545 lines
28 KiB
Python
545 lines
28 KiB
Python
# !/usr/bin/env python
|
|
# -*- coding: UTF-8 -*-
|
|
import cv2
|
|
import torch
|
|
import os
|
|
from PIL import Image
|
|
import numpy as np
|
|
from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMScheduler, ControlNetModel,
|
|
KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
|
|
AutoPipelineForInpainting, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
|
|
EulerDiscreteScheduler, HeunDiscreteScheduler, UNet2DConditionModel,
|
|
StableDiffusionXLImg2ImgPipeline, AutoPipelineForImage2Image,
|
|
AutoPipelineForText2Image, StableDiffusionXLControlNetImg2ImgPipeline, KDPM2DiscreteScheduler,
|
|
EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
|
|
StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler,
|
|
StableDiffusionPipeline, StableDiffusionControlNetPipeline, StableDiffusionXLInpaintPipeline)
|
|
try:
|
|
from diffusers.loaders.single_file_utils import load_single_file_checkpoint,infer_diffusers_model_type
|
|
except:
|
|
from diffusers.loaders.single_file_utils import load_single_file_model_checkpoint as load_single_file_checkpoint,infer_model_type as infer_diffusers_model_type
|
|
from .hidiffusion.hidiffusion import apply_hidiffusion,remove_hidiffusion
|
|
import folder_paths
|
|
from safetensors.torch import load_file
|
|
import yaml
|
|
import diffusers
|
|
import random
|
|
from omegaconf import OmegaConf
|
|
from comfy.model_management import cleanup_models
|
|
from comfy.clip_vision import load as load_clip
|
|
|
|
dif_version = str(diffusers.__version__)
|
|
dif_version_int = int(dif_version.split(".")[1])
|
|
if dif_version_int >= 28:
|
|
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
|
|
else:
|
|
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
|
from comfy.utils import common_upscale
|
|
from .guided_filter import FastGuidedFilter
|
|
from .ip_adapter import IPAdapterXL,IPAdapter
|
|
|
|
dir_path = os.path.dirname(os.path.abspath(__file__))
|
|
path_dir = os.path.dirname(dir_path)
|
|
file_path = os.path.dirname(path_dir)
|
|
|
|
|
|
scheduler_list = ["DDIM",
|
|
"Euler",
|
|
"Euler a",
|
|
"DDPM",
|
|
"DPM++ 2M",
|
|
"DPM++ 2M Karras",
|
|
"DPM++ 2M SDE",
|
|
"DPM++ 2M SDE Karras",
|
|
"DPM++ SDE",
|
|
"DPM++ SDE Karras",
|
|
"DPM2",
|
|
"DPM2 Karras",
|
|
"DPM2 a",
|
|
"DPM2 a Karras",
|
|
"Heun",
|
|
"LCM",
|
|
"LMS",
|
|
"LMS Karras",
|
|
"UniPC",
|
|
]
|
|
|
|
fs = open(os.path.join(dir_path, "model.yaml"), encoding="UTF-8")
|
|
datas = yaml.load(fs, Loader=yaml.FullLoader)
|
|
|
|
normal_model_list = datas["surport_model"]
|
|
sdxl_lightning_list = datas["lightning_unet"]
|
|
controlnet_suport = datas["surport_controlnet"]
|
|
xl_model_support = datas["sdxl_model"]
|
|
lightning_lora=datas["lightning_lora"]
|
|
lightning_xl_lora=datas["lightning_xl_lora"]
|
|
|
|
lcm_unet = ["dmd2_sdxl_4step_unet_fp16.bin", "dmd2_sdxl_1step_unet_fp16.bin", "lcm-sdxl-base-1.0.safetensors",
|
|
"Hyper-SDXL-1step-Unet.safetensors"]
|
|
|
|
def tensor_to_image(tensor):
|
|
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
|
image = Image.fromarray(image_np, mode='RGB')
|
|
return image
|
|
|
|
def nomarl_upscale(img_tensor, width, height):
|
|
samples = img_tensor.movedim(-1, 1)
|
|
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
|
samples = img.movedim(1, -1)
|
|
img_pil = tensor_to_image(samples)
|
|
return img_pil
|
|
|
|
def resize_image_control(control_image, resolution):
|
|
HH, WW, _ = control_image.shape
|
|
crop_h = random.randint(0, HH - resolution[1])
|
|
crop_w = random.randint(0, WW - resolution[0])
|
|
crop_image = control_image[crop_h:crop_h+resolution[1], crop_w:crop_w+resolution[0], :]
|
|
return crop_image, crop_w, crop_h
|
|
|
|
def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0):
|
|
if ksize % 2 == 0:
|
|
ksize += 1 # ksize must be odd
|
|
blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX)
|
|
return blurred_image
|
|
|
|
def apply_guided_filter(image_np, radius, eps, scale):
|
|
filter = FastGuidedFilter(image_np, radius, eps, scale)
|
|
return filter.filter(image_np)
|
|
|
|
def input_size_adaptation_output(img_tensor,base_in, width, height):
|
|
#basein=1024
|
|
if width == height:
|
|
img_pil = nomarl_upscale(img_tensor, base_in, base_in) # 2pil
|
|
else:
|
|
if min(1,width/ height)==1: #高
|
|
r=height/base_in
|
|
img_pil = nomarl_upscale(img_tensor, round(width/r), base_in) # 2pil
|
|
else: #宽
|
|
r=width/base_in
|
|
img_pil = nomarl_upscale(img_tensor, base_in, round(height/r)) # 2pil
|
|
return img_pil
|
|
|
|
def get_sheduler(name):
|
|
scheduler = False
|
|
if name == "Euler":
|
|
scheduler = EulerDiscreteScheduler()
|
|
elif name == "Euler a":
|
|
scheduler = EulerAncestralDiscreteScheduler()
|
|
elif name == "DDIM":
|
|
scheduler = DDIMScheduler()
|
|
elif name == "DDPM":
|
|
scheduler = DDPMScheduler()
|
|
elif name == "DPM++ 2M":
|
|
scheduler = DPMSolverMultistepScheduler()
|
|
elif name == "DPM++ 2M Karras":
|
|
scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True)
|
|
elif name == "DPM++ 2M SDE":
|
|
scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")
|
|
elif name == "DPM++ 2M SDE Karras":
|
|
scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
|
|
elif name == "DPM++ SDE":
|
|
scheduler = DPMSolverSinglestepScheduler()
|
|
elif name == "DPM++ SDE Karras":
|
|
scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True)
|
|
elif name == "DPM2":
|
|
scheduler = KDPM2DiscreteScheduler()
|
|
elif name == "DPM2 Karras":
|
|
scheduler = KDPM2DiscreteScheduler(use_karras_sigmas=True)
|
|
elif name == "DPM2 a":
|
|
scheduler = KDPM2AncestralDiscreteScheduler()
|
|
elif name == "DPM2 a Karras":
|
|
scheduler = KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)
|
|
elif name == "Heun":
|
|
scheduler = HeunDiscreteScheduler()
|
|
elif name == "LCM":
|
|
scheduler = LCMScheduler()
|
|
elif name == "LMS":
|
|
scheduler = LMSDiscreteScheduler()
|
|
elif name == "LMS Karras":
|
|
scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
|
|
elif name == "UniPC":
|
|
scheduler = UniPCMultistepScheduler()
|
|
return scheduler
|
|
|
|
|
|
class HI_Diffusers_Model_Loader:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"function_choice": (["txt2img", "img2img", ],),
|
|
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
|
"vae_id": (["none"] + folder_paths.get_filename_list("vae"),),
|
|
"unet_model": (["none"] + folder_paths.get_filename_list("unet"),),
|
|
"controlnet_model": (["none"] + folder_paths.get_filename_list("controlnet"),),
|
|
"lora": (["none"] + folder_paths.get_filename_list("loras"),),
|
|
"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
|
|
"trigger_words": ("STRING", {"default": "best quality"}),
|
|
"scheduler": (scheduler_list,),
|
|
"apply_window_attn":("BOOLEAN", {"default": False},),
|
|
"ip_ckpt": (["none"] + folder_paths.get_filename_list("photomaker"),),
|
|
"clip_vision": (["none"] + folder_paths.get_filename_list("clip_vision"),),
|
|
|
|
}
|
|
}
|
|
|
|
|
|
RETURN_TYPES = ("HIDIF_MODEL", )
|
|
RETURN_NAMES = ("pipe", )
|
|
FUNCTION = "loader_models"
|
|
CATEGORY = "Hidiffusion_Pro"
|
|
|
|
def loader_models(self,function_choice, ckpt_name,vae_id,unet_model, controlnet_model,
|
|
lora,lora_scale,trigger_words,scheduler,apply_window_attn,ip_ckpt,clip_vision):
|
|
|
|
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) if ckpt_name!="none" else None
|
|
sd_type=""
|
|
if ckpt_path:
|
|
sd = load_single_file_checkpoint(ckpt_path)
|
|
try:
|
|
sd_type = infer_diffusers_model_type(sd)
|
|
del sd
|
|
except:
|
|
raise "diffuser need >0.27.2"
|
|
|
|
|
|
vae_id=vae_id if vae_id!="none" else None
|
|
controlnet_path=folder_paths.get_full_path("controlnet", controlnet_model) if controlnet_model!="none" else None
|
|
unet_ckpt = folder_paths.get_full_path("unet", unet_model) if unet_model!="none" else None
|
|
ip_ckpt = folder_paths.get_full_path("photomaker", ip_ckpt) if ip_ckpt != "none" else None
|
|
clip_vision = folder_paths.get_full_path("clip_vision", clip_vision) if clip_vision != "none" else None
|
|
|
|
scheduler_used = get_sheduler(scheduler)
|
|
|
|
if sd_type == "v1" or sd_type == "v2":
|
|
model_type="stable-diffusion-v1-5"
|
|
model_config=os.path.join(dir_path,"sd15_config")
|
|
original_config_file = os.path.join(folder_paths.models_dir, "configs", "v1-inference.yaml")
|
|
if dif_version_int >= 28:
|
|
model = StableDiffusionPipeline.from_single_file(
|
|
ckpt_path, config=model_config,original_config=original_config_file, torch_dtype=torch.float16).to("cuda")
|
|
else:
|
|
model = StableDiffusionPipeline.from_single_file(
|
|
ckpt_path,config=model_config, original_config_file=original_config_file, torch_dtype=torch.float16).to("cuda")
|
|
|
|
elif sd_type =="playground-v2-5":
|
|
model_type = "playground-v2-1024px-aesthetic"
|
|
model_config ="playgroundai/playground-v2.5-1024px-aesthetic"
|
|
model = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=model_config,torch_dtype=torch.float16).to("cuda")
|
|
|
|
elif sd_type == "xl_inpaint":
|
|
model_type ="stable-diffusion-xl-1.0-inpainting-0.1"
|
|
model_config = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
|
|
original_config_file = os.path.join(dir_path, "weights", "sd_xl_base.yaml")
|
|
if dif_version_int >= 28:
|
|
model = StableDiffusionXLInpaintPipeline.from_single_file(ckpt_path,config=model_config,
|
|
original_config=original_config_file,
|
|
torch_dtype=torch.float16,
|
|
)
|
|
else:
|
|
model = StableDiffusionXLInpaintPipeline.from_single_file(ckpt_path,config=model_config,
|
|
original_config_file=original_config_file,
|
|
torch_dtype=torch.float16,
|
|
)
|
|
if unet_model in sdxl_lightning_list:
|
|
if unet_model.rsplit('.', 1)[-1] == "bin":
|
|
model.unet.load_state_dict(torch.load(unet_ckpt),strict=False,)
|
|
else:
|
|
model.unet.load_state_dict(load_file(unet_ckpt), strict=False, )
|
|
elif sd_type == "xl_base":
|
|
model_type = "stable-diffusion-xl-base-1.0"
|
|
model_config=os.path.join(dir_path,"sdxl_config")
|
|
original_config_file = os.path.join(dir_path, "weights", "sd_xl_base.yaml")
|
|
|
|
if dif_version_int >= 28:
|
|
model = StableDiffusionXLPipeline.from_single_file(
|
|
ckpt_path, config=model_config,original_config=original_config_file, torch_dtype=torch.float16)
|
|
else:
|
|
model = StableDiffusionXLPipeline.from_single_file(
|
|
ckpt_path,config=model_config, original_config_file=original_config_file, torch_dtype=torch.float16)
|
|
|
|
if controlnet_path:
|
|
controlnet = ControlNetModel.from_unet(model.unet)
|
|
cn_state_dict = load_file(controlnet_path)
|
|
controlnet.load_state_dict(cn_state_dict, strict=False)
|
|
controlnet.to(torch.float16)
|
|
if function_choice == "img2img":
|
|
model = StableDiffusionXLControlNetImg2ImgPipeline.from_pipe(model,controlnet=controlnet)
|
|
else:
|
|
model = StableDiffusionXLControlNetPipeline.from_pipe(model,controlnet=controlnet)
|
|
|
|
if unet_model in sdxl_lightning_list:
|
|
if unet_model.rsplit('.', 1)[-1] == "bin":
|
|
model.unet.load_state_dict(torch.load(unet_ckpt), strict=False,)
|
|
else:
|
|
model.unet.load_state_dict(load_file(unet_ckpt), strict=False, )
|
|
else:
|
|
raise "unsupport model!!"
|
|
if vae_id:
|
|
vae_id = folder_paths.get_full_path("vae", vae_id)
|
|
if sd_type == "xl_base" or sd_type == "xl_inpaint":
|
|
vae_config=os.path.join(dir_path,"sdxl_config","vae")
|
|
elif sd_type == "v1" or sd_type == "v2" :
|
|
vae_config=os.path.join(dir_path, "sd15_config","vae")
|
|
elif sd_type == "playground-v2-5" :
|
|
vae_config=os.path.join(dir_path,"weights/playground")
|
|
else:
|
|
raise "vae not support"
|
|
model.vae = AutoencoderKL.from_single_file(vae_id,config=vae_config, torch_dtype=torch.float16).to("cuda")
|
|
if sd_type == "xl_inpaint":
|
|
model.scheduler =scheduler_used.from_pretrained(os.path.join(dir_path,"sdxl_config"), subfolder="scheduler")
|
|
else:
|
|
model.scheduler = scheduler_used.from_config(model.scheduler.config, timestep_spacing="trailing")
|
|
|
|
if lora!="none":
|
|
lora_path = folder_paths.get_full_path("loras", lora)
|
|
model.load_lora_weights(lora_path, adapter_name=trigger_words)
|
|
model.fuse_lora(lora_scale=lora_scale, adapter_names=[trigger_words,])
|
|
|
|
model.enable_xformers_memory_efficient_attention()
|
|
model.enable_vae_tiling()
|
|
apply_hidiffusion(model,apply_window_attn=apply_window_attn,model_type_str=model_type)
|
|
model.enable_model_cpu_offload() # need below apply_hidiffusion(model)
|
|
ip_adapter = False
|
|
if ip_ckpt is not None and clip_vision is not None:
|
|
model.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
|
|
device = "cuda"
|
|
remove_hidiffusion(model)
|
|
image_encoder = load_clip(clip_vision)
|
|
if sd_type == "xl_base":
|
|
config_path=os.path.join(dir_path,"weights","sdxl","config.json")
|
|
image_encoder_config = OmegaConf.load(config_path)
|
|
model = IPAdapterXL(model, image_encoder, ip_ckpt, device,image_encoder_config,
|
|
target_blocks=["up_blocks.0.attentions.1"])
|
|
elif sd_type == "v1":
|
|
config_path = os.path.join(dir_path, "weights", "sd15","config.json")
|
|
image_encoder_config = OmegaConf.load(config_path)
|
|
model = IPAdapter(model, image_encoder, ip_ckpt, device,image_encoder_config, target_blocks=["block"])
|
|
else:
|
|
raise "unsupport model,only support SDXL or SD1.5"
|
|
torch.cuda.empty_cache()
|
|
ip_adapter=True
|
|
|
|
torch.cuda.empty_cache()
|
|
pipe={"model":model,"controlnet_path":controlnet_path,"sd_type":sd_type,"lora":lora,"trigger_words":trigger_words,"ip_adapter":ip_adapter,"function_choice":function_choice}
|
|
torch.cuda.empty_cache()
|
|
return (pipe,)
|
|
|
|
|
|
class Hi_Sampler:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"pipe": ("HIDIF_MODEL",),
|
|
"prompt": ("STRING", {"multiline": True,
|
|
"default": "a girl,8k,smile,best quality"}),
|
|
"negative_prompt": ("STRING", {"multiline": True,
|
|
"default": "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry"}),
|
|
"controlnet_scale": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
|
|
"clip_skip": ("INT", {"default": 1, "min": -5, "max": 100,"step": 1}),
|
|
"pre_input": ("INT", {"default": 512, "min": 256, "max": 1024, "step": 64}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
|
|
"width": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 64, "display": "number"}),
|
|
"height": ("INT", {"default": 2048, "min": 64, "max": 8192, "step": 64, "display": "number"}),
|
|
"adapter_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1,}),
|
|
},
|
|
"optional": {"image": ("IMAGE",),
|
|
"control_image": ("IMAGE",),
|
|
"ip_image": ("IMAGE",)}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("image",)
|
|
FUNCTION = "hi_sampler"
|
|
CATEGORY = "Hidiffusion_Pro"
|
|
|
|
|
|
def hi_sampler(self, pipe, prompt, negative_prompt,controlnet_scale,clip_skip,pre_input,
|
|
seed,steps, cfg, width,height,adapter_scale,**kwargs):
|
|
model=pipe.get("model",None)
|
|
controlnet_path = pipe["controlnet_path"]
|
|
sd_type = pipe["sd_type"]
|
|
lora = pipe["lora"]
|
|
trigger_words = pipe["trigger_words"]
|
|
ip_adapter = pipe["ip_adapter"]
|
|
function_choice =pipe["function_choice"]
|
|
|
|
if ip_adapter:
|
|
ip_image = kwargs.get("ip_image")
|
|
#ip_image = input_size_adaptation_output(ip_image, pre_input, width, height)
|
|
if lora != "none":
|
|
prompt = prompt + " " + trigger_words
|
|
if controlnet_path is None:
|
|
if function_choice == "img2img":
|
|
image = kwargs.get("image")
|
|
image = input_size_adaptation_output(image, pre_input, width, height)
|
|
images = \
|
|
model.generate(prompt=prompt, negative_prompt=negative_prompt,pil_image=ip_image, image=image, scale=adapter_scale,num_inference_steps=steps,
|
|
guidance_scale=cfg, clip_skip=clip_skip,
|
|
height=height, width=width, seed=seed, )
|
|
else:
|
|
images = \
|
|
model.generate(pil_image=ip_image,prompt=prompt, negative_prompt=negative_prompt,scale=adapter_scale, num_inference_steps=steps,
|
|
guidance_scale=cfg, clip_skip=clip_skip,
|
|
height=height, width=width, seed=seed, )
|
|
|
|
else:
|
|
control_image = kwargs.get("control_image")
|
|
if "tile" in controlnet_path:
|
|
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
|
controlnet_img = cv2.cvtColor(np.asarray(control_image), cv2.COLOR_RGB2BGR)
|
|
new_height, new_width, _ = controlnet_img.shape
|
|
ratio = np.sqrt(1024. * 1024. / (new_width * new_height))
|
|
W, H = int(new_width * ratio), int(new_height * ratio)
|
|
|
|
crop_w, crop_h = 0, 0
|
|
controlnet_img = cv2.resize(controlnet_img, (W, H))
|
|
|
|
blur_strength = random.sample([i / 10. for i in range(10, 201, 2)], k=1)[0]
|
|
radius = random.sample([i for i in range(1, 40, 2)], k=1)[0]
|
|
eps = random.sample([i / 1000. for i in range(1, 101, 2)], k=1)[0]
|
|
scale_factor = random.sample([i / 10. for i in range(10, 181, 5)], k=1)[0]
|
|
|
|
if random.random() > 0.5:
|
|
controlnet_img = apply_gaussian_blur(controlnet_img, ksize=int(blur_strength),
|
|
sigmaX=blur_strength / 2)
|
|
|
|
if random.random() > 0.5:
|
|
# Apply Guided Filter
|
|
controlnet_img = apply_guided_filter(controlnet_img, radius, eps, scale_factor)
|
|
|
|
# Resize image
|
|
controlnet_img = cv2.resize(controlnet_img, (int(W / scale_factor), int(H / scale_factor)),
|
|
interpolation=cv2.INTER_AREA)
|
|
controlnet_img = cv2.resize(controlnet_img, (W, H), interpolation=cv2.INTER_CUBIC)
|
|
|
|
controlnet_img = cv2.cvtColor(controlnet_img, cv2.COLOR_BGR2RGB)
|
|
control_image = Image.fromarray(controlnet_img)
|
|
else:
|
|
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
|
if function_choice == "img2img":
|
|
image = kwargs["image"]
|
|
image = input_size_adaptation_output(image, pre_input, width, height)
|
|
if sd_type == "xl_inpaint":
|
|
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, image=image,pil_image=ip_image, scale=adapter_scale,mask_image=control_image,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, clip_skip=clip_skip,
|
|
width=width, controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, )
|
|
else:
|
|
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, image=image, pil_image=ip_image,scale=adapter_scale,control_image=control_image,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
|
clip_skip=clip_skip,
|
|
controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, )
|
|
else:
|
|
images = model.generate(prompt=prompt, negative_prompt=negative_prompt, pil_image=ip_image,scale=adapter_scale,control_image=control_image,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
|
clip_skip=clip_skip,
|
|
controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, )
|
|
images = images[0]
|
|
else:
|
|
if lora != "none":
|
|
prompt = prompt + " " + trigger_words
|
|
# print(model_type, unet_model, control_net, function_choice)
|
|
if controlnet_path is None:
|
|
if function_choice == "img2img":
|
|
image = kwargs["image"]
|
|
image = input_size_adaptation_output(image, pre_input, width, height)
|
|
images = \
|
|
model(prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=steps,
|
|
guidance_scale=cfg, clip_skip=clip_skip,
|
|
height=height, width=width, seed=seed, ).images[0]
|
|
else:
|
|
images = \
|
|
model(prompt, negative_prompt=negative_prompt, num_inference_steps=steps,
|
|
guidance_scale=cfg, clip_skip=clip_skip,
|
|
height=height, width=width, seed=seed, ).images[0]
|
|
else:
|
|
control_image = kwargs["control_image"]
|
|
if "tile" in controlnet_path:
|
|
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
|
controlnet_img = cv2.cvtColor(np.asarray(control_image), cv2.COLOR_RGB2BGR)
|
|
new_height, new_width, _ = controlnet_img.shape
|
|
ratio = np.sqrt(1024. * 1024. / (new_width * new_height))
|
|
W, H = int(new_width * ratio), int(new_height * ratio)
|
|
|
|
crop_w, crop_h = 0, 0
|
|
controlnet_img = cv2.resize(controlnet_img, (W, H))
|
|
|
|
blur_strength = random.sample([i / 10. for i in range(10, 201, 2)], k=1)[0]
|
|
radius = random.sample([i for i in range(1, 40, 2)], k=1)[0]
|
|
eps = random.sample([i / 1000. for i in range(1, 101, 2)], k=1)[0]
|
|
scale_factor = random.sample([i / 10. for i in range(10, 181, 5)], k=1)[0]
|
|
|
|
if random.random() > 0.5:
|
|
controlnet_img = apply_gaussian_blur(controlnet_img, ksize=int(blur_strength),
|
|
sigmaX=blur_strength / 2)
|
|
|
|
if random.random() > 0.5:
|
|
# Apply Guided Filter
|
|
controlnet_img = apply_guided_filter(controlnet_img, radius, eps, scale_factor)
|
|
|
|
# Resize image
|
|
controlnet_img = cv2.resize(controlnet_img, (int(W / scale_factor), int(H / scale_factor)),
|
|
interpolation=cv2.INTER_AREA)
|
|
controlnet_img = cv2.resize(controlnet_img, (W, H), interpolation=cv2.INTER_CUBIC)
|
|
|
|
controlnet_img = cv2.cvtColor(controlnet_img, cv2.COLOR_BGR2RGB)
|
|
control_image = Image.fromarray(controlnet_img)
|
|
else:
|
|
control_image = input_size_adaptation_output(control_image, pre_input, width, height)
|
|
|
|
if function_choice == "img2img":
|
|
image = kwargs["image"]
|
|
image = input_size_adaptation_output(image, pre_input, width, height)
|
|
if sd_type == "xl_inpaint":
|
|
print("controlnet inpainting")
|
|
images = \
|
|
model(prompt, negative_prompt=negative_prompt, image=image, mask_image=control_image,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, clip_skip=clip_skip,
|
|
width=width, controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, ).images[0]
|
|
else:
|
|
print("controlnet img2img")
|
|
images = model(prompt, negative_prompt=negative_prompt, image=image, control_image=control_image,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
|
clip_skip=clip_skip,
|
|
controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, ).images[0]
|
|
else:
|
|
print("controlnet txt2img")
|
|
images = model(prompt,control_image=control_image, negative_prompt=negative_prompt,
|
|
num_inference_steps=steps, guidance_scale=cfg, height=height, width=width,
|
|
clip_skip=clip_skip,
|
|
controlnet_conditioning_scale=controlnet_scale,
|
|
seed=seed, ).images[0]
|
|
|
|
|
|
output_image = torch.from_numpy(np.array(images).astype(np.float32) / 255.0).unsqueeze(0)
|
|
if lora != "none":
|
|
if ip_adapter is None:
|
|
model.unfuse_lora()
|
|
torch.cuda.empty_cache()
|
|
return (output_image,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"HI_Diffusers_Model_Loader": HI_Diffusers_Model_Loader,
|
|
"Hi_Sampler": Hi_Sampler
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"HI_Diffusers_Model_Loader": "HI_Diffusers_Model_Loader",
|
|
"Hi_Hi_Sampler": "Hi_Sampler"
|
|
}
|