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LCM_Nodes.py
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LCM_Nodes.py
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from PIL import ImageDraw, ImageOps, ImageFilter
import json
from PIL.PngImagePlugin import PngInfo
import numpy as np
import folder_paths
from .LCM.lcm_pipeline_inpaint import LatentConsistencyModelPipeline_inpaint, LCMScheduler_X
from .LCM.lcm_pipeline_2 import LatentConsistencyModelPipeline_ipadapter
from .LCM.LCM_img2img_pipeline import LatentConsistencyModelPipeline_img2img
from .LCM.LCM_reference_pipeline import LatentConsistencyModelPipeline_reference
from .LCM.LCM_refinpaint_pipeline import LatentConsistencyModelPipeline_refinpaint
from .LCM.pipeline_cn_inpaint import LatentConsistencyModelPipeline_inpaintV2
from .LCM.pipeline_cn_inpaint_ipadapter import LatentConsistencyModelPipeline_inpaintV3
from .LCM.pipeline_inpaint_cn_reference import LatentConsistencyModelPipeline_refinpaintcn
from .LCM.pipeline_cn_reference_img2img import LatentConsistencyModelPipeline_reference_img2img
from .LCM.stable_diffusion_reference_img2img import StableDiffusionImg2ImgPipeline_reference
from .LCM.LCM_lora_inpaint import LCM_inpaint_final
from .LCM.LCM_lora_inpaint_ipadapter import LCM_lora_inpaint_ipadapter
from .LCM.pipeline_cn import LatentConsistencyModelPipeline_controlnet
from .LCM.stable_diffusion_reference_img2img import StableDiffusionImg2ImgPipeline_reference
from .LCM.stable_diffusion_reference_img2img_controlnet import StableDiffusionControlNetImg2ImgPipeline_ref
from .LCM.stable_diffusion_ipadapter_img2img_controlnet import StableDiffusionControlNetImg2ImgPipeline_ipadapter
from diffusers import DDIMScheduler,StableDiffusionControlNetImg2ImgPipeline,StableDiffusionXLPipeline, AutoPipelineForImage2Image,AutoencoderKL, UNet2DConditionModel, T2IAdapter, ControlNetModel, StableDiffusionPipeline, AutoencoderTiny, DiffusionPipeline, LCMScheduler, AutoPipelineForInpainting, StableDiffusionControlNetPipeline, StableDiffusionControlNetInpaintPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline
from diffusers.utils import get_class_from_dynamic_module
from transformers import CLIPTokenizer, CLIPTextModel, CLIPImageProcessor
import os
from pathlib import Path
import torch
from PIL import Image
from urllib import request
import tomesd
import random
from compel import Compel
import tomesd
from .IPA.ip_adapter import IPAdapter, IPAdapterPlus
from icecream import ic
import utils
import types
from comfy.cli_args import args
######Copied from WAS_Node_Suite
# Tensor to PIL
def tensor2pil(image):
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
# PIL to Tensor
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
#######
#os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:192"
def set_max_split_size_mb(model, max_split_size_mb):
"""
Set the max_split_size_mb parameter in PyTorch to avoid fragmentation.
Args:
model (torch.nn.Module): The PyTorch model.
max_split_size_mb (int): The desired value for max_split_size_mb in megabytes.
"""
for param in model.parameters():
param.requires_grad = False # Disable gradient calculation to prevent unnecessary memory allocations
# Dummy forward pass to initialize the memory allocator
dummy_input = torch.randn(1, 1)
model(dummy_input)
# Get the current memory allocator state
allocator = torch.cuda.memory._get_memory_allocator()
# Update max_split_size_mb in the memory allocator
allocator.set_max_split_size(max_split_size_mb * 1024 * 1024)
class LCMLoader_controlnet_inpaint:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),
"mode":([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,model_path,mode):
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor")
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
try:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"/{mode}"
except:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"\{mode}"
controlnet = ControlNetModel.from_pretrained(mpath)
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_refinpaintcn(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor,controlnet=controlnet)
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 LCMLoader_controlnet:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),
"mode":([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,model_path,mode):
torch.backends.cuda.matmul.allow_tf32 = True
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor")
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
try:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"/{mode}"
except:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"\{mode}"
controlnet = ControlNetModel.from_pretrained(mpath)
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_controlnet(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor,controlnet=controlnet)
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 LCMLoader_img2img:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
})
}
}
RETURN_TYPES = ("class",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self,device,tomesd_value,model_path):
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae",torch_dtype=torch.float32)
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder",torch_dtype=torch.float32)
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer",torch_dtype=torch.float32)
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True,torch_dtype=torch.float32)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor",torch_dtype=torch.float32)
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_img2img(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor)
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 LCMLoader_ReferenceOnly:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),
"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,model_path,controlnet_model):
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor")
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
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)
'''
# Replace the unet with LCM:
lcm_unet_ckpt = "./Downloads/LCM_Dreamshaper_v7_4k-prune-fp32.safetensors"
ckpt = load_file(lcm_unet_ckpt)
m, u = unet.load_state_dict(ckpt, strict=False)
if len(m) > 0:
print("missing keys:")
print(m)
if len(u) > 0:
print("unexpected keys:")
print(u)
'''
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_reference_img2img(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor,controlnet=controlnet)
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 LCMLoader_RefInpaint:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
})
}
}
RETURN_TYPES = ("class",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self,device,tomesd_value,model_path):
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor")
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
'''
# Replace the unet with LCM:
lcm_unet_ckpt = "./Downloads/LCM_Dreamshaper_v7_4k-prune-fp32.safetensors"
ckpt = load_file(lcm_unet_ckpt)
m, u = unet.load_state_dict(ckpt, strict=False)
if len(m) > 0:
print("missing keys:")
print(m)
if len(u) > 0:
print("unexpected keys:")
print(u)
'''
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_refinpaint(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor)
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 LCMLoader:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device": (["GPU", "CPU"],),
"model_path": ("STRING", {"default": '', "multiline": False}),
"tomesd_value": ("FLOAT", {
"default": 0.6,
"min": 0.0,
"max": 1.0,
"step": 0.1,
})
}
}
RETURN_TYPES = ("class",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self,device,tomesd_value,model_path):
save_path = "./lcm_images"
if model_path != "":
model_id = model_path
else:
try:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
except:
model_id = folder_paths.get_folder_paths("diffusers")[0]+"\LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer")
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor")
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
'''
# Replace the unet with LCM:
lcm_unet_ckpt = "./Downloads/LCM_Dreamshaper_v7_4k-prune-fp32.safetensors"
ckpt = load_file(lcm_unet_ckpt)
m, u = unet.load_state_dict(ckpt, strict=False)
if len(m) > 0:
print("missing keys:")
print(m)
if len(u) > 0:
print("unexpected keys:")
print(u)
'''
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_inpaint(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor)
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 LCMT2IAdapter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"T2Iadapter": ([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,T2Iadapter):
try:
model_id = folder_paths.get_folder_paths("controlnet")[0]+f"/{T2Iadapter}"
except:
model_id = folder_paths.get_folder_paths("controlnet")[0]+f"\{T2Iadapter}"
self.adapter = adapter = T2IAdapter.from_pretrained(model_id)
self.adapter = self.adapter.to(torch.device('cuda'))
return (adapter,)
class LCM_IPAdapter_inpaint:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device":(["cpu","cuda"],),
"ip_adapter":([i for i in os.listdir(folder_paths.get_folder_paths("controlnet")[0]) if str(i).endswith((".ckpt",".safetensors",".bin"))],),
"ip_adapter_full_path":("STRING", {"default": '', "multiline": False}),
"mode":([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,ip_adapter,ip_adapter_full_path,mode):
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae",torch_dtype=torch.float32)
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder",torch_dtype=torch.float32)
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer",torch_dtype=torch.float32)
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True,torch_dtype=torch.float32)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor",torch_dtype=torch.float32)
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
# LCM Pipeline:
try:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"/{mode}"
except:
mpath = folder_paths.get_folder_paths("controlnet")[0]+f"\{mode}"
controlnet = ControlNetModel.from_pretrained(mpath,torch_dtype=torch.float32)
pipe = LatentConsistencyModelPipeline_inpaintV3(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor,controlnet=controlnet)
tomesd.apply_patch(pipe, ratio=0.6)
'''if device == "cuda":
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_sequential_cpu_offload()
else:
pipe.to("cpu")'''
if ip_adapter_full_path == "":
try:
ip_ckpt = folder_paths.get_folder_paths("controlnet")[0]+f"/{ip_adapter}"
except:
ip_ckpt = folder_paths.get_folder_paths("controlnet")[0]+f"\{ip_adapter}"
else:
ip_ckpt = ip_adapter_full_path
image_encoder_path = folder_paths.get_folder_paths("clip_vision")[0]
if "plus" not in ip_adapter:
ip_model = IPAdapter(pipe, image_encoder_path, ip_ckpt, device)
else:
ip_model = IPAdapterPlus(pipe, image_encoder_path, ip_ckpt, device, num_tokens=16)
return (ip_model,)
class LCM_IPAdapter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"device":(["cpu","cuda"],),
"ip_adapter":([i for i in os.listdir(folder_paths.get_folder_paths("controlnet")[0]) if str(i).endswith((".ckpt",".safetensors",".bin"))],),
"ip_adapter_full_path":("STRING", {"default": '', "multiline": False}),
}
}
RETURN_TYPES = ("class",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self,device,ip_adapter,ip_adapter_full_path):
model_id = folder_paths.get_folder_paths("diffusers")[0]+"/LCM_Dreamshaper_v7"
# Initalize Diffusers Model:
vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae",torch_dtype=torch.float32)
text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder",torch_dtype=torch.float32)
tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer",torch_dtype=torch.float32)
unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", device_map=None, low_cpu_mem_usage=False, local_files_only=True,torch_dtype=torch.float32)
#safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker")
feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor",torch_dtype=torch.float32)
# Initalize Scheduler:
scheduler = LCMScheduler_X(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon")
# LCM Pipeline:
pipe = LatentConsistencyModelPipeline_ipadapter(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, feature_extractor=feature_extractor)
tomesd.apply_patch(pipe, ratio=0.6)
'''if device == "cuda":
pipe.to("cuda")
else:
pipe.to("cpu")'''
if ip_adapter_full_path == "":
try:
ip_ckpt = folder_paths.get_folder_paths("controlnet")[0]+f"/{ip_adapter}"
except:
ip_ckpt = folder_paths.get_folder_paths("controlnet")[0]+f"\{ip_adapter}"
else:
ip_ckpt = ip_adapter_full_path
image_encoder_path = folder_paths.get_folder_paths("clip_vision")[0]
if "plus" not in ip_adapter:
ip_model = IPAdapter(pipe, image_encoder_path, ip_ckpt, device)
else:
ip_model = IPAdapterPlus(pipe, image_encoder_path, ip_ckpt, device, num_tokens=16)
return (ip_model,)
class LCMGenerate_img2img_IPAdapter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"text": ("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,
}),
"batch": ("INT", {
"default": 1,
"min": 1,
"max": 100,
"step": 1,
}),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),
"prompt_weighting":(["disable","enable"],),
"loopback":(["disable","enable"],),
"loopback_iterations":("INT", {
"default": 4,
"min": 1,
"max": 5000,
"step": 1,
}),
"image": ("IMAGE", ),
"ip_model":("class",),
"pil_image":("IMAGE",),
"scale": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self, text: str,steps: int,width:int,height:int,cfg:float,seed: int,batch,strength,prompt_weighting,loopback,loopback_iterations,pil_image,ip_model,image,scale):
img = pil_image[0].numpy()
img = img*255.0
pil_image = Image.fromarray(np.uint8(img))
img = image[0].numpy()
img = img*255.0
image = Image.fromarray(np.uint8(img))
res = []
prompt = text
if prompt_weighting == "enable":
compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
prompt_embeds = compel_proc(prompt)
for i in range(0,batch):
seed = random.randint(0,1000000000000000)
torch.manual_seed(seed)
# Output Images:
images = ip_model.generate(pil_image=pil_image, num_samples=1, num_inference_steps=steps, seed=seed, image=image, strength=strength,scale=scale)
res.append(images[0])
if loopback == "enable" and batch==1:
for j in range(0,loopback_iterations):
images = ip_model.generate(pil_image=pil_image, num_samples=1, num_inference_steps=steps, seed=seed, image=image, strength=strength,scale=scale)
res.append(images[0])
else:
for i in range(0,batch):
seed = random.randint(0,1000000000000000)
torch.manual_seed(seed)
# Output Images:
images = ip_model.generate(pil_image=pil_image, num_samples=1, num_inference_steps=steps, seed=seed, image=image, strength=strength,scale=scale)
res.append(images[0])
if loopback == "enable" and batch==1:
for j in range(0,loopback_iterations):
images = ip_model.generate(pil_image=pil_image, num_samples=1, num_inference_steps=steps, seed=seed, image=image, strength=strength,scale=scale)
res.append(images[0])
return (res,)
class LCMGenerate:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mode": (["Inpaint", "Outpaint"],),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"text": ("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", ),
"mask": ("IMAGE", ),
"original_image": ("IMAGE", ),
"outpaint_size": ("INT", {
"default": 256,
"min": 0,
"max": 5000,
"step": 64,
}),
"outpaint_direction": (["left", "right","top","bottom"],),
"pipe":("class",),
"batch": ("INT", {
"default": 1,
"min": 1,
"max": 100,
"step": 1,
}),
"prompt_weighting":(["disable","enable"],),
"reference_image": ("IMAGE", ),
"style_fidelity": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.1,
}),
"Reference_Only":(["disable","enable"],),
"oupaint_quality":(["higher","lower"],),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mainfunc"
CATEGORY = "LCM_Nodes/nodes"
def mainfunc(self, text: str,steps: int,width:int,height:int,cfg:float,seed: int,image,mask,original_image,outpaint_size,outpaint_direction,mode,pipe,batch,prompt_weighting,style_fidelity,reference_image,Reference_Only,oupaint_quality):
img = image[0].numpy()
img = img*255.0
image = Image.fromarray(np.uint8(img)).convert("RGB")
img = mask[0].numpy()
img = img*255.0
mask = Image.fromarray(np.uint8(img)).convert("RGB")
img = reference_image[0].numpy()
img = img*255.0
reference_image = Image.fromarray(np.uint8(img)).convert("RGB")
img = original_image[0].numpy()
img = img*255.0
original_image = Image.fromarray(np.uint8(img)).convert("RGB")
res = []
prompt = text
if prompt_weighting == "enable":
compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
prompt_embeds = compel_proc(prompt)
for i in range(0,batch):
seed = random.randint(0,1000000000000000)
torch.manual_seed(seed)
# Output Images:
if Reference_Only == "enable":
images = pipe(prompt_embeds=prompt_embeds, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=width,height=height,strength = 1.0, image=image, mask_image=mask,ref_image=reference_image,style_fidelity=style_fidelity).images
else:
images = pipe(prompt_embeds=prompt_embeds, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=width,height=height,strength = 1.0, image=image, mask_image=mask).images
else:
for i in range(0,batch):
seed = random.randint(0,1000000000000000)
torch.manual_seed(seed)
# Output Images:
if Reference_Only == "enable":
images = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=width,height=height,strength = 1.0, image=image, mask_image=mask,ref_image=reference_image,style_fidelity=style_fidelity).images
else:
images = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=width,height=height,strength = 1.0, image=image, mask_image=mask).images
res.append(images[0])
if mode == "Outpaint":
if outpaint_direction == "right":
newbg = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newbg2 = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newmaskbg = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newmaskbg.paste(mask,(outpaint_size,0))
newbg.paste(original_image,(0,0))
newbg2.paste(images[0],(outpaint_size,0))
newmaskbg =newmaskbg.convert('L')
newmaskbg = ImageOps.invert(newmaskbg)
image = Image.composite(newbg, newbg2, newmaskbg)
elif outpaint_direction == "left":
newbg = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newbg2 = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newmaskbg = Image.new("RGBA",(images[0].size[0]+outpaint_size,images[0].size[1]),(0,0,0))
newmaskbg.paste(mask,(0,0))
newbg.paste(original_image,(outpaint_size,0))
newbg2.paste(images[0],(0,0))
newmaskbg =newmaskbg.convert('L')
newmaskbg = ImageOps.invert(newmaskbg)
image = Image.composite(newbg, newbg2, newmaskbg)
elif outpaint_direction =="top":
newbg = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newbg2 = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newmaskbg = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newmaskbg.paste(mask,(0,0))
newbg.paste(original_image,(0,outpaint_size))
newbg2.paste(images[0],(0,0))
newmaskbg =newmaskbg.convert('L')
newmaskbg = ImageOps.invert(newmaskbg)
image = Image.composite(newbg, newbg2, newmaskbg)
elif outpaint_direction == "bottom":
newbg = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newbg2 = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newmaskbg = Image.new("RGBA",(images[0].size[0],images[0].size[1]+outpaint_size),(0,0,0))
newmaskbg.paste(mask,(0,outpaint_size))
newbg.paste(original_image,(0,0))
newbg2.paste(images[0],(0,outpaint_size))
newmaskbg =newmaskbg.convert('L')
newmaskbg = ImageOps.invert(newmaskbg)
image = Image.composite(newbg, newbg2, newmaskbg)
else:
newres = []
for i in range(0,1):
image = res[0]
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
newres.append(image)
return (res,)
seed = random.randint(0,1000000000000000)
torch.manual_seed(seed)
# Output Images:
if mode == "Outpaint" and oupaint_quality=="higher":
newres = []
if outpaint_direction == "left":
mask = Image.new("RGB", (image.size[0],image.size[1]), (0,0,0))
draw = ImageDraw.Draw(mask)
draw.rectangle((outpaint_size-30,0,outpaint_size+30,res[0].size[1]-outpaint_size), fill=(255,255,255))
mask_blur = mask.filter(ImageFilter.GaussianBlur(10))
masknew = mask_blur
elif outpaint_direction == "right":
mask = Image.new("RGB", (image.size[0],image.size[1]), (0,0,0))
draw = ImageDraw.Draw(mask)
draw.rectangle((res[0].size[1]-outpaint_size-30,0,res[0].size[1]-outpaint_size+30,res[0].size[1]-outpaint_size), fill=(255,255,255))
mask_blur = mask.filter(ImageFilter.GaussianBlur(10))
masknew = mask_blur
elif outpaint_direction == "top":
mask = Image.new("RGB", (image.size[0],image.size[1]), (0,0,0))
draw = ImageDraw.Draw(mask)
draw.rectangle((0,outpaint_size-30,res[0].size[1]-outpaint_size,outpaint_size+30), fill=(255,255,255))
mask_blur = mask.filter(ImageFilter.GaussianBlur(10))
masknew = mask_blur
elif outpaint_direction == "bottom":
mask = Image.new("RGB", (image.size[0],image.size[1]), (0,0,0))
draw = ImageDraw.Draw(mask)
draw.rectangle((0,res[0].size[1]-outpaint_size-30,res[0].size[0]-outpaint_size,res[0].size[1]-outpaint_size+30), fill=(255,255,255))
mask_blur = mask.filter(ImageFilter.GaussianBlur(10))
masknew = mask_blur
image = image.convert("RGB")
if Reference_Only == "enable":
images = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=image.size[0],height=image.size[1],strength = 1.0, image=image, mask_image=masknew,ref_image=image,style_fidelity=style_fidelity).images
else:
images = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=steps, guidance_scale=cfg, lcm_origin_steps=50,width=image.size[0],height=image.size[1],strength = 1.0, image=image, mask_image=masknew).images
newres.append(images[0])
return (newres,)
else:
return ([image],)
class LCMGenerate_inpaintv2:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mode": (["Inpaint", "Outpaint"],),
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