import spaces import os from stablepy import ( Model_Diffusers, SCHEDULE_TYPE_OPTIONS, SCHEDULE_PREDICTION_TYPE_OPTIONS, check_scheduler_compatibility, ) from constants import ( PREPROCESSOR_CONTROLNET, TASK_STABLEPY, TASK_MODEL_LIST, UPSCALER_DICT_GUI, UPSCALER_KEYS, PROMPT_W_OPTIONS, WARNING_MSG_VAE, SDXL_TASK, MODEL_TYPE_TASK, POST_PROCESSING_SAMPLER, ) from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES import torch import re from stablepy import ( scheduler_names, IP_ADAPTERS_SD, IP_ADAPTERS_SDXL, ) import time from PIL import ImageFile from utils import ( get_model_list, extract_parameters, get_model_type, extract_exif_data, create_mask_now, download_diffuser_repo, get_used_storage_gb, delete_model, progress_step_bar, html_template_message, escape_html, ) from datetime import datetime import gradio as gr import logging import diffusers import warnings from stablepy import logger # import urllib.parse ImageFile.LOAD_TRUNCATED_IMAGES = True # os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1" print(os.getenv("SPACES_ZERO_GPU")) ## BEGIN MOD 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.DEBUG) 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) 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) single_file_model_list = get_model_list(DIRECTORY_MODELS) model_list = list_uniq(get_model_id_list() + LOAD_DIFFUSERS_FORMAT_MODEL + single_file_model_list) ## BEGIN MOD lora_model_list = get_lora_model_list() vae_model_list = get_model_list(DIRECTORY_VAES) vae_model_list.insert(0, "BakedVAE") 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') ## BEGIN MOD class GuiSD: def __init__(self, stream=True): self.model = None self.status_loading = False self.sleep_loading = 4 self.last_load = datetime.now() self.inventory = [] def update_storage_models(self, storage_floor_gb=32, required_inventory_for_purge=3): while get_used_storage_gb() > storage_floor_gb: if len(self.inventory) < required_inventory_for_purge: break removal_candidate = self.inventory.pop(0) delete_model(removal_candidate) def update_inventory(self, model_name): if model_name not in single_file_model_list: self.inventory = [ m for m in self.inventory if m != model_name ] + [model_name] print(self.inventory) 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)): self.update_storage_models() # download link model > model_name vae_model = vae_model if vae_model != "None" else None model_type = get_model_type(model_name) dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16 if not os.path.exists(model_name): _ = download_diffuser_repo( repo_name=model_name, model_type=model_type, revision="main", token=True, ) self.update_inventory(model_name) for i in range(68): if not self.status_loading: self.status_loading = True if i > 0: time.sleep(self.sleep_loading) print("Previous model ops...") break time.sleep(0.5) print(f"Waiting queue {i}") yield "Waiting queue" self.status_loading = True yield f"Loading model: {model_name}" if vae_model == "BakedVAE": if not os.path.exists(model_name): vae_model = model_name else: vae_model = None elif vae_model: vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5" if model_type != vae_type: gr.Warning(WARNING_MSG_VAE) print("Loading model...") try: start_time = time.time() if self.model is None: self.model = Model_Diffusers( base_model_id=model_name, task_name=TASK_STABLEPY[task], vae_model=vae_model, type_model_precision=dtype_model, retain_task_model_in_cache=False, device="cpu", ) else: if self.model.base_model_id != model_name: load_now_time = datetime.now() elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0) if elapsed_time <= 8: print("Waiting for the previous model's time ops...") time.sleep(8-elapsed_time) self.model.device = torch.device("cpu") self.model.load_pipe( model_name, task_name=TASK_STABLEPY[task], vae_model=vae_model, type_model_precision=dtype_model, retain_task_model_in_cache=False, ) end_time = time.time() self.sleep_loading = max(min(int(end_time - start_time), 10), 4) except Exception as e: self.last_load = datetime.now() self.status_loading = False self.sleep_loading = 4 raise e self.last_load = datetime.now() self.status_loading = 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, schedule_type, schedule_prediction_type, 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, filename_pattern, 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, ): info_state = html_template_message("Navigating latent space...") yield info_state, gr.update(), gr.update() 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 = "" ## BEGIN MOD loras_list = [s if s else "None" for s in loras_list] global lora_model_list lora_model_list = get_lora_model_list() ## END MOD print("Config model:", model_name, vae_model, loras_list) 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), ] if not hasattr(self.model.pipe, "transformer"): 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) concurrency = 5 self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False) 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" 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, ## BEGIN MOD "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [], ## END MOD "syntax_weights": syntax_weights, # "Classic" "sampler": sampler, "schedule_type": schedule_type, "schedule_prediction_type": schedule_prediction_type, "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, "filename_pattern": filename_pattern, "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: self.model.pipe.transformer.to(self.model.device) print("transformer to cuda") #return self.infer_short(self.model, pipe_params), info_state actual_progress = 0 info_images = gr.update() for img, [seed, image_path, metadata] in self.model(**pipe_params): info_state = progress_step_bar(actual_progress, steps) actual_progress += concurrency if image_path: info_images = f"Seeds: {str(seed)}" if vae_msg: info_images = info_images + "
" + vae_msg if "Cannot copy out of meta tensor; no data!" in self.model.last_lora_error: msg_ram = "Unable to process the LoRAs due to high RAM usage; please try again later." print(msg_ram) msg_lora += f"
{msg_ram}" for status, lora in zip(self.model.lora_status, self.model.lora_memory): if status: msg_lora += f"
Loaded: {lora}" elif status is not None: msg_lora += f"
Error with: {lora}" if msg_lora: info_images += msg_lora info_images = info_images + "
" + "GENERATION DATA:
" + escape_html(metadata[0]) + "
-------
" download_links = "
".join( [ f'Download Image {i + 1}' for i, path in enumerate(image_path) ] ) if save_generated_images: info_images += f"
{download_links}" ## BEGIN MOD if not isinstance(img, list): img = [img] img = save_images(img, metadata) img = [(i, None) for i in img] ## END MOD info_state = "COMPLETE" yield info_state, img, info_images #return info_state, img, info_images def dynamic_gpu_duration(func, duration, *args): @spaces.GPU(duration=duration) def wrapped_func(): yield from 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 # MOD 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 msg_load_lora, gr.update(), gr.update() # 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) sampler_name = args[17] schedule_type_name = args[18] _, _, msg_sampler = check_scheduler_compatibility( sd_gen.model.class_name, sampler_name, schedule_type_name ) if msg_sampler: gr.Warning(msg_sampler) 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.\nModel: {sd_gen.model.base_model_id}" if verbose_arg: gr.Info(msg_request) print(msg_request) yield msg_request.replace("\n", "
"), gr.update(), gr.update() start_time = time.time() # yield from sd_gen.generate_pipeline(*generation_args) yield from dynamic_gpu_duration( #return dynamic_gpu_duration( sd_gen.generate_pipeline, gpu_duration_arg, *generation_args, ) end_time = time.time() execution_time = end_time - start_time msg_task_complete = ( f"GPU task complete in: {int(round(execution_time, 0) + 1)} seconds" ) if verbose_arg: gr.Info(msg_task_complete) print(msg_task_complete) yield msg_task_complete, gr.update(), gr.update() @spaces.GPU(duration=15) 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 sd_gen = GuiSD() from pathlib import Path from PIL import Image import PIL import numpy as np import random import json import shutil 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_state, set_state, get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL, normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history) #@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", vae=None, translate=False, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0], clip_skip=True, pag_scale=0.0, free_u=False, gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)): MAX_SEED = np.iinfo(np.int32).max image_previews = True load_lora_cpu = False verbose_info = False filename_pattern = "model,seed" images: list[tuple[PIL.Image.Image, str | None]] = [] 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, recom_prompt) 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...") for _ in sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0]): pass progress(1, desc="Model loaded.") progress(0, desc="Starting Inference...") for info_state, stream_images, info_images in sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps, guidance_scale, clip_skip, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, sampler, schedule_type, schedule_prediction_type, 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, image_previews, False, False, filename_pattern, "./images", False, False, False, True, 1, 0.55, False, free_u, 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, pag_scale, load_lora_cpu, verbose_info, gpu_duration ): images = stream_images if isinstance(stream_images, list) else images 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", vae=None, translate=False, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0], clip_skip=True, pag_scale=0.0, free_u=False, gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)): return gr.update() 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 cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model) def get_diffusers_model_list(state: dict = {}): show_diffusers_model_list_detail = get_state(state, "show_diffusers_model_list_detail") 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 = "", state: dict = {}): 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] set_state(state, "show_diffusers_model_list_detail", show_diffusers_model_list_detail) return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list(state)), state def load_model_prompt_dict(): 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() 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", model_recom_prompt_enabled = True): 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 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_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): 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