xinyun/ComfyUI-CogVideoXWrapper
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Update model_loading.py

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kijai 2024-11-19 19:17:42 +02:00
parent 67f2f6abb1
commit 516655b689
1 changed files with 1 additions and 30 deletions
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model_loading.py
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@ -36,8 +36,6 @@ except:
import torch
import torch.nn as nn
from .utils import check_diffusers_version, remove_specific_blocks, log
check_diffusers_version()
from diffusers.models import AutoencoderKLCogVideoX
from diffusers.schedulers import CogVideoXDDIMScheduler
@ -45,15 +43,6 @@ from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .pipeline_cogvideox import CogVideoXPipeline
from contextlib import nullcontext
from .cogvideox_fun.transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelFun
from .cogvideox_fun.fun_pab_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelFunPAB
from .cogvideox_fun.autoencoder_magvit import AutoencoderKLCogVideoX as AutoencoderKLCogVideoXFun
from .cogvideox_fun.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint
from .cogvideox_fun.pipeline_cogvideox_control import CogVideoX_Fun_Pipeline_Control
from .videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
@ -231,8 +220,6 @@ class DownloadAndLoadCogVideoModel:
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
with open(scheduler_path) as f:
scheduler_config = json.load(f)
@ -274,22 +261,6 @@ class DownloadAndLoadCogVideoModel:
for l in lora:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
pipe.fuse_lora(lora_scale=lora[-1]["strength"] / lora_rank, components=["transformer"])
#fp8
if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
for name, param in pipe.transformer.named_parameters():
params_to_keep = {"patch_embed", "lora", "pos_embedding"}
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
if fp8_transformer == "fastmode":
from .fp8_optimization import convert_fp8_linear
convert_fp8_linear(pipe.transformer, dtype)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
lora_scale = 1
dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
if any(item in lora[-1]["path"].lower() for item in dimension_loras):
@ -1057,4 +1028,4 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"CogVideoLoraSelect": "CogVideo LoraSelect",
"CogVideoXVAELoader": "CogVideoX VAE Loader",
"CogVideoXModelLoader": "CogVideoX Model Loader",
}
}