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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from transformers import pipeline | |
| from diffusers import StableDiffusionDepth2ImgPipeline, StableDiffusionPipeline, StableDiffusionControlNetPipeline, StableDiffusionUpscalePipeline, StableDiffusionImg2ImgPipeline, AutoPipelineForImage2Image | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler, DPMSolverMultistepScheduler | |
| import torch | |
| from controlnet_aux import HEDdetector | |
| from diffusers.utils import load_image | |
| from huggingface_hub import notebook_login, login | |
| import concurrent.futures | |
| from threading import Thread | |
| hidden_booster_text = "beautiful face, beautiful hand, 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" | |
| 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_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() | |
| def dummy(images, **kwargs): | |
| return images, False | |
| pipe_scribble.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) | |
| pipe_scribble.to("cuda") | |
| def scribble_to_image(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) | |
| """ | |
| # 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 | |
| prompt = text + hidden_booster_text | |
| res_image0 = pipe_scribble(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image1 = pipe_scribble(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image2 = pipe_scribble(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image3 = pipe_scribble(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' | |
| ) | |
| 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_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() | |
| def dummy(images, **kwargs): | |
| return images, False | |
| pipe_img2img.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) | |
| pipe_img2img = pipe_img2img.to("cuda") | |
| def real_img2img_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) | |
| prompt = text + hidden_booster_text | |
| # input_img = depth_estimator(input_img)['depth'] | |
| res_image0 = pipe_img2img(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image1 = pipe_img2img(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image2 = pipe_img2img(prompt, input_img, negative_prompt=hidden_negative, num_inference_steps=40).images[0] | |
| res_image3 = pipe_img2img(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' | |
| ) | |
| generator = torch.manual_seed(33) | |
| 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(): | |
| prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt") | |
| 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): | |
| with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor: | |
| future = executor.submit(real_img2img_to_anime, prompt_box, image_box) | |
| image1, image2, image3, image4 = future.result() | |
| return image1, image2, image3, image4 | |
| gen_btn.click(mult_thread, [prompt_box, image_box], [image1, image2, image3, image4]) | |
| with gr.Tab("AniSketch"): | |
| with gr.Row(equal_height=True): | |
| with gr.Column(): | |
| prompt_box = gr.Textbox(label="Prompt", placeholder="Enter a prompt") | |
| 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): | |
| with concurrent.futures.ThreadPoolExecutor(max_workers=12000) as executor: | |
| future = executor.submit(scribble_to_image, prompt_box, image_box) | |
| image1, image2, image3, image4 = future.result() | |
| return image1, image2, image3, image4 | |
| gen_btn.click(mult_thread, [prompt_box, image_box], [image1, image2, image3, image4]) | |
| iface.launch(inline=False) | |