Duplicate from johnsu6616/SD_Helper_01

Co-authored-by: johnsu <johnsu6616@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: SD_Helper_01
+emoji: 📊
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.30.0
+app_file: app.py
+pinned: false
+license: openrail
+duplicated_from: johnsu6616/SD_Helper_01
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import random
+import re
+
+import gradio as gr
+import torch
+
+from transformers import AutoModelForCausalLM
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+from transformers import AutoProcessor
+
+from transformers import pipeline
+
+from transformers import set_seed
+
+global ButtonIndex
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+big_processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
+big_model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
+
+pipeline_01 = pipeline('text-generation', model='succinctly/text2image-prompt-generator', max_new_tokens=256)
+pipeline_02 = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', max_new_tokens=256)
+pipeline_03 = pipeline('text-generation', model='johnsu6616/ModelExport', max_new_tokens=256)
+
+zh2en_model = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-zh-en').eval()
+zh2en_tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-zh-en')
+
+en2zh_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-zh").eval()
+en2zh_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
+
+def translate_zh2en(text):
+    with torch.no_grad():
+        text = re.sub(r"[:\-–.!;?_#]", '', text)
+
+        text = re.sub(r'([^\u4e00-\u9fa5])([\u4e00-\u9fa5])', r'\1\n\2', text)
+        text = re.sub(r'([\u4e00-\u9fa5])([^\u4e00-\u9fa5])', r'\1\n\2', text)
+
+        text = text.replace('\n', ',')
+
+        text =re.sub(r'(?<![a-zA-Z])\s+|\s+(?![a-zA-Z])', '', text)
+
+        text = re.sub(r',+', ',', text)
+
+        encoded = zh2en_tokenizer([text], return_tensors='pt')
+        sequences = zh2en_model.generate(**encoded)
+        result = zh2en_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+        result = result.strip() 
+
+        if result == "No,no," :
+            result = text
+
+        result = re.sub(r'<.*?>', '', result)
+
+        result = re.sub(r'\b(\w+)\b(?:\W+\1\b)+', r'\1', result, flags=re.IGNORECASE)
+        return result
+
+
+def translate_en2zh(text):
+    with torch.no_grad():
+
+        encoded = en2zh_tokenizer([text], return_tensors="pt")
+        sequences = en2zh_model.generate(**encoded)
+        result = en2zh_tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+
+        result = re.sub(r'\b(\w+)\b(?:\W+\1\b)+', r'\1', result, flags=re.IGNORECASE)
+        return result
+
+def load_prompter():
+    prompter_model = AutoModelForCausalLM.from_pretrained("microsoft/Promptist")
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "left"
+    return prompter_model, tokenizer
+
+prompter_model, prompter_tokenizer = load_prompter()
+
+
+def generate_prompter_pipeline_01(text):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate_zh2en(text)
+    response = pipeline_01(text_in_english, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
+
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
+
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
+
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+','', result)
+    result = result.replace("<", "").replace(">", "")
+
+    if result != "":
+        return result
+
+
+def generate_prompter_tokenizer_01(text):
+
+    text_in_english = translate_zh2en(text)
+
+    input_ids = prompter_tokenizer(text_in_english.strip()+" Rephrase:", return_tensors="pt").input_ids
+    
+    outputs = prompter_model.generate(
+        input_ids,
+        do_sample=False,
+
+        num_beams=3,
+        num_return_sequences=3,
+        pad_token_id= 50256,
+        eos_token_id = 50256,
+        length_penalty=-1.0
+    )
+    output_texts = prompter_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    result = []
+    for output_text in output_texts:
+
+        output_text = output_text.replace('<', '').replace('>', '')
+        output_text = output_text.split("Rephrase:", 1)[-1].strip()
+
+        result.append(translate_en2zh(output_text)+"\n")
+        result.append(output_text+"\n")
+        result.append("\n")
+    return "".join(result)
+
+def generate_prompter_pipeline_02(text):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate_zh2en(text)
+    response = pipeline_02(text_in_english, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
+
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
+
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+','', result)
+    result = result.replace("<", "").replace(">", "")
+
+    if result != "":
+        return result
+
+def generate_prompter_pipeline_03(text):
+    seed = random.randint(100, 1000000)
+    set_seed(seed)
+    text_in_english = translate_zh2en(text)
+    response = pipeline_03(text_in_english, num_return_sequences=3)
+    response_list = []
+    for x in response:
+        resp = x['generated_text'].strip()
+        if resp != text_in_english and len(resp) > (len(text_in_english) + 4):
+
+            response_list.append(translate_en2zh(resp)+"\n")
+            response_list.append(resp+"\n")
+            response_list.append("\n")
+
+    result = "".join(response_list)
+    result = re.sub('[^ ]+\.[^ ]+','', result)
+    result = result.replace("<", "").replace(">", "")
+
+    if result != "":
+        return result
+
+def generate_render(text,choice):
+    if choice == '★pipeline模式(succinctly)':
+        outputs = generate_prompter_pipeline_01(text)
+        return outputs,choice
+    elif choice == '★★tokenizer模式':
+        outputs = generate_prompter_tokenizer_01(text)
+        return outputs,choice
+    elif choice == '★★★pipeline模型(Gustavosta)':
+        outputs = generate_prompter_pipeline_02(text)
+        return outputs,choice
+    elif choice == 'pipeline模型(John)_自訓測試，資料不穩定':
+        outputs = generate_prompter_pipeline_03(text)
+        return outputs,choice
+
+def get_prompt_from_image(input_image,choice):
+    image = input_image.convert('RGB')
+    pixel_values = big_processor(images=image, return_tensors="pt").to(device).pixel_values
+    generated_ids = big_model.to(device).generate(pixel_values=pixel_values)
+    generated_caption = big_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    text = re.sub(r"[:\-–.!;?_#]", '', generated_caption)
+
+    if choice == '★pipeline模式(succinctly)':
+        outputs = generate_prompter_pipeline_01(text)
+        return outputs
+    elif choice == '★★tokenizer模式':
+        outputs = generate_prompter_tokenizer_01(text)
+        return outputs
+    elif choice == '★★★pipeline模型(Gustavosta)':
+        outputs = generate_prompter_pipeline_02(text)
+        return outputs
+    elif choice == 'pipeline模型(John)_自訓測試，資料不穩定':
+        outputs = generate_prompter_pipeline_03(text)
+        return outputs
+
+with gr.Blocks() as block:
+    with gr.Column():
+        with gr.Tab('工作區'):
+            with gr.Row():
+                input_text = gr.Textbox(lines=12, label='輸入文字', placeholder='在此输入文字...')
+                input_image = gr.Image(type='pil', label="選擇圖片(辨識度不佳)")
+            with gr.Row():
+                txt_prompter_btn = gr.Button('文生文')
+                pic_prompter_btn = gr.Button('圖生文')
+            with gr.Row():
+                radio_btn = gr.Radio(
+                    label="請選擇產出方式",
+                    choices=['★pipeline模式(succinctly)', '★★tokenizer模式', '★★★pipeline模型(Gustavosta)',
+                            'pipeline模型(John)_自訓測試，資料不穩定'], 
+
+                    value='★pipeline模式(succinctly)'
+                )
+
+            with gr.Row():
+                Textbox_1 = gr.Textbox(lines=6, label='提示詞生成')
+            with gr.Row():
+                Textbox_2 = gr.Textbox(lines=6, label='測試資訊')
+
+        with gr.Tab('測試區'):
+            with gr.Row():
+                input_test01 = gr.Textbox(lines=2, label='中英翻譯', placeholder='在此输入文字...')
+                test01_btn = gr.Button('執行')
+                Textbox_test01 = gr.Textbox(lines=2, label='輸出結果')
+            with gr.Row():
+                input_test02 = gr.Textbox(lines=2, label='英中翻譯(不精準)', placeholder='在此输入文字...')
+                test02_btn = gr.Button('執行')
+                Textbox_test02 = gr.Textbox(lines=2, label='輸出結果')
+            with gr.Row():
+                input_test03 = gr.Textbox(lines=2, label='★pipeline模式(succinctly)', placeholder='在此输入文字...')
+                test03_btn = gr.Button('執行')
+                Textbox_test03 = gr.Textbox(lines=2, label='輸出結果')
+            with gr.Row():
+                input_test04 = gr.Textbox(lines=2, label='★★tokenizer模式', placeholder='在此输入文字...')
+                test04_btn = gr.Button('執行')
+                Textbox_test04 = gr.Textbox(lines=2, label='輸出結果')
+            with gr.Row():
+                input_test05 = gr.Textbox(lines=2, label='★★★pipeline模型(Gustavosta)', placeholder='在此输入文字...')
+                test05_btn = gr.Button('執行')
+                Textbox_test05 = gr.Textbox(lines=2, label='輸出結果')
+            with gr.Row():
+                input_test06 = gr.Textbox(lines=2, label='pipeline模型(John)_自訓測試，資料不穩定', placeholder='在此输入文字...')
+                test06_btn = gr.Button('執行')
+                Textbox_test06 = gr.Textbox(lines=2, label='輸出結果')
+
+    txt_prompter_btn.click (
+        fn=generate_render,
+        inputs=[input_text,radio_btn],
+        outputs=[Textbox_1,Textbox_2]
+      )
+
+    pic_prompter_btn.click(
+        fn=get_prompt_from_image,
+        inputs=[input_image,radio_btn],
+        outputs=Textbox_1
+    )
+
+    test01_btn.click(
+        fn=translate_zh2en,
+        inputs=input_test01,
+        outputs=Textbox_test01
+    )
+
+    test02_btn.click(
+        fn=translate_en2zh,
+        inputs=input_test02,
+        outputs=Textbox_test02
+    )
+
+    test03_btn.click(
+        fn= generate_prompter_pipeline_01,
+        inputs=input_test03,
+        outputs=Textbox_test03
+    )
+
+    test04_btn.click(
+        fn= generate_prompter_tokenizer_01,
+        inputs=input_test04,
+        outputs=Textbox_test04
+    )
+
+    test05_btn.click(
+        fn= generate_prompter_pipeline_02,
+        inputs=input_test05,
+        outputs=Textbox_test05
+    )
+
+
+    test06_btn.click(
+        fn= generate_prompter_pipeline_03,
+        inputs= input_test06,
+        outputs= Textbox_test06
+    )
+
+block.queue(max_size=64).launch(show_api=False, enable_queue=True, debug=True, share=False, server_name='0.0.0.0')
+

requirements.txt

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+transformers==4.29.2
+torch==2.0.0
+pytorch_lightning==2.0.2
+gradio==3.30.0
+sentencepiece==0.1.99
+sacremoses==0.0.53