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| import os | |
| import json | |
| import gradio as gr | |
| from PIL import Image | |
| from huggingface_hub import HfApi | |
| from requests import HTTPError, Timeout | |
| HF_LORA_PRIVATE_REPOS1 = ['John6666/loratest1', 'John6666/loratest3', 'John6666/loratest4', 'John6666/loratest6'] | |
| HF_LORA_PRIVATE_REPOS2 = ['John6666/loratest10', 'John6666/loratest'] | |
| HF_LORA_PRIVATE_REPOS = HF_LORA_PRIVATE_REPOS1 + HF_LORA_PRIVATE_REPOS2 | |
| HF_LORA_ESSENTIAL_PRIVATE_REPO = 'John6666/loratest1' | |
| directory_loras = 'loras' | |
| CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY") | |
| def get_user_agent(): | |
| return 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0' | |
| def change_interface_mode(mode: str): | |
| if mode == "Fast": | |
| return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(value="Fast") | |
| elif mode == "Simple": # t2i mode | |
| return gr.update(open=True), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(open=False), gr.update(visible=False), gr.update(open=True),\ | |
| gr.update(visible=False), gr.update(value="Standard") | |
| elif mode == "LoRA": # t2i LoRA mode | |
| return gr.update(open=True), gr.update(visible=True), gr.update(open=True), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(open=True), gr.update(visible=True), gr.update(open=False),\ | |
| gr.update(visible=False), gr.update(value="Standard") | |
| else: # Standard | |
| return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\ | |
| gr.update(visible=True), gr.update(value="Standard") | |
| def get_local_model_list(dir_path): | |
| from pathlib import Path | |
| model_list = [] | |
| valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin') | |
| for file in Path(dir_path).glob("*"): | |
| if file.suffix in valid_extensions: | |
| file_path = str(Path(f"{dir_path}/{file.name}")) | |
| model_list.append(file_path) | |
| return model_list | |
| def list_uniq(l): | |
| return sorted(set(l), key=l.index) | |
| def list_sub(a, b): | |
| return [e for e in a if e not in b] | |
| def normalize_prompt_list(tags): | |
| prompts = [] | |
| for tag in tags: | |
| tag = str(tag).strip() | |
| if tag: | |
| prompts.append(tag) | |
| return prompts | |
| def escape_lora_basename(basename: str): | |
| return basename.replace(".", "_").replace(" ", "_").replace(",", "") | |
| def download_private_repo(repo_id, dir_path, is_replace): | |
| from huggingface_hub import snapshot_download | |
| hf_read_token = os.environ.get('HF_READ_TOKEN') | |
| if not hf_read_token: return | |
| try: | |
| snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token) | |
| except Exception as e: | |
| return | |
| else: | |
| if is_replace: | |
| from pathlib import Path | |
| for file in Path(dir_path).glob("*"): | |
| if file.exists() and "." in file.stem or " " in file.stem and file.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']: | |
| newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') | |
| file.resolve().rename(newpath.resolve()) | |
| private_model_path_repo_dict = {} | |
| def get_private_model_list(repo_id, dir_path): | |
| global private_model_path_repo_dict | |
| api = HfApi() | |
| hf_read_token = os.environ.get('HF_READ_TOKEN') | |
| if not hf_read_token: return [] | |
| try: | |
| files = api.list_repo_files(repo_id, token=hf_read_token) | |
| except Exception as e: | |
| return [] | |
| else: | |
| model_list = [] | |
| for file in files: | |
| from pathlib import Path | |
| path = Path(f"{dir_path}/{file}") | |
| if path.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']: | |
| model_list.append(str(path)) | |
| for model in model_list: | |
| private_model_path_repo_dict[model] = repo_id | |
| return model_list | |
| def get_private_lora_model_lists(): | |
| models1 = [] | |
| models2 = [] | |
| for repo in HF_LORA_PRIVATE_REPOS1: | |
| models1.extend(get_private_model_list(repo, directory_loras)) | |
| for repo in HF_LORA_PRIVATE_REPOS2: | |
| models2.extend(get_private_model_list(repo, directory_loras)) | |
| models = list_uniq(models1 + sorted(models2)) | |
| return models | |
| def download_private_file(repo_id, path, is_replace): | |
| from huggingface_hub import hf_hub_download | |
| from pathlib import Path | |
| file = Path(path) | |
| newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file | |
| hf_read_token = os.environ.get('HF_READ_TOKEN') | |
| if not hf_read_token or newpath.exists(): return | |
| filename = file.name | |
| dirname = file.parent.name | |
| try: | |
| hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token) | |
| except Exception as e: | |
| return | |
| else: | |
| if is_replace: | |
| file.resolve().rename(newpath.resolve()) | |
| def download_private_file_from_somewhere(path, is_replace): | |
| if not path in private_model_path_repo_dict.keys(): return | |
| repo_id = private_model_path_repo_dict.get(path, None) | |
| download_private_file(repo_id, path, is_replace) | |
| def get_model_id_list(): | |
| api = HfApi() | |
| model_ids = [] | |
| try: | |
| models_vp = api.list_models(author="votepurchase", cardData=True, sort="likes") | |
| models_john = api.list_models(author="John6666", cardData=True, sort="last_modified") | |
| except Exception as e: | |
| return model_ids | |
| else: | |
| for model in models_vp: | |
| model_ids.append(model.id) if not model.private else "" | |
| anime_models = [] | |
| real_models = [] | |
| for model in models_john: | |
| if not model.private: | |
| anime_models.append(model.id) if 'anime' in model.tags else real_models.append(model.id) | |
| model_ids.extend(anime_models) | |
| model_ids.extend(real_models) | |
| return model_ids | |
| def get_t2i_model_info(repo_id: str): | |
| api = HfApi() | |
| try: | |
| if " " in repo_id or not api.repo_exists(repo_id): return "" | |
| model = api.model_info(repo_id=repo_id) | |
| except (EnvironmentError, OSError, ValueError, HTTPError, Timeout) as e: | |
| return "" | |
| else: | |
| if model.private or model.gated: return "" | |
| tags = model.tags | |
| info = [] | |
| url = f"https://huggingface.co/{repo_id}/" | |
| if not 'diffusers' in tags: return "" | |
| if 'diffusers:StableDiffusionXLPipeline' in tags: | |
| info.append("SDXL") | |
| elif 'diffusers:StableDiffusionPipeline' in tags: | |
| info.append("SD1.5") | |
| if model.card_data and model.card_data.tags: | |
| info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl'])) | |
| info.append(f"DLs: {model.downloads}") | |
| info.append(f"likes: {model.likes}") | |
| info.append(model.last_modified.strftime("lastmod: %Y-%m-%d")) | |
| md = f"Model Info: {', '.join(info)}, [Model Repo]({url})" | |
| return gr.update(value=md) | |
| def get_tupled_model_list(model_list): | |
| if not model_list: return [] | |
| tupled_list = [] | |
| for repo_id in model_list: | |
| api = HfApi() | |
| try: | |
| if not api.repo_exists(repo_id): continue | |
| model = api.model_info(repo_id=repo_id) | |
| except Exception as e: | |
| continue | |
| else: | |
| if model.private or model.gated: continue | |
| tags = model.tags | |
| info = [] | |
| if not 'diffusers' in tags: continue | |
| if 'diffusers:StableDiffusionXLPipeline' in tags: | |
| info.append("SDXL") | |
| elif 'diffusers:StableDiffusionPipeline' in tags: | |
| info.append("SD1.5") | |
| if model.card_data and model.card_data.tags: | |
| info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl'])) | |
| if "pony" in info: | |
| info.remove("pony") | |
| name = f"{repo_id} (Pony🐴, {', '.join(info)})" | |
| else: | |
| name = f"{repo_id} ({', '.join(info)})" | |
| tupled_list.append((name, repo_id)) | |
| return tupled_list | |
| def save_gallery_images(images): | |
| from datetime import datetime, timezone, timedelta | |
| japan_tz = timezone(timedelta(hours=9)) | |
| dt_now = datetime.utcnow().replace(tzinfo=timezone.utc).astimezone(japan_tz) | |
| basename = dt_now.strftime('%Y%m%d_%H%M%S_') | |
| i = 1 | |
| if not images: return images | |
| output_images = [] | |
| output_paths = [] | |
| for image in images: | |
| from pathlib import Path | |
| filename = basename + str(i) + ".png" | |
| oldpath = Path(image[0]) | |
| newpath = oldpath.resolve().rename(Path(filename).resolve()) | |
| output_paths.append(str(newpath)) | |
| output_images.append((str(newpath), str(filename))) | |
| i += 1 | |
| return gr.update(value=output_images), gr.update(value=output_paths), gr.update(visible=True), | |
| optimization_list = { | |
| "None": [28, 7., 'Euler a', False, 'None', 1.], | |
| "Default": [28, 7., 'Euler a', False, 'None', 1.], | |
| "SPO": [28, 7., 'Euler a', True, 'loras/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors', 1.], | |
| "DPO": [28, 7., 'Euler a', True, 'loras/sdxl-DPO-LoRA.safetensors', 1.], | |
| "DPO Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_dpo_turbo_lora_v1-128dim.safetensors', 1.], | |
| "SDXL Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_turbo_lora_v1.safetensors', 1.], | |
| "Hyper-SDXL 12step": [12, 5., 'TCD', True, 'loras/Hyper-SDXL-12steps-CFG-lora.safetensors', 1.], | |
| "Hyper-SDXL 8step": [8, 5., 'TCD', True, 'loras/Hyper-SDXL-8steps-CFG-lora.safetensors', 1.], | |
| "Hyper-SDXL 4step": [4, 0, 'TCD', True, 'loras/Hyper-SDXL-4steps-lora.safetensors', 1.], | |
| "Hyper-SDXL 2step": [2, 0, 'TCD', True, 'loras/Hyper-SDXL-2steps-lora.safetensors', 1.], | |
| "Hyper-SDXL 1step": [1, 0, 'TCD', True, 'loras/Hyper-SDXL-1steps-lora.safetensors', 1.], | |
| "PCM 16step": [16, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_16step_converted.safetensors', 1.], | |
| "PCM 8step": [8, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_8step_converted.safetensors', 1.], | |
| "PCM 4step": [4, 2., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_4step_converted.safetensors', 1.], | |
| "PCM 2step": [2, 1., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_2step_converted.safetensors', 1.], | |
| } | |
| def set_optimization(opt, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora1_gui, lora_scale_1_gui): | |
| if not opt in list(optimization_list.keys()): opt = "None" | |
| def_steps_gui = 28 | |
| def_cfg_gui = 7. | |
| steps = optimization_list.get(opt, "None")[0] | |
| cfg = optimization_list.get(opt, "None")[1] | |
| sampler = optimization_list.get(opt, "None")[2] | |
| clip_skip = optimization_list.get(opt, "None")[3] | |
| lora1 = optimization_list.get(opt, "None")[4] | |
| lora_scale_1 = optimization_list.get(opt, "None")[5] | |
| if opt == "None": | |
| steps = max(steps_gui, def_steps_gui) | |
| cfg = max(cfg_gui, def_cfg_gui) | |
| clip_skip = clip_skip_gui | |
| elif opt == "SPO" or opt == "DPO": | |
| steps = max(steps_gui, def_steps_gui) | |
| cfg = max(cfg_gui, def_cfg_gui) | |
| return gr.update(value=steps), gr.update(value=cfg), gr.update(value=sampler),\ | |
| gr.update(value=clip_skip), gr.update(value=lora1), gr.update(value=lora_scale_1), | |
| def set_lora_prompt(prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,\ | |
| lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui): | |
| import os | |
| if not "Classic" in str(prompt_syntax_gui): return prompt_gui | |
| loras = [] | |
| if lora1_gui and lora1_gui != "None": | |
| basename = os.path.splitext(os.path.basename(lora1_gui))[0] | |
| loras.append(f"<lora:{basename}:{lora_scale_1_gui:.2f}>") | |
| if lora2_gui and lora2_gui != "None": | |
| basename = os.path.splitext(os.path.basename(lora2_gui))[0] | |
| loras.append(f"<lora:{basename}:{lora_scale_2_gui:.2f}>") | |
| if lora3_gui and lora3_gui != "None": | |
| basename = os.path.splitext(os.path.basename(lora3_gui))[0] | |
| loras.append(f"<lora:{basename}:{lora_scale_3_gui:.2f}>") | |
| if lora4_gui and lora4_gui != "None": | |
| basename = os.path.splitext(os.path.basename(lora4_gui))[0] | |
| loras.append(f"<lora:{basename}:{lora_scale_4_gui:.2f}>") | |
| if lora5_gui and lora5_gui != "None": | |
| basename = os.path.splitext(os.path.basename(lora5_gui))[0] | |
| loras.append(f"<lora:{basename}:{lora_scale_5_gui:.2f}>") | |
| tags = prompt_gui.split(",") if prompt_gui else [] | |
| prompts = [] | |
| for tag in tags: | |
| tag = str(tag).strip() | |
| if tag and not "<lora" in tag: | |
| prompts.append(tag) | |
| empty = [""] | |
| prompt = ", ".join(prompts + loras + empty) | |
| return gr.update(value=prompt) | |
| temp_dict = {} | |
| lora_trigger_dict = {} | |
| with open('lora_dict.json', encoding='utf-8') as f: | |
| temp_dict = json.load(f) | |
| for k, v in temp_dict.items(): | |
| lora_trigger_dict[escape_lora_basename(k)] = v | |
| civitai_not_exists_list = [] | |
| def get_civitai_info(path): | |
| global civitai_not_exists_list | |
| import requests | |
| from urllib3.util import Retry | |
| from requests.adapters import HTTPAdapter | |
| if path in set(civitai_not_exists_list): return ["", "", "", "", ""] | |
| from pathlib import Path | |
| if not Path(path).exists(): return None | |
| user_agent = get_user_agent() | |
| headers = {'User-Agent': user_agent, 'content-type': 'application/json'} | |
| base_url = 'https://civitai.com/api/v1/model-versions/by-hash/' | |
| params = {} | |
| session = requests.Session() | |
| retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504]) | |
| session.mount("https://", HTTPAdapter(max_retries=retries)) | |
| import hashlib | |
| with open(path, 'rb') as file: | |
| file_data = file.read() | |
| hash_sha256 = hashlib.sha256(file_data).hexdigest() | |
| url = base_url + hash_sha256 | |
| try: | |
| r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15)) | |
| except (HTTPError, Timeout) as e: | |
| return ["", "", "", "", ""] | |
| else: | |
| if not r.ok: return None | |
| json = r.json() | |
| if not 'baseModel' in json: | |
| civitai_not_exists_list.append(path) | |
| return ["", "", "", "", ""] | |
| items = [] | |
| items.append(" / ".join(json['trainedWords'])) | |
| items.append(json['baseModel']) | |
| items.append(json['model']['name']) | |
| items.append(f"https://civitai.com/models/{json['modelId']}") | |
| items.append(json['images'][0]['url']) | |
| return items | |
| def update_lora_dict(path): | |
| global lora_trigger_dict | |
| from pathlib import Path | |
| key = escape_lora_basename(Path(path).stem) | |
| if key in lora_trigger_dict.keys(): return | |
| items = get_civitai_info(path) | |
| if items == None: return | |
| lora_trigger_dict[key] = items | |
| def get_lora_tupled_list(lora_model_list): | |
| global lora_trigger_dict | |
| from pathlib import Path | |
| if not lora_model_list: return [] | |
| tupled_list = [] | |
| local_models = set(get_local_model_list(directory_loras)) | |
| for model in lora_model_list: | |
| if not model: continue | |
| basename = Path(model).stem | |
| key = escape_lora_basename(basename) | |
| items = None | |
| if key in lora_trigger_dict.keys(): | |
| items = lora_trigger_dict.get(key, None) | |
| elif model in local_models: | |
| items = get_civitai_info(model) | |
| if items != None: | |
| lora_trigger_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 set_lora_trigger(lora_gui: str): | |
| from pathlib import Path | |
| if not lora_gui or lora_gui == "None": return gr.update(value="", visible=False), gr.update(visible=False),\ | |
| gr.update(value="", visible=False), gr.update(value="None", visible=True) | |
| path = Path(lora_gui) | |
| new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}') | |
| if not new_path.stem in lora_trigger_dict.keys() and not str(path) in set(get_private_lora_model_lists() + get_local_model_list(directory_loras)): | |
| return gr.update(value="", visible=False), gr.update(visible=False),\ | |
| gr.update(value="", visible=False), gr.update(value="", visible=True) | |
| if not new_path.exists(): | |
| download_private_file_from_somewhere(str(path), True) | |
| basename = new_path.stem | |
| tag = "" | |
| label = f'Trigger: {basename} / Prompt:' | |
| value = "None" | |
| md = "None" | |
| flag = False | |
| items = lora_trigger_dict.get(basename, None) | |
| if items == None: | |
| items = get_civitai_info(str(new_path)) | |
| if items != None: | |
| lora_trigger_dict[basename] = items | |
| flag = True | |
| if items and items[2] != "": | |
| tag = items[0] | |
| label = f'Trigger: {basename} / Prompt:' | |
| if items[1] == "Pony": | |
| label = f'Trigger: {basename} / Prompt (for Pony🐴):' | |
| if items[4]: | |
| md = f'<img src="{items[4]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL]({items[3]})' | |
| elif items[3]: | |
| md = f'[LoRA Model URL]({items[3]})' | |
| if tag and flag: | |
| new_lora_model_list = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras)) | |
| return gr.update(value=tag, label=label, visible=True), gr.update(visible=True),\ | |
| gr.update(value=md, visible=True), gr.update(value=str(new_path), choices=get_lora_tupled_list(new_lora_model_list)) | |
| elif tag: | |
| return gr.update(value=tag, label=label, visible=True), gr.update(visible=True),\ | |
| gr.update(value=md, visible=True), gr.update(value=str(new_path)) | |
| else: | |
| return gr.update(value=value, label=label, visible=True), gr.update(visible=True),\ | |
| gr.update(value=md, visible=True), gr.update(visible=True) | |
| def apply_lora_prompt(prompt_gui: str, lora_trigger_gui: str): | |
| if lora_trigger_gui == "None": return gr.update(value=prompt_gui) | |
| tags = prompt_gui.split(",") if prompt_gui else [] | |
| prompts = normalize_prompt_list(tags) | |
| lora_tag = lora_trigger_gui.replace("/",",") | |
| lora_tags = lora_tag.split(",") if str(lora_trigger_gui) != "None" else [] | |
| lora_prompts = normalize_prompt_list(lora_tags) | |
| empty = [""] | |
| prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty) | |
| return gr.update(value=prompt) | |
| def upload_file_lora(files): | |
| file_paths = [file.name for file in files] | |
| return gr.update(value=file_paths, visible=True), gr.update(visible=True) | |
| def move_file_lora(filepaths): | |
| import shutil | |
| from pathlib import Path | |
| for file in filepaths: | |
| path = Path(shutil.move(Path(file).resolve(), Path(f"./{directory_loras}").resolve())) | |
| newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}') | |
| path.resolve().rename(newpath.resolve()) | |
| update_lora_dict(str(newpath)) | |
| new_lora_model_list = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras)) | |
| new_lora_model_list.insert(0, "None") | |
| return gr.update( | |
| choices=get_lora_tupled_list(new_lora_model_list), value=new_lora_model_list[-1] | |
| ), gr.update( | |
| choices=get_lora_tupled_list(new_lora_model_list) | |
| ), gr.update( | |
| choices=get_lora_tupled_list(new_lora_model_list) | |
| ), gr.update( | |
| choices=get_lora_tupled_list(new_lora_model_list) | |
| ), gr.update( | |
| choices=get_lora_tupled_list(new_lora_model_list) | |
| ), | |
| def search_lora_on_civitai(query: str, allow_model: list[str]): | |
| import requests | |
| from urllib3.util import Retry | |
| from requests.adapters import HTTPAdapter | |
| if not query: return None | |
| user_agent = get_user_agent() | |
| headers = {'User-Agent': user_agent, 'content-type': 'application/json'} | |
| base_url = 'https://civitai.com/api/v1/models' | |
| params = {'query': query, 'types': ['LORA'], 'sort': 'Highest Rated', 'period': 'AllTime', | |
| 'nsfw': 'true', 'supportsGeneration ': 'true'} | |
| session = requests.Session() | |
| retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504]) | |
| session.mount("https://", HTTPAdapter(max_retries=retries)) | |
| try: | |
| r = session.get(base_url, params=params, headers=headers, stream=True, timeout=(3.0, 30)) | |
| except (HTTPError, Timeout) as e: | |
| return None | |
| else: | |
| if not r.ok: return None | |
| json = r.json() | |
| if not 'items' in json: return None | |
| items = [] | |
| for j in json['items']: | |
| for model in j['modelVersions']: | |
| item = {} | |
| if not model['baseModel'] in set(allow_model): continue | |
| item['name'] = j['name'] | |
| item['creator'] = j['creator']['username'] | |
| item['tags'] = j['tags'] | |
| item['model_name'] = model['name'] | |
| item['base_model'] = model['baseModel'] | |
| item['dl_url'] = model['downloadUrl'] | |
| item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})' | |
| items.append(item) | |
| return items | |
| civitai_lora_last_results = {} | |
| def search_civitai_lora(query, base_model): | |
| global civitai_lora_last_results | |
| items = search_lora_on_civitai(query, base_model) | |
| if not items: return gr.update(choices=[("", "")], value="", visible=False),\ | |
| gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True) | |
| civitai_lora_last_results = {} | |
| choices = [] | |
| 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)) | |
| civitai_lora_last_results[value] = item['md'] | |
| if not choices: return gr.update(choices=[("", "")], value="", visible=False),\ | |
| gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True) | |
| md = civitai_lora_last_results.get(choices[0][1], "None") | |
| 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) | |
| def select_civitai_lora(search_result): | |
| if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True) | |
| md = civitai_lora_last_results.get(search_result, "None") | |
| return gr.update(value=search_result), gr.update(value=md, visible=True) | |
| quality_prompt_list = [ | |
| { | |
| "name": "None", | |
| "prompt": "", | |
| "negative_prompt": "lowres", | |
| }, | |
| { | |
| "name": "Animagine Common", | |
| "prompt": "anime artwork, anime style, key visual, 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, key visual, 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", | |
| }, | |
| ] | |
| # [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui] | |
| preset_sampler_setting = { | |
| "None": ["Euler a", 28, 7., True, 1024, 1024, "None"], | |
| "Anime 3:4 Fast": ["LCM", 8, 2.5, True, 896, 1152, "DPO Turbo"], | |
| "Anime 3:4 Standard": ["Euler a", 28, 7., True, 896, 1152, "None"], | |
| "Anime 3:4 Heavy": ["Euler a", 40, 7., True, 896, 1152, "None"], | |
| "Anime 1:1 Fast": ["LCM", 8, 2.5, True, 1024, 1024, "DPO Turbo"], | |
| "Anime 1:1 Standard": ["Euler a", 28, 7., True, 1024, 1024, "None"], | |
| "Anime 1:1 Heavy": ["Euler a", 40, 7., True, 1024, 1024, "None"], | |
| "Photo 3:4 Fast": ["LCM", 8, 2.5, False, 896, 1152, "DPO Turbo"], | |
| "Photo 3:4 Standard": ["DPM++ 2M Karras", 28, 7., False, 896, 1152, "None"], | |
| "Photo 3:4 Heavy": ["DPM++ 2M Karras", 40, 7., False, 896, 1152, "None"], | |
| "Photo 1:1 Fast": ["LCM", 8, 2.5, False, 1024, 1024, "DPO Turbo"], | |
| "Photo 1:1 Standard": ["DPM++ 2M Karras", 28, 7., False, 1024, 1024, "None"], | |
| "Photo 1:1 Heavy": ["DPM++ 2M Karras", 40, 7., False, 1024, 1024, "None"], | |
| } | |
| def set_sampler_settings(sampler_setting): | |
| if not sampler_setting in list(preset_sampler_setting.keys()) or sampler_setting == "None": | |
| return gr.update(value="Euler a"), gr.update(value=28), gr.update(value=7.), gr.update(value=True),\ | |
| gr.update(value=1024), gr.update(value=1024), gr.update(value="None") | |
| v = preset_sampler_setting.get(sampler_setting, ["Euler a", 28, 7., True, 1024, 1024]) | |
| # sampler, steps, cfg, clip_skip, width, height, optimization | |
| return gr.update(value=v[0]), gr.update(value=v[1]), gr.update(value=v[2]), gr.update(value=v[3]),\ | |
| gr.update(value=v[4]), gr.update(value=v[5]), gr.update(value=v[6]) | |
| 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", type: 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, key visual, 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 styles_key != "None" and quality_key != "None" else [] | |
| last_empty_np = [""] if not neg_prompts and type != "None" 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 prompt, neg_prompt | |
| def set_quick_presets(genre:str = "None", type:str = "None", speed:str = "None", aspect:str = "None"): | |
| quality = "None" | |
| style = "None" | |
| sampler = "None" | |
| opt = "None" | |
| if genre == "Anime": | |
| style = "Anime" | |
| if aspect == "1:1": | |
| if speed == "Heavy": | |
| sampler = "Anime 1:1 Heavy" | |
| elif speed == "Fast": | |
| sampler = "Anime 1:1 Fast" | |
| else: | |
| sampler = "Anime 1:1 Standard" | |
| elif aspect == "3:4": | |
| if speed == "Heavy": | |
| sampler = "Anime 3:4 Heavy" | |
| elif speed == "Fast": | |
| sampler = "Anime 3:4 Fast" | |
| else: | |
| sampler = "Anime 3:4 Standard" | |
| if type == "Pony": | |
| quality = "Pony Anime Common" | |
| else: | |
| quality = "Animagine Common" | |
| elif genre == "Photo": | |
| style = "Photographic" | |
| if aspect == "1:1": | |
| if speed == "Heavy": | |
| sampler = "Photo 1:1 Heavy" | |
| elif speed == "Fast": | |
| sampler = "Photo 1:1 Fast" | |
| else: | |
| sampler = "Photo 1:1 Standard" | |
| elif aspect == "3:4": | |
| if speed == "Heavy": | |
| sampler = "Photo 3:4 Heavy" | |
| elif speed == "Fast": | |
| sampler = "Photo 3:4 Fast" | |
| else: | |
| sampler = "Photo 3:4 Standard" | |
| if type == "Pony": | |
| quality = "Pony Common" | |
| else: | |
| quality = "None" | |
| if speed == "Fast": | |
| opt = "DPO Turbo" | |
| if genre == "Anime" and type != "Pony": quality = "Animagine Light v3.1" | |
| return gr.update(value=quality), gr.update(value=style), gr.update(value=sampler), gr.update(value=opt) | |
| textual_inversion_dict = {} | |
| with open('textual_inversion_dict.json', encoding='utf-8') as f: | |
| textual_inversion_dict = json.load(f) | |
| textual_inversion_file_token_list = [] | |
| def get_tupled_embed_list(embed_list): | |
| from pathlib import Path | |
| global textual_inversion_file_list | |
| tupled_list = [] | |
| for file in embed_list: | |
| token = textual_inversion_dict.get(Path(file).name, [Path(file).stem.replace(",",""), False])[0] | |
| tupled_list.append((token, file)) | |
| textual_inversion_file_token_list.append(token) | |
| return tupled_list | |
| def set_textual_inversion_prompt(textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui): | |
| ti_tags = list(textual_inversion_dict.values()) + textual_inversion_file_token_list | |
| tags = prompt_gui.split(",") if prompt_gui else [] | |
| prompts = [] | |
| for tag in tags: | |
| tag = str(tag).strip() | |
| if tag and not tag in ti_tags: | |
| prompts.append(tag) | |
| ntags = neg_prompt_gui.split(",") if neg_prompt_gui else [] | |
| neg_prompts = [] | |
| for tag in ntags: | |
| tag = str(tag).strip() | |
| if tag and not tag in ti_tags: | |
| neg_prompts.append(tag) | |
| ti_prompts = [] | |
| ti_neg_prompts = [] | |
| for ti in textual_inversion_gui: | |
| from pathlib import Path | |
| tokens = textual_inversion_dict.get(Path(ti).name, [Path(ti).stem.replace(",",""), False]) | |
| is_positive = tokens[1] == True or "positive" in Path(ti).parent.name | |
| if is_positive: # positive prompt | |
| ti_prompts.append(tokens[0]) | |
| else: # negative prompt (default) | |
| ti_neg_prompts.append(tokens[0]) | |
| empty = [""] | |
| prompt = ", ".join(prompts + ti_prompts + empty) | |
| neg_prompt = ", ".join(neg_prompts + ti_neg_prompts + empty) | |
| return gr.update(value=prompt), gr.update(value=neg_prompt), | |
| def get_model_pipeline(repo_id: str): | |
| from huggingface_hub import HfApi | |
| api = HfApi() | |
| default = "StableDiffusionPipeline" | |
| try: | |
| if " " in repo_id or not api.repo_exists(repo_id): return default | |
| model = api.model_info(repo_id=repo_id) | |
| except Exception as e: | |
| return default | |
| else: | |
| if model.private or model.gated: return default | |
| tags = model.tags | |
| if not 'diffusers' in tags: return default | |
| if 'diffusers:StableDiffusionXLPipeline' in tags: | |
| return "StableDiffusionXLPipeline" | |
| elif 'diffusers:StableDiffusionPipeline' in tags: | |
| return "StableDiffusionPipeline" | |
| else: | |
| return default | |