Upload 4 files

dc.py

@@ -696,7 +696,7 @@ 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)
 
@@ -761,62 +761,6 @@ 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,
@@ -843,17 +787,18 @@ def get_vaes():
     return vae_model_list
 
 
-show_diffusers_model_list_detail = False
+#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 = {}):
+#    global show_diffusers_model_list_detail
     show_diffusers_model_list_detail = is_enable
     new_value = model_name
     index = 0
@@ -863,7 +808,8 @@ 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
 
 
 def load_model_prompt_dict():

llmdolphin.py

@@ -1434,12 +1434,10 @@ def dolphin_respond_auto(
     state: dict = {},
     progress=gr.Progress(track_tqdm=True),
 ):
-    #try:
+    try:
         #if not is_japanese(message): return [(None, None)]
         progress(0, desc="Processing...")
 
-        print(state)#
-
         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]
@@ -1498,21 +1496,19 @@ def dolphin_respond_auto(
         for output in stream:
             outputs += output
             yield [(outputs, None)], gr.update(), gr.update()
-    #except Exception as e:
-    #    print(e)
-    #    yield [("", None)], gr.update(), gr.update()
+    except Exception as e:
+        print(e)
+        yield [("", None)], gr.update(), gr.update()
 
 
 def dolphin_parse_simple(
     message: str,
     history: list[tuple[str, str]],
-    state: dict = {},
+    state: dict,
 ):
     try:
-        print(state)#
         #if not is_japanese(message): return message
         dolphin_sysprompt_mode = get_state(state, "dolphin_sysprompt_mode")
-        print(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)

modutils.py

@@ -52,6 +52,25 @@ 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: