import spaces import os from stablepy import Model_Diffusers from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES from stablepy.diffusers_vanilla.constants import FLUX_CN_UNION_MODES import torch import re from huggingface_hub import HfApi from stablepy import ( CONTROLNET_MODEL_IDS, VALID_TASKS, T2I_PREPROCESSOR_NAME, FLASH_LORA, SCHEDULER_CONFIG_MAP, scheduler_names, IP_ADAPTER_MODELS, IP_ADAPTERS_SD, IP_ADAPTERS_SDXL, REPO_IMAGE_ENCODER, ALL_PROMPT_WEIGHT_OPTIONS, SD15_TASKS, SDXL_TASKS, ) import time from PIL import ImageFile #import urllib.parse ImageFile.LOAD_TRUNCATED_IMAGES = True print(os.getenv("SPACES_ZERO_GPU")) import gradio as gr import logging logging.getLogger("diffusers").setLevel(logging.ERROR) import diffusers diffusers.utils.logging.set_verbosity(40) import warnings warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers") warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers") warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers") from stablepy import logger logger.setLevel(logging.CRITICAL) from env import ( HF_TOKEN, hf_read_token, # to use only for private repos CIVITAI_API_KEY, HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2, HF_LORA_ESSENTIAL_PRIVATE_REPO, HF_VAE_PRIVATE_REPO, HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, directory_models, directory_loras, directory_vaes, directory_embeds, directory_embeds_sdxl, directory_embeds_positive_sdxl, load_diffusers_format_model, download_model_list, download_lora_list, download_vae_list, download_embeds) PREPROCESSOR_CONTROLNET = { "openpose": [ "Openpose", "None", ], "scribble": [ "HED", "PidiNet", "None", ], "softedge": [ "PidiNet", "HED", "HED safe", "PidiNet safe", "None", ], "segmentation": [ "UPerNet", "None", ], "depth": [ "DPT", "Midas", "None", ], "normalbae": [ "NormalBae", "None", ], "lineart": [ "Lineart", "Lineart coarse", "Lineart (anime)", "None", "None (anime)", ], "lineart_anime": [ "Lineart", "Lineart coarse", "Lineart (anime)", "None", "None (anime)", ], "shuffle": [ "ContentShuffle", "None", ], "canny": [ "Canny", "None", ], "mlsd": [ "MLSD", "None", ], "ip2p": [ "ip2p" ], "recolor": [ "Recolor luminance", "Recolor intensity", "None", ], "tile": [ "Mild Blur", "Moderate Blur", "Heavy Blur", "None", ], } TASK_STABLEPY = { 'txt2img': 'txt2img', 'img2img': 'img2img', 'inpaint': 'inpaint', # 'canny T2I Adapter': 'sdxl_canny_t2i', # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0 # 'sketch T2I Adapter': 'sdxl_sketch_t2i', # 'lineart T2I Adapter': 'sdxl_lineart_t2i', # 'depth-midas T2I Adapter': 'sdxl_depth-midas_t2i', # 'openpose T2I Adapter': 'sdxl_openpose_t2i', 'openpose ControlNet': 'openpose', 'canny ControlNet': 'canny', 'mlsd ControlNet': 'mlsd', 'scribble ControlNet': 'scribble', 'softedge ControlNet': 'softedge', 'segmentation ControlNet': 'segmentation', 'depth ControlNet': 'depth', 'normalbae ControlNet': 'normalbae', 'lineart ControlNet': 'lineart', 'lineart_anime ControlNet': 'lineart_anime', 'shuffle ControlNet': 'shuffle', 'ip2p ControlNet': 'ip2p', 'optical pattern ControlNet': 'pattern', 'recolor ControlNet': 'recolor', 'tile ControlNet': 'tile', } TASK_MODEL_LIST = list(TASK_STABLEPY.keys()) UPSCALER_DICT_GUI = { None: None, "Lanczos": "Lanczos", "Nearest": "Nearest", 'Latent': 'Latent', 'Latent (antialiased)': 'Latent (antialiased)', 'Latent (bicubic)': 'Latent (bicubic)', 'Latent (bicubic antialiased)': 'Latent (bicubic antialiased)', 'Latent (nearest)': 'Latent (nearest)', 'Latent (nearest-exact)': 'Latent (nearest-exact)', "RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth", "RealESRNet_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth", "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth", "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth", "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth", "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth", "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth", "4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth", "4x_foolhardy_Remacri": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth", "Remacri4xExtraSmoother": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth", "AnimeSharp4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth", "lollypop": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth", "RealisticRescaler4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth", "NickelbackFS4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth" } UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys()) def get_model_list(directory_path): model_list = [] valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'} for filename in os.listdir(directory_path): if os.path.splitext(filename)[1] in valid_extensions: # name_without_extension = os.path.splitext(filename)[0] file_path = os.path.join(directory_path, filename) # model_list.append((name_without_extension, file_path)) model_list.append(file_path) print('\033[34mFILE: ' + file_path + '\033[0m') return model_list ## BEGIN MOD from modutils import (to_list, list_uniq, list_sub, get_model_id_list, get_tupled_embed_list, get_tupled_model_list, get_lora_model_list, download_private_repo, download_things) # - **Download Models** download_model = ", ".join(download_model_list) # - **Download VAEs** download_vae = ", ".join(download_vae_list) # - **Download LoRAs** download_lora = ", ".join(download_lora_list) #download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, directory_loras, True) download_private_repo(HF_VAE_PRIVATE_REPO, directory_vaes, False) load_diffusers_format_model = list_uniq(load_diffusers_format_model + get_model_id_list()) ## END MOD # Download stuffs for url in [url.strip() for url in download_model.split(',')]: if not os.path.exists(f"./models/{url.split('/')[-1]}"): download_things(directory_models, url, HF_TOKEN, CIVITAI_API_KEY) for url in [url.strip() for url in download_vae.split(',')]: if not os.path.exists(f"./vaes/{url.split('/')[-1]}"): download_things(directory_vaes, url, HF_TOKEN, CIVITAI_API_KEY) for url in [url.strip() for url in download_lora.split(',')]: if not os.path.exists(f"./loras/{url.split('/')[-1]}"): download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY) # Download Embeddings for url_embed in download_embeds: if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"): download_things(directory_embeds, url_embed, HF_TOKEN, CIVITAI_API_KEY) # Build list models embed_list = get_model_list(directory_embeds) model_list = get_model_list(directory_models) model_list = load_diffusers_format_model + model_list ## BEGIN MOD lora_model_list = get_lora_model_list() vae_model_list = get_model_list(directory_vaes) vae_model_list.insert(0, "None") #download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, directory_embeds_sdxl, False) #download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, directory_embeds_positive_sdxl, False) embed_sdxl_list = get_model_list(directory_embeds_sdxl) + get_model_list(directory_embeds_positive_sdxl) def get_embed_list(pipeline_name): return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list) ## END MOD print('\033[33m🏁 Download and listing of valid models completed.\033[0m') msg_inc_vae = ( "Use the right VAE for your model to maintain image quality. The wrong" " VAE can lead to poor results, like blurriness in the generated images." ) SDXL_TASK = [k for k, v in TASK_STABLEPY.items() if v in SDXL_TASKS] SD_TASK = [k for k, v in TASK_STABLEPY.items() if v in SD15_TASKS] FLUX_TASK = list(TASK_STABLEPY.keys())[:3] + [k for k, v in TASK_STABLEPY.items() if v in FLUX_CN_UNION_MODES.keys()] MODEL_TYPE_TASK = { "SD 1.5": SD_TASK, "SDXL": SDXL_TASK, "FLUX": FLUX_TASK, } MODEL_TYPE_CLASS = { "diffusers:StableDiffusionPipeline": "SD 1.5", "diffusers:StableDiffusionXLPipeline": "SDXL", "diffusers:FluxPipeline": "FLUX", } POST_PROCESSING_SAMPLER = ["Use same sampler"] + scheduler_names[:-2] def extract_parameters(input_string): parameters = {} input_string = input_string.replace("\n", "") if "Negative prompt:" not in input_string: if "Steps:" in input_string: input_string = input_string.replace("Steps:", "Negative prompt: Steps:") else: print("Invalid metadata") parameters["prompt"] = input_string return parameters parm = input_string.split("Negative prompt:") parameters["prompt"] = parm[0].strip() if "Steps:" not in parm[1]: print("Steps not detected") parameters["neg_prompt"] = parm[1].strip() return parameters parm = parm[1].split("Steps:") parameters["neg_prompt"] = parm[0].strip() input_string = "Steps:" + parm[1] # Extracting Steps steps_match = re.search(r'Steps: (\d+)', input_string) if steps_match: parameters['Steps'] = int(steps_match.group(1)) # Extracting Size size_match = re.search(r'Size: (\d+x\d+)', input_string) if size_match: parameters['Size'] = size_match.group(1) width, height = map(int, parameters['Size'].split('x')) parameters['width'] = width parameters['height'] = height # Extracting other parameters other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string) for param in other_parameters: parameters[param[0]] = param[1].strip('"') return parameters def get_model_type(repo_id: str): api = HfApi(token=os.environ.get("HF_TOKEN")) # if use private or gated model default = "SD 1.5" try: model = api.model_info(repo_id=repo_id, timeout=5.0) tags = model.tags for tag in tags: if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default) except Exception: return default return default ## BEGIN MOD class GuiSD: def __init__(self): self.model = None print("Loading model...") self.model = Model_Diffusers( base_model_id="Lykon/dreamshaper-8", task_name="txt2img", vae_model=None, type_model_precision=torch.float16, retain_task_model_in_cache=False, device="cpu", ) self.model.load_beta_styles() #self.model.device = torch.device("cpu") # def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)): #progress(0, desc="Start inference...") images, seed, image_list, metadata = model(**pipe_params) #progress(1, desc="Inference completed.") if not isinstance(images, list): images = [images] images = save_images(images, metadata) img = [] for image in images: img.append((image, None)) return img def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)): #yield f"Loading model: {model_name}" vae_model = vae_model if vae_model != "None" else None model_type = get_model_type(model_name) if vae_model: vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5" if model_type != vae_type: gr.Warning(msg_inc_vae) self.model.device = torch.device("cpu") dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16 self.model.load_pipe( model_name, task_name=TASK_STABLEPY[task], vae_model=vae_model if vae_model != "None" else None, type_model_precision=dtype_model, retain_task_model_in_cache=False, ) #yield f"Model loaded: {model_name}" #@spaces.GPU @torch.inference_mode() 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, pag_scale, #progress=gr.Progress(track_tqdm=True), ): #progress(0, desc="Preparing inference...") vae_model = vae_model if vae_model != "None" else None loras_list = [lora1, lora2, lora3, lora4, lora5] vae_msg = f"VAE: {vae_model}" if vae_model else "" msg_lora = "" print("Config model:", model_name, vae_model, loras_list) ## BEGIN MOD prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name) global lora_model_list lora_model_list = get_lora_model_list() ## END MOD task = TASK_STABLEPY[task] params_ip_img = [] params_ip_msk = [] params_ip_model = [] params_ip_mode = [] params_ip_scale = [] 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) 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 UPSCALER_KEYS[:9]: 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) adetailer_params_A = { "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 = { "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 = { "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, "pag_scale": float(pag_scale), "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" and lora1 != "" else None, "lora_scale_A": lora_scale1, "lora_B": lora2 if lora2 != "None" and lora2 != "" else None, "lora_scale_B": lora_scale2, "lora_C": lora3 if lora3 != "None" and lora3 != "" else None, "lora_scale_C": lora_scale3, "lora_D": lora4 if lora4 != "None" and lora4 != "" else None, "lora_scale_D": lora_scale4, "lora_E": lora5 if lora5 != "None" and lora5 != "" else None, "lora_scale_E": lora_scale5, ## BEGIN MOD "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [], ## END MOD "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, } self.model.device = torch.device("cuda:0") if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * 5 and loras_list != [""] * 5: self.model.pipe.transformer.to(self.model.device) print("transformer to cuda") #progress(1, desc="Inference preparation completed. Starting inference...") info_state = "" # for yield version return self.infer_short(self.model, pipe_params), info_state ## END MOD def dynamic_gpu_duration(func, duration, *args): @spaces.GPU(duration=duration) def wrapped_func(): return func(*args) return wrapped_func() @spaces.GPU def dummy_gpu(): return None def sd_gen_generate_pipeline(*args): gpu_duration_arg = int(args[-1]) if args[-1] else 59 verbose_arg = int(args[-2]) load_lora_cpu = args[-3] generation_args = args[:-3] lora_list = [ None if item == "None" or item == "" else item for item in [args[7], args[9], args[11], args[13], args[15]] ] lora_status = [None] * 5 msg_load_lora = "Updating LoRAs in GPU..." if load_lora_cpu: msg_load_lora = "Updating LoRAs in CPU (Slow but saves GPU usage)..." #if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5: # yield None, msg_load_lora # Load lora in CPU if load_lora_cpu: lora_status = sd_gen.model.lora_merge( lora_A=lora_list[0], lora_scale_A=args[8], lora_B=lora_list[1], lora_scale_B=args[10], lora_C=lora_list[2], lora_scale_C=args[12], lora_D=lora_list[3], lora_scale_D=args[14], lora_E=lora_list[4], lora_scale_E=args[16], ) print(lora_status) if verbose_arg: for status, lora in zip(lora_status, lora_list): if status: gr.Info(f"LoRA loaded in CPU: {lora}") elif status is not None: gr.Warning(f"Failed to load LoRA: {lora}") if lora_status == [None] * 5 and sd_gen.model.lora_memory != [None] * 5 and load_lora_cpu: lora_cache_msg = ", ".join( str(x) for x in sd_gen.model.lora_memory if x is not None ) gr.Info(f"LoRAs in cache: {lora_cache_msg}") msg_request = f"Requesting {gpu_duration_arg}s. of GPU time" gr.Info(msg_request) print(msg_request) # yield from sd_gen.generate_pipeline(*generation_args) start_time = time.time() return dynamic_gpu_duration( sd_gen.generate_pipeline, gpu_duration_arg, *generation_args, ) end_time = time.time() if verbose_arg: execution_time = end_time - start_time msg_task_complete = ( f"GPU task complete in: {round(execution_time, 0) + 1} seconds" ) gr.Info(msg_task_complete) print(msg_task_complete) def extract_exif_data(image): if image is None: return "" try: metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment'] for key in metadata_keys: if key in image.info: return image.info[key] return str(image.info) except Exception as e: return f"Error extracting metadata: {str(e)}" @spaces.GPU(duration=20) def esrgan_upscale(image, upscaler_name, upscaler_size): if image is None: return None from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata from stablepy import UpscalerESRGAN exif_image = extract_exif_data(image) url_upscaler = UPSCALER_DICT_GUI[upscaler_name] directory_upscalers = 'upscalers' os.makedirs(directory_upscalers, exist_ok=True) if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"): download_things(directory_upscalers, url_upscaler, HF_TOKEN) scaler_beta = UpscalerESRGAN(0, 0) image_up = scaler_beta.upscale(image, upscaler_size, f"./upscalers/{url_upscaler.split('/')[-1]}") image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image) return image_path dynamic_gpu_duration.zerogpu = True sd_gen_generate_pipeline.zerogpu = True from pathlib import Path from PIL import Image import random, json from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path, get_local_model_list, get_private_lora_model_lists, get_valid_lora_name, get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD, normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en) sd_gen = GuiSD() #@spaces.GPU def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0, lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0, sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)): import PIL import numpy as np MAX_SEED = np.iinfo(np.int32).max load_lora_cpu = False verbose_info = False gpu_duration = 59 images: list[tuple[PIL.Image.Image, str | None]] = [] info: str = "" progress(0, desc="Preparing...") if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed).seed() if translate: prompt = translate_to_en(prompt) negative_prompt = translate_to_en(prompt) prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name) progress(0.5, desc="Preparing...") lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \ set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt) lora1 = get_valid_lora_path(lora1) lora2 = get_valid_lora_path(lora2) lora3 = get_valid_lora_path(lora3) lora4 = get_valid_lora_path(lora4) lora5 = get_valid_lora_path(lora5) progress(1, desc="Preparation completed. Starting inference...") progress(0, desc="Loading model...") sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0]) progress(1, desc="Model loaded.") progress(0, desc="Starting Inference...") images, info = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps, guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, sampler, height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024, None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None, 1.0, 100, 10, 30, 0.55, "Use same sampler", "", "", False, True, 1, True, False, False, False, False, "./images", False, False, False, True, 1, 0.55, False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32, False, "", "", 0.35, True, True, False, 4, 4, 32, True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7, 0.0, load_lora_cpu, verbose_info, gpu_duration ) progress(1, desc="Inference completed.") output_image = images[0][0] if images else None return output_image #@spaces.GPU def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0, lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0, sampler = "Euler a", vae = None, translate = True, progress=gr.Progress(track_tqdm=True)): return gr.update(visible=True) infer.zerogpu = True _infer.zerogpu = True def pass_result(result): return result def get_samplers(): return scheduler_names def get_vaes(): return vae_model_list show_diffusers_model_list_detail = False cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model) def get_diffusers_model_list(): if show_diffusers_model_list_detail: return cached_diffusers_model_tupled_list else: return load_diffusers_format_model def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = ""): global show_diffusers_model_list_detail show_diffusers_model_list_detail = is_enable new_value = model_name index = 0 if model_name in set(load_diffusers_format_model): index = load_diffusers_format_model.index(model_name) if is_enable: new_value = cached_diffusers_model_tupled_list[index][1] else: new_value = load_diffusers_format_model[index] return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list()) def load_model_prompt_dict(): import json dict = {} try: with open('model_dict.json', encoding='utf-8') as f: dict = json.load(f) except Exception: pass return dict model_prompt_dict = load_model_prompt_dict() model_recom_prompt_enabled = True animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres") animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]") pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres") pony_nps = to_list("source_pony, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends") other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed") other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly") default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres") default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]") def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"): if not model_recom_prompt_enabled or not model_name: return prompt, neg_prompt prompts = to_list(prompt) neg_prompts = to_list(neg_prompt) prompts = list_sub(prompts, animagine_ps + pony_ps + other_ps) neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + other_nps) last_empty_p = [""] if not prompts and type != "None" else [] last_empty_np = [""] if not neg_prompts and type != "None" else [] ps = [] nps = [] if model_name in model_prompt_dict.keys(): ps = to_list(model_prompt_dict[model_name]["prompt"]) nps = to_list(model_prompt_dict[model_name]["negative_prompt"]) else: ps = default_ps nps = default_nps prompts = prompts + ps neg_prompts = neg_prompts + nps prompt = ", ".join(list_uniq(prompts) + last_empty_p) neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np) return prompt, neg_prompt def enable_model_recom_prompt(is_enable: bool = True): global model_recom_prompt_enabled model_recom_prompt_enabled = is_enable return is_enable private_lora_dict = {} try: with open('lora_dict.json', encoding='utf-8') as f: d = json.load(f) for k, v in d.items(): private_lora_dict[escape_lora_basename(k)] = v except Exception: pass private_lora_model_list = get_private_lora_model_lists() loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy() loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...} civitai_lora_last_results = {} # {"URL to download": {search results}, ...} all_lora_list = [] def get_all_lora_list(): global all_lora_list loras = get_lora_model_list() all_lora_list = loras.copy() return loras def get_all_lora_tupled_list(): global loras_dict models = get_all_lora_list() if not models: return [] tupled_list = [] for model in models: #if not model: continue # to avoid GUI-related bug basename = Path(model).stem key = to_lora_key(model) items = None if key in loras_dict.keys(): items = loras_dict.get(key, None) else: items = get_civitai_info(model) if items != None: loras_dict[key] = items name = basename value = model if items and items[2] != "": if items[1] == "Pony": name = f"{basename} (for {items[1]}🐴, {items[2]})" else: name = f"{basename} (for {items[1]}, {items[2]})" tupled_list.append((name, value)) return tupled_list def update_lora_dict(path: str): global loras_dict key = to_lora_key(path) if key in loras_dict.keys(): return items = get_civitai_info(path) if items == None: return loras_dict[key] = items def download_lora(dl_urls: str): global loras_url_to_path_dict dl_path = "" before = get_local_model_list(directory_loras) urls = [] for url in [url.strip() for url in dl_urls.split(',')]: local_path = f"{directory_loras}/{url.split('/')[-1]}" if not Path(local_path).exists(): download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY) urls.append(url) after = get_local_model_list(directory_loras) new_files = list_sub(after, before) i = 0 for file in new_files: path = Path(file) if path.exists(): new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}') path.resolve().rename(new_path.resolve()) loras_url_to_path_dict[urls[i]] = str(new_path) update_lora_dict(str(new_path)) dl_path = str(new_path) i += 1 return dl_path def copy_lora(path: str, new_path: str): import shutil if path == new_path: return new_path cpath = Path(path) npath = Path(new_path) if cpath.exists(): try: shutil.copy(str(cpath.resolve()), str(npath.resolve())) except Exception: return None update_lora_dict(str(npath)) return new_path else: return None def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str): path = download_lora(dl_urls) if path: if not lora1 or lora1 == "None": lora1 = path elif not lora2 or lora2 == "None": lora2 = path elif not lora3 or lora3 == "None": lora3 = path elif not lora4 or lora4 == "None": lora4 = path elif not lora5 or lora5 == "None": lora5 = path choices = get_all_lora_tupled_list() return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\ gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices) def set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt): import re lora1 = get_valid_lora_name(lora1, model_name) lora2 = get_valid_lora_name(lora2, model_name) lora3 = get_valid_lora_name(lora3, model_name) lora4 = get_valid_lora_name(lora4, model_name) lora5 = get_valid_lora_name(lora5, model_name) if not "', p) if not result: continue key = result[0][0] wt = result[0][1] path = to_lora_path(key) if not key in loras_dict.keys() or not path: path = get_valid_lora_name(path) if not path or path == "None": continue if path in lora_paths: continue elif not on1: lora1 = path lora_paths = [lora1, lora2, lora3, lora4, lora5] lora1_wt = safe_float(wt) on1 = True elif not on2: lora2 = path lora_paths = [lora1, lora2, lora3, lora4, lora5] lora2_wt = safe_float(wt) on2 = True elif not on3: lora3 = path lora_paths = [lora1, lora2, lora3, lora4, lora5] lora3_wt = safe_float(wt) on3 = True elif not on4: lora4 = path lora_paths = [lora1, lora2, lora3, lora4, lora5] lora4_wt = safe_float(wt) on4, label4, tag4, md4 = get_lora_info(lora4) elif not on5: lora5 = path lora_paths = [lora1, lora2, lora3, lora4, lora5] lora5_wt = safe_float(wt) on5 = True return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt def apply_lora_prompt(prompt: str, lora_info: str): if lora_info == "None": return gr.update(value=prompt) tags = prompt.split(",") if prompt else [] prompts = normalize_prompt_list(tags) lora_tag = lora_info.replace("/",",") lora_tags = lora_tag.split(",") if str(lora_info) != "None" else [] lora_prompts = normalize_prompt_list(lora_tags) empty = [""] prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty) return gr.update(value=prompt) def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt): import re on1, label1, tag1, md1 = get_lora_info(lora1) on2, label2, tag2, md2 = get_lora_info(lora2) on3, label3, tag3, md3 = get_lora_info(lora3) on4, label4, tag4, md4 = get_lora_info(lora4) on5, label5, tag5, md5 = get_lora_info(lora5) lora_paths = [lora1, lora2, lora3, lora4, lora5] prompts = prompt.split(",") if prompt else [] output_prompts = [] for p in prompts: p = str(p).strip() if "', p) if not result: continue key = result[0][0] wt = result[0][1] path = to_lora_path(key) if not key in loras_dict.keys() or not path: continue if path in lora_paths: output_prompts.append(f"") elif p: output_prompts.append(p) lora_prompts = [] if on1: lora_prompts.append(f"") if on2: lora_prompts.append(f"") if on3: lora_prompts.append(f"") if on4: lora_prompts.append(f"") if on5: lora_prompts.append(f"") output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""])) choices = get_all_lora_tupled_list() return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\ gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\ gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\ gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\ gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\ gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\ gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\ gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\ gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\ gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5) def search_civitai_lora(query, base_model=[], sort=CIVITAI_SORT[0], period=CIVITAI_PERIOD[0], tag="", user="", gallery=[]): global civitai_last_results, civitai_last_choices, civitai_last_gallery civitai_last_choices = [("", "")] civitai_last_gallery = [] civitai_last_results = {} items = search_lora_on_civitai(query, base_model, 100, sort, period, tag, user) if not items: return gr.update(choices=[("", "")], value="", visible=False),\ gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) civitai_last_results = {} choices = [] gallery = [] for item in items: base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model'] name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})" value = item['dl_url'] choices.append((name, value)) gallery.append((item['img_url'], name)) civitai_last_results[value] = item if not choices: return gr.update(choices=[("", "")], value="", visible=False),\ gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) civitai_last_choices = choices civitai_last_gallery = gallery result = civitai_last_results.get(choices[0][1], "None") md = result['md'] if result else "" return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\ gr.update(visible=True), gr.update(visible=True), gr.update(value=gallery) def update_civitai_selection(evt: gr.SelectData): try: selected_index = evt.index selected = civitai_last_choices[selected_index][1] return gr.update(value=selected) except Exception: return gr.update(visible=True) def select_civitai_lora(search_result): if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True) result = civitai_last_results.get(search_result, "None") md = result['md'] if result else "" return gr.update(value=search_result), gr.update(value=md, visible=True) def search_civitai_lora_json(query, base_model): results = {} items = search_lora_on_civitai(query, base_model) if not items: return gr.update(value=results) for item in items: results[item['dl_url']] = item return gr.update(value=results) quality_prompt_list = [ { "name": "None", "prompt": "", "negative_prompt": "lowres", }, { "name": "Animagine Common", "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres", "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]", }, { "name": "Pony Anime Common", "prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres", "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends", }, { "name": "Pony Common", "prompt": "source_anime, score_9, score_8_up, score_7_up", "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends", }, { "name": "Animagine Standard v3.0", "prompt": "masterpiece, best quality", "negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name", }, { "name": "Animagine Standard v3.1", "prompt": "masterpiece, best quality, very aesthetic, absurdres", "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]", }, { "name": "Animagine Light v3.1", "prompt": "(masterpiece), best quality, very aesthetic, perfect face", "negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn", }, { "name": "Animagine Heavy v3.1", "prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details", "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing", }, ] style_list = [ { "name": "None", "prompt": "", "negative_prompt": "", }, { "name": "Cinematic", "prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", "negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", }, { "name": "Photographic", "prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed", "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", }, { "name": "Anime", "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed", "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", }, { "name": "Manga", "prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", }, { "name": "Digital Art", "prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed", "negative_prompt": "photo, photorealistic, realism, ugly", }, { "name": "Pixel art", "prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics", "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", }, { "name": "Fantasy art", "prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", }, { "name": "Neonpunk", "prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", }, { "name": "3D Model", "prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting", "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", }, ] preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list} def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None"): def to_list(s): return [x.strip() for x in s.split(",") if not s == ""] def list_sub(a, b): return [e for e in a if e not in b] def list_uniq(l): return sorted(set(l), key=l.index) animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres") animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]") pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres") pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends") prompts = to_list(prompt) neg_prompts = to_list(neg_prompt) all_styles_ps = [] all_styles_nps = [] for d in style_list: all_styles_ps.extend(to_list(str(d.get("prompt", "")))) all_styles_nps.extend(to_list(str(d.get("negative_prompt", "")))) all_quality_ps = [] all_quality_nps = [] for d in quality_prompt_list: all_quality_ps.extend(to_list(str(d.get("prompt", "")))) all_quality_nps.extend(to_list(str(d.get("negative_prompt", "")))) quality_ps = to_list(preset_quality[quality_key][0]) quality_nps = to_list(preset_quality[quality_key][1]) styles_ps = to_list(preset_styles[styles_key][0]) styles_nps = to_list(preset_styles[styles_key][1]) prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps) neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps) last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else [] last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else [] if type == "Animagine": prompts = prompts + animagine_ps neg_prompts = neg_prompts + animagine_nps elif type == "Pony": prompts = prompts + pony_ps neg_prompts = neg_prompts + pony_nps prompts = prompts + styles_ps + quality_ps neg_prompts = neg_prompts + styles_nps + quality_nps prompt = ", ".join(list_uniq(prompts) + last_empty_p) neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np) return gr.update(value=prompt), gr.update(value=neg_prompt) def save_images(images: list[Image.Image], metadatas: list[str]): from PIL import PngImagePlugin try: output_images = [] for image, metadata in zip(images, metadatas): info = PngImagePlugin.PngInfo() info.add_text("parameters", metadata) savefile = "image.png" image.save(savefile, "PNG", pnginfo=info) output_images.append(str(Path(savefile).resolve())) return output_images except Exception as e: print(f"Failed to save image file: {e}") raise Exception(f"Failed to save image file:") from e