Upload app.py

app.py

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+# -*- 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
+"""
+
+
+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
+
+import torch
+from controlnet_aux import HEDdetector
+from diffusers.utils import load_image
+import concurrent.futures
+from threading import Thread
+from compel import Compel
+
+
+
+device="cuda" if torch.cuda.is_available() else "cpu"
+
+hidden_booster_text = "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_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"
+
+
+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
+
+from PIL import Image
+
+hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
+
+controlnet_scribble = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16
+)
+
+pipe_scribble = StableDiffusionControlNetPipeline.from_single_file(
+    "https://huggingface.co/shellypeng/anime-god/blob/main/animeGod_v10.safetensors", controlnet=controlnet_scribble,
+    torch_dtype=torch.float16,
+)
+
+# pipe.load_lora_weights("shellypeng/detail-tweaker")
+# 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_img2img.load_lora_weights("yenojunie/slit-pupils")
+
+# 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/beautiful-eyes")
+# pipe_scribble.fuse_lora(lora_scale=0.1)
+
+pipe_scribble.load_textual_inversion("shellypeng/bad-prompt")
+pipe_scribble.load_textual_inversion("shellypeng/badhandv4")
+# pipe.load_textual_inversion("shellypeng/easynegative")
+pipe_scribble.load_textual_inversion("shellypeng/deepnegative")
+pipe_scribble.load_textual_inversion("shellypeng/verybadimagenegative")
+pipe_scribble.scheduler = DPMSolverMultistepScheduler.from_config(pipe_scribble.scheduler.config, use_karras_sigmas=True)
+# pipe.enable_model_cpu_offload()
+pipe_scribble.safety_checker = None
+pipe_scribble.requires_safety_checker = False
+pipe_scribble.to(device)
+
+
+def scribble_to_image(text, input_img, chinese_check):
+    """
+    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)
+    """
+    # 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
+    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
+    if chinese_check:
+        text = translate(text)
+        print("prompt text:", text)
+    prompt = text + hidden_booster_text
+    prompt_embeds = compel_proc(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]
+
+    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):
+    """
+    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)
+    compel_proc = Compel(tokenizer=pipe_scribble.tokenizer, text_encoder=pipe_scribble.text_encoder)
+    if chinese_check:
+        text = translate(text)
+        print("prompt text:", text)
+    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]
+
+    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'
+)
+
+from transformers import pipeline
+
+text = [
+    "Brevity is the soul of wit.",
+    "Amor, ch'a nullo amato amar perdona."
+]
+
+model_ckpt = "papluca/xlm-roberta-base-language-detection"
+pipe = pipeline("text-classification", model=model_ckpt)
+pipe(text, top_k=1, truncation=True)
+
+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)
+
+                image_box = gr.Image(label="Input Image", height=350)
+                gen_btn = gr.Button(value="Generate")
+
+        with gr.Row(equal_height=True):
+            global image1
+            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])
+
+    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)
+
+                image_box = gr.Image(label="Input Image", height=350)
+                gen_btn = gr.Button(value="Generate")
+        with gr.Row(equal_height=True):
+            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
+
+        gen_btn.click(mult_thread, [prompt_box, image_box, chinese_check], [image1, image2, image3, image4])
+
+iface.launch(debug=True, share=True)