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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() | |
| 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: [](https://twitter.com/hahahahohohe) | |
|  | |
| ''') | |
| if not is_colab: | |
| demo.queue(concurrency_count=1) | |
| demo.launch(debug=is_colab, share=is_colab) | |