Update app.py

app.py

@@ -1,12 +1,343 @@
-from huggingface_hub import Repository
+# -*- coding: utf-8 -*-
+"""Test_gradio_push.ipynb
 
-repo = Repository(
-    local_dir="anime_pack_app_py",
-    repo_type="model",
-    clone_from="shellypeng/anime_pack_app_py",
-    token=True
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1mlZpAq-EWRmmLHH4Ok533awreqtJwzzW
+"""
+
+"""# HF Script
+
+"""
+
+# -*- coding: utf-8 -*-
+"""Copy of Anime_Pack_Gradio.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1RxVCwOkq3Q5qlEkQxhFGeUxICBujjEjR
+"""
+
+import os
+
+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")
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline
+
+import torch
+from controlnet_aux import HEDdetector
+from diffusers.utils import load_image
+
+import concurrent.futures
+from threading import Thread
+from compel import Compel
+
+
+from transformers import pipeline
+
+
+model_ckpt = "papluca/xlm-roberta-base-language-detection"
+pipe = pipeline("text-classification", model=model_ckpt)
+
+HF_TOKEN = os.environ.get("HUGGING_FACE_HUB_TOKEN")
+
+device="cuda" if torch.cuda.is_available() else "cpu"
+
+hidden_booster_text = "masterpiece++, best quality++, ultra-detailed+ +, unity 8k wallpaper+, illustration+, anime style+, intricate, fluid simulation, sharp edges. glossy++, Smooth++, detailed eyes++, best quality++,4k++,8k++,highres++,masterpiece++,ultra- detailed,realistic++,photorealistic++,photo-realistic++,depth of field, ultra-high definition, highly detailed, natural lighting, sharp focus, cinematic, hyperrealism,extremely detailed"
+hidden_negative = "bad anatomy, disfigured, poorly drawn,deformed, mutation, malformation, deformed, mutated, disfigured, deformed eyes+, bad face++, bad hands, poorly drawn hands, malformed hands, extra arms++, extra legs++, Fused body+, Fused hands+, Fused legs+, missing arms, missing limb, extra digit+, fewer digits, floating limbs, disconnected limbs, inaccurate limb, bad fingers, missing fingers, ugly face, long body++"
+hidden_cn_booster_text = ",漂亮的脸"
+hidden_cn_negative = ""
+
+def translate(prompt):
+    trans_text = prompt
+    translated = model.generate(**tokenizer(trans_text, return_tensors="pt", padding=True))
+    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+    tgt_text = ''.join(tgt_text)[:-1]
+    return tgt_text
+
+
+hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
+
+controlnet_scribble = ControlNetModel.from_pretrained(
+    "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, safety_checker=None, requires_safety_checker=False,
+    torch_dtype=torch.float16, token=HF_TOKEN
+)
+
+pipe_scribble.load_lora_weights("shellypeng/lora2")
+pipe_scribble.fuse_lora(lora_scale=0.1)
+
+pipe_scribble.load_textual_inversion("shellypeng/textinv1")
+pipe_scribble.load_textual_inversion("shellypeng/textinv2")
+pipe_scribble.load_textual_inversion("shellypeng/textinv3")
+pipe_scribble.load_textual_inversion("shellypeng/textinv4")
+pipe_scribble.scheduler = DPMSolverMultistepScheduler.from_config(pipe_scribble.scheduler.config, use_karras_sigmas=True)
+pipe_scribble.safety_checker = None
+pipe_scribble.requires_safety_checker = False
+pipe_scribble.to(device)
+pipe_scribble.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+
+
+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
+    expression to improve hand)
+    """
+
+
+
+# if auto detect detects chinese => auto turn on chinese prompting checkbox
+    # change param "bag" below to text, image param below to input_img
+    input_img = Image.fromarray(input_img)
+    input_img = hed(input_img, scribble=True)
+    input_img = load_image(input_img)
+    # global prompt
+    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)
+    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_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
+
+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
+    expression to improve hand)
+    """
+    input_img = Image.fromarray(input_img)
+    input_img = load_image(input_img)
+    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)
+
+    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)
+    # 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
+
+
+theme = gr.themes.Soft(
+    primary_hue="orange",
+    secondary_hue="orange",
+).set(
+    block_background_fill='*primary_50'
 )
-repo.git_pull()
 
-from anime_pack_app_py import model
-model.run()
+
+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, token=HF_TOKEN)
+
+pipe_img2img.load_lora_weights("shellypeng/lora1")
+pipe_img2img.fuse_lora(lora_scale=0.1)
+pipe_img2img.load_lora_weights("shellypeng/lora2", token=HF_TOKEN)
+pipe_img2img.fuse_lora(lora_scale=0.1)
+
+pipe_img2img.load_textual_inversion("shellypeng/textinv1")
+pipe_img2img.load_textual_inversion("shellypeng/textinv2")
+pipe_img2img.load_textual_inversion("shellypeng/textinv3")
+pipe_img2img.load_textual_inversion("shellypeng/textinv4")
+pipe_img2img.scheduler = DPMSolverMultistepScheduler.from_config(pipe_img2img.scheduler.config, use_karras_sigmas=True)
+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))
+
+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_live_scribble(prompt_box, neg_prompt_box, image_box):
+    image_box = image_box["composite"]
+    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（安妮漫风）"):
+        gr.Markdown(
+            """
+            # Animefier（安妮漫风）
+            Turns realistic photos into one in the anime style.
+            将真实图片转为动漫风图片。
+            """
+        )
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                with gr.Row(equal_height=True):
+                    with gr.Column(scale=4):
+                        prompt_box = gr.Textbox(label="Prompt（提示词）", placeholder="Enter a prompt\n输入提示词", lines=3)
+                        neg_prompt_box = gr.Textbox(label="Negative Prompt（负面提示词）", placeholder="Enter a negative prompt(things you don't want to include in the generated image)\n输入负面提示词：输入您不想生成的部分", lines=3)
+                    with gr.Row(equal_height=True):
+                        chinese_check = gr.Checkbox(label="Chinese Prompt Mode（中文提示词模式）", 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（上传图片）", height=400)
+                gen_btn = gr.Button(value="Generate（生成）")
+
+        with gr.Row(equal_height=True):
+            image1 = gr.Image(label="Result 1（结果图 1）")
+            image2 = gr.Image(label="Result 2（结果图 2）")
+            image3 = gr.Image(label="Result 3（结果图 3）")
+            image4 = gr.Image(label="Result 4（结果图 4）")
+        example_img2img = [
+            ["漂亮的女孩，微笑，长发，黑发，粉色外套，白色内衬，优雅，红色背景，红色窗帘", "低画质", "sunmi.jpg"],
+            ["Beautiful girl, smiling, bun, bun hair, black hair, beautiful eyes, black dress, elegant, red carpet photo","ugly, bad quality", "emma.jpg"]
+        ]
+        
+        gr.Examples(examples=example_img2img, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4], fn=mult_thread_img2img, cache_examples=True)
+
+        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("Live Sketch（实时涂鸦）"):
+        gr.Markdown(
+            """
+            # Live Sketch（实时涂鸦）
+            Live draw sketches/scribbles and turns into one in the anime style.
+            实时涂鸦，将粗线条涂鸦转为动漫风图片。
+            """
+        )
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                with gr.Row(equal_height=True):
+                    with gr.Column(scale=4):
+                        prompt_box = gr.Textbox(label="Prompt（提示词）", placeholder="Enter a prompt\n输入提示词", lines=3)
+                        neg_prompt_box = gr.Textbox(label="Negative Prompt（负面提示词）", placeholder="Enter a negative prompt(things you don't want to include in the generated image)\n输入负面提示词：输入您不想生成的部分", lines=3)
+                    with gr.Row(equal_height=True):
+                        chinese_check = gr.Checkbox(label="Chinese Prompt Mode（中文提示词模式）", info="Click here to enable Chinese Prompting（点此触发中文提示词输入）")
+                image_box = gr.ImageEditor(sources=(), brush=gr.Brush(default_size="5", color_mode="fixed", colors=["#000000"]), height=400)
+
+                gen_btn = gr.Button(value="Generate（生成）")
+        with gr.Row(equal_height=True):
+            image1 = gr.Image(label="Result 1（结果图 1）")
+            image2 = gr.Image(label="Result 2（结果图 2）")
+            image3 = gr.Image(label="Result 3（结果图 3）")
+            image4 = gr.Image(label="Result 4（结果图 4）")
+        # sketch_image_box.change(fn=mult_thread_scribble, inputs=[prompt_box, neg_prompt_box, sketch_image_box], outputs=[image1, image2, image3, image4])
+        example_scribble_live2img = [
+            ["帅气的男孩，橙色头发++，皱眉，闭眼，深蓝色开襟毛衣，白色内衬，酷，冷漠，帅气，硝烟背景", "劣质", "sketch_boy.png"],
+            ["a beautiful girl spreading her arms, blue hair, long hair, hat with flowers on its edge, smiling++, dynamic, black dress, park background, birds, trees, flowers, grass","ugly, worst quality", "girl_spread.jpg"]
+        ]
+        
+        gr.Examples(examples=example_scribble_live2img, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4], fn=mult_thread_live_scribble, cache_examples=True)
+
+        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_live_scribble, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4])
+
+    with gr.Tab("AniSketch（安妮涂鸦）"):
+        gr.Markdown(
+            """
+            # AniSketch（安妮涂鸦）
+            Turns sketches/scribbles into one in the anime style.
+            将草图、粗线条涂鸦转为动漫风图片。
+            """
+        )
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                with gr.Row(equal_height=True):
+                    with gr.Column(scale=4):
+                        prompt_box = gr.Textbox(label="Prompt（提示词）", placeholder="Enter a prompt\n输入提示词", lines=3)
+                        neg_prompt_box = gr.Textbox(label="Negative Prompt（负面提示词）", placeholder="Enter a negative prompt(things you don't want to include in the generated image)\n输入负面提示词：输入您不想生成的部分", lines=3)
+                    with gr.Row(equal_height=True):
+                        chinese_check = gr.Checkbox(label="Chinese Prompt Mode（中文提示词模式）", 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（上传图片）", height=400)
+
+                gen_btn = gr.Button(value="Generate（生成）")
+        with gr.Row(equal_height=True):
+            image1 = gr.Image(label="Result 1（结果图 1）")
+            image2 = gr.Image(label="Result 2（结果图 2）")
+            image3 = gr.Image(label="Result 3（结果图 3）")
+            image4 = gr.Image(label="Result 4（结果图 4）")
+        example_scribble2img = [
+            ["漂亮的女人，散开的长发，巫师，巫师袍，微笑，拍手，优雅，成熟，月夜背景", "水印", "final_witch.jpg"],
+            ["a man wearing a chinese clothes, closed eyes, handsome face, dragon on the clothes, expressionless face, indifferent, chinese building background","poor quality", "chinese_man.jpg"]
+        ]
+        
+        gr.Examples(examples=example_scribble2img, inputs=[prompt_box, neg_prompt_box, image_box], outputs=[image1, image2, image3, image4], fn=mult_thread_scribble, cache_examples=True)
+
+        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])
+
+
+def run():
+    iface.queue(default_concurrency_limit=20).launch(debug=True, share=True)
+
+run()
+
+"""# Separator
+
+"""
+
+