Update app.py

app.py

@@ -8,12 +8,10 @@ import time
 import psutil
 import random
 
-
 start_time = time.time()
 is_colab = utils.is_google_colab()
 state = None
 current_steps = 25
-
 class Model:
     def __init__(self, name, path="", prefix=""):
         self.name = name
@@ -21,7 +19,6 @@ class Model:
         self.prefix = prefix
         self.pipe_t2i = None
         self.pipe_i2i = None
-
 models = [
      Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "),
      Model("Dreamlike Photoreal 2.0", "dreamlike-art/dreamlike-photoreal-2.0", ""),
@@ -121,20 +118,14 @@ models = [
      Model("Realistic_Vision_V1.4", "SG161222/Realistic_Vision_V1.4", ""),
 
 
-
-
-
 ]
-
 custom_model = None
 if is_colab:
   models.insert(0, Model("Custom model"))
   custom_model = models[0]
-
 last_mode = "txt2img"
 current_model = models[1] if is_colab else models[0]
 current_model_path = current_model.path
-
 if is_colab:
   pipe = StableDiffusionPipeline.from_pretrained(
       current_model.path,
@@ -142,7 +133,6 @@ if is_colab:
       scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
       safety_checker=None
       )
-
 else:
   pipe = StableDiffusionPipeline.from_pretrained(
       current_model.path,
@@ -153,57 +143,41 @@ else:
 if torch.cuda.is_available():
   pipe = pipe.to("cuda")
   pipe.enable_xformers_memory_efficient_attention()
-
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
 def error_str(error, title="Error"):
     return f"""#### {title}
             {error}"""  if error else ""
-
 def update_state(new_state):
   global state
   state = new_state
-
 def update_state_info(old_state):
   if state and state != old_state:
     return gr.update(value=state)
-
 def custom_model_changed(path):
   models[0].path = path
   global current_model
   current_model = models[0]
-
 def on_model_change(model_name):
   
   prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
   return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
 def on_steps_change(steps):
   global current_steps
   current_steps = steps
-
 def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):
     update_state(f"{step}/{current_steps} steps")#\nTime left, sec: {timestep/100:.0f}")
-
 def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
   update_state(" ")
-
   print(psutil.virtual_memory()) # print memory usage
-
   global current_model
   for model in models:
     if model.name == model_name:
       current_model = model
       model_path = current_model.path
-
   # generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
   if seed == 0:
     seed = random.randint(0, 2147483647)
-
   generator = torch.Generator('cuda').manual_seed(seed)
-
   try:
     if img is not None:
       return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
@@ -211,19 +185,14 @@ def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height
       return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
   except Exception as e:
     return None, error_str(e)
-
 def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):
-
     print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
     global last_mode
     global pipe
     global current_model_path
     if model_path != current_model_path or last_mode != "txt2img":
         current_model_path = model_path
-
         update_state(f"Loading {current_model.name} text-to-image model...")
-
         if is_colab or current_model == custom_model:
           pipe = StableDiffusionPipeline.from_pretrained(
               current_model_path,
@@ -239,12 +208,10 @@ def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width,
               )
           # pipe = pipe.to("cpu")
           # pipe = current_model.pipe_t2i
-
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
           pipe.enable_xformers_memory_efficient_attention()
         last_mode = "txt2img"
-
     prompt = current_model.prefix + prompt  
     result = pipe(
       prompt,
@@ -256,23 +223,17 @@ def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width,
       height = height,
       generator = generator,
       callback=pipe_callback)
-
     # update_state(f"Done. Seed: {seed}")
     
     return replace_nsfw_images(result)
-
 def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):
-
     print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
     global last_mode
     global pipe
     global current_model_path
     if model_path != current_model_path or last_mode != "img2img":
         current_model_path = model_path
-
         update_state(f"Loading {current_model.name} image-to-image model...")
-
         if is_colab or current_model == custom_model:
           pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
               current_model_path,
@@ -293,7 +254,6 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
           pipe = pipe.to("cuda")
           pipe.enable_xformers_memory_efficient_attention()
         last_mode = "img2img"
-
     prompt = current_model.prefix + prompt
     ratio = min(height / img.height, width / img.width)
     img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
@@ -309,13 +269,10 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
         # height = height,
         generator = generator,
         callback=pipe_callback)
-
     # update_state(f"Done. Seed: {seed}")
         
     return replace_nsfw_images(result)
-
 def replace_nsfw_images(results):
-
     if is_colab:
       return results.images
       
@@ -323,7 +280,6 @@ def replace_nsfw_images(results):
       if results.nsfw_content_detected[i]:
         results.images[i] = Image.open("nsfw.png")
     return results.images
-
 # css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-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="style.css") as demo:
@@ -358,46 +314,36 @@ with gr.Blocks(css="style.css") as demo:
                 prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
                 generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
 
-
               # image_out = gr.Image(height=512)
               gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
           
           state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False)
           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")
-
               n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=10, step=1)
-
               with gr.Row():
                 guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                 steps = gr.Slider(label="Steps", value=current_steps, minimum=2, maximum=250, step=1)
-
               with gr.Row():
                 width = gr.Slider(label="Width", value=512, minimum=64, maximum=2048, step=8)
                 height = gr.Slider(label="Height", value=512, minimum=64, maximum=2048, 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)
-
     if is_colab:
         model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
         custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
     # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
     steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)
-
     inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt]
     outputs = [gallery, error_output]
     prompt.submit(inference, inputs=inputs, outputs=outputs)
     generate.click(inference, inputs=inputs, outputs=outputs)
-
     ex = gr.Examples([
         [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25],
         [models[4].name, "portrait of dwayne johnson", 7.0, 35],
@@ -405,7 +351,6 @@ with gr.Blocks(css="style.css") as demo:
         [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30],
         [models[5].name, "fantasy portrait painting, digital art", 4.0, 20],
     ], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False)
-
     gr.HTML("""
     <div style="border-top: 1px solid #303030;">
       <br>
@@ -418,11 +363,8 @@ with gr.Blocks(css="style.css") as demo:
       <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
     </div>
     """)
-
     demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)
-
 print(f"Space built in {time.time() - start_time:.2f} seconds")
-
 # if not is_colab:
 demo.queue(concurrency_count=1)
 demo.launch(debug=is_colab, share=True)