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Update LucidFlux_node.py

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smthemex
2025-12-10 20:00:53 +08:00
committed by GitHub
parent ba02fbb594
commit b90de4e64f
1 changed files with 35 additions and 31 deletions
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LucidFlux_node.py
+35 -31
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@@ -38,18 +38,20 @@ class LucidFlux_SM_Model(io.ComfyNode):
inputs=[
io.Combo.Input("LucidFlux",options= ["none"] + [i for i in folder_paths.get_filename_list("LucidFlux") if "lucid" in i.lower()]),
io.Combo.Input("diffusion_models",options= ["none"] + folder_paths.get_filename_list("diffusion_models")),
io.Boolean.Input("user_accelerate", default=True),
io.Boolean.Input("use_accelerate", default=True),
io.Boolean.Input("use_quantize", default=False),
io.Boolean.Input("use_mmgp", default=False),
io.Boolean.Input("mmgp_quantize", default=False),
io.Int.Input("profile_number", default=2, min=1, max=5,step=1),
io.Model.Input("cf_model", optional=True),
],
outputs=[
io.Custom("LucidFlux_SM").Output(),
io.Custom("LucidFlux_SD").Output(),
io.Custom("LucidFlux_SM").Output(display_name="model"),
io.Custom("LucidFlux_SD").Output(display_name="state"),
],
)
@classmethod
def execute(cls, LucidFlux,diffusion_models,user_accelerate,use_quantize,cf_model=None) -> io.NodeOutput:
def execute(cls, LucidFlux,diffusion_models,use_accelerate,use_quantize,use_mmgp,mmgp_quantize,profile_number,cf_model=None) -> io.NodeOutput:
is_dev="flux-dev" if "dev" in diffusion_models.lower() else "flux-schnell"
if cf_model is not None:
if "guidance_in.in_layer.weight" in cf_model.model.diffusion_model.state_dict().keys():
@@ -69,7 +71,18 @@ class LucidFlux_SM_Model(io.ComfyNode):
"checkpoint":LucidFlux_path,
}
args=OmegaConf.create(origin_dict)
model,state=load_lucidflux_model(args,ckpt_path,cf_model,user_accelerate,use_quantize,device,)
model,state=load_lucidflux_model(args,ckpt_path,cf_model,use_accelerate,use_quantize,device,)
if use_quantize:
mmgp_quantize=False
if use_accelerate and use_mmgp:
print("if use accelerate ,can not use_mmgp")
use_mmgp=False
if use_mmgp:
from mmgp import offload
pipeline = { "transformer": model }
offload.profile(pipeline, quantizeTransformer = mmgp_quantize, profile_no = profile_number ) # uncomment this line and comment the previous one if you have 24 GB of VRAM and wants faster generation
state["use_mmgp"]=use_mmgp
model.use_mmgp=use_mmgp
return io.NodeOutput(model,state)
@@ -142,8 +155,8 @@ class LucidFlux_SM_Cond(io.ComfyNode):
io.Custom("LucidFlux_SM").Input("model"),
io.Combo.Input("lora1",options= ["none"] + folder_paths.get_filename_list("loras")),
io.Combo.Input("lora2",options= ["none"] + folder_paths.get_filename_list("loras")),
io.Float.Input("scale1", default=1.0, min=0.0, max=1.0, step=0.1,display_mode=io.NumberDisplay.slider),
io.Float.Input("scale2", default=1.0, min=0.0, max=1.0, step=0.1,display_mode=io.NumberDisplay.slider),
io.Float.Input("scale1", default=1.0, min=0.0, max=1.0, step=0.1,display_mode=io.NumberDisplay.number),
io.Float.Input("scale2", default=1.0, min=0.0, max=1.0, step=0.1,display_mode=io.NumberDisplay.number),
],
outputs=[io.Custom("LucidFlux_SM").Output()],
)
@@ -165,7 +178,7 @@ class LucidFlux_SM_Encode(io.ComfyNode):
display_name="LucidFlux_SM_Encode",
category="LucidFlux_SM",
inputs=[
io.Custom("LucidFlux_SD").Input("state_dict"),
io.Custom("LucidFlux_SD").Input("state"),
io.ClipVision.Input("CLIP_VISION"),
io.Image.Input("image"),# B H W C C=3
io.Combo.Input("emb",options= ["none"] + [i for i in folder_paths.get_filename_list("LucidFlux") if "prompt" in i.lower() ]),
@@ -176,13 +189,13 @@ class LucidFlux_SM_Encode(io.ComfyNode):
],
)
@classmethod
def execute(cls, state_dict,CLIP_VISION, image,emb,positive=None) -> io.NodeOutput:
def execute(cls, state,CLIP_VISION, image,emb,positive=None) -> io.NodeOutput:
emb_path=folder_paths.get_full_path("LucidFlux", emb) if emb != "none" else None
_,height,width,_=image.shape
tensor_list=tensor2list(image)
tensor_list=[i.to(device) for i in tensor_list]
inp_cond=get_cond(positive,emb_path,height,width,device)
postive=preprocess_data(state_dict,CLIP_VISION,tensor_list, inp_cond,device)
postive=preprocess_data(state,CLIP_VISION,tensor_list, inp_cond,device)
cf_models=mm.loaded_models()
try:
for pipe in cf_models:
@@ -211,35 +224,26 @@ class LucidFlux_SM_KSampler(io.ComfyNode):
io.Float.Input("cfg", default=4.0, min=0.0, max=100.0, step=0.1, round=0.01,),
io.Boolean.Input("wavelet", default=True),
io.Conditioning.Input("condition"),
io.Boolean.Input("use_mmgp", default=True),
io.Boolean.Input("mmgp_quantize", default=True),
io.Int.Input("profile_number", default=4, min=1, max=5,step=1),
],
outputs=[
io.Image.Output(display_name="Image"),
],
)
@classmethod
def execute(cls, model,vae, steps,seed, cfg,wavelet, condition,use_mmgp,mmgp_quantize,profile_number ) -> io.NodeOutput:
pipe=model.get("model")
user_accelerate=model.get("user_accelerate",True)
if not user_accelerate:
if use_mmgp:
from mmgp import offload
pipeline = { "transformer": pipe, }
offload.profile(pipeline, quantizeTransformer = mmgp_quantize, profile_no = profile_number ) # uncomment this line and comment the previous one if you have 24 GB of VRAM and wants faster generation
else:
pipe.to(device)
def execute(cls, model,vae, steps,seed, cfg,wavelet, condition, ) -> io.NodeOutput:
use_accelerate=condition.get("use_accelerate",True)
use_mmgp=condition.get("use_mmgp",True)
if not use_accelerate and not use_mmgp:
model.to(device)
dual_condition_branch=model.get("dual_condition_branch")
x=lucidflux_inference(pipe,dual_condition_branch,condition,cfg,steps,seed,device,model.get("is_schnell",False)) #torch.Size([1, 16, 128, 128])
pipe=None
del pipe
dual_condition_branch=condition.get("dual_condition_branch")
x=lucidflux_inference(model,dual_condition_branch,condition["data_list"],cfg,steps,seed,device,condition.get("is_schnell",False)) #torch.Size([1, 16, 128, 128])
torch.cuda.empty_cache()
images=[]
for i ,j in zip(x,condition):
for i ,j in zip(x,condition["data_list"]):
if not wavelet:
hq=vae.decode(i)
else: