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| import gradio as gr | |
| model_ids = {"models/runwayml/stable-diffusion-v1-5":"stable-diffusion-v1-5", | |
| "models/stabilityai/stable-diffusion-2":"stable-diffusion-2", | |
| "models/prompthero/openjourney":"openjourney", | |
| } | |
| tab_actions = [] | |
| tab_titles = [] | |
| for model_id in model_ids.keys(): | |
| print(model_id, model_ids[model_id]) | |
| try: | |
| tab = gr.Interface.load(model_id) | |
| tab_actions.append(tab) | |
| tab_titles.append(model_ids[model_id]) | |
| except: | |
| pass | |
| def infer(prompt): | |
| # gr.Interface.load("models/runwayml/stable-diffusion-v1-5",prompt=prompt).launch() | |
| return prompt | |
| start_work = """async() => { | |
| function isMobile() { | |
| try { | |
| document.createEvent("TouchEvent"); return true; | |
| } catch(e) { | |
| return false; | |
| } | |
| } | |
| function getClientHeight() | |
| { | |
| var clientHeight=0; | |
| if(document.body.clientHeight&&document.documentElement.clientHeight) { | |
| var clientHeight = (document.body.clientHeight<document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight; | |
| } else { | |
| var clientHeight = (document.body.clientHeight>document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight; | |
| } | |
| return clientHeight; | |
| } | |
| function setNativeValue(element, value) { | |
| const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set; | |
| const prototype = Object.getPrototypeOf(element); | |
| const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set; | |
| if (valueSetter && valueSetter !== prototypeValueSetter) { | |
| prototypeValueSetter.call(element, value); | |
| } else { | |
| valueSetter.call(element, value); | |
| } | |
| } | |
| var gradioEl = document.querySelector('body > gradio-app').shadowRoot; | |
| if (!gradioEl) { | |
| gradioEl = document.querySelector('body > gradio-app'); | |
| } | |
| if (typeof window['gradioEl'] === 'undefined') { | |
| window['gradioEl'] = gradioEl; | |
| tabitems = window['gradioEl'].querySelectorAll('.tabitem'); | |
| for (var i = 0; i < tabitems.length; i++) { | |
| tabitems[i].childNodes[0].children[0].style.display='none'; | |
| tabitems[i].childNodes[0].children[1].children[0].style.display='none'; | |
| tabitems[i].childNodes[0].children[1].children[1].children[0].children[1].style.display="none"; | |
| } | |
| tab_demo = window['gradioEl'].querySelectorAll('#tab_demo')[0]; | |
| tab_demo.style.display = "block"; | |
| tab_demo.setAttribute('style', 'height: 100%;'); | |
| const page1 = window['gradioEl'].querySelectorAll('#page_1')[0]; | |
| const page2 = window['gradioEl'].querySelectorAll('#page_2')[0]; | |
| page1.style.display = "none"; | |
| page2.style.display = "block"; | |
| window['prevPrompt'] = ''; | |
| window['doCheckPrompt'] = 0; | |
| window['checkPrompt'] = function checkPrompt() { | |
| try { | |
| texts = window['gradioEl'].querySelectorAll('textarea'); | |
| text0 = texts[0]; | |
| text1 = texts[1]; | |
| if (window['doCheckPrompt'] == 0 && window['prevPrompt'] != text1.value) { | |
| window['doCheckPrompt'] = 1; | |
| window['prevPrompt'] = text1.value; | |
| for (var i = 2; i < texts.length; i++) { | |
| setNativeValue(texts[i], text1.value); | |
| texts[i].dispatchEvent(new Event('input', { bubbles: true })); | |
| } | |
| setTimeout(function() { | |
| text1 = window['gradioEl'].querySelectorAll('textarea')[1]; | |
| //console.log('do_click()_1_' + text1.value); | |
| btns = window['gradioEl'].querySelectorAll('button'); | |
| for (var i = 0; i < btns.length; i++) { | |
| if (btns[i].innerText == 'Submit') { | |
| btns[i].focus(); | |
| btns[i].click(); | |
| //break; | |
| } | |
| } | |
| //console.log('do_click()_3_'); | |
| window['doCheckPrompt'] = 0; | |
| }, 10); | |
| } | |
| } catch(e) { | |
| } | |
| } | |
| window['checkPrompt_interval'] = window.setInterval("window.checkPrompt()", 100); | |
| } | |
| /* | |
| texts = gradioEl.querySelectorAll('textarea'); | |
| text0 = gradioEl.querySelectorAll('textarea')[0]; | |
| text1 = gradioEl.querySelectorAll('textarea')[0]; | |
| for (var i = 1; i < texts.length; i++) { | |
| setNativeValue(texts[i], text0.value); | |
| texts[i].dispatchEvent(new Event('input', { bubbles: true })); | |
| } | |
| var st = setTimeout(function() { | |
| text1 = window['gradioEl'].querySelectorAll('textarea')[1]; | |
| console.log('do_click()_1_' + text1.value); | |
| btns = window['gradioEl'].querySelectorAll('button'); | |
| for (var i = 0; i < btns.length; i++) { | |
| if (btns[i].innerText == 'Submit') { | |
| btns[i].focus(); | |
| btns[i].click(); | |
| //break; | |
| } | |
| } | |
| console.log('do_click()_3_'); | |
| }, 10); | |
| */ | |
| return false; | |
| }""" | |
| with gr.Blocks(title='Text to Image') as demo: | |
| with gr.Group(elem_id="page_1", visible=True) as page_1: | |
| with gr.Box(): | |
| with gr.Row(): | |
| start_button = gr.Button("Let's GO!", elem_id="start-btn", visible=True) | |
| start_button.click(fn=None, inputs=[], outputs=[], _js=start_work) | |
| with gr.Group(elem_id="page_2", visible=False) as page_2: | |
| with gr.Row(elem_id="prompt_row"): | |
| prompt_input0 = gr.Textbox(lines=4, label="prompt") | |
| prompt_input1 = gr.Textbox(lines=4, label="prompt", visible=False) | |
| with gr.Row(): | |
| submit_btn = gr.Button(value = "submit",elem_id="erase-btn").style( | |
| margin=True, | |
| rounded=(True, True, True, True), | |
| ) | |
| with gr.Row(elem_id='tab_demo', visible=True).style(height=5): | |
| tab_demo = gr.TabbedInterface(tab_actions, tab_titles) | |
| submit_btn.click(fn=infer, inputs=[prompt_input0], outputs=[prompt_input1]) | |
| # prompt_input = gr.Textbox(lines=4, label="Input prompt") | |
| # tab_demo = gr.TabbedInterface([sd15_demo, sd20_demo, openjourney_demo], ["stable-diffusion-v1-5", "stable-diffusion-2", "openjourney"]) | |
| # demo = gr.Interface(fn=infer, | |
| # inputs=[prompt_input], | |
| # outputs=[tab_demo], | |
| # ) | |
| if __name__ == "__main__": | |
| demo.launch() | |
| # import os | |
| # os.environ['CUDA_LAUNCH_BLOCKING'] = "1" | |
| # from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy | |
| # import gradio as gr | |
| # import PIL.Image | |
| # import numpy as np | |
| # import random | |
| # import torch | |
| # import subprocess | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # # print('Using device:', device) | |
| # HF_TOKEN_SD=os.environ.get('HF_TOKEN_SD') | |
| # if 0==0: | |
| # model_id = "runwayml/stable-diffusion-v1-5" | |
| # model_id = "prompthero/openjourney" | |
| # # pipeClass = StableDiffusionImg2ImgPipeline | |
| # pipeClass = StableDiffusionPipeline | |
| # className = pipeClass.__name__ | |
| # if className == 'StableDiffusionInpaintPipeline': | |
| # model_id = "runwayml/stable-diffusion-inpainting" | |
| # sd_pipe = pipeClass.from_pretrained( | |
| # model_id, | |
| # # revision="fp16", | |
| # torch_dtype=torch.float16, | |
| # # use_auth_token=HF_TOKEN_SD | |
| # ) # .to(device) | |
| # def predict(prompt, steps=100, seed=42, guidance_scale=6.0): | |
| # #torch.cuda.empty_cache() | |
| # # print(subprocess.check_output(["nvidia-smi"], stderr=subprocess.STDOUT).decode("utf8")) | |
| # generator = torch.manual_seed(seed) | |
| # images = sd_pipe([prompt], | |
| # generator=generator, | |
| # num_inference_steps=steps, | |
| # eta=0.3, | |
| # guidance_scale=guidance_scale)["sample"] | |
| # # print(subprocess.check_output(["nvidia-smi"], stderr=subprocess.STDOUT).decode("utf8")) | |
| # return images[0] | |
| # random_seed = random.randint(0, 2147483647) | |
| # gr.Interface( | |
| # predict, | |
| # inputs=[ | |
| # gr.inputs.Textbox(label='Prompt', default='a chalk pastel drawing of a llama wearing a wizard hat'), | |
| # gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1), | |
| # gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1), | |
| # gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1), | |
| # ], | |
| # outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"), | |
| # css="#output_image{width: 256px}", | |
| # title="Text-to-Image_Latent_Diffusion", | |
| # # description="This Spaces contains a text-to-image Latent Diffusion process for the <a href=\"https://huggingface.co/CompVis/ldm-text2im-large-256\">ldm-text2im-large-256</a> model by <a href=\"https://huggingface.co/CompVis\">CompVis</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. The goal of this demo is to showcase the diffusers library and you can check how the code works here. If you want the state-of-the-art experience with Latent Diffusion text-to-image check out the <a href=\"https://huggingface.co/spaces/multimodalart/latentdiffusion\">main Spaces</a>.", | |
| # ).launch() | |