refactored to move stable wrapper nodes into nodes.py and remainder in init.py
This commit is contained in:
John Pollock
+58
-676
@@ -1,4 +1,3 @@
|
||||
# __init__.py
|
||||
import copy
|
||||
import torch
|
||||
import sys
|
||||
@@ -255,27 +254,22 @@ class HunyuanVideoEmbeddingsAdapter:
|
||||
CATEGORY = "multigpu"
|
||||
|
||||
def adapt_embeddings(self, hyvid_embeds):
|
||||
# Create main conditioning tensor
|
||||
cond = hyvid_embeds["prompt_embeds"]
|
||||
|
||||
# Create pooled dict with all our extra information
|
||||
pooled_dict = {
|
||||
"pooled_output": hyvid_embeds["prompt_embeds_2"],
|
||||
"cross_attn": hyvid_embeds["prompt_embeds"],
|
||||
"attention_mask": hyvid_embeds["attention_mask"],
|
||||
}
|
||||
|
||||
# Add CLIP's attention mask if present
|
||||
if hyvid_embeds["attention_mask_2"] is not None:
|
||||
pooled_dict["attention_mask_controlnet"] = hyvid_embeds["attention_mask_2"]
|
||||
|
||||
# Add guidance if present - typically these and negative_xxxx are empty for HunyuanVideo
|
||||
if hyvid_embeds["cfg"] is not None:
|
||||
pooled_dict["guidance"] = float(hyvid_embeds["cfg"])
|
||||
pooled_dict["start_percent"] = float(hyvid_embeds["start_percent"]) if hyvid_embeds["start_percent"] is not None else 0.0
|
||||
pooled_dict["end_percent"] = float(hyvid_embeds["end_percent"]) if hyvid_embeds["end_percent"] is not None else 1.0
|
||||
|
||||
# Finally create the conditioning list in the exact format that encode_from_tokens_scheduled returns
|
||||
return ([[cond, pooled_dict]],)
|
||||
|
||||
def override_class(cls):
|
||||
@@ -365,690 +359,78 @@ def override_class_with_distorch(cls):
|
||||
|
||||
return NodeOverrideDisTorch
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"DeviceSelectorMultiGPU": DeviceSelectorMultiGPU,
|
||||
"HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter,
|
||||
}
|
||||
|
||||
def check_module_exists(module_path):
|
||||
full_path = os.path.join(folder_paths.get_folder_paths("custom_nodes")[0], module_path)
|
||||
logging.info(f"MultiGPU: Checking for module at {full_path}")
|
||||
|
||||
if not os.path.exists(full_path):
|
||||
logging.info(f"MultiGPU: Module {module_path} not found - skipping")
|
||||
return False
|
||||
|
||||
logging.info(f"MultiGPU: Found {module_path}, creating compatible MultiGPU nodes")
|
||||
return True
|
||||
|
||||
def register_module(target_nodes):
|
||||
from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS
|
||||
from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS
|
||||
from .nodes import (
|
||||
UnetLoaderGGUF, UnetLoaderGGUFAdvanced,
|
||||
CLIPLoaderGGUF, DualCLIPLoaderGGUF, TripleCLIPLoaderGGUF,
|
||||
LTXVLoader,
|
||||
Florence2ModelLoader, DownloadAndLoadFlorence2Model,
|
||||
CheckpointLoaderNF4,
|
||||
LoadFluxControlNet,
|
||||
MMAudioModelLoader, MMAudioFeatureUtilsLoader, MMAudioSampler,
|
||||
PulidModelLoader, PulidInsightFaceLoader, PulidEvaClipLoader,
|
||||
HyVideoModelLoader, HyVideoModelLoaderDiffSynth, HyVideoVAELoader, DownloadAndLoadHyVideoTextEncoder
|
||||
)
|
||||
|
||||
for node in target_nodes:
|
||||
NODE_CLASS_MAPPINGS[f"{node}MultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS[node])
|
||||
return
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"DeviceSelectorMultiGPU": DeviceSelectorMultiGPU,
|
||||
"HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter
|
||||
}
|
||||
NODE_CLASS_MAPPINGS["UNETLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["UNETLoader"])
|
||||
NODE_CLASS_MAPPINGS["VAELoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["VAELoader"])
|
||||
NODE_CLASS_MAPPINGS["CLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["CLIPLoader"])
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["DualCLIPLoader"])
|
||||
NODE_CLASS_MAPPINGS["TripleCLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["TripleCLIPLoader"])
|
||||
NODE_CLASS_MAPPINGS["CheckpointLoaderSimpleMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"])
|
||||
NODE_CLASS_MAPPINGS["ControlNetLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["ControlNetLoader"])
|
||||
|
||||
def register_UnetLoaderGGUFMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class UnetLoaderGGUF:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (unet_names,),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_unet"
|
||||
CATEGORY = "bootleg"
|
||||
TITLE = "Unet Loader (GGUF)"
|
||||
|
||||
def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]()
|
||||
return original_loader.load_unet(unet_name, dequant_dtype, patch_dtype, patch_on_device)
|
||||
|
||||
# Create both MultiGPU versions of the base class
|
||||
UnetLoaderGGUFMultiGPU = override_class(UnetLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = UnetLoaderGGUFMultiGPU
|
||||
logging.info(f"MultiGPU: Registered UnetLoaderGGUFMultiGPU")
|
||||
|
||||
UnetLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(UnetLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFDisTorchMultiGPU"] = UnetLoaderGGUFDisTorchMultiGPU
|
||||
logging.info(f"MultiGPU: Registered UnetLoaderGGUFDisTorchMultiGPU")
|
||||
|
||||
class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (unet_names,),
|
||||
"dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
||||
"patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
||||
"patch_on_device": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
TITLE = "Unet Loader (GGUF/Advanced)"
|
||||
|
||||
# Create both MultiGPU versions of the advanced class
|
||||
UnetLoaderGGUFAdvancedMultiGPU = override_class(UnetLoaderGGUFAdvanced)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = UnetLoaderGGUFAdvancedMultiGPU
|
||||
logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedMultiGPU")
|
||||
|
||||
UnetLoaderGGUFAdvancedDisTorchMultiGPU = override_class_with_distorch(UnetLoaderGGUFAdvanced)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedDisTorchMultiGPU"] = UnetLoaderGGUFAdvancedDisTorchMultiGPU
|
||||
logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedDisTorchMultiGPU")
|
||||
|
||||
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")
|
||||
|
||||
CLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(CLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["CLIPLoaderGGUFDisTorchMultiGPU"] = CLIPLoaderGGUFDisTorchMultiGPU
|
||||
logging.info(f"MultiGPU: Registered CLIPLoaderGGUFDisTorchMultiGPU")
|
||||
|
||||
# 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"]()
|
||||
clip = original_loader.load_clip(clip_name1, clip_name2, type)
|
||||
clip[0].patcher.load(force_patch_weights=True)
|
||||
return clip
|
||||
# Create the MultiGPU version of the advanced class
|
||||
DualCLIPLoaderGGUFMultiGPU = override_class(DualCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = DualCLIPLoaderGGUFMultiGPU
|
||||
logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFMultiGPU")
|
||||
|
||||
DualCLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(DualCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFDisTorchMultiGPU"] = DualCLIPLoaderGGUFDisTorchMultiGPU
|
||||
logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFDisTorchMultiGPU")
|
||||
|
||||
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")
|
||||
|
||||
TripleCLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(TripleCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFDisTorchMultiGPU"] = TripleCLIPLoaderGGUFDisTorchMultiGPU
|
||||
logging.info(f"MultiGPU: Registered TripleCLIPLoaderGGUFDisTorchMultiGPU")
|
||||
|
||||
def register_LTXVLoaderMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class LTXVLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),
|
||||
{"tooltip": "The name of the checkpoint (model) to load."}),
|
||||
"dtype": (["bfloat16", "float32"], {"default": "bfloat16"})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "VAE")
|
||||
RETURN_NAMES = ("model", "vae")
|
||||
FUNCTION = "load"
|
||||
CATEGORY = "lightricks/LTXV"
|
||||
TITLE = "LTXV Loader"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
def load(self, ckpt_name, dtype):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader.load(ckpt_name, dtype)
|
||||
def _load_unet(self, load_device, offload_device, weights, num_latent_channels, dtype, config=None ):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader._load_unet(load_device, offload_device, weights, num_latent_channels, dtype, config=None )
|
||||
def _load_vae(self, weights, config=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader._load_vae(weights, config=None)
|
||||
if check_module_exists("ComfyUI-LTXVideo") or check_module_exists("comfyui-ltxvideo"):
|
||||
NODE_CLASS_MAPPINGS["LTXVLoaderMultiGPU"] = override_class(LTXVLoader)
|
||||
|
||||
logging.info(f"MultiGPU: Registered LTXVLoaderMultiGPU")
|
||||
|
||||
def register_Florence2ModelLoaderMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class Florence2ModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ([item.name for item in Path(folder_paths.models_dir, "LLM").iterdir() if item.is_dir()],
|
||||
{"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
|
||||
"precision": (['fp16','bf16','fp32'],),
|
||||
"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
|
||||
},
|
||||
"optional": {
|
||||
"lora": ("PEFTLORA",),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FL2MODEL",)
|
||||
RETURN_NAMES = ("florence2_model",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "Florence2"
|
||||
|
||||
def loadmodel(self, model, precision, attention, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
|
||||
return original_loader.loadmodel(model, precision, attention, lora)
|
||||
NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader)
|
||||
logging.info(f"MultiGPU: Registered Florence2ModelLoaderMultiGPU")
|
||||
|
||||
def register_DownloadAndLoadFlorence2ModelMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class DownloadAndLoadFlorence2Model:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ([
|
||||
'microsoft/Florence-2-base',
|
||||
'microsoft/Florence-2-base-ft',
|
||||
'microsoft/Florence-2-large',
|
||||
'microsoft/Florence-2-large-ft',
|
||||
'HuggingFaceM4/Florence-2-DocVQA',
|
||||
'thwri/CogFlorence-2.1-Large',
|
||||
'thwri/CogFlorence-2.2-Large',
|
||||
'gokaygokay/Florence-2-SD3-Captioner',
|
||||
'gokaygokay/Florence-2-Flux-Large',
|
||||
'MiaoshouAI/Florence-2-base-PromptGen-v1.5',
|
||||
'MiaoshouAI/Florence-2-large-PromptGen-v1.5',
|
||||
'MiaoshouAI/Florence-2-base-PromptGen-v2.0',
|
||||
'MiaoshouAI/Florence-2-large-PromptGen-v2.0'
|
||||
], {"default": 'microsoft/Florence-2-base'}),
|
||||
"precision": (['fp16','bf16','fp32'], {"default": 'fp16'}),
|
||||
"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
|
||||
},
|
||||
"optional": {
|
||||
"lora": ("PEFTLORA",),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FL2MODEL",)
|
||||
RETURN_NAMES = ("florence2_model",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "Florence2"
|
||||
|
||||
def loadmodel(self, model, precision, attention, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
|
||||
return original_loader.loadmodel(model, precision, attention, lora)
|
||||
NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model)
|
||||
logging.info(f"MultiGPU: Registered DownloadAndLoadFlorence2ModelMultiGPU")
|
||||
|
||||
def register_CheckpointLoaderNF4():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class CheckpointLoaderNF4:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
|
||||
}}
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
FUNCTION = "load_checkpoint"
|
||||
|
||||
CATEGORY = "loaders"
|
||||
|
||||
|
||||
def load_checkpoint(self, ckpt_name):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
|
||||
return original_loader.load_checkpoint(ckpt_name)
|
||||
|
||||
NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
|
||||
logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")
|
||||
|
||||
def register_LoadFluxControlNetMultiGPU():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class LoadFluxControlNet:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],),
|
||||
"controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FluxControlNet",)
|
||||
RETURN_NAMES = ("ControlNet",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "XLabsNodes"
|
||||
|
||||
def loadmodel(self, model_name, controlnet_path):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
|
||||
return original_loader.loadmodel(model_name, controlnet_path)
|
||||
|
||||
NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet)
|
||||
logging.info(f"MultiGPU: Registered LoadFluxControlNetMultiGPU")
|
||||
|
||||
def register_MMAudioModelLoaderMultiGPU():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class MMAudioModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
|
||||
|
||||
"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MMAUDIO_MODEL",)
|
||||
RETURN_NAMES = ("mmaudio_model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def loadmodel(self, mmaudio_model, base_precision):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
|
||||
return original_loader.loadmodel(mmaudio_model, base_precision)
|
||||
|
||||
NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
|
||||
logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")
|
||||
|
||||
def register_MMAudioFeatureUtilsLoaderMultiGPU():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
|
||||
class MMAudioFeatureUtilsLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"vae_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"synchformer_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"clip_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
},
|
||||
"optional": {
|
||||
"bigvgan_vocoder_model": ("VOCODER_MODEL", {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"mode": (["16k", "44k"], {"default": "44k"}),
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "fp16"}
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MMAUDIO_FEATUREUTILS",)
|
||||
RETURN_NAMES = ("mmaudio_featureutils", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def loadmodel(self, vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoader"]()
|
||||
return original_loader.loadmodel(vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model)
|
||||
|
||||
NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader)
|
||||
logging.info(f"MultiGPU: Registered MMAudioFeatureUtilsLoaderMultiGPU")
|
||||
|
||||
def register_MMAudioSamplerMultiGPU():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class MMAudioSampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"mmaudio_model": ("MMAUDIO_MODEL",),
|
||||
"feature_utils": ("MMAUDIO_FEATUREUTILS",),
|
||||
"duration": ("FLOAT", {"default": 8, "step": 0.01, "tooltip": "Duration of the audio in seconds"}),
|
||||
"steps": ("INT", {"default": 25, "step": 1, "tooltip": "Number of steps to interpolate"}),
|
||||
"cfg": ("FLOAT", {"default": 4.5, "step": 0.1, "tooltip": "Strength of the conditioning"}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"prompt": ("STRING", {"default": "", "multiline": True} ),
|
||||
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
|
||||
"mask_away_clip": ("BOOLEAN", {"default": False, "tooltip": "If true, the clip video will be masked away"}),
|
||||
"force_offload": ("BOOLEAN", {"default": True, "tooltip": "If true, the model will be offloaded to the offload device"}),
|
||||
},
|
||||
"optional": {
|
||||
"images": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("AUDIO",)
|
||||
RETURN_NAMES = ("audio", )
|
||||
FUNCTION = "sample"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def sample(self, mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioSampler"]()
|
||||
return original_loader.sample(mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images)
|
||||
|
||||
NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler)
|
||||
logging.info(f"MultiGPU: Registered MMAudioSamplerMultiGPU")
|
||||
|
||||
def register_PulidModelLoader():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class PulidModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "pulid_file": (folder_paths.get_filename_list("pulid"), )}}
|
||||
|
||||
RETURN_TYPES = ("PULID",)
|
||||
FUNCTION = "load_model"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_model(self, pulid_file):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidModelLoader"]()
|
||||
return original_loader.load_model(pulid_file)
|
||||
|
||||
NODE_CLASS_MAPPINGS["PulidModelLoaderMultiGPU"] = override_class(PulidModelLoader)
|
||||
logging.info(f"MultiGPU: Registered PulidModelLoaderMultiGPU")
|
||||
|
||||
def register_PulidInsightFaceLoader():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class PulidInsightFaceLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"provider": (["CPU", "CUDA", "ROCM", "CoreML"], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("FACEANALYSIS",)
|
||||
FUNCTION = "load_insightface"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_insightface(self, provider):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidInsightFaceLoader"]()
|
||||
return original_loader.load_insightface(provider)
|
||||
|
||||
NODE_CLASS_MAPPINGS["PulidInsightFaceLoaderMultiGPU"] = override_class(PulidInsightFaceLoader)
|
||||
logging.info(f"MultiGPU: Registered PulidInsightFaceLoaderMultiGPU")
|
||||
|
||||
def register_PulidEvaClipLoader():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class PulidEvaClipLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("EVA_CLIP",)
|
||||
FUNCTION = "load_eva_clip"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_eva_clip(self):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidEvaClipLoader"]()
|
||||
return original_loader.load_eva_clip()
|
||||
|
||||
NODE_CLASS_MAPPINGS["PulidEvaClipLoaderMultiGPU"] = override_class(PulidEvaClipLoader)
|
||||
logging.info(f"MultiGPU: Registered PulidEvaClipLoaderMultiGPU")
|
||||
|
||||
def register_HyVideoModelLoader():
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
# Keep original MultiGPU wrapper unchanged
|
||||
class HyVideoModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
||||
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
||||
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"], {"default": 'disabled', "tooltip": "optional quantization method"}),
|
||||
"load_device": (["main_device"], {"default": "main_device"}),
|
||||
},
|
||||
"optional": {
|
||||
"attention_mode": ([
|
||||
"sdpa",
|
||||
"flash_attn_varlen",
|
||||
"sageattn_varlen",
|
||||
"comfy",
|
||||
], {"default": "flash_attn"}),
|
||||
"compile_args": ("COMPILEARGS", ),
|
||||
"block_swap_args": ("BLOCKSWAPARGS", ),
|
||||
"lora": ("HYVIDLORA", {"default": None}),
|
||||
"auto_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enable auto offloading for reduced VRAM usage, implementation from DiffSynth-Studio, slightly different from block swapping and uses even less VRAM, but can be slower as you can't define how much VRAM to use"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDEOMODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, base_precision, load_device, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, auto_cpu_offload=False):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
||||
return original_loader.loadmodel(model, base_precision, load_device, quantization, compile_args, attention_mode, block_swap_args, lora, auto_cpu_offload)
|
||||
|
||||
# Add new DiffSynth-style node
|
||||
class HyVideoModelLoaderDiffSynth:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
||||
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
||||
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"],
|
||||
{"default": 'disabled', "tooltip": "optional quantization method"}),
|
||||
},
|
||||
"optional": {
|
||||
"attention_mode": ([
|
||||
"sdpa",
|
||||
"flash_attn_varlen",
|
||||
"sageattn_varlen",
|
||||
"comfy",
|
||||
], {"default": "flash_attn"}),
|
||||
"compile_args": ("COMPILEARGS", ),
|
||||
"block_swap_args": ("BLOCKSWAPARGS", ),
|
||||
"lora": ("HYVIDLORA", {"default": None}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDEOMODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, base_precision, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
||||
# Use DiffSynth's auto offloading approach
|
||||
return original_loader.loadmodel(model, base_precision, "main_device", quantization,
|
||||
compile_args, attention_mode, block_swap_args, lora,
|
||||
auto_cpu_offload=True)
|
||||
|
||||
# Register both with MultiGPU wrapper
|
||||
NODE_CLASS_MAPPINGS["HyVideoModelLoaderMultiGPU"] = override_class(HyVideoModelLoader)
|
||||
NODE_CLASS_MAPPINGS["HyVideoModelLoaderDiffSynthMultiGPU"] = override_class_with_offload(HyVideoModelLoaderDiffSynth)
|
||||
|
||||
logging.info(f"MultiGPU: Registered HyVideoModelLoader nodes")
|
||||
|
||||
def register_HyVideoVAELoader():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class HyVideoVAELoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
|
||||
},
|
||||
"optional": {
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "bf16"}
|
||||
),
|
||||
"compile_args":("COMPILEARGS", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("VAE",)
|
||||
RETURN_NAMES = ("vae", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
DESCRIPTION = "Loads Hunyuan VAE model from 'ComfyUI/models/vae'"
|
||||
|
||||
def loadmodel(self, model_name, precision, compile_args=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoVAELoader"]()
|
||||
return original_loader.loadmodel(model_name, precision, compile_args)
|
||||
|
||||
NODE_CLASS_MAPPINGS["HyVideoVAELoaderMultiGPU"] = override_class(HyVideoVAELoader)
|
||||
logging.info(f"MultiGPU: Registered HyVideoVAELoaderMultiGPU")
|
||||
|
||||
def register_DownloadAndLoadHyVideoTextEncoder():
|
||||
|
||||
global NODE_CLASS_MAPPINGS
|
||||
|
||||
class DownloadAndLoadHyVideoTextEncoder:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"llm_model": (["Kijai/llava-llama-3-8b-text-encoder-tokenizer","xtuner/llava-llama-3-8b-v1_1-transformers"],),
|
||||
"clip_model": (["disabled","openai/clip-vit-large-patch14",],),
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "bf16"}
|
||||
),
|
||||
},
|
||||
"optional": {
|
||||
"apply_final_norm": ("BOOLEAN", {"default": False}),
|
||||
"hidden_state_skip_layer": ("INT", {"default": 2}),
|
||||
"quantization": (['disabled', 'bnb_nf4', "fp8_e4m3fn"], {"default": 'disabled'}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDTEXTENCODER",)
|
||||
RETURN_NAMES = ("hyvid_text_encoder", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
DESCRIPTION = "Loads Hunyuan text_encoder model from 'ComfyUI/models/LLM'"
|
||||
|
||||
def loadmodel(self, llm_model, clip_model, precision, apply_final_norm=False, hidden_state_skip_layer=2, quantization="disabled"):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoder"]()
|
||||
return original_loader.loadmodel(llm_model, clip_model, precision, apply_final_norm, hidden_state_skip_layer, quantization)
|
||||
|
||||
NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoderMultiGPU"] = override_class(DownloadAndLoadHyVideoTextEncoder)
|
||||
logging.info(f"MultiGPU: Registered DownloadAndLoadHyVideoTextEncoderMultiGPU")
|
||||
|
||||
# Register desired nodes
|
||||
register_module(["UNETLoader", "VAELoader", "CLIPLoader", "DualCLIPLoader", "TripleCLIPLoader", "CheckpointLoaderSimple", "ControlNetLoader"])
|
||||
if check_module_exists("ComfyUI-LTXVideo") or check_module_exists("comfyui-ltxvideo"):
|
||||
register_LTXVLoaderMultiGPU()
|
||||
if check_module_exists("ComfyUI-Florence2") or check_module_exists("comfyui-florence2"):
|
||||
register_Florence2ModelLoaderMultiGPU()
|
||||
register_DownloadAndLoadFlorence2ModelMultiGPU()
|
||||
if check_module_exists("ComfyUI_bitsandbytes_NF4") or check_module_exists("comfyui_bitsandbytes_nf4"):
|
||||
register_CheckpointLoaderNF4()
|
||||
if check_module_exists("x-flux-comfyui") or check_module_exists("x-flux-comfyui"):
|
||||
register_LoadFluxControlNetMultiGPU()
|
||||
if check_module_exists("ComfyUI-MMAudio") or check_module_exists("comfyui-mmaudio"):
|
||||
register_MMAudioModelLoaderMultiGPU()
|
||||
register_MMAudioFeatureUtilsLoaderMultiGPU()
|
||||
register_MMAudioSamplerMultiGPU()
|
||||
if check_module_exists("ComfyUI-GGUF") or check_module_exists("comfyui-gguf"):
|
||||
register_UnetLoaderGGUFMultiGPU()
|
||||
register_CLIPLoaderGGUFMultiGPU()
|
||||
if check_module_exists("PuLID_ComfyUI") or check_module_exists("pulid_comfyui"):
|
||||
register_PulidModelLoader()
|
||||
register_PulidInsightFaceLoader()
|
||||
register_PulidEvaClipLoader()
|
||||
if check_module_exists("ComfyUI-HunyuanVideoWrapper") or check_module_exists("comfyui-hunyuanvideowrapper"):
|
||||
register_HyVideoModelLoader()
|
||||
register_HyVideoVAELoader()
|
||||
register_DownloadAndLoadHyVideoTextEncoder()
|
||||
NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader)
|
||||
NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model)
|
||||
|
||||
if check_module_exists("ComfyUI_bitsandbytes_NF4") or check_module_exists("comfyui_bitsandbytes_nf4"):
|
||||
NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
|
||||
|
||||
if check_module_exists("x-flux-comfyui") or check_module_exists("x-flux-comfyui"):
|
||||
NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet)
|
||||
|
||||
if check_module_exists("ComfyUI-MMAudio") or check_module_exists("comfyui-mmaudio"):
|
||||
NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
|
||||
NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader)
|
||||
NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler)
|
||||
|
||||
if check_module_exists("ComfyUI-GGUF") or check_module_exists("comfyui-gguf"):
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = override_class(UnetLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(UnetLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = override_class(UnetLoaderGGUFAdvanced)
|
||||
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedDisTorchMultiGPU"] = override_class_with_distorch(UnetLoaderGGUFAdvanced)
|
||||
NODE_CLASS_MAPPINGS["CLIPLoaderGGUFMultiGPU"] = override_class(CLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["CLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(CLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = override_class(DualCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(DualCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFMultiGPU"] = override_class(TripleCLIPLoaderGGUF)
|
||||
NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(TripleCLIPLoaderGGUF)
|
||||
|
||||
if check_module_exists("PuLID_ComfyUI") or check_module_exists("pulid_comfyui"):
|
||||
NODE_CLASS_MAPPINGS["PulidModelLoaderMultiGPU"] = override_class(PulidModelLoader)
|
||||
NODE_CLASS_MAPPINGS["PulidInsightFaceLoaderMultiGPU"] = override_class(PulidInsightFaceLoader)
|
||||
NODE_CLASS_MAPPINGS["PulidEvaClipLoaderMultiGPU"] = override_class(PulidEvaClipLoader)
|
||||
|
||||
if check_module_exists("ComfyUI-HunyuanVideoWrapper") or check_module_exists("comfyui-hunyuanvideowrapper"):
|
||||
NODE_CLASS_MAPPINGS["HyVideoModelLoaderMultiGPU"] = override_class(HyVideoModelLoader)
|
||||
NODE_CLASS_MAPPINGS["HyVideoVAELoaderMultiGPU"] = override_class(HyVideoVAELoader)
|
||||
NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoderMultiGPU"] = override_class(DownloadAndLoadHyVideoTextEncoder)
|
||||
|
||||
logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")
|
||||
|
||||
@@ -0,0 +1,496 @@
|
||||
import os
|
||||
import torch
|
||||
import folder_paths
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
|
||||
class UnetLoaderGGUF:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (unet_names,),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_unet"
|
||||
CATEGORY = "bootleg"
|
||||
TITLE = "Unet Loader (GGUF)"
|
||||
|
||||
def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]()
|
||||
return original_loader.load_unet(unet_name, dequant_dtype, patch_dtype, patch_on_device)
|
||||
|
||||
class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (unet_names,),
|
||||
"dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
||||
"patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
||||
"patch_on_device": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
TITLE = "Unet Loader (GGUF/Advanced)"
|
||||
|
||||
|
||||
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)
|
||||
|
||||
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"]()
|
||||
clip = original_loader.load_clip(clip_name1, clip_name2, type)
|
||||
clip[0].patcher.load(force_patch_weights=True)
|
||||
return clip
|
||||
|
||||
|
||||
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)
|
||||
|
||||
|
||||
class LTXVLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),
|
||||
{"tooltip": "The name of the checkpoint (model) to load."}),
|
||||
"dtype": (["bfloat16", "float32"], {"default": "bfloat16"})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "VAE")
|
||||
RETURN_NAMES = ("model", "vae")
|
||||
FUNCTION = "load"
|
||||
CATEGORY = "lightricks/LTXV"
|
||||
TITLE = "LTXV Loader"
|
||||
OUTPUT_NODE = False
|
||||
|
||||
def load(self, ckpt_name, dtype):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader.load(ckpt_name, dtype)
|
||||
def _load_unet(self, load_device, offload_device, weights, num_latent_channels, dtype, config=None ):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader._load_unet(load_device, offload_device, weights, num_latent_channels, dtype, config=None )
|
||||
def _load_vae(self, weights, config=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
|
||||
return original_loader._load_vae(weights, config=None)
|
||||
|
||||
class Florence2ModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ([item.name for item in Path(folder_paths.models_dir, "LLM").iterdir() if item.is_dir()],
|
||||
{"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
|
||||
"precision": (['fp16','bf16','fp32'],),
|
||||
"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
|
||||
},
|
||||
"optional": {
|
||||
"lora": ("PEFTLORA",),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FL2MODEL",)
|
||||
RETURN_NAMES = ("florence2_model",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "Florence2"
|
||||
|
||||
def loadmodel(self, model, precision, attention, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
|
||||
return original_loader.loadmodel(model, precision, attention, lora)
|
||||
|
||||
class DownloadAndLoadFlorence2Model:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ([
|
||||
'microsoft/Florence-2-base',
|
||||
'microsoft/Florence-2-base-ft',
|
||||
'microsoft/Florence-2-large',
|
||||
'microsoft/Florence-2-large-ft',
|
||||
'HuggingFaceM4/Florence-2-DocVQA',
|
||||
'thwri/CogFlorence-2.1-Large',
|
||||
'thwri/CogFlorence-2.2-Large',
|
||||
'gokaygokay/Florence-2-SD3-Captioner',
|
||||
'gokaygokay/Florence-2-Flux-Large',
|
||||
'MiaoshouAI/Florence-2-base-PromptGen-v1.5',
|
||||
'MiaoshouAI/Florence-2-large-PromptGen-v1.5',
|
||||
'MiaoshouAI/Florence-2-base-PromptGen-v2.0',
|
||||
'MiaoshouAI/Florence-2-large-PromptGen-v2.0'
|
||||
], {"default": 'microsoft/Florence-2-base'}),
|
||||
"precision": (['fp16','bf16','fp32'], {"default": 'fp16'}),
|
||||
"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
|
||||
},
|
||||
"optional": {
|
||||
"lora": ("PEFTLORA",),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FL2MODEL",)
|
||||
RETURN_NAMES = ("florence2_model",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "Florence2"
|
||||
|
||||
def loadmodel(self, model, precision, attention, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
|
||||
return original_loader.loadmodel(model, precision, attention, lora)
|
||||
|
||||
class CheckpointLoaderNF4:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
|
||||
}}
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
FUNCTION = "load_checkpoint"
|
||||
|
||||
CATEGORY = "loaders"
|
||||
|
||||
|
||||
def load_checkpoint(self, ckpt_name):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
|
||||
return original_loader.load_checkpoint(ckpt_name)
|
||||
|
||||
class LoadFluxControlNet:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],),
|
||||
"controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ),
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("FluxControlNet",)
|
||||
RETURN_NAMES = ("ControlNet",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "XLabsNodes"
|
||||
|
||||
def loadmodel(self, model_name, controlnet_path):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
|
||||
return original_loader.loadmodel(model_name, controlnet_path)
|
||||
|
||||
class MMAudioModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
|
||||
|
||||
"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MMAUDIO_MODEL",)
|
||||
RETURN_NAMES = ("mmaudio_model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def loadmodel(self, mmaudio_model, base_precision):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
|
||||
return original_loader.loadmodel(mmaudio_model, base_precision)
|
||||
|
||||
class MMAudioFeatureUtilsLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"vae_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"synchformer_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"clip_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
},
|
||||
"optional": {
|
||||
"bigvgan_vocoder_model": ("VOCODER_MODEL", {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
|
||||
"mode": (["16k", "44k"], {"default": "44k"}),
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "fp16"}
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MMAUDIO_FEATUREUTILS",)
|
||||
RETURN_NAMES = ("mmaudio_featureutils", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def loadmodel(self, vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoader"]()
|
||||
return original_loader.loadmodel(vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model)
|
||||
|
||||
class MMAudioSampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"mmaudio_model": ("MMAUDIO_MODEL",),
|
||||
"feature_utils": ("MMAUDIO_FEATUREUTILS",),
|
||||
"duration": ("FLOAT", {"default": 8, "step": 0.01, "tooltip": "Duration of the audio in seconds"}),
|
||||
"steps": ("INT", {"default": 25, "step": 1, "tooltip": "Number of steps to interpolate"}),
|
||||
"cfg": ("FLOAT", {"default": 4.5, "step": 0.1, "tooltip": "Strength of the conditioning"}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"prompt": ("STRING", {"default": "", "multiline": True} ),
|
||||
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
|
||||
"mask_away_clip": ("BOOLEAN", {"default": False, "tooltip": "If true, the clip video will be masked away"}),
|
||||
"force_offload": ("BOOLEAN", {"default": True, "tooltip": "If true, the model will be offloaded to the offload device"}),
|
||||
},
|
||||
"optional": {
|
||||
"images": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("AUDIO",)
|
||||
RETURN_NAMES = ("audio", )
|
||||
FUNCTION = "sample"
|
||||
CATEGORY = "MMAudio"
|
||||
|
||||
def sample(self, mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["MMAudioSampler"]()
|
||||
return original_loader.sample(mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images)
|
||||
|
||||
class PulidModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "pulid_file": (folder_paths.get_filename_list("pulid"), )}}
|
||||
|
||||
RETURN_TYPES = ("PULID",)
|
||||
FUNCTION = "load_model"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_model(self, pulid_file):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidModelLoader"]()
|
||||
return original_loader.load_model(pulid_file)
|
||||
|
||||
class PulidInsightFaceLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"provider": (["CPU", "CUDA", "ROCM", "CoreML"], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("FACEANALYSIS",)
|
||||
FUNCTION = "load_insightface"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_insightface(self, provider):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidInsightFaceLoader"]()
|
||||
return original_loader.load_insightface(provider)
|
||||
|
||||
class PulidEvaClipLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("EVA_CLIP",)
|
||||
FUNCTION = "load_eva_clip"
|
||||
CATEGORY = "pulid"
|
||||
|
||||
def load_eva_clip(self):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["PulidEvaClipLoader"]()
|
||||
return original_loader.load_eva_clip()
|
||||
|
||||
|
||||
class HyVideoModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
||||
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
||||
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"], {"default": 'disabled', "tooltip": "optional quantization method"}),
|
||||
"load_device": (["main_device"], {"default": "main_device"}),
|
||||
},
|
||||
"optional": {
|
||||
"attention_mode": ([
|
||||
"sdpa",
|
||||
"flash_attn_varlen",
|
||||
"sageattn_varlen",
|
||||
"comfy",
|
||||
], {"default": "flash_attn"}),
|
||||
"compile_args": ("COMPILEARGS", ),
|
||||
"block_swap_args": ("BLOCKSWAPARGS", ),
|
||||
"lora": ("HYVIDLORA", {"default": None}),
|
||||
"auto_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enable auto offloading for reduced VRAM usage, implementation from DiffSynth-Studio, slightly different from block swapping and uses even less VRAM, but can be slower as you can't define how much VRAM to use"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDEOMODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, base_precision, load_device, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, auto_cpu_offload=False):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
||||
return original_loader.loadmodel(model, base_precision, load_device, quantization, compile_args, attention_mode, block_swap_args, lora, auto_cpu_offload)
|
||||
|
||||
# Add new DiffSynth-style node
|
||||
class HyVideoModelLoaderDiffSynth:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
||||
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
||||
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"],
|
||||
{"default": 'disabled', "tooltip": "optional quantization method"}),
|
||||
},
|
||||
"optional": {
|
||||
"attention_mode": ([
|
||||
"sdpa",
|
||||
"flash_attn_varlen",
|
||||
"sageattn_varlen",
|
||||
"comfy",
|
||||
], {"default": "flash_attn"}),
|
||||
"compile_args": ("COMPILEARGS", ),
|
||||
"block_swap_args": ("BLOCKSWAPARGS", ),
|
||||
"lora": ("HYVIDLORA", {"default": None}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDEOMODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, base_precision, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
||||
# Use DiffSynth's auto offloading approach
|
||||
return original_loader.loadmodel(model, base_precision, "main_device", quantization,
|
||||
compile_args, attention_mode, block_swap_args, lora,
|
||||
auto_cpu_offload=True)
|
||||
|
||||
class HyVideoVAELoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
|
||||
},
|
||||
"optional": {
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "bf16"}
|
||||
),
|
||||
"compile_args":("COMPILEARGS", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("VAE",)
|
||||
RETURN_NAMES = ("vae", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
DESCRIPTION = "Loads Hunyuan VAE model from 'ComfyUI/models/vae'"
|
||||
|
||||
def loadmodel(self, model_name, precision, compile_args=None):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["HyVideoVAELoader"]()
|
||||
return original_loader.loadmodel(model_name, precision, compile_args)
|
||||
|
||||
class DownloadAndLoadHyVideoTextEncoder:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"llm_model": (["Kijai/llava-llama-3-8b-text-encoder-tokenizer","xtuner/llava-llama-3-8b-v1_1-transformers"],),
|
||||
"clip_model": (["disabled","openai/clip-vit-large-patch14",],),
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "bf16"}
|
||||
),
|
||||
},
|
||||
"optional": {
|
||||
"apply_final_norm": ("BOOLEAN", {"default": False}),
|
||||
"hidden_state_skip_layer": ("INT", {"default": 2}),
|
||||
"quantization": (['disabled', 'bnb_nf4', "fp8_e4m3fn"], {"default": 'disabled'}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("HYVIDTEXTENCODER",)
|
||||
RETURN_NAMES = ("hyvid_text_encoder", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "HunyuanVideoWrapper"
|
||||
DESCRIPTION = "Loads Hunyuan text_encoder model from 'ComfyUI/models/LLM'"
|
||||
|
||||
def loadmodel(self, llm_model, clip_model, precision, apply_final_norm=False, hidden_state_skip_layer=2, quantization="disabled"):
|
||||
from nodes import NODE_CLASS_MAPPINGS
|
||||
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoder"]()
|
||||
return original_loader.loadmodel(llm_model, clip_model, precision, apply_final_norm, hidden_state_skip_layer, quantization)
|
||||
Reference in New Issue
Block a user