Update app.py

app.py

@@ -7,18 +7,17 @@ Original file is located at
     https://colab.research.google.com/drive/1RxVCwOkq3Q5qlEkQxhFGeUxICBujjEjR
 """
 
+
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
 
 model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
 
-# from retrying import retry
-from transformers import CLIPTextModel, CLIPTokenizer, BertTokenizer, BertForSequenceClassification, ChineseCLIPProcessor, ChineseCLIPModel, AutoModel
 import gradio as gr
 import numpy as np
 from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler, DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline
 
 import torch
 from controlnet_aux import HEDdetector
@@ -28,6 +27,7 @@ import concurrent.futures
 from threading import Thread
 from compel import Compel
 
+
 from transformers import pipeline
 
 
@@ -38,15 +38,11 @@ pipe = pipeline("text-classification", model=model_ckpt)
 
 device="cuda" if torch.cuda.is_available() else "cpu"
 
-hidden_booster_text = "beautiful face, small boobs, a cup"
+hidden_booster_text = ", loraeyes, beautiful face, small boobs, a cup"
 hidden_negative = "big boobs, huge boobs, sexy, dirty, d cup, e cup, g cup, slutty, badhandv4, ng_deepnegative_v1_75t, worst quality, low quality, extra digits, text, signature, bad anatomy, mutated hand, error, missing finger, cropped, worse quality, bad quality, lowres, floating limbs, bad hands, anatomical nonsense"
-hidden_cn_booster_text = "漂亮的脸,小胸,贫乳,a罩杯"
+hidden_cn_booster_text = ",loraeyes漂亮的脸,小胸,贫乳,a罩杯"
 hidden_cn_negative = "大胸, ，, ！, 。, ；,巨乳,性感,脏,d罩杯,e罩杯,g罩杯,骚,骚气,badhandv4, ng_deepnegative_v1_75t"
 
-# text_tokenizer = CLIPTokenizer.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1")
-# text_encoder = CLIPTextModel.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1").eval()
-# text_encoder = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16").eval()
-
 def translate(prompt):
     trans_text = prompt
     translated = model.generate(**tokenizer(trans_text, return_tensors="pt", padding=True))
@@ -54,16 +50,13 @@ def translate(prompt):
     tgt_text = ''.join(tgt_text)[:-1]
     return tgt_text
 
-from PIL import Image
-
 hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
 
 controlnet_scribble = ControlNetModel.from_pretrained(
-    "lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16
-)
+    "lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16, safety_checker=None, requires_safety_checker=False, )
 
 pipe_scribble = StableDiffusionControlNetPipeline.from_single_file(
-    "https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors", controlnet=controlnet_scribble,
+    "https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors", controlnet=controlnet_scribble, safety_checker=None, requires_safety_checker=False,
     torch_dtype=torch.float16,
 )
 
@@ -78,8 +71,8 @@ pipe_scribble = StableDiffusionControlNetPipeline.from_single_file(
 
 # pipe_scribble.load_lora_weights("shellypeng/detail-tweaker")
 # pipe_scribble.fuse_lora(lora_scale=0.1)
-# pipe_scribble.load_lora_weights("shellypeng/lora-eyes")
-# pipe_scribble.fuse_lora(lora_scale=0.1)
+pipe_scribble.load_lora_weights("shellypeng/lora-eyes")
+pipe_scribble.fuse_lora(lora_scale=0.1)
 # pipe_scribble.load_lora_weights("shellypeng/beautiful-eyes")
 # pipe_scribble.fuse_lora(lora_scale=0.1)
 
@@ -94,9 +87,7 @@ pipe_scribble.safety_checker = None
 pipe_scribble.requires_safety_checker = False
 pipe_scribble.to(device)
 
-text = "handsome doctor, black eyes, sparkling eyes, smiling, handsome, glasses, blue tie with yellow dots, doctor's white coat, white collar, blue gradient background"
-
-def scribble_to_image(text, input_img, chinese_check):
+def scribble_to_image(text, neg_prompt_box, input_img):
     """
     pass the sd model and do scribble to image
     include Adetailer, detail tweaker lora, prompt backend include: beautiful eyes, beautiful face, beautiful hand, (maybe infer from user's prompt for gesture and facial
@@ -111,54 +102,24 @@ def scribble_to_image(text, input_img, chinese_check):
     input_img = hed(input_img, scribble=True)
     input_img = load_image(input_img)
     # global prompt
-    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
     lang_check_label = pipe(text, top_k=1, truncation=True)[0]['label']
     lang_check_score = pipe(text, top_k=1, truncation=True)[0]['score']
     if lang_check_label == 'zh' and lang_check_score >= 0.85:
         text = translate(text)
-        print("prompt text:", text)
+    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
     prompt = text + hidden_booster_text
     prompt_embeds = compel_proc(prompt)
+    negative_prompt = neg_prompt_box + hidden_negative
+    negative_prompt_embeds = compel_proc(negative_prompt)
 
-    res_image0 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image1 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image2 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image3 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+    res_image0 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image1 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image2 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image3 = pipe_scribble(image=input_img, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
 
     return res_image0, res_image1, res_image2, res_image3
 
-
-from PIL import Image
-
-from transformers import pipeline
-from diffusers import StableDiffusionDepth2ImgPipeline, StableDiffusionPipeline, StableDiffusionControlNetPipeline, StableDiffusionUpscalePipeline,  StableDiffusionImg2ImgPipeline, AutoPipelineForImage2Image
-
-# Commented out IPython magic to ensure Python compatibility.
-# %cd /content/drive/MyDrive/stable-diffusion-webui-colab/stable-diffusion-webui
-
-pipe_img2img = StableDiffusionImg2ImgPipeline.from_single_file("https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors",
-                                                               torch_dtype=torch.float16)
-
-# pipe_img2img.load_lora_weights("shellypeng/detail-tweaker")
-# pipe_img2img.fuse_lora(lora_scale=0.1)
-# pipe_img2img.load_lora_weights("shellypeng/lora-eyes")
-# pipe_img2img.fuse_lora(lora_scale=0.1)
-# pipe_img2img.load_lora_weights("shellypeng/beautiful-eyes")
-# pipe_img2img.fuse_lora(lora_scale=0.1)
-
-pipe_img2img.load_textual_inversion("shellypeng/bad-prompt")
-pipe_img2img.load_textual_inversion("shellypeng/badhandv4")
-# pipe.load_textual_inversion("shellypeng/easynegative")
-pipe_img2img.load_textual_inversion("shellypeng/deepnegative")
-pipe_img2img.load_textual_inversion("shellypeng/verybadimagenegative")
-pipe_img2img.scheduler = DPMSolverMultistepScheduler.from_config(pipe_img2img.scheduler.config, use_karras_sigmas=True)
-# pipe.enable_model_cpu_offload()
-pipe_img2img.safety_checker = None
-pipe_img2img.requires_safety_checker = False
-pipe_img2img.to(device)
-
-
-def real_img2img_to_anime(text, input_img, chinese_check):
+def real_img2img_to_anime(text, neg_prompt_box, input_img):
     """
     pass the sd model and do scribble to image
     include Adetailer, detail tweaker lora, prompt backend include: beautiful eyes, beautiful face, beautiful hand, (maybe infer from user's prompt for gesture and facial
@@ -166,19 +127,22 @@ def real_img2img_to_anime(text, input_img, chinese_check):
     """
     input_img = Image.fromarray(input_img)
     input_img = load_image(input_img)
-    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
     lang_check_label = pipe(text, top_k=1, truncation=True)[0]['label']
     lang_check_score = pipe(text, top_k=1, truncation=True)[0]['score']
     if lang_check_label == 'zh' and lang_check_score >= 0.85:
         text = translate(text)
-        print("prompt text:", text)
-    prompt = text + hidden_booster_text
 
+    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
+    prompt = text + hidden_booster_text
     prompt_embeds = compel_proc(prompt)
-    res_image0 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image1 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image2 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
-    res_image3 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+
+    negative_prompt = neg_prompt_box + hidden_negative
+    negative_prompt_embeds = compel_proc(negative_prompt)
+    # input_img = depth_estimator(input_img)['depth']
+    res_image0 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image1 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image2 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
+    res_image3 = pipe_img2img(image=input_img, strength=0.6, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=40).images[0]
 
     return res_image0, res_image1, res_image2, res_image3
 
@@ -190,24 +154,151 @@ theme = gr.themes.Soft(
     block_background_fill='*primary_50'
 )
 
-from transformers import pipeline
+# %cd /content/drive/MyDrive/stable-diffusion-webui-colab/stable-diffusion-webui
 
-text = [
-    "Brevity is the soul of wit.",
-    "Amor, ch'a nullo amato amar perdona."
-]
+pipe_img2img = StableDiffusionImg2ImgPipeline.from_single_file("https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors",
+                                                               torch_dtype=torch.float16, safety_checker=None, requires_safety_checker=False)
 
-model_ckpt = "papluca/xlm-roberta-base-language-detection"
-pipe = pipeline("text-classification", model=model_ckpt)
-pipe(text, top_k=1, truncation=True)
+# pipe_img2img.load_lora_weights("shellypeng/detail-tweaker")
+# pipe_img2img.fuse_lora(lora_scale=0.1)
+pipe_img2img.load_lora_weights("shellypeng/lora-eyes")
+pipe_img2img.fuse_lora(lora_scale=0.1)
+# pipe_img2img.load_lora_weights("shellypeng/beautiful-eyes")
+# pipe_img2img.fuse_lora(lora_scale=0.1)
+
+pipe_img2img.load_textual_inversion("shellypeng/bad-prompt")
+pipe_img2img.load_textual_inversion("shellypeng/badhandv4")
+# pipe.load_textual_inversion("shellypeng/easynegative")
+pipe_img2img.load_textual_inversion("shellypeng/deepnegative")
+pipe_img2img.load_textual_inversion("shellypeng/verybadimagenegative")
+pipe_img2img.scheduler = DPMSolverMultistepScheduler.from_config(pipe_img2img.scheduler.config, use_karras_sigmas=True)
+# pipe.enable_model_cpu_offload()
+pipe_img2img.safety_checker = None
+pipe_img2img.requires_safety_checker = False
+pipe_img2img.to(device)
+
+# pipe_img2img.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+
+# depth_estimator = pipeline('depth-estimation')
+
+# controlnet_depth = ControlNetModel.from_pretrained(
+#     "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16
+# )
+
+
+# # models that worked well: anime god, pastel dream,
+# # https://huggingface.co/shellypeng/featureless/tree/main
+# pipe_depth = StableDiffusionControlNetPipeline.from_single_file(
+#     "https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors", controlnet=controlnet_depth,
+#     torch_dtype=torch.float16,
+# )
+# # pipe = StableDiffusionControlNetPipeline.from_pretrained("furusu/SSD-1B-anime",
+# #     torch_dtype=torch.float16
+# # )
+
+# pipe_depth.load_lora_weights("shellypeng/detail-tweaker")
+# pipe_depth.fuse_lora(lora_scale=0.1)
+# # pipe.load_lora_weights("shellypeng/stylized-3d")
+# # pipe.load_lora_weights("shellypeng/midjourney-anime")
+
+# # pipe.load_lora_weights("shellypeng/animetarot")
+# # pipe.load_lora_weights("shellypeng/anime-stickers-v3")
+# # pipe.load_lora_weights("shellypeng/anime-magazine")
+
+
+
+# pipe_depth.load_textual_inversion("shellypeng/bad-prompt")
+# pipe_depth.load_textual_inversion("shellypeng/badhandv4")
+# # pipe.load_textual_inversion("shellypeng/easynegative")
+# pipe_depth.load_textual_inversion("shellypeng/deepnegative")
+# pipe_depth.load_textual_inversion("shellypeng/verybadimagenegative")
+# pipe_depth.scheduler = DPMSolverMultistepScheduler.from_config(pipe_depth.scheduler.config, use_karras_sigmas=True)
+# # pipe.enable_model_cpu_offload()
+# def dummy(images, **kwargs):
+#     return images, False
+# pipe_depth.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+# pipe_depth.to(device)
+# # pipe.load_lora_weights("shellypeng/detail-tweaker", weight_name="add_detail.safetensors")
+
+# # load textual inversion negative embeddings!!!: pipe.load_textual_inversion("sd-concepts-library/cat-toy")
+
+# def real_to_anime(text, input_img):
+#     """
+#     pass the sd model and do scribble to image
+#     include Adetailer, detail tweaker lora, prompt backend include: beautiful eyes, beautiful face, beautiful hand, (maybe infer from user's prompt for gesture and facial
+#     expression to improve hand)
+#     """
+#     input_img = Image.fromarray(input_img)
+#     input_img = load_image(input_img)
+#     input_img = depth_estimator(input_img)['depth']
+#     res_image0 = pipe_depth(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+#     res_image1 = pipe_depth(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+#     res_image2 = pipe_depth(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+#     res_image3 = pipe_depth(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0]
+
+#     return res_image0, res_image1, res_image2, res_image3
+
+
+# theme = gr.themes.Soft(
+#     primary_hue="orange",
+#     secondary_hue="orange",
+# ).set(
+#     block_background_fill='*primary_50'
+# )
+
+def zh_prompt_info(text, neg_text, chinese_check):
+    can_raise_info = ""
+    lang_check_label = pipe(text, top_k=1, truncation=True)[0]['label']
+    lang_check_score = pipe(text, top_k=1, truncation=True)[0]['score']
+    neg_lang_check_label = pipe(neg_text, top_k=1, truncation=True)[0]['label']
+    neg_lang_check_score = pipe(neg_text, top_k=1, truncation=True)[0]['score']
+    print(lang_check_label)
+    if lang_check_label == 'zh' and lang_check_score >= 0.85:
+        if not chinese_check:
+            chinese_check = True
+            can_raise_info = "zh"
+        if neg_lang_check_label == 'en' and neg_lang_check_score >= 0.85:
+            can_raise_info = "invalid"
+            return True, can_raise_info
+    elif lang_check_label == 'en' and lang_check_score >= 0.85:
+        if chinese_check:
+            chinese_check = False
+            can_raise_info = "en"
+        if neg_lang_check_label == 'zh' and neg_lang_check_score >= 0.85:
+            can_raise_info = "invalid"
+            return False, can_raise_info
+    return chinese_check, can_raise_info
+def mult_thread_img2img(prompt_box, neg_prompt_box, image_box):
+    with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor:
+        future = executor.submit(real_img2img_to_anime, prompt_box, neg_prompt_box, image_box)
+        image1, image2, image3, image4 = future.result()
+    return image1, image2, image3, image4
+def mult_thread_scribble(prompt_box, neg_prompt_box, image_box):
+    with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor:
+        future = executor.submit(scribble_to_image, prompt_box, neg_prompt_box, image_box)
+        image1, image2, image3, image4 = future.result()
+    return image1, image2, image3, image4
+def mult_thread_lang_class(prompt_box, neg_prompt_box, chinese_check):
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor:
+        future = executor.submit(zh_prompt_info, prompt_box, neg_prompt_box, chinese_check)
+        chinese_check, can_raise_info = future.result()
+    if can_raise_info == "zh":
+        gr.Info("Chinese Language Detected, Switching to Chinese Prompt Mode")
+    elif can_raise_info == "en":
+        gr.Info("English Language Detected, Disabling Chinese Prompt Mode")
+    return chinese_check
 
 with gr.Blocks(theme=theme, css="footer {visibility: hidden}", title="ShellAI Apps") as iface:
     with gr.Tab("Animefier"):
         with gr.Row(equal_height=True):
             with gr.Column():
                 with gr.Row(equal_height=True):
-                    prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt", scale=1)
-                    chinese_check = gr.Checkbox(label="Chinese Prompt Mode", info="Click here to enable Chinese Prompting（点此触发中文提示词输入）", scale=0.3)
+                    with gr.Column(scale=4):
+                        prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt", lines=3)
+                        neg_prompt_box = gr.Textbox(label="Negative Prompt", placeholder="Enter a negative prompt(things you don't want to include in the generated image)", lines=3)
+                    with gr.Row(equal_height=True):
+                        chinese_check = gr.Checkbox(label="Chinese Prompt Mode", info="Click here to enable Chinese Prompting（点此触发中文提示词输入）")
 
                 image_box = gr.Image(label="Input Image", height=350)
                 gen_btn = gr.Button(value="Generate")
@@ -217,41 +308,37 @@ with gr.Blocks(theme=theme, css="footer {visibility: hidden}", title="ShellAI Ap
             global image2
             global image3
             global image4
-            image1 = gr.Image()
-            image2 = gr.Image()
-            image3 = gr.Image()
-            image4 = gr.Image()
-
-        def mult_thread(prompt_box, image_box, chinese_check):
-            with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor:
-                future = executor.submit(real_img2img_to_anime, prompt_box, image_box, chinese_check)
-                image1, image2, image3, image4 = future.result()
-            return image1, image2, image3, image4
-        gen_btn.click(mult_thread, [prompt_box, image_box, chinese_check], [image1, image2, image3, image4])
+            image1 = gr.Image(label="Result 1")
+            image2 = gr.Image(label="Result 2")
+            image3 = gr.Image(label="Result 3")
+            image4 = gr.Image(label="Result 4")
+
+
+        gr.on(triggers=[prompt_box.submit, gen_btn.click],fn=mult_thread_lang_class, inputs=[prompt_box, neg_prompt_box, chinese_check], outputs=[chinese_check], show_progress=False)
+        gr.on(triggers=[prompt_box.submit, gen_btn.click],fn=mult_thread_img2img, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4])
 
     with gr.Tab("AniSketch"):
         with gr.Row(equal_height=True):
             with gr.Column():
                 with gr.Row(equal_height=True):
-                    prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt", scale=1)
-                    chinese_check = gr.Checkbox(label="Chinese Prompt Mode", info="Click here to enable Chinese Prompting（点此触发中文提示词输入）", scale=0.3)
+                    with gr.Column(scale=4):
+                        prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt", lines=3)
+                        neg_prompt_box = gr.Textbox(label="Negative Prompt", placeholder="Enter a negative prompt(things you don't want to include in the generated image)", lines=3)
+                    with gr.Row(equal_height=True):
+                        chinese_check = gr.Checkbox(label="Chinese Prompt Mode", info="Click here to enable Chinese Prompting（点此触发中文提示词输入）")
 
                 image_box = gr.Image(label="Input Image", height=350)
                 gen_btn = gr.Button(value="Generate")
         with gr.Row(equal_height=True):
+            image1 = gr.Image(label="Result 1")
+            image2 = gr.Image(label="Result 2")
+            image3 = gr.Image(label="Result 3")
+            image4 = gr.Image(label="Result 4")
 
-            image1 = gr.Image()
-            image2 = gr.Image()
-            image3 = gr.Image()
-            image4 = gr.Image()
-
-        def mult_thread(prompt_box, image_box, chinese_check):
-            with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor:
-                future = executor.submit(scribble_to_image, prompt_box, image_box, chinese_check)
-                image1, image2, image3, image4 = future.result()
-
-            return image1, image2, image3, image4
+        gr.on(triggers=[prompt_box.submit, gen_btn.click],fn=mult_thread_lang_class, inputs=[prompt_box, neg_prompt_box, chinese_check], outputs=[chinese_check], show_progress=False)
+        gr.on(triggers=[prompt_box.submit, gen_btn.click],fn=mult_thread_scribble, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4])
 
-        gen_btn.click(mult_thread, [prompt_box, image_box, chinese_check], [image1, image2, image3, image4])
 
+        # gen_btn.click(mult_thread, [prompt_box, image_box, chinese_check], [image1, image2, image3, image4, chinese_check])
+iface.dependencies[0]["show_progress"] = "hidden"
 iface.launch(debug=True, share=True, auth=["shenrym", "shjdqw%23-sw2&"])