# -*- 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, auth=["user", "1qh-32ba"])