Upload 12 files

app.py

@@ -51,8 +51,9 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 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")
-                run_translate_button = gr.Button("Translate")
+                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=True, scale=2)
 
             result = gr.Image(label="Result", elem_id="result", format="png", show_label=False, interactive=False,
                               show_download_button=True, show_share_button=False, container=True)
@@ -174,7 +175,7 @@ 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],
+                sampler, vae_model, auto_trans],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -187,7 +188,7 @@ 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],
+                sampler, vae_model, auto_trans],
         outputs=[result],
         queue=False,
         show_api=True,

dc.py

@@ -697,14 +697,14 @@ from pathlib import Path
 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,
-    normalize_prompt_list, get_civitai_info, search_lora_on_civitai)
+    normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en)
 
 sd_gen = GuiSD()
 #@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, progress=gr.Progress(track_tqdm=True)):
+           sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)):
     import PIL
     import numpy as np
     MAX_SEED = np.iinfo(np.int32).max
@@ -718,6 +718,10 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 
     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 = \
@@ -752,7 +756,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 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, progress=gr.Progress(track_tqdm=True)):
+            sampler = "Euler a", vae = None, translate = True, progress=gr.Progress(track_tqdm=True)):
     return gr.update(visible=True)
 
 

modutils.py

@@ -27,6 +27,17 @@ def list_sub(a, b):
     return [e for e in a if e not in b]
 
 
+from translatepy import Translator
+translator = Translator()
+def translate_to_en(input: str):
+    try:
+        output = str(translator.translate(input, 'English'))
+    except Exception as e:
+        output = input
+        print(e)
+    return output
+
+
 def get_local_model_list(dir_path):
     model_list = []
     valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin')

requirements.txt

@@ -17,8 +17,7 @@ rapidfuzz
 torchvision
 optimum[onnxruntime]
 dartrs
-httpx==0.13.3
-httpcore
-googletrans==4.0.0rc1
+translatepy
 timm
-wrapt-timeout-decorator
+wrapt-timeout-decorator
+sentencepiece

tagger/tagger.py

@@ -1,11 +1,8 @@
+import spaces
 from PIL import Image
 import torch
 import gradio as gr
-import spaces
-from transformers import (
-    AutoImageProcessor,
-    AutoModelForImageClassification,
-)
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 from pathlib import Path
 
 
@@ -190,18 +187,16 @@ def convert_danbooru_to_e621_prompt(input_prompt: str = "", prompt_type: str = "
     return output_prompt
 
 
+from translatepy import Translator
+translator = Translator()
 def translate_prompt(prompt: str = ""):
-    def translate_to_english(prompt):
-        import httpcore
-        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
-        from googletrans import Translator
-        translator = Translator()
+    def translate_to_english(input: str):
         try:
-            translated_prompt = translator.translate(prompt, src='auto', dest='en').text
-            return translated_prompt
+            output = str(translator.translate(input, 'English'))
         except Exception as e:
+            output = input
             print(e)
-            return prompt
+        return output
 
     def is_japanese(s):
         import unicodedata
@@ -224,17 +219,13 @@ def translate_prompt(prompt: str = ""):
 
 
 def translate_prompt_to_ja(prompt: str = ""):
-    def translate_to_japanese(prompt):
-        import httpcore
-        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
-        from googletrans import Translator
-        translator = Translator()
+    def translate_to_japanese(input: str):
         try:
-            translated_prompt = translator.translate(prompt, src='en', dest='ja').text
-            return translated_prompt
+            output = str(translator.translate(input, 'Japanese'))
         except Exception as e:
+            output = input
             print(e)
-            return prompt
+        return output
 
     def is_japanese(s):
         import unicodedata