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| 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 False: | |
| model_id_or_path = "CompVis/stable-diffusion-v1-4" | |
| scheduler = DDIMScheduler.from_config(model_id_or_path, 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 🤗. | |
| </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: [](https://twitter.com/ChenHenryWu) | |
|  | |
| ''') | |
| if not is_colab: | |
| demo.queue(concurrency_count=1) | |
| demo.launch(debug=is_colab, share=is_colab) | |