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model_loading.py
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model_loading.py
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import os
import json
import folder_paths
import comfy.model_management as mm
from typing import Union
def patched_write_atomic(
path_: str,
content: Union[str, bytes],
make_dirs: bool = False,
encode_utf_8: bool = False,
) -> None:
# Write into temporary file first to avoid conflicts between threads
# Avoid using a named temporary file, as those have restricted permissions
from pathlib import Path
import os
import shutil
import threading
assert isinstance(
content, (str, bytes)
), "Only strings and byte arrays can be saved in the cache"
path = Path(path_)
if make_dirs:
path.parent.mkdir(parents=True, exist_ok=True)
tmp_path = path.parent / f".{os.getpid()}.{threading.get_ident()}.tmp"
write_mode = "w" if isinstance(content, str) else "wb"
with tmp_path.open(write_mode, encoding="utf-8" if encode_utf_8 else None) as f:
f.write(content)
shutil.copy2(src=tmp_path, dst=path) #changed to allow overwriting cache files
os.remove(tmp_path)
try:
import torch._inductor.codecache
torch._inductor.codecache.write_atomic = patched_write_atomic
except:
pass
import torch
import torch.nn as nn
from diffusers.models import AutoencoderKLCogVideoX
from diffusers.schedulers import CogVideoXDDIMScheduler
from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .pipeline_cogvideox import CogVideoXPipeline
from contextlib import nullcontext
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from .utils import remove_specific_blocks, log
from comfy.utils import load_torch_file
script_directory = os.path.dirname(os.path.abspath(__file__))
class CogVideoLoraSelect:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora": (folder_paths.get_filename_list("cogvideox_loras"),
{"tooltip": "LORA models are expected to be in ComfyUI/models/CogVideo/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":("COGLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
"fuse_lora": ("BOOLEAN", {"default": False, "tooltip": "Fuse the LoRA weights into the transformer"}),
}
}
RETURN_TYPES = ("COGLORA",)
RETURN_NAMES = ("lora", )
FUNCTION = "getlorapath"
CATEGORY = "CogVideoWrapper"
def getlorapath(self, lora, strength, prev_lora=None, fuse_lora=False):
cog_loras_list = []
cog_lora = {
"path": folder_paths.get_full_path("cogvideox_loras", lora),
"strength": strength,
"name": lora.split(".")[0],
"fuse_lora": fuse_lora
}
if prev_lora is not None:
cog_loras_list.extend(prev_lora)
cog_loras_list.append(cog_lora)
print(cog_loras_list)
return (cog_loras_list,)
#region DownloadAndLoadCogVideoModel
class DownloadAndLoadCogVideoModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"THUDM/CogVideoX-2b",
"THUDM/CogVideoX-5b",
"THUDM/CogVideoX-5b-I2V",
"kijai/CogVideoX-5b-1.5-T2V",
"kijai/CogVideoX-5b-1.5-I2V",
"bertjiazheng/KoolCogVideoX-5b",
"kijai/CogVideoX-Fun-2b",
"kijai/CogVideoX-Fun-5b",
"kijai/CogVideoX-5b-Tora",
"alibaba-pai/CogVideoX-Fun-V1.1-2b-InP",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-InP",
"alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-Control",
"feizhengcong/CogvideoX-Interpolation",
"NimVideo/cogvideox-2b-img2vid"
],
),
},
"optional": {
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"}
),
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fastmode', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
"lora": ("COGLORA", {"default": None}),
"compile_args":("COMPILEARGS", ),
"attention_mode": (["sdpa", "sageattn", "fused_sdpa", "fused_sageattn", "comfy"], {"default": "sdpa"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
RETURN_NAMES = ("model", "vae", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Downloads and loads the selected CogVideo model from Huggingface to 'ComfyUI/models/CogVideo'"
def loadmodel(self, model, precision, quantization="disabled", compile="disabled",
enable_sequential_cpu_offload=False, block_edit=None, lora=None, compile_args=None,
attention_mode="sdpa", load_device="main_device"):
if precision == "fp16" and "1.5" in model:
raise ValueError("1.5 models do not currently work in fp16")
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
mm.soft_empty_cache()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
download_path = folder_paths.get_folder_paths("CogVideo")[0]
if "Fun" in model:
if not "1.1" in model:
repo_id = "kijai/CogVideoX-Fun-pruned"
if "2b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-2b-InP")
elif "5b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
elif "1.1" in model:
repo_id = model
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", (model.split("/")[-1])) # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
elif "2b" in model:
if 'img2vid' in model:
base_path = os.path.join(download_path, "cogvideox-2b-img2vid")
download_path = base_path
repo_id = model
else:
base_path = os.path.join(download_path, "CogVideo2B")
download_path = base_path
repo_id = model
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
elif "1.5-T2V" in model or "1.5-I2V" in model:
base_path = os.path.join(download_path, "CogVideoX-5b-1.5")
download_path = base_path
subfolder = "transformer_T2V" if "1.5-T2V" in model else "transformer_I2V"
allow_patterns = [f"*{subfolder}*", "*vae*", "*scheduler*"]
repo_id = "kijai/CogVideoX-5b-1.5"
else:
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
repo_id = model
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
if "2b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
else:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
if not os.path.exists(base_path) or not os.path.exists(os.path.join(base_path, subfolder)):
log.info(f"Downloading model to: {base_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
allow_patterns=allow_patterns,
ignore_patterns=["*text_encoder*", "*tokenizer*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder=subfolder)
transformer = transformer.to(dtype).to(transformer_load_device)
if "1.5" in model:
transformer.config.sample_height = 300
transformer.config.sample_width = 300
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
with open(scheduler_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config)
# VAE
vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
#pipeline
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if "cogvideox-2b-img2vid" in model:
pipe.input_with_padding = False
#LoRAs
if lora is not None:
dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
dimensionx_lora = False
adapter_list = []
adapter_weights = []
for l in lora:
if any(item in l["path"].lower() for item in dimensionx_loras):
dimensionx_lora = True
fuse = True if l["fuse_lora"] else False
lora_sd = load_torch_file(l["path"])
lora_rank = None
for key, val in lora_sd.items():
if "lora_B" in key:
lora_rank = val.shape[1]
break
if lora_rank is not None:
log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
adapter_name = l['path'].split("/")[-1].split(".")[0]
adapter_weight = l['strength']
pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
adapter_list.append(adapter_name)
adapter_weights.append(adapter_weight)
else:
try: #Fun trainer LoRAs are loaded differently
from .lora_utils import merge_lora
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
pipe.transformer = merge_lora(pipe.transformer, l["path"], l["strength"], device=transformer_load_device, state_dict=lora_sd)
except:
raise ValueError(f"Can't recognize LoRA {l['path']}")
if adapter_list:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
lora_scale = 1
if dimensionx_lora:
lora_scale = lora_scale / lora_rank
pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
if "fused" in attention_mode:
from diffusers.models.attention import Attention
pipe.transformer.fuse_qkv_projections = True
for module in pipe.transformer.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
pipe.transformer.attention_mode = attention_mode
if compile_args is not None:
pipe.transformer.to(memory_format=torch.channels_last)
#fp8
if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fastmode":
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
if "1.5" in model:
params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
for name, param in pipe.transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
if quantization == "fp8_e4m3fn_fastmode":
from .fp8_optimization import convert_fp8_linear
if "1.5" in model:
params_to_keep.update({"ff"}) #otherwise NaNs
convert_fp8_linear(pipe.transformer, dtype, params_to_keep=params_to_keep)
# compilation
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if "torchao" in quantization:
try:
from torchao.quantization import (
quantize_,
fpx_weight_only,
float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight
)
except:
raise ImportError("torchao is not installed, please install torchao to use fp8dq")
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: #slower for some reason on 4090
quant_func = fpx_weight_only(3, 2)
elif "fp8dq" in quantization: #very fast on 4090 when compiled
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()
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
quantize_(block, quant_func, filter_fn=filter_fn)
manual_offloading = False # to disable manual .to(device) calls
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
manual_offloading = False
# CogVideoXBlock(
# (norm1): CogVideoXLayerNormZero(
# (silu): SiLU()
# (linear): Linear(in_features=512, out_features=18432, bias=True)
# (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
# )
# (attn1): Attention(
# (norm_q): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
# (norm_k): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
# (to_q): Linear(in_features=3072, out_features=3072, bias=True)
# (to_k): Linear(in_features=3072, out_features=3072, bias=True)
# (to_v): Linear(in_features=3072, out_features=3072, bias=True)
# (to_out): ModuleList(
# (0): Linear(in_features=3072, out_features=3072, bias=True)
# (1): Dropout(p=0.0, inplace=False)
# )
# )
# (norm2): CogVideoXLayerNormZero(
# (silu): SiLU()
# (linear): Linear(in_features=512, out_features=18432, bias=True)
# (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
# )
# (ff): FeedForward(
# (net): ModuleList(
# (0): GELU(
# (proj): Linear(in_features=3072, out_features=12288, bias=True)
# )
# (1): Dropout(p=0.0, inplace=False)
# (2): Linear(in_features=12288, out_features=3072, bias=True)
# (3): Dropout(p=0.0, inplace=False)
# )
# )
# )
# if compile == "onediff":
# from onediffx import compile_pipe
# os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
# pipe = compile_pipe(
# pipe,
# backend="nexfort",
# options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
# ignores=["vae"],
# fuse_qkv_projections= False,
# )
pipeline = {
"pipe": pipe,
"dtype": dtype,
"quantization": quantization,
"base_path": base_path,
"onediff": True if compile == "onediff" else False,
"cpu_offloading": enable_sequential_cpu_offload,
"manual_offloading": manual_offloading,
"scheduler_config": scheduler_config,
"model_name": model,
}
return (pipeline, vae)
#region GGUF
class DownloadAndLoadCogVideoGGUFModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"CogVideoX_5b_GGUF_Q4_0.safetensors",
"CogVideoX_5b_I2V_GGUF_Q4_0.safetensors",
"CogVideoX_5b_1_5_I2V_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_Pose_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Interpolation_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Tora_GGUF_Q4_0.safetensors",
],
),
"vae_precision": (["fp16", "fp32", "bf16"], {"default": "bf16", "tooltip": "VAE dtype"}),
"fp8_fastmode": ("BOOLEAN", {"default": False, "tooltip": "only supported on 4090 and later GPUs, also requires torch 2.4.0 with cu124 minimum"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
},
"optional": {
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
#"compile_args":("COMPILEARGS", ),
"attention_mode": (["sdpa", "sageattn"], {"default": "sdpa"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
RETURN_NAMES = ("model", "vae",)
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model, vae_precision, fp8_fastmode, load_device, enable_sequential_cpu_offload,
block_edit=None, compile_args=None, attention_mode="sdpa"):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
vae_dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[vae_precision]
download_path = os.path.join(folder_paths.models_dir, 'CogVideo', 'GGUF')
gguf_path = os.path.join(folder_paths.models_dir, 'diffusion_models', model) # check MinusZone's model path first
if not os.path.exists(gguf_path):
gguf_path = os.path.join(download_path, model)
if not os.path.exists(gguf_path):
if "I2V" in model or "1_1" in model or "Interpolation" in model or "Tora" in model:
repo_id = "Kijai/CogVideoX_GGUF"
else:
repo_id = "MinusZoneAI/ComfyUI-CogVideoX-MZ"
log.info(f"Downloading model to: {gguf_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
allow_patterns=[f"*{model}*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
if "5b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
transformer_path = os.path.join(script_directory, 'configs', 'transformer_config_5b.json')
elif "2b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
transformer_path = os.path.join(script_directory, 'configs', 'transformer_config_2b.json')
with open(transformer_path) as f:
transformer_config = json.load(f)
from . import mz_gguf_loader
import importlib
importlib.reload(mz_gguf_loader)
with mz_gguf_loader.quantize_lazy_load():
if "fun" in model:
if "Pose" in model:
transformer_config["in_channels"] = 32
else:
transformer_config["in_channels"] = 33
elif "I2V" in model or "Interpolation" in model:
transformer_config["in_channels"] = 32
if "1_5" in model:
transformer_config["ofs_embed_dim"] = 512
transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False
transformer_config["sample_height"] = 300
transformer_config["sample_width"] = 300
else:
transformer_config["in_channels"] = 16
transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
cast_dtype = vae_dtype
params_to_keep = {"patch_embed", "pos_embedding", "time_embedding"}
if "2b" in model:
cast_dtype = torch.float16
elif "1_5" in model:
params_to_keep = {"norm1.linear.weight", "patch_embed", "time_embedding", "ofs_embedding", "norm_final", "norm_out", "proj_out"}
cast_dtype = torch.bfloat16
for name, param in transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
else:
param.data = param.data.to(cast_dtype)
#for name, param in transformer.named_parameters():
# print(name, param.data.dtype)
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
transformer.attention_mode = attention_mode
if fp8_fastmode:
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
if "1.5" in model:
params_to_keep.update({"ff","norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"})
from .fp8_optimization import convert_fp8_linear
convert_fp8_linear(transformer, vae_dtype, params_to_keep=params_to_keep)
with open(scheduler_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config, subfolder="scheduler")
# VAE
vae_dl_path = os.path.join(folder_paths.models_dir, 'CogVideo', 'VAE')
vae_path = os.path.join(vae_dl_path, "cogvideox_vae.safetensors")
if not os.path.exists(vae_path):
log.info(f"Downloading VAE model to: {vae_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Kijai/CogVideoX-Fun-pruned",
allow_patterns=["*cogvideox_vae.safetensors*"],
local_dir=vae_dl_path,
local_dir_use_symlinks=False,
)
with open(os.path.join(script_directory, 'configs', 'vae_config.json')) as f:
vae_config = json.load(f)
#VAE
vae_sd = load_torch_file(vae_path)
vae = AutoencoderKLCogVideoX.from_config(vae_config).to(vae_dtype).to(offload_device)
vae.load_state_dict(vae_sd)
del vae_sd
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=vae_dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
sd = load_torch_file(gguf_path)
pipe.transformer = mz_gguf_loader.quantize_load_state_dict(pipe.transformer, sd, device="cpu")
del sd
if load_device == "offload_device":
pipe.transformer.to(offload_device)
else:
pipe.transformer.to(device)
pipeline = {
"pipe": pipe,
"dtype": vae_dtype,
"quantization": "GGUF",
"base_path": model,
"onediff": False,
"cpu_offloading": enable_sequential_cpu_offload,
"scheduler_config": scheduler_config,
"model_name": model,
"manual_offloading": True,
}
return (pipeline, vae)
#region ModelLoader
class CogVideoXModelLoader:
@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": (["fp16", "fp32", "bf16"], {"default": "bf16"}),
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "optional quantization method"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
},
"optional": {
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
"lora": ("COGLORA", {"default": None}),
"compile_args":("COMPILEARGS", ),
"attention_mode": (["sdpa", "sageattn", "fused_sdpa", "fused_sageattn"], {"default": "sdpa"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model, base_precision, load_device, enable_sequential_cpu_offload,
block_edit=None, compile_args=None, lora=None, attention_mode="sdpa", quantization="disabled"):
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
mm.soft_empty_cache()
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)
model_type = ""
if sd["patch_embed.proj.weight"].shape == (3072, 33, 2, 2):
model_type = "fun_5b"
elif sd["patch_embed.proj.weight"].shape == (3072, 16, 2, 2):
model_type = "5b"
elif sd["patch_embed.proj.weight"].shape == (3072, 128):
model_type = "5b_1_5"
elif sd["patch_embed.proj.weight"].shape == (3072, 256):
model_type = "5b_I2V_1_5"
elif sd["patch_embed.proj.weight"].shape == (1920, 33, 2, 2):
model_type = "fun_2b"
elif sd["patch_embed.proj.weight"].shape == (1920, 16, 2, 2):
model_type = "2b"
elif sd["patch_embed.proj.weight"].shape == (3072, 32, 2, 2):
if "pos_embedding" in sd:
model_type = "fun_5b_pose"
else:
model_type = "I2V_5b"
else:
raise Exception("Selected model is not recognized")
log.info(f"Detected CogVideoX model type: {model_type}")
if "5b" in model_type:
scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_5b.json')
elif "2b" in model_type:
scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_2b.json')
with open(transformer_config_path) as f:
transformer_config = json.load(f)
if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5"]:
transformer_config["in_channels"] = 32
if "1_5" in model_type:
transformer_config["ofs_embed_dim"] = 512
elif "fun" in model_type:
transformer_config["in_channels"] = 33
else:
transformer_config["in_channels"] = 16
if "1_5" in model_type:
transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False
transformer_config["sample_height"] = 300
transformer_config["sample_width"] = 300
with init_empty_weights():
transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
#load weights
#params_to_keep = {}
log.info("Using accelerate to load and assign model weights to device...")
for name, param in transformer.named_parameters():
#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=base_dtype, value=sd[name])
del sd
#scheduler
with open(scheduler_config_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config, subfolder="scheduler")
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
if "fused" in attention_mode:
from diffusers.models.attention import Attention
transformer.fuse_qkv_projections = True
for module in transformer.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
transformer.attention_mode = attention_mode
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=base_dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
#LoRAs
if lora is not None:
dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
dimensionx_lora = False
adapter_list = []
adapter_weights = []
for l in lora:
if any(item in l["path"].lower() for item in dimensionx_loras):
dimensionx_lora = True
fuse = True if l["fuse_lora"] else False
lora_sd = load_torch_file(l["path"])
lora_rank = None
for key, val in lora_sd.items():
if "lora_B" in key:
lora_rank = val.shape[1]
break
if lora_rank is not None:
log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
adapter_name = l['path'].split("/")[-1].split(".")[0]
adapter_weight = l['strength']
pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
adapter_list.append(adapter_name)
adapter_weights.append(adapter_weight)
else:
try: #Fun trainer LoRAs are loaded differently
from .lora_utils import merge_lora
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
pipe.transformer = merge_lora(pipe.transformer, l["path"], l["strength"], device=transformer_load_device, state_dict=lora_sd)
except:
raise ValueError(f"Can't recognize LoRA {l['path']}")
if adapter_list:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
lora_scale = 1
if dimensionx_lora:
lora_scale = lora_scale / lora_rank
pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
if compile_args is not None:
pipe.transformer.to(memory_format=torch.channels_last)
#quantization
if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fast":
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
if "1.5" in model:
params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
for name, param in pipe.transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
if quantization == "fp8_e4m3fn_fast":
from .fp8_optimization import convert_fp8_linear
if "1.5" in model:
params_to_keep.update({"ff"}) #otherwise NaNs
convert_fp8_linear(pipe.transformer, base_dtype, params_to_keep=params_to_keep)
#compile
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if "torchao" in quantization:
try:
from torchao.quantization import (
quantize_,
fpx_weight_only,
float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight
)
except:
raise ImportError("torchao is not installed, please install torchao to use fp8dq")
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: #slower for some reason on 4090
quant_func = fpx_weight_only(3, 2)
elif "fp8dq" in quantization: #very fast on 4090 when compiled
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()
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
quantize_(block, quant_func, filter_fn=filter_fn)
manual_offloading = False # to disable manual .to(device) calls
log.info(f"Quantized transformer blocks to {quantization}")
pipeline = {
"pipe": pipe,
"dtype": base_dtype,
"quantization": quantization,
"base_path": model,
"onediff": False,
"cpu_offloading": enable_sequential_cpu_offload,
"scheduler_config": scheduler_config,
"model_name": model,
"manual_offloading": manual_offloading,
}
return (pipeline,)
#region VAE
class CogVideoXVAELoader:
@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"}
),
}
}
RETURN_TYPES = ("VAE",)
RETURN_NAMES = ("vae", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Loads CogVideoX VAE model from 'ComfyUI/models/vae'"
def loadmodel(self, model_name, precision):
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', '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)
vae = AutoencoderKLCogVideoX.from_config(vae_config).to(dtype).to(offload_device)
vae.load_state_dict(vae_sd)
return (vae,)
#region Tora
class DownloadAndLoadToraModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"kijai/CogVideoX-5b-Tora",
],
),
},
}
RETURN_TYPES = ("TORAMODEL",)
RETURN_NAMES = ("tora_model", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Downloads and loads the the Tora model from Huggingface to 'ComfyUI/models/CogVideo/CogVideoX-5b-Tora'"
def loadmodel(self, model):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
download_path = folder_paths.get_folder_paths("CogVideo")[0]
from .tora.traj_module import MGF
try:
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
is_accelerate_available = True
except:
is_accelerate_available = False
pass
download_path = os.path.join(folder_paths.models_dir, 'CogVideo', "CogVideoX-5b-Tora")
fuser_path = os.path.join(download_path, "fuser", "fuser.safetensors")
if not os.path.exists(fuser_path):
log.info(f"Downloading Fuser model to: {fuser_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=model,
allow_patterns=["*fuser.safetensors*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
hidden_size = 3072
num_layers = 42
with (init_empty_weights() if is_accelerate_available else nullcontext()):
fuser_list = nn.ModuleList([MGF(128, hidden_size) for _ in range(num_layers)])
fuser_sd = load_torch_file(fuser_path)
if is_accelerate_available:
for key in fuser_sd:
set_module_tensor_to_device(fuser_list, key, dtype=torch.float16, device=device, value=fuser_sd[key])
else:
fuser_list.load_state_dict(fuser_sd)
for module in fuser_list:
for param in module.parameters():
param.data = param.data.to(torch.bfloat16).to(device)
del fuser_sd
traj_extractor_path = os.path.join(download_path, "traj_extractor", "traj_extractor.safetensors")
if not os.path.exists(traj_extractor_path):
log.info(f"Downloading trajectory extractor model to: {traj_extractor_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="kijai/CogVideoX-5b-Tora",
allow_patterns=["*traj_extractor.safetensors*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
from .tora.traj_module import TrajExtractor
with (init_empty_weights() if is_accelerate_available else nullcontext()):
traj_extractor = TrajExtractor(
vae_downsize=(4, 8, 8),
patch_size=2,
nums_rb=2,
cin=16,
channels=[128] * 42,
sk=True,
use_conv=False,
)
traj_sd = load_torch_file(traj_extractor_path)
if is_accelerate_available:
for key in traj_sd:
set_module_tensor_to_device(traj_extractor, key, dtype=torch.float32, device=device, value=traj_sd[key])
else:
traj_extractor.load_state_dict(traj_sd)
traj_extractor.to(torch.float32).to(device)
toramodel = {
"fuser_list": fuser_list,
"traj_extractor": traj_extractor,
}
return (toramodel,)
#region controlnet
class DownloadAndLoadCogVideoControlNet:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"TheDenk/cogvideox-2b-controlnet-hed-v1",
"TheDenk/cogvideox-2b-controlnet-canny-v1",
"TheDenk/cogvideox-5b-controlnet-hed-v1",
"TheDenk/cogvideox-5b-controlnet-canny-v1"
],
),
},
}
RETURN_TYPES = ("COGVIDECONTROLNETMODEL",)
RETURN_NAMES = ("cogvideo_controlnet", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model):
from .cogvideo_controlnet import CogVideoXControlnet
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()