John Pollock
898169fccf
This commit refactors the codebase by extracting major components from the main `__init__.py` file into their own dedicated modules. This improves code organization, readability, and maintainability. - **`distorch.py`**: New file containing the `DisTorch` class, which manages multi-GPU device patching and distribution logic. - **`block_swap.py`**: New file containing the generic `BlockSwap` class for UNet block swapping to manage VRAM. - **`wanvideo.py`**: New file containing the `WanVideoBlockSwap` class, a specialized implementation for WanVideo models. - **`__init__.py`**: Simplified to handle node registration and imports from the new modules.
549 lines
20 KiB
Python
549 lines
20 KiB
Python
import torch
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import folder_paths
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from pathlib import Path
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from nodes import NODE_CLASS_MAPPINGS
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def _has_xpu():
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try:
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return hasattr(torch, "xpu") and hasattr(torch.xpu, "is_available") and torch.xpu.is_available()
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except Exception:
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return False
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def get_device_list():
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devs = ["cpu"]
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try:
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if hasattr(torch, "cuda") and hasattr(torch.cuda, "is_available") and torch.cuda.is_available():
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devs += [f"cuda:{i}" for i in range(torch.cuda.device_count())]
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except Exception:
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pass
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try:
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if _has_xpu():
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devs += [f"xpu:{i}" for i in range(torch.xpu.device_count())]
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except Exception:
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pass
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return devs
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class DeviceSelectorMultiGPU:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0]})
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}
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}
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RETURN_TYPES = (get_device_list(),)
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RETURN_NAMES = ("device",)
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FUNCTION = "select_device"
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CATEGORY = "multigpu"
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def select_device(self, device):
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return (device,)
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class HunyuanVideoEmbeddingsAdapter:
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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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"hyvid_embeds": ("HYVIDEMBEDS",),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "adapt_embeddings"
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CATEGORY = "multigpu"
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def adapt_embeddings(self, hyvid_embeds):
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cond = hyvid_embeds["prompt_embeds"]
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pooled_dict = {
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"pooled_output": hyvid_embeds["prompt_embeds_2"],
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"cross_attn": hyvid_embeds["prompt_embeds"],
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"attention_mask": hyvid_embeds["attention_mask"],
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}
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if hyvid_embeds["attention_mask_2"] is not None:
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pooled_dict["attention_mask_controlnet"] = hyvid_embeds["attention_mask_2"]
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if hyvid_embeds["cfg"] is not None:
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pooled_dict["guidance"] = float(hyvid_embeds["cfg"])
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pooled_dict["start_percent"] = float(hyvid_embeds["start_percent"]) if hyvid_embeds["start_percent"] is not None else 0.0
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pooled_dict["end_percent"] = float(hyvid_embeds["end_percent"]) if hyvid_embeds["end_percent"] is not None else 1.0
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return ([[cond, pooled_dict]],)
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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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RETURN_TYPES = ("MODEL",)
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FUNCTION = "load_unet"
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CATEGORY = "bootleg"
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TITLE = "Unet Loader (GGUF)"
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def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
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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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class UnetLoaderGGUFAdvanced(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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"dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
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"patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
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"patch_on_device": ("BOOLEAN", {"default": False}),
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}
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}
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TITLE = "Unet Loader (GGUF/Advanced)"
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class CLIPLoaderGGUF:
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@classmethod
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def INPUT_TYPES(s):
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import nodes
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base = nodes.CLIPLoader.INPUT_TYPES()
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return {
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"required": {
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"clip_name": (s.get_filename_list(),),
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"type": base["required"]["type"],
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}
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}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "load_clip"
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CATEGORY = "bootleg"
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TITLE = "CLIPLoader (GGUF)"
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@classmethod
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def get_filename_list(s):
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files = []
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files += folder_paths.get_filename_list("clip")
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files += folder_paths.get_filename_list("clip_gguf")
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return sorted(files)
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def load_data(self, ckpt_paths):
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original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
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return original_loader.load_data(ckpt_paths)
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def load_patcher(self, clip_paths, clip_type, clip_data):
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original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
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return original_loader.load_patcher(clip_paths, clip_type, clip_data)
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def load_clip(self, clip_name, type="stable_diffusion"):
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original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
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return original_loader.load_clip(clip_name, type)
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class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
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@classmethod
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def INPUT_TYPES(s):
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import nodes
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base = nodes.DualCLIPLoader.INPUT_TYPES()
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file_options = (s.get_filename_list(), )
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return {
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"required": {
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"clip_name1": file_options,
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"clip_name2": file_options,
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"type": base["required"]["type"],
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}
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}
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TITLE = "DualCLIPLoader (GGUF)"
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def load_clip(self, clip_name1, clip_name2, type):
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original_loader = NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUF"]()
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clip = original_loader.load_clip(clip_name1, clip_name2, type)
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clip[0].patcher.load(force_patch_weights=True)
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return clip
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class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
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@classmethod
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def INPUT_TYPES(s):
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file_options = (s.get_filename_list(), )
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return {
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"required": {
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"clip_name1": file_options,
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"clip_name2": file_options,
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"clip_name3": file_options,
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}
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}
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TITLE = "TripleCLIPLoader (GGUF)"
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def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
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original_loader = NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUF"]()
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return original_loader.load_clip(clip_name1, clip_name2, clip_name3, type)
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class QuadrupleCLIPLoaderGGUF(CLIPLoaderGGUF):
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@classmethod
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def INPUT_TYPES(s):
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file_options = (s.get_filename_list(), )
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return {
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"required": {
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"clip_name1": file_options,
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"clip_name2": file_options,
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"clip_name3": file_options,
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"clip_name4": file_options,
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}
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}
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TITLE = "QuadrupleCLIPLoader (GGUF)"
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def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable_diffusion"):
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original_loader = NODE_CLASS_MAPPINGS["QuadrupleCLIPLoaderGGUF"]()
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return original_loader.load_clip(clip_name1, clip_name2, clip_name3, clip_name4, type)
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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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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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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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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader._load_vae(weights, config=None)
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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()], {"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
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"precision": (['fp16','bf16','fp32'],),
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"attention": (
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[ 'flash_attention_2', 'sdpa', 'eager'],
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{
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"default": 'sdpa'
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}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}
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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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original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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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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[
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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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],
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{
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"default": 'microsoft/Florence-2-base'
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}),
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"precision": ([ 'fp16','bf16','fp32'],
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{
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"default": 'fp16'
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}),
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"attention": (
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[ 'flash_attention_2', 'sdpa', 'eager'],
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{
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"default": 'sdpa'
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}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}
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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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original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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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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original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
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return original_loader.load_checkpoint(ckpt_name)
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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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original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
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return original_loader.loadmodel(model_name, controlnet_path)
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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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original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
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return original_loader.loadmodel(mmaudio_model, base_precision)
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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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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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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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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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class PulidModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "pulid_file": (folder_paths.get_filename_list("pulid"), )}}
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RETURN_TYPES = ("PULID",)
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FUNCTION = "load_model"
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CATEGORY = "pulid"
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def load_model(self, pulid_file):
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original_loader = NODE_CLASS_MAPPINGS["PulidModelLoader"]()
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return original_loader.load_model(pulid_file)
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class PulidInsightFaceLoader:
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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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"provider": (["CPU", "CUDA", "ROCM", "CoreML"], ),
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},
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}
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RETURN_TYPES = ("FACEANALYSIS",)
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FUNCTION = "load_insightface"
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CATEGORY = "pulid"
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def load_insightface(self, provider):
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original_loader = NODE_CLASS_MAPPINGS["PulidInsightFaceLoader"]()
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return original_loader.load_insightface(provider)
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class PulidEvaClipLoader:
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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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RETURN_TYPES = ("EVA_CLIP",)
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FUNCTION = "load_eva_clip"
|
|
CATEGORY = "pulid"
|
|
|
|
def load_eva_clip(self):
|
|
original_loader = NODE_CLASS_MAPPINGS["PulidEvaClipLoader"]()
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|
return original_loader.load_eva_clip()
|
|
|
|
|
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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):
|
|
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)
|
|
|
|
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):
|
|
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"):
|
|
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoder"]()
|
|
return original_loader.loadmodel(llm_model, clip_model, precision, apply_final_norm, hidden_state_skip_layer, quantization)
|