spec.loader.exec_module(module)
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logging.info(f"MultiGPU: Executed {module_path} initialization")
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except Exception as exec_error:
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logging.error(f"MultiGPU: Failed to execute {module_path}: {exec_error}")
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if hasattr(module, '__file__') and os.path.isfile(module.__file__):
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with open(module.__file__, 'r', encoding='utf-8') as f:
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logging.error(f"MultiGPU: Failed module code:\n{f.read()}")
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raise
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# Use the module's local dictionary instead of the global one
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local_map_name = "NODE_CLASS_MAPPINGS"
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local_map = getattr(module, local_map_name, None)
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if not local_map:
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logging.info(f"MultiGPU: {module_path} has no '{local_map_name}' dictionary, skipping override.")
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return
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all_defined_nodes = list(local_map.keys())
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logging.info(f"MultiGPU: {module_path} local dict keys: {all_defined_nodes}")
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for node in target_nodes:
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if node in local_map:
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mgpu_class = override_class(local_map[node])
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NODE_CLASS_MAPPINGS[f"{node}MultiGPU"] = mgpu_class
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logging.info(f"MultiGPU: Successfully wrapped {node} from {module_path}")
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else:
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logging.info(f"MultiGPU: Node '{node}' not found in {module_path}'s local dictionary")
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except Exception as e:
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logging.info(f"MultiGPU: Error processing {module_path}: {str(e)}")
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def register_LTXVLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class LTXVLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"),
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{"tooltip": "The name of the checkpoint (model) to load."}),
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"dtype": (["bfloat16", "float32"], {"default": "bfloat16"})
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}
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}
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RETURN_TYPES = ("MODEL", "VAE")
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RETURN_NAMES = ("model", "vae")
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FUNCTION = "load"
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CATEGORY = "lightricks/LTXV"
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TITLE = "LTXV Loader"
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OUTPUT_NODE = False
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def load(self, ckpt_name, dtype):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader.load(ckpt_name, dtype)
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def _load_unet(self, load_device, offload_device, weights, num_latent_channels, dtype, config=None ):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader._load_unet(load_device, offload_device, weights, num_latent_channels, dtype, config=None )
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def _load_vae(self, weights, config=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader._load_vae(weights, config=None)
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NODE_CLASS_MAPPINGS["LTXVLoaderMultiGPU"] = override_class(LTXVLoader)
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logging.info(f"MultiGPU: Registered LTXVLoaderMultiGPU")
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def register_Florence2ModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class Florence2ModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ([item.name for item in Path(folder_paths.models_dir, "LLM").iterdir() if item.is_dir()],
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{"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
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"precision": (['fp16','bf16','fp32'],),
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"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}}
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RETURN_TYPES = ("FL2MODEL",)
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RETURN_NAMES = ("florence2_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "Florence2"
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def loadmodel(self, model, precision, attention, lora=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader)
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logging.info(f"MultiGPU: Registered Florence2ModelLoaderMultiGPU")
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def register_DownloadAndLoadFlorence2ModelMultiGPU():
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global NODE_CLASS_MAPPINGS
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class DownloadAndLoadFlorence2Model:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ([
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'microsoft/Florence-2-base',
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'microsoft/Florence-2-base-ft',
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'microsoft/Florence-2-large',
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'microsoft/Florence-2-large-ft',
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'HuggingFaceM4/Florence-2-DocVQA',
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'thwri/CogFlorence-2.1-Large',
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'thwri/CogFlorence-2.2-Large',
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'gokaygokay/Florence-2-SD3-Captioner',
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'gokaygokay/Florence-2-Flux-Large',
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'MiaoshouAI/Florence-2-base-PromptGen-v1.5',
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'MiaoshouAI/Florence-2-large-PromptGen-v1.5',
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'MiaoshouAI/Florence-2-base-PromptGen-v2.0',
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'MiaoshouAI/Florence-2-large-PromptGen-v2.0'
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], {"default": 'microsoft/Florence-2-base'}),
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"precision": (['fp16','bf16','fp32'], {"default": 'fp16'}),
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"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}}
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RETURN_TYPES = ("FL2MODEL",)
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RETURN_NAMES = ("florence2_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "Florence2"
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def loadmodel(self, model, precision, attention, lora=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model)
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logging.info(f"MultiGPU: Registered DownloadAndLoadFlorence2ModelMultiGPU")
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def register_CheckpointLoaderNF4():
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global NODE_CLASS_MAPPINGS
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class CheckpointLoaderNF4:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
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return original_loader.load_checkpoint(ckpt_name)
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NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
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logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")
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def register_CheckpointLoaderNF4():
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global NODE_CLASS_MAPPINGS
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class CheckpointLoaderNF4:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
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return original_loader.load_checkpoint(ckpt_name)
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NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
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logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")
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def register_LoadFluxControlNetMultiGPU():
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global NODE_CLASS_MAPPINGS
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class LoadFluxControlNet:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],),
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"controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ),
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}}
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RETURN_TYPES = ("FluxControlNet",)
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RETURN_NAMES = ("ControlNet",)
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FUNCTION = "loadmodel"
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CATEGORY = "XLabsNodes"
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def loadmodel(self, model_name, controlnet_path):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
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return original_loader.loadmodel(model_name, controlnet_path)
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NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet)
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logging.info(f"MultiGPU: Registered LoadFluxControlNetMultiGPU")
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def register_MMAudioModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
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"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
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},
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}
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RETURN_TYPES = ("MMAUDIO_MODEL",)
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RETURN_NAMES = ("mmaudio_model", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, mmaudio_model, base_precision):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
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return original_loader.loadmodel(mmaudio_model, base_precision)
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NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
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logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")
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def register_MMAudioModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
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"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
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},
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}
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RETURN_TYPES = ("MMAUDIO_MODEL",)
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RETURN_NAMES = ("mmaudio_model", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, mmaudio_model, base_precision):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
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return original_loader.loadmodel(mmaudio_model, base_precision)
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NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
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logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")
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def register_MMAudioFeatureUtilsLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioFeatureUtilsLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"vae_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"synchformer_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"clip_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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},
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"optional": {
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"bigvgan_vocoder_model": ("VOCODER_MODEL", {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"mode": (["16k", "44k"], {"default": "44k"}),
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"precision": (["fp16", "fp32", "bf16"],
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{"default": "fp16"}
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),
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}
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}
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RETURN_TYPES = ("MMAUDIO_FEATUREUTILS",)
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RETURN_NAMES = ("mmaudio_featureutils", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoader"]()
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return original_loader.loadmodel(vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model)
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NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader)
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logging.info(f"MultiGPU: Registered MMAudioFeatureUtilsLoaderMultiGPU")
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def register_MMAudioSamplerMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"mmaudio_model": ("MMAUDIO_MODEL",),
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"feature_utils": ("MMAUDIO_FEATUREUTILS",),
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"duration": ("FLOAT", {"default": 8, "step": 0.01, "tooltip": "Duration of the audio in seconds"}),
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"steps": ("INT", {"default": 25, "step": 1, "tooltip": "Number of steps to interpolate"}),
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"cfg": ("FLOAT", {"default": 4.5, "step": 0.1, "tooltip": "Strength of the conditioning"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"prompt": ("STRING", {"default": "", "multiline": True} ),
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"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
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"mask_away_clip": ("BOOLEAN", {"default": False, "tooltip": "If true, the clip video will be masked away"}),
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"force_offload": ("BOOLEAN", {"default": True, "tooltip": "If true, the model will be offloaded to the offload device"}),
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},
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"optional": {
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"images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio", )
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FUNCTION = "sample"
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CATEGORY = "MMAudio"
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def sample(self, mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioSampler"]()
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return original_loader.sample(mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images)
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NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler)
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logging.info(f"MultiGPU: Registered MMAudioSamplerMultiGPU")
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def register_UnetLoaderGGUFMultiGPU():
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global NODE_CLASS_MAPPINGS
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# First define the base UnetLoaderGGUF class
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class UnetLoaderGGUF:
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@classmethod
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def INPUT_TYPES(s):
|
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unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
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||||
return {
|
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"required": {
|
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"unet_name": (unet_names,),
|
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}
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}
|
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|
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RETURN_TYPES = ("MODEL",)
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||||
FUNCTION = "load_unet"
|
||||
CATEGORY = "bootleg"
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TITLE = "Unet Loader (GGUF)"
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||||
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def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]()
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return original_loader.load_unet(unet_name, dequant_dtype, patch_dtype, patch_on_device)
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# Create the MultiGPU version of the base class
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UnetLoaderGGUFMultiGPU = override_class(UnetLoaderGGUF)
|
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NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = UnetLoaderGGUFMultiGPU
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logging.info(f"MultiGPU: Registered UnetLoaderGGUFMultiGPU")
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# Now create the advanced version that inherits from the MultiGPU base class
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class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
|
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@classmethod
|
||||
def INPUT_TYPES(s):
|
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unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (unet_names,),
|
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"dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
||||
"patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
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"patch_on_device": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
TITLE = "Unet Loader (GGUF/Advanced)"
|
||||
|
||||
# Create the MultiGPU version of the advanced class
|
||||
UnetLoaderGGUFAdvancedMultiGPU = override_class(UnetLoaderGGUFAdvanced)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = UnetLoaderGGUFAdvancedMultiGPU
|
||||
logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedMultiGPU")
|
||||
|
||||
def register_CLIPLoaderGGUFMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class CLIPLoaderGGUF:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"clip_name": (s.get_filename_list(),),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv"],),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
CATEGORY = "bootleg"
|
||||
TITLE = "CLIPLoader (GGUF)"
|
||||
|
||||
@classmethod
|
||||
def get_filename_list(s):
|
||||
files = []
|
||||
files += folder_paths.get_filename_list("clip")
|
||||
files += folder_paths.get_filename_list("clip_gguf")
|
||||
return sorted(files)
|
||||
|
||||
def load_data(self, ckpt_paths):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
||||
return original_loader.load_data(ckpt_paths)
|
||||
|
||||
def load_patcher(self, clip_paths, clip_type, clip_data):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
||||
return original_loader.load_patcher(clip_paths, clip_type, clip_data)
|
||||
|
||||
def load_clip(self, clip_name, type="stable_diffusion"):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
||||
return original_loader.load_clip(clip_name, type)
|
||||
|
||||
# Create the MultiGPU version of the base class
|
||||
CLIPLoaderGGUFMultiGPU = override_class(CLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["CLIPLoaderGGUFMultiGPU"] = CLIPLoaderGGUFMultiGPU
|
||||
logging.info(f"MultiGPU: Registered CLIPLoaderGGUFMultiGPU")
|
||||
|
||||
# Now create the advanced version that inherits from the MultiGPU base class
|
||||
|
||||
class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
file_options = (s.get_filename_list(), )
|
||||
return {
|
||||
"required": {
|
||||
"clip_name1": file_options,
|
||||
"clip_name2": file_options,
|
||||
"type": (("sdxl", "sd3", "flux", "hunyuan_video"),),
|
||||
}
|
||||
}
|
||||
|
||||
TITLE = "DualCLIPLoader (GGUF)"
|
||||
|
||||
def load_clip(self, clip_name1, clip_name2, type):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUF"]()
|
||||
return original_loader.load_clip(clip_name1, clip_name2, type)
|
||||
# Create the MultiGPU version of the advanced class
|
||||
DualCLIPLoaderGGUFMultiGPU = override_class(DualCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = DualCLIPLoaderGGUFMultiGPU
|
||||
logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFMultiGPU")
|
||||
|
||||
class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
file_options = (s.get_filename_list(), )
|
||||
return {
|
||||
"required": {
|
||||
"clip_name1": file_options,
|
||||
"clip_name2": file_options,
|
||||
"clip_name3": file_options,
|
||||
}
|
||||
}
|
||||
|
||||
TITLE = "TripleCLIPLoader (GGUF)"
|
||||
|
||||
def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUF"]()
|
||||
return original_loader.load_clip(clip_name1, clip_name2, clip_name3, type)
|
||||
# Create the MultiGPU version of the advanced class
|
||||
TripleCLIPLoaderGGUFMultiGPU = override_class(TripleCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFMultiGPU"] = TripleCLIPLoaderGGUFMultiGPU
|
||||
logging.info(f"MultiGPU: Registered TripleCLIPLoaderGGUFMultiGPU")
|
||||
|
||||
|
||||
# Register desired nodes
|
||||
register_module("", ["UNETLoader", "VAELoader", "CLIPLoader", "DualCLIPLoader", "TripleCLIPLoader", "CheckpointLoaderSimple", "ControlNetLoader"])
|
||||
register_module("ComfyUI-GGUF", ["UnetLoaderGGUF","UnetLoaderGGUFAdvanced","CLIPLoaderGGUF","DualCLIPLoaderGGUF","TripleCLIPLoaderGGUF"])
|
||||
register_module("x-flux-comfyui", ["LoadFluxControlNet"])
|
||||
register_module("ComfyUI-Florence2", ["Florence2ModelLoader","DownloadAndLoadFlorence2Model"])
|
||||
register_module("ComfyUI-LTXVideo", ["LTXVLoader"])
|
||||
register_module("ComfyUI-MMAudio", ["MMAudioFeatureUtilsLoader","MMAudioModelLoader","MMAudioSampler"])
|
||||
register_module("ComfyUI_bitsandbytes_NF4", ["CheckpointLoaderNF4",])
|
||||
|
||||
register_LTXVLoaderMultiGPU()
|
||||
register_Florence2ModelLoaderMultiGPU()
|
||||
register_DownloadAndLoadFlorence2ModelMultiGPU()
|
||||
register_CheckpointLoaderNF4()
|
||||
register_LoadFluxControlNetMultiGPU()
|
||||
register_MMAudioModelLoaderMultiGPU()
|
||||
register_MMAudioFeatureUtilsLoaderMultiGPU()
|
||||
register_MMAudioSamplerMultiGPU()
|
||||
register_UnetLoaderGGUFMultiGPU()
|
||||
register_CLIPLoaderGGUFMultiGPU()
|
||||
|
||||
|
||||
logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")
|
||||
|
||||
Reference in New Issue
Block a user