Upload app.py

app.py

@@ -1,299 +1,660 @@
+import spaces
 import gradio as gr
-import requests
-from pathlib import Path
-import re
-import os
-import tempfile
-import shutil
-import urllib
-from huggingface_hub import whoami, HfApi, hf_hub_download, RepoCard
-from huggingface_hub.utils import build_hf_headers, hf_raise_for_status
-from gradio_huggingfacehub_search import HuggingfaceHubSearch
-
-ENDPOINT = "https://huggingface.co"
-# ENDPOINT = "http://localhost:5564"
-
-REPO_TYPES = ["model", "dataset", "space"]
-HF_REPO = os.environ.get("HF_REPO") if os.environ.get("HF_REPO") else "" # set your default repo
-HF_REPO_PREFIX = os.environ.get("HF_REPO_PREFIX") if os.environ.get("HF_REPO_PREFIX") else "" # set your default repo prefix
-HF_REPO_SUFFIX = os.environ.get("HF_REPO_SUFFIX") if os.environ.get("HF_REPO_SUFFIX") else "" # set your default repo suffix
-HF_USER = os.environ.get("HF_USER") if os.environ.get("HF_USER") else "" # set your username
-REGEX_HF_REPO = r'^[\w_\-\.]+/[\w_\-\.]+$'
-
-def remove_repo_tags(repo_id: str, tags: list[str], repo_type: str, hf_token: str):
-    try:
-        card = RepoCard.load(repo_id, repo_type=repo_type, token=hf_token)
-        orig_content = card.content
-        for tag in tags:
-            if 'tags' in card.data and tag in card.data['tags']: card.data['tags'].remove(tag)
-        if card.content == orig_content: return
-        card.push_to_hub(repo_id=repo_id, repo_type=repo_type, token=hf_token)
-    except Exception as e:
-        print(f"Failed to remove tags from repocard. {e}")
-
-def duplicate(source_repo, dst_repo, repo_type, private, overwrite, auto_dir, remove_tag, oauth_token: gr.OAuthToken | None, progress=gr.Progress(track_tqdm=True)):
-    hf_token = oauth_token.token
-    api = HfApi(token=hf_token)
-    try:
-        if not repo_type in REPO_TYPES:
-            raise ValueError("need to select valid repo type")
-        _ = whoami(oauth_token.token)
-        # ^ this will throw if token is invalid
-    except Exception as e:
-        raise gr.Error(f"""Oops, you forgot to login. Please use the loggin button on the top left to migrate your repo {e}""")
-    
-    try:
-        if re.fullmatch(REGEX_HF_REPO, source_repo): target = ""
-        else:
-            source_repo, target = re.findall(r'^(?:http.+\.co/)?(?:datasets)?(?:spaces)?([\w_\-\.]+/[\w_\-\.]+)/?(?:blob/main/)?(?:resolve/main/)?(.+)?$', source_repo)[0]
-            target = urllib.parse.unquote(target.removesuffix("/"))
-
-        if re.fullmatch(REGEX_HF_REPO, dst_repo): subfolder = ""
-        else:
-            dst_repo, subfolder = re.findall(r'^([\w_\-\.]+/[\w_\-\.]+)/?(.+)?$', dst_repo)[0]
-            subfolder = subfolder.removesuffix("/")
-        if auto_dir: subfolder = source_repo
-
-        if not overwrite and api.repo_exists(repo_id=dst_repo, repo_type=repo_type, token=hf_token): raise gr.Error(f"Repo already exists {dst_repo}")
-
-        if overwrite or subfolder:
-            temp_dir = tempfile.mkdtemp()
-            api.create_repo(repo_id=dst_repo, repo_type=repo_type, private=private, exist_ok=True, token=hf_token)
-            for path in api.list_repo_files(repo_id=source_repo, repo_type=repo_type, token=hf_token):
-                if target and target not in path: continue
-                file = hf_hub_download(repo_id=source_repo, filename=path, repo_type=repo_type, local_dir=temp_dir, token=hf_token)
-                if not Path(file).exists(): continue
-                if Path(file).is_dir(): # unused for now
-                    api.upload_folder(repo_id=dst_repo, folder_path=file, path_in_repo=f"{subfolder}/{path}" if subfolder else path, repo_type=repo_type, token=hf_token)
-                elif Path(file).is_file():
-                    api.upload_file(repo_id=dst_repo, path_or_fileobj=file, path_in_repo=f"{subfolder}/{path}" if subfolder else path, repo_type=repo_type, token=hf_token)
-                if Path(file).exists(): Path(file).unlink()
-            if repo_type == "dataset": repo_url = f"https://huggingface.co/datasets/{dst_repo}"
-            elif repo_type == "space": repo_url = f"https://huggingface.co/spaces/{dst_repo}"
-            else: repo_url = f"https://huggingface.co/{dst_repo}"
-            shutil.rmtree(temp_dir)
-        else:
-            r = requests.post(
-                f"{ENDPOINT}/api/{repo_type}s/{source_repo}/duplicate",
-                headers=build_hf_headers(token=oauth_token.token),
-                json={"repository": dst_repo, "private": private},
-            )
-            hf_raise_for_status(r)
-
-            repo_url = r.json().get("url")
-
-        if remove_tag: remove_repo_tags(dst_repo, ["not-for-all-audiences"], repo_type, hf_token)
-
-        return (
-            f'Find your repo <a href=\'{repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
-            "sp.jpg",
-        )
-
-    except Exception as e:
-        print(e)
-        raise gr.Error(f"Error occured: {e}")
-
-def parse_repos(s):
-    repo_pattern = r'[^\w_\-\.]?([\w_\-\.]+/[\w_\-\.]+)[^\w_\-\.]?'
-    try:
-        s = re.sub("https?://[\\w/:%#\\$&\\?\\(\\)~\\.=\\+\\-]+", "", s)
-        repos = re.findall(repo_pattern, s)
-        return list(repos)
-    except Exception:
-        return []
-
-def duplicate_m2o(source_repos_str, dst_repo, repo_type, private, overwrite, oauth_token: gr.OAuthToken | None, progress=gr.Progress(track_tqdm=True)):
-    hf_token = oauth_token.token
-    api = HfApi(token=hf_token)
-    try:
-        if not repo_type in REPO_TYPES:
-            raise ValueError("need to select valid repo type")
-        _ = whoami(oauth_token.token)
-        # ^ this will throw if token is invalid
-    except Exception as e:
-        raise gr.Error(f"""Oops, you forgot to login. Please use the loggin button on the top left to migrate your repo {e}""")
-    
-    try:
-        if re.fullmatch(REGEX_HF_REPO, dst_repo): subfolder_prefix = ""
-        else:
-            dst_repo, subfolder_prefix = re.findall(r'^([\w_\-\.]+/[\w_\-\.]+)/?(.+)?$', dst_repo)[0]
-            subfolder_prefix = subfolder.removesuffix("/")
-        if not overwrite and api.repo_exists(repo_id=dst_repo, repo_type=repo_type, token=hf_token): raise gr.Error(f"Repo already exists {dst_repo}")
-        source_repos = parse_repos(source_repos_str)
-        for source_repo in source_repos:
-            if re.fullmatch(REGEX_HF_REPO, source_repo): target = ""
-            else:
-                source_repo, target = re.findall(r'^(?:http.+\.co/)?(?:datasets)?(?:spaces)?([\w_\-\.]+/[\w_\-\.]+)/?(?:blob/main/)?(?:resolve/main/)?(.+)?$', source_repo)[0]
-                target = urllib.parse.unquote(target.removesuffix("/"))
-
-            subfolder = subfolder_prefix + "/" + source_repo if subfolder_prefix else source_repo
-
-            temp_dir = tempfile.mkdtemp()
-            api.create_repo(repo_id=dst_repo, repo_type=repo_type, private=private, exist_ok=True, token=hf_token)
-            for path in api.list_repo_files(repo_id=source_repo, repo_type=repo_type, token=hf_token):
-                if target and target not in path: continue
-                file = hf_hub_download(repo_id=source_repo, filename=path, repo_type=repo_type, local_dir=temp_dir, token=hf_token)
-                if not Path(file).exists(): continue
-                if Path(file).is_dir(): # unused for now
-                    api.upload_folder(repo_id=dst_repo, folder_path=file, path_in_repo=f"{subfolder}/{path}" if subfolder else path, repo_type=repo_type, token=hf_token)
-                elif Path(file).is_file():
-                    api.upload_file(repo_id=dst_repo, path_or_fileobj=file, path_in_repo=f"{subfolder}/{path}" if subfolder else path, repo_type=repo_type, token=hf_token)
-                if Path(file).exists(): Path(file).unlink()
-            if repo_type == "dataset": repo_url = f"https://huggingface.co/datasets/{dst_repo}"
-            elif repo_type == "space": repo_url = f"https://huggingface.co/spaces/{dst_repo}"
-            else: repo_url = f"https://huggingface.co/{dst_repo}"
-            shutil.rmtree(temp_dir)
-
-        return (
-            f'Find your repo <a href=\'{repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
-            "sp.jpg",
-        )
+import numpy as np
 
-    except Exception as e:
-        print(e)
-        raise gr.Error(f"Error occured: {e}")
-
-def duplicate_m2m(source_repos_str, hf_user, repo_type, private, overwrite, remove_tag, repo_prefix, repo_suffix, oauth_token: gr.OAuthToken | None, progress=gr.Progress(track_tqdm=True)):
-    hf_token = oauth_token.token
-    api = HfApi(token=hf_token)
-    try:
-        if not repo_type in REPO_TYPES:
-            raise ValueError("need to select valid repo type")
-        _ = whoami(oauth_token.token)
-        # ^ this will throw if token is invalid
-    except Exception as e:
-        raise gr.Error(f"""Oops, you forgot to login. Please use the loggin button on the top left to migrate your repo {e}""")
-    
-    try:
-        source_repos = parse_repos(source_repos_str)
-        repo_url_result = 'Find your repo '
-        for source_repo in source_repos:
-            if not re.fullmatch(REGEX_HF_REPO, source_repo) or not api.repo_exists(repo_id=source_repo, repo_type=repo_type, token=hf_token): continue
-            dst_repo = hf_user + "/" + repo_prefix + source_repo.split("/")[-1] + repo_suffix
-            if not re.fullmatch(REGEX_HF_REPO, dst_repo): continue
-            if not overwrite and api.repo_exists(repo_id=dst_repo, repo_type=repo_type, token=hf_token):
-                gr.Info(f"Repo already exists {dst_repo}")
-                continue
-
-            r = requests.post(
-                f"{ENDPOINT}/api/{repo_type}s/{source_repo}/duplicate",
-                headers=build_hf_headers(token=oauth_token.token),
-                json={"repository": dst_repo, "private": private},
-            )
-            hf_raise_for_status(r)
-
-            repo_url = r.json().get("url")
-            repo_url_result += f'<a href=\'{repo_url}\' target="_blank" style="text-decoration:underline">{dst_repo}</a><br>\n'
-
-            if remove_tag: remove_repo_tags(dst_repo, ["not-for-all-audiences"], repo_type, hf_token)
-
-        return (
-            repo_url_result,
-            "sp.jpg",
-        )
+# DiffuseCraft
+from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers, save_image_history,
+    get_vaes, enable_diffusers_model_detail, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
+    preset_quality, preset_styles, process_style_prompt, get_all_lora_tupled_list, update_loras, apply_lora_prompt,
+    download_my_lora, search_civitai_lora, update_civitai_selection, select_civitai_lora, search_civitai_lora_json,
+    get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
+    SCHEDULE_TYPE_OPTIONS, SCHEDULE_PREDICTION_TYPE_OPTIONS, preprocessor_tab, SDXL_TASK, TASK_MODEL_LIST,
+    PROMPT_W_OPTIONS, POST_PROCESSING_SAMPLER, IP_ADAPTERS_SD, IP_ADAPTERS_SDXL, DIFFUSERS_CONTROLNET_MODEL,
+    TASK_AND_PREPROCESSORS, update_task_options, change_preprocessor_choices, get_ti_choices,
+    update_textual_inversion, set_textual_inversion_prompt, create_mask_now)
+# Translator
+from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
+    get_llm_formats, get_dolphin_model_format, get_dolphin_models,
+    get_dolphin_model_info, select_dolphin_model, select_dolphin_format, get_dolphin_sysprompt)
+# Tagger
+from tagger.v2 import v2_upsampling_prompt, V2_ALL_MODELS
+from tagger.utils import (gradio_copy_text, gradio_copy_prompt, COPY_ACTION_JS,
+    V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
+from tagger.tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
+    remove_specific_prompt, insert_recom_prompt, compose_prompt_to_copy,
+    translate_prompt, select_random_character)
+from tagger.fl2sd3longcap import predict_tags_fl2_sd3
+def description_ui():
+    gr.Markdown(
+        """
+## Danbooru Tags Transformer V2 Demo with WD Tagger & SD3 Long Captioner
+(Image =>) Prompt => Upsampled longer prompt
+- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).
+- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft), [dart-v2-sft](https://huggingface.co/p1atdev/dart-v2-sft)\
+, gokaygokay's [Florence-2-SD3-Captioner](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner)
+"""
+    )
 
-    except Exception as e:
-        print(e)
-        raise gr.Error(f"Error occured: {e}")
 
-def add_repo_text(repo_id: str, source_repos: str):
-    return source_repos + "\n" + repo_id if source_repos else repo_id
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 4096
+MIN_IMAGE_SIZE = 256
 
-def swap_visibilty(profile: gr.OAuthProfile | None):
-    return gr.update(elem_classes=["main_ui_logged_in"]) if profile else gr.update(elem_classes=["main_ui_logged_out"])
+css = """
+#container { margin: 0 auto; !important; }
+#col-container { margin: 0 auto; !important; }
+#result { max-width: 520px; max-height: 520px; margin: 0px auto; !important; }
+.lora { min-width: 480px; !important; }
+.title { font-size: 3em; align-items: center; text-align: center; }
+.info { align-items: center; text-align: center; }
+.desc [src$='#float'] { float: right; margin: 20px; }
+"""
 
-css = '''
-.main_ui_logged_out{opacity: 0.3; pointer-events: none}
-.title {text-align: center; align-items: center}
-'''
-with gr.Blocks(css=css) as demo:
-    gr.LoginButton()
-    with gr.Column(elem_classes="main_ui_logged_out") as main_ui:
-        gr.Markdown("# Duplicate your repo!", elem_classes="title")
-        gr.Markdown("Duplicate a Hugging Face repository! This Space is a an experimental demo.")
-        with gr.Tab("One to One"):
+with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60, 3600)) as demo:
+    gr.Markdown("# Votepurchase Multiple Model", elem_classes="title")
+    state = gr.State(value={})
+    with gr.Tab("Image Generator"):
+        with gr.Column(elem_id="col-container"):
             with gr.Row():
-                with gr.Column():
-                    search = HuggingfaceHubSearch(
-                        label="source_repo",
-                        placeholder="Source repository (e.g. osanseviero/src)",
-                        search_type=["model", "dataset", "space"],
-                        sumbit_on_select=False,
-                    )
-                    with gr.Group():
-                        dst_repo = gr.Textbox(label="dst_repo", placeholder="Destination repository (e.g. osanseviero/dst)", value=HF_REPO)
-                        repo_type = gr.Dropdown(label="repo_type", choices=REPO_TYPES, value="model")
+                prompt = gr.Text(label="Prompt", show_label=False, lines=1, max_lines=8, placeholder="Enter your prompt", container=False)
+            
+            with gr.Row():
+                run_button = gr.Button("Run", variant="primary", scale=5)
+                run_translate_button = gr.Button("Run with LLM Enhance", variant="secondary", scale=3)
+                auto_trans = gr.Checkbox(label="Auto translate to English", value=False, scale=2)
+
+            result = gr.Image(label="Result", elem_id="result", format="png", type="filepath", show_label=False, interactive=False,
+                              show_download_button=True, show_share_button=False, container=True)
+
+            with gr.Accordion("History", open=False):
+                history_files = gr.Files(interactive=False, visible=False)
+                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False,
+                show_download_button=True)
+                history_clear_button = gr.Button(value="Clear History", variant="secondary")
+                history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
+
+            with gr.Accordion("Advanced Settings", open=True):
+                task = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
+                with gr.Tab("Generation Settings"):
+                    with gr.Row():
+                        negative_prompt = gr.Text(label="Negative prompt", lines=1, max_lines=6, placeholder="Enter a negative prompt", show_copy_button=True,
+                                                value="(low quality, worst quality:1.2), very displeasing, watermark, signature, ugly")
+                    with gr.Accordion("Prompt Settings", open=False):
+                        with gr.Row():
+                            quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None", scale=3)
+                            style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None", scale=3)
                         with gr.Row():
-                            is_private = gr.Checkbox(label="Make new repo private?", value=True)
-                            is_overwrite = gr.Checkbox(label="Overwrite existing repo?", value=True)
-                            is_subdir = gr.Checkbox(label="Create subdirectories automatically?", value=True)
-                            is_remtag = gr.Checkbox(label="Remove NFAA tag?", value=True)
+                            recom_prompt = gr.Checkbox(label="Recommended prompt", value=True, scale=1)
+                            prompt_syntax = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
                     with gr.Row():
-                        submit_button = gr.Button("Submit", variant="primary")
-                        clear_button = gr.Button("Clear", variant="secondary")
-                with gr.Column():
-                    output_md = gr.Markdown(label="output")
-                    output_image = gr.Image(show_label=False)
-        with gr.Tab("Multi to One"):
-            with gr.Row():
-                with gr.Column():
-                    m2o_search = HuggingfaceHubSearch(
-                        label="source_repo",
-                        placeholder="Source repository (e.g. osanseviero/src)",
-                        search_type=["model", "dataset", "space"],
-                        sumbit_on_select=True,
-                    )
-                    m2o_source_repos = gr.Textbox(label="source_repos", placeholder="Source repositories (e.g. osanseviero/src)\n...", value="", lines=10)
-                    with gr.Group():
-                        m2o_dst_repo = gr.Textbox(label="dst_repo", placeholder="Destination repository (e.g. osanseviero/dst)", value=HF_REPO)
-                        m2o_repo_type = gr.Dropdown(label="repo_type", choices=REPO_TYPES, value="model")
+                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                        gpu_duration = gr.Slider(label="GPU time duration (seconds)", minimum=5, maximum=240, value=59)
+                    with gr.Row():
+                        width = gr.Slider(label="Width", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=32, value=1024) # 832
+                        height = gr.Slider(label="Height", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=32, value=1024) # 1216
+                        guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=30.0, step=0.1, value=7)
+                        guidance_rescale = gr.Slider(label="CFG rescale", value=0., step=0.01, minimum=0., maximum=1.5)
+                    with gr.Row():
+                        num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=100, step=1, value=28)
+                        pag_scale = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
+                        clip_skip = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
+                        free_u = gr.Checkbox(value=False, label="FreeU")
+                    with gr.Row():
+                        sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler")
+                        schedule_type = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
+                        schedule_prediction_type = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
+                        vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
+                    with gr.Accordion("Other Settings", open=False):
+                        with gr.Accordion("Textual inversion", open=True):
+                            active_textual_inversion = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
+                            use_textual_inversion = gr.CheckboxGroup(choices=get_ti_choices(model_name.value) if active_textual_inversion.value else [], value=None, label="Use Textual Invertion in prompt")
+                    with gr.Row():
+                        with gr.Column(scale=4):
+                            model_name = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.",
+                                                    choices=get_diffusers_model_list(), value=get_diffusers_model_list()[0],
+                                                    allow_custom_value=True, interactive=True, min_width=320)
+                            model_info = gr.Markdown(elem_classes="info")
+                        with gr.Column(scale=1):
+                            model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
+    
+                with gr.Tab("LoRA"):
+                    def lora_dropdown(label, visible=True):
+                        return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible)
+
+                    def lora_scale_slider(label, visible=True):
+                        return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible)
+                    
+                    def lora_textbox():
+                        return gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                lora1 = lora_dropdown("LoRA 1")
+                                lora1_wt = lora_scale_slider("LoRA 1: weight")
+                            with gr.Row():
+                                lora1_info = lora_textbox()
+                                lora1_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora1_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora2 = lora_dropdown("LoRA 2")
+                                lora2_wt = lora_scale_slider("LoRA 2: weight")
+                            with gr.Row():
+                                lora2_info = lora_textbox()
+                                lora2_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora2_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora3 = lora_dropdown("LoRA 3")
+                                lora3_wt = lora_scale_slider("LoRA 3: weight")
+                            with gr.Row():
+                                lora3_info = lora_textbox()
+                                lora3_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora3_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora4 = lora_dropdown("LoRA 4")
+                                lora4_wt = lora_scale_slider("LoRA 4: weight")
+                            with gr.Row():
+                                lora4_info = lora_textbox()
+                                lora4_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora4_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora5 = lora_dropdown("LoRA 5")
+                                lora5_wt = lora_scale_slider("LoRA 5: weight")
+                            with gr.Row():
+                                lora5_info = lora_textbox()
+                                lora5_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora5_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora6 = lora_dropdown("LoRA 6", visible=False)
+                                lora6_wt = lora_scale_slider("LoRA 6: weight", visible=False)
+                            with gr.Row():
+                                lora6_info = lora_textbox()
+                                lora6_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora6_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora7 = lora_dropdown("LoRA 7", visible=False)
+                                lora7_wt = lora_scale_slider("LoRA 7: weight", visible=False)
+                            with gr.Row():
+                                lora7_info = lora_textbox()
+                                lora7_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora7_md = gr.Markdown(value="", visible=False)
+                    with gr.Accordion("From URL", open=True, visible=True):
                         with gr.Row():
-                            m2o_is_private = gr.Checkbox(label="Make new repo private?", value=True)
-                            m2o_is_overwrite = gr.Checkbox(label="Overwrite existing repo?", value=True)
+                            lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
+                            lora_search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Highest Rated")
+                            lora_search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="AllTime")
+                        with gr.Row():
+                            lora_search_civitai_query = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
+                            lora_search_civitai_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
+                            lora_search_civitai_user = gr.Textbox(label="Username", lines=1)
+                        lora_search_civitai_submit = gr.Button("Search on Civitai")
+                        with gr.Row():
+                            lora_search_civitai_json = gr.JSON(value={}, visible=False)
+                            lora_search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc")
+                        with gr.Accordion("Select from Gallery", open=False):
+                            lora_search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
+                        lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
+                        lora_download_url = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1)
+                        lora_download = gr.Button("Get and set LoRA and apply to prompt")
+                
+                with gr.Tab("ControlNet / Img2img / Inpaint"):
                     with gr.Row():
-                        m2o_submit_button = gr.Button("Submit", variant="primary")
-                        m2o_clear_button = gr.Button("Clear", variant="secondary")
-                with gr.Column():
-                    m2o_output_md = gr.Markdown(label="output")
-                    m2o_output_image = gr.Image(show_label=False)
-        with gr.Tab("Multi to Multi"):
-            with gr.Row():
-                with gr.Column():
-                    m2m_search = HuggingfaceHubSearch(
-                        label="source_repo",
-                        placeholder="Source repository (e.g. osanseviero/src)",
-                        search_type=["model", "dataset", "space"],
-                        sumbit_on_select=True,
-                    )
-                    m2m_source_repos = gr.Textbox(label="source_repos", placeholder="Source repositories (e.g. osanseviero/src)\n...", value="", lines=10)
-                    with gr.Group():
+                        #image_control = gr.Image(label="Image ControlNet / Inpaint / Img2img", type="filepath", height=384, sources=["upload", "clipboard", "webcam"], show_share_button=False)
+                        image_control = gr.ImageEditor(label="Image ControlNet / Inpaint / Img2img", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                        image_mask = gr.Image(label="Image Mask", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                    with gr.Row():
+                        strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
+                                            info="This option adjusts the level of changes for img2img and inpainting.")
+                        image_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
+                                                    info="The maximum proportional size of the generated image based on the uploaded image.")
+                    with gr.Row():
+                        controlnet_model = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
+                        control_net_output_scaling = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
+                        control_net_start_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
+                        control_net_stop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
+                    with gr.Row():
+                        preprocessor_name = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
+                        preprocess_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
+                        low_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
+                        high_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
+                    with gr.Row():
+                        value_threshold = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
+                        distance_threshold = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
+                        recolor_gamma_correction = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
+                        tile_blur_sigma = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
+
+                with gr.Tab("IP-Adapter"):
+                    IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
+                    MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
+                    with gr.Accordion("IP-Adapter 1", open=True, visible=True):
                         with gr.Row():
-                            m2m_user = gr.Textbox(label="hf_user", placeholder="Your HF username", value=HF_USER)
-                            m2m_prefix = gr.Textbox(label="repo_prefix", value=HF_REPO_PREFIX)
-                            m2m_suffix = gr.Textbox(label="repo_suffix", value=HF_REPO_SUFFIX)
-                        m2m_repo_type = gr.Dropdown(label="repo_type", choices=REPO_TYPES, value="model")
+                            #image_ip1 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            image_ip1 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                            mask_ip1 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
                         with gr.Row():
-                            m2m_is_private = gr.Checkbox(label="Make new repo private?", value=True)
-                            m2m_is_overwrite = gr.Checkbox(label="Overwrite existing repo?", value=False)
-                            m2m_is_remtag = gr.Checkbox(label="Remove NFAA tag?", value=True)
+                            model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
+                            mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
+                            scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+                    with gr.Accordion("IP-Adapter 2", open=True, visible=True):
+                        with gr.Row():
+                            #image_ip2 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            image_ip2 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                            mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                        with gr.Row():
+                            model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
+                            mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
+                            scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+
+                with gr.Tab("Inpaint Mask Maker"):
                     with gr.Row():
-                        m2m_submit_button = gr.Button("Submit", variant="primary")
-                        m2m_clear_button = gr.Button("Clear", variant="secondary")
-                with gr.Column():
-                    m2m_output_md = gr.Markdown(label="output")
-                    m2m_output_image = gr.Image(show_label=False)
-    demo.load(fn=swap_visibilty, outputs=main_ui)
-    submit_button.click(duplicate, [search, dst_repo, repo_type, is_private, is_overwrite, is_subdir, is_remtag], [output_md, output_image])
-    clear_button.click(lambda: ("", HF_REPO, "model", True, True, True, True), None, [search, dst_repo, repo_type, is_private, is_overwrite, is_subdir, is_remtag], queue=False)
-    m2o_search.submit(add_repo_text, [m2o_search, m2o_source_repos], [m2o_source_repos], queue=False)
-    m2o_submit_button.click(duplicate_m2o, [m2o_source_repos, m2o_dst_repo, m2o_repo_type, m2o_is_private, m2o_is_overwrite], [m2o_output_md, m2o_output_image])
-    m2o_clear_button.click(lambda: ("", HF_REPO, "model", True, True, ""), None,
-                           [m2o_search, m2o_dst_repo, m2o_repo_type, m2o_is_private, m2o_is_overwrite, m2o_source_repos], queue=False)
-    m2m_search.submit(add_repo_text, [m2m_search, m2m_source_repos], [m2m_source_repos], queue=False)
-    m2m_submit_button.click(duplicate_m2m, [m2m_source_repos, m2m_user, m2m_repo_type, m2m_is_private, m2m_is_overwrite, m2m_is_remtag, m2m_prefix, m2m_suffix],
-                            [m2m_output_md, m2m_output_image])
-    m2m_clear_button.click(lambda: ("", HF_USER, "model", True, False, True, "", HF_REPO_PREFIX, HF_REPO_SUFFIX), None,
-                           [m2m_search, m2m_user, m2m_repo_type, m2m_is_private, m2m_is_overwrite, m2m_is_remtag, m2m_source_repos, m2m_prefix, m2m_suffix], queue=False)
+                        with gr.Column():
+                            image_base = gr.ImageEditor(sources=["upload", "clipboard", "webcam"],
+                                brush=gr.Brush(default_size="32", color_mode="fixed", colors=["rgba(0, 0, 0, 1)", "rgba(0, 0, 0, 0.1)", "rgba(255, 255, 255, 0.1)"]),
+                                eraser=gr.Eraser(default_size="32"), show_share_button=False, show_fullscreen_button=False,
+                                canvas_size=(384, 384), width=384, height=512)
+                            invert_mask = gr.Checkbox(value=False, label="Invert mask")
+                            cm_btn = gr.Button("Create mask")
+                        with gr.Column():
+                            img_source = gr.Image(interactive=False, height=384, show_share_button=False)
+                            img_result = gr.Image(label="Mask image", show_label=True, interactive=False, height=384, show_share_button=False)
+                            cm_btn_send = gr.Button("Send to ControlNet / Img2img / Inpaint")
+                            cm_btn_send_ip1 = gr.Button("Send to IP-Adapter 1")
+                            cm_btn_send_ip2 = gr.Button("Send to IP-Adapter 2")
+                        cm_btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result], show_api=False)
+                        def send_img(img_source, img_result):
+                            return img_source, img_result
+                        cm_btn_send.click(send_img, [img_source, img_result], [image_control, image_mask], queue=False, show_api=False)
+                        cm_btn_send_ip1.click(send_img, [img_source, img_result], [image_ip1, mask_ip1], queue=False, show_api=False)
+                        cm_btn_send_ip2.click(send_img, [img_source, img_result], [image_ip2, mask_ip2], queue=False, show_api=False)
+
+                with gr.Tab("Hires fix / Detailfix"):
+                    with gr.Accordion("Hires fix", open=True):
+                        with gr.Row():
+                            upscaler_model_path = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
+                            upscaler_increases_size = gr.Slider(minimum=1.1, maximum=4., step=0.1, value=1.2, label="Upscale by")
+                            esrgan_tile = gr.Slider(minimum=0, value=0, maximum=500, step=1, label="ESRGAN Tile")
+                            esrgan_tile_overlap = gr.Slider(minimum=1, maximum=200, step=1, value=8, label="ESRGAN Tile Overlap")
+                        with gr.Row():
+                            hires_steps = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
+                            hires_denoising_strength = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
+                            hires_sampler = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+                            hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
+                            hires_schedule_type = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
+                            hires_guidance_scale = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
+                        with gr.Row():
+                            hires_prompt = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
+                            hires_negative_prompt = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
+                    with gr.Accordion("Detail fix", open=True):
+                        with gr.Row():
+                            # Adetailer Inpaint Only
+                            adetailer_inpaint_only = gr.Checkbox(label="Inpaint only", value=True)
+                            # Adetailer Verbose
+                            adetailer_verbose = gr.Checkbox(label="Verbose", value=False)
+                            # Adetailer Sampler
+                            adetailer_sampler = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+                        with gr.Row():
+                            with gr.Accordion("Detailfix A", open=True, visible=True):
+                                # Adetailer A
+                                adetailer_active_a = gr.Checkbox(label="Enable Adetailer A", value=False)
+                                prompt_ad_a = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+                                negative_prompt_ad_a = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+                                with gr.Row():
+                                    strength_ad_a = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+                                    face_detector_ad_a = gr.Checkbox(label="Face detector", value=False)
+                                    person_detector_ad_a = gr.Checkbox(label="Person detector", value=True)
+                                    hand_detector_ad_a = gr.Checkbox(label="Hand detector", value=False)
+                                with gr.Row():
+                                    mask_dilation_a = gr.Number(label="Mask dilation:", value=4, minimum=1)
+                                    mask_blur_a = gr.Number(label="Mask blur:", value=4, minimum=1)
+                                    mask_padding_a = gr.Number(label="Mask padding:", value=32, minimum=1)
+                            with gr.Accordion("Detailfix B", open=True, visible=True):
+                                # Adetailer B
+                                adetailer_active_b = gr.Checkbox(label="Enable Adetailer B", value=False)
+                                prompt_ad_b = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+                                negative_prompt_ad_b = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+                                with gr.Row():
+                                    strength_ad_b = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+                                    face_detector_ad_b = gr.Checkbox(label="Face detector", value=False)
+                                    person_detector_ad_b = gr.Checkbox(label="Person detector", value=True)
+                                    hand_detector_ad_b = gr.Checkbox(label="Hand detector", value=False)
+                                with gr.Row():
+                                    mask_dilation_b = gr.Number(label="Mask dilation:", value=4, minimum=1)
+                                    mask_blur_b = gr.Number(label="Mask blur:", value=4, minimum=1)
+                                    mask_padding_b = gr.Number(label="Mask padding:", value=32, minimum=1)
+                                    
+                with gr.Tab("Translation Settings"):
+                    chatbot = gr.Chatbot(render_markdown=False, visible=False) # component for auto-translation
+                    chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model")
+                    chat_model_info = gr.Markdown(value=get_dolphin_model_info(get_dolphin_models()[0][1]), label="Model info")
+                    chat_format = gr.Dropdown(choices=get_llm_formats(), value=get_dolphin_model_format(get_dolphin_models()[0][1]), label="Message format")
+                    with gr.Row():
+                        chat_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max tokens")
+                        chat_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+                        chat_topp = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
+                        chat_topk = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k")
+                        chat_rp = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
+                    chat_sysmsg = gr.Textbox(value=get_dolphin_sysprompt(), label="System message")
+
+        examples = gr.Examples(
+            examples = [
+                ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
+                ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
+                ["kafuu chino, 1girl, solo"],
+                ["1girl"],
+                ["beautiful sunset"],
+            ],
+            inputs=[prompt],
+            cache_examples=False,
+        )
+
+    model_name.change(update_task_options, [model_name, task], [task], queue=False, show_api=False)
+    task.change(change_preprocessor_choices, [task], [preprocessor_name], queue=False, show_api=False)
+    active_textual_inversion.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False)
+    model_name.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False)
+    use_textual_inversion.change(set_textual_inversion_prompt, [use_textual_inversion, prompt, negative_prompt, prompt_syntax], [prompt, negative_prompt])
+
+    gr.on( #lambda x: None, inputs=None, outputs=result).then(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
+                guidance_scale, num_inference_steps, model_name,
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                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,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+        outputs=[result],
+        queue=True,
+        show_progress="full",
+        show_api=True,
+    )
+
+    gr.on( #lambda x: None, inputs=None, outputs=result).then(
+        triggers=[run_translate_button.click],
+        fn=_infer, # dummy fn for api
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
+                guidance_scale, num_inference_steps, model_name,
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                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,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+        outputs=[result],
+        queue=False,
+        show_api=True,
+        api_name="infer_translate",
+    ).success(
+        fn=dolphin_respond_auto,
+        inputs=[prompt, chatbot, chat_model, chat_sysmsg, chat_tokens, chat_temperature, chat_topp, chat_topk, chat_rp, state],
+        outputs=[chatbot, result, prompt],
+        queue=True,
+        show_progress="full",
+        show_api=False,
+    ).success(
+        fn=dolphin_parse_simple,
+        inputs=[prompt, chatbot, state],
+        outputs=[prompt],
+        queue=False,
+        show_api=False,
+    ).success(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
+                guidance_scale, num_inference_steps, model_name,
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                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,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+        outputs=[result],
+        queue=True,
+        show_progress="full",
+        show_api=False,
+    ).success(lambda: None, None, chatbot, queue=False, show_api=False)\
+    .success(pass_result, [result], [result], queue=False, show_api=False) # dummy fn for api
+
+    result.change(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False)
+
+    gr.on(
+        triggers=[lora1.change, lora1_wt.change, lora2.change, lora2_wt.change, lora3.change, lora3_wt.change,
+                  lora4.change, lora4_wt.change, lora5.change, lora5_wt.change, lora6.change, lora6_wt.change, lora7.change, lora7_wt.change, prompt_syntax.change],
+        fn=update_loras,
+        inputs=[prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt],
+        outputs=[prompt, lora1, lora1_wt, lora1_info, lora1_copy, lora1_md,
+                 lora2, lora2_wt, lora2_info, lora2_copy, lora2_md, lora3, lora3_wt, lora3_info, lora3_copy, lora3_md, 
+                 lora4, lora4_wt, lora4_info, lora4_copy, lora4_md, lora5, lora5_wt, lora5_info, lora5_copy, lora5_md,
+                 lora6, lora6_wt, lora6_info, lora6_copy, lora6_md, lora7, lora7_wt, lora7_info, lora7_copy, lora7_md],
+        queue=False,
+        trigger_mode="once",
+        show_api=False,
+    )
+    lora1_copy.click(apply_lora_prompt, [prompt, lora1_info], [prompt], queue=False, show_api=False)
+    lora2_copy.click(apply_lora_prompt, [prompt, lora2_info], [prompt], queue=False, show_api=False)
+    lora3_copy.click(apply_lora_prompt, [prompt, lora3_info], [prompt], queue=False, show_api=False)
+    lora4_copy.click(apply_lora_prompt, [prompt, lora4_info], [prompt], queue=False, show_api=False)
+    lora5_copy.click(apply_lora_prompt, [prompt, lora5_info], [prompt], queue=False, show_api=False)
+    lora6_copy.click(apply_lora_prompt, [prompt, lora6_info], [prompt], queue=False, show_api=False)
+    lora7_copy.click(apply_lora_prompt, [prompt, lora7_info], [prompt], queue=False, show_api=False)
+
+    gr.on(
+        triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
+        fn=search_civitai_lora,
+        inputs=[lora_search_civitai_query, lora_search_civitai_basemodel, lora_search_civitai_sort, lora_search_civitai_period, lora_search_civitai_tag, lora_search_civitai_user, lora_search_civitai_gallery],
+        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query, lora_search_civitai_gallery],
+        scroll_to_output=True,
+        queue=True,
+        show_api=False,
+    )
+    lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)  # fn for api
+    lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)
+    gr.on(
+        triggers=[lora_download.click, lora_download_url.submit],
+        fn=download_my_lora,
+        inputs=[lora_download_url, lora1, lora2, lora3, lora4, lora5, lora6, lora7],
+        outputs=[lora1, lora2, lora3, lora4, lora5, lora6, lora7],
+        scroll_to_output=True,
+        queue=True,
+        show_api=False,
+    )
+    lora_search_civitai_gallery.select(update_civitai_selection, None, [lora_search_civitai_result], queue=False, show_api=False)
+
+    #recom_prompt.change(enable_model_recom_prompt, [recom_prompt], [recom_prompt], queue=False, show_api=False)
+    gr.on(
+        triggers=[quality_selector.change, style_selector.change],
+        fn=process_style_prompt,
+        inputs=[prompt, negative_prompt, style_selector, quality_selector],
+        outputs=[prompt, negative_prompt],
+        queue=False,
+        trigger_mode="once",
+        show_api=False,
+    )
+
+    model_detail.change(enable_diffusers_model_detail, [model_detail, model_name, state], [model_detail, model_name, state], queue=False, show_api=False)
+    model_name.change(get_t2i_model_info, [model_name], [model_info], queue=False, show_api=False)
+
+    chat_model.change(select_dolphin_model, [chat_model, state], [chat_model, chat_format, chat_model_info, state], queue=True, show_progress="full", show_api=False)\
+    .success(lambda: None, None, chatbot, queue=False, show_api=False)
+    chat_format.change(select_dolphin_format, [chat_format, state], [chat_format, state], queue=False, show_api=False)\
+    .success(lambda: None, None, chatbot, queue=False, show_api=False)
+
+    # Tagger
+    with gr.Tab("Tags Transformer with Tagger"):
+        with gr.Column():
+                with gr.Group():
+                    input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
+                    with gr.Accordion(label="Advanced options", open=False):
+                        general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
+                        character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
+                        input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
+                        recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
+                    image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"])
+                    keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
+                    generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
+                with gr.Group():
+                    with gr.Row():
+                        input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
+                        input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
+                        random_character = gr.Button(value="Random character 🎲", size="sm")
+                    input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
+                    input_tags_to_copy = gr.Textbox(value="", visible=False)
+                    with gr.Row():
+                        copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+                        copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+                    translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
+                    tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
+                    input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
+                    with gr.Accordion(label="Advanced options", open=False):
+                        input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
+                        input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
+                        input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")                    
+                        input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
+                        model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
+                        dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
+                        recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
+                        recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
+                    generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
+                with gr.Row():
+                    with gr.Group():
+                        output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
+                        with gr.Row():
+                            copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+                            copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+                    with gr.Group():
+                        output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
+                        with gr.Row():
+                            copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+                            copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+
+        random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character], queue=False, show_api=False)
+
+        translate_input_prompt_button.click(translate_prompt, [input_general], [input_general], queue=False, show_api=False)
+        translate_input_prompt_button.click(translate_prompt, [input_character], [input_character], queue=False, show_api=False)
+        translate_input_prompt_button.click(translate_prompt, [input_copyright], [input_copyright], queue=False, show_api=False)
+
+        generate_from_image_btn.click(
+            lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False, show_api=False,
+        ).success(
+            predict_tags_wd,
+            [input_image, input_general, image_algorithms, general_threshold, character_threshold],
+            [input_copyright, input_character, input_general, copy_input_btn],
+            show_api=False,
+        ).success(
+            predict_tags_fl2_sd3, [input_image, input_general, image_algorithms], [input_general], show_api=False,
+        ).success(
+            remove_specific_prompt, [input_general, keep_tags], [input_general], queue=False, show_api=False,
+        ).success(
+            convert_danbooru_to_e621_prompt, [input_general, input_tag_type], [input_general], queue=False, show_api=False,
+        ).success(
+            insert_recom_prompt, [input_general, dummy_np, recom_prompt], [input_general, dummy_np], queue=False, show_api=False,
+        ).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False, show_api=False)
+        copy_input_btn.click(compose_prompt_to_copy, [input_character, input_copyright, input_general], [input_tags_to_copy], show_api=False)\
+            .success(gradio_copy_text, [input_tags_to_copy], js=COPY_ACTION_JS, show_api=False)
+        copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy], show_api=False)\
+            .success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt], show_api=False)
+        
+        generate_btn.click(
+            v2_upsampling_prompt,
+            [model_name, input_copyright, input_character, input_general,
+            input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
+            [output_text],
+            show_api=False,
+        ).success(
+            convert_danbooru_to_e621_prompt, [output_text, tag_type], [output_text_pony], queue=False, show_api=False,
+        ).success(
+            insert_recom_prompt, [output_text, dummy_np, recom_animagine], [output_text, dummy_np], queue=False, show_api=False,
+        ).success(
+            insert_recom_prompt, [output_text_pony, dummy_np, recom_pony], [output_text_pony, dummy_np], queue=False, show_api=False,
+        ).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
+                  None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False, show_api=False)
+        copy_btn.click(gradio_copy_text, [output_text], js=COPY_ACTION_JS, show_api=False)
+        copy_btn_pony.click(gradio_copy_text, [output_text_pony], js=COPY_ACTION_JS, show_api=False)
+        copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt], show_api=False)
+        copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt], show_api=False)
+
+    with gr.Tab("PNG Info"):
+        with gr.Row():
+            with gr.Column():
+                image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
+
+            with gr.Column():
+                result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
+
+                image_metadata.change(
+                    fn=extract_exif_data,
+                    inputs=[image_metadata],
+                    outputs=[result_metadata],
+                )
+
+    with gr.Tab("Upscaler"):
+        with gr.Row():
+            with gr.Column():
+                image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
+                upscaler_tab = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS[9:], value=UPSCALER_KEYS[11])
+                upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
+                generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
+
+            with gr.Column():
+                result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
+
+                generate_button_up_tab.click(
+                    fn=esrgan_upscale,
+                    inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
+                    outputs=[result_up_tab],
+                )
+
+    with gr.Tab("Preprocessor", render=True):
+        preprocessor_tab()
+
+    gr.LoginButton()
+    gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
 
 demo.queue()
-demo.launch()
+demo.launch(show_error=True, debug=True)