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Update LCM_Nodes.py

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taabata
2024-01-01 00:51:37 +03:00
committed by GitHub
parent 67c2625040
commit ce0f73115e
1 changed files with 225 additions and 1 deletions
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LCM_Nodes.py
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@@ -2695,6 +2695,225 @@ class SaveImage_PuzzleV2:
counter += 1
return { "ui": { "images": results } }
class LCMLoraLoader_ipadapter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
files = []
for j in ["/IPAdapter/models","\IPAdapter\models"]:
try:
for i in os.listdir(folder_paths.get_folder_paths("controlnet")[0]+j):
if os.path.isfile(os.path.join(folder_paths.get_folder_paths("controlnet")[0]+j,i)):
files.append(i)
except:
pass
return {
"required": {
"device": (["GPU", "CPU"],),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.01,
}),
"ip_adapter_model":(files,),
"reference_only":(["disable","enable"],),
"ip_adapter":(["disable","enable"],),
"control_net":(["disable","enable"],),
"model_name":([i for i in os.listdir(folder_paths.get_folder_paths("diffusers")[0]) if os.path.isdir(folder_paths.get_folder_paths("diffusers")[0]+f"/{i}") or os.path.isdir(folder_paths.get_folder_paths("diffusers")[0]+f"\{i}")],),
"controlnet_model":([i for i in os.listdir(folder_paths.get_folder_paths("controlnet")[0]) if os.path.isdir(folder_paths.get_folder_paths("controlnet")[0]+f"/{i}") or os.path.isdir(folder_paths.get_folder_paths("controlnet")[0]+f"\{i}")],),
}
}
RETURN_TYPES = ("class",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self,device,tomesd_value,ip_adapter_model,model_name,reference_only,ip_adapter,controlnet_model,control_net):
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+f"/{model_name}"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+f"\{model_name}"
try:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"/{controlnet_model}"
except:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"\{controlnet_model}"
controlnet = ControlNetModel.from_pretrained(mpath)
if control_net == "disable":
if ip_adapter == "enable" and reference_only=="disable":
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id,safety_checker=None)
pipe.load_ip_adapter(folder_paths.get_folder_paths("controlnet")[0]+"/IPAdapter", subfolder="models", weight_name=ip_adapter_model)
elif reference_only == "disable":
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id,safety_checker=None)
else:
pipe = StableDiffusionImg2ImgPipeline_reference.from_pretrained(model_id,safety_checker=None)
pipe.load_ip_adapter(folder_paths.get_folder_paths("controlnet")[0]+"/IPAdapter", subfolder="models", weight_name=ip_adapter_model)
else:
if ip_adapter == "enable" and reference_only=="disable":
pipe = StableDiffusionControlNetImg2ImgPipeline_ipadapter.from_pretrained(model_id,safety_checker=None,controlnet=controlnet)
pipe.load_ip_adapter(folder_paths.get_folder_paths("controlnet")[0]+"/IPAdapter", subfolder="models", weight_name=ip_adapter_model)
elif reference_only == "disable":
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(model_id,safety_checker=None,controlnet=controlnet)
else:
pipe = StableDiffusionControlNetImg2ImgPipeline_ref.from_pretrained(model_id,safety_checker=None,controlnet=controlnet)
pipe.load_ip_adapter(folder_paths.get_folder_paths("controlnet")[0]+"/IPAdapter", subfolder="models", weight_name=ip_adapter_model)
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# load LCM-LoRA
pipe.load_lora_weights(folder_paths.get_folder_paths("loras")[0]+"/pytorch_lora_weights.safetensors")
pipe.fuse_lora()
tomesd.apply_patch(pipe, ratio=tomesd_value)
if device == "GPU":
#pipe.enable_xformers_memory_efficient_attention()
pipe.enable_sequential_cpu_offload()
else:
pipe.to("cpu")
return (pipe,)
class LCMLora_ipadapter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipe":("class",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"prompt": ("STRING", {"default": '', "multiline": True}),
"negative_prompt": ("STRING", {"default": '', "multiline": True}),
"steps": ("INT", {
"default": 4,
"min": 0,
"max": 360,
"step": 1,
}),
"width": ("INT", {
"default": 512,
"min": 0,
"max": 5000,
"step": 64,
}),
"height": ("INT", {
"default": 512,
"min": 0,
"max": 5000,
"step": 64,
}),
"cfg": ("FLOAT", {
"default": 8.0,
"min": 0,
"max": 30.0,
"step": 0.5,
}),
"image": ("IMAGE", ),
"control_image": ("IMAGE", ),
"reference_image": ("IMAGE", ),
"ipadapter_image": ("IMAGE", ),
"style_fidelity": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01,
}),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
}),
"batch": ("INT", {
"default": 1,
"min": 0,
"max": 1000,
"step": 1,
}),
"ipadapter_scale":("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 10.0,
"step": 0.01,
}),
"controlnet_conditioning_scale":("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 10.0,
"step": 0.01,
}),
"reference_only":(["disable","enable"],),
"ip_adapter":(["disable","enable"],),
"control_net":(["disable","enable"],),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self, prompt: str,steps: int,width:int,height:int,cfg:float,seed: int,image,pipe,batch,strength,reference_image,style_fidelity,ipadapter_scale,reference_only,ip_adapter,ipadapter_image,negative_prompt,control_image,controlnet_conditioning_scale,control_net):
if ip_adapter =="enable":
pipe.set_ip_adapter_scale(ipadapter_scale)
img = image[0].numpy()
img = img*255.0
image = Image.fromarray(np.uint8(img))
img = reference_image[0].numpy()
img = img*255.0
reference_image = Image.fromarray(np.uint8(img))
img = control_image[0].numpy()
img = img*255.0
control_image = Image.fromarray(np.uint8(img))
img = ipadapter_image[0].numpy()
img = img*255.0
ipadapter_image = Image.fromarray(np.uint8(img))
res = []
for m in range(batch):
if control_net == "disable":
if ip_adapter == "enable" and reference_only=="disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ip_adapter_image = ipadapter_image).images
elif reference_only == "disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg).images
elif reference_only=="enable" and ip_adapter == "disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ref_image=reference_image,style_fidelity=style_fidelity).images
else:
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ip_adapter_image = ipadapter_image,ref_image=reference_image,style_fidelity=style_fidelity).images
else:
if ip_adapter == "enable" and reference_only=="disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ip_adapter_image = ipadapter_image,controlnet_conditioning_scale=controlnet_conditioning_scale,control_image=control_image).images
elif reference_only == "disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,controlnet_conditioning_scale=controlnet_conditioning_scale,control_image=control_image).images
elif reference_only=="enable" and ip_adapter == "disable":
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ref_image=reference_image,style_fidelity=style_fidelity,control_image=control_image,controlnet_conditioning_scale=controlnet_conditioning_scale).images
else:
images = pipe(prompt,negative_prompt=negative_prompt,width=width,height=height, image=image, num_inference_steps=steps, strength=strength, guidance_scale=cfg,ip_adapter_image = ipadapter_image,ref_image=reference_image,style_fidelity=style_fidelity,controlnet_conditioning_scale=controlnet_conditioning_scale,control_image=control_image).images
res.append(images[0])
return (res,)
NODE_CLASS_MAPPINGS = {
"LCMGenerate": LCMGenerate,
"LoadImageNode_LCM":LoadImageNode_LCM,
@@ -2727,5 +2946,10 @@ NODE_CLASS_MAPPINGS = {
"Loader_SegmindVega":Loader_SegmindVega,
"SegmindVega":SegmindVega,
"SaveImage_Puzzle":SaveImage_Puzzle,
"SaveImage_PuzzleV2":SaveImage_PuzzleV2
"SaveImage_PuzzleV2":SaveImage_PuzzleV2,
"ImageSwitch":ImageSwitch,
"SettingsSwitch":SettingsSwitch,
"FloatNumber":FloatNumber,
"LCMLora_ipadapter":LCMLora_ipadapter,
"LCMLoraLoader_ipadapter":LCMLoraLoader_ipadapter,
}