Upload 18 files

app.py

@@ -4,7 +4,7 @@ import numpy as np
 
 # DiffuseCraft
 from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers, save_image_history,
-    get_vaes, enable_model_recom_prompt, enable_diffusers_model_detail, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
+    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,
@@ -48,6 +48,7 @@ css = """
 
 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():
@@ -203,7 +204,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type],
+                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -216,21 +217,21 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type],
+                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type, recom_prompt],
         outputs=[result],
         queue=False,
         show_api=True,
         api_name="infer_translate",
     ).success(
         fn=dolphin_respond_auto,
-        inputs=[prompt, chatbot],
+        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],
+        inputs=[prompt, chatbot, state],
         outputs=[prompt],
         queue=False,
         show_api=False,
@@ -239,7 +240,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type],
+                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -289,7 +290,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     )
     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)
+    #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,
@@ -300,12 +301,12 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         show_api=False,
     )
 
-    model_detail.change(enable_diffusers_model_detail, [model_detail, model_name], [model_detail, model_name], queue=False, 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], [chat_model, chat_format, chat_model_info], queue=True, show_progress="full", 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], [chat_format], 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

dc.py

@@ -351,7 +351,6 @@ class GuiSD:
 
 ## BEGIN MOD
         loras_list = [s if s else "None" for s in loras_list]
-        prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
         global lora_model_list
         lora_model_list = get_lora_model_list()
 ## END MOD
@@ -696,7 +695,7 @@ import random
 import json
 import shutil
 from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path,
-    get_local_model_list, get_private_lora_model_lists, get_valid_lora_name,
+    get_local_model_list, get_private_lora_model_lists, get_valid_lora_name, get_state, set_state,
     get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
     normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history)
 
@@ -706,7 +705,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
            lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
            sampler = "Euler", vae = None, translate=True, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-           progress=gr.Progress(track_tqdm=True)):
+           recom_prompt = True, progress=gr.Progress(track_tqdm=True)):
     MAX_SEED = np.iinfo(np.int32).max
 
     image_previews = True
@@ -727,7 +726,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         prompt = translate_to_en(prompt)
         negative_prompt = translate_to_en(prompt)
 
-    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
+    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name, recom_prompt)
     progress(0.5, desc="Preparing...")
     lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
         set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
@@ -761,70 +760,14 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 
     return output_image
 
-#@spaces.GPU
-def __infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
-           model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
-           lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
-           sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)):
-    MAX_SEED = np.iinfo(np.int32).max
-
-    load_lora_cpu = False
-    verbose_info = False
-    gpu_duration = 59
-
-    images: list[tuple[PIL.Image.Image, str | None]] = []
-    info_state = info_images = ""
-    progress(0, desc="Preparing...")
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed).seed()
-
-    if translate:
-        prompt = translate_to_en(prompt)
-        negative_prompt = translate_to_en(prompt)
-
-    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
-    progress(0.5, desc="Preparing...")
-    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
-        set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
-    lora1 = get_valid_lora_path(lora1)
-    lora2 = get_valid_lora_path(lora2)
-    lora3 = get_valid_lora_path(lora3)
-    lora4 = get_valid_lora_path(lora4)
-    lora5 = get_valid_lora_path(lora5)
-    progress(1, desc="Preparation completed. Starting inference...")
-
-    progress(0, desc="Loading model...")
-    sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
-    progress(1, desc="Model loaded.")
-    progress(0, desc="Starting Inference...")
-    info_state, images, info_images = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
-        guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
-        lora4, lora4_wt, lora5, lora5_wt, sampler,
-        height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,
-        None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None,
-        1.0, 100, 10, 30, 0.55, "Use same sampler", "", "",
-        False, True, 1, True, False, False, False, False, "./images", False, False, False, True, 1, 0.55,
-        False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32,
-        False, "", "", 0.35, True, True, False, 4, 4, 32,
-        True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7, 0.0,
-        load_lora_cpu, verbose_info, gpu_duration
-    )
-    progress(1, desc="Inference completed.")
-    output_image = images[0][0] if images else None
-
-    return output_image
-
 
 #@spaces.GPU
 def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
             model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
             lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
             sampler = "Euler", vae = None, translate = True, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-            progress=gr.Progress(track_tqdm=True)):
-    return gr.update(visible=True)
+            recom_prompt = True, progress=gr.Progress(track_tqdm=True)):
+    return gr.update()
 
 
 infer.zerogpu = True
@@ -843,17 +786,16 @@ def get_vaes():
     return vae_model_list
 
 
-show_diffusers_model_list_detail = False
 cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model)
-def get_diffusers_model_list():
+def get_diffusers_model_list(state: dict = {}):
+    show_diffusers_model_list_detail = get_state(state, "show_diffusers_model_list_detail")
     if show_diffusers_model_list_detail:
         return cached_diffusers_model_tupled_list
     else:
         return load_diffusers_format_model
 
 
-def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = ""):
-    global show_diffusers_model_list_detail
+def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = "", state: dict = {}):
     show_diffusers_model_list_detail = is_enable
     new_value = model_name
     index = 0
@@ -863,7 +805,8 @@ def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = "")
         new_value = cached_diffusers_model_tupled_list[index][1]
     else:
         new_value = load_diffusers_format_model[index]
-    return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list())
+    set_state(state, "show_diffusers_model_list_detail", show_diffusers_model_list_detail)
+    return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list(state)), state
 
 
 def load_model_prompt_dict():
@@ -879,7 +822,6 @@ def load_model_prompt_dict():
 model_prompt_dict = load_model_prompt_dict()
 
 
-model_recom_prompt_enabled = True
 animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
 animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
 pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
@@ -888,7 +830,7 @@ other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, c
 other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly")
 default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
 default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
-def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
+def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None", model_recom_prompt_enabled = True):
     if not model_recom_prompt_enabled or not model_name: return prompt, neg_prompt
     prompts = to_list(prompt)
     neg_prompts = to_list(neg_prompt)
@@ -911,12 +853,6 @@ def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name
     return prompt, neg_prompt
 
 
-def enable_model_recom_prompt(is_enable: bool = True):
-    global model_recom_prompt_enabled
-    model_recom_prompt_enabled = is_enable
-    return is_enable
-
-
 private_lora_dict = {}
 try:
     with open('lora_dict.json', encoding='utf-8') as f:

llmdolphin.py

@@ -3,6 +3,7 @@ import gradio as gr
 from pathlib import Path
 import re
 import torch
+from typing import Any
 from huggingface_hub import hf_hub_download, HfApi
 from llama_cpp import Llama
 from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
@@ -75,6 +76,27 @@ llm_models = {
     "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],
+    "dolphin-2.6-mistral-7b-dpo-laser.Q4_K_S.gguf": ["mradermacher/dolphin-2.6-mistral-7b-dpo-laser-GGUF", MessagesFormatterType.MISTRAL],
+    "Flowable-Docs-Llama-3.1-8B.Q5_K_M.gguf": ["mradermacher/Flowable-Docs-Llama-3.1-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "slimorca-gemma2-9b-fft.Q4_K_M.gguf": ["mradermacher/slimorca-gemma2-9b-fft-GGUF", MessagesFormatterType.ALPACA],
+    "TQ2.5-14B-Sugarquill-v1-Q4_K_M.gguf": ["bartowski/TQ2.5-14B-Sugarquill-v1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "magnum-v3-9b-customgemma2.i1-Q4_K_M.gguf": ["mradermacher/magnum-v3-9b-customgemma2-i1-GGUF", MessagesFormatterType.ALPACA],
+    "Captain_BMO-12B.Q4_K_M.gguf": ["mradermacher/Captain_BMO-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "LemonP-8B-Model_Stock.i1-Q5_K_M.gguf": ["mradermacher/LemonP-8B-Model_Stock-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Ice0.31-08.11-RP.i1-Q5_K_M.gguf": ["mradermacher/Ice0.31-08.11-RP-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "EVA-Qwen2.5-14B-v0.2.i1-Q4_K_M.gguf": ["mradermacher/EVA-Qwen2.5-14B-v0.2-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "L3.1-Dark-Planet-SpinFire-Uncensored-8B-D_AU-Q4_k_m.gguf": ["DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "EndlessRP-v2-7B.Q5_K_M.gguf": ["mradermacher/EndlessRP-v2-7B-GGUF", MessagesFormatterType.MISTRAL],
+    "badger-lambda-0-llama-3-8b.i1-Q5_K_M.gguf": ["mradermacher/badger-lambda-0-llama-3-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3.1-Artemis-e2-8B.i1-Q5_K_M.gguf": ["mradermacher/L3.1-Artemis-e2-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "MN-12B-Inferor-v0.0.Q4_K_M.gguf": ["mradermacher/MN-12B-Inferor-v0.0-GGUF", MessagesFormatterType.MISTRAL],
+    "Eclipse-13B-dpo.i1-Q4_K_M.gguf": ["mradermacher/Eclipse-13B-dpo-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Epic_Fiction-8b-v0.1.Q5_K_M.gguf": ["mradermacher/Epic_Fiction-8b-v0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama-3-8B-StoryGenerator.i1-Q5_K_M.gguf": ["mradermacher/Llama-3-8B-StoryGenerator-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "badger-mu-llama-3-8b.i1-Q5_K_M.gguf": ["mradermacher/badger-mu-llama-3-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "badger-writer-llama-3-8b.i1-Q5_K_M.gguf": ["mradermacher/badger-writer-llama-3-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen-Rui-SE.Q5_K_M.gguf": ["mradermacher/Qwen-Rui-SE-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Valor-7B-v0.1.i1-Q4_K_M.gguf": ["mradermacher/Valor-7B-v0.1-i1-GGUF", MessagesFormatterType.MISTRAL],
     "Fraken-Maid-TW-Slerp.i1-Q5_K_M.gguf": ["mradermacher/Fraken-Maid-TW-Slerp-i1-GGUF", MessagesFormatterType.MISTRAL],
     "KunoichiLake-2x7b.Q4_K_M.gguf": ["mradermacher/KunoichiLake-2x7b-GGUF", MessagesFormatterType.MISTRAL],
     "Llama-3.1-SuperNova-8B-Lite_TIES_with_Base-Q4_K_M.gguf": ["bartowski/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base-GGUF", MessagesFormatterType.LLAMA_3],
@@ -908,25 +930,44 @@ llm_formats = {
 llm_languages = ["English", "Japanese", "Chinese", "Korean", "Spanish", "Portuguese", "German", "French", "Finnish", "Russian"]
 llm_models_tupled_list = []
 default_llm_model_filename = list(llm_models.keys())[0]
-override_llm_format = None
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 
-def to_list(s):
+def to_list(s: str):
     return [x.strip() for x in s.split(",") if not s == ""]
 
 
-def list_uniq(l):
+def list_uniq(l: list):
     return sorted(set(l), key=l.index)
 
 
+DEFAULT_STATE = {
+    "dolphin_sysprompt_mode": "Default",
+    "dolphin_output_language": llm_languages[0],
+}
+
+
+def get_state(state: dict, key: str):
+    if key in state.keys(): return state[key]
+    elif key in DEFAULT_STATE.keys():
+        print(f"State '{key}' not found. Use dedault value.")
+        return DEFAULT_STATE[key]
+    else:
+        print(f"State '{key}' not found.")
+        return None
+
+
+def set_state(state: dict, key: str, value: Any):
+    state[key] = value
+
+
 @wrapt_timeout_decorator.timeout(dec_timeout=3.5)
-def to_list_ja(s):
+def to_list_ja(s: str):
     s = re.sub(r'[、。]', ',', s)
     return [x.strip() for x in s.split(",") if not s == ""]
 
 
-def is_japanese(s):
+def is_japanese(s: str):
     import unicodedata
     for ch in s:
         name = unicodedata.name(ch, "") 
@@ -964,7 +1005,7 @@ def download_llm_models():
         llm_models_tupled_list.append((name, value))
 
 
-def download_llm_model(filename):
+def download_llm_model(filename: str):
     if not filename in llm_models.keys(): return default_llm_model_filename
     try:
         hf_hub_download(repo_id = llm_models[filename][0], filename = filename, local_dir = llm_models_dir)
@@ -975,7 +1016,7 @@ def download_llm_model(filename):
     return filename
 
 
-def get_dolphin_model_info(filename):
+def get_dolphin_model_info(filename: str):
     md = "None"
     items = llm_models.get(filename, None)
     if items:
@@ -983,20 +1024,18 @@ def get_dolphin_model_info(filename):
     return md
 
 
-def select_dolphin_model(filename, progress=gr.Progress(track_tqdm=True)):
-    global override_llm_format
-    override_llm_format = None
+def select_dolphin_model(filename: str, state: dict, progress=gr.Progress(track_tqdm=True)):
+    set_state(state, "override_llm_format", None)
     progress(0, desc="Loading model...")
     value = download_llm_model(filename)
     progress(1, desc="Model loaded.")
     md = get_dolphin_model_info(filename)
-    return gr.update(value=value, choices=get_dolphin_models()), gr.update(value=get_dolphin_model_format(value)), gr.update(value=md)
+    return gr.update(value=value, choices=get_dolphin_models()), gr.update(value=get_dolphin_model_format(value)), gr.update(value=md), state
 
 
-def select_dolphin_format(format_name):
-    global override_llm_format
-    override_llm_format = llm_formats[format_name]
-    return gr.update(value=format_name)
+def select_dolphin_format(format_name: str, state: dict):
+    set_state(state, "override_llm_format", llm_formats[format_name])
+    return gr.update(value=format_name), state
 
 
 download_llm_model(default_llm_model_filename)
@@ -1017,14 +1056,14 @@ def get_key_from_value(d, val):
     return None
 
 
-def get_dolphin_model_format(filename):
+def get_dolphin_model_format(filename: str):
     if not filename in llm_models.keys(): filename = default_llm_model_filename
     format = llm_models[filename][1]
     format_name = get_key_from_value(llm_formats, format)
     return format_name
 
 
-def add_dolphin_models(query, format_name):
+def add_dolphin_models(query: str, format_name: str):
     global llm_models
     api = HfApi()
     add_models = {}
@@ -1055,8 +1094,6 @@ def add_dolphin_models(query, format_name):
     return gr.update(choices=choices, value=choices[-1][1])
 
 
-dolphin_output_language = "English"
-dolphin_sysprompt_mode = "Default"
 dolphin_system_prompt = {"Default": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
 The message must have the following [Tags] generated in strict accordance with the following [Rules]:
 ```
@@ -1247,8 +1284,11 @@ Output should be enclosed in //GENBEGIN//:// and //://GENEND//. The text to be g
 "Chat with LLM": r"You are a helpful AI assistant. Respond in <LANGUAGE>."}
 
 
-def get_dolphin_sysprompt():
-    prompt = re.sub('<LANGUAGE>', dolphin_output_language, dolphin_system_prompt.get(dolphin_sysprompt_mode, ""))
+def get_dolphin_sysprompt(state: dict={}):
+    dolphin_sysprompt_mode = get_state(state, "dolphin_sysprompt_mode")
+    dolphin_output_language = get_state(state, "dolphin_output_language")
+    prompt = re.sub('<LANGUAGE>', dolphin_output_language if dolphin_output_language else llm_languages[0],
+                    dolphin_system_prompt.get(dolphin_sysprompt_mode, dolphin_system_prompt[list(dolphin_system_prompt.keys())[0]]))
     return prompt
 
 
@@ -1256,23 +1296,21 @@ def get_dolphin_sysprompt_mode():
     return list(dolphin_system_prompt.keys())
 
 
-def select_dolphin_sysprompt(key: str):
-    global dolphin_sysprompt_mode
-    if not key in dolphin_system_prompt.keys():
-        dolphin_sysprompt_mode = "Default"
-    else:
-        dolphin_sysprompt_mode = key
-    return gr.update(value=get_dolphin_sysprompt())
+def select_dolphin_sysprompt(key: str, state: dict):
+    dolphin_sysprompt_mode = get_state(state, "dolphin_sysprompt_mode")
+    if not key in dolphin_system_prompt.keys(): dolphin_sysprompt_mode = "Default"
+    else: dolphin_sysprompt_mode = key
+    set_state(state, "dolphin_sysprompt_mode", dolphin_sysprompt_mode)
+    return gr.update(value=get_dolphin_sysprompt(state)), state
 
 
 def get_dolphin_languages():
     return llm_languages
 
 
-def select_dolphin_language(lang: str):
-    global dolphin_output_language
-    dolphin_output_language = lang
-    return gr.update(value=get_dolphin_sysprompt())
+def select_dolphin_language(lang: str, state: dict):
+    set_state(state, "dolphin_output_language", lang)
+    return gr.update(value=get_dolphin_sysprompt(state)), state
 
 
 @wrapt_timeout_decorator.timeout(dec_timeout=5.0)
@@ -1293,15 +1331,14 @@ def dolphin_respond(
     top_p: float = 0.95,
     top_k: int = 40,
     repeat_penalty: float = 1.1,
+    state: dict = {},
     progress=gr.Progress(track_tqdm=True),
 ):
     try:
         progress(0, desc="Processing...")
-
-        if override_llm_format:
-            chat_template = override_llm_format
-        else:
-            chat_template = llm_models[model][1]
+        override_llm_format = get_state(state, "override_llm_format")
+        if override_llm_format: chat_template = override_llm_format
+        else: chat_template = llm_models[model][1]
 
         llm = Llama(
             model_path=str(Path(f"{llm_models_dir}/{model}")),
@@ -1363,8 +1400,10 @@ def dolphin_respond(
 
 def dolphin_parse(
     history: list[tuple[str, str]],
+    state: dict,
 ):
     try:
+        dolphin_sysprompt_mode = get_state(state, "dolphin_sysprompt_mode")
         if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1:
             return "", gr.update(), gr.update()
         msg = history[-1][0]
@@ -1392,16 +1431,16 @@ def dolphin_respond_auto(
     top_p: float = 0.95,
     top_k: int = 40,
     repeat_penalty: float = 1.1,
+    state: dict = {},
     progress=gr.Progress(track_tqdm=True),
 ):
     try:
         #if not is_japanese(message): return [(None, None)]
         progress(0, desc="Processing...")
 
-        if override_llm_format:
-            chat_template = override_llm_format
-        else:
-            chat_template = llm_models[model][1]
+        override_llm_format = get_state(state, "override_llm_format")
+        if override_llm_format: chat_template = override_llm_format
+        else: chat_template = llm_models[model][1]
 
         llm = Llama(
             model_path=str(Path(f"{llm_models_dir}/{model}")),
@@ -1465,9 +1504,11 @@ def dolphin_respond_auto(
 def dolphin_parse_simple(
     message: str,
     history: list[tuple[str, str]],
+    state: dict,
 ):
     try:
         #if not is_japanese(message): return message
+        dolphin_sysprompt_mode = get_state(state, "dolphin_sysprompt_mode")
         if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1: return message
         msg = history[-1][0]
         raw_prompt = get_raw_prompt(msg)
@@ -1499,12 +1540,12 @@ def respond_playground(
     top_p,
     top_k,
     repeat_penalty,
+    state,
 ):
     try:
-        if override_llm_format:
-            chat_template = override_llm_format
-        else:
-            chat_template = llm_models[model][1]
+        override_llm_format = get_state(state, "override_llm_format")
+        if override_llm_format: chat_template = override_llm_format
+        else: chat_template = llm_models[model][1]
 
         llm = Llama(
             model_path=str(Path(f"{llm_models_dir}/{model}")),

modutils.py

@@ -12,6 +12,7 @@ from requests.adapters import HTTPAdapter
 from urllib3.util import Retry
 import urllib.parse
 import pandas as pd
+from typing import Any
 from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
 from translatepy import Translator
 from unidecode import unidecode
@@ -52,6 +53,25 @@ def is_repo_name(s):
     return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
 
 
+DEFAULT_STATE = {
+    "show_diffusers_model_list_detail": False,
+}
+
+
+def get_state(state: dict, key: str):
+    if key in state.keys(): return state[key]
+    elif key in DEFAULT_STATE.keys():
+        print(f"State '{key}' not found. Use dedault value.")
+        return DEFAULT_STATE[key]
+    else:
+        print(f"State '{key}' not found.")
+        return None
+
+
+def set_state(state: dict, key: str, value: Any):
+    state[key] = value
+
+
 translator = Translator()
 def translate_to_en(input: str):
     try:
@@ -753,10 +773,10 @@ def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2,
             key = result[0][0]
             wt = result[0][1]
             path = to_lora_path(key)
-            if not key in loras_dict.keys() or not path:
+            if not key in loras_dict.keys() or not Path(path).exists():
                 path = get_valid_lora_name(path)
                 if not path or path == "None": continue
-            if path in lora_paths:
+            if path in lora_paths or key in lora_paths:
                 continue
             elif not on1:
                 lora1 = path
@@ -777,7 +797,7 @@ def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2,
                 lora4 = path
                 lora_paths = [lora1, lora2, lora3, lora4, lora5]
                 lora4_wt = safe_float(wt)
-                on4, label4, tag4, md4 = get_lora_info(lora4)
+                on4 = True
             elif not on5:
                 lora5 = path
                 lora_paths = [lora1, lora2, lora3, lora4, lora5]

tagger/tagger.py

@@ -285,9 +285,6 @@ def convert_tags_to_ja(input_prompt: str = ""):
     return ", ".join(out_tags)
 
 
-enable_auto_recom_prompt = True
-
-
 animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
 animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
 pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
@@ -297,7 +294,6 @@ other_nps = to_list("photo, deformed, black and white, realism, disfigured, low
 default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
 default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
 def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
-    global enable_auto_recom_prompt
     prompts = to_list(prompt)
     neg_prompts = to_list(neg_prompt)
 
@@ -307,16 +303,12 @@ def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "Non
     last_empty_p = [""] if not prompts and type != "None" else []
     last_empty_np = [""] if not neg_prompts and type != "None" else []
 
-    if type == "Auto":
-        enable_auto_recom_prompt = True
-    else:
-        enable_auto_recom_prompt = False
-        if type == "Animagine":
-            prompts = prompts + animagine_ps
-            neg_prompts = neg_prompts + animagine_nps
-        elif type == "Pony":
-            prompts = prompts + pony_ps
-            neg_prompts = neg_prompts + pony_nps
+    if type == "Animagine":
+        prompts = prompts + animagine_ps
+        neg_prompts = neg_prompts + animagine_nps
+    elif type == "Pony":
+        prompts = prompts + pony_ps
+        neg_prompts = neg_prompts + pony_nps
 
     prompt = ", ".join(list_uniq(prompts) + last_empty_p)
     neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
@@ -329,7 +321,7 @@ def load_model_prompt_dict():
     dict = {}
     path = 'model_dict.json' if Path('model_dict.json').exists() else './tagger/model_dict.json'
     try:
-        with open('model_dict.json', encoding='utf-8') as f:
+        with open(path, encoding='utf-8') as f:
             dict = json.load(f)
     except Exception:
         pass
@@ -339,7 +331,8 @@ def load_model_prompt_dict():
 model_prompt_dict = load_model_prompt_dict()
 
 
-def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
+def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None", type = "Auto"):
+    enable_auto_recom_prompt = True if type == "Auto" else False
     if not model_name or not enable_auto_recom_prompt: return prompt, neg_prompt
     prompts = to_list(prompt)
     neg_prompts = to_list(neg_prompt)