Upload 6 files

app.py

@@ -4,11 +4,10 @@ import numpy as np
 
 # DiffuseCraft
 from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers,
-    get_vaes, enable_model_recom_prompt, enable_diffusers_model_detail,
-    get_t2i_model_info, get_all_lora_tupled_list, update_loras,
-    apply_lora_prompt, download_my_lora, search_civitai_lora,
-    select_civitai_lora, search_civitai_lora_json, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
-    preset_quality, preset_styles, process_style_prompt)
+    get_vaes, enable_model_recom_prompt, 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)
+from modutils import get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL
 # Translator
 from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
     get_llm_formats, get_dolphin_model_format, get_dolphin_models,
@@ -41,8 +40,9 @@ css = """
 #col-container { margin: 0 auto; !important; }
 #result { max-width: 520px; max-height: 520px; margin: 0px auto; !important; }
 .lora { min-width: 480px; !important; }
-#model-info { text-align: center; !important; }
-.title{ text-align: center; !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; }
 """
 
 with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60, 3600)) as demo:
@@ -80,7 +80,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                         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_id="model-info")
+                        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)
 
@@ -141,17 +141,20 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                             lora5_md = gr.Markdown(value="", visible=False)
                     with gr.Accordion("From URL", open=True, visible=True):
                         with gr.Row():
-                            lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Pony", "SD 1.5", "SDXL 1.0", "Flux.1 D", "Flux.1 S"], value=["Pony", "SDXL 1.0"])
-                            lora_search_civitai_sort = gr.Radio(label="Sort", choices=["Highest Rated", "Most Downloaded", "Newest"], value="Highest Rated")
-                            lora_search_civitai_period = gr.Radio(label="Period", choices=["AllTime", "Year", "Month", "Week", "Day"], value="AllTime")
+                            lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "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.Textbox(label="Tag", lines=1)
-                            lora_search_civitai_submit = gr.Button("Search on Civitai")
+                            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_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                             lora_search_civitai_json = gr.JSON(value={}, visible=False)
-                            lora_search_civitai_desc = gr.Markdown(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 URL", placeholder="https://civitai.com/api/download/models/28907", lines=1)
                         lora_download = gr.Button("Get and set LoRA and apply to prompt")
 
@@ -254,10 +257,10 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     lora5_copy.click(apply_lora_prompt, [prompt, lora5_info], [prompt], queue=False, show_api=False)
 
     gr.on(
-        triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit, lora_search_civitai_tag.submit],
+        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],
-        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
+        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,
@@ -273,6 +276,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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(

dc.py

@@ -783,7 +783,7 @@ from PIL import Image
 import random, json
 from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path,
     get_local_model_list, get_private_lora_model_lists, get_valid_lora_name,
-    get_valid_lora_path, get_valid_lora_wt, get_lora_info,
+    get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD,
     normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en)
 
 sd_gen = GuiSD()
@@ -893,35 +893,6 @@ def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = "")
     return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list())
 
 
-def get_t2i_model_info(repo_id: str):
-    from huggingface_hub import HfApi
-    api = HfApi()
-    try:
-        if " " in repo_id or not api.repo_exists(repo_id): return ""
-        model = api.model_info(repo_id=repo_id)
-    except Exception as e:
-        print(f"Error: Failed to get {repo_id}'s info. {e}")
-        return ""
-    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:FluxPipeline' in tags:
-        info.append("FLUX.1")
-    elif '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 load_model_prompt_dict():
     import json
     dict = {}
@@ -1209,30 +1180,46 @@ def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora
      gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
 
 
-def search_civitai_lora(query, base_model, sort="Highest Rated", period="AllTime", tag=""):
-    global civitai_lora_last_results
-    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag)
+def search_civitai_lora(query, base_model=[], sort=CIVITAI_SORT[0], period=CIVITAI_PERIOD[0], tag="", user="", gallery=[]):
+    global civitai_last_results, civitai_last_choices, civitai_last_gallery
+    civitai_last_choices = [("", "")]
+    civitai_last_gallery = []
+    civitai_last_results = {}
+    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag, user)
     if not items: return gr.update(choices=[("", "")], value="", visible=False),\
-          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
-    civitai_lora_last_results = {}
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    civitai_last_results = {}
     choices = []
+    gallery = []
     for item in items:
         base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
         name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
         value = item['dl_url']
         choices.append((name, value))
-        civitai_lora_last_results[value] = item
+        gallery.append((item['img_url'], name))
+        civitai_last_results[value] = item
     if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
-          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
-    result = civitai_lora_last_results.get(choices[0][1], "None")
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    civitai_last_choices = choices
+    civitai_last_gallery = gallery
+    result = civitai_last_results.get(choices[0][1], "None")
     md = result['md'] if result else ""
     return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
-          gr.update(visible=True), gr.update(visible=True)
+          gr.update(visible=True), gr.update(visible=True), gr.update(value=gallery)
+
+
+def update_civitai_selection(evt: gr.SelectData):
+    try:
+        selected_index = evt.index
+        selected = civitai_last_choices[selected_index][1]
+        return gr.update(value=selected)
+    except Exception:
+        return gr.update(visible=True)
 
 
 def select_civitai_lora(search_result):
     if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
-    result = civitai_lora_last_results.get(search_result, "None")
+    result = civitai_last_results.get(search_result, "None")
     md = result['md'] if result else ""
     return gr.update(value=search_result), gr.update(value=md, visible=True)
 

llmdolphin.py

@@ -59,11 +59,37 @@ llm_models = {
     "Qwen2.5-14B_Uncensored_Instruct.Q4_K_M.gguf": ["mradermacher/Qwen2.5-14B_Uncensored_Instruct-GGUF", MessagesFormatterType.OPEN_CHAT],
     "EVA-Qwen2.5-14B-v0.0.i1-IQ4_XS.gguf": ["mradermacher/EVA-Qwen2.5-14B-v0.0-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
     "MN-12B-Vespa-x1.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Vespa-x1-i1-GGUF", MessagesFormatterType.CHATML],
+    "Mistral-Nemo-12B-ArliAI-RPMax-v1.1.i1-Q4_K_M.gguf": ["mradermacher/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-i1-GGUF", MessagesFormatterType.MISTRAL],
     "Trinas_Nectar-8B-model_stock.i1-Q4_K_M.gguf": ["mradermacher/Trinas_Nectar-8B-model_stock-i1-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_12B_v2.0.Q5_K_M.gguf": ["mradermacher/ChatWaifu_12B_v2.0-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_22B_v2.0_preview.Q4_K_S.gguf": ["mradermacher/ChatWaifu_22B_v2.0_preview-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.4.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.4-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.3.1.Q4_K_M.gguf": ["mradermacher/ChatWaifu_v1.3.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Aster-G2-9B-v1.Q4_K_S.gguf": ["mradermacher/Aster-G2-9B-v1-GGUF", MessagesFormatterType.ALPACA],
+    "nemo-12b-rp-merge.Q4_K_S.gguf": ["mradermacher/nemo-12b-rp-merge-GGUF", MessagesFormatterType.MISTRAL],
+    "SthenoMix3.3.Q5_K_M.gguf": ["mradermacher/SthenoMix3.3-GGUF", MessagesFormatterType.LLAMA_3],
+    "Celestial-Harmony-14b-v1.0-Experimental-1016-Q4_K_M.gguf": ["bartowski/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF", MessagesFormatterType.MISTRAL],
+    "Gemma-2-Ataraxy-v4c-9B.Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4c-9B-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma-2-Ataraxy-v4b-9B.Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4b-9B-GGUF", MessagesFormatterType.ALPACA],
+    "L3.1-EtherealRainbow-v1.0-rc1-8B.Q5_K_M.gguf": ["mradermacher/L3.1-EtherealRainbow-v1.0-rc1-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "MN-Lulanum-12B-FIX.i1-Q4_K_M.gguf": ["mradermacher/MN-Lulanum-12B-FIX-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Ministral-8B-Instruct-2410-HF-Q4_K_M.gguf": ["bartowski/Ministral-8B-Instruct-2410-HF-GGUF-TEST", MessagesFormatterType.MISTRAL],
+    "QevaCoT-7B-Stock.Q5_K_M.gguf": ["mradermacher/QevaCoT-7B-Stock-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Mixtronix-8B.i1-Q4_K_M.gguf": ["mradermacher/Mixtronix-8B-i1-GGUF", MessagesFormatterType.CHATML],
+    "Tsunami-0.5x-7B-Instruct.i1-Q5_K_M.gguf": ["mradermacher/Tsunami-0.5x-7B-Instruct-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "mt3-gemma-2-9b-q6_k.gguf": ["zelk12/MT3-gemma-2-9B-Q6_K-GGUF", MessagesFormatterType.ALPACA],
+    "NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated.Q5_K_M.gguf": ["mradermacher/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated-GGUF", MessagesFormatterType.LLAMA_3],
+    "MadMix-Unleashed-12B.Q4_K_M.gguf": ["mradermacher/MadMix-Unleashed-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Gemma-2-Ataraxy-v4a-Advanced-9B.i1-Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4a-Advanced-9B-i1-GGUF", MessagesFormatterType.ALPACA],
+    "writing-roleplay-20k-context-nemo-12b-v1.0.i1-Q4_K_M.gguf": ["mradermacher/writing-roleplay-20k-context-nemo-12b-v1.0-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "GEMMA2-9b-Pollux-exp.Q4_K_M.gguf": ["mradermacher/GEMMA2-9b-Pollux-exp-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma-2-Ataraxy-v4a-Advanced-9B.Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4a-Advanced-9B-GGUF", MessagesFormatterType.ALPACA],
+    "llama-3.1-8b-titanfusion-mix-2.1-q4_k_m-imat.gguf": ["bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "Gemma-2-Ataraxy-v4-Advanced-9B.Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4-Advanced-9B-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma-2-9B-ArliAI-RPMax-v1.1.i1-Q4_K_S.gguf": ["mradermacher/Gemma-2-9B-ArliAI-RPMax-v1.1-i1-GGUF", MessagesFormatterType.ALPACA],
+    "SuperNeuralDreadDevil-8b.Q5_K_M.gguf": ["mradermacher/SuperNeuralDreadDevil-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "astral-fusion-neural-happy-l3.1-8b-q4_0.gguf": ["ZeroXClem/Astral-Fusion-Neural-Happy-L3.1-8B-Q4_0-GGUF", MessagesFormatterType.LLAMA_3],
+    "LexiMaid-L3-8B.Q5_K_M.gguf": ["mradermacher/LexiMaid-L3-8B-GGUF", MessagesFormatterType.LLAMA_3],
     "ModeliCo-8B.i1-Q5_K_M.gguf": ["mradermacher/ModeliCo-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "Llama3-8B-function-calling-dpo-slerp.i1-Q5_K_M.gguf": ["mradermacher/Llama3-8B-function-calling-dpo-slerp-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "Aspire1.2-8B-TIES.i1-Q5_K_M.gguf": ["mradermacher/Aspire1.2-8B-TIES-i1-GGUF", MessagesFormatterType.LLAMA_3],

lora_dict.json

@@ -4381,6 +4381,13 @@
     "https://civitai.com/models/577378",
     "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/459bd20d-a9d6-4a0b-8947-7dcebc061c0f/width=450/19781986.jpeg"
   ],
+  "genbaneko_v4_illustrious_uo_1024-000040": [
+    "genbaneko / cat, headwear, hat, grey headwear, baseball cap, / speech bubble, speech text,",
+    "SDXL 1.0",
+    "Shigotoneko(Genbaneko) Style - illustrious | \u4ed5\u4e8b\u732b\uff08\u73fe\u5834\u732b\uff09",
+    "https://civitai.com/models/859355",
+    "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0f145509-d867-418c-b545-0c0e49275f48/width=450/34849585.jpeg"
+  ],
   "genshin_v4": [
     "hina_(genshin_impact) / sethos_(genshin_impact) / raiden_shogun_mitake",
     "Pony",

modutils.py

@@ -2,11 +2,16 @@ import spaces
 import json
 import gradio as gr
 import os
+import re
 from pathlib import Path
 from PIL import Image
-from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
+import shutil
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util import Retry
 import urllib.parse
-import re
+import pandas as pd
+from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
 
 
 from env import (HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
@@ -38,7 +43,6 @@ def list_sub(a, b):
 
 
 def is_repo_name(s):
-    import re
     return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
 
 
@@ -99,10 +103,12 @@ def download_hf_file(directory, url, progress=gr.Progress(track_tqdm=True)):
     repo_id, filename, subfolder, repo_type = split_hf_url(url)
     try:
         print(f"Downloading {url} to {directory}")
-        if subfolder is not None: hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder, repo_type=repo_type, local_dir=directory, token=hf_token)
-        else: hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type, local_dir=directory, token=hf_token)
+        if subfolder is not None: path = hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder, repo_type=repo_type, local_dir=directory, token=hf_token)
+        else: path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type, local_dir=directory, token=hf_token)
+        return path
     except Exception as e:
         print(f"Failed to download: {e}")
+        return None
 
 
 def download_things(directory, url, hf_token="", civitai_api_key=""):
@@ -224,7 +230,6 @@ def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
 
 
 def download_private_repo(repo_id, dir_path, is_replace):
-    from huggingface_hub import snapshot_download
     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)
@@ -263,7 +268,6 @@ def get_private_model_list(repo_id, dir_path):
 
 
 def download_private_file(repo_id, path, is_replace):
-    from huggingface_hub import hf_hub_download
     file = Path(path)
     newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
     if not hf_read_token or newpath.exists(): return
@@ -387,7 +391,9 @@ except Exception as e:
 loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
 civitai_not_exists_list = []
 loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
-civitai_lora_last_results = {}  # {"URL to download": {search results}, ...}
+civitai_last_results = {}  # {"URL to download": {search results}, ...}
+civitai_last_choices = [("", "")]
+civitai_last_gallery = []
 all_lora_list = []
 
 
@@ -411,9 +417,6 @@ private_lora_model_list = get_private_lora_model_lists()
 
 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 ["", "", "", "", ""]
     if not Path(path).exists(): return None
     user_agent = get_user_agent()
@@ -448,7 +451,7 @@ def get_civitai_info(path):
 
 
 def get_lora_model_list():
-    loras = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras) + DIFFUSERS_FORMAT_LORAS)
+    loras = list_uniq(get_private_lora_model_lists() + DIFFUSERS_FORMAT_LORAS + get_local_model_list(directory_loras))
     loras.insert(0, "None")
     loras.insert(0, "")
     return loras
@@ -523,7 +526,6 @@ def download_lora(dl_urls: str):
 
 
 def copy_lora(path: str, new_path: str):
-    import shutil
     if path == new_path: return new_path
     cpath = Path(path)
     npath = Path(new_path)
@@ -587,7 +589,6 @@ def get_valid_lora_path(query: str):
 
 
 def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
-    import re
     wt = lora_wt
     result = re.findall(f'<lora:{to_lora_key(lora_path)}:(.+?)>', prompt)
     if not result: return wt
@@ -596,7 +597,6 @@ def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
 
 
 def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
-    import re
     if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
     lora1 = get_valid_lora_name(lora1, model_name)
     lora2 = get_valid_lora_name(lora2, model_name)
@@ -716,7 +716,6 @@ def apply_lora_prompt(prompt: str = "", lora_info: str = ""):
 
 
 def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
-    import re
     on1, label1, tag1, md1 = get_lora_info(lora1)
     on2, label2, tag2, md2 = get_lora_info(lora2)
     on3, label3, tag3, md3 = get_lora_info(lora3)
@@ -763,7 +762,6 @@ def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3,
 
 
 def get_my_lora(link_url):
-    from pathlib import Path
     before = get_local_model_list(directory_loras)
     for url in [url.strip() for url in link_url.split(',')]:
         if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
@@ -800,7 +798,6 @@ def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
 
 
 def move_file_lora(filepaths):
-    import shutil
     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}')
@@ -823,11 +820,13 @@ def move_file_lora(filepaths):
     )
 
 
+CIVITAI_SORT = ["Highest Rated", "Most Downloaded", "Newest"]
+CIVITAI_PERIOD = ["AllTime", "Year", "Month", "Week", "Day"]
+CIVITAI_BASEMODEL = ["Pony", "SD 1.5", "SDXL 1.0", "Flux.1 D", "Flux.1 S"]
+
+
 def get_civitai_info(path):
     global civitai_not_exists_list, loras_url_to_path_dict
-    import requests
-    from requests.adapters import HTTPAdapter
-    from urllib3.util import Retry
     default = ["", "", "", "", ""]
     if path in set(civitai_not_exists_list): return default
     if not Path(path).exists(): return None
@@ -865,16 +864,14 @@ def get_civitai_info(path):
 
 
 def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1.0"], limit: int = 100,
-                           sort: str = "Highest Rated", period: str = "AllTime", tag: str = ""):
-    import requests
-    from requests.adapters import HTTPAdapter
-    from urllib3.util import Retry
+                           sort: str = "Highest Rated", period: str = "AllTime", tag: str = "", user: str = "", page: int = 1):
     user_agent = get_user_agent()
     headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
     base_url = 'https://civitai.com/api/v1/models'
-    params = {'types': ['LORA'], 'sort': sort, 'period': period, 'limit': limit, 'nsfw': 'true'}
+    params = {'types': ['LORA'], 'sort': sort, 'period': period, 'limit': limit, 'page': int(page), 'nsfw': 'true'}
     if query: params["query"] = query
     if tag: params["tag"] = tag
+    if user: params["username"] = user
     session = requests.Session()
     retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
     session.mount("https://", HTTPAdapter(max_retries=retries))
@@ -891,46 +888,129 @@ def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1
         for j in json['items']:
             for model in j['modelVersions']:
                 item = {}
-                if model['baseModel'] not in set(allow_model): continue
+                if len(allow_model) != 0 and model['baseModel'] not 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['creator'] = j['creator']['username'] if 'creator' in j.keys() and 'username' in j['creator'].keys() else ""
+                item['tags'] = j['tags'] if 'tags' in j.keys() else []
+                item['model_name'] = model['name'] if 'name' in model.keys() else ""
+                item['base_model'] = model['baseModel'] if 'baseModel' in model.keys() else ""
+                item['description'] = model['description'] if 'description' in model.keys() else ""
                 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"]})'
+                item['md'] = ""
+                if 'images' in model.keys() and len(model["images"]) != 0:
+                    item['img_url'] = model["images"][0]["url"]
+                    item['md'] += f'<img src="{model["images"][0]["url"]}#float" alt="thumbnail" width="150" height="240"><br>'
+                else: item['img_url'] = "/home/user/app/null.png"
+                item['md'] += f'''Model URL: [https://civitai.com/models/{j["id"]}](https://civitai.com/models/{j["id"]})<br>Model Name: {item["name"]}<br>
+                    Creator: {item["creator"]}<br>Tags: {", ".join(item["tags"])}<br>Base Model: {item["base_model"]}<br>Description: {item["description"]}'''
                 items.append(item)
         return items
 
 
-def search_civitai_lora(query, base_model, sort="Highest Rated", period="AllTime", tag=""):
-    global civitai_lora_last_results
-    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag)
+def search_civitai_lora(query, base_model=[], sort=CIVITAI_SORT[0], period=CIVITAI_PERIOD[0], tag="", user="", gallery=[]):
+    global civitai_last_results, civitai_last_choices, civitai_last_gallery
+    civitai_last_choices = [("", "")]
+    civitai_last_gallery = []
+    civitai_last_results = {}
+    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag, user)
     if not items: return gr.update(choices=[("", "")], value="", visible=False),\
-          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
-    civitai_lora_last_results = {}
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    civitai_last_results = {}
     choices = []
+    gallery = []
     for item in items:
         base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
         name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
         value = item['dl_url']
         choices.append((name, value))
-        civitai_lora_last_results[value] = item
+        gallery.append((item['img_url'], name))
+        civitai_last_results[value] = item
     if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
-          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
-    result = civitai_lora_last_results.get(choices[0][1], "None")
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    civitai_last_choices = choices
+    civitai_last_gallery = gallery
+    result = civitai_last_results.get(choices[0][1], "None")
     md = result['md'] if result else ""
     return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
-          gr.update(visible=True), gr.update(visible=True)
+          gr.update(visible=True), gr.update(visible=True), gr.update(value=gallery)
+
+
+def update_civitai_selection(evt: gr.SelectData):
+    try:
+        selected_index = evt.index
+        selected = civitai_last_choices[selected_index][1]
+        return gr.update(value=selected)
+    except Exception:
+        return gr.update(visible=True)
 
 
 def select_civitai_lora(search_result):
     if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
-    result = civitai_lora_last_results.get(search_result, "None")
+    result = civitai_last_results.get(search_result, "None")
     md = result['md'] if result else ""
     return gr.update(value=search_result), gr.update(value=md, visible=True)
 
 
+def download_my_lora_flux(dl_urls: str, lora):
+    path = download_lora(dl_urls)
+    if path: lora = path
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=lora, choices=choices)
+
+
+def apply_lora_prompt_flux(lora_info: str):
+    if lora_info == "None": return ""
+    lora_tag = lora_info.replace("/",",")
+    lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
+    lora_prompts = normalize_prompt_list(lora_tags)
+    prompt = ", ".join(list_uniq(lora_prompts))
+    return prompt
+
+
+def update_loras_flux(prompt, lora, lora_wt):
+    on, label, tag, md = get_lora_info(lora)
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=prompt), gr.update(value=lora, choices=choices), gr.update(value=lora_wt),\
+     gr.update(value=tag, label=label, visible=on), gr.update(value=md, visible=on)
+
+
+def search_civitai_lora_json(query, base_model):
+    results = {}
+    items = search_lora_on_civitai(query, base_model)
+    if not items: return gr.update(value=results)
+    for item in items:
+        results[item['dl_url']] = item
+    return gr.update(value=results)
+
+
+def get_civitai_tag():
+    default = [""]
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/tags'
+    params = {'limit': 200}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    url = base_url
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+        if not r.ok: return default
+        j = dict(r.json()).copy()
+        if "items" not in j.keys(): return default
+        items = []
+        for item in j["items"]:
+            items.append([str(item.get("name", "")), int(item.get("modelCount", 0))])
+        df = pd.DataFrame(items)
+        df.sort_values(1, ascending=False)
+        tags = df.values.tolist()
+        tags = [""] + [l[0] for l in tags]
+        return tags
+    except Exception as e:
+        print(e)
+        return default
+
+
 LORA_BASE_MODEL_DICT = {
     "diffusers:StableDiffusionPipeline": ["SD 1.5"],
     "diffusers:StableDiffusionXLPipeline": ["Pony", "SDXL 1.0"],
@@ -1175,15 +1255,6 @@ preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in qualit
 
 
 def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None", type: str = "Auto"):
-    def to_list(s):
-        return [x.strip() for x in s.split(",") if not s == ""]
-    
-    def list_sub(a, b):
-        return [e for e in a if e not in b]
-    
-    def list_uniq(l):
-        return sorted(set(l), key=l.index)
-
     animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
     animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
     pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
@@ -1335,7 +1406,6 @@ def set_textual_inversion_prompt(textual_inversion_gui, prompt_gui, neg_prompt_g
 
 
 def get_model_pipeline(repo_id: str):
-    from huggingface_hub import HfApi
     api = HfApi(token=HF_TOKEN)
     default = "StableDiffusionPipeline"
     try: