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nodes.py
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import os
import torch
import json
import gc
from .utils import log, print_memory
from diffusers.video_processor import VideoProcessor
from typing import List, Dict, Any, Tuple
import numpy as np
from .hyvideo.constants import PROMPT_TEMPLATE
from .hyvideo.text_encoder import TextEncoder
from .hyvideo.utils.data_utils import align_to
from .hyvideo.diffusion.schedulers import FlowMatchDiscreteScheduler
from .hyvideo.diffusion.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .hyvideo.diffusion.schedulers.scheduling_sasolver import SASolverScheduler
from. hyvideo.diffusion.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
scheduler_mapping = {
"FlowMatchDiscreteScheduler": FlowMatchDiscreteScheduler,
"SDE-DPMSolverMultistepScheduler": DPMSolverMultistepScheduler,
"DPMSolverMultistepScheduler": DPMSolverMultistepScheduler,
"SASolverScheduler": SASolverScheduler,
"UniPCMultistepScheduler": UniPCMultistepScheduler,
}
available_schedulers = list(scheduler_mapping.keys())
from .hyvideo.diffusion.pipelines import HunyuanVideoPipeline
from .hyvideo.vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
from .hyvideo.modules.models import HYVideoDiffusionTransformer
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
import folder_paths
folder_paths.add_model_folder_path("hyvid_embeds", os.path.join(folder_paths.get_output_directory(), "hyvid_embeds"))
import comfy.model_management as mm
from comfy.utils import load_torch_file, save_torch_file
from comfy.clip_vision import clip_preprocess
import comfy.model_base
import comfy.latent_formats
script_directory = os.path.dirname(os.path.abspath(__file__))
VAE_SCALING_FACTOR = 0.476986
def add_noise_to_reference_video(image, ratio=None):
if ratio is None:
sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
sigma = torch.exp(sigma).to(image.dtype)
else:
sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
image = image + image_noise
return image
def filter_state_dict_by_blocks(state_dict, blocks_mapping):
filtered_dict = {}
for key in state_dict:
if 'double_blocks.' in key or 'single_blocks.' in key:
block_pattern = key.split('diffusion_model.')[1].split('.', 2)[0:2]
block_key = f'{block_pattern[0]}.{block_pattern[1]}.'
if block_key in blocks_mapping:
filtered_dict[key] = state_dict[key]
return filtered_dict
def standardize_lora_key_format(lora_sd):
new_sd = {}
for k, v in lora_sd.items():
# Diffusers format
if k.startswith('transformer.'):
k = k.replace('transformer.', 'diffusion_model.')
if "img_attn.proj" in k:
k = k.replace("img_attn.proj", "img_attn_proj")
if "img_attn.qkv" in k:
k = k.replace("img_attn.qkv", "img_attn_qkv")
if "txt_attn.proj" in k:
k = k.replace("txt_attn.proj ", "txt_attn_proj")
if "txt_attn.qkv" in k:
k = k.replace("txt_attn.qkv", "txt_attn_qkv")
new_sd[k] = v
return new_sd
class HyVideoLoraBlockEdit:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
arg_dict = {}
argument = ("BOOLEAN", {"default": True})
for i in range(20):
arg_dict["double_blocks.{}.".format(i)] = argument
for i in range(40):
arg_dict["single_blocks.{}.".format(i)] = argument
return {"required": arg_dict}
RETURN_TYPES = ("SELECTEDBLOCKS", )
RETURN_NAMES = ("blocks", )
OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
FUNCTION = "select"
CATEGORY = "HunyuanVideoWrapper"
def select(self, **kwargs):
selected_blocks = {k: v for k, v in kwargs.items() if v is True}
print("Selected blocks: ", selected_blocks)
return (selected_blocks,)
class HyVideoLoraSelect:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora": (folder_paths.get_filename_list("loras"),
{"tooltip": "LORA models are expected to be in ComfyUI/models/loras with .safetensors extension"}),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
},
"optional": {
"prev_lora":("HYVIDLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
"blocks":("SELECTEDBLOCKS", ),
}
}
RETURN_TYPES = ("HYVIDLORA",)
RETURN_NAMES = ("lora", )
FUNCTION = "getlorapath"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras"
def getlorapath(self, lora, strength, blocks=None, prev_lora=None, fuse_lora=False):
loras_list = []
lora = {
"path": folder_paths.get_full_path("loras", lora),
"strength": strength,
"name": lora.split(".")[0],
"fuse_lora": fuse_lora,
"blocks": blocks
}
if prev_lora is not None:
loras_list.extend(prev_lora)
loras_list.append(lora)
return (loras_list,)
class HyVideoBlockSwap:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"double_blocks_to_swap": ("INT", {"default": 20, "min": 0, "max": 20, "step": 1, "tooltip": "Number of double blocks to swap"}),
"single_blocks_to_swap": ("INT", {"default": 0, "min": 0, "max": 40, "step": 1, "tooltip": "Number of single blocks to swap"}),
"offload_txt_in": ("BOOLEAN", {"default": False, "tooltip": "Offload txt_in layer"}),
"offload_img_in": ("BOOLEAN", {"default": False, "tooltip": "Offload img_in layer"}),
},
}
RETURN_TYPES = ("BLOCKSWAPARGS",)
RETURN_NAMES = ("block_swap_args",)
FUNCTION = "setargs"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "Settings for block swapping, reduces VRAM use by swapping blocks to CPU memory"
def setargs(self, **kwargs):
return (kwargs, )
class HyVideoEnhanceAVideo:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"weight": ("FLOAT", {"default": 2.0, "min": 0, "max": 100, "step": 0.01, "tooltip": "The feta Weight of the Enhance-A-Video"}),
"single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Enable Enhance-A-Video for single blocks"}),
"double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Enable Enhance-A-Video for double blocks"}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply Enhance-A-Video"}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the steps to apply Enhance-A-Video"}),
},
}
RETURN_TYPES = ("FETAARGS",)
RETURN_NAMES = ("feta_args",)
FUNCTION = "setargs"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video"
def setargs(self, **kwargs):
return (kwargs, )
class HyVideoSTG:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"stg_mode": (["STG-A", "STG-R"],),
"stg_block_idx": ("INT", {"default": 0, "min": -1, "max": 39, "step": 1, "tooltip": "Block index to apply STG"}),
"stg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Recommended values are ≤2.0"}),
"stg_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply STG"}),
"stg_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the steps to apply STG"}),
},
}
RETURN_TYPES = ("STGARGS",)
RETURN_NAMES = ("stg_args",)
FUNCTION = "setargs"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "Spatio Temporal Guidance, https://github.com/junhahyung/STGuidance"
def setargs(self, **kwargs):
return (kwargs, )
class HyVideoTeaCache:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"rel_l1_thresh": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01,
"tooltip": "Higher values will make TeaCache more aggressive, faster, but may cause artifacts"}),
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
},
}
RETURN_TYPES = ("TEACACHEARGS",)
RETURN_NAMES = ("teacache_args",)
FUNCTION = "process"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "TeaCache settings for HunyuanVideo to speed up inference"
def process(self, rel_l1_thresh, cache_device):
if cache_device == "main_device":
teacache_device = mm.get_torch_device()
else:
teacache_device = mm.unet_offload_device()
teacache_args = {
"rel_l1_thresh": rel_l1_thresh,
"cache_device": teacache_device
}
return (teacache_args,)
class HyVideoModel(comfy.model_base.BaseModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.pipeline = {}
def __getitem__(self, k):
return self.pipeline[k]
def __setitem__(self, k, v):
self.pipeline[k] = v
class HyVideoModelConfig:
def __init__(self, dtype):
self.unet_config = {}
self.unet_extra_config = {}
self.latent_format = comfy.latent_formats.HunyuanVideo
self.latent_format.latent_channels = 16
self.manual_cast_dtype = dtype
self.sampling_settings = {"multiplier": 1.0}
# Don't know what this is. Value taken from ComfyUI Mochi model.
self.memory_usage_factor = 2.0
# denoiser is handled by extension
self.unet_config["disable_unet_model_creation"] = True
#region Model loading
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_e5m2', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"], {"default": 'disabled', "tooltip": "optional quantization method"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
},
"optional": {
"attention_mode": ([
"sdpa",
"flash_attn_varlen",
"sageattn",
"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"}),
"upcast_rope": ("BOOLEAN", {"default": True, "tooltip": "Upcast RoPE to fp32 for better accuracy, this is the default behaviour, disabling can improve speed and reduce memory use slightly"}),
}
}
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, upcast_rope=True):
transformer = None
mm.unload_all_models()
mm.soft_empty_cache()
manual_offloading = True
if "sage" in attention_mode:
try:
from sageattention import sageattn_varlen
except Exception as e:
raise ValueError(f"Can't import SageAttention: {str(e)}")
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
manual_offloading = True
transformer_load_device = device if load_device == "main_device" else offload_device
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[base_precision]
model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)
in_channels = sd["img_in.proj.weight"].shape[1]
if in_channels == 16 and "i2v" in model.lower():
i2v_condition_type = "token_replace"
else:
i2v_condition_type = "latent_concat"
log.info(f"Condition type: {i2v_condition_type}")
guidance_embed = sd.get("guidance_in.mlp.0.weight", False) is not False
out_channels = 16
factor_kwargs = {"device": transformer_load_device, "dtype": base_dtype}
HUNYUAN_VIDEO_CONFIG = {
"mm_double_blocks_depth": 20,
"mm_single_blocks_depth": 40,
"rope_dim_list": [16, 56, 56],
"hidden_size": 3072,
"heads_num": 24,
"mlp_width_ratio": 4,
"guidance_embed": guidance_embed,
"i2v_condition_type": i2v_condition_type,
}
with init_empty_weights():
transformer = HYVideoDiffusionTransformer(
in_channels=in_channels,
out_channels=out_channels,
attention_mode=attention_mode,
main_device=device,
offload_device=offload_device,
**HUNYUAN_VIDEO_CONFIG,
**factor_kwargs
)
transformer.eval()
transformer.upcast_rope = upcast_rope
comfy_model = HyVideoModel(
HyVideoModelConfig(base_dtype),
model_type=comfy.model_base.ModelType.FLOW,
device=device,
)
scheduler_config = {
"flow_shift": 7.0,
"reverse": True,
"solver": "euler",
"use_flow_sigmas": True,
"prediction_type": 'flow_prediction'
}
scheduler = FlowMatchDiscreteScheduler.from_config(scheduler_config)
pipe = HunyuanVideoPipeline(
transformer=transformer,
scheduler=scheduler,
progress_bar_config=None,
base_dtype=base_dtype,
comfy_model=comfy_model,
)
if not "torchao" in quantization:
log.info("Using accelerate to load and assign model weights to device...")
if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fast" or quantization == "fp8_scaled":
dtype = torch.float8_e4m3fn
elif quantization == "fp8_e5m2":
dtype = torch.float8_e5m2
else:
dtype = base_dtype
params_to_keep = {"norm", "bias", "time_in", "vector_in", "guidance_in", "txt_in", "img_in"}
for name, param in transformer.named_parameters():
#print("Assigning Parameter name: ", name)
dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
comfy_model.diffusion_model = transformer
patcher = comfy.model_patcher.ModelPatcher(comfy_model, device, offload_device)
pipe.comfy_model = patcher
del sd
gc.collect()
mm.soft_empty_cache()
if lora is not None:
from comfy.sd import load_lora_for_models
for l in lora:
log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}")
lora_path = l["path"]
lora_strength = l["strength"]
lora_sd = load_torch_file(lora_path, safe_load=True)
lora_sd = standardize_lora_key_format(lora_sd)
if l["blocks"]:
lora_sd = filter_state_dict_by_blocks(lora_sd, l["blocks"])
# patch in channels for keyframe LoRA
if "diffusion_model.img_in.proj.lora_A.weight" in lora_sd:
from .hyvideo.modules.embed_layers import PatchEmbed
if lora_sd["diffusion_model.img_in.proj.lora_A.weight"].shape[1] != in_channels:
log.info(f"Different in_channels {lora_sd['diffusion_model.img_in.proj.lora_A.weight'].shape[1]} vs {in_channels}, patching...")
new_img_in = PatchEmbed(
patch_size=patcher.model.diffusion_model.patch_size,
in_chans=32,
embed_dim=patcher.model.diffusion_model.hidden_size,
).to(patcher.model.diffusion_model.device, dtype=patcher.model.diffusion_model.dtype)
new_img_in.proj.weight.zero_()
new_img_in.proj.weight[:, :in_channels].copy_(patcher.model.diffusion_model.img_in.proj.weight)
if patcher.model.diffusion_model.img_in.proj.bias is not None:
new_img_in.proj.bias.copy_(patcher.model.diffusion_model.img_in.proj.bias)
patcher.model.diffusion_model.img_in = new_img_in
patcher, _ = load_lora_for_models(patcher, None, lora_sd, lora_strength, 0)
comfy.model_management.load_models_gpu([patcher])
if load_device == "offload_device":
patcher.model.diffusion_model.to(offload_device)
if quantization == "fp8_e4m3fn_fast":
from .fp8_optimization import convert_fp8_linear
convert_fp8_linear(patcher.model.diffusion_model, base_dtype, params_to_keep=params_to_keep)
elif quantization == "fp8_scaled":
from .hyvideo.modules.fp8_optimization import convert_fp8_linear
convert_fp8_linear(patcher.model.diffusion_model, base_dtype)
if auto_cpu_offload:
transformer.enable_auto_offload(dtype=dtype, device=device)
#compile
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
if compile_args["compile_single_blocks"]:
for i, block in enumerate(patcher.model.diffusion_model.single_blocks):
patcher.model.diffusion_model.single_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if compile_args["compile_double_blocks"]:
for i, block in enumerate(patcher.model.diffusion_model.double_blocks):
patcher.model.diffusion_model.double_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if compile_args["compile_txt_in"]:
patcher.model.diffusion_model.txt_in = torch.compile(patcher.model.diffusion_model.txt_in, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if compile_args["compile_vector_in"]:
patcher.model.diffusion_model.vector_in = torch.compile(patcher.model.diffusion_model.vector_in, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if compile_args["compile_final_layer"]:
patcher.model.diffusion_model.final_layer = torch.compile(patcher.model.diffusion_model.final_layer, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
elif "torchao" in quantization:
try:
from torchao.quantization import (
quantize_,
fpx_weight_only,
float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight,
int8_weight_only,
int4_weight_only
)
except:
raise ImportError("torchao is not installed")
# def filter_fn(module: nn.Module, fqn: str) -> bool:
# target_submodules = {'attn1', 'ff'} # avoid norm layers, 1.5 at least won't work with quantized norm1 #todo: test other models
# if any(sub in fqn for sub in target_submodules):
# return isinstance(module, nn.Linear)
# return False
if "fp6" in quantization:
quant_func = fpx_weight_only(3, 2)
elif "int4" in quantization:
quant_func = int4_weight_only()
elif "int8" in quantization:
quant_func = int8_weight_only()
elif "fp8dq" in quantization:
quant_func = float8_dynamic_activation_float8_weight()
elif 'fp8dqrow' in quantization:
from torchao.quantization.quant_api import PerRow
quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
elif 'int8dq' in quantization:
quant_func = int8_dynamic_activation_int8_weight()
log.info(f"Quantizing model with {quant_func}")
comfy_model.diffusion_model = transformer
patcher = comfy.model_patcher.ModelPatcher(comfy_model, device, offload_device)
if lora is not None:
from comfy.sd import load_lora_for_models
for l in lora:
lora_path = l["path"]
lora_strength = l["strength"]
lora_sd = load_torch_file(lora_path, safe_load=True)
lora_sd = standardize_lora_key_format(lora_sd)
patcher, _ = load_lora_for_models(patcher, None, lora_sd, lora_strength, 0)
comfy.model_management.load_models_gpu([patcher])
for i, block in enumerate(patcher.model.diffusion_model.single_blocks):
log.info(f"Quantizing single_block {i}")
for name, _ in block.named_parameters(prefix=f"single_blocks.{i}"):
#print(f"Parameter name: {name}")
set_module_tensor_to_device(patcher.model.diffusion_model, name, device=patcher.model.diffusion_model_load_device, dtype=base_dtype, value=sd[name])
if compile_args is not None:
patcher.model.diffusion_model.single_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
quantize_(block, quant_func)
print(block)
block.to(offload_device)
for i, block in enumerate(patcher.model.diffusion_model.double_blocks):
log.info(f"Quantizing double_block {i}")
for name, _ in block.named_parameters(prefix=f"double_blocks.{i}"):
#print(f"Parameter name: {name}")
set_module_tensor_to_device(patcher.model.diffusion_model, name, device=patcher.model.diffusion_model_load_device, dtype=base_dtype, value=sd[name])
if compile_args is not None:
patcher.model.diffusion_model.double_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
quantize_(block, quant_func)
for name, param in patcher.model.diffusion_model.named_parameters():
if "single_blocks" not in name and "double_blocks" not in name:
set_module_tensor_to_device(patcher.model.diffusion_model, name, device=patcher.model.diffusion_model_load_device, dtype=base_dtype, value=sd[name])
manual_offloading = False # to disable manual .to(device) calls
log.info(f"Quantized transformer blocks to {quantization}")
for name, param in patcher.model.diffusion_model.named_parameters():
print(name, param.dtype)
#param.data = param.data.to(self.vae_dtype).to(device)
del sd
mm.soft_empty_cache()
patcher.model["pipe"] = pipe
patcher.model["dtype"] = base_dtype
patcher.model["base_path"] = model_path
patcher.model["model_name"] = model
patcher.model["manual_offloading"] = manual_offloading
patcher.model["quantization"] = "disabled"
patcher.model["block_swap_args"] = block_swap_args
patcher.model["auto_cpu_offload"] = auto_cpu_offload
patcher.model["scheduler_config"] = scheduler_config
for model in mm.current_loaded_models:
if model._model() == patcher:
mm.current_loaded_models.remove(model)
return (patcher,)
#region load VAE
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):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
with open(os.path.join(script_directory, 'configs', 'hy_vae_config.json')) as f:
vae_config = json.load(f)
model_path = folder_paths.get_full_path("vae", model_name)
vae_sd = load_torch_file(model_path, safe_load=True)
if not "decoder.conv_norm_out.weight" in vae_sd:
raise ValueError("""
Incompatible VAE model selected, the HunyuanVideoWrapper's VAE nodes require using the original VAE model: 'https://huggingface.co/Kijai/HunyuanVideo_comfy/blob/main/hunyuan_video_vae_bf16.safetensors'
Alternatively you can also use the ComfyUI native VAELoader and the usual VAE nodes with the wrapper.""")
vae = AutoencoderKLCausal3D.from_config(vae_config)
vae.load_state_dict(vae_sd)
del vae_sd
vae.requires_grad_(False)
vae.eval()
vae.to(device = device, dtype = dtype)
#compile
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
vae = torch.compile(vae, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
return (vae,)
class HyVideoTorchCompileSettings:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"backend": (["inductor","cudagraphs"], {"default": "inductor"}),
"fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
"mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
"dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
"dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
"compile_single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}),
"compile_double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}),
"compile_txt_in": ("BOOLEAN", {"default": False, "tooltip": "Compile txt_in layers"}),
"compile_vector_in": ("BOOLEAN", {"default": False, "tooltip": "Compile vector_in layers"}),
"compile_final_layer": ("BOOLEAN", {"default": False, "tooltip": "Compile final layer"}),
},
}
RETURN_TYPES = ("COMPILEARGS",)
RETURN_NAMES = ("torch_compile_args",)
FUNCTION = "loadmodel"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "torch.compile settings, when connected to the model loader, torch.compile of the selected layers is attempted. Requires Triton and torch 2.5.0 is recommended"
def loadmodel(self, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_single_blocks, compile_double_blocks, compile_txt_in, compile_vector_in, compile_final_layer):
compile_args = {
"backend": backend,
"fullgraph": fullgraph,
"mode": mode,
"dynamic": dynamic,
"dynamo_cache_size_limit": dynamo_cache_size_limit,
"compile_single_blocks": compile_single_blocks,
"compile_double_blocks": compile_double_blocks,
"compile_txt_in": compile_txt_in,
"compile_vector_in": compile_vector_in,
"compile_final_layer": compile_final_layer
}
return (compile_args, )
#region TextEncode
class HyVideoTextEmbedBridge:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"positive": ("CONDITIONING", ),
},
"optional": {
"negative": ("CONDITIONING", ),
"hyvid_cfg": ("HYVID_CFG", {"tooltip": "The prompt from the cfg node is not used, only the settings"}),
}
}
RETURN_TYPES = ("HYVIDEMBEDS",)
RETURN_NAMES = ("hyvid_embeds",)
FUNCTION = "convert"
CATEGORY = "HunyuanVideoWrapper"
DESCRIPTION = "Acts as a bridge between the native ComfyUI conditioning and the HunyuanVideoWrapper embeds"
def convert(self, positive, negative=None, hyvid_cfg=None):
positive_cond = positive[0][0]
positive_pooled = positive[0][1]["pooled_output"]
positive_attention_mask = torch.ones(positive_cond.shape[1], dtype=torch.bool, device=positive_cond.device).unsqueeze(0)
negative_cond, negative_attention_mask, negative_pooled = None, None, None
if negative is not None:
negative_cond = negative[0][0]
negative_pooled = negative[0][1]["pooled_output"]
negative_attention_mask = torch.ones(negative_cond.shape[1], dtype=torch.bool, device=negative_cond.device).unsqueeze(0)
prompt_embeds_dict = {
"prompt_embeds": positive_cond,
"negative_prompt_embeds": negative_cond,
"attention_mask": positive_attention_mask,
"negative_attention_mask": negative_attention_mask,
"prompt_embeds_2": positive_pooled,
"negative_prompt_embeds_2": negative_pooled,
"cfg": torch.tensor(hyvid_cfg["cfg"]) if hyvid_cfg is not None else None,
"start_percent": torch.tensor(hyvid_cfg["start_percent"]) if hyvid_cfg is not None else None,
"end_percent": torch.tensor(hyvid_cfg["end_percent"]) if hyvid_cfg is not None else None,
"batched_cfg": torch.tensor(hyvid_cfg["batched_cfg"]) if hyvid_cfg is not None else None,
}
return (prompt_embeds_dict,)
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'}),
"load_device": (["main_device", "offload_device"], {"default": "offload_device"}),
}
}
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", load_device="offload_device"):
lm_type_mapping = {
"Kijai/llava-llama-3-8b-text-encoder-tokenizer": "llm",
"xtuner/llava-llama-3-8b-v1_1-transformers": "vlm",
}
lm_type = lm_type_mapping[llm_model]
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
text_encoder_load_device = device if load_device == "main_device" else offload_device
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
quantization_config = None
if quantization == "bnb_nf4":
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
if clip_model != "disabled":
clip_model_path = os.path.join(folder_paths.models_dir, "clip", "clip-vit-large-patch14")
if not os.path.exists(clip_model_path):
log.info(f"Downloading clip model to: {clip_model_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=clip_model,
ignore_patterns=["*.msgpack", "*.bin", "*.h5"],
local_dir=clip_model_path,
local_dir_use_symlinks=False,
)
text_encoder_2 = TextEncoder(
text_encoder_path=clip_model_path,
text_encoder_type="clipL",
max_length=77,
text_encoder_precision=precision,
tokenizer_type="clipL",
logger=log,
device=text_encoder_load_device,
)
else:
text_encoder_2 = None
download_path = os.path.join(folder_paths.models_dir,"LLM")
base_path = os.path.join(download_path, (llm_model.split("/")[-1]))
if not os.path.exists(base_path):
log.info(f"Downloading model to: {base_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=llm_model,
local_dir=base_path,
local_dir_use_symlinks=False,
)
text_encoder = TextEncoder(
text_encoder_path=base_path,
text_encoder_type=lm_type,
max_length=256,
text_encoder_precision=precision,
tokenizer_type=lm_type,
hidden_state_skip_layer=hidden_state_skip_layer,
apply_final_norm=apply_final_norm,
logger=log,
device=text_encoder_load_device,
dtype=dtype,
quantization_config=quantization_config
)
if quantization == "fp8_e4m3fn":
text_encoder.is_fp8 = True
text_encoder.to(torch.float8_e4m3fn)
def forward_hook(module):
def forward(hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + module.variance_epsilon)
return module.weight.to(input_dtype) * hidden_states.to(input_dtype)
return forward
for module in text_encoder.model.modules():
if module.__class__.__name__ in ["Embedding"]:
module.to(dtype)
if module.__class__.__name__ in ["LlamaRMSNorm"]:
module.forward = forward_hook(module)
else:
text_encoder.is_fp8 = False
hyvid_text_encoders = {
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
}
return (hyvid_text_encoders,)
class HyVideoCustomPromptTemplate:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"custom_prompt_template": ("STRING", {"default": f"{PROMPT_TEMPLATE['dit-llm-encode-video']['template']}", "multiline": True}),
"crop_start": ("INT", {"default": PROMPT_TEMPLATE['dit-llm-encode-video']["crop_start"], "tooltip": "To cropt the system prompt"}),
},
}
RETURN_TYPES = ("PROMPT_TEMPLATE", )
RETURN_NAMES = ("hyvid_prompt_template",)
FUNCTION = "process"
CATEGORY = "HunyuanVideoWrapper"
def process(self, custom_prompt_template, crop_start):
prompt_template_dict = {
"template": custom_prompt_template,
"crop_start": crop_start,
}
return (prompt_template_dict,)
class HyVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"text_encoders": ("HYVIDTEXTENCODER",),
"prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"force_offload": ("BOOLEAN", {"default": True}),
"prompt_template": (["video", "image", "custom", "disabled"], {"default": "video", "tooltip": "Use the default prompt templates for the llm text encoder"}),
"custom_prompt_template": ("PROMPT_TEMPLATE", {"default": PROMPT_TEMPLATE["dit-llm-encode-video"], "multiline": True}),
"clip_l": ("CLIP", {"tooltip": "Use comfy clip model instead, in this case the text encoder loader's clip_l should be disabled"}),
"hyvid_cfg": ("HYVID_CFG", ),
"model_to_offload": ("HYVIDEOMODEL", {"tooltip": "If connected, moves the video model to the offload device"}),
}
}
RETURN_TYPES = ("HYVIDEMBEDS", )
RETURN_NAMES = ("hyvid_embeds",)
FUNCTION = "process"
CATEGORY = "HunyuanVideoWrapper"
def process(self, text_encoders, prompt, force_offload=True, prompt_template="video", custom_prompt_template=None, clip_l=None, image_token_selection_expr="::4",
hyvid_cfg=None, image=None, image1=None, image2=None, clip_text_override=None, image_embed_interleave=2, model_to_offload=None):
if clip_text_override is not None and len(clip_text_override) == 0:
clip_text_override = None
device = mm.text_encoder_device()
offload_device = mm.text_encoder_offload_device()
if model_to_offload is not None:
log.info(f"Moving video model to {offload_device}...")
model_to_offload.model.to(offload_device)
text_encoder_1 = text_encoders["text_encoder"]
if clip_l is None:
text_encoder_2 = text_encoders["text_encoder_2"]
else:
text_encoder_2 = None
if hyvid_cfg is not None:
negative_prompt = hyvid_cfg["negative_prompt"]
do_classifier_free_guidance = True
else:
do_classifier_free_guidance = False
negative_prompt = None
if prompt_template != "disabled":
if prompt_template == "custom":
prompt_template_dict = custom_prompt_template
elif prompt_template == "video":
prompt_template_dict = PROMPT_TEMPLATE["dit-llm-encode-video"]
elif prompt_template == "image":
prompt_template_dict = PROMPT_TEMPLATE["dit-llm-encode"]
elif prompt_template == "I2V_video":
prompt_template_dict = PROMPT_TEMPLATE["dit-llm-encode-video-i2v"]
elif prompt_template == "I2V_image":
prompt_template_dict = PROMPT_TEMPLATE["dit-llm-encode-i2v"]
else:
raise ValueError(f"Invalid prompt_template: {prompt_template_dict}")
assert (
isinstance(prompt_template_dict, dict)
and "template" in prompt_template_dict
), f"`prompt_template` must be a dictionary with a key 'template', got {prompt_template_dict}"
assert "{}" in str(prompt_template_dict["template"]), (
"`prompt_template['template']` must contain a placeholder `{}` for the input text, "
f"got {prompt_template_dict['template']}"
)
else:
prompt_template_dict = None
def encode_prompt(self, prompt, negative_prompt, text_encoder, image_token_selection_expr="::4", semantic_images=None, image1=None, image2=None, clip_text_override=None, image_embed_interleave=2):
batch_size = 1
num_videos_per_prompt = 1
if image is not None:
#pixel_values = clip_preprocess(image.to(device), size=336, crop=True).float() * 255
#print(pixel_values.min(), pixel_values.max())
text_inputs = text_encoder.text2tokens(prompt,
prompt_template=prompt_template_dict)
prompt_outputs = text_encoder.encode(text_inputs,
prompt_template=prompt_template_dict,
image_token_selection_expr=image_token_selection_expr,
semantic_images = [semantic_images.squeeze(0) * 255] if text_encoder.text_encoder_type == "vlm" else None,
image_embed_interleave=image_embed_interleave,
device=device,
data_type=prompt_template,
)
else:
text_inputs = text_encoder.text2tokens(prompt,
prompt_template=prompt_template_dict,
image1=image1,
image2=image2,
clip_text_override=clip_text_override)
prompt_outputs = text_encoder.encode(text_inputs,
prompt_template=prompt_template_dict,
image_token_selection_expr=image_token_selection_expr,
semantic_images = None,
device=device
)
prompt_embeds = prompt_outputs.hidden_state
attention_mask = prompt_outputs.attention_mask
log.info(f"{text_encoder.text_encoder_type} prompt attention_mask shape: {attention_mask.shape}, masked tokens: {attention_mask[0].sum().item()}")
if attention_mask is not None:
attention_mask = attention_mask.to(device)
bs_embed, seq_len = attention_mask.shape
attention_mask = attention_mask.repeat(1, num_videos_per_prompt)
attention_mask = attention_mask.view(
bs_embed * num_videos_per_prompt, seq_len
)
prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# max_length = prompt_embeds.shape[1]
uncond_input = text_encoder.text2tokens(uncond_tokens, prompt_template=prompt_template_dict)
uncond_image = None
if image is not None:
if text_encoder.text_encoder_type == "vlm":
uncond_image = torch.zeros_like(semantic_images.squeeze(0))
negative_prompt_outputs = text_encoder.encode(
uncond_input,
prompt_template=prompt_template_dict,
device=device,
image_token_selection_expr=image_token_selection_expr,
semantic_images = [uncond_image] if text_encoder.text_encoder_type == "vlm" else None,
image_embed_interleave=image_embed_interleave,
data_type=prompt_template,
)
negative_prompt_embeds = negative_prompt_outputs.hidden_state
negative_attention_mask = negative_prompt_outputs.attention_mask
if negative_attention_mask is not None:
negative_attention_mask = negative_attention_mask.to(device)
_, seq_len = negative_attention_mask.shape