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import transformers
transformers.utils.move_cache()
import random
import tempfile
import time
import gradio as gr
import numpy as np
import torch
import math
import re
import sys
from gradio import inputs
from diffusers import (
AutoencoderKL,
#UNet2DConditionModel,
ControlNetModel,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2AncestralDiscreteScheduler,
KDPM2DiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
DEISMultistepScheduler,
DDPMScheduler,
DDIMScheduler,
DPMSolverSDEScheduler,
DPMSolverSinglestepScheduler,
T2IAdapter,
SASolverScheduler,
EDMEulerScheduler,
EDMDPMSolverMultistepScheduler,
ConsistencyDecoderVAE,
)
from modules.u_net_condition_modify import UNet2DConditionModel
from modules.model_diffusers import (
StableDiffusionPipeline_finetune,
StableDiffusionControlNetPipeline_finetune,
StableDiffusionControlNetImg2ImgPipeline_finetune,
StableDiffusionImg2ImgPipeline_finetune,
StableDiffusionInpaintPipeline_finetune,
StableDiffusionControlNetInpaintPipeline_finetune,
)
from modules.attention_modify import AttnProcessor,IPAdapterAttnProcessor,AttnProcessor2_0,IPAdapterAttnProcessor2_0
from modules.model_k_diffusion import StableDiffusionPipeline
from torchvision import transforms
from transformers import CLIPTokenizer, CLIPTextModel,CLIPImageProcessor
from PIL import Image,ImageOps, ImageChops
from pathlib import Path
from safetensors.torch import load_file
import modules.safe as _
from modules.lora import LoRANetwork
import os
import cv2
from controlnet_aux import PidiNetDetector, HEDdetector,LineartAnimeDetector,LineartDetector,MLSDdetector,OpenposeDetector,MidasDetector,NormalBaeDetector,ContentShuffleDetector,ZoeDetector
from transformers import pipeline
from modules import samplers_extra_k_diffusion
import gc
import copy
from modules.preprocessing_segmentation import preprocessing_segmentation
import torch.nn.functional as F
from modules.t2i_adapter import setup_model_t2i_adapter
from diffusers.image_processor import IPAdapterMaskProcessor
from typing import Callable, Dict, List, Optional, Union
from insightface.app import FaceAnalysis
from insightface.utils import face_align
from diffusers.utils import load_image
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
)
embeddings_dict = dict()
lora_dict = dict()
lora_scale_dict = dict()
# lora_dict = {'Not using Lora':None,}
# lora_scale_dict = {'Not using Lora':1.0,}
# lora_lst = ['Not using Lora']
lora_lst = ['Not using Lora']
formula = [
['w = token_weight_martix * sigma * std(qk)',0],
['w = token_weight_martix * log(1 + sigma) * max(qk)',1],
['w = token_weight_martix * log(1 + sigma) * std(qk)',2],
['w = token_weight_martix * log(1 + sigma^2) * std(qk)',3],
]
encoding_type ={
"Automatic111 Encoding": 0,
"Long Prompt Encoding": 1,
"Short Prompt Encoding": 2,
}
model_ip_adapter_lst = ['IP-Adapter','IP-Adapter VIT-G','IP-Adapter Light','IP-Adapter Light v1.1','IP-Adapter Face','IP-Adapter FaceID','IP-Adapter Plus','IP-Adapter Plus Face',"IP-Adapter Plus FaceID","IP-Adapter Plus FaceIDv2"]
model_ip_adapter_type = {
"IP-Adapter": "ip-adapter_sd15.bin",
"IP-Adapter VIT-G": "ip-adapter_sd15_vit-G.bin",
"IP-Adapter Light": "ip-adapter_sd15_light.bin",
"IP-Adapter Light v1.1": "ip-adapter_sd15_light_v11.bin",
"IP-Adapter Face":"ip-adapter-full-face_sd15.bin",
"IP-Adapter FaceID":"ip-adapter-faceid_sd15.bin",
"IP-Adapter Plus": "ip-adapter-plus_sd15.bin",
"IP-Adapter Plus Face": "ip-adapter-plus-face_sd15.bin",
"IP-Adapter Plus FaceID": "ip-adapter-faceid-plus_sd15.bin",
"IP-Adapter Plus FaceIDv2": "ip-adapter-faceid-plusv2_sd15.bin",
}
controlnet_lst = ["Canny","Depth","Openpose","Soft Edge","Lineart","Lineart (anime)","Scribble","MLSD","Semantic Segmentation","Normal Map","Shuffle","Instruct Pix2Pix"]
adapter_lst = ["Canny","Sketch","Color","Depth","Openpose","Semantic Segmentation","Zoedepth"]
controlnet_type ={
"Canny": "lllyasviel/control_v11p_sd15_canny",
"Depth": "lllyasviel/control_v11f1p_sd15_depth",
"Openpose": "lllyasviel/control_v11p_sd15_openpose",
"Soft Edge": "lllyasviel/control_v11p_sd15_softedge",
"Lineart":"ControlNet-1-1-preview/control_v11p_sd15_lineart",
"Lineart (anime)":"lllyasviel/control_v11p_sd15s2_lineart_anime",
"Scribble":"lllyasviel/control_v11p_sd15_scribble",
"MLSD":"lllyasviel/control_v11p_sd15_mlsd",
"Semantic Segmentation":"lllyasviel/control_v11p_sd15_seg",
"Normal Map":"lllyasviel/control_v11p_sd15_normalbae",
"Shuffle":"lllyasviel/control_v11e_sd15_shuffle",
"Instruct Pix2Pix":"lllyasviel/control_v11e_sd15_ip2p",
}
adapter_type ={
"Canny": "TencentARC/t2iadapter_canny_sd15v2",
"Sketch": "TencentARC/t2iadapter_sketch_sd15v2",
"Color": "TencentARC/t2iadapter_color_sd14v1",
"Depth": "TencentARC/t2iadapter_depth_sd15v2",
"Openpose":"TencentARC/t2iadapter_openpose_sd14v1",
"Semantic Segmentation":"TencentARC/t2iadapter_seg_sd14v1",
"Zoedepth":"TencentARC/t2iadapter_zoedepth_sd15v1",
}
models_single_file = []
models = [
("AbyssOrangeMix2", "Korakoe/AbyssOrangeMix2-HF"),
("BloodOrangeMix", "WarriorMama777/BloodOrangeMix"),
("ElyOrangeMix", "WarriorMama777/ElyOrangeMix"),
("Pastal Mix", "JamesFlare/pastel-mix"),
("Basil Mix", "nuigurumi/basil_mix"),
("Stable Diffusion v1.5", "runwayml/stable-diffusion-v1-5"),
("Stable Diffusion v2.1", "stabilityai/stable-diffusion-2-1-base"),
("Realistic Vision v1.4", "SG161222/Realistic_Vision_V1.4"),
("Dreamlike Photoreal v2.0", "dreamlike-art/dreamlike-photoreal-2.0"),
("Waifu-diffusion v1.4", "hakurei/waifu-diffusion"),
("Stable diffusion PixelArt v1.4", "Onodofthenorth/SD_PixelArt_SpriteSheet_Generator"),
("Anything v3", "Linaqruf/anything-v3.0"),
("Sketch style", "Cosk/sketchstyle-cutesexyrobutts"),
("Anything v5", "stablediffusionapi/anything-v5"),
("Counterfeit v2.5", "gsdf/Counterfeit-V2.5"),
("Edge of realism", "stablediffusionapi/edge-of-realism"),
("Photorealistic fuen", "claudfuen/photorealistic-fuen-v1"),
("Protogen x5.8 (Scifi-Anime)", "darkstorm2150/Protogen_x5.8_Official_Release"),
("Dreamlike Anime", "dreamlike-art/dreamlike-anime-1.0"),
("Something V2.2", "NoCrypt/SomethingV2_2"),
("Realistic Vision v3.0", "SG161222/Realistic_Vision_V3.0_VAE"),
("Noosphere v3.0", "digiplay/Noosphere_v3"),
("Beauty Fool v1.2", "digiplay/BeautyFool_v1.2VAE_pruned"),
("Prefix RealisticMix v1.0", "digiplay/PrefixRealisticMix_v1"),
("Prefix FantasyMix v1.0", "digiplay/PrefixFantasyMix_v1"),
("Unstable Diffusers YamerMIX v3.0", "digiplay/unstableDiffusersYamerMIX_v3"),
("GTA5 Artwork Diffusion", "ItsJayQz/GTA5_Artwork_Diffusion"),
("Open Journey", "prompthero/openjourney"),
("SoapMix2.5D v2.0", "digiplay/SoapMix2.5D_v2"),
("CoffeeMix v2.0", "digiplay/CoffeeMix_v2"),
("helloworld v3.0", "digiplay/helloworld_v3"),
("ARRealVX v1.1", "digiplay/ARRealVX1.1"),
("Fishmix v1.0", "digiplay/fishmix_other_v1"),
("DiamondCoalMix v2.0", "digiplay/DiamondCoalMix_v2_pruned_diffusers"),
("ISOMix v3.22", "digiplay/ISOmix_v3.22"),
("Pika v2", "digiplay/Pika_v2"),
("BluePencil v0.9b", "digiplay/bluePencil_v09b"),
("MeinaPastel v6", "Meina/MeinaPastel_V6"),
("Realistic Vision v4", "SG161222/Realistic_Vision_V4.0"),
("Revanimated v1.2.2", "stablediffusionapi/revanimated"),
("NeverEnding Dream v1.2.2", "Lykon/NeverEnding-Dream"),
("CetusMixCoda", "Stax124/CetusMixCoda"),
("NewMarsMix R11", "digiplay/NewMarsMix_R11"),
("Juggernaut Final", "digiplay/Juggernaut_final"),
("BlankCanvas v1.0", "digiplay/BlankCanvas_v1"),
("FumizukiMix v1.0", "digiplay/FumizukiMix_v1"),
("CampurSari v1.0", "digiplay/CampurSari_Gen1"),
("Realisian v1.0", "digiplay/Realisian_v5"),
("Real Epic Majic Revolution v1.0", "digiplay/RealEpicMajicRevolution_v1"),
("QuinceMix v2.0", "digiplay/quincemix_v2"),
("Counterfeit v3.0", "stablediffusionapi/counterfeit-v30"),
("MeinaMix v11.0", "Meina/MeinaMix_V11"),
("MeinaPastel V7.0", "Meina/MeinaPastel_V7"),
("Alter V3.0", "Meina/Alter_V3"),
("MeinaUnreal V5.0", "Meina/MeinaUnreal_V5"),
("MeinaHentai V5.0", "Meina/MeinaHentai_V5"),
("AnyOrangeMix Mint", "GraydientPlatformAPI/anyorange-mint"),
]
#Name / link / True = single file , False = need config.json
vae_link ={
"Vae ft MSE": "stabilityai/sd-vae-ft-mse",
"Vae ft MSE original": "stabilityai/sd-vae-ft-mse-original/vae-ft-mse-840000-ema-pruned.safetensors",
"Vae ft EMA": "stabilityai/sd-vae-ft-ema",
"Vae ft EMA original": "stabilityai/sd-vae-ft-ema-original/vae-ft-ema-560000-ema-pruned.safetensors",
"ClearVAE V2.1" : "digiplay/VAE/ClearVAE_V2.1.safetensors",
"Blessed": "digiplay/VAE/blessed.vae.pt",
"Color101VAE v1": "digiplay/VAE/color101VAE_v1.safetensors",
"kl-f8-anime2": "digiplay/VAE/klF8Anime2VAE_klF8Anime2VAE.ckpt",
"Mangled Merge": "digiplay/VAE/mangledMergeVAE_v10.pt",
"Orangemix": "digiplay/VAE/orangemix.vae.pt",
"Stable 780000": "digiplay/VAE/stable-780000.vae.pt",
"CustomVAE Q6": "duongve/VAE/customvae_q6.safetensors",
"Voidnoise VAE": "duongve/VAE/voidnoiseVAE_baseonR0829.safetensors",
"Lastpiece Contrast": "duongve/VAE/lastpieceVAE_contrast.safetensors",
"Lastpiece Brightness": "duongve/VAE/lastpieceVAE_brightness.safetensors",
"Berry's Mix v1.0": "duongve/VAE/berrysMixVAE_v10.safetensors",
"Async's VAE v1.0": "duongve/VAE/asyncsVAE_v10.safetensors",
"WD-VAE v1.0": "duongve/VAE/wdVAE_v10.safetensors",
"Nocturnal": "duongve/VAE/nocturnalVAE_.safetensors",
"Apricots": "duongve/VAE/apricotsVAESeries_tensorQuantizerV10.safetensors",
"Earth & Dusk v1.0": "duongve/VAE/earthDuskVAE_v10.safetensors",
"HotaruVAE Anime v1.0": "duongve/VAE/hotaruvae_AnimeV10.safetensors",
"HotaruVAE Real v1.0": "duongve/VAE/hotaruvae_RealV10.safetensors",
"Consistency Decoder": "openai/consistency-decoder",
}
vae_single_file ={
"Vae ft MSE": False,
"Vae ft MSE original": True,
"Vae ft EMA": False,
"Vae ft EMA original": True,
"ClearVAE V2.1": True,
"Blessed": True,
"Color101VAE v1": True,
"kl-f8-anime2": True,
"Mangled Merge": True,
"Orangemix": True,
"Stable 780000": True,
"CustomVAE Q6": True,
"Voidnoise VAE": True,
"Lastpiece Contrast": True,
"Lastpiece Brightness": True,
"Berry's Mix v1.0": True,
"Async's VAE v1.0": True,
"WD-VAE v1.0": True,
"Nocturnal": True,
"Apricots": True,
"Earth & Dusk v1.0": True,
"HotaruVAE Anime v1.0": True,
"HotaruVAE Real v1.0": True,
"Consistency Decoder": False,
}
vae_lst = [
"Default",
"Vae ft MSE",
"Vae ft MSE original",
"Vae ft EMA",
"Vae ft EMA original",
"ClearVAE V2.1",
"Blessed",
"Color101VAE v1",
"kl-f8-anime2",
"Mangled Merge",
"Orangemix",
"Stable 780000",
"CustomVAE Q6",
"Voidnoise VAE",
"Lastpiece Contrast",
"Lastpiece Brightness",
"Berry's Mix v1.0",
"Async's VAE v1.0",
"WD-VAE v1.0",
"Nocturnal",
"Apricots",
"Earth & Dusk v1.0",
"HotaruVAE Anime v1.0",
"HotaruVAE Real v1.0",
"Consistency Decoder",
]
keep_vram = [
"Korakoe/AbyssOrangeMix2-HF",
"WarriorMama777/BloodOrangeMix",
"WarriorMama777/ElyOrangeMix",
"JamesFlare/pastel-mix",
"nuigurumi/basil_mix",
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1-base",
"SG161222/Realistic_Vision_V1.4",
"dreamlike-art/dreamlike-photoreal-2.0",
"hakurei/waifu-diffusion",
"Onodofthenorth/SD_PixelArt_SpriteSheet_Generator",
"Linaqruf/anything-v3.0",
"Cosk/sketchstyle-cutesexyrobutts",
"stablediffusionapi/anything-v5",
"gsdf/Counterfeit-V2.5",
"stablediffusionapi/edge-of-realism",
"claudfuen/photorealistic-fuen-v1",
"darkstorm2150/Protogen_x5.8_Official_Release",
"dreamlike-art/dreamlike-anime-1.0",
"NoCrypt/SomethingV2_2",
"SG161222/Realistic_Vision_V3.0_VAE",
"digiplay/Noosphere_v3",
"digiplay/BeautyFool_v1.2VAE_pruned",
"digiplay/PrefixRealisticMix_v1",
"digiplay/PrefixFantasyMix_v1",
"digiplay/unstableDiffusersYamerMIX_v3",
"ItsJayQz/GTA5_Artwork_Diffusion",
"prompthero/openjourney",
"digiplay/SoapMix2.5D_v2",
"digiplay/CoffeeMix_v2",
"digiplay/helloworld_v3",
"digiplay/ARRealVX1.1",
"digiplay/fishmix_other_v1",
"digiplay/DiamondCoalMix_v2_pruned_diffusers",
"digiplay/ISOmix_v3.22",
"digiplay/Pika_v2",
"digiplay/bluePencil_v09b",
"Meina/MeinaPastel_V6",
"SG161222/Realistic_Vision_V4.0",
"stablediffusionapi/revanimated",
"Lykon/NeverEnding-Dream",
"Stax124/CetusMixCoda",
"digiplay/NewMarsMix_R11",
"digiplay/Juggernaut_final",
"digiplay/BlankCanvas_v1",
"digiplay/FumizukiMix_v1",
"digiplay/CampurSari_Gen1",
"digiplay/Realisian_v5",
"digiplay/RealEpicMajicRevolution_v1",
"stablediffusionapi/counterfeit-v30",
"Meina/MeinaMix_V11",
"Meina/MeinaPastel_V7",
"Meina/Alter_V3",
"Meina/MeinaUnreal_V5",
"Meina/MeinaHentai_V5",
"GraydientPlatformAPI/anyorange-mint",
]
base_name, base_model = models[0]
samplers_k_diffusion = [
('Euler', 'sample_euler', {}),
('Euler a', 'sample_euler_ancestral', {"uses_ensd": True}),
('LMS', 'sample_lms', {}),
('LCM', samplers_extra_k_diffusion.sample_lcm, {"second_order": True}),
('Heun', 'sample_heun', {"second_order": True}),
('Heun++', samplers_extra_k_diffusion.sample_heunpp2, {"second_order": True}),
('DDPM', samplers_extra_k_diffusion.sample_ddpm, {"second_order": True}),
('DPM2', 'sample_dpm_2', {'discard_next_to_last_sigma': True}),
('DPM2 a', 'sample_dpm_2_ancestral', {'discard_next_to_last_sigma': True, "uses_ensd": True}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', {}),
('DPM++ SDE', 'sample_dpmpp_sde', {"second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', {"brownian_noise": True}),
('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', {'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM fast (img-to-img)', 'sample_dpm_fast', {"uses_ensd": True}),
('DPM adaptive (img-to-img)', 'sample_dpm_adaptive', {"uses_ensd": True}),
('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', {"brownian_noise": True, "solver_type": "heun"}),
('Restart', samplers_extra_k_diffusion.restart_sampler, {"second_order": True}),
('Euler Karras', 'sample_euler', {'scheduler': 'karras'}),
('Euler a Karras', 'sample_euler_ancestral', {'scheduler': 'karras',"uses_ensd": True}),
('LMS Karras', 'sample_lms', {'scheduler': 'karras'}),
('LCM Karras', samplers_extra_k_diffusion.sample_lcm, {'scheduler': 'karras',"second_order": True}),
('Heun Karras', 'sample_heun', {'scheduler': 'karras',"second_order": True}),
('Heun++ Karras', samplers_extra_k_diffusion.sample_heunpp2, {'scheduler': 'karras',"second_order": True}),
('DDPM Karras', samplers_extra_k_diffusion.sample_ddpm, {'scheduler': 'karras', "second_order": True}),
('DPM2 Karras', 'sample_dpm_2', {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM2 a Karras', 'sample_dpm_2_ancestral', {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('DPM++ 2M Karras', 'sample_dpmpp_2m', {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', {'scheduler': 'karras', "brownian_noise": True}),
('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('Restart Karras', samplers_extra_k_diffusion.restart_sampler, {'scheduler': 'karras', "second_order": True}),
('Euler Exponential', 'sample_euler', {'scheduler': 'exponential'}),
('Euler a Exponential', 'sample_euler_ancestral', {'scheduler': 'exponential',"uses_ensd": True}),
('LMS Exponential', 'sample_lms', {'scheduler': 'exponential'}),
('LCM Exponential', samplers_extra_k_diffusion.sample_lcm, {'scheduler': 'exponential',"second_order": True}),
('Heun Exponential', 'sample_heun', {'scheduler': 'exponential',"second_order": True}),
('Heun++ Exponential', samplers_extra_k_diffusion.sample_heunpp2, {'scheduler': 'exponential',"second_order": True}),
('DDPM Exponential', samplers_extra_k_diffusion.sample_ddpm, {'scheduler': 'exponential', "second_order": True}),
('DPM++ 2M Exponential', 'sample_dpmpp_2m', {'scheduler': 'exponential'}),
('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', {'scheduler': 'exponential', "brownian_noise": True}),
('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('Restart Exponential', samplers_extra_k_diffusion.restart_sampler, {'scheduler': 'exponential', "second_order": True}),
('Euler Polyexponential', 'sample_euler', {'scheduler': 'polyexponential'}),
('Euler a Polyexponential', 'sample_euler_ancestral', {'scheduler': 'polyexponential',"uses_ensd": True}),
('LMS Polyexponential', 'sample_lms', {'scheduler': 'polyexponential'}),
('LCM Polyexponential', samplers_extra_k_diffusion.sample_lcm, {'scheduler': 'polyexponential',"second_order": True}),
('Heun Polyexponential', 'sample_heun', {'scheduler': 'polyexponential',"second_order": True}),
('Heun++ Polyexponential', samplers_extra_k_diffusion.sample_heunpp2, {'scheduler': 'polyexponential',"second_order": True}),
('DDPM Polyexponential', samplers_extra_k_diffusion.sample_ddpm, {'scheduler': 'polyexponential', "second_order": True}),
('DPM++ 2M Polyexponential', 'sample_dpmpp_2m', {'scheduler': 'polyexponential'}),
('DPM++ 2M SDE Heun Polyexponential', 'sample_dpmpp_2m_sde', {'scheduler': 'polyexponential', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 3M SDE Polyexponential', 'sample_dpmpp_3m_sde', {'scheduler': 'polyexponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('Restart Polyexponential', samplers_extra_k_diffusion.restart_sampler, {'scheduler': 'polyexponential', "second_order": True}),
]
#Add to sigma sp which library is missing
'''class DEISMultistepScheduler_modify(DEISMultistepScheduler):
def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
"""Constructs the noise schedule of Karras et al. (2022)."""
sigma_min: float = in_sigmas[-1].item()
sigma_max: float = in_sigmas[0].item()
rho = 7.0 # 7.0 is the value used in the paper
ramp = np.linspace(0, 1, num_inference_steps)
min_inv_rho = sigma_min ** (1 / rho)
max_inv_rho = sigma_max ** (1 / rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(sigma)
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1
low = log_sigmas[low_idx]
high = log_sigmas[high_idx]
# interpolate sigmas
w = (low - log_sigma) / (low - high)
w = np.clip(w, 0, 1)
# transform interpolation to time range
t = (1 - w) * low_idx + w * high_idx
t = t.reshape(sigma.shape)
return t'''
samplers_diffusers = [
('Euler a', lambda ddim_scheduler_config: EulerAncestralDiscreteScheduler.from_config(ddim_scheduler_config), {}),
('Euler', lambda ddim_scheduler_config: EulerDiscreteScheduler.from_config(ddim_scheduler_config), {}),
#('EDM Euler', lambda ddim_scheduler_config: EDMEulerScheduler.from_config(ddim_scheduler_config), {}),
('LMS', lambda ddim_scheduler_config: LMSDiscreteScheduler.from_config(ddim_scheduler_config), {}),
('Heun',lambda ddim_scheduler_config: HeunDiscreteScheduler.from_config(ddim_scheduler_config), {}),
('DPM2',lambda ddim_scheduler_config: KDPM2DiscreteScheduler.from_config(ddim_scheduler_config), {}),
('DPM2 a',lambda ddim_scheduler_config: KDPM2AncestralDiscreteScheduler.from_config(ddim_scheduler_config), {}),
('DPM++ 2S a',lambda ddim_scheduler_config: DPMSolverSinglestepScheduler.from_config(ddim_scheduler_config), {}),
('DPM++ 2M',lambda ddim_scheduler_config: DPMSolverMultistepScheduler.from_config(ddim_scheduler_config), {}),
#('EDM DPM++ 2M',lambda ddim_scheduler_config: EDMDPMSolverMultistepScheduler.from_config(ddim_scheduler_config), {}),
('DPM++ SDE',lambda ddim_scheduler_config: DPMSolverSDEScheduler.from_config(ddim_scheduler_config), {}),
('DPM++ 2M SDE',lambda ddim_scheduler_config: DPMSolverMultistepScheduler.from_config(ddim_scheduler_config,algorithm_type="sde-dpmsolver++"), {}),
#('EDM DPM++ 2M SDE',lambda ddim_scheduler_config: EDMDPMSolverMultistepScheduler.from_config(ddim_scheduler_config,algorithm_type="sde-dpmsolver++"), {}),
('DEIS',lambda ddim_scheduler_config: DEISMultistepScheduler.from_config(ddim_scheduler_config), {}),
('UniPC Time Uniform 1',lambda ddim_scheduler_config: UniPCMultistepScheduler.from_config(ddim_scheduler_config,solver_type = "bh1"), {}),
('UniPC Time Uniform 2',lambda ddim_scheduler_config: UniPCMultistepScheduler.from_config(ddim_scheduler_config,solver_type = "bh2"), {}),
('SA-Solver',lambda ddim_scheduler_config: SASolverScheduler.from_config(ddim_scheduler_config), {}),
('Euler Karras', lambda ddim_scheduler_config: EulerDiscreteScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('LMS Karras',lambda ddim_scheduler_config: LMSDiscreteScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('Heun Karras',lambda ddim_scheduler_config: HeunDiscreteScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM2 Karras',lambda ddim_scheduler_config: KDPM2DiscreteScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM2 a Karras',lambda ddim_scheduler_config: KDPM2AncestralDiscreteScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM++ 2S a Karras',lambda ddim_scheduler_config: DPMSolverSinglestepScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM++ 2M Karras',lambda ddim_scheduler_config: DPMSolverMultistepScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM++ SDE Karras',lambda ddim_scheduler_config: DPMSolverSDEScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('DPM++ 2M SDE Karras',lambda ddim_scheduler_config: DPMSolverMultistepScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True,algorithm_type="sde-dpmsolver++"), {}),
('DEIS Karras',lambda ddim_scheduler_config: DEISMultistepScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
('UniPC Time Uniform 1 Karras',lambda ddim_scheduler_config: UniPCMultistepScheduler.from_config(ddim_scheduler_config,solver_type = "bh1",use_karras_sigmas=True), {}),
('UniPC Time Uniform 2 Karras',lambda ddim_scheduler_config: UniPCMultistepScheduler.from_config(ddim_scheduler_config,solver_type = "bh2",use_karras_sigmas=True), {}),
('SA-Solver Karras',lambda ddim_scheduler_config: SASolverScheduler.from_config(ddim_scheduler_config,use_karras_sigmas=True), {}),
]
# samplers_diffusers = [
# ("DDIMScheduler", "diffusers.schedulers.DDIMScheduler", {})
# ("DDPMScheduler", "diffusers.schedulers.DDPMScheduler", {})
# ("DEISMultistepScheduler", "diffusers.schedulers.DEISMultistepScheduler", {})
# ]
start_time = time.time()
timeout = 360
scheduler = DDIMScheduler.from_pretrained(
base_model,
subfolder="scheduler",
)
'''vae = AutoencoderKL.from_pretrained(
"stabilityai/sd-vae-ft-mse",
torch_dtype=torch.float16
)'''
vae = AutoencoderKL.from_pretrained(base_model,
subfolder="vae",
torch_dtype=torch.float16,
)
if vae is None:
vae = AutoencoderKL.from_pretrained(
"stabilityai/sd-vae-ft-mse",
torch_dtype=torch.float16,
)
text_encoder = CLIPTextModel.from_pretrained(
base_model,
subfolder="text_encoder",
torch_dtype=torch.float16,
)
tokenizer = CLIPTokenizer.from_pretrained(
base_model,
subfolder="tokenizer",
torch_dtype=torch.float16,
)
unet = UNet2DConditionModel.from_pretrained(
base_model,
subfolder="unet",
torch_dtype=torch.float16,
)
feature_extract = CLIPImageProcessor.from_pretrained(
base_model,
subfolder="feature_extractor",
)
pipe = StableDiffusionPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
vae=vae,
scheduler=scheduler,
feature_extractor = feature_extract,
)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
def get_model_list():
return models
scheduler_cache ={
base_name: scheduler
}
te_cache = {
base_name: text_encoder
}
vae_cache = {
base_name: vae
}
unet_cache = {
base_name: unet
}
lora_cache = {
base_name: LoRANetwork(text_encoder, unet)
}
tokenizer_cache ={
base_name: tokenizer
}
feature_cache ={
base_name: feature_extract
}
controlnetmodel_cache ={
}
adapter_cache ={
}
vae_enhance_cache ={
}
te_base_weight_length = text_encoder.get_input_embeddings().weight.data.shape[0]
original_prepare_for_tokenization = tokenizer.prepare_for_tokenization
current_model = base_name
def setup_controlnet(name_control,device):
global controlnet_type,controlnetmodel_cache
if name_control not in controlnetmodel_cache:
model_control = ControlNetModel.from_pretrained(name_control, torch_dtype=torch.float16).to(device)
controlnetmodel_cache[name_control] = model_control
return controlnetmodel_cache[name_control]
def setup_adapter(adapter_sp,device):
global model_ip_adapter_type,adapter_cache
if adapter_sp not in adapter_cache:
model_control = T2IAdapter.from_pretrained(adapter_sp, torch_dtype=torch.float16).to(device)
adapter_cache[adapter_sp] = model_control
return adapter_cache[adapter_sp]
def setup_vae(model,vae_used = "Default"):
global vae_link,vae_single_file
vae_model = None
if vae_used == "Default":
vae_model = AutoencoderKL.from_pretrained(model,subfolder="vae",torch_dtype=torch.float16)
elif vae_used == "Consistency Decoder":
vae_model = ConsistencyDecoderVAE.from_pretrained(vae_link[vae_used], torch_dtype=torch.float16)
else:
if vae_single_file[vae_used]:
vae_model = AutoencoderKL.from_single_file(vae_link[vae_used],torch_dtype=torch.float16)
else:
vae_model = AutoencoderKL.from_pretrained(vae_link[vae_used],torch_dtype=torch.float16)
if vae_model is None:
vae_model = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
return vae_model
def setup_model(name,clip_skip, lora_group=None,diffuser_pipeline = False ,control_net_model = None,img_input = None,device = "cpu",mask_inpaiting = None,vae_used = "Default"):
global current_model,vae_link,vae_single_file,models_single_file
keys = [k[0] for k in models]
model = models[keys.index(name)][1]
if name not in unet_cache:
if name not in models_single_file:
try:
vae_model = AutoencoderKL.from_pretrained(model,subfolder="vae",torch_dtype=torch.float16)
except OSError:
vae_model = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
try:
unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet", torch_dtype=torch.float16)
except OSError:
unet = UNet2DConditionModel.from_pretrained(base_model, subfolder="unet", torch_dtype=torch.float16)
try:
text_encoder = CLIPTextModel.from_pretrained(model, subfolder="text_encoder", torch_dtype=torch.float16)
except OSError:
text_encoder = CLIPTextModel.from_pretrained(base_model, subfolder="text_encoder", torch_dtype=torch.float16)
try:
tokenizer = CLIPTokenizer.from_pretrained(model,subfolder="tokenizer",torch_dtype=torch.float16)
except OSError:
tokenizer = CLIPTokenizer.from_pretrained(base_model,subfolder="tokenizer",torch_dtype=torch.float16)
try:
scheduler = DDIMScheduler.from_pretrained(model,subfolder="scheduler")
except OSError:
scheduler = DDIMScheduler.from_pretrained(base_model,subfolder="scheduler")
try:
feature_extract = CLIPImageProcessor.from_pretrained(model,subfolder="feature_extractor")
except OSError:
feature_extract = CLIPImageProcessor.from_pretrained(base_model,subfolder="feature_extractor")
else:
pipe_get = StableDiffusionPipeline_finetune.from_single_file(model,safety_checker= None,requires_safety_checker = False,torch_dtype=torch.float16).to(device)
vae_model = pipe_get.vae
unet = pipe_get.unet
text_encoder = pipe_get.text_encoder
tokenizer = pipe_get.tokenizer
scheduler = pipe_get.scheduler
feature_extract = pipe_get.feature_extractor if pipe_get.feature_extractor is not None else CLIPImageProcessor.from_pretrained(base_model,subfolder="feature_extractor")
del pipe_get
# if vae_model is None:
# vae_model = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
scheduler_cache[name] = scheduler
unet_cache[name] = unet
te_cache[name] = text_encoder
vae_cache[name] = vae_model
tokenizer_cache[name] = tokenizer
feature_cache[name] = feature_extract
#lora_cache[model] = LoRANetwork(text_encoder, unet)
if vae_used != "Default" and vae_used not in vae_enhance_cache:
vae_enhance_cache[vae_used] = setup_vae(model,vae_used)
if current_model != name:
#if current_model not in keep_vram:
# offload current model
unet_cache[current_model].to(device)
te_cache[current_model].to(device)
vae_cache[current_model].to(device)
current_model = name
local_te, local_unet,local_sche,local_vae,local_token,local_feature = copy.deepcopy(te_cache[name]), copy.deepcopy(unet_cache[name]),scheduler_cache[name],vae_cache[name], copy.deepcopy(tokenizer_cache[name]),feature_cache[name]
if vae_used != "Default":
local_vae = vae_enhance_cache[vae_used]
if torch.cuda.is_available():
local_unet.to("cuda")
local_te.to("cuda")
local_vae.to("cuda")
#local_unet.set_attn_processor(AttnProcessor())
#local_lora.reset()
if diffuser_pipeline:
if control_net_model is not None:
if mask_inpaiting and img_input:
pipe = StableDiffusionControlNetInpaintPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
controlnet = control_net_model,
safety_checker= None,
scheduler = local_sche,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
elif img_input is not None:
#pipe = StableDiffusionControlNetImg2ImgPipeline_finetune.from_pretrained(model,safety_checker = None,controlnet=control_net_model, torch_dtype=torch.float16).to(device)
pipe = StableDiffusionControlNetImg2ImgPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
controlnet = control_net_model,
safety_checker= None,
scheduler = local_sche,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
else:
#pipe = StableDiffusionControlNetPipeline_finetune.from_pretrained(model,safety_checker = None,controlnet=control_net_model, torch_dtype=torch.float16).to(device)
pipe = StableDiffusionControlNetPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
controlnet = control_net_model,
scheduler = local_sche,
safety_checker= None,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
else:
if mask_inpaiting and img_input:
pipe = StableDiffusionInpaintPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
scheduler = local_sche,
safety_checker= None,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
elif img_input is not None:
#pipe = StableDiffusionImg2ImgPipeline_finetune.from_pretrained(model,safety_checker = None, torch_dtype=torch.float16).to(device)
pipe = StableDiffusionImg2ImgPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
scheduler = local_sche,
safety_checker= None,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
else:
#pipe = StableDiffusionPipeline_finetune.from_pretrained(model,safety_checker = None, torch_dtype=torch.float16).to(device)
pipe = StableDiffusionPipeline_finetune(
vae= local_vae,
text_encoder= local_te,
tokenizer=local_token,
unet=local_unet,
scheduler = local_sche,
safety_checker= None,
feature_extractor=local_feature,
requires_safety_checker = False,
).to(device)
else:
#global pipe
#pipe.text_encoder, pipe.unet,pipe.scheduler,pipe.vae = local_te, local_unet,local_sche,local_vae
pipe = StableDiffusionPipeline(
text_encoder=local_te,
tokenizer=local_token,
unet=local_unet,
vae=local_vae,
scheduler=local_sche,
feature_extractor=local_feature,
).to(device)
#if lora_state is not None and lora_state != "":
if lora_group is not None and len(lora_group) > 0:
global lora_scale_dict
adapter_name_lst = []
adapter_weights_lst = []
for name, file in lora_group.items():
pipe.load_lora_weights(file, adapter_name = name)
adapter_name_lst.append(name)
adapter_weights_lst.append(lora_scale_dict[name])
pipe.set_adapters(adapter_name_lst, adapter_weights=adapter_weights_lst)
#pipe.fuse_lora(lora_scale=lora_scale_dict[name])
#pipe = load_lora_control_pipeline(pipe,lora_state,lora_scale,device)
pipe.unet.set_attn_processor(AttnProcessor())
if hasattr(F, "scaled_dot_product_attention"):
pipe.unet.set_attn_processor(AttnProcessor2_0())
if diffuser_pipeline == False:
pipe.setup_unet(pipe.unet)
pipe.tokenizer.prepare_for_tokenization = local_token.prepare_for_tokenization
#pipe.tokenizer.added_tokens_encoder = {}
#pipe.tokenizer.added_tokens_decoder = {}
#pipe.setup_text_encoder(clip_skip, local_te)
'''if lora_state is not None and lora_state != "":
local_lora.load(lora_state, lora_scale)
local_lora.to(local_unet.device, dtype=local_unet.dtype)
pipe.text_encoder, pipe.unet,pipe.scheduler,pipe.vae = local_te, local_unet,local_sche,local_vae
pipe.setup_unet(local_unet)
pipe.tokenizer.prepare_for_tokenization = local_token.prepare_for_tokenization
pipe.tokenizer.added_tokens_encoder = {}
pipe.tokenizer.added_tokens_decoder = {}
pipe.setup_text_encoder(clip_skip, local_te)'''
torch.cuda.empty_cache()
gc.collect()
return pipe
def error_str(error, title="Error"):
return (
f"""#### {title}
{error}"""
if error
else ""
)
def make_token_names(embs):
all_tokens = []
for name, vec in embs.items():
tokens = [f'emb-{name}-{i}' for i in range(len(vec))]
all_tokens.append(tokens)
return all_tokens
def setup_tokenizer(tokenizer, embs):
reg_match = [re.compile(fr"(?:^|(?<=\s|,)){k}(?=,|\s|$)") for k in embs.keys()]
clip_keywords = [' '.join(s) for s in make_token_names(embs)]
def parse_prompt(prompt: str):
for m, v in zip(reg_match, clip_keywords):
prompt = m.sub(v, prompt)
return prompt
def prepare_for_tokenization(self, text: str, is_split_into_words: bool = False, **kwargs):
text = parse_prompt(text)
r = original_prepare_for_tokenization(text, is_split_into_words, **kwargs)
return r
tokenizer.prepare_for_tokenization = prepare_for_tokenization.__get__(tokenizer, CLIPTokenizer)
return [t for sublist in make_token_names(embs) for t in sublist]
def convert_size(size_bytes):
if size_bytes == 0:
return "0B"
size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
i = int(math.floor(math.log(size_bytes, 1024)))
p = math.pow(1024, i)
s = round(size_bytes / p, 2)
return "%s %s" % (s, size_name[i])
def load_lora_control_pipeline(pipeline_control,file_path,lora_scale,device):
state_dict = load_file(file_path,device=device)
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
alpha = lora_scale
visited = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if '.alpha' in key or key in visited:
continue
if 'text' in key:
layer_infos = key.split('.')[0].split(LORA_PREFIX_TEXT_ENCODER+'_')[-1].split('_')
curr_layer = pipeline_control.text_encoder
else:
layer_infos = key.split('.')[0].split(LORA_PREFIX_UNET+'_')[-1].split('_')
curr_layer = pipeline_control.unet
# find the target layer
temp_name = layer_infos.pop(0)
while len(layer_infos) > -1:
try:
curr_layer = curr_layer.__getattr__(temp_name)
if len(layer_infos) > 0:
temp_name = layer_infos.pop(0)
elif len(layer_infos) == 0:
break
except Exception:
if len(temp_name) > 0:
temp_name += '_'+layer_infos.pop(0)
else:
temp_name = layer_infos.pop(0)
# org_forward(x) + lora_up(lora_down(x)) * multiplier
pair_keys = []
if 'lora_down' in key:
pair_keys.append(key.replace('lora_down', 'lora_up'))
pair_keys.append(key)
else:
pair_keys.append(key)
pair_keys.append(key.replace('lora_up', 'lora_down'))
# update weight
if len(state_dict[pair_keys[0]].shape) == 4:
weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
else:
weight_up = state_dict[pair_keys[0]].to(torch.float32)
weight_down = state_dict[pair_keys[1]].to(torch.float32)
curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)
# update visited list
for item in pair_keys:
visited.append(item)
torch.cuda.empty_cache()
gc.collect()
return pipeline_control
def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
"""Converts a depth map to a color image.
Args:
value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
Returns:
numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
"""
if isinstance(value, torch.Tensor):
value = value.detach().cpu().numpy()
value = value.squeeze()
if invalid_mask is None:
invalid_mask = value == invalid_val
mask = np.logical_not(invalid_mask)
# normalize
vmin = np.percentile(value[mask],2) if vmin is None else vmin
vmax = np.percentile(value[mask],85) if vmax is None else vmax
if vmin != vmax:
value = (value - vmin) / (vmax - vmin) # vmin..vmax
else:
# Avoid 0-division
value = value * 0.
# squeeze last dim if it exists
# grey out the invalid values
value[invalid_mask] = np.nan
cmapper = matplotlib.cm.get_cmap(cmap)
if value_transform:
value = value_transform(value)
# value = value / value.max()
value = cmapper(value, bytes=True) # (nxmx4)
img = value[...]
img[invalid_mask] = background_color
if gamma_corrected:
img = img / 255
img = np.power(img, 2.2)
img = img * 255
img = img.astype(np.uint8)
return img
def adapter_preprocessing(model_adapter,img_control,low_threshold_adapter = None,high_threshold_adapter=None,has_body=False,has_hand=False,has_face=False,preprocessor_adapter=None,disable_preprocessing_adapter=False):
if disable_preprocessing_adapter == True :
return img_control.copy()
device = 'cpu'
if torch.cuda.is_available():
device = 'cuda'
if model_adapter == 'Canny':
img_control = np.array(img_control)
img_control = cv2.Canny(img_control, low_threshold_adapter, high_threshold_adapter)
img_control = Image.fromarray(img_control)
elif model_adapter == 'Openpose':
#model_openpose = OpenposeDetector()
processor = OpenposeDetector.from_pretrained('lllyasviel/ControlNet').to(device)
img_control = processor(img_control, include_body=has_body, include_hand=has_hand, include_face=has_face)
#img_control = model_openpose(img_control, has_hand)[0]
elif model_adapter == 'Depth':
#model_midas = MidasDetector()
#img_control = model_midas(resize_image(img_control))[0]
if preprocessor_adapter == 'DPT':
processor = pipeline('depth-estimation')
img_control = processor(img_control)['depth']
img_control = np.array(img_control)
img_control = img_control[:, :, None]
img_control = np.concatenate([img_control, img_control, img_control], axis=2)
img_control = Image.fromarray(img_control)
else:
processor = MidasDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)
elif model_adapter == 'Semantic Segmentation':
img_control = preprocessing_segmentation(preprocessor_adapter,img_control)
elif model_adapter == 'Color':
img_control = img_control.resize((8, 8))
img_control = img_control.resize((512, 512), resample=Image.Resampling.NEAREST)
elif model_adapter == 'Zoedepth':
'''processor = torch.hub.load("isl-org/ZoeDepth", "ZoeD_N", pretrained=True).to(device)
img_control = processor.infer_pil(img_control)
img_control = Image.fromarray(colorize(img_control)).convert('RGB')'''
'''processor = ZoeDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)'''
processor = ZoeDetector.from_pretrained("valhalla/t2iadapter-aux-models", filename="zoed_nk.pth", model_type="zoedepth_nk").to(device)
img_control = processor(img_control, gamma_corrected=True)
else:
active_model = False
if model_adapter == 'Sketch':
active_model = True
if preprocessor_name == 'HED':
processor = HEDdetector.from_pretrained('lllyasviel/Annotators').to(device)
else:
processor = PidiNetDetector.from_pretrained('lllyasviel/Annotators').to(device)
img_control = processor(img_control,scribble=active_model)
#img_control = np.array(img_control)
#img = cv2.resize(img_control,(width, height))
#img_input = img_input.resize((width, height), Image.LANCZOS)
#img_control = img_control.resize((width, height), Image.LANCZOS)
if model_adapter != 'Canny' and model_adapter != 'Semantic Segmentation' and model_adapter != 'Color':
del processor
torch.cuda.empty_cache()
gc.collect()
return img_control
def control_net_preprocessing(control_net_model,img_control,low_threshold = None,high_threshold=None,has_body=False,has_hand=False,has_face=False,preprocessor_name=None,disable_preprocessing=False):
if disable_preprocessing == True or control_net_model == 'Instruct Pix2Pix':
return img_control.copy()
device = 'cpu'
if torch.cuda.is_available():
device = 'cuda'
if control_net_model == 'Canny':
img_control = np.array(img_control)
img_control = cv2.Canny(img_control, low_threshold, high_threshold)
img_control = img_control[:, :, None]
img_control = np.concatenate([img_control, img_control, img_control], axis=2)
img_control = Image.fromarray(img_control)
elif control_net_model == 'Openpose':
#model_openpose = OpenposeDetector()
processor = OpenposeDetector.from_pretrained('lllyasviel/ControlNet').to(device)
img_control = processor(img_control, include_body=has_body, include_hand=has_hand, include_face=has_face)
#img_control = model_openpose(img_control, has_hand)[0]
elif control_net_model == 'Depth':
#model_midas = MidasDetector()
#img_control = model_midas(resize_image(img_control))[0]
if preprocessor_name == 'DPT':
processor = pipeline('depth-estimation')
img_control = processor(img_control)['depth']
img_control = np.array(img_control)
img_control = img_control[:, :, None]
img_control = np.concatenate([img_control, img_control, img_control], axis=2)
img_control = Image.fromarray(img_control)
else:
processor = MidasDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)
elif control_net_model == 'Lineart (anime)':
processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)
#img_control = np.array(img_control)
elif control_net_model == 'Lineart':
processor = LineartDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)
#img_control = np.array(img_control)
elif control_net_model == 'MLSD':
processor = MLSDdetector.from_pretrained("lllyasviel/ControlNet").to(device)
img_control = processor(img_control)
#img_control = np.array(img_control)
elif control_net_model == 'Semantic Segmentation':
img_control = preprocessing_segmentation(preprocessor_name,img_control)
elif control_net_model == 'Normal Map':
processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators").to(device)
img_control = processor(img_control)
elif control_net_model == 'Shuffle':
processor = ContentShuffleDetector()
img_control = processor(img_control)
else:
active_model = False
if control_net_model == 'Scribble':
active_model = True
if preprocessor_name == 'HED':
processor = HEDdetector.from_pretrained('lllyasviel/Annotators').to(device)
else:
processor = PidiNetDetector.from_pretrained('lllyasviel/Annotators').to(device)
img_control = processor(img_control,scribble=active_model)
#img_control = np.array(img_control)
#img = cv2.resize(img_control,(width, height))
#img_input = img_input.resize((width, height), Image.LANCZOS)
#img_control = img_control.resize((width, height), Image.LANCZOS)
if control_net_model != 'Canny' and control_net_model != 'Semantic Segmentation':
del processor
torch.cuda.empty_cache()
gc.collect()
return img_control
def add_embedding(pipe_model,embs):
tokenizer, text_encoder = pipe_model.tokenizer, pipe_model.text_encoder
if embs is not None and len(embs) > 0:
ti_embs = {}
for name, file in embs.items():
if str(file).endswith(".pt"):
loaded_learned_embeds = torch.load(file, map_location="cpu")
else:
loaded_learned_embeds = load_file(file, device="cpu")
loaded_learned_embeds = loaded_learned_embeds["string_to_param"]["*"] if "string_to_param" in loaded_learned_embeds else loaded_learned_embeds
if isinstance(loaded_learned_embeds, dict):
#loaded_learned_embeds = list(loaded_learned_embeds.values())[-1]
ti_embs.update(loaded_learned_embeds)
else:
ti_embs[name] = loaded_learned_embeds
if len(ti_embs) > 0:
'''for key, value in ti_embs.items():
if isinstance(value, dict):
ti_embs.pop(key)
ti_embs.update(value)'''
tokens = setup_tokenizer(tokenizer, ti_embs)
added_tokens = tokenizer.add_tokens(tokens)
delta_weight = torch.cat([val for val in ti_embs.values()], dim=0)
assert added_tokens == delta_weight.shape[0]
text_encoder.resize_token_embeddings(len(tokenizer))
token_embeds = text_encoder.get_input_embeddings().weight.data
token_embeds[-delta_weight.shape[0]:] = delta_weight
torch.cuda.empty_cache()
gc.collect()
return pipe_model
def add_embedding_with_diffusers(pipe,embs):
if embs is not None and len(embs) > 0:
for name, file in embs.items():
pipe.load_textual_inversion(file)
torch.cuda.empty_cache()
gc.collect()
return pipe
def mask_region_apply_ip_adapter(mask,invert_ip_adapter_mask_mode):
if mask is None:
return None
#define black is region masked
if not isinstance(mask,List):
mask = [mask]
if len(mask) == 0:
return None
if invert_ip_adapter_mask_mode:
mask = [ImageOps.invert(i).convert('RGB') for i in mask]
processor = IPAdapterMaskProcessor()
masks = processor.preprocess(mask)
'''mask = mask.resize((width, height), Image.BICUBIC)
mask = np.array(mask).astype(np.float32) / 255.0
#If the region is black apply ( 0 = black)
mask = np.expand_dims(np.where(mask==0, 1,0)[:, :, 0], axis=0)
if mask.ndim == 3:
mask = mask[..., None]
mask = torch.from_numpy(mask.transpose(0, 3, 1, 2))
return mask[0]'''
return masks
def ip_adapter_face_id_embedding(lst_img_face_id_embed,device,dtype,guidance_scale,plus_faceid = False):
ref_images_embeds = []
ref_unc_images_embeds = []
ip_adapter_images = []
app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
if not isinstance(lst_img_face_id_embed,list):
lst_img_face_id_embed = [lst_img_face_id_embed]
for im in lst_img_face_id_embed:
#im = load_image(im)
image = cv2.cvtColor(np.asarray(im), cv2.COLOR_BGR2RGB)
faces = app.get(image) #faces is a list
if len(faces) == 0:
raise ValueError(
"Can not find any faces in the image."
)
if plus_faceid:
ip_adapter_images.append(face_align.norm_crop(image, landmark=faces[0].kps, image_size=224)) #For plus faceid
image = torch.from_numpy(faces[0].normed_embedding)
image_embeds = image.unsqueeze(0)
uncond_image_embeds = torch.zeros_like(image_embeds)
ref_images_embeds.append(image_embeds)
ref_unc_images_embeds.append(uncond_image_embeds)
ref_images_embeds = torch.stack(ref_images_embeds, dim=0)
if guidance_scale > 1 :
ref_unc_images_embeds = torch.stack(ref_unc_images_embeds, dim=0)
single_image_embeds = torch.cat([ref_unc_images_embeds, ref_images_embeds], dim=0).to(device,dtype=dtype)
else:
single_image_embeds = ref_images_embeds.to(device,dtype=dtype)
return single_image_embeds,ip_adapter_images
lst_control = []
lst_adapter =[]
lst_ip_adapter = []
current_number_ip_adapter = 0
current_number_control = 0
current_number_adapter = 0
def inference(
prompt,
guidance,
steps,
width=512,
height=512,
clip_skip =2,
seed=0,
neg_prompt="",
state=None,
img_input=None,
i2i_scale=0.5,
hr_enabled=False,
hr_method="Latent",
hr_scale=1.5,
hr_denoise=0.8,
sampler="DPM++ 2M Karras",
embs=None,
model=None,
lora_group = None,
#lora_state=None,
#lora_scale=None,
formula_setting = None,
controlnet_enabled = False,
control_net_model = None,
low_threshold = None,
high_threshold = None,
has_body = False,
has_hand = False,
has_face = False,
img_control = None,
image_condition = None,
controlnet_scale = 0,
preprocessor_name = None,
diffuser_pipeline = False,
sampler_hires="DPM++ 2M Karras",
latent_processing = 0,
control_guidance_start = 0.0,
control_guidance_end = 1.0,
multi_controlnet = False,
disable_preprocessing = False,
region_condition = False,
hr_process_enabled = False,
ip_adapter = False,
model_ip_adapter = None,
inf_adapt_image = None,
inf_adapt_image_strength = 1.0,
hr_region_condition = False,
adapter_enabled = False,
model_adapter = None,
low_threshold_adapter = None,
high_threshold_adapter = None,
has_body_openpose_adapter = False,
has_hand_openpose_adapter = False,
has_face_openpose_adapter = False,
adapter_img = None,
image_condition_adapter = None,
preprocessor_adapter = None,
adapter_conditioning_scale = 0,
adapter_conditioning_factor = None,
multi_adapter = False,
disable_preprocessing_adapter = False,
ip_adapter_multi = False,
guidance_rescale = 0,
inf_control_adapt_image = None,
long_encode = 0,
inpaiting_mode = False,
invert_mask_mode = False,
mask_upload = None,
inf_image_inpaiting = None,
invert_ip_adapter_mask_mode = True,
vae_used = "Default",
):
global formula,controlnet_type,lst_control,lst_adapter,model_ip_adapter_type,adapter_type,lst_ip_adapter,current_number_ip_adapter,encoding_type
img_control_input = None
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
if region_condition == False:
state = None
mask_inpaiting = None
if inpaiting_mode and isinstance(inf_image_inpaiting,dict):
mask_inpaiting = inf_image_inpaiting["mask"]
img_input = inf_image_inpaiting["image"]
diff = ImageChops.difference(mask_inpaiting, img_input)
if diff.getbbox() is None:
mask_inpaiting = None
if inpaiting_mode and mask_upload:
mask_inpaiting = mask_upload
if mask_inpaiting and invert_mask_mode:
mask_inpaiting = ImageOps.invert(mask_inpaiting).convert('RGB')
if adapter_enabled:
if len(lst_adapter) > 0 and multi_adapter:
adapter_img = []
model_adapter = []
adapter_conditioning_scale = []
adapter_conditioning_factor = []
for i in range( len(lst_adapter)):
setting_processing = list(lst_adapter[i].items())
setting_processing = setting_processing[:-2]
setting_processing = dict(setting_processing)
image_sp_adapter = adapter_preprocessing(**setting_processing)
adapter_img.append(image_sp_adapter)
adapter_sp = adapter_type[lst_adapter[i]["model_adapter"]]
model_adapter.append(setup_adapter(adapter_sp,device))
adapter_conditioning_scale.append(float(lst_adapter[i]["adapter_conditioning_scale"]))
adapter_conditioning_factor.append(float(lst_adapter[i]["adapter_conditioning_factor"]))
adapter_conditioning_factor = adapter_conditioning_factor[-1]
torch.cuda.empty_cache()
gc.collect()
elif adapter_img is not None and multi_adapter ==False:
adapter_img = adapter_preprocessing(model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,disable_preprocessing_adapter)
model_adapter = adapter_type[model_adapter]
adapter_conditioning_scale = float(adapter_conditioning_scale)
adapter_conditioning_factor = float(adapter_conditioning_factor)
torch.cuda.empty_cache()
gc.collect()
model_adapter=setup_adapter(model_adapter,device)
torch.cuda.empty_cache()
gc.collect()
else:
model_adapter = None
adapter_img = None
else:
model_adapter = None
adapter_img = None
if controlnet_enabled:
if len(lst_control) > 0 and multi_controlnet:
img_control = []
control_net_model = []
controlnet_scale = []
control_guidance_start = []
control_guidance_end = []
for i in range( len(lst_control)):
setting_processing = list(lst_control[i].items())
setting_processing = setting_processing[:-3]
setting_processing = dict(setting_processing)
image_sp_control = control_net_preprocessing(**setting_processing)
img_control.append(image_sp_control)
conrol_net_sp = controlnet_type[lst_control[i]["control_net_model"]]
control_net_model.append(setup_controlnet(conrol_net_sp,device))
controlnet_scale.append(float(lst_control[i]["controlnet_scale"]))
control_guidance_start.append(float(lst_control[i]["control_guidance_start"]))
control_guidance_end.append(float(lst_control[i]["control_guidance_end"]))
torch.cuda.empty_cache()
gc.collect()
elif img_control is not None and multi_controlnet ==False:
img_control = control_net_preprocessing(control_net_model,img_control,low_threshold,high_threshold,has_body,has_hand,has_face,preprocessor_name,disable_preprocessing)
control_net_model = controlnet_type[control_net_model]
controlnet_scale = float(controlnet_scale)
control_guidance_start = float(control_guidance_start)
control_guidance_end = float(control_guidance_end)
torch.cuda.empty_cache()
gc.collect()
control_net_model=setup_controlnet(control_net_model,device)
torch.cuda.empty_cache()
gc.collect()
else:
control_net_model = None
img_control = None
else:
control_net_model = None
img_control = None
keys_f = [k[0] for k in formula]
formula_setting = formula[keys_f.index(formula_setting)][1]
if seed is None or seed < 0:
seed = random.randint(0, sys.maxsize)
#lora_state = lora_dict[lora_state]
pipe = setup_model(model,clip_skip, lora_group,diffuser_pipeline,control_net_model,img_input,device,mask_inpaiting,vae_used)
generator = torch.Generator(device).manual_seed(int(seed))
if formula_setting == 0:
weight_func = lambda w, sigma, qk: w * sigma * qk.std()
elif formula_setting == 1:
weight_func = lambda w, sigma, qk: w * math.log(1 + sigma) * qk.max()
elif formula_setting == 2:
weight_func = lambda w, sigma, qk: w * math.log(1 + sigma) * qk.std()
else:
weight_func = lambda w, sigma, qk: w * math.log(1 + sigma**2) * qk.std()
start_time = time.time()
sampler_name, sampler_opt = None, None
'''for label, funcname, options in samplers_k_diffusion:
if label == sampler_hires:
sampler_name_hires, sampler_opt_hires = funcname, options'''
#add_Textual Inversion or text embeddings
pipe = add_embedding(pipe,embs)
width_resize_mask_ipadapter = width
height_resize_mask_ipadapter = height
if img_input is not None:
width_resize_mask_ipadapter = img_input.width
height_resize_mask_ipadapter = img_input.height
setup_model_t2i_adapter(pipe,model_adapter)
cross_attention_kwargs = {}
#Get type encoding
long_encode = encoding_type[long_encode]
ip_adapter_image_embeds = None
faceid_plus_v2 = False
#clip_embeds = None #Support for faceid_plus
if ip_adapter == True:
#inf_adapt_image = None
ip_adapter_images_faceid_plus = []
if ip_adapter_multi and len(lst_ip_adapter) > 0:
ip_adapter_image_lst =[]
model_ip_adapter_lst = []
scale_ip_adapter_lst = []
region_aplly_lst = []
ip_adapter_image_vitg_lst =[]
model_ip_adapter_vitg_lst = []
scale_ip_adapter_vitg_lst = []
region_aplly_vitg_lst = []
ip_adapter_faceid_image_lst =[]
model_ip_adapter_faceid_lst = []
scale_ip_adapter_faceid_lst = []
region_aplly_lst_faceid = []
ip_adapter_faceid_plus_image_lst =[]
model_ip_adapter_faceid_plus_lst = []
scale_ip_adapter_faceid_plus_lst = []
region_aplly_lst_faceid_plus = []
#Support not marks
img_full_black = Image.new('RGB', (width, height), (0, 0, 0))
img_full_white = Image.new('RGB', (width, height), (255, 255, 255))
for i in lst_ip_adapter:
if 'VIT-G' in i["model"]:
ip_adapter_image_vitg_lst.append(i["image"])
model_ip_adapter_vitg_lst.append(model_ip_adapter_type[i["model"]])
scale_ip_adapter_vitg_lst.append(float(i["scale"]))
if i["region_apply"] is not None:
region_aplly_vitg_lst.append(i["region_apply"])
else:
if invert_ip_adapter_mask_mode:
region_aplly_vitg_lst.append(img_full_black)
else:
region_aplly_vitg_lst.append(img_full_white)
elif 'FaceID' not in i["model"]:
ip_adapter_image_lst.append(i["image"])
model_ip_adapter_lst.append(model_ip_adapter_type[i["model"]])
scale_ip_adapter_lst.append(float(i["scale"]))
if i["region_apply"] is not None:
region_aplly_lst.append(i["region_apply"])
else:
if invert_ip_adapter_mask_mode:
region_aplly_lst.append(img_full_black)
else:
region_aplly_lst.append(img_full_white)
elif 'Plus FaceID' in i["model"]:
if 'Plus FaceIDv2' in i["model"]:
faceid_plus_v2 = True
ip_adapter_faceid_plus_image_lst.append(i["image"])
model_ip_adapter_faceid_plus_lst.append(model_ip_adapter_type[i["model"]])
scale_ip_adapter_faceid_plus_lst.append(float(i["scale"]))
if i["region_apply"] is not None:
region_aplly_lst_faceid_plus.append(i["region_apply"])
else:
if invert_ip_adapter_mask_mode:
region_aplly_lst_faceid_plus.append(img_full_black)
else:
region_aplly_lst_faceid_plus.append(img_full_white)
else:
ip_adapter_faceid_image_lst.append(i["image"])
model_ip_adapter_faceid_lst.append(model_ip_adapter_type[i["model"]])
scale_ip_adapter_faceid_lst.append(float(i["scale"]))
if i["region_apply"] is not None:
region_aplly_lst_faceid.append(i["region_apply"])
else:
if invert_ip_adapter_mask_mode:
region_aplly_lst_faceid.append(img_full_black)
else:
region_aplly_lst_faceid.append(img_full_white)
#Concat faceid and ipadapter
none_img_encoder = False
# if len(model_ip_adapter_lst) == 0:
# only_face_id = 1
if len(ip_adapter_faceid_image_lst) > 0 or len(ip_adapter_image_vitg_lst) > 0 or len(ip_adapter_faceid_plus_image_lst) > 0:
#Image_encode vit-H
ip_adapter_embeds = []
ip_adapter_vitg_embeds = []
ip_adapter_image_embeds_faceid = []
ip_adapter_image_embeds_faceid_plus = []
if len(model_ip_adapter_lst) > 0:
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=model_ip_adapter_lst)
pipe.set_ip_adapter_scale(scale_ip_adapter_lst)
ip_adapter_embeds = pipe.prepare_ip_adapter_image_embeds(ip_adapter_image_lst,None,device,1, guidance>1)
pipe.unload_ip_adapter()
if len(ip_adapter_faceid_image_lst) > 0:
ip_adapter_image_embeds_faceid,_ = ip_adapter_face_id_embedding(ip_adapter_faceid_image_lst,device,pipe.unet.dtype,guidance,False)
ip_adapter_image_embeds_faceid = [ip_adapter_image_embeds_faceid]
if len(ip_adapter_faceid_plus_image_lst) >0:
ip_adapter_image_embeds_faceid_plus,ip_adapter_images_faceid_plus = ip_adapter_face_id_embedding(ip_adapter_faceid_plus_image_lst,device,pipe.unet.dtype,guidance,True)
ip_adapter_image_embeds_faceid_plus = [ip_adapter_image_embeds_faceid_plus]
#Image encoder vit-G
if len(ip_adapter_image_vitg_lst) > 0:
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=model_ip_adapter_vitg_lst,image_encoder_folder=None)
pipe.set_ip_adapter_scale(scale_ip_adapter_vitg_lst)
pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter", subfolder="sdxl_models/image_encoder",
).to(device, dtype=pipe.unet.dtype)
ip_adapter_vitg_embeds = pipe.prepare_ip_adapter_image_embeds(ip_adapter_image_vitg_lst,None,device,1, guidance>1)
pipe.unload_ip_adapter()
ip_adapter_image_embeds = ip_adapter_embeds + ip_adapter_image_embeds_faceid + ip_adapter_vitg_embeds + ip_adapter_image_embeds_faceid_plus
inf_adapt_image = None
none_img_encoder = True
if not isinstance(ip_adapter_image_embeds, list):
ip_adapter_image_embeds = [ip_adapter_image_embeds]
else:
inf_adapt_image = ip_adapter_image_lst
ip_adapter_image_embeds = None
region_aplly_lst = region_aplly_lst + region_aplly_lst_faceid + region_aplly_vitg_lst + region_aplly_lst_faceid_plus
load_model = ["h94/IP-Adapter"]*len(model_ip_adapter_lst) + ["h94/IP-Adapter-FaceID"]*len(model_ip_adapter_faceid_lst) + ["h94/IP-Adapter"]*len(model_ip_adapter_vitg_lst) + ["h94/IP-Adapter-FaceID"]*len(model_ip_adapter_faceid_plus_lst)
subfolder = ["models"]*len(model_ip_adapter_lst) + [None]*len(model_ip_adapter_faceid_lst) + ["models"] * len(model_ip_adapter_vitg_lst) + [None]*len(model_ip_adapter_faceid_plus_lst)
model_ip_adapter_lst = model_ip_adapter_lst + model_ip_adapter_faceid_lst + model_ip_adapter_vitg_lst + model_ip_adapter_faceid_plus_lst
scale_ip_adapter_lst = scale_ip_adapter_lst + scale_ip_adapter_faceid_lst + scale_ip_adapter_vitg_lst + scale_ip_adapter_faceid_plus_lst
clip_embeds = None
if len(ip_adapter_images_faceid_plus) > 0:
pipe.load_ip_adapter("h94/IP-Adapter-FaceID", subfolder=None, weight_name=model_ip_adapter_faceid_plus_lst,image_encoder_folder=None)
pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
).to(device, dtype=pipe.unet.dtype)
# Extract CLIP embeddings
clip_embeds = pipe.prepare_ip_adapter_image_embeds([ip_adapter_images_faceid_plus], None, device, 1, guidance>1)[0] #num_images = 1
pipe.unload_ip_adapter()
if none_img_encoder:
pipe.load_ip_adapter(load_model, subfolder=subfolder, weight_name=model_ip_adapter_lst,image_encoder_folder=None)
else:
pipe.load_ip_adapter(load_model, subfolder=subfolder, weight_name=model_ip_adapter_lst)
pipe.set_ip_adapter_scale(scale_ip_adapter_lst)
if len(ip_adapter_images_faceid_plus) > 0:
pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
).to(device, dtype=pipe.unet.dtype)
# Set CLIP embeddings as class parameter
pipe.unet.encoder_hid_proj.image_projection_layers[0].clip_embeds = clip_embeds.to(dtype=pipe.unet.dtype)
pipe.unet.encoder_hid_proj.image_projection_layers[0].shortcut = faceid_plus_v2
cross_attention_kwargs = {"ip_adapter_masks":mask_region_apply_ip_adapter(region_aplly_lst,invert_ip_adapter_mask_mode)}
elif inf_adapt_image is not None and ip_adapter_multi == False:
if 'VIT-G' in model_ip_adapter:
model_ip_adapter = model_ip_adapter_type[model_ip_adapter]
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=model_ip_adapter,image_encoder_folder=None)
pipe.set_ip_adapter_scale(float(inf_adapt_image_strength))
pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter", subfolder="sdxl_models/image_encoder",
).to(device, dtype=pipe.unet.dtype)
cross_attention_kwargs = {"ip_adapter_masks":mask_region_apply_ip_adapter(inf_control_adapt_image,invert_ip_adapter_mask_mode)}
elif 'FaceID' not in model_ip_adapter:
model_ip_adapter = model_ip_adapter_type[model_ip_adapter]
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=model_ip_adapter)
pipe.set_ip_adapter_scale(float(inf_adapt_image_strength))
cross_attention_kwargs = {"ip_adapter_masks":mask_region_apply_ip_adapter(inf_control_adapt_image,invert_ip_adapter_mask_mode)}
elif 'Plus FaceID' in model_ip_adapter:
if 'Plus FaceIDv2' in model_ip_adapter:
faceid_plus_v2 = True
model_ip_adapter = model_ip_adapter_type[model_ip_adapter]
pipe.load_ip_adapter("h94/IP-Adapter-FaceID", subfolder=None, weight_name=model_ip_adapter,image_encoder_folder=None)
pipe.set_ip_adapter_scale(float(inf_adapt_image_strength))
ip_adapter_image_embeds,ip_adapter_images_faceid_plus = ip_adapter_face_id_embedding([inf_adapt_image],device,pipe.unet.dtype,guidance,True)
if not isinstance(ip_adapter_image_embeds, list):
ip_adapter_image_embeds = [ip_adapter_image_embeds]
cross_attention_kwargs = {"ip_adapter_masks":mask_region_apply_ip_adapter(inf_control_adapt_image,invert_ip_adapter_mask_mode)}
if len(ip_adapter_images_faceid_plus) > 0:
pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
).to(device, dtype=pipe.unet.dtype)
# Extract CLIP embeddings
clip_embeds = pipe.prepare_ip_adapter_image_embeds([ip_adapter_images_faceid_plus], None, device, 1, guidance>1)[0] #num_images = 1
# Set CLIP embeddings as class parameter
pipe.unet.encoder_hid_proj.image_projection_layers[0].clip_embeds = clip_embeds.to(dtype=pipe.unet.dtype)
pipe.unet.encoder_hid_proj.image_projection_layers[0].shortcut = faceid_plus_v2
#pipe.unload_ip_adapter()
inf_adapt_image = None
else:
model_ip_adapter = model_ip_adapter_type[model_ip_adapter]
pipe.load_ip_adapter("h94/IP-Adapter-FaceID", subfolder=None, weight_name=model_ip_adapter,image_encoder_folder=None)
pipe.set_ip_adapter_scale(float(inf_adapt_image_strength))
ip_adapter_image_embeds,_ = ip_adapter_face_id_embedding([inf_adapt_image],device,pipe.unet.dtype,guidance,False)
if not isinstance(ip_adapter_image_embeds, list):
ip_adapter_image_embeds = [ip_adapter_image_embeds]
cross_attention_kwargs = {"ip_adapter_masks":mask_region_apply_ip_adapter(inf_control_adapt_image,invert_ip_adapter_mask_mode)}
inf_adapt_image = None
else:
inf_adapt_image = None
else:
inf_adapt_image = None
if diffuser_pipeline:
for label, funcname, options in samplers_diffusers:
if label == sampler:
sampler_name, sampler_opt = funcname, options
if label == sampler_hires:
sampler_name_hires, sampler_opt_hires = funcname, options
pipe.scheduler = sampler_name(pipe.scheduler.config)
output_type = 'pil'
if hr_enabled and img_input is None:
output_type = 'latent'
#Need to reduce clip_skip by 1 because when using clip_skip the value will increase in the encode_prompt
config = {
"prompt": prompt,
"negative_prompt": neg_prompt,
"num_inference_steps": int(steps),
"guidance_scale": guidance,
"generator": generator,
"region_map_state": state,
#"region_map_attn_weight": g_strength,
"latent_processing": latent_processing,
'weight_func':weight_func,
'clip_skip' :int(clip_skip),
"output_type" : output_type,
"image_t2i_adapter":adapter_img,
"adapter_conditioning_scale":adapter_conditioning_scale,
"adapter_conditioning_factor":adapter_conditioning_factor,
"guidance_rescale":guidance_rescale,
"long_encode" : int(long_encode),
"ip_adapter_image_embeds": ip_adapter_image_embeds,
"cross_attention_kwargs": cross_attention_kwargs
}
'''if ip_adapter == False:
inf_adapt_image = None'''
if mask_inpaiting and img_input and inpaiting_mode and control_net_model:
result = pipe(mask_image = mask_inpaiting,width=img_input.width,height=img_input.height, controlnet_conditioning_scale = controlnet_scale,inf_adapt_image=inf_adapt_image,image =img_input , control_image=img_control,strength = i2i_scale,control_guidance_start=control_guidance_start,control_guidance_end=control_guidance_end,**config)
elif control_net_model is not None and img_input is not None:
result = pipe(controlnet_conditioning_scale = controlnet_scale,inf_adapt_image=inf_adapt_image,image =img_input , control_image=img_control,strength = i2i_scale,control_guidance_start=control_guidance_start,control_guidance_end=control_guidance_end,**config)
elif control_net_model is not None:
result = pipe(width = width,height = height,controlnet_conditioning_scale = controlnet_scale, image=img_control,control_guidance_start=control_guidance_start,control_guidance_end=control_guidance_end,ip_adapter_image=inf_adapt_image,**config)
elif mask_inpaiting and img_input and inpaiting_mode:
result = pipe(image =img_input,ip_adapter_image=inf_adapt_image,mask_image = mask_inpaiting,strength=i2i_scale,width=img_input.width,height=img_input.height,**config)
elif img_input is not None:
result = pipe(image =img_input,strength = i2i_scale,ip_adapter_image=inf_adapt_image,**config)
else:
result = pipe(height = height, width = width,ip_adapter_image=inf_adapt_image,**config)
if hr_enabled and img_input is None:
del pipe
torch.cuda.empty_cache()
gc.collect()
pipe = setup_model(model,clip_skip, lora_group,diffuser_pipeline,control_net_model,True,device,vae_used)
#add_Textual Inversion or text embeddings
pipe = add_embedding(pipe,embs)
pipe.scheduler = sampler_name_hires(pipe.scheduler.config)
vae_scale_factor = 2 ** (len(pipe.vae.config.block_out_channels) - 1)
target_height = int(height * upscale_x // vae_scale_factor )* 8
target_width = int(width * upscale_x // vae_scale_factor)*8
latents = result[-1].unsqueeze(0)
#print(latents.shape)
latents = torch.nn.functional.interpolate(
latents,
size=(
int(target_height // vae_scale_factor),
int(target_width // vae_scale_factor),
),
mode=latent_upscale_modes[hr_method]["upscale_method"],
antialias=latent_upscale_modes[hr_method]["upscale_antialias"],
)
config = {
"prompt": prompt,
"negative_prompt": neg_prompt,
"num_inference_steps": int(steps),
"guidance_scale": guidance,
"generator": generator,
"region_map_state": state,
#"region_map_attn_weight": g_strength,
"latent_processing": hr_process_enabled,
'weight_func':weight_func,
'clip_skip' :int(clip_skip),
"image_t2i_adapter":adapter_img,
"adapter_conditioning_scale":adapter_conditioning_scale,
"adapter_conditioning_factor":adapter_conditioning_factor,
"guidance_rescale":guidance_rescale,
"long_encode" : int(long_encode),
"ip_adapter_image_embeds": ip_adapter_image_embeds,
"cross_attention_kwargs":cross_attention_kwargs,
}
if control_net_model is not None:
upscale_result = pipe(width=int(target_width),height=int(target_height),controlnet_conditioning_scale = controlnet_scale,image = latents, control_image=img_control,strength = hr_denoise,control_guidance_start=control_guidance_start,control_guidance_end=control_guidance_end,**config)
else:
upscale_result = pipe(width=int(target_width),height=int(target_height),image = latents,strength = hr_denoise,**config)
#print(type(upscale_result[-1]))
#print(upscale_result)
result = result[:-1] + upscale_result
else:
for label, funcname, options in samplers_k_diffusion:
if label == sampler:
sampler_name, sampler_opt = funcname, options
if label == sampler_hires:
sampler_name_hires, sampler_opt_hires = funcname, options
config = {
"negative_prompt": neg_prompt,
"num_inference_steps": int(steps),
"guidance_scale": guidance,
"generator": generator,
"sampler_name": sampler_name,
"sampler_opt": sampler_opt,
"region_map_state": state,
#"region_map_attn_weight": g_strength,
"start_time": start_time,
"timeout": timeout,
"latent_processing": latent_processing,
'weight_func':weight_func,
'seed': int(seed),
'sampler_name_hires': sampler_name_hires,
'sampler_opt_hires': sampler_opt_hires,
"latent_upscale_processing": hr_process_enabled,
"ip_adapter_image":inf_adapt_image,
"controlnet_conditioning_scale":controlnet_scale,
"control_img": img_control,
"control_guidance_start":control_guidance_start,
"control_guidance_end":control_guidance_end,
"image_t2i_adapter":adapter_img,
"adapter_conditioning_scale":adapter_conditioning_scale,
"adapter_conditioning_factor":adapter_conditioning_factor,
"guidance_rescale":guidance_rescale,
'clip_skip' :int(clip_skip),
"long_encode" : int(long_encode),
"ip_adapter_image_embeds": ip_adapter_image_embeds,
"cross_attention_kwargs":cross_attention_kwargs,
}
#if control_net_model is not None:
pipe.setup_controlnet(control_net_model)
if mask_inpaiting and img_input and inpaiting_mode:
result = pipe.inpaiting(prompt, image=img_input,mask_image = mask_inpaiting,strength=i2i_scale,width=img_input.width,height=img_input.height, **config)
elif img_input is not None:
result = pipe.img2img(prompt, image=img_input, strength=i2i_scale,width=img_input.width,height=img_input.height, **config)
elif hr_enabled:
result = pipe.txt2img(
prompt,
width=width,
height=height,
upscale=True,
upscale_x=hr_scale,
upscale_denoising_strength=hr_denoise,
**config,
**latent_upscale_modes[hr_method],
)
else:
result = pipe.txt2img(prompt, width=width, height=height, **config)
end_time = time.time()
vram_free, vram_total = torch.cuda.mem_get_info()
if ip_adapter :
pipe.unload_ip_adapter()
if lora_group is not None and len(lora_group) > 0:
#pipe.unfuse_lora()#Unload lora
pipe.unload_lora_weights()
#if embs is not None and len(embs) > 0:
#pipe.unload_textual_inversion()
del pipe
torch.cuda.empty_cache()
gc.collect()
print(f"done: model={model}, res={result[-1].width}x{result[-1].height}, step={steps}, time={round(end_time-start_time, 2)}s, vram_alloc={convert_size(vram_total-vram_free)}/{convert_size(vram_total)}")
return gr.Image.update(result[-1], label=f"Initial Seed: {seed}"),result
color_list = []
def get_color(n):
for _ in range(n - len(color_list)):
color_list.append(tuple(np.random.random(size=3) * 256))
return color_list
def create_mixed_img(current, state, w=512, h=512):
w, h = int(w), int(h)
image_np = np.full([h, w, 4], 255)
if state is None:
state = {}
colors = get_color(len(state))
idx = 0
for key, item in state.items():
if item["map"] is not None:
m = item["map"] < 255
alpha = 150
if current == key:
alpha = 200
image_np[m] = colors[idx] + (alpha,)
idx += 1
return image_np
def apply_size_sketch(width,height,state,inf_image,inpaiting_mode,inf_image_inpaiting):
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
#update_img = gr.Image.update(value=create_mixed_img("", state, w_change, h_change))
#return state, update_img,gr.Image.update(width=w_change,height = h_change)
else:
w_change = int(width)
h_change = int(height)
if state is not None:
for key, item in state.items():
if item["map"] is not None:
#inverted_image = PIL.ImageOps.invert(item["map"].convert('RGB'))
item["map"] = resize(item["map"], w_change, h_change)
update_img = gr.Image.update(value=create_mixed_img("", state, w_change, h_change))
return state, update_img,gr.Image.update(width=w_change,height = h_change)
# width.change(apply_new_res, inputs=[width, height, global_stats], outputs=[global_stats, sp, rendered])
'''def apply_new_res(w, h, state,inf_image,rendered):
if inf_image is not None:
return state, rendered
w, h = int(w), int(h)
if state is not None:
for key, item in state.items():
if item["map"] is not None:
item["map"] = resize(item["map"], w, h)
update_img = gr.Image.update(value=create_mixed_img("", state, w, h))
return state, update_img'''
def detect_text(text, state, width, height,formula_button,inf_image,inpaiting_mode,inf_image_inpaiting):
global formula
if text is None or text == "":
return None, None, gr.Radio.update(value=None,visible = False), None,gr.Dropdown.update(value = formula_button)
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(width)
h_change = int(height)
t = text.split(",")
new_state = {}
for item in t:
item = item.strip()
if item == "":
continue
if state is not None and item in state:
new_state[item] = {
"map": state[item]["map"],
"weight": state[item]["weight"],
"mask_outsides": state[item]["mask_outsides"],
}
else:
new_state[item] = {
"map": None,
"weight": 0.5,
"mask_outsides": 0
}
update = gr.Radio.update(choices=[key for key in new_state.keys()], value=None,visible = True)
update_img = gr.update(value=create_mixed_img("", new_state, w_change, h_change))
update_sketch = gr.update(value=None, interactive=False)
return new_state, update_sketch, update, update_img,gr.Dropdown.update(value = formula_button)
def detect_text1(text, state, width, height,formula_button,inf_image,inpaiting_mode,inf_image_inpaiting):
global formula
if text is None or text == "":
return None, None, gr.Radio.update(value=None,visible = False), None,gr.Dropdown.update(value = formula_button)
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(width)
h_change = int(height)
t = text.split(",")
new_state = {}
for item in t:
item = item.strip()
if item == "":
continue
if state is not None and item in state:
new_state[item] = {
"map": state[item]["map"],
"weight": state[item]["weight"],
"mask_outsides": state[item]["mask_outsides"],
}
else:
new_state[item] = {
"map": None,
"weight": 0.5,
"mask_outsides": False
}
update = gr.Radio.update(choices=[key for key in new_state.keys()], value=None,visible = True)
update_img = gr.update(value=create_mixed_img("", new_state, w_change, h_change))
return new_state, update, update_img,gr.Dropdown.update(value = formula_button)
def resize(img, w, h):
trs = transforms.Compose(
[
transforms.ToPILImage(),
#transforms.Resize(min(h, w)),
transforms.Resize((h, w),interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop((h, w)),
]
)
result = np.array(trs(img), dtype=np.uint8)
return result
def switch_canvas(entry, state, width, height,inf_image,inpaiting_mode,inf_image_inpaiting):
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(width)
h_change = int(height)
if entry is None or state is None:
return None, 0.5, False, create_mixed_img("", state, w_change, h_change)
return (
gr.update(value=None, interactive=True),
gr.update(value=state[entry]["weight"] if entry in state else 0.5),
gr.update(value=state[entry]["mask_outsides"] if entry in state else False),
create_mixed_img(entry, state, w_change, h_change),
)
def apply_canvas(selected, draw, state, w, h,inf_image,inpaiting_mode,inf_image_inpaiting):
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(w)
h_change = int(h)
if state is not None and selected in state and draw is not None:
w, h = int(w_change), int(h_change)
state[selected]["map"] = resize(draw, w, h)
return state, gr.Image.update(value=create_mixed_img(selected, state, w, h))
def apply_weight(selected, weight, state):
if state is not None and selected in state:
state[selected]["weight"] = weight
return state
def apply_option(selected, mask, state):
if state is not None and selected in state:
state[selected]["mask_outsides"] = mask
return state
clustering_image =[]
number_clustering = 0
def is_image_black(image):
average_intensity = image.mean()
if average_intensity < 10:
return True
else:
return False
def change_diferent_black_to_white(image):
width, height = image.size
for x in range(width):
for y in range(height):
r, g, b = image.getpixel((x, y))
if r != 0 and g != 0 and b != 0:
image.putpixel((x, y), (255, 255, 255))
return image
def change_black_to_other_color(image,color_list):
width, height = image.size
new_pixel = (random.randrange(1,256), random.randrange(1,256), random.randrange(1,256))
while new_pixel in color_list:
new_pixel = (random.randrange(1,256), random.randrange(1,256), random.randrange(1,256))
for x in range(width):
for y in range(height):
pixel = image.getpixel((x, y))
if pixel == (0, 0, 0):
image.putpixel((x, y), new_pixel)
return image
def get_color_mask(color, image, threshold=30):
"""
Returns a color mask for the given color in the given image.
"""
img_array = np.array(image, dtype=np.uint8)
color_diff = np.sum((img_array - color) ** 2, axis=-1)
img_array[color_diff > threshold] = img_array[color_diff > threshold] * 0
return Image.fromarray(img_array)
def unique_colors(image, threshold=0.01):
colors = image.getcolors(image.size[0] * image.size[1])
total_pixels = image.size[0] * image.size[1]
unique_colors = []
for count, color in colors:
if count / total_pixels > threshold:
unique_colors.append(color)
return unique_colors
def extract_color_textboxes(color_map_image,MAX_NUM_COLORS):
#color_map_image = Image.open(color_map_image)
#color_map_image = cv2.imread(color_map_image)
color_map_image= Image.fromarray(color_map_image.astype('uint8'), 'RGB')
# Get unique colors in color_map_image
colors = unique_colors(color_map_image)
color_map_image = change_black_to_other_color(color_map_image,colors)
colors = unique_colors(color_map_image)
color_masks = [get_color_mask(color, color_map_image) for color in colors]
# Append white blocks to color_masks to fill up to MAX_NUM_COLORS
num_missing_masks = MAX_NUM_COLORS - len(color_masks)
white_mask = Image.new("RGB", color_map_image.size, color=(32, 32, 32))
color_masks += [white_mask] * num_missing_masks
color_output =[]
for i in range(0,len(color_masks)) :
#color_masks[i] = color_masks[i].convert('L')
color_masks[i] = change_diferent_black_to_white(color_masks[i])
color_masks[i] = np.array(color_masks[i])
color_masks[i] = cv2.cvtColor(color_masks[i], cv2.COLOR_RGB2GRAY)
color_masks[i] = 255.0 - color_masks[i]
if is_image_black(color_masks[i]) == False:
color_masks[i] = color_masks[i].astype(np.uint8)
color_output.append(color_masks[i])
return color_output
def apply_image_clustering(image, selected, w, h, strength, mask, state,inf_image,inpaiting_mode,inf_image_inpaiting):
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(w)
h_change = int(h)
if state is not None and selected in state:
state[selected] = {
"map": resize(image, w_change, h_change),
"weight": strength,
"mask_outsides": mask
}
return state, gr.Image.update(value=create_mixed_img(selected, state, w_change, h_change))
# sp2, radio, width, height, global_stats
def apply_image(image, selected, w, h, strength, mask, state,inf_image,inpaiting_mode,inf_image_inpaiting):
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(w)
h_change = int(h)
if state is not None and selected in state:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
state[selected] = {
"map": resize(image, w_change, h_change),
"weight": strength,
"mask_outsides": mask
}
elif state is not None:
key_state = list(state.keys())
global number_clustering,clustering_image
number_clustering = 0
clustering_image = []
clustering_image = extract_color_textboxes(image,len(state)+1)
number_clustering = len(clustering_image)
if len(state) > len(clustering_image):
amount_add = len(clustering_image)
else:
amount_add = len(state)
for i in range(0,amount_add):
state[key_state[i]] = {
"map": resize(clustering_image[i], w_change, h_change),
"weight": strength,
"mask_outsides": mask
}
return state, gr.Image.update(value=create_mixed_img(selected, state, w_change, h_change))
#rendered, apply_style, apply_clustering_style,Previous,Next,Completed,sp2,sp3
def apply_base_on_color(sp2,state, width, height,inf_image,inpaiting_mode,inf_image_inpaiting):
global number_clustering,clustering_image
if inpaiting_mode and inf_image_inpaiting:
w_change = inf_image_inpaiting["image"].width
h_change = inf_image_inpaiting["image"].height
elif inf_image is not None:
w_change = inf_image.width
h_change = inf_image.height
else:
w_change = int(width)
h_change = int(height)
number_clustering = 0
clustering_image = []
clustering_image = extract_color_textboxes(sp2,len(state)+1)
new_state = {}
for i in state:
new_state[i] = {
"map": None,
"weight": 0.5,
"mask_outsides": False
}
return gr.Image.update(value = create_mixed_img("", new_state, w_change, h_change)),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Image.update(visible = False),gr.Image.update(value=clustering_image[0],visible = True),gr.Button.update(visible = True),new_state
def completing_clustering(sp2):
return gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Image.update(visible = True),gr.Image.update(visible = False),gr.Button.update(visible = False)
def previous_image_page(sp3):
global clustering_image,number_clustering
number_clustering = number_clustering - 1
if number_clustering < 0:
number_clustering = len(clustering_image)-1
return gr.Image.update(value = clustering_image[number_clustering])
def next_image_page(sp3):
global clustering_image,number_clustering
number_clustering = number_clustering + 1
if number_clustering >= len(clustering_image):
number_clustering = 0
return gr.Image.update(value = clustering_image[number_clustering])
# [ti_state, lora_state, ti_vals, lora_vals, uploads]
def get_file_link_sp(link):
import requests
import os
from urllib.parse import unquote
file_name = None
absolute_path = None
try:
response = requests.get(link)
response.raise_for_status()
except requests.exceptions.HTTPError as err:
print(f"There was an error downloading: {err}")
else:
content_disposition = response.headers.get("content-disposition")
if content_disposition:
file_name = content_disposition.split("filename=")[1]
file_name = unquote(file_name)
# remove quotation marks
file_name = file_name.strip('"')
else:
file_name = "downloaded_file"
with open(file_name, "wb") as f:
f.write(response.content)
#Get absolute_path
absolute_path = os.path.abspath(file_name)
#Change format file_name
file_name = file_name.split('.')[0]
file_name = file_name.replace('_',' ')
file_name = file_name.replace('-',' ')
file_name = file_name.title()
return absolute_path, file_name
def get_file_link(link):
import requests
import os
from urllib.parse import unquote
file_name = None
absolute_path = None
try:
with requests.get(link, stream=True) as response:
response.raise_for_status()
# Get file size from headers
total_size = int(response.headers.get('content-length', 0))
content_disposition = response.headers.get("content-disposition")
if content_disposition:
file_name = content_disposition.split("filename=")[1]
file_name = unquote(file_name)
# remove quotation marks
file_name = file_name.strip('"')
else:
file_name = "downloaded_file"
# Stream download and write to file
chunk_size = 1024
downloaded_size = 0
with open(file_name, "wb") as f:
for chunk in response.iter_content(chunk_size=chunk_size):
if chunk:
f.write(chunk)
downloaded_size += len(chunk)
# Print download progress
progress = (downloaded_size / total_size) * 100
if progress%10 == 0:
print(f"Download progress: {progress:.2f}% ({downloaded_size / 1024:.2f} KB / {total_size / 1024:.2f} KB)")
# Get absolute_path
absolute_path = os.path.abspath(file_name)
# Change format file_name
file_name = file_name.split('.')[0]
file_name = file_name.replace('_', ' ')
file_name = file_name.replace('-', ' ')
file_name = file_name.title()
except requests.exceptions.HTTPError as err:
print(f"There was an error downloading: {err}")
return absolute_path, file_name
def add_net(files,link_download):
global lora_scale_dict, lora_lst, lora_dict, embeddings_dict
if files is None and (link_download is None or link_download == ''):
return gr.CheckboxGroup.update(choices=list(embeddings_dict.keys())),gr.CheckboxGroup.update(choices=list(lora_dict.keys())),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],),gr.File.update(value=None),gr.Textbox.update(value = ''),
if link_download is not None and link_download != '':
path_file, file_name_download = get_file_link(link_download)
if file_name_download:
items_dl = Path(path_file)
if items_dl.suffix == ".pt":
state_dict = torch.load(path_file, map_location="cpu")
else:
state_dict = load_file(path_file, device="cpu")
if any("lora" in k for k in state_dict.keys()):
#lora_state = file.name
if file_name_download not in lora_dict:
lora_lst.append(file_name_download)
lora_dict[file_name_download] = path_file
lora_scale_dict[file_name_download] = 1.0
else:
if file_name_download not in embeddings_dict:
embeddings_dict[file_name_download] = path_file
if files is not None:
for file in files:
item = Path(file.name)
stripedname = str(item.stem).strip()
stripedname = stripedname.replace('_',' ')
stripedname = stripedname.replace('-',' ')
stripedname = stripedname.title()
if item.suffix == ".pt":
state_dict = torch.load(file.name, map_location="cpu")
else:
state_dict = load_file(file.name, device="cpu")
if any("lora" in k for k in state_dict.keys()):
#lora_state = file.name
if stripedname not in lora_dict:
lora_lst.append(stripedname)
lora_dict[stripedname] = file.name
lora_scale_dict[stripedname] = 1.0
else:
#ti_state[stripedname] = file.name
if stripedname not in embeddings_dict:
embeddings_dict[stripedname] = file.name
return gr.CheckboxGroup.update(choices=list(embeddings_dict.keys())), gr.CheckboxGroup.update(choices=list(lora_dict.keys())),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],),gr.File.update(value=None),gr.Textbox.update(value = ''),
def change_lora_value(lora_vals):
global lora_scale_dict
if len(lora_scale_dict) == 0 or lora_vals == 'Not using Lora':
return gr.Slider.update(value = 1.0)
return gr.Slider.update(value = lora_scale_dict[lora_vals])
def update_lora_value(lora_scale,lora_vals):
global lora_scale_dict
if len(lora_scale_dict) and lora_vals != 'Not using Lora':
lora_scale_dict[lora_vals] = float(lora_scale)
# [ti_state, lora_state, ti_vals, lora_vals, uploads]
def clean_states(ti_state,lora_group):
global lora_dict,embeddings_dict,lora_lst,lora_scale_dict
delete_lora = list(lora_dict.values())
for i in delete_lora:
os.remove(i)
delete_embed_lst = list(embeddings_dict.values())
for i in delete_embed_lst:
os.remove(i)
embeddings_dict = dict()
lora_dict = dict()
lora_scale_dict = dict()
lora_lst = ['Not using Lora']
return dict(),dict(),gr.CheckboxGroup.update(choices=list(embeddings_dict.keys()),value = None),gr.CheckboxGroup.update(choices=list(lora_dict.keys()),value = None),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],),gr.File.update(value=None),gr.Text.update(f""),gr.Text.update(f""),gr.Textbox.update(value = ''),
def add_model(insert_model):
global models,keep_vram,models_single_file
insert_model=insert_model.replace(" ", "")
if len(insert_model) == 0:
return gr.Dropdown.update(choices=[k[0] for k in get_model_list()],value=base_name),gr.Textbox.update(value = '')
if 'https' in insert_model:
path_file, file_name_download = get_file_link(insert_model)
for i in models:
if file_name_download in i:
return gr.Dropdown.update(choices=[k[0] for k in get_model_list()],value=base_name),gr.Textbox.update(value = '')
models.append((file_name_download,path_file))
keep_vram.append(path_file)
models_single_file.append(file_name_download)
else:
author,name = insert_model.split('/')
name = name.replace('_',' ')
name = name.replace('-',' ')
name = name.title()
for i in models:
if name in i or insert_model in i:
return gr.Dropdown.update(choices=[k[0] for k in get_model_list()],value=base_name),gr.Textbox.update(value = '')
models.append((name,insert_model))
keep_vram.append(insert_model)
return gr.Dropdown.update(choices=[k[0] for k in get_model_list()],value=base_name),gr.Textbox.update(value = '')
def add_vae(insert_vae,single_load_file):
global vae_link,vae_single_file,vae_lst
insert_vae=insert_vae.replace(" ", "")
if len(insert_vae) == 0:
return gr.Dropdown.update(choices=[k for k in vae_lst],value=vae_lst[0]),gr.Textbox.update(value = ''),gr.Checkbox.update(value = False),
if 'https' in insert_vae:
path_file, file_name_download = get_file_link(insert_vae)
if file_name_download not in vae_lst:
vae_lst.append(file_name_download)
vae_link[file_name_download] = path_file
vae_single_file[file_name_download] = True
else:
name = insert_vae.split('/')[-1]
name = name.split('.')[0]
name = name.replace('_',' ')
name = name.replace('-',' ')
name = name.title()
if name not in vae_lst:
vae_lst.append(name)
vae_link[name] = insert_vae
vae_single_file[name] = single_load_file
return gr.Dropdown.update(choices=[k for k in vae_lst],value=vae_lst[0]),gr.Textbox.update(value = ''),gr.Checkbox.update(value = False),
def reset_model_button(insert_model):
return gr.Textbox.update(value = '')
def choose_tistate(ti_vals):
if len(ti_vals) == 0:
return dict(),gr.Text.update(""),gr.CheckboxGroup.update(choices=list(embeddings_dict.keys()),value = None)
dict_copy = dict()
for key, value in embeddings_dict.items():
if key in ti_vals:
dict_copy[key] = value
lst_key = [key for key in dict_copy.keys()]
lst_key = '; '.join(map(str, lst_key))
return dict_copy,gr.Text.update(lst_key),gr.CheckboxGroup.update(choices=list(embeddings_dict.keys()),value = None)
def choose_lora_function(lora_list):
global lora_dict
if len(lora_list) == 0:
return dict(),gr.Text.update(""),gr.CheckboxGroup.update(choices=list(lora_dict.keys()),value = None),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],)
dict_copy = dict()
for key, value in lora_dict.items():
if key in lora_list:
dict_copy[key] = value
lst_key = [key for key in dict_copy.keys()]
lst_key = '; '.join(map(str, lst_key))
return dict_copy,gr.Text.update(lst_key),gr.CheckboxGroup.update(choices=list(lora_dict.keys()),value = None),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],)
def delete_embed(ti_vals,ti_state,embs_choose):
if len(ti_vals) == 0:
return gr.CheckboxGroup.update(choices=list(embeddings_dict.keys())),ti_state,gr.Text.update(embs_choose)
for key in ti_vals:
if key in ti_state:
ti_state.pop(key)
if key in embeddings_dict:
os.remove(embeddings_dict[key])
embeddings_dict.pop(key)
if len(ti_state) >= 1:
lst_key = [key for key in ti_state.keys()]
lst_key = '; '.join(map(str, lst_key))
else:
lst_key =""
return gr.CheckboxGroup.update(choices=list(embeddings_dict.keys()),value = None),ti_state,gr.Text.update(lst_key)
def delete_lora_function(lora_list,lora_group,lora_choose):
global lora_dict,lora_lst,lora_scale_dict
if len(lora_list) == 0:
return gr.CheckboxGroup.update(choices=list(lora_dict.keys())),lora_group,gr.Text.update(lora_choose),gr.Dropdown.update()
for key in lora_list:
if key in lora_group:
lora_group.pop(key)
if key in lora_scale_dict:
lora_scale_dict.pop(key)
if key in lora_dict:
os.remove(lora_dict[key])
lora_dict.pop(key)
if len(lora_group) >= 1:
lst_key = [key for key in lora_group.keys()]
lst_key = '; '.join(map(str, lst_key))
else:
lst_key =""
lora_lst = ["Not using Lora"]+[key for key in lora_dict.keys()]
return gr.CheckboxGroup.update(choices=list(lora_dict.keys()),value = None),lora_group,gr.Text.update(lst_key),gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],)
def lora_delete(lora_vals):
global lora_dict
global lora_lst
if lora_vals == 'Not using Lora':
return gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],)
os.remove(lora_dict[lora_vals])
lora_dict.pop(lora_vals)
lora_lst.remove(lora_vals)
return gr.Dropdown.update(choices=[k for k in lora_lst],value=lora_lst[0],)
#diffuser_pipeline,sampler,gallery,hr_enabled
def mode_diffuser_pipeline( controlnet_enabled):
if controlnet_enabled == True:
return gr.Checkbox.update(value = True),gr.Checkbox.update()
return gr.Checkbox.update(value = False),gr.Checkbox.update(value = False)
'''def mode_diffuser_pipeline1(diffuser_pipeline, controlnet_enabled):
assert diffuser_pipeline == False, "Please enable diffusers pipeline to use this option"
return gr.Checkbox.update(value = True)'''
def res_cap(g, w, h, x):
if g:
return f"Enable upscaler: {w}x{h} to {int(w*x)//8 *8}x{int(h*x)//8 *8}"
else:
return "Enable upscaler"
#diffuser_pipeline,hr_enabled,sampler,gallery,controlnet_enabled
def mode_upscale(diffuser_pipeline, hr_scale, width, height,hr_enabled):
if hr_enabled == True:
return gr.Checkbox.update(value = False),gr.Checkbox.update(value = True,label=res_cap(True, width, height, hr_scale)),gr.Dropdown.update(value="DPM++ 2M Karras",choices=[s[0] for s in samplers_k_diffusion]),gr.Checkbox.update(value = False)
return gr.Checkbox.update(value = False),gr.Checkbox.update(value = False,label=res_cap(False, width, height, hr_scale)),gr.Dropdown.update(value="DPM++ 2M Karras",choices=[s[0] for s in samplers_k_diffusion]),gr.Checkbox.update()
def change_control_net(model_control_net, low_threshold, high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose):
if model_control_net == 'Canny':
return gr.Slider.update(visible = True),gr.Slider.update(visible = True),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Radio.update(visible = False)
if model_control_net == 'Depth':
return gr.Slider.update(visible = False),gr.Slider.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Radio.update(visible = True,choices=["Midas","DPT"])
if model_control_net == 'Openpose':
return gr.Slider.update(visible = False),gr.Slider.update(visible = False),gr.Checkbox.update(visible = True),gr.Checkbox.update(visible = True),gr.Checkbox.update(visible = True),gr.Radio.update(visible = False)
if model_control_net == 'Semantic Segmentation':
return gr.Slider.update(visible = False),gr.Slider.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Radio.update(visible = True,choices=["Convnet tiny","Convnet small","Convnet base","Convnet large","Convnet xlarge","Swin tiny","Swin small","Swin base","Swin large"])
if model_control_net =='Soft Edge' or model_control_net == 'Scribble' or model_control_net == 'Sketch':
return gr.Slider.update(visible = False),gr.Slider.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Radio.update(visible = True,choices=["HED","PidiNet"])
return gr.Slider.update(visible = False),gr.Slider.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Checkbox.update(visible = False),gr.Radio.update(visible = False)
previous_sampler = 'DPM++ 2M Karras'
previous_sampler_hires = 'DPM++ 2M Karras'
#sampler,gallery,hr_enabled,controlnet_enabled
def mode_diffuser_pipeline_sampler(diffuser_pipeline, sampler,sampler_hires):
global previous_sampler, previous_sampler_hires
sample_now = previous_sampler
sampler_hires_now = previous_sampler_hires
previous_sampler = sampler
previous_sampler_hires = sampler_hires
if diffuser_pipeline == False:
return gr.Checkbox.update(value = False), gr.Dropdown.update(value=sample_now,choices=[s[0] for s in samplers_k_diffusion]),gr.Dropdown.update(value=sampler_hires_now,choices=[s[0] for s in samplers_k_diffusion])
return gr.Checkbox.update(value = True),gr.Dropdown.update(value=sample_now,choices=[s[0] for s in samplers_diffusers]),gr.Dropdown.update(value=sampler_hires_now,choices=[s[0] for s in samplers_diffusers])
def change_gallery(latent_processing,hr_process_enabled):
if latent_processing or hr_process_enabled:
return gr.Gallery.update(visible = True)
return gr.Gallery.update(visible = False)
in_edit_mode = False
in_edit_mode_adapter = False
def preview_image(model_control_net,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,img_control,preprocessor_name,multi_controlnet,disable_preprocessing):
global in_edit_mode
if multi_controlnet == True and in_edit_mode == True:
global lst_control,current_number_control
if model_control_net == lst_control[current_number_control]["control_net_model"]:
setting_processing = list(lst_control[current_number_control].items())
setting_processing = setting_processing[:-3]
setting_processing = dict(setting_processing)
else:
setting_processing = {
"control_net_model": model_control_net,
"img_control": img_control,
"low_threshold": low_threshold,
"high_threshold": high_threshold,
"has_body": has_body_openpose,
"has_face": has_face_openpose,
"has_hand": has_hand_openpose,
"preprocessor_name": preprocessor_name,
"disable_preprocessing":disable_preprocessing,
}
image_sp_control = control_net_preprocessing(**setting_processing)
return gr.Image.update(image_sp_control)
elif img_control is not None:
image_show = control_net_preprocessing(model_control_net,img_control,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,disable_preprocessing)
return gr.Image.update(image_show)
return gr.Image.update(value = None)
def change_image_condition(image_condition):
if image_condition is None:
return gr.Image.update()
return gr.Image.update(value= None)
#control_net_model,img_control,low_threshold = None,high_threshold=None,has_hand=None,preprocessor_name=None
def control_net_muti(control_net_model,img_control,low_threshold ,high_threshold,has_body,has_hand,has_face,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing):
global lst_control
if img_control is not None:
config = {
"control_net_model": control_net_model,
"img_control": img_control,
"low_threshold": low_threshold,
"high_threshold": high_threshold,
"has_body": has_body,
"has_face": has_face,
"has_hand": has_hand,
"preprocessor_name": preprocessor_name,
"disable_preprocessing":disable_preprocessing,
"controlnet_scale": controlnet_scale,
"control_guidance_start": control_guidance_start,
"control_guidance_end": control_guidance_end,
}
lst_control.append(config)
return gr.Image.update(value = None)
def previous_view_control():
global lst_control,current_number_control
if current_number_control <= 0:
current_number_control = len(lst_control)-1
else:
current_number_control -= 1
return gr.Dropdown.update(value = lst_control[current_number_control]["control_net_model"]),gr.Image.update(value = lst_control[current_number_control]["img_control"]),gr.Slider.update(value = lst_control[current_number_control]["low_threshold"]),gr.Slider.update(value = lst_control[current_number_control]["high_threshold"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_body"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_hand"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_face"]),gr.Radio.update(value = lst_control[current_number_control]["preprocessor_name"]),gr.Slider.update(value= lst_control[current_number_control]["controlnet_scale"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_start"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_end"]),gr.Checkbox.update(value = lst_control[current_number_control]["disable_preprocessing"])
def next_view_control():
global lst_control,current_number_control
if current_number_control >= len(lst_control)-1:
current_number_control = 0
else:
current_number_control += 1
return gr.Dropdown.update(value = lst_control[current_number_control]["control_net_model"]),gr.Image.update(value = lst_control[current_number_control]["img_control"]),gr.Slider.update(value = lst_control[current_number_control]["low_threshold"]),gr.Slider.update(value = lst_control[current_number_control]["high_threshold"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_body"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_hand"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_face"]),gr.Radio.update(value = lst_control[current_number_control]["preprocessor_name"]),gr.Slider.update(value= lst_control[current_number_control]["controlnet_scale"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_start"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_end"]),gr.Checkbox.update(value = lst_control[current_number_control]["disable_preprocessing"])
def apply_edit_control_net(control_net_model,img_control,low_threshold ,high_threshold,has_body,has_hand,has_face,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing):
global lst_control,current_number_control,in_edit_mode
if img_control is not None:
config = {
"control_net_model": control_net_model,
"img_control": img_control,
"low_threshold": low_threshold,
"high_threshold": high_threshold,
"has_body": has_body,
"has_face": has_face,
"has_hand": has_hand,
"preprocessor_name": preprocessor_name,
"disable_preprocessing":disable_preprocessing,
"controlnet_scale": controlnet_scale,
"control_guidance_start": control_guidance_start,
"control_guidance_end": control_guidance_end,
}
lst_control[current_number_control] = config
return gr.Dropdown.update(),gr.Image.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Checkbox.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Slider.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
else:
lst_control.pop(current_number_control)
current_number_control -=1
if current_number_control == -1:
current_number_control = len(lst_control)-1
if len(lst_control) == 0:
in_edit_mode = False
return gr.Dropdown.update(),gr.Image.update(value = None),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Checkbox.update(value = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Slider.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
return gr.Dropdown.update(value = lst_control[current_number_control]["control_net_model"]),gr.Image.update(value = lst_control[current_number_control]["img_control"]),gr.Slider.update(value = lst_control[current_number_control]["low_threshold"]),gr.Slider.update(value = lst_control[current_number_control]["high_threshold"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_body"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_hand"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_face"]),gr.Radio.update(value = lst_control[current_number_control]["preprocessor_name"]),gr.Checkbox.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Slider.update(value= lst_control[current_number_control]["controlnet_scale"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_start"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_end"]),gr.Checkbox.update(value = lst_control[current_number_control]["disable_preprocessing"])
def complete_edit_multi():
global current_number_control,in_edit_mode
current_number_control = 0
in_edit_mode = False
return gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Image.update(value= None),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False)
def multi_controlnet_function(multi_controlnet):
if multi_controlnet:
return gr.Checkbox.update(value = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update()
return gr.Checkbox.update(),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False)
def edit_multi_control_image_function():
global lst_control,current_number_control,in_edit_mode
if len(lst_control) > 0:
in_edit_mode = True
return gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Dropdown.update(value = lst_control[current_number_control]["control_net_model"]),gr.Image.update(value = lst_control[current_number_control]["img_control"]),gr.Slider.update(value = lst_control[current_number_control]["low_threshold"]),gr.Slider.update(value = lst_control[current_number_control]["high_threshold"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_body"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_hand"]),gr.Checkbox.update(value = lst_control[current_number_control]["has_face"]),gr.Radio.update(value = lst_control[current_number_control]["preprocessor_name"]),gr.Slider.update(value= lst_control[current_number_control]["controlnet_scale"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_start"]),gr.Slider.update(value= lst_control[current_number_control]["control_guidance_end"]),gr.Checkbox.update(value = lst_control[current_number_control]["disable_preprocessing"])
in_edit_mode = False
return gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Dropdown.update(),gr.Image.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Slider.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
def ip_adapter_work(ip_adapter):
if ip_adapter:
return gr.Checkbox.update(value = True)
return gr.Checkbox.update()
def preview_image_adapter(model_adapter,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,img_control,preprocessor_adapter,multi_adapter,disable_preprocessing_adapter):
global in_edit_mode_adapter
if multi_adapter == True and in_edit_mode_adapter == True:
global lst_adapter,current_number_adapter
if model_adapter == lst_adapter[current_number_adapter]["model_adapter"]:
setting_processing = list(lst_adapter[current_number_adapter].items())
setting_processing = setting_processing[:-3]
setting_processing = dict(setting_processing)
else:
setting_processing = {
"model_adapter": model_adapter,
"img_control": img_control,
"low_threshold_adapter": low_threshold_adapter,
"high_threshold_adapter": high_threshold_adapter,
"has_body": has_body_openpose_adapter,
"has_face": has_face_openpose_adapter,
"has_hand": has_hand_openpose_adapter,
"preprocessor_adapter": preprocessor_adapter,
"disable_preprocessing_adapter":disable_preprocessing_adapter,
}
image_sp_control = adapter_preprocessing(**setting_processing)
return gr.Image.update(image_sp_control)
elif img_control is not None:
image_show = adapter_preprocessing(model_adapter,img_control,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,disable_preprocessing_adapter)
return gr.Image.update(image_show)
return gr.Image.update(value = None)
def change_image_condition_adapter(image_condition_adapter):
if image_condition_adapter is None:
return gr.Image.update()
return gr.Image.update(value= None)
#control_net_model,img_control,low_threshold_adapter = None,high_threshold_adapter=None,has_hand=None,preprocessor_adapter=None
def adapter_muti(model_adapter,img_control,low_threshold_adapter ,high_threshold_adapter,has_body,has_hand,has_face,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter):
global lst_adapter
if img_control is not None:
config = {
"model_adapter": model_adapter,
"img_control": img_control,
"low_threshold_adapter": low_threshold_adapter,
"high_threshold_adapter": high_threshold_adapter,
"has_body": has_body,
"has_face": has_face,
"has_hand": has_hand,
"preprocessor_adapter": preprocessor_adapter,
"disable_preprocessing_adapter":disable_preprocessing_adapter,
"adapter_conditioning_scale": adapter_conditioning_scale,
"adapter_conditioning_factor": adapter_conditioning_factor,
}
lst_adapter.append(config)
return gr.Image.update(value = None)
def previous_view_adapter():
global lst_adapter,current_number_adapter
if current_number_adapter <= 0:
current_number_adapter = len(lst_adapter)-1
else:
current_number_adapter -= 1
return gr.Dropdown.update(value = lst_adapter[current_number_adapter]["model_adapter"]),gr.Image.update(value = lst_adapter[current_number_adapter]["img_control"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["low_threshold_adapter"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["high_threshold_adapter"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_body"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_hand"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_face"]),gr.Radio.update(value = lst_adapter[current_number_adapter]["preprocessor_adapter"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_scale"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_factor"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["disable_preprocessing_adapter"])
def next_view_adapter():
global lst_adapter,current_number_adapter
if current_number_adapter >= len(lst_adapter)-1:
current_number_adapter = 0
else:
current_number_adapter += 1
return gr.Dropdown.update(value = lst_adapter[current_number_adapter]["model_adapter"]),gr.Image.update(value = lst_adapter[current_number_adapter]["img_control"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["low_threshold_adapter"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["high_threshold_adapter"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_body"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_hand"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_face"]),gr.Radio.update(value = lst_adapter[current_number_adapter]["preprocessor_adapter"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_scale"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_factor"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["disable_preprocessing_adapter"])
def apply_edit_adapter(model_adapter,img_control,low_threshold_adapter ,high_threshold_adapter,has_body,has_hand,has_face,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter):
global lst_adapter,current_number_adapter,in_edit_mode_adapter
if img_control is not None:
config = {
"model_adapter": model_adapter,
"img_control": img_control,
"low_threshold_adapter": low_threshold_adapter,
"high_threshold_adapter": high_threshold_adapter,
"has_body": has_body,
"has_face": has_face,
"has_hand": has_hand,
"preprocessor_adapter": preprocessor_adapter,
"disable_preprocessing_adapter":disable_preprocessing_adapter,
"adapter_conditioning_scale": adapter_conditioning_scale,
"adapter_conditioning_factor": adapter_conditioning_factor,
}
lst_adapter[current_number_adapter] = config
return gr.Dropdown.update(),gr.Image.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Checkbox.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
else:
lst_adapter.pop(current_number_adapter)
current_number_adapter -=1
if current_number_adapter == -1:
current_number_adapter = len(lst_adapter)-1
if len(lst_adapter) == 0:
in_edit_mode_adapter = False
return gr.Dropdown.update(),gr.Image.update(value = None),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Checkbox.update(value = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
return gr.Dropdown.update(value = lst_adapter[current_number_adapter]["model_adapter"]),gr.Image.update(value = lst_adapter[current_number_adapter]["img_control"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["low_threshold_adapter"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["high_threshold_adapter"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_body"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_hand"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_face"]),gr.Radio.update(value = lst_adapter[current_number_adapter]["preprocessor_adapter"]),gr.Checkbox.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_scale"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_factor"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["disable_preprocessing_adapter"])
def complete_edit_multi_adapter():
global current_number_adapter,in_edit_mode_adapter
current_number_adapter = 0
in_edit_mode_adapter = False
return gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Image.update(value= None),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False)
def multi_adapter_function(multi_adapter):
if multi_adapter:
return gr.Checkbox.update(value = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update()
return gr.Checkbox.update(),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False)
def edit_multi_adapter_image_function():
global lst_adapter,current_number_adapter,in_edit_mode_adapter
if len(lst_adapter) > 0:
in_edit_mode_adapter = True
return gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = False),gr.Button.update(visible = False),gr.Dropdown.update(value = lst_adapter[current_number_adapter]["model_adapter"]),gr.Image.update(value = lst_adapter[current_number_adapter]["img_control"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["low_threshold_adapter"]),gr.Slider.update(value = lst_adapter[current_number_adapter]["high_threshold_adapter"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_body"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_hand"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["has_face"]),gr.Radio.update(value = lst_adapter[current_number_adapter]["preprocessor_adapter"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_scale"]),gr.Slider.update(value= lst_adapter[current_number_adapter]["adapter_conditioning_factor"]),gr.Checkbox.update(value = lst_adapter[current_number_adapter]["disable_preprocessing_adapter"])
in_edit_mode_adapter = False
return gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Button.update(),gr.Dropdown.update(),gr.Image.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Checkbox.update(),gr.Radio.update(),gr.Slider.update(),gr.Slider.update(),gr.Checkbox.update()
def ip_adpater_function(ip_adapter):
if ip_adapter:
return gr.Checkbox.update()
return gr.Checkbox.update(value = False)
#ip_adapter,inf_adapt_image,inf_adapt_image_multi,inf_adapt_image_strength,inf_adapt_image_strength_multi,edit_ip_adapter_setting,apply_ip_adapter_setting
def ip_adpater_multi_function(ip_adapter_multi):
if ip_adapter_multi:
return gr.Dropdown.update(choices=[k for k in model_ip_adapter_lst[:-2]],value=model_ip_adapter_lst[0]),gr.Checkbox.update(value = True), gr.Image.update(visible = False), gr.Image.update(visible = True), gr.Slider.update(visible = False), gr.Slider.update(visible = True),gr.Button.update(visible = True),gr.Button.update(visible = True), gr.Image.update(visible = False), gr.Image.update(visible = True)
return gr.Dropdown.update(choices=[k for k in model_ip_adapter_lst],value=model_ip_adapter_lst[0]),gr.Checkbox.update(), gr.Image.update(visible = True), gr.Image.update(visible = False), gr.Slider.update(visible = True), gr.Slider.update(visible = False),gr.Button.update(visible = False),gr.Button.update(visible = False), gr.Image.update(visible = True), gr.Image.update(visible = False)
def apply_ip_adapter_setting_function(model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi):
global lst_ip_adapter,current_number_ip_adapter
if inf_adapt_image_multi is not None:
config ={
"model" : model_ip_adapter,
"image" : inf_adapt_image_multi,
"region_apply": inf_control_adapt_image_multi,
"scale" : float(inf_adapt_image_strength_multi),
}
lst_ip_adapter.append(config)
return gr.Image.update(value = None),gr.Image.update(value = None)
return gr.Image.update(value = None),gr.Image.update(value = None)
#model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,previous_ip_adapter_setting,next_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting
def edit_ip_adapter_setting_function():
global lst_ip_adapter,current_number_ip_adapter
if len(lst_ip_adapter) == 0:
return (
gr.Dropdown.update(),
gr.Image.update(),
gr.Slider.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Image.update(),
)
return (
gr.Dropdown.update(value = lst_ip_adapter[current_number_ip_adapter]["model"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["image"]),
gr.Slider.update(value = lst_ip_adapter[current_number_ip_adapter]["scale"]),
gr.Button.update(visible = True),
gr.Button.update(visible = True),
gr.Button.update(visible = True),
gr.Button.update(visible = True),
gr.Button.update(visible = False),
gr.Button.update(visible = False),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["region_apply"]),
)
def previous_ip_adapter_setting_function():
global lst_ip_adapter,current_number_ip_adapter
current_number_ip_adapter -= 1
if current_number_ip_adapter < 0:
current_number_ip_adapter = len(lst_ip_adapter) -1
return (
gr.Dropdown.update(value = lst_ip_adapter[current_number_ip_adapter]["model"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["image"]),
gr.Slider.update(value = lst_ip_adapter[current_number_ip_adapter]["scale"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["region_apply"]),
)
def next_ip_adapter_setting_function():
global lst_ip_adapter,current_number_ip_adapter
current_number_ip_adapter += 1
if current_number_ip_adapter == len(lst_ip_adapter):
current_number_ip_adapter = 0
return (
gr.Dropdown.update(value = lst_ip_adapter[current_number_ip_adapter]["model"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["image"]),
gr.Slider.update(value = lst_ip_adapter[current_number_ip_adapter]["scale"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["region_apply"]),
)
#inf_adapt_image_multi,previous_ip_adapter_setting,next_ip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting
def complete_cip_adapter_setting_function():
return (
gr.Image.update(value = None),
gr.Button.update(visible = False),
gr.Button.update(visible = False),
gr.Button.update(visible = True),
gr.Button.update(visible = True),
gr.Button.update(visible = False),
gr.Button.update(visible = False),
gr.Image.update(value = None),
)
#model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,previous_ip_adapter_setting,next_ip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting
def apply_edit_ip_adapter_setting_function(model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi):
global lst_ip_adapter,current_number_ip_adapter
if inf_adapt_image_multi is not None:
config_change = lst_ip_adapter[current_number_ip_adapter]
config_change["model"] = model_ip_adapter
config_change["image"] = inf_adapt_image_multi
config_change["scale"] = float(inf_adapt_image_strength_multi)
config_change["region_apply"] = inf_control_adapt_image_multi
return (
gr.Dropdown.update(),
gr.Image.update(),
gr.Slider.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Image.update(),
)
#Delete
lst_ip_adapter.pop(current_number_ip_adapter)
current_number_ip_adapter -= 1
if len(lst_ip_adapter) == 0:
return (
gr.Dropdown.update(),
gr.Image.update(value = None),
gr.Slider.update(),
gr.Button.update(visible = False),
gr.Button.update(visible = False),
gr.Button.update(visible = True),
gr.Button.update(visible = True),
gr.Button.update(visible = False),
gr.Button.update(visible = False),
gr.Image.update(value = None),
)
if current_number_ip_adapter == -1:
current_number_ip_adapter = len(lst_ip_adapter)-1
return (
gr.Dropdown.update(value = lst_ip_adapter[current_number_ip_adapter]["model"]),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["image"]),
gr.Slider.update(value = lst_ip_adapter[current_number_ip_adapter]["scale"]),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Button.update(),
gr.Image.update(value = lst_ip_adapter[current_number_ip_adapter]["region_apply"]),
)
def inpaiting_mode_fuction(inpaiting_mode):
if inpaiting_mode:
return gr.Image.update(visible = False),gr.Image.update(visible = True), gr.Image.update(visible = True),gr.Checkbox.update(visible = True),gr.Button.update(visible = True),gr.Slider.update(value = 1.0)
return gr.Image.update(visible = True),gr.Image.update(visible = False), gr.Image.update(visible = False),gr.Checkbox.update(visible = False),gr.Button.update(visible = False),gr.Slider.update(value = 0.5)
def get_mask_fuction(inf_image_inpaiting):
img_mask = None
if isinstance(inf_image_inpaiting,dict):
img_mask = inf_image_inpaiting["mask"].copy()
return gr.Image.update(img_mask)
latent_upscale_modes = {
"Latent (bilinear)": {"upscale_method": "bilinear", "upscale_antialias": False},
"Latent (bilinear antialiased)": {"upscale_method": "bilinear", "upscale_antialias": True},
"Latent (bicubic)": {"upscale_method": "bicubic", "upscale_antialias": False},
"Latent (bicubic antialiased)": {
"upscale_method": "bicubic",
"upscale_antialias": True,
},
"Latent (nearest)": {"upscale_method": "nearest", "upscale_antialias": False},
"Latent (nearest-exact)": {
"upscale_method": "nearest-exact",
"upscale_antialias": False,
},
#"Latent (linear)": {"upscale_method": "linear", "upscale_antialias": False},
#"Latent (trilinear)": {"upscale_method": "trilinear", "upscale_antialias": False},
"Latent (area)": {"upscale_method": "area", "upscale_antialias": False},
}
css = """
.finetuned-diffusion-div div{
display:inline-flex;
align-items:center;
gap:.8rem;
font-size:1.75rem;
padding-top:2rem;
}
.finetuned-diffusion-div div h1{
font-weight:900;
margin-bottom:7px
}
.finetuned-diffusion-div p{
margin-bottom:10px;
font-size:94%
}
.box {
float: left;
height: 20px;
width: 20px;
margin-bottom: 15px;
border: 1px solid black;
clear: both;
}
a{
text-decoration:underline
}
.tabs{
margin-top:0;
margin-bottom:0
}
#gallery{
min-height:20rem
}
.no-border {
border: none !important;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
f"""
<div class="finetuned-diffusion-div">
<div>
<h1>Demo for diffusion models</h1>
</div>
<p>Running on CPU 🥶 This demo does not work on CPU.</p>
</div>
"""
)
global_stats = gr.State(value={})
with gr.Row():
with gr.Column(scale=55):
model = gr.Dropdown(
choices=[k[0] for k in get_model_list()],
label="Model",
value=base_name,
)
with gr.Row():
image_out = gr.Image()
gallery = gr.Gallery(label="Generated images", show_label=True, elem_id="gallery",visible = False).style(grid=[1], height="auto")
with gr.Column(scale=45):
with gr.Group():
with gr.Row():
with gr.Column(scale=70):
prompt = gr.Textbox(
label="Prompt",
value="loli cat girl, blue eyes, flat chest, solo, long messy silver hair, blue capelet, cat ears, cat tail, upper body",
show_label=True,
#max_lines=4,
placeholder="Enter prompt.",
)
neg_prompt = gr.Textbox(
label="Negative Prompt",
value="bad quality, low quality, jpeg artifact, cropped",
show_label=True,
#max_lines=4,
placeholder="Enter negative prompt.",
)
generate = gr.Button(value="Generate").style(
rounded=(False, True, True, False)
)
with gr.Tab("Options"):
with gr.Group():
# n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
with gr.Row():
diffuser_pipeline = gr.Checkbox(label="Using diffusers pipeline", value=False)
latent_processing = gr.Checkbox(label="Show processing", value=False)
region_condition = gr.Checkbox(label="Enable region condition", value=False)
with gr.Row():
guidance = gr.Slider(
label="Guidance scale", value=7.5, maximum=20
)
guidance_rescale = gr.Slider(
label="Guidance rescale", value=0, maximum=20
)
with gr.Row():
width = gr.Slider(
label="Width", value=512, minimum=64, maximum=1920, step=8
)
height = gr.Slider(
label="Height", value=512, minimum=64, maximum=1920, step=8
)
with gr.Row():
clip_skip = gr.Slider(
label="Clip Skip", value=2, minimum=1, maximum=12, step=1
)
steps = gr.Slider(
label="Steps", value=25, minimum=2, maximum=100, step=1
)
with gr.Row():
long_encode = sampler = gr.Dropdown(
value="Automatic111 Encoding",
label="Encoding prompt type",
choices=[s for s in encoding_type],
)
sampler = gr.Dropdown(
value="DPM++ 2M Karras",
label="Sampler",
choices=[s[0] for s in samplers_k_diffusion],
)
with gr.Row():
seed = gr.Number(label="Seed (Lower than 0 = random)", value=-1)
Insert_model = gr.Textbox(
label="Insert model",
show_label=True,
placeholder="Enter a model's link.",
)
insert_model = gr.Button(value="Insert")
#reset_model = gr.Button(value="Reset")
insert_model.click(
add_model,
inputs=[Insert_model],
outputs=[model, Insert_model],
queue=False,
)
with gr.Tab("Image to image/Inpaiting"):
with gr.Group():
with gr.Row():
inpaiting_mode = gr.Checkbox(label="Inpaiting", value=False)
invert_mask_mode = gr.Checkbox(label="Black areas are used", value=False,visible = False)
with gr.Row():
inf_image = gr.Image(
label="Image", source="upload", type="pil",
)
inf_image_inpaiting = gr.Image(
label="Image", source="upload", type="pil", tool="sketch",visible = False
)
mask_upload = gr.Image(
label="Mask", source="upload", type="pil",image_mode='L',visible = False,
)
inf_strength = gr.Slider(
label="Transformation strength",
minimum=0,
maximum=1,
step=0.01,
value=0.5,
)
get_mask = gr.Button(value="Get mask",visible = False)
inpaiting_mode.change(
inpaiting_mode_fuction,
inputs=[inpaiting_mode],
outputs=[inf_image,inf_image_inpaiting,mask_upload,invert_mask_mode,get_mask,inf_strength],
queue=False,
)
get_mask.click(
get_mask_fuction,
inputs=[inf_image_inpaiting],
outputs=[mask_upload],
queue=False,
)
with gr.Tab("Hires fix"):
with gr.Group():
with gr.Row():
hr_enabled = gr.Checkbox(label="Enable upscaler", value=False)
hr_process_enabled = gr.Checkbox(label="Show processing upscaler", value=False)
hr_region_condition = gr.Checkbox(label="Enable region condition upscaler", value=False)
with gr.Row():
hr_method = gr.Dropdown(
[key for key in latent_upscale_modes.keys()],
value="Latent (bilinear)",
label="Upscale method",
)
sampler_hires = gr.Dropdown(
value="DPM++ 2M Karras",
label="Sampler",
choices=[s[0] for s in samplers_k_diffusion],
)
hr_scale = gr.Slider(
label="Upscale factor",
minimum=1.0,
maximum=2.0,
step=0.1,
value=1.2,
)
hr_denoise = gr.Slider(
label="Denoising strength",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.8,
)
hr_scale.change(
lambda g, x, w, h: gr.Checkbox.update(
label=res_cap(g, w, h, x)
),
inputs=[hr_enabled, hr_scale, width, height],
outputs=hr_enabled,
queue=False,
)
hr_process_enabled.change(
change_gallery,
inputs=[latent_processing,hr_process_enabled],
outputs=[gallery],
queue=False,
)
latent_processing.change(
change_gallery,
inputs=[latent_processing,hr_process_enabled],
outputs=[gallery],
queue=False,
)
with gr.Tab("IP-Adapter"):
with gr.Group():
with gr.Row():
ip_adapter = gr.Checkbox(label="Using IP-Adapter", value=False)
ip_adapter_multi = gr.Checkbox(label="Using Multi IP-Adapter", value=False)
invert_ip_adapter_mask_mode = gr.Checkbox(label="Black areas are used", value=True)
model_ip_adapter = gr.Dropdown(
choices=[k for k in model_ip_adapter_lst],
label="Model IP-Adapter",
value=model_ip_adapter_lst[0],
)
with gr.Row():
inf_adapt_image = gr.Image(
label="IP-Adapter", source="upload", type="pil"
)
inf_control_adapt_image = gr.Image(
label="Region apply", source="upload", type="pil",image_mode='L'
)
inf_adapt_image_multi = gr.Image(
label="IP-Adapter", source="upload", type="pil",visible= False
)
inf_control_adapt_image_multi = gr.Image(
label="Region apply", source="upload", type="pil",image_mode='L',visible= False
)
inf_adapt_image_strength = gr.Slider(
label="IP-Adapter scale",
minimum=0,
maximum=2,
step=0.01,
value=1,
)
inf_adapt_image_strength_multi = gr.Slider(
label="IP-Adapter scale",
minimum=0,
maximum=2,
step=0.01,
value=1,
visible= False,
)
with gr.Row():
previous_ip_adapter_setting = gr.Button(value="Previous setting",visible = False)
next_ip_adapter_setting = gr.Button(value="Next setting",visible = False)
with gr.Row():
edit_ip_adapter_setting = gr.Button(value="Edit previous setting",visible = False)
apply_ip_adapter_setting = gr.Button(value="Apply setting",visible = False)
with gr.Row():
apply_edit_ip_adapter_setting = gr.Button(value="Apply change",visible = False)
complete_cip_adapter_setting = gr.Button(value="Complete change",visible = False)
ip_adapter.change(
ip_adpater_function,
inputs=[ip_adapter],
outputs=[ip_adapter_multi],
queue=False,
)
ip_adapter_multi.change(
ip_adpater_multi_function,
inputs=[ip_adapter_multi],
outputs=[model_ip_adapter,ip_adapter,inf_adapt_image,inf_adapt_image_multi,inf_adapt_image_strength,inf_adapt_image_strength_multi,edit_ip_adapter_setting,apply_ip_adapter_setting,inf_control_adapt_image,inf_control_adapt_image_multi],
queue=False,
)
apply_ip_adapter_setting.click(
apply_ip_adapter_setting_function,
inputs = [model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi],
outputs = [inf_adapt_image_multi,inf_control_adapt_image_multi],
)
edit_ip_adapter_setting.click(
edit_ip_adapter_setting_function,
inputs = [],
outputs =[model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,previous_ip_adapter_setting,next_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting,inf_control_adapt_image_multi],
queue =False,
)
previous_ip_adapter_setting.click(
previous_ip_adapter_setting_function,
inputs = [],
outputs = [model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi],
queue = False,
)
next_ip_adapter_setting.click(
next_ip_adapter_setting_function,
inputs = [],
outputs = [model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi],
queue = False,
)
apply_edit_ip_adapter_setting.click(
apply_edit_ip_adapter_setting_function,
inputs = [model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,inf_control_adapt_image_multi],
outputs =[model_ip_adapter,inf_adapt_image_multi,inf_adapt_image_strength_multi,previous_ip_adapter_setting,next_ip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting,inf_control_adapt_image_multi],
queue = False,
)
complete_cip_adapter_setting.click(
complete_cip_adapter_setting_function,
inputs = [],
outputs = [inf_adapt_image_multi,previous_ip_adapter_setting,next_ip_adapter_setting,edit_ip_adapter_setting,apply_ip_adapter_setting,apply_edit_ip_adapter_setting,complete_cip_adapter_setting,inf_control_adapt_image_multi],
queue = False,
)
with gr.Tab("Controlnet"):
with gr.Group():
with gr.Row():
controlnet_enabled = gr.Checkbox(label="Enable Controlnet", value=False)
disable_preprocessing = gr.Checkbox(label="Disable preprocessing", value=False)
multi_controlnet = gr.Checkbox(label="Enable Multi Controlnet", value=False)
#sketch_enabled = gr.Checkbox(label="Sketch image", value=False)
model_control_net = gr.Dropdown(
choices=[k for k in controlnet_lst],
label="Model Controlnet",
value=controlnet_lst[0],
)
with gr.Row():
low_threshold = gr.Slider(
label="Canny low threshold", value=100, minimum=1, maximum=255, step=1
)
high_threshold = gr.Slider(
label="Canny high threshold", value=200, minimum=1, maximum=255, step=1
)
with gr.Row():
has_body_openpose = gr.Checkbox(label="Has body", value=True,visible= False)
has_hand_openpose = gr.Checkbox(label="Has hand", value=False,visible= False)
has_face_openpose = gr.Checkbox(label="Has face", value=False,visible= False)
preprocessor_name = gr.Radio(
label="Preprocessor",
type="value",
visible= False,
)
with gr.Row():
control_guidance_start = gr.Slider(
label="Control guidance start", value=0, minimum=0, maximum=1, step=0.01
)
control_guidance_end = gr.Slider(
label="Control guidance end", value=1, minimum=0, maximum=1, step=0.01
)
controlnet_scale = gr.Slider(
label="Controlnet scale", value=1, minimum=0, maximum=2, step=0.01
)
with gr.Row():
controlnet_img = gr.Image(
image_mode="RGB",
source="upload",
label = "Image",
type = 'pil',
)
image_condition = gr.Image(interactive=False,image_mode="RGB",label = "Preprocessor Preview",type = 'pil')
control_image_click = gr.Button(value="Preview")
with gr.Row():
previous_multi_control_image = gr.Button(value="Previous control setting",visible = False)
next_multi_control_image = gr.Button(value="Next control setting",visible = False)
with gr.Row():
edit_multi_control_image = gr.Button(value="Edit previous setting",visible = False)
apply_multi_control_image = gr.Button(value="Apply setting",visible = False)
with gr.Row():
apply_edit_multi = gr.Button(value="Apply change",visible = False)
complete_change_multi = gr.Button(value="Complete change",visible = False)
control_image_click.click(
preview_image,
inputs=[model_control_net,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,controlnet_img,preprocessor_name,multi_controlnet,disable_preprocessing],
outputs=[image_condition],
queue=False,
)
multi_controlnet.change(
multi_controlnet_function,
inputs=[multi_controlnet],
outputs=[controlnet_enabled,edit_multi_control_image,apply_multi_control_image,previous_multi_control_image,next_multi_control_image,apply_edit_multi,complete_change_multi],
queue=False,
)
edit_multi_control_image.click(
edit_multi_control_image_function,
inputs=[],
outputs=[previous_multi_control_image,next_multi_control_image,apply_edit_multi,complete_change_multi,edit_multi_control_image,apply_multi_control_image,model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
queue=False,
)
previous_multi_control_image.click(
previous_view_control,
inputs=[],
outputs=[model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
queue=False,
)
next_multi_control_image.click(
next_view_control,
inputs=[],
outputs=[model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
queue=False,
)
apply_multi_control_image.click(
control_net_muti,
inputs=[model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
outputs=[controlnet_img],
queue=False,
)
apply_edit_multi.click(
apply_edit_control_net,
inputs=[model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
outputs=[model_control_net,controlnet_img,low_threshold,high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name,multi_controlnet,previous_multi_control_image,next_multi_control_image,apply_edit_multi,complete_change_multi,controlnet_scale,control_guidance_start,control_guidance_end,disable_preprocessing],
queue=False,
)
complete_change_multi.click(
complete_edit_multi,
inputs=[],
outputs=[edit_multi_control_image,apply_multi_control_image,controlnet_img,apply_edit_multi,complete_change_multi,next_multi_control_image,previous_multi_control_image],
queue=False,
)
controlnet_img.change(
change_image_condition,
inputs=[image_condition],
outputs=[image_condition],
queue=False,
)
model_control_net.change(
change_control_net,
inputs=[model_control_net, low_threshold, high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose],
outputs=[low_threshold, high_threshold,has_body_openpose,has_hand_openpose,has_face_openpose,preprocessor_name],
queue=False,
)
with gr.Tab("T2I Adapter"):
with gr.Group():
with gr.Row():
adapter_enabled = gr.Checkbox(label="Enable T2I Adapter", value=False)
disable_preprocessing_adapter = gr.Checkbox(label="Disable preprocessing", value=False)
multi_adapter = gr.Checkbox(label="Enable Multi T2I Adapter", value=False)
#sketch_enabled = gr.Checkbox(label="Sketch image", value=False)
model_adapter = gr.Dropdown(
choices=[k for k in adapter_lst],
label="Model Controlnet",
value=adapter_lst[0],
)
with gr.Row():
low_threshold_adapter = gr.Slider(
label="Canny low threshold", value=100, minimum=1, maximum=255, step=1
)
high_threshold_adapter = gr.Slider(
label="Canny high threshold", value=200, minimum=1, maximum=255, step=1
)
with gr.Row():
has_body_openpose_adapter = gr.Checkbox(label="Has body", value=True,visible= False)
has_hand_openpose_adapter = gr.Checkbox(label="Has hand", value=False,visible= False)
has_face_openpose_adapter = gr.Checkbox(label="Has face", value=False,visible= False)
preprocessor_adapter = gr.Radio(
label="Preprocessor",
type="value",
visible= False,
)
with gr.Row():
adapter_conditioning_scale = gr.Slider(
label="Conditioning scale", value=1, minimum=0, maximum=2, step=0.01
)
adapter_conditioning_factor = gr.Slider(
label="Conditioning factor", value=1, minimum=0, maximum=1, step=0.01
)
'''controlnet_scale = gr.Slider(
label="Controlnet scale", value=1, minimum=0, maximum=2, step=0.01
)'''
with gr.Row():
adapter_img = gr.Image(
image_mode="RGB",
source="upload",
label = "Image",
type = 'pil',
)
image_condition_adapter = gr.Image(interactive=False,image_mode="RGB",label = "Preprocessor Preview",type = 'pil')
adapter_image_click = gr.Button(value="Preview")
with gr.Row():
previous_multi_adapter_image = gr.Button(value="Previous adapter setting",visible = False)
next_multi_adapter_image = gr.Button(value="Next adapter setting",visible = False)
with gr.Row():
edit_multi_adapter_image = gr.Button(value="Edit previous setting",visible = False)
apply_multi_adapter_image = gr.Button(value="Apply setting",visible = False)
with gr.Row():
apply_edit_multi_adapter = gr.Button(value="Apply change",visible = False)
complete_change_multi_adapter = gr.Button(value="Complete change",visible = False)
adapter_image_click.click(
preview_image_adapter,
inputs=[model_adapter,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,adapter_img,preprocessor_adapter,multi_adapter,disable_preprocessing_adapter],
outputs=[image_condition_adapter],
queue=False,
)
multi_adapter.change(
multi_adapter_function,
inputs=[multi_adapter],
outputs=[adapter_enabled,edit_multi_adapter_image,apply_multi_adapter_image,previous_multi_adapter_image,next_multi_adapter_image,apply_edit_multi_adapter,complete_change_multi_adapter],
queue=False,
)
edit_multi_adapter_image.click(
edit_multi_adapter_image_function,
inputs=[],
outputs=[previous_multi_adapter_image,next_multi_adapter_image,apply_edit_multi_adapter,complete_change_multi_adapter,edit_multi_adapter_image,apply_multi_adapter_image,model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
queue=False,
)
previous_multi_adapter_image.click(
previous_view_adapter,
inputs=[],
outputs=[model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
queue=False,
)
next_multi_adapter_image.click(
next_view_adapter,
inputs=[],
outputs=[model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
queue=False,
)
apply_multi_adapter_image.click(
adapter_muti,
inputs=[model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
outputs=[adapter_img],
queue=False,
)
apply_edit_multi_adapter.click(
apply_edit_adapter,
inputs=[model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
outputs=[model_adapter,adapter_img,low_threshold_adapter,high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter,multi_adapter,previous_multi_adapter_image,next_multi_adapter_image,apply_edit_multi_adapter,complete_change_multi_adapter,adapter_conditioning_scale,adapter_conditioning_factor,disable_preprocessing_adapter],
queue=False,
)
complete_change_multi_adapter.click(
complete_edit_multi_adapter,
inputs=[],
outputs=[edit_multi_adapter_image,apply_multi_adapter_image,adapter_img,apply_edit_multi_adapter,complete_change_multi_adapter,next_multi_adapter_image,previous_multi_adapter_image],
queue=False,
)
adapter_img.change(
change_image_condition_adapter,
inputs=[image_condition_adapter],
outputs=[image_condition_adapter],
queue=False,
)
model_adapter.change(
change_control_net,
inputs=[model_adapter, low_threshold_adapter, high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter],
outputs=[low_threshold_adapter, high_threshold_adapter,has_body_openpose_adapter,has_hand_openpose_adapter,has_face_openpose_adapter,preprocessor_adapter],
queue=False,
)
diffuser_pipeline.change(
mode_diffuser_pipeline_sampler,
inputs=[diffuser_pipeline, sampler,sampler_hires],
outputs=[diffuser_pipeline,sampler,sampler_hires],
queue=False,
)
hr_enabled.change(
lambda g, x, w, h: gr.Checkbox.update(
label=res_cap(g, w, h, x)
),
inputs=[hr_enabled, hr_scale, width, height],
outputs=hr_enabled,
queue=False,
)
adapter_enabled.change(
mode_diffuser_pipeline,
inputs=[adapter_enabled],
outputs=[adapter_enabled,multi_adapter],
queue=False,
)
controlnet_enabled.change(
mode_diffuser_pipeline,
inputs=[controlnet_enabled],
outputs=[controlnet_enabled,multi_controlnet],
queue=False,
)
'''controlnet_enabled.change(
mode_diffuser_pipeline1,
inputs=[diffuser_pipeline, controlnet_enabled],
outputs=[controlnet_enabled],
queue=False,
)'''
with gr.Tab("Vae Setting"):
with gr.Group():
vae_used = gr.Dropdown(
choices=[k for k in vae_lst],
label="Chosing Vae",
value=vae_lst[0],
)
with gr.Row():
with gr.Column():
Insert_vae = gr.Textbox(
label="Insert Vae's link",
show_label=True,
placeholder="Enter a Vae's link.",
)
single_load_file = gr.Checkbox(label="Is Single File", value=False)
insert_vae = gr.Button(value="Insert")
insert_vae.click(
add_vae,
inputs=[Insert_vae,single_load_file],
outputs=[vae_used, Insert_vae,single_load_file],
queue=False,
)
with gr.Tab("Embeddings/Loras"):
ti_state = gr.State(dict())
lora_group = gr.State(dict())
with gr.Group():
with gr.Row():
with gr.Column():
ti_vals = gr.CheckboxGroup(label="Chosing embeddings")
embs_choose = gr.Text(label="Embeddings chosen")
with gr.Row():
choose_em = gr.Button(value="Select Embeddings")
delete_em = gr.Button(value="Delete Embeddings")
choose_em.click(choose_tistate,inputs=[ti_vals],outputs=[ti_state,embs_choose,ti_vals],queue=False,)
delete_em.click(delete_embed,inputs=[ti_vals,ti_state,embs_choose],outputs=[ti_vals,ti_state,embs_choose],queue=False,)
with gr.Row():
with gr.Column():
lora_list = gr.CheckboxGroup(label="Chosing Loras")
lora_choose = gr.Text(label="Loras chosen")
with gr.Row():
choose_lora = gr.Button(value="Select Loras")
delete_lora = gr.Button(value="Delete Loras")
lora_vals = gr.Dropdown(choices=[k for k in lora_lst],label="Loras Scale",value=lora_lst[0],)
choose_lora.click(choose_lora_function,inputs=[lora_list],outputs=[lora_group,lora_choose,lora_list,lora_vals],queue=False,)
delete_lora.click(delete_lora_function,inputs=[lora_list,lora_group,lora_choose],outputs=[lora_list,lora_group,lora_choose,lora_vals],queue=False,)
# delete_lora_but = gr.Button(value="Delete Lora")
link_download = gr.Textbox(
label="Insert lora's/embedding's link",
show_label=True,
placeholder="Enter a link download.",
)
#delete_lora_but.click(lora_delete,inputs=[lora_vals],outputs=[lora_vals],queue=False,)
with gr.Row():
uploads = gr.Files(label="Upload new embeddings/lora")
with gr.Column():
lora_scale = gr.Slider(
label="Lora scale",
minimum=0,
maximum=2,
step=0.01,
value=1.0,
)
btn = gr.Button(value="Upload/Download")
btn_del = gr.Button(value="Reset")
lora_vals.change(
change_lora_value,
inputs=[lora_vals],
outputs=[lora_scale],
queue=False,
)
lora_scale.change(
update_lora_value,
inputs=[lora_scale,lora_vals],
outputs=[],
queue=False,
)
btn.click(
add_net,
inputs=[uploads,link_download],
outputs=[ti_vals,lora_list, lora_vals, uploads,link_download],
queue=False,
)
btn_del.click(
clean_states,
inputs=[ti_state,lora_group],
outputs=[ti_state,lora_group, ti_vals,lora_list, lora_vals, uploads,embs_choose,lora_choose,link_download],
queue=False,
)
# error_output = gr.Markdown()
gr.HTML(
f"""
<div class="finetuned-diffusion-div">
<div>
<h1>Define the object's region.</h1>
</div>
<p>
Using the following formula as default: w = scale * token_weight_martix * sigma * std(qk).
</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
formula_button = gr.Dropdown(
choices=[k[0] for k in formula],
label="Formual",
value=formula[0][0],
)
rendered = gr.Image(
invert_colors=True,
source="canvas",
interactive=False,
image_mode="RGBA",
)
with gr.Column(scale=45):
with gr.Group():
with gr.Row():
with gr.Column(scale=70):
# g_strength = gr.Slider(
# label="Compliance rate",
# minimum=0,
# maximum=2,
# step=0.01,
# value=0.4,
# )
text = gr.Textbox(
lines=2,
interactive=True,
label="Token to Draw: (Separate by comma)",
)
radio = gr.Radio([], label="Tokens",visible = False)
sk_update = gr.Button(value="Update").style(
rounded=(False, True, True, False)
)
# g_strength.change(lambda b: gr.update(f"Scaled additional attn: $w = {b} \log (1 + \sigma) \std (Q^T K)$."), inputs=g_strength, outputs=[g_output])
with gr.Tab("SketchPad"):
sp = gr.Image(
width = 512,
height = 512,
image_mode="L",
tool="sketch",
source="canvas",
interactive=False
)
'''mask_outsides = gr.Checkbox(
label="Mask other areas",
value=False
)'''
with gr.Row():
mask_outsides = gr.Slider(
label="Decrease unmarked region weight",
minimum=0,
maximum=3,
step=0.01,
value=0,
)
strength = gr.Slider(
label="Token-Region strength",
minimum=0,
maximum=3,
step=0.01,
value=0.5,
)
width.change(
apply_size_sketch,
inputs=[width, height,global_stats,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, rendered,sp],
queue=False,
)
height.change(
apply_size_sketch,
inputs=[width, height,global_stats,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, rendered,sp],
queue=False,
)
inf_image.change(
apply_size_sketch,
inputs=[width, height,global_stats,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, rendered,sp],
queue=False,
)
sk_update.click(
detect_text,
inputs=[text, global_stats, width, height,formula_button,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, sp, radio, rendered,formula_button],
queue=False,
)
radio.change(
switch_canvas,
inputs=[radio, global_stats, width, height,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[sp, strength, mask_outsides, rendered],
queue=False,
)
sp.edit(
apply_canvas,
inputs=[radio, sp, global_stats, width, height,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, rendered],
queue=False,
)
strength.change(
apply_weight,
inputs=[radio, strength, global_stats],
outputs=[global_stats],
queue=False,
)
mask_outsides.change(
apply_option,
inputs=[radio, mask_outsides, global_stats],
outputs=[global_stats],
queue=False,
)
with gr.Tab("UploadFile"):
sp2 = gr.Image(
image_mode="RGB",
source="upload",
)
sp3 = gr.Image(
image_mode="L",
source="canvas",
visible = False,
interactive = False,
)
with gr.Row():
previous_page = gr.Button(value="Previous",visible = False,)
next_page = gr.Button(value="Next",visible = False,)
'''mask_outsides2 = gr.Checkbox(
label="Mask other areas",
value=False,
)'''
with gr.Row():
mask_outsides2 = gr.Slider(
label="Decrease unmarked region weight",
minimum=0,
maximum=3,
step=0.01,
value=0,
)
strength2 = gr.Slider(
label="Token-Region strength",
minimum=0,
maximum=3,
step=0.01,
value=0.5,
)
'''sk_update.click(
detect_text1,
inputs=[text, global_stats, width, height,formula_button,inf_image],
outputs=[global_stats, radio, rendered,formula_button],
queue=False,
)'''
with gr.Row():
apply_style = gr.Button(value="Apply")
apply_clustering_style = gr.Button(value="Extracting color regions")
with gr.Row():
add_style = gr.Button(value="Apply",visible = False)
complete_clustering = gr.Button(value="Complete",visible = False)
apply_style.click(
apply_image,
inputs=[sp2, radio, width, height, strength2, mask_outsides2, global_stats,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats, rendered],
queue=False,
)
apply_clustering_style.click(
apply_base_on_color,
inputs=[sp2,global_stats,width, height,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[rendered,apply_style,apply_clustering_style,previous_page,next_page,complete_clustering,sp2,sp3,add_style,global_stats],
queue=False,
)
previous_page.click(
previous_image_page,
inputs=[sp3],
outputs=[sp3],
queue=False,
)
next_page.click(
next_image_page,
inputs=[sp3],
outputs=[sp3],
queue=False,
)
add_style.click(
apply_image_clustering,
inputs=[sp3, radio, width, height, strength2, mask_outsides2, global_stats,inf_image,inpaiting_mode,inf_image_inpaiting],
outputs=[global_stats,rendered],
queue=False,
)
complete_clustering.click(
completing_clustering,
inputs=[sp2],
outputs=[apply_style,apply_clustering_style,previous_page,next_page,complete_clustering,sp2,sp3,add_style],
queue=False,
)
'''width.change(
apply_new_res,
inputs=[width, height, global_stats,inf_image,rendered],
outputs=[global_stats, rendered],
queue=False,
)
height.change(
apply_new_res,
inputs=[width, height, global_stats,inf_image,rendered],
outputs=[global_stats, rendered],
queue=False,
)'''
# color_stats = gr.State(value={})
# text.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered])
# sp.change(detect_color, inputs=[sp, text, color_stats], outputs=[color_stats, rendered])
inputs = [
prompt,
guidance,
steps,
width,
height,
clip_skip,
seed,
neg_prompt,
global_stats,
#g_strength,
inf_image,
inf_strength,
hr_enabled,
hr_method,
hr_scale,
hr_denoise,
sampler,
ti_state,
model,
lora_group,
#lora_vals,
#lora_scale,
formula_button,
controlnet_enabled,
model_control_net,
low_threshold,
high_threshold,
has_body_openpose,
has_hand_openpose,
has_face_openpose,
controlnet_img,
image_condition,
controlnet_scale,
preprocessor_name,
diffuser_pipeline,
sampler_hires,
latent_processing,
control_guidance_start,
control_guidance_end,
multi_controlnet,
disable_preprocessing,
region_condition,
hr_process_enabled,
ip_adapter,
model_ip_adapter,
inf_adapt_image,
inf_adapt_image_strength,
hr_region_condition,
adapter_enabled,
model_adapter,
low_threshold_adapter,
high_threshold_adapter,
has_body_openpose_adapter,
has_hand_openpose_adapter,
has_face_openpose_adapter,
adapter_img,
image_condition_adapter,
preprocessor_adapter,
adapter_conditioning_scale,
adapter_conditioning_factor,
multi_adapter,
disable_preprocessing_adapter,
ip_adapter_multi,
guidance_rescale,
inf_control_adapt_image,
long_encode,
inpaiting_mode,
invert_mask_mode,
mask_upload,
inf_image_inpaiting,
invert_ip_adapter_mask_mode,
vae_used,
]
outputs = [image_out,gallery]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs)
print(f"Space built in {time.time() - start_time:.2f} seconds")
demo.queue().launch(share=True,debug=True)
demo.launch(enable_queue=True, server_name="0.0.0.0", server_port=7860)
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