Update app.py

app.py

@@ -1,149 +1,154 @@
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+import gradio as gr
+import torch
+from PIL import Image
 
+model_id = 'Souleater_Diffusion/souleater-diffusion.ckpt'
+prefix = ''
+     
+scheduler = DPMSolverMultistepScheduler(
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    num_train_timesteps=1000,
+    trained_betas=None,
+    predict_epsilon=True,
+    thresholding=True,
+    algorithm_type="dpmsolver++",
+    solver_type="midpoint",
+    lower_order_final=True,
+)
 
-import gradio as gr
-import sys
-import random
-import paddlehub as hub
-
-language_translation_model = hub.Module(directory=f'./baidu_translate')
-def getTextTrans(text, source='zh', target='en'):
-    try:
-        text_translation = language_translation_model.translate(text, source, target)
-        return text_translation
-    except Exception as e:
-        return text 
-
-
-model_ids = {
-            "models/DeadfoxX/Souleater_Diffusion":"sd-v1-0",
-            
-           }
-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):
-    prompt = getTextTrans(prompt, source='zh', target='en') + f',{random.randint(0,sys.maxsize)}'
-    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');
-    }
+pipe = StableDiffusionPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+  scheduler=scheduler)
+
+pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+  scheduler=scheduler)
+
+if torch.cuda.is_available():
+  pipe = pipe.to("cuda")
+  pipe_i2i = pipe_i2i.to("cuda")
+
+def error_str(error, title="Error"):
+    return f"""#### {title}
+            {error}"""  if error else ""
+
+def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=True):
+
+  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
+
+  try:
+    if img is not None:
+      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
+    else:
+      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
+  except Exception as e:
+    return None, error_str(e)
+
+def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
+
+    result = pipe(
+      prompt,
+      negative_prompt = neg_prompt,
+      num_inference_steps = int(steps),
+      guidance_scale = guidance,
+      width = width,
+      height = height,
+      generator = generator)
     
-    if (typeof window['gradioEl'] === 'undefined') {
-        window['gradioEl'] = gradioEl;
+    return replace_nsfw_images(result)
+
+def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
+
+    ratio = min(height / img.height, width / img.width)
+    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    result = pipe_i2i(
+        prompt,
+        negative_prompt = neg_prompt,
+        init_image = img,
+        num_inference_steps = int(steps),
+        strength = strength,
+        guidance_scale = guidance,
+        width = width,
+        height = height,
+        generator = generator)
         
-        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];   
-                                
-                                btns = window['gradioEl'].querySelectorAll('button');
-                                for (var i = 0; i < btns.length; i++) {
-                                    if (btns[i].innerText == 'Submit') {
-                                        //btns[i].focus();
-                                        btns[i].click();                
-                                    }
-                                }
-                                window['doCheckPrompt'] = 0;
-                            }, 10);                   
-                    }
-            } catch(e) {
-            }        
-        }
-        window['checkPrompt_interval'] = window.setInterval("window.checkPrompt()", 100);         
-    }
-   
-    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=True)
-            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])
-
-if __name__ == "__main__":
-    demo.launch()
+    return replace_nsfw_images(result)
+
+def replace_nsfw_images(results):
+
+    for i in range(len(results.images)):
+      if results.nsfw_content_detected[i]:
+        results.images[i] = Image.open("nsfw.png")
+    return results.images[0]
+
+css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
+"""
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        f"""
+            <div class="main-div">
+              <div>
+                <h1>Stable Diffusion 2</h1>
+              </div>
+              <p>
+               Demo for <a href="https://huggingface.co/stabilityai/stable-diffusion-2">Stable Diffusion 2</a> Stable Diffusion model.<br>
+        Add the following tokens to your prompts for the model to work properly: <b></b>.
+              </p>
+              Running on <b>{"GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"}</b>
+            </div>
+        """
+    )
+    with gr.Row():
+        
+        with gr.Column(scale=55):
+          with gr.Group():
+              with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
+                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
+
+              image_out = gr.Image(height=512)
+          error_output = gr.Markdown()
+
+        with gr.Column(scale=45):
+          with gr.Tab("Options"):
+            with gr.Group():
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
+              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=True)
+
+              with gr.Row():
+                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
+                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
+
+              with gr.Row():
+                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
+                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
+
+              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+
+          with gr.Tab("Image to image"):
+              with gr.Group():
+                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
+                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
+
+    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
 
+    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
+    outputs = [image_out, error_output]
+    prompt.submit(inference, inputs=inputs, outputs=outputs)
+    generate.click(inference, inputs=inputs, outputs=outputs)
 
+    gr.HTML("""
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
+    </div>
+    """)
 
+demo.queue(concurrency_count=1)
+demo.launch()