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model_loader_utils.py
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# !/usr/bin/env python
# -*- coding: UTF-8 -*-
import datetime
import gc
import logging
import os
import sys
import re
import random
import torch
from diffusers.image_processor import VaeImageProcessor
from omegaconf import OmegaConf
from PIL import Image
import numpy as np
import cv2
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
from transformers import CLIPImageProcessor
from diffusers import (StableDiffusionXLPipeline, DDIMScheduler, ControlNetModel,
KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
EulerDiscreteScheduler, HeunDiscreteScheduler,
KDPM2DiscreteScheduler,
EulerAncestralDiscreteScheduler, UniPCMultistepScheduler,
StableDiffusionXLControlNetPipeline, DDPMScheduler, LCMScheduler)
from .msdiffusion.models.projection import Resampler
from .msdiffusion.models.model import MSAdapter
from .msdiffusion.utils import get_phrase_idx, get_eot_idx
from .utils.style_template import styles
from .utils.load_models_utils import get_lora_dict,get_instance_path
from .PuLID.pulid.utils import resize_numpy_image_long
from transformers import AutoModel, AutoTokenizer
from comfy.utils import common_upscale,ProgressBar
import folder_paths
from comfy.clip_vision import load as clip_load
cur_path = os.path.dirname(os.path.abspath(__file__))
photomaker_dir=os.path.join(folder_paths.models_dir, "photomaker")
base_pt = os.path.join(photomaker_dir,"pt")
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
lora_get = get_lora_dict()
lora_lightning_list = lora_get["lightning_xl_lora"]
global total_count, attn_count, cur_step, mask1024, mask4096, attn_procs, unet
global sa32, sa64
global write
global height_s, width_s
SAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis","ddim", "uni_pc", "uni_pc_bh2"]
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
def get_scheduler(name,scheduler_):
scheduler = False
if name == "euler" or name =="euler_cfg_pp":
scheduler = EulerDiscreteScheduler()
elif name == "euler_ancestral" or name =="euler_ancestral_cfg_pp":
scheduler = EulerAncestralDiscreteScheduler()
elif name == "ddim":
scheduler = DDIMScheduler()
elif name == "ddpm":
scheduler = DDPMScheduler()
elif name == "dpmpp_2m":
scheduler = DPMSolverMultistepScheduler()
elif name == "dpmpp_2m" and scheduler_=="karras":
scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True)
elif name == "dpmpp_2m_sde":
scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")
elif name == "dpmpp_2m" and scheduler_=="karras":
scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
elif name == "dpmpp_sde" or name == "dpmpp_sde_gpu":
scheduler = DPMSolverSinglestepScheduler()
elif (name == "dpmpp_sde" or name == "dpmpp_sde_gpu") and scheduler_=="karras":
scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True)
elif name == "dpm_2":
scheduler = KDPM2DiscreteScheduler()
elif name == "dpm_2" and scheduler_=="karras":
scheduler = KDPM2DiscreteScheduler(use_karras_sigmas=True)
elif name == "dpm_2_ancestral":
scheduler = KDPM2AncestralDiscreteScheduler()
elif name == "dpm_2_ancestral" and scheduler_=="karras":
scheduler = KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)
elif name == "heun":
scheduler = HeunDiscreteScheduler()
elif name == "lcm":
scheduler = LCMScheduler()
elif name == "lms":
scheduler = LMSDiscreteScheduler()
elif name == "lms" and scheduler_=="karras":
scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
elif name == "uni_pc":
scheduler = UniPCMultistepScheduler()
else:
scheduler = EulerDiscreteScheduler()
return scheduler
def get_easy_function(easy_function, clip_vision, character_weights, ckpt_name, lora, repo_id,photomake_mode):
auraface = False
NF4 = False
save_model = False
kolor_face = False
flux_pulid_name = "flux-dev"
pulid = False
quantized_mode = "fp16"
story_maker = False
make_dual_only = False
clip_vision_path = None
char_files = ""
lora_path = None
use_kolor = False
use_flux = False
ckpt_path = None
onnx_provider="gpu"
low_vram=False
TAG_mode=False
SD35_mode=False
consistory=False
cached=False
inject=False
use_quantize=True
if easy_function:
easy_function = easy_function.strip().lower()
if "auraface" in easy_function:
auraface = True
if "nf4" in easy_function:
NF4 = True
if "save" in easy_function:
save_model = True
if "face" in easy_function:
kolor_face = True
if "schnell" in easy_function:
flux_pulid_name = "flux-schnell"
if "pulid" in easy_function:
pulid = True
if "fp8" in easy_function:
quantized_mode = "fp8"
if "maker" in easy_function:
story_maker = True
if "dual" in easy_function:
make_dual_only = True
if "cpu" in easy_function:
onnx_provider="cpu"
if "low" in easy_function:
low_vram=True
if "tag" in easy_function:
TAG_mode=True
if "consi" in easy_function:
consistory=True
if "cache" in easy_function:
cached=True
if "inject" in easy_function:
inject=True
if "noquan" in easy_function:
use_quantize=False
if clip_vision != "none":
clip_vision_path = folder_paths.get_full_path("clip_vision", clip_vision)
if character_weights != "none":
character_weights_path = get_instance_path(os.path.join(base_pt, character_weights))
weights_list = os.listdir(character_weights_path)
if weights_list:
char_files = character_weights_path
if ckpt_name != "none":
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
if lora != "none":
lora_path = folder_paths.get_full_path("loras", lora)
lora_path = get_instance_path(lora_path)
if "/" in lora:
lora = lora.split("/")[-1]
if "\\" in lora:
lora = lora.split("\\")[-1]
else:
lora = None
if repo_id:
if "kolors" in repo_id.lower():
use_kolor = True
photomake_mode = ""
elif "flux" in repo_id.lower():
use_flux = True
photomake_mode = ""
elif "3.5" in repo_id.lower():
SD35_mode = True
else:
pass
if pulid:
use_flux = True
photomake_mode = ""
return (auraface, NF4, save_model, kolor_face, flux_pulid_name, pulid, quantized_mode, story_maker, make_dual_only,
clip_vision_path, char_files, ckpt_path, lora, lora_path, use_kolor, photomake_mode, use_flux,onnx_provider,low_vram,TAG_mode,SD35_mode,consistory,cached,inject,use_quantize)
def pre_checkpoint(photomaker_path, photomake_mode, kolor_face, pulid, story_maker, clip_vision_path, use_kolor,
model_type):
if photomake_mode == "v1":
if not os.path.exists(photomaker_path):
photomaker_path = hf_hub_download(
repo_id="TencentARC/PhotoMaker",
filename="photomaker-v1.bin",
local_dir=photomaker_dir,
)
else:
if not os.path.exists(photomaker_path):
photomaker_path = hf_hub_download(
repo_id="TencentARC/PhotoMaker-V2",
filename="photomaker-v2.bin",
local_dir=photomaker_dir,
)
if kolor_face:
face_ckpt = os.path.join(photomaker_dir, "ipa-faceid-plus.bin")
if not os.path.exists(face_ckpt):
hf_hub_download(
repo_id="Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus",
filename="ipa-faceid-plus.bin",
local_dir=photomaker_dir,
)
photomake_mode = ""
else:
face_ckpt = ""
if pulid:
pulid_ckpt = os.path.join(photomaker_dir, "pulid_flux_v0.9.0.safetensors")
if not os.path.exists(pulid_ckpt):
hf_hub_download(
repo_id="guozinan/PuLID",
filename="pulid_flux_v0.9.0.safetensors",
local_dir=photomaker_dir,
)
photomake_mode = ""
else:
pulid_ckpt = ""
if story_maker:
photomake_mode = ""
if not clip_vision_path:
raise ("using story_maker need choice a clip_vision model")
# image_encoder_path='laion/CLIP-ViT-H-14-laion2B-s32B-b79K'
face_adapter = os.path.join(photomaker_dir, "mask.bin")
if not os.path.exists(face_adapter):
hf_hub_download(
repo_id="RED-AIGC/StoryMaker",
filename="mask.bin",
local_dir=photomaker_dir,
)
else:
face_adapter = ""
kolor_ip_path=""
if use_kolor:
if model_type == "img2img" and not kolor_face:
kolor_ip_path = os.path.join(photomaker_dir, "ip_adapter_plus_general.bin")
if not os.path.exists(kolor_ip_path):
hf_hub_download(
repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus",
filename="ip_adapter_plus_general.bin",
local_dir=photomaker_dir,
)
photomake_mode = ""
return photomaker_path, face_ckpt, photomake_mode, pulid_ckpt, face_adapter, kolor_ip_path
def phi2narry(img):
img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
return img
def tensor_to_image(tensor):
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
image = Image.fromarray(image_np, mode='RGB')
return image
def tensortopil_list(tensor_in):
d1, _, _, _ = tensor_in.size()
if d1 == 1:
img_list = [tensor_to_image(tensor_in)]
else:
tensor_list = torch.chunk(tensor_in, chunks=d1)
img_list=[tensor_to_image(i) for i in tensor_list]
return img_list
def nomarl_tensor_upscale(tensor, width, height):
samples = tensor.movedim(-1, 1)
samples = common_upscale(samples, width, height, "nearest-exact", "center")
samples = samples.movedim(1, -1)
return samples
def nomarl_upscale(img, width, height):
samples = img.movedim(-1, 1)
img = common_upscale(samples, width, height, "nearest-exact", "center")
samples = img.movedim(1, -1)
img = tensor_to_image(samples)
return img
def nomarl_upscale_tensor(img, width, height):
samples = img.movedim(-1, 1)
img = common_upscale(samples, width, height, "nearest-exact", "center")
samples = img.movedim(1, -1)
return samples
def center_crop(img):
width, height = img.size
square = min(width, height)
left = (width - square) / 2
top = (height - square) / 2
right = (width + square) / 2
bottom = (height + square) / 2
return img.crop((left, top, right, bottom))
def center_crop_s(img, new_width, new_height):
width, height = img.size
left = (width - new_width) / 2
top = (height - new_height) / 2
right = (width + new_width) / 2
bottom = (height + new_height) / 2
return img.crop((left, top, right, bottom))
def contains_brackets(s):
return '[' in s or ']' in s
def has_parentheses(s):
return bool(re.search(r'\(.*?\)', s))
def extract_content_from_brackets(text):
# 正则表达式匹配多对方括号内的内容
return re.findall(r'\[(.*?)\]', text)
def narry_list(list_in):
for i in range(len(list_in)):
value = list_in[i]
modified_value = phi2narry(value)
list_in[i] = modified_value
return list_in
def remove_punctuation_from_strings(lst):
pattern = r"[\W]+$" # 匹配字符串末尾的所有非单词字符
return [re.sub(pattern, '', s) for s in lst]
def phi_list(list_in):
for i in range(len(list_in)):
value = list_in[i]
list_in[i] = value
return list_in
def narry_list_pil(list_in):
for i in range(len(list_in)):
value = list_in[i]
modified_value = tensor_to_image(value)
list_in[i] = modified_value
return list_in
def get_local_path(file_path, model_path):
path = os.path.join(file_path, "models", "diffusers", model_path)
model_path = os.path.normpath(path)
if sys.platform.startswith('win32'):
model_path = model_path.replace('\\', "/")
return model_path
def setup_seed(seed):
torch.manual_seed(seed)
if device == "cuda":
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def apply_style_positive(style_name: str, positive: str):
p, n = styles.get(style_name, styles[style_name])
#print(p, "test0", n)
return p.replace("{prompt}", positive),n
def apply_style(style_name: str, positives: list, negative: str = ""):
p, n = styles.get(style_name, styles[style_name])
#print(p,"test1",n)
return [
p.replace("{prompt}", positive) for positive in positives
], n + " " + negative
def array2string(arr):
stringtmp = ""
for i, part in enumerate(arr):
if i != len(arr) - 1:
stringtmp += part + "\n"
else:
stringtmp += part
return stringtmp
def find_directories(base_path):
directories = []
for root, dirs, files in os.walk(base_path):
for name in dirs:
directories.append(name)
return directories
def load_character_files(character_files: str):
if character_files == "":
raise "Please set a character file!"
character_files_arr = character_files.splitlines()
primarytext = []
for character_file_name in character_files_arr:
character_file = torch.load(
character_file_name, map_location=torch.device("cpu")
)
character_file.eval()
primarytext.append(character_file["character"] + character_file["description"])
return array2string(primarytext)
def face_bbox_to_square(bbox):
## l, t, r, b to square l, t, r, b
l,t,r,b = bbox
cent_x = (l + r) / 2
cent_y = (t + b) / 2
w, h = r - l, b - t
r = max(w, h) / 2
l0 = cent_x - r
r0 = cent_x + r
t0 = cent_y - r
b0 = cent_y + r
return [l0, t0, r0, b0]
def story_maker_loader(clip_load,clip_vision_path,dir_path,ckpt_path,face_adapter,UniPCMultistepScheduler,controlnet_path,lora_scale,low_vram):
logging.info("loader story_maker processing...")
from .StoryMaker.pipeline_sdxl_storymaker import StableDiffusionXLStoryMakerPipeline
original_config_file = os.path.join(dir_path, 'config', 'sd_xl_base.yaml')
add_config = os.path.join(dir_path, "local_repo")
try:
pipe = StableDiffusionXLStoryMakerPipeline.from_single_file(
ckpt_path, config=add_config, original_config=original_config_file,
torch_dtype=torch.float16)
except:
try:
pipe = StableDiffusionXLStoryMakerPipeline.from_single_file(
ckpt_path, config=add_config, original_config_file=original_config_file,
torch_dtype=torch.float16)
except:
raise "load pipe error!,check you diffusers"
controlnet=None
if controlnet_path:
controlnet = ControlNetModel.from_unet(pipe.unet)
cn_state_dict = load_file(controlnet_path, device="cpu")
controlnet.load_state_dict(cn_state_dict, strict=False)
controlnet.to(torch.float16)
if device != "mps":
if not low_vram:
pipe.cuda()
image_encoder = clip_load(clip_vision_path)
pipe.load_storymaker_adapter(image_encoder, face_adapter, scale=0.8, lora_scale=lora_scale,controlnet=controlnet)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
#pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
#pipe.enable_vae_slicing()
if device != "mps":
if low_vram:
pipe.enable_model_cpu_offload()
return pipe
def kolor_loader(repo_id,model_type,set_attention_processor,id_length,kolor_face,clip_vision_path,clip_load,CLIPVisionModelWithProjection,CLIPImageProcessor,
photomaker_dir,face_ckpt,AutoencoderKL,EulerDiscreteScheduler,UNet2DConditionModel):
from .kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import \
StableDiffusionXLPipeline as StableDiffusionXLPipelineKolors
from .kolors.models.modeling_chatglm import ChatGLMModel
from .kolors.models.tokenization_chatglm import ChatGLMTokenizer
from .kolors.models.unet_2d_condition import UNet2DConditionModel as UNet2DConditionModelkolor
logging.info("loader story_maker processing...")
text_encoder = ChatGLMModel.from_pretrained(
f'{repo_id}/text_encoder', torch_dtype=torch.float16).half()
vae = AutoencoderKL.from_pretrained(f"{repo_id}/vae", revision=None).half()
tokenizer = ChatGLMTokenizer.from_pretrained(f'{repo_id}/text_encoder')
scheduler = EulerDiscreteScheduler.from_pretrained(f"{repo_id}/scheduler")
if model_type == "txt2img":
unet = UNet2DConditionModel.from_pretrained(f"{repo_id}/unet", revision=None,
use_safetensors=True).half()
pipe = StableDiffusionXLPipelineKolors(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=False, )
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
else:
if kolor_face is False:
from .kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import \
StableDiffusionXLPipeline as StableDiffusionXLPipelinekoloripadapter
if clip_vision_path:
image_encoder = clip_load(clip_vision_path).model
ip_img_size = 224 # comfyUI defualt is use 224
use_singel_clip = True
else:
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
f'{repo_id}/Kolors-IP-Adapter-Plus/image_encoder', ignore_mismatched_sizes=True).to(
dtype=torch.float16)
ip_img_size = 336
use_singel_clip = False
clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
unet = UNet2DConditionModelkolor.from_pretrained(f"{repo_id}/unet", revision=None, ).half()
pipe = StableDiffusionXLPipelinekoloripadapter(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=clip_image_processor,
force_zeros_for_empty_prompt=False,
use_single_clip=use_singel_clip
)
if hasattr(pipe.unet, 'encoder_hid_proj'):
pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
pipe.load_ip_adapter(photomaker_dir, subfolder="", weight_name=["ip_adapter_plus_general.bin"])
else: # kolor ip faceid
from .kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter_FaceID import \
StableDiffusionXLPipeline as StableDiffusionXLPipelineFaceID
unet = UNet2DConditionModel.from_pretrained(f'{repo_id}/unet', revision=None).half()
if clip_vision_path:
clip_image_encoder = clip_load(clip_vision_path).model
clip_image_processor = CLIPImageProcessor(size=224, crop_size=224)
use_singel_clip = True
else:
clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
f'{repo_id}/clip-vit-large-patch14-336', ignore_mismatched_sizes=True)
clip_image_encoder.to("cuda")
clip_image_processor = CLIPImageProcessor(size=336, crop_size=336)
use_singel_clip = False
pipe = StableDiffusionXLPipelineFaceID(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
face_clip_encoder=clip_image_encoder,
face_clip_processor=clip_image_processor,
force_zeros_for_empty_prompt=False,
use_single_clip=use_singel_clip,
)
pipe = pipe.to("cuda")
pipe.load_ip_adapter_faceid_plus(face_ckpt, device="cuda")
pipe.set_face_fidelity_scale(0.8)
return pipe
def quantized_nf4_extra(ckpt_path,dir_path,mode):
if mode=="flux":
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
config_file = os.path.join(dir_path, "config.json")
else:
from diffusers import SD3Transformer2DModel
config_file = os.path.join(dir_path, "config/sd35/config.json")
from accelerate.utils import set_module_tensor_to_device
from accelerate import init_empty_weights
from .utils.convert_nf4_flux import _replace_with_bnb_linear, create_quantized_param, \
check_quantized_param
import gc
dtype = torch.bfloat16
is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
original_state_dict = load_file(ckpt_path)
with init_empty_weights():
if mode == "flux":
config = FluxTransformer2DModel.load_config(config_file)
model = FluxTransformer2DModel.from_config(config).to(dtype)
expected_state_dict_keys = list(model.state_dict().keys())
else:
config = SD3Transformer2DModel.load_config(config_file)
model = SD3Transformer2DModel.from_config(config).to(dtype)
expected_state_dict_keys = list(model.state_dict().keys())
_replace_with_bnb_linear(model, "nf4")
for param_name, param in original_state_dict.items():
if param_name not in expected_state_dict_keys:
continue
is_param_float8_e4m3fn = is_torch_e4m3fn_available and param.dtype == torch.float8_e4m3fn
if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
param = param.to(dtype)
if not check_quantized_param(model, param_name):
set_module_tensor_to_device(model, param_name, device=0, value=param)
else:
create_quantized_param(
model, param, param_name, target_device=0, state_dict=original_state_dict,
pre_quantized=True
)
del original_state_dict
gc.collect()
return model
def flux_loader(folder_paths,ckpt_path,repo_id,AutoencoderKL,save_model,model_type,pulid,clip_vision_path,NF4,vae_id,offload,aggressive_offload,pulid_ckpt,quantized_mode,
if_repo,dir_path,clip,onnx_provider,use_quantize):
# pip install optimum-quanto
# https://gist.github.com/AmericanPresidentJimmyCarter/873985638e1f3541ba8b00137e7dacd9
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
weight_transformer = os.path.join(folder_paths.models_dir, "checkpoints", f"transformer_{timestamp}.pt")
dtype = torch.bfloat16
if not ckpt_path:
logging.info("using repo_id ,start flux fp8 quantize processing...")
from optimum.quanto import freeze, qfloat8, quantize
from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
from diffusers import FlowMatchEulerDiscreteScheduler
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
revision = "refs/pr/1"
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler",
revision=revision)
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2",
torch_dtype=dtype,
revision=revision)
tokenizer_2 = T5TokenizerFast.from_pretrained(repo_id, subfolder="tokenizer_2",
torch_dtype=dtype,
revision=revision)
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype,
revision=revision)
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer",
torch_dtype=dtype, revision=revision)
quantize(transformer, weights=qfloat8)
freeze(transformer)
if save_model:
print(f"saving fp8 pt on '{weight_transformer}'")
torch.save(transformer,
weight_transformer) # https://pytorch.org/tutorials/beginner/saving_loading_models.html.
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)
if model_type == "img2img":
# https://github.com/deforum-studio/flux/blob/main/flux_pipeline.py#L536
from .utils.flux_pipeline import FluxImg2ImgPipeline
pipe = FluxImg2ImgPipeline(
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=None,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=None,
)
else:
pipe = FluxPipeline(
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=None,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=None,
)
pipe.text_encoder_2 = text_encoder_2
pipe.transformer = transformer
pipe.enable_model_cpu_offload()
else: # flux diff unet ,diff 0.30 ckpt or repo
from diffusers import FluxTransformer2DModel, FluxPipeline
from transformers import T5EncoderModel, CLIPTextModel
from optimum.quanto import freeze, qfloat8, quantize
if pulid:
logging.info("using repo_id and ckpt ,start flux-pulid processing...")
from .PuLID.app_flux import FluxGenerator
if not clip_vision_path:
raise "need 'EVA02_CLIP_L_336_psz14_s6B.pt' in comfyUI/models/clip_vision"
if NF4:
quantized_mode = "nf4"
if vae_id == "none":
raise "Now,using pulid must choice ae from comfyUI vae menu"
else:
vae_path = folder_paths.get_full_path("vae", vae_id)
pipe = FluxGenerator(repo_id, ckpt_path, "cuda", offload=offload,
aggressive_offload=aggressive_offload, pretrained_model=pulid_ckpt,
quantized_mode=quantized_mode, clip_vision_path=clip_vision_path, clip_cf=clip,
vae_cf=vae_path, if_repo=if_repo,onnx_provider=onnx_provider,use_quantize=use_quantize)
else:
if NF4:
logging.info("using repo_id and ckpt ,start flux nf4 quantize processing...")
# https://github.com/huggingface/diffusers/issues/9165
mode="flux"
model=quantized_nf4_extra(ckpt_path, dir_path, mode)
if model_type == "img2img":
from .utils.flux_pipeline import FluxImg2ImgPipeline
pipe = FluxImg2ImgPipeline.from_pretrained(repo_id, transformer=model,
torch_dtype=dtype)
else:
pipe = FluxPipeline.from_pretrained(repo_id, transformer=model, torch_dtype=dtype)
else:
logging.info("using repo_id and ckpt ,start flux fp8 quantize processing...")
if os.path.splitext(ckpt_path)[-1] == ".pt":
transformer = torch.load(ckpt_path)
transformer.eval()
else:
config_file = os.path.join(dir_path, "utils", "config.json")
transformer = FluxTransformer2DModel.from_single_file(ckpt_path, config=config_file,
torch_dtype=dtype)
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2",
torch_dtype=dtype)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)
if model_type == "img2img":
from .utils.flux_pipeline import FluxImg2ImgPipeline
pipe = FluxImg2ImgPipeline.from_pretrained(repo_id, transformer=None,
text_encoder_2=clip,
torch_dtype=dtype)
else:
pipe = FluxPipeline.from_pretrained(repo_id,transformer=None,text_encoder_2=None,
torch_dtype=dtype)
pipe.transformer = transformer
pipe.text_encoder_2 = text_encoder_2
pipe.enable_model_cpu_offload()
return pipe
def insight_face_loader(photomake_mode,auraface,kolor_face,story_maker,make_dual_only,use_storydif):
if use_storydif and photomake_mode == "v2" and not story_maker:
from .utils.insightface_package import FaceAnalysis2, analyze_faces
if auraface:
from huggingface_hub import snapshot_download
snapshot_download(
"fal/AuraFace-v1",
local_dir="models/auraface",
)
app_face = FaceAnalysis2(name="auraface",
providers=["CUDAExecutionProvider", "CPUExecutionProvider"], root=".",
allowed_modules=['detection', 'recognition'])
else:
app_face = FaceAnalysis2(providers=['CUDAExecutionProvider'],
allowed_modules=['detection', 'recognition'])
app_face.prepare(ctx_id=0, det_size=(640, 640))
pipeline_mask = None
app_face_ = None
elif kolor_face:
from .kolors.models.sample_ipadapter_faceid_plus import FaceInfoGenerator
from huggingface_hub import snapshot_download
snapshot_download(
'DIAMONIK7777/antelopev2',
local_dir='models/antelopev2',
)
app_face = FaceInfoGenerator(root_dir=".")
pipeline_mask = None
app_face_ = None
elif story_maker:
from insightface.app import FaceAnalysis
from transformers import pipeline
pipeline_mask = pipeline("image-segmentation", model="briaai/RMBG-1.4",
trust_remote_code=True)
if make_dual_only: # 前段用story 双人用maker
if photomake_mode == "v2" and use_storydif:
from .utils.insightface_package import FaceAnalysis2
if auraface:
from huggingface_hub import snapshot_download
snapshot_download(
"fal/AuraFace-v1",
local_dir="models/auraface",
)
app_face = FaceAnalysis2(name="auraface",
providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
root=".",
allowed_modules=['detection', 'recognition'])
else:
app_face = FaceAnalysis2(providers=['CUDAExecutionProvider'],
allowed_modules=['detection', 'recognition'])
app_face.prepare(ctx_id=0, det_size=(640, 640))
app_face_ = FaceAnalysis(name='buffalo_l', root='./',
providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app_face_.prepare(ctx_id=0, det_size=(640, 640))
else:
app_face = FaceAnalysis(name='buffalo_l', root='./',
providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app_face.prepare(ctx_id=0, det_size=(640, 640))
app_face_ = None
else:
app_face = FaceAnalysis(name='buffalo_l', root='./',
providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app_face.prepare(ctx_id=0, det_size=(640, 640))
app_face_ = None
else:
app_face = None
pipeline_mask = None
app_face_ = None
return app_face,pipeline_mask,app_face_
def main_normal(prompt,pipe,phrases,ms_model,input_images,num_samples,steps,seed,negative_prompt,scale,image_encoder,cfg,image_processor,
boxes,mask_threshold,start_step,image_proj_type,image_encoder_type,drop_grounding_tokens,height,width,phrase_idxes, eot_idxes,in_img,use_repo):
if use_repo:
in_img = None
images = ms_model.generate(pipe=pipe, pil_images=[input_images],processed_images=in_img, num_samples=num_samples,
num_inference_steps=steps,
seed=seed,
prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
image_encoder=image_encoder, guidance_scale=cfg,
image_processor=image_processor, boxes=boxes,
mask_threshold=mask_threshold,
start_step=start_step,
image_proj_type=image_proj_type,
image_encoder_type=image_encoder_type,
phrases=phrases,
drop_grounding_tokens=drop_grounding_tokens,
phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
width=width)
return images
def main_control(prompt,width,height,pipe,phrases,ms_model,input_images,num_samples,steps,seed,negative_prompt,scale,image_encoder,cfg,
image_processor,boxes,mask_threshold,start_step,image_proj_type,image_encoder_type,drop_grounding_tokens,controlnet_scale,control_image,phrase_idxes, eot_idxes,in_img,use_repo):
if use_repo:
in_img=None
images = ms_model.generate(pipe=pipe, pil_images=[input_images],processed_images=in_img, num_samples=num_samples,
num_inference_steps=steps,
seed=seed,
prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
image_encoder=image_encoder, guidance_scale=cfg,
image_processor=image_processor, boxes=boxes,
mask_threshold=mask_threshold,
start_step=start_step,
image_proj_type=image_proj_type,
image_encoder_type=image_encoder_type,
phrases=phrases,
drop_grounding_tokens=drop_grounding_tokens,
phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
width=width,
image=control_image, controlnet_conditioning_scale=controlnet_scale)
return images
def get_float(str_in):
list_str=str_in.split(",")
float_box=[float(x) for x in list_str]
return float_box
def get_phrases_idx(tokenizer, phrases, prompt):
res = []
phrase_cnt = {}
for phrase in phrases:
if phrase in phrase_cnt:
cur_cnt = phrase_cnt[phrase]
phrase_cnt[phrase] += 1
else:
cur_cnt = 0
phrase_cnt[phrase] = 1
res.append(get_phrase_idx(tokenizer, phrase, prompt, num=cur_cnt)[0])
return res
def msdiffusion_main(image_1, image_2, prompts_dual, width, height, steps, seed, style_name, char_describe, char_origin,
negative_prompt,
clip_vision, _model_type, lora, lora_path, lora_scale, trigger_words, ckpt_path, dif_repo,
guidance, mask_threshold, start_step, controlnet_path, control_image, controlnet_scale, cfg,
guidance_list, scheduler_choice,pipe):
tensor_a = phi2narry(image_1.copy())
tensor_b = phi2narry(image_2.copy())
in_img = torch.cat((tensor_a, tensor_b), dim=0)
original_config_file = os.path.join(cur_path, 'config', 'sd_xl_base.yaml')
if dif_repo:
single_files = False
elif not dif_repo and ckpt_path:
single_files = True
elif dif_repo and ckpt_path:
single_files = False
else:
raise "no model"
add_config = os.path.join(cur_path, "local_repo")
if _model_type=="img2img":
del pipe
gc.collect()
torch.cuda.empty_cache()
if single_files:
try:
pipe = StableDiffusionXLPipeline.from_single_file(
ckpt_path, config=add_config, original_config=original_config_file,
torch_dtype=torch.float16)
except:
try:
pipe = StableDiffusionXLPipeline.from_single_file(
ckpt_path, config=add_config, original_config_file=original_config_file,
torch_dtype=torch.float16)
except:
raise "load pipe error!,check you diffusers"
else:
pipe = StableDiffusionXLPipeline.from_pretrained(dif_repo, torch_dtype=torch.float16)
if controlnet_path:
controlnet = ControlNetModel.from_unet(pipe.unet)
cn_state_dict = load_file(controlnet_path, device="cpu")
controlnet.load_state_dict(cn_state_dict, strict=False)
controlnet.to(torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pipe(pipe, controlnet=controlnet)
del cn_state_dict
torch.cuda.empty_cache()
if lora:
if lora in lora_lightning_list:
pipe.load_lora_weights(lora_path)
pipe.fuse_lora()
else:
pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)
pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
pipe.enable_vae_slicing()
if device != "mps":
pipe.enable_model_cpu_offload()
torch.cuda.empty_cache()
# 预加载 ms
photomaker_local_path = os.path.join(photomaker_dir, "ms_adapter.bin")
if not os.path.exists(photomaker_local_path):
ms_path = hf_hub_download(
repo_id="doge1516/MS-Diffusion",
filename="ms_adapter.bin",
repo_type="model",
local_dir=photomaker_dir,
)
else:
ms_path = photomaker_local_path
ms_ckpt = get_instance_path(ms_path)
image_processor = CLIPImageProcessor()
image_encoder_type = "clip"
image_encoder = clip_load(clip_vision)
gc.collect()
torch.cuda.empty_cache()
use_repo = False
config_path = os.path.join(cur_path, "config", "config.json")
image_encoder_config = OmegaConf.load(config_path)
image_encoder_projection_dim = image_encoder_config["vision_config"]["projection_dim"]
num_tokens = 16
image_proj_type = "resampler"
latent_init_mode = "grounding"
# latent_init_mode = "random"
image_proj_model = Resampler(
dim=1280,
depth=4,
dim_head=64,
heads=20,
num_queries=num_tokens,
embedding_dim=image_encoder_config["vision_config"]["hidden_size"],
output_dim=pipe.unet.config.cross_attention_dim,
ff_mult=4,
latent_init_mode=latent_init_mode,
phrase_embeddings_dim=pipe.text_encoder.config.projection_dim,
).to(device, dtype=torch.float16)
ms_model = MSAdapter(pipe.unet, image_proj_model, ckpt_path=ms_ckpt, device=device, num_tokens=num_tokens)
ms_model.to(device, dtype=torch.float16)
torch.cuda.empty_cache()
input_images = [image_1, image_2]
batch_size = 1
guidance_list = guidance_list.strip().split(";")
box_add = [] # 获取预设box
for i in range(len(guidance_list)):
box_add.append(get_float(guidance_list[i]))
if mask_threshold == 0.:
mask_threshold = None
image_ouput = []
# get n p prompt
prompts_dual, negative_prompt = apply_style(
style_name, prompts_dual, negative_prompt
)
# 添加Lora trigger
add_trigger_words = " " + trigger_words + " style "
if lora:
prompts_dual = remove_punctuation_from_strings(prompts_dual)
if lora not in lora_lightning_list: # 加速lora不需要trigger
prompts_dual = [item + add_trigger_words for item in prompts_dual]
prompts_dual = [item.replace(char_origin[0], char_describe[0]) for item in prompts_dual if char_origin[0] in item]
prompts_dual = [item.replace(char_origin[1], char_describe[1]) for item in prompts_dual if char_origin[1] in item]
#print(char_origin,char_describe)# ['[Taylor]', '[Lecun]']
if "(" in char_describe[0] and "(" in char_describe[1] :
role_a = char_describe[0].split(")")[0].split("(")[-1]
role_b = char_describe[1].split(")")[0].split("(")[-1]
prompts_dual = [i.replace(char_origin[0], "") for i in prompts_dual if char_origin[0] in i]
prompts_dual=[i.replace(char_origin[1], "") for i in prompts_dual if char_origin[1] in i]
else:
# get role name
role_a = char_origin[0].replace("]", "").replace("[", "")
role_b = char_origin[1].replace("]", "").replace("[", "")
prompts_dual = [item.replace("[", " ", ).replace("]", " ", ) for item in prompts_dual]
#print(prompts_dual,role_a,role_b)
torch.cuda.empty_cache()
phrases = [[role_a, role_b]]
drop_grounding_tokens = [0] # set to 1 if you want to drop the grounding tokens
if mask_threshold:
boxes = [box_add[:2]]
# boxes = [[[0., 0.25, 0.4, 0.75], [0.6, 0.25, 1., 0.75]]] # man+women
else:
boxes = [[[0., 0., 0., 0.], [0., 0., 0., 0.]]]