Update app.py

app.py

@@ -28,7 +28,7 @@ for model_id in model_ids.keys():
         pass
 
 def infer(prompt):
-    random.randint(0,sys.maxsize)    prompt = getTextTrans(prompt, source='zh', target='en') + f',{random.randint(0,sys.maxsize)}'
+    prompt = getTextTrans(prompt, source='zh', target='en') + f',{random.randint(0,sys.maxsize)}'
     return prompt
 
 start_work = """async() => {
@@ -122,33 +122,7 @@ start_work = """async() => {
         }
         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;
 }"""
 
@@ -173,76 +147,6 @@ with gr.Blocks(title='Text to Image') as demo:
 
             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()