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.clientHeightdocument.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 ldm-text2im-large-256 model by CompVis using the diffusers library. 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 main Spaces.", # ).launch()