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()

if True:
    model_id_or_path = "CompVis/stable-diffusion-v1-4"
    scheduler = DDIMScheduler.from_config(model_id_or_path,
                                          use_auth_token=st.secrets["USER_TOKEN"],
                                          subfolder="scheduler")
    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(source_prompt, target_prompt, source_guidance_scale=1, guidance_scale=5, num_inference_steps=100,
              width=512, height=512, seed=0, img=None, strength=0.7):

    torch.manual_seed(seed)

    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)))

    result = pipe(prompt=target_prompt,
                  source_prompt=source_prompt,
                  init_image=img,
                  num_inference_steps=num_inference_steps,
                  eta=0.1,
                  strength=strength,
                  guidance_scale=guidance_scale,
                  source_guidance_scale=source_guidance_scale,
                  ).images[0]

    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 = """.cycle-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.cycle-diffusion-div div h1{font-weight:900;margin-bottom:7px}.cycle-diffusion-div p{margin-bottom:10px;font-size:94%}.cycle-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="cycle-diffusion-div">
              <div>
                <h1>CycleDiffusion with Stable Diffusion</h1>
              </div>
              <p>
                Demo for CycleDiffusion with Stable Diffusion, built with Diffusers 🧨 by HuggingFace 🤗. <br>
                <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">Pipeline doc</a> | <a href="https://arxiv.org/abs/2210.05559">Paper</a>
              </p>
              <p>You can skip the queue in the colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/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://colab.research.google.com/assets/colab-badge.svg"></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():

                img = gr.Image(label="Input image", height=512, tool="editor", type="pil")

                image_out = gr.Image(label="Output 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():
                    with gr.Row():
                        source_prompt = gr.Textbox(label="Source prompt", placeholder="Source prompt describes the input image")
                    with gr.Row():
                        target_prompt = gr.Textbox(label="Target prompt", placeholder="Target prompt describes the output image")

                    with gr.Row():
                        source_guidance_scale = gr.Slider(label="Source guidance scale", value=1, minimum=1, maximum=10)
                        guidance_scale = gr.Slider(label="Target guidance scale", value=5, minimum=1, maximum=10)

                    with gr.Row():
                        num_inference_steps = gr.Slider(label="Number of inference steps", value=100, minimum=25, maximum=500, step=1)
                        strength = gr.Slider(label="Strength", value=0.7, minimum=0.5, maximum=1, step=0.01)

                    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)

                    with gr.Row():
                        seed = gr.Slider(0, 2147483647, label='Seed', value=0, step=1)
                        generate = gr.Button(value="Edit")

    inputs = [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps,
              width, height, seed, img, strength]
    generate.click(inference, inputs=inputs, outputs=image_out)

    ex = gr.Examples(
        [
            ["An astronaut riding a horse", "An astronaut riding an elephant", 1, 2, 100, 512, 512, 0, "images/astronaut_horse.png", 0.8],
        ],
        [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps,
         width, height, seed, img, strength],
        image_out, inference, cache_examples=False)

    gr.Markdown('''
      Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/ChenHenryWu?style=social)](https://twitter.com/ChenHenryWu)
        
      ![visitors](https://visitor-badge.glitch.me/badge?page_id=ChenWu98.CycleDiffusion)
    ''')

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