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import spaces | |
import os | |
from stablepy import Model_Diffusers | |
from stablepy.diffusers_vanilla.model import scheduler_names | |
from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES | |
import torch | |
import re | |
import shutil | |
import random | |
import spaces | |
import gradio as gr | |
from PIL import Image | |
import IPython.display | |
import time, json | |
from IPython.utils import capture | |
import logging | |
from utils.string_utils import extract_parameters | |
from stablepy import logger | |
from models.upscaler import upscaler_dict_gui | |
logging.getLogger("diffusers").setLevel(logging.ERROR) | |
import diffusers | |
diffusers.utils.logging.set_verbosity(40) | |
import warnings | |
from utils.download_utils import download_things | |
hf_token: str = os.environ.get("HF_TOKEN") | |
class GuiSD: | |
def __init__(self, | |
model_list, | |
task_stablepy, | |
lora_model_list, | |
embed_list, | |
stream=True): | |
self.model = None | |
print("Loading model...") | |
self.model = Model_Diffusers( | |
base_model_id="cagliostrolab/animagine-xl-3.1", | |
task_name="txt2img", | |
vae_model=None, | |
type_model_precision=torch.float16, | |
retain_task_model_in_cache=False, | |
) | |
self.model_list = model_list | |
self.task_stablepy = task_stablepy | |
self.lora_model_list = lora_model_list | |
self.embed_list = embed_list | |
self.stream = stream | |
def load_new_model( | |
self, | |
model_name, | |
vae_model, | |
task, | |
progress=gr.Progress(track_tqdm=True)): | |
""" | |
:param model_name: | |
:param vae_model: | |
:param task: | |
:param progress: | |
""" | |
yield f"Loading model: {model_name}" | |
vae_model = vae_model if vae_model != "None" else None | |
if model_name in self.model_list: | |
model_is_xl = "xl" in model_name.lower() | |
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower() | |
model_type = "SDXL" if model_is_xl else "SD 1.5" | |
incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae) | |
if incompatible_vae: | |
vae_model = None | |
self.model.load_pipe( | |
model_name, | |
task_name=self.task_stablepy[task], | |
vae_model=vae_model if vae_model != "None" else None, | |
type_model_precision=torch.float16, | |
retain_task_model_in_cache=False, | |
) | |
yield f"Model loaded: {model_name}" | |
def generate_pipeline( | |
self, | |
prompt, | |
neg_prompt, | |
num_images, | |
steps, | |
cfg, | |
clip_skip, | |
seed, | |
lora1, | |
lora_scale1, | |
lora2, | |
lora_scale2, | |
lora3, | |
lora_scale3, | |
lora4, | |
lora_scale4, | |
lora5, | |
lora_scale5, | |
sampler, | |
img_height, | |
img_width, | |
model_name, | |
vae_model, | |
task, | |
image_control, | |
preprocessor_name, | |
preprocess_resolution, | |
image_resolution, | |
style_prompt, # list [] | |
style_json_file, | |
image_mask, | |
strength, | |
low_threshold, | |
high_threshold, | |
value_threshold, | |
distance_threshold, | |
controlnet_output_scaling_in_unet, | |
controlnet_start_threshold, | |
controlnet_stop_threshold, | |
textual_inversion, | |
syntax_weights, | |
upscaler_model_path, | |
upscaler_increases_size, | |
esrgan_tile, | |
esrgan_tile_overlap, | |
hires_steps, | |
hires_denoising_strength, | |
hires_sampler, | |
hires_prompt, | |
hires_negative_prompt, | |
hires_before_adetailer, | |
hires_after_adetailer, | |
loop_generation, | |
leave_progress_bar, | |
disable_progress_bar, | |
image_previews, | |
display_images, | |
save_generated_images, | |
image_storage_location, | |
retain_compel_previous_load, | |
retain_detailfix_model_previous_load, | |
retain_hires_model_previous_load, | |
t2i_adapter_preprocessor, | |
t2i_adapter_conditioning_scale, | |
t2i_adapter_conditioning_factor, | |
xformers_memory_efficient_attention, | |
freeu, | |
generator_in_cpu, | |
adetailer_inpaint_only, | |
adetailer_verbose, | |
adetailer_sampler, | |
adetailer_active_a, | |
prompt_ad_a, | |
negative_prompt_ad_a, | |
strength_ad_a, | |
face_detector_ad_a, | |
person_detector_ad_a, | |
hand_detector_ad_a, | |
mask_dilation_a, | |
mask_blur_a, | |
mask_padding_a, | |
adetailer_active_b, | |
prompt_ad_b, | |
negative_prompt_ad_b, | |
strength_ad_b, | |
face_detector_ad_b, | |
person_detector_ad_b, | |
hand_detector_ad_b, | |
mask_dilation_b, | |
mask_blur_b, | |
mask_padding_b, | |
retain_task_cache_gui, | |
image_ip1, | |
mask_ip1, | |
model_ip1, | |
mode_ip1, | |
scale_ip1, | |
image_ip2, | |
mask_ip2, | |
model_ip2, | |
mode_ip2, | |
scale_ip2): | |
vae_model = vae_model if vae_model != "None" else None | |
loras_list: list = [lora1, lora2, lora3, lora4, lora5] | |
vae_msg: str = f"VAE: {vae_model}" if vae_model else "" | |
msg_lora: list = [] | |
if model_name in self.model_list: | |
model_is_xl = "xl" in model_name.lower() | |
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower() | |
model_type = "SDXL" if model_is_xl else "SD 1.5" | |
incompatible_vae = ((model_is_xl and | |
vae_model and | |
not sdxl_in_vae) or | |
(not model_is_xl and | |
sdxl_in_vae)) | |
if incompatible_vae: | |
msg_inc_vae = ( | |
f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you" | |
f" are using a {model_type} model. The default VAE " | |
"will be used." | |
) | |
gr.Info(msg_inc_vae) | |
vae_msg = msg_inc_vae | |
vae_model = None | |
for la in loras_list: | |
if la is None or la == "None" or la not in self.lora_model_list: | |
continue | |
print(la) | |
lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower()) | |
if (model_is_xl and lora_type) or (not model_is_xl and not lora_type): | |
msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}." | |
gr.Info(msg_inc_lora) | |
msg_lora.append(msg_inc_lora) | |
task = self.task_stablepy[task] | |
params_ip_img: list = [] | |
params_ip_msk: list = [] | |
params_ip_model: list = [] | |
params_ip_mode: list = [] | |
params_ip_scale: list = [] | |
all_adapters = [ | |
(image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1), | |
(image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2), | |
] | |
for (imgip, | |
mskip, | |
modelip, | |
modeip, | |
scaleip) in all_adapters: | |
if imgip: | |
params_ip_img.append(imgip) | |
if mskip: | |
params_ip_msk.append(mskip) | |
params_ip_model.append(modelip) | |
params_ip_mode.append(modeip) | |
params_ip_scale.append(scaleip) | |
# First load | |
model_precision = torch.float16 | |
if not self.model: | |
from modelstream import Model_Diffusers2 | |
print("Loading model...") | |
self.model = Model_Diffusers2( | |
base_model_id=model_name, | |
task_name=task, | |
vae_model=vae_model if vae_model != "None" else None, | |
type_model_precision=model_precision, | |
retain_task_model_in_cache=retain_task_cache_gui, | |
) | |
if task != "txt2img" and not image_control: | |
raise ValueError( | |
"No control image found: To use this function, " | |
"you have to upload an image in 'Image ControlNet/Inpaint/Img2img'" | |
) | |
if task == "inpaint" and not image_mask: | |
raise ValueError("No mask image found: Specify one in 'Image Mask'") | |
if upscaler_model_path in [None, "Lanczos", "Nearest"]: | |
upscaler_model = upscaler_model_path | |
else: | |
directory_upscalers = 'upscalers' | |
os.makedirs(directory_upscalers, exist_ok=True) | |
url_upscaler = upscaler_dict_gui[upscaler_model_path] | |
if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"): | |
download_things( | |
directory_upscalers, | |
url_upscaler, | |
hf_token | |
) | |
upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}" | |
logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR) | |
print("Config model:", model_name, vae_model, loras_list) | |
self.model.load_pipe( | |
model_name, | |
task_name=task, | |
vae_model=vae_model if vae_model != "None" else None, | |
type_model_precision=model_precision, | |
retain_task_model_in_cache=retain_task_cache_gui, | |
) | |
if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline": | |
print("No Textual inversion for SDXL") | |
adetailer_params_A: dict = { | |
"face_detector_ad": face_detector_ad_a, | |
"person_detector_ad": person_detector_ad_a, | |
"hand_detector_ad": hand_detector_ad_a, | |
"prompt": prompt_ad_a, | |
"negative_prompt": negative_prompt_ad_a, | |
"strength": strength_ad_a, | |
# "image_list_task" : None, | |
"mask_dilation": mask_dilation_a, | |
"mask_blur": mask_blur_a, | |
"mask_padding": mask_padding_a, | |
"inpaint_only": adetailer_inpaint_only, | |
"sampler": adetailer_sampler, | |
} | |
adetailer_params_B: dict = { | |
"face_detector_ad": face_detector_ad_b, | |
"person_detector_ad": person_detector_ad_b, | |
"hand_detector_ad": hand_detector_ad_b, | |
"prompt": prompt_ad_b, | |
"negative_prompt": negative_prompt_ad_b, | |
"strength": strength_ad_b, | |
# "image_list_task" : None, | |
"mask_dilation": mask_dilation_b, | |
"mask_blur": mask_blur_b, | |
"mask_padding": mask_padding_b, | |
} | |
pipe_params: dict = { | |
"prompt": prompt, | |
"negative_prompt": neg_prompt, | |
"img_height": img_height, | |
"img_width": img_width, | |
"num_images": num_images, | |
"num_steps": steps, | |
"guidance_scale": cfg, | |
"clip_skip": clip_skip, | |
"seed": seed, | |
"image": image_control, | |
"preprocessor_name": preprocessor_name, | |
"preprocess_resolution": preprocess_resolution, | |
"image_resolution": image_resolution, | |
"style_prompt": style_prompt if style_prompt else "", | |
"style_json_file": "", | |
"image_mask": image_mask, # only for Inpaint | |
"strength": strength, # only for Inpaint or ... | |
"low_threshold": low_threshold, | |
"high_threshold": high_threshold, | |
"value_threshold": value_threshold, | |
"distance_threshold": distance_threshold, | |
"lora_A": lora1 if lora1 != "None" else None, | |
"lora_scale_A": lora_scale1, | |
"lora_B": lora2 if lora2 != "None" else None, | |
"lora_scale_B": lora_scale2, | |
"lora_C": lora3 if lora3 != "None" else None, | |
"lora_scale_C": lora_scale3, | |
"lora_D": lora4 if lora4 != "None" else None, | |
"lora_scale_D": lora_scale4, | |
"lora_E": lora5 if lora5 != "None" else None, | |
"lora_scale_E": lora_scale5, | |
"textual_inversion": self.embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [], | |
"syntax_weights": syntax_weights, # "Classic" | |
"sampler": sampler, | |
"xformers_memory_efficient_attention": xformers_memory_efficient_attention, | |
"gui_active": True, | |
"loop_generation": loop_generation, | |
"controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet), | |
"control_guidance_start": float(controlnet_start_threshold), | |
"control_guidance_end": float(controlnet_stop_threshold), | |
"generator_in_cpu": generator_in_cpu, | |
"FreeU": freeu, | |
"adetailer_A": adetailer_active_a, | |
"adetailer_A_params": adetailer_params_A, | |
"adetailer_B": adetailer_active_b, | |
"adetailer_B_params": adetailer_params_B, | |
"leave_progress_bar": leave_progress_bar, | |
"disable_progress_bar": disable_progress_bar, | |
"image_previews": image_previews, | |
"display_images": display_images, | |
"save_generated_images": save_generated_images, | |
"image_storage_location": image_storage_location, | |
"retain_compel_previous_load": retain_compel_previous_load, | |
"retain_detailfix_model_previous_load": retain_detailfix_model_previous_load, | |
"retain_hires_model_previous_load": retain_hires_model_previous_load, | |
"t2i_adapter_preprocessor": t2i_adapter_preprocessor, | |
"t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale), | |
"t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor), | |
"upscaler_model_path": upscaler_model, | |
"upscaler_increases_size": upscaler_increases_size, | |
"esrgan_tile": esrgan_tile, | |
"esrgan_tile_overlap": esrgan_tile_overlap, | |
"hires_steps": hires_steps, | |
"hires_denoising_strength": hires_denoising_strength, | |
"hires_prompt": hires_prompt, | |
"hires_negative_prompt": hires_negative_prompt, | |
"hires_sampler": hires_sampler, | |
"hires_before_adetailer": hires_before_adetailer, | |
"hires_after_adetailer": hires_after_adetailer, | |
"ip_adapter_image": params_ip_img, | |
"ip_adapter_mask": params_ip_msk, | |
"ip_adapter_model": params_ip_model, | |
"ip_adapter_mode": params_ip_mode, | |
"ip_adapter_scale": params_ip_scale, | |
} | |
# print(pipe_params) | |
random_number = random.randint(1, 100) | |
if random_number < 25 and num_images < 3: | |
if (not upscaler_model and | |
steps < 45 and | |
task in ["txt2img", "img2img"] and | |
not adetailer_active_a and | |
not adetailer_active_b): | |
num_images *= 2 | |
pipe_params["num_images"] = num_images | |
gr.Info("Num images x 2 🎉") | |
# Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!'' | |
self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu") | |
info_state = f"PROCESSING" | |
for img, seed, data in self.model(**pipe_params): | |
info_state += "." | |
if data: | |
info_state = f"COMPLETED. Seeds: {str(seed)}" | |
if vae_msg: | |
info_state = info_state + "<br>" + vae_msg | |
if msg_lora: | |
info_state = info_state + "<br>" + "<br>".join(msg_lora) | |
yield img, info_state | |