682 lines
36 KiB
Python
682 lines
36 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import cv2
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import torch
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import os
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from PIL import Image
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import numpy as np
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import sys
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from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMScheduler, ControlNetModel,
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KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
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AutoPipelineForInpainting, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
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EulerDiscreteScheduler, HeunDiscreteScheduler, UNet2DConditionModel,
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StableDiffusionXLImg2ImgPipeline, AutoPipelineForImage2Image,
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AutoPipelineForText2Image, StableDiffusionXLControlNetImg2ImgPipeline, KDPM2DiscreteScheduler,
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EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
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StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler,
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StableDiffusionPipeline, StableDiffusionControlNetPipeline, StableDiffusionXLInpaintPipeline)
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from huggingface_hub import hf_hub_download
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from .hidiffusion.hidiffusion import apply_hidiffusion,remove_hidiffusion
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import folder_paths
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from safetensors.torch import load_file, load
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import yaml
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import diffusers
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import random
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dif_version = str(diffusers.__version__)
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dif_version_int = int(dif_version.split(".")[1])
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if dif_version_int >= 28:
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from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
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else:
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from diffusers.models.unet_2d_condition import UNet2DConditionModel
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from comfy.utils import common_upscale
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from .guided_filter import FastGuidedFilter
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from .ip_adapter import IPAdapterXL,IPAdapter
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dir_path = os.path.dirname(os.path.abspath(__file__))
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path_dir = os.path.dirname(dir_path)
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file_path = os.path.dirname(path_dir)
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scheduler_list = ["DDIM",
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"Euler",
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"Euler a",
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"DDPM",
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"DPM++ 2M",
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"DPM++ 2M Karras",
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"DPM++ 2M SDE",
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"DPM++ 2M SDE Karras",
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"DPM++ SDE",
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"DPM++ SDE Karras",
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"DPM2",
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"DPM2 Karras",
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"DPM2 a",
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"DPM2 a Karras",
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"Heun",
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"LCM",
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"LMS",
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"LMS Karras",
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"UniPC",
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]
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fs = open(os.path.join(dir_path, "model.yaml"), encoding="UTF-8")
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datas = yaml.load(fs, Loader=yaml.FullLoader)
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normal_model_list = datas["surport_model"]
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sdxl_lightning_list = datas["lightning_unet"]
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controlnet_suport = datas["surport_controlnet"]
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xl_model_support = datas["sdxl_model"]
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lightning_lora=datas["lightning_lora"]
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lightning_xl_lora=datas["lightning_xl_lora"]
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lcm_unet = ["dmd2_sdxl_4step_unet_fp16.bin", "dmd2_sdxl_1step_unet_fp16.bin", "lcm-sdxl-base-1.0.safetensors",
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"Hyper-SDXL-1step-Unet.safetensors"]
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def tensor_to_image(tensor):
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#tensor = tensor.cpu()
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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image = Image.fromarray(image_np, mode='RGB')
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return image
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def nomarl_upscale(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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img_pil = tensor_to_image(samples)
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return img_pil
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def resize_image_control(control_image, resolution):
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HH, WW, _ = control_image.shape
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crop_h = random.randint(0, HH - resolution[1])
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crop_w = random.randint(0, WW - resolution[0])
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crop_image = control_image[crop_h:crop_h+resolution[1], crop_w:crop_w+resolution[0], :]
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return crop_image, crop_w, crop_h
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def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0):
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if ksize % 2 == 0:
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ksize += 1 # ksize must be odd
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blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX)
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return blurred_image
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def apply_guided_filter(image_np, radius, eps, scale):
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filter = FastGuidedFilter(image_np, radius, eps, scale)
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return filter.filter(image_np)
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def input_size_adaptation_output(img_tensor,base_in, width, height):
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#basein=1024
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if width == height:
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img_pil = nomarl_upscale(img_tensor, base_in, base_in) # 2pil
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else:
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if min(1,width/ height)==1: #高
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r=height/base_in
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img_pil = nomarl_upscale(img_tensor, round(width/r), base_in) # 2pil
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else: #宽
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r=width/base_in
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img_pil = nomarl_upscale(img_tensor, base_in, round(height/r)) # 2pil
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return img_pil
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def get_sheduler(name):
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scheduler = False
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if name == "Euler":
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scheduler = EulerDiscreteScheduler()
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elif name == "Euler a":
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scheduler = EulerAncestralDiscreteScheduler()
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elif name == "DDIM":
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scheduler = DDIMScheduler()
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elif name == "DDPM":
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scheduler = DDPMScheduler()
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elif name == "DPM++ 2M":
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scheduler = DPMSolverMultistepScheduler()
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elif name == "DPM++ 2M Karras":
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scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True)
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elif name == "DPM++ 2M SDE":
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scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")
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elif name == "DPM++ 2M SDE Karras":
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scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
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elif name == "DPM++ SDE":
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scheduler = DPMSolverSinglestepScheduler()
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elif name == "DPM++ SDE Karras":
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scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True)
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elif name == "DPM2":
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scheduler = KDPM2DiscreteScheduler()
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elif name == "DPM2 Karras":
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scheduler = KDPM2DiscreteScheduler(use_karras_sigmas=True)
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elif name == "DPM2 a":
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scheduler = KDPM2AncestralDiscreteScheduler()
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elif name == "DPM2 a Karras":
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scheduler = KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)
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elif name == "Heun":
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scheduler = HeunDiscreteScheduler()
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elif name == "LCM":
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scheduler = LCMScheduler()
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elif name == "LMS":
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scheduler = LMSDiscreteScheduler()
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elif name == "LMS Karras":
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scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
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elif name == "UniPC":
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scheduler = UniPCMultistepScheduler()
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return scheduler
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def get_local_path(file_path, model_path):
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path = os.path.join(file_path, "models", "diffusers", model_path)
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model_path = os.path.normpath(path)
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if sys.platform == 'win32':
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model_path = model_path.replace('\\', "/")
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return model_path
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def get_instance_path(path):
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instance_path = os.path.normpath(path)
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if sys.platform == 'win32':
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instance_path = instance_path.replace('\\', "/")
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return instance_path
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def instance_path(path, repo):
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if repo == "":
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if path == "none":
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repo = "none"
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else:
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model_path = get_local_path(file_path, path)
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repo = get_instance_path(model_path)
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return repo
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class HI_Diffusers_Model_Loader:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"repo_id": ("STRING", {"default":""}),
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"vae_id": ("STRING", {"default": ""}),
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"controlnet_repo_id": ("STRING", {"default": ""}),
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"model_category": (["sdxl","sd15","sd21","xl_turbo","inpainting","ghibli","playground"],),
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"function_choice": (["txt2img", "img2img", ],),
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
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"unet_model": (["none"] + folder_paths.get_filename_list("unet"),),
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"controlnet_model": (["none"] + folder_paths.get_filename_list("controlnet"),),
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"lora": (["none"] + folder_paths.get_filename_list("loras"),),
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"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
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"trigger_words": ("STRING", {"default": "best quality"}),
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"scheduler": (scheduler_list,),
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"apply_window_attn":("BOOLEAN", {"default": False},),
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"ip_adapter": ("BOOLEAN", {"default": False},),
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}
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}
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RETURN_TYPES = ("MODEL", "STRING",)
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RETURN_NAMES = ("model", "model_info",)
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FUNCTION = "loader_models"
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CATEGORY = "Hidiffusion_Pro"
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def loader_models(self, repo_id,vae_id,controlnet_repo_id, model_category,function_choice,ckpt_name,unet_model, controlnet_model,
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lora,lora_scale,trigger_words,scheduler,apply_window_attn,ip_adapter):
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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controlnet_single_model=folder_paths.get_full_path("controlnet", controlnet_model)
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scheduler_used = get_sheduler(scheduler)
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ip_path = os.path.join(folder_paths.models_dir, "photomaker")
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sdxl_models_pre = os.path.join(ip_path, "sdxl_models", "image_encoder")
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if not os.path.exists(sdxl_models_pre):
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os.makedirs(sdxl_models_pre)
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sd_models_pre = os.path.join(ip_path, "models", "image_encoder")
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if not os.path.exists(sd_models_pre):
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os.makedirs(sd_models_pre)
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if controlnet_repo_id:
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control_model_type = controlnet_repo_id.rsplit("/")[-1]
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else:
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control_model_type = "none"
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if controlnet_single_model:
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control_model_type=controlnet_model
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if model_category=="sd15" or model_category=="sd21" :
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model_type="stable-diffusion-v1-5"
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if not repo_id: # using single model
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original_config_file = os.path.join(folder_paths.models_dir, "configs", "v1-inference.yaml")
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if dif_version_int >= 28:
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model = StableDiffusionPipeline.from_single_file(
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ckpt_path, original_config=original_config_file, torch_dtype=torch.float16).to("cuda")
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else:
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model = StableDiffusionPipeline.from_single_file(
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ckpt_path, original_config_file=original_config_file, torch_dtype=torch.float16).to("cuda")
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else:
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model = DiffusionPipeline.from_pretrained(repo_id,torch_dtype=torch.float16, variant="fp16").to("cuda")
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elif model_category=="ghibli":
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model_type = "stable-diffusion-xl-base-1.0"
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if not repo_id:
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model = StableDiffusionPipeline.from_single_file(ckpt_path,torch_dtype=torch.float16, variant="fp16").to("cuda")
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else:
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model = StableDiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
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elif model_category=="xl_turbo":
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model_type = "sdxl-turbo"
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if not repo_id:
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original_config_file = os.path.join(dir_path, "weights", "sd_xl_base.yaml")
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if dif_version_int >= 28:
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model = StableDiffusionXLPipeline.from_single_file(
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ckpt_path, original_config=original_config_file, torch_dtype=torch.float16).to("cuda")
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else:
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model = StableDiffusionXLPipeline.from_single_file(
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ckpt_path, original_config_file=original_config_file, torch_dtype=torch.float16).to("cuda")
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else:
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if function_choice == "img2img":
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model = AutoPipelineForImage2Image.from_pretrained(repo_id, torch_dtype=torch.float16,
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variant="fp16").to('cuda')
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else:
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model = AutoPipelineForText2Image.from_pretrained(repo_id, torch_dtype=torch.float16,
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variant="fp16").to('cuda')
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elif model_category=="playground":
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model_type = "playground-v2-1024px-aesthetic"
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if not repo_id:
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model = StableDiffusionXLPipeline.from_single_file(ckpt_path,torch_dtype=torch.float16, variant="fp16").to("cuda")
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else:
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model = DiffusionPipeline.from_pretrained(repo_id,torch_dtype=torch.float16, variant="fp16").to("cuda")
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elif model_category == "inpainting":
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model_type = "stable-diffusion-xl-1.0-inpainting-0.1"
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control_model_type="stable-diffusion-xl-1.0-inpainting-0.1"
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if not controlnet_repo_id:
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original_config_file = os.path.join(dir_path, "weights", "sd_xl_base.yaml")
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if controlnet_model!="none" and "inpaint" in controlnet_model :
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if dif_version_int >= 28:
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model = StableDiffusionXLInpaintPipeline.from_single_file(ckpt_path,
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original_config=original_config_file,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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model = StableDiffusionXLInpaintPipeline.from_single_file(ckpt_path,
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original_config_file=original_config_file,
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torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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else:
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raise "need inpaiting model"
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else:
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model = AutoPipelineForInpainting.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
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variant="fp16").to("cuda")
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if unet_model in sdxl_lightning_list:
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ckpt = folder_paths.get_full_path("unet", unet_model)
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unet = UNet2DConditionModel.from_config("stabilityai/stable-diffusion-xl-base-1.0",
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subfolder="unet").to("cuda", torch.float16)
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if unet_model.rsplit('.', 1)[-1] == "bin":
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unet.load_state_dict(torch.load(ckpt))
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else:
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unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
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model.unet = unet
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else:
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model_type = "stable-diffusion-xl-base-1.0"
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if not repo_id:
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original_config_file = os.path.join(dir_path, "weights", "sd_xl_base.yaml")
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if dif_version_int >= 28:
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model = StableDiffusionXLPipeline.from_single_file(
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ckpt_path, original_config=original_config_file, torch_dtype=torch.float16).to("cuda")
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else:
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model = StableDiffusionXLPipeline.from_single_file(
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ckpt_path, original_config_file=original_config_file, torch_dtype=torch.float16).to("cuda")
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if control_model_type!="none":
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if not controlnet_repo_id:
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controlnet = ControlNetModel.from_unet(model.unet)
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cn_state_dict = load_file( controlnet_single_model, device="cuda")
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controlnet.load_state_dict(cn_state_dict, strict=False)
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else:
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controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
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).to("cuda")
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if function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pipe(model,controlnet=controlnet,
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torch_dtype=torch.float16,
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).to("cuda")
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else:
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model = StableDiffusionXLControlNetPipeline.from_pipe(
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model, controlnet=controlnet,
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torch_dtype=torch.float16).to("cuda")
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else:
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if function_choice == "img2img":
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model = StableDiffusionXLImg2ImgPipeline.from_pretrained(repo_id,
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torch_dtype=torch.float16,
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).to("cuda")
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else:
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model = StableDiffusionXLPipeline.from_pretrained(repo_id,
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torch_dtype=torch.float16,
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).to("cuda")
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if control_model_type != "none":
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if not controlnet_repo_id:
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controlnet = ControlNetModel.from_unet(model.unet)
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cn_state_dict = load_file(controlnet_single_model, device="cuda")
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controlnet.load_state_dict(cn_state_dict, strict=False)
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else:
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controlnet = ControlNetModel.from_pretrained(controlnet_repo_id, torch_dtype=torch.float16,
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).to("cuda")
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if function_choice == "img2img":
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model = StableDiffusionXLControlNetImg2ImgPipeline.from_pipe(model, controlnet=controlnet,
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torch_dtype=torch.float16,
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).to("cuda")
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else:
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model = StableDiffusionXLControlNetPipeline.from_pipe(
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model, controlnet=controlnet,
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torch_dtype=torch.float16).to("cuda")
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if unet_model in sdxl_lightning_list:
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ckpt = folder_paths.get_full_path("unet", unet_model)
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unet = UNet2DConditionModel.from_config("stabilityai/stable-diffusion-xl-base-1.0",
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subfolder="unet").to("cuda", torch.float16)
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if unet_model.rsplit('.', 1)[-1] == "bin":
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unet.load_state_dict(torch.load(ckpt))
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else:
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unet.load_state_dict(load_file(ckpt, device="cuda"), strict=False, )
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model.unet = unet
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if vae_id != "":
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model.vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16).to("cuda")
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if control_model_type == "stable-diffusion-xl-1.0-inpainting-0.1":
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model.scheduler =scheduler_used.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
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else:
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model.scheduler = scheduler_used.from_config(model.scheduler.config, timestep_spacing="trailing")
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if lora!="none":
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lora_path = folder_paths.get_full_path("loras", lora)
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lora_path = get_instance_path(lora_path)
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model.load_lora_weights(lora_path, adapter_name=trigger_words)
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model.fuse_lora(lora_scale=lora_scale, adapter_names=[trigger_words,])
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model.enable_xformers_memory_efficient_attention()
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model.enable_vae_tiling()
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apply_hidiffusion(model,apply_window_attn=apply_window_attn,model_type_str=model_type)
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#model.enable_model_cpu_offload() # need below apply_hidiffusion(model)
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adapter_info = "none"
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if ip_adapter:
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model.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
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ip_model_dir_origin = get_instance_path(ip_path)
|
||
device = "cuda"
|
||
if model_type == "stable-diffusion-xl-base-1.0": # #ip-adapter_sdxl.bin
|
||
ip_model_path_xl = os.path.join(ip_path, "sdxl_models", "ip-adapter_sdxl.bin")
|
||
|
||
if not os.path.exists(ip_model_path_xl):
|
||
adapter_path_xl = hf_hub_download(
|
||
repo_id="h94/IP-Adapter",
|
||
subfolder="sdxl_models",
|
||
filename="ip-adapter_sdxl.bin",
|
||
local_dir=ip_model_dir_origin,
|
||
)
|
||
else:
|
||
adapter_path_xl = get_instance_path(ip_model_path_xl)
|
||
image_encoder_path_xl = get_instance_path(
|
||
os.path.join(ip_path, "sdxl_models", "image_encoder", "model.safetensors"))
|
||
if not os.path.exists(ip_model_path_xl):
|
||
os.makedirs(ip_model_path_xl)
|
||
ip_model_path_dir = get_instance_path(os.path.join(ip_path, "sdxl_models", "image_encoder"))
|
||
if not os.path.exists(image_encoder_path_xl):
|
||
adapter_encoder_xl = hf_hub_download(
|
||
repo_id="h94/IP-Adapter",
|
||
subfolder="sdxl_models/image_encoder",
|
||
local_dir=ip_model_dir_origin,
|
||
)
|
||
else:
|
||
adapter_encoder_xl = get_instance_path(ip_model_path_dir)
|
||
model = IPAdapterXL(model, adapter_encoder_xl, adapter_path_xl, device,
|
||
target_blocks=["up_blocks.0.attentions.1"])
|
||
adapter_info = "ture"
|
||
elif model_type == "stable-diffusion-v1-5": # SD1.5 ip-adapter_sd15.bin
|
||
# sd1.5
|
||
ip_model_path = os.path.join(ip_path, "models", "ip-adapter_sd15.bin")
|
||
if not os.path.exists(ip_model_path):
|
||
adapter_path_sd = hf_hub_download(
|
||
repo_id="h94/IP-Adapter",
|
||
subfolder="models",
|
||
filename="ip-adapter_sd15.bin",
|
||
local_dir=ip_model_dir_origin,
|
||
)
|
||
else:
|
||
adapter_path_sd = get_instance_path(ip_model_path)
|
||
|
||
image_encoder_path = get_instance_path(
|
||
os.path.join(ip_path, "models", "image_encoder", "model.safetensors"))
|
||
ip_model_encoder_dir = get_instance_path(os.path.join(ip_path, "models", "image_encoder"))
|
||
if not os.path.exists(image_encoder_path):
|
||
adapter_encoder = hf_hub_download(
|
||
repo_id="h94/IP-Adapter",
|
||
subfolder="models/image_encoder",
|
||
filename="model.safetensors",
|
||
local_dir=ip_model_dir_origin,
|
||
)
|
||
else:
|
||
adapter_encoder = get_instance_path(ip_model_encoder_dir)
|
||
model = IPAdapter(model, adapter_encoder, adapter_path_sd, device, target_blocks=["block"])
|
||
adapter_info = "ture"
|
||
|
||
else:
|
||
adapter_info = "none"
|
||
torch.cuda.empty_cache()
|
||
torch.cuda.empty_cache()
|
||
model_info = ";".join([model_type, unet_model, control_model_type, function_choice,lora,trigger_words, adapter_info])
|
||
torch.cuda.empty_cache()
|
||
return (model, model_info,)
|
||
|
||
|
||
@classmethod
|
||
def IS_CHANGED(cls, model, **kwargs):
|
||
return remove_hidiffusion(model)
|
||
|
||
|
||
class Hi_Sampler:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"model": ("MODEL",),
|
||
"model_info": ("STRING", {"forceInput": True}),
|
||
"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, model, model_info, prompt, negative_prompt,controlnet_scale,clip_skip,pre_input,
|
||
seed,steps, cfg, width,height,adapter_scale,**kwargs):
|
||
model_type, unet_model, control_net, function_choice, lora, trigger_words, adapter_info = model_info.split(";")
|
||
|
||
# control_image = np.transpose(control_image, (
|
||
# # 0, 3, 1, 2)) # 将comfy输入的（batch_size,channels,rows,cols）改成条件需求的（3通道，16输出，3，1 ，pading=1）
|
||
if adapter_info!="none":
|
||
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
|
||
# print(model_type, unet_model, control_net, function_choice)
|
||
if control_net == "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 control_net:
|
||
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 control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||
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 control_net == "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 control_net:
|
||
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 control_net == "stable-diffusion-xl-1.0-inpainting-0.1":
|
||
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, negative_prompt=negative_prompt, 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]
|
||
|
||
|
||
output_image = torch.from_numpy(np.array(images).astype(np.float32) / 255.0).unsqueeze(0)
|
||
if lora != "none":
|
||
if adapter_info=="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"
|
||
}
|