app.py

from diffusers import CycleDiffusionPipeline, DDIMScheduler
import gradio as gr
import torch
from PIL import Image
import utils
import streamlit as st

is_colab = utils.is_google_colab()

scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
                          num_train_timesteps=1000, clip_sample=False, set_alpha_to_one=False)

model_id_or_path = "CompVis/stable-diffusion-v1-4"
pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path,
                                              use_auth_token=st.secrets["USER_TOKEN"],
                                              scheduler=scheduler)

if torch.cuda.is_available():
    pipe = pipe.to("cuda")

device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"


def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", inpaint_image=None):

  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 img is not None:
    return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
  else:
    return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, inpaint_image)


def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):

    global last_mode
    global pipe
    global current_model_path
    if model_path != current_model_path or last_mode != "img2img":
        current_model_path = model_path

        if is_colab or current_model == custom_model:
          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
        else:
          pipe.to("cpu")
          pipe = current_model.pipe_i2i
        
        if torch.cuda.is_available():
          pipe = pipe.to("cuda")
        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)
    result = pipe(
        prompt,
        negative_prompt = neg_prompt,
        # num_images_per_prompt=n_images,
        init_image = img,
        num_inference_steps = int(steps),
        strength = strength,
        guidance_scale = guidance,
        width = width,
        height = height,
        generator = generator)
        
    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 = """.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%}.finetuned-diffusion-div p 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="finetuned-diffusion-div">
              <div>
                <h1>CycleDiffusion with Stable Diffusion</h1>
              </div>
              <p>
               Demo for CycleDiffusion with Stable Diffusion, built with Diffusers 🧨 by HuggingFace 🤗.
              </p>
              <p>You can skip the queue in the colab: <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
              </p>
            </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="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=[1], height="auto")

        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=4, step=1)

              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)

          with gr.Tab("Inpainting"):
            inpaint_image = gr.Image(source='upload', tool='sketch', type="pil", label="Upload").style(height=256)

    model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
    if is_colab:
      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)

    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, inpaint_image]
    prompt.submit(inference, inputs=inputs, outputs=image_out)
    generate.click(inference, inputs=inputs, outputs=image_out)

    ex = gr.Examples([
        [models[1].name, "jason bateman disassembling the demon core", 7.5, 50],
        [models[4].name, "portrait of dwayne johnson", 7.0, 75],
        [models[5].name, "portrait of a beautiful alyx vance half life", 10, 50],
        [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
        [models[5].name, "fantasy portrait painting, digital art", 4.0, 30],
    ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)

    gr.Markdown('''
      Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@haruu1367](https://twitter.com/haruu1367), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
      Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)
        
      ![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion)
    ''')

if not is_colab:
    demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab)