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| from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import utils | |
| import datetime | |
| import time | |
| import psutil | |
| start_time = time.time() | |
| is_colab = utils.is_google_colab() | |
| class Model: | |
| def __init__(self, name, path="", prefix=""): | |
| self.name = name | |
| self.path = path | |
| self.prefix = prefix | |
| self.pipe_t2i = None | |
| self.pipe_i2i = None | |
| models = [ | |
| Model("Yoji Shinkawa", "Evel/YoShin", "yoshin style"), | |
| ] | |
| # Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "), | |
| # Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "), | |
| # Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "), | |
| # Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ") | |
| #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""), | |
| #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""), | |
| #Model("Robo Diffusion", "nousr/robo-diffusion", ""), | |
| scheduler = DPMSolverMultistepScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| num_train_timesteps=1000, | |
| trained_betas=None, | |
| predict_epsilon=True, | |
| thresholding=False, | |
| algorithm_type="dpmsolver++", | |
| solver_type="midpoint", | |
| lower_order_final=True, | |
| ) | |
| 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, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) | |
| else: # download all models | |
| print(f"{datetime.datetime.now()} Downloading vae...") | |
| vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16) | |
| for model in models: | |
| try: | |
| print(f"{datetime.datetime.now()} Downloading {model.name} model...") | |
| unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16) | |
| model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler) | |
| model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler) | |
| except Exception as e: | |
| print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e)) | |
| models.remove(model) | |
| pipe = models[0].pipe_t2i | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def error_str(error, title="Error"): | |
| return f"""#### {title} | |
| {error}""" if error else "" | |
| 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 inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""): | |
| 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 | |
| try: | |
| if img is not None: | |
| return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None | |
| else: | |
| return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None | |
| except Exception as e: | |
| return None, error_str(e) | |
| def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator): | |
| 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 | |
| if is_colab or current_model == custom_model: | |
| pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) | |
| else: | |
| pipe = pipe.to("cpu") | |
| pipe = current_model.pipe_t2i | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| last_mode = "txt2img" | |
| prompt = current_model.prefix + prompt | |
| result = pipe( | |
| prompt, | |
| negative_prompt = neg_prompt, | |
| # num_images_per_prompt=n_images, | |
| num_inference_steps = int(steps), | |
| guidance_scale = guidance, | |
| width = width, | |
| height = height, | |
| generator = generator) | |
| return replace_nsfw_images(result) | |
| def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): | |
| 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 | |
| if is_colab or current_model == custom_model: | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False)) | |
| else: | |
| pipe = 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): | |
| if is_colab: | |
| return results.images[0] | |
| 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%}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>Finetuned Diffusion</h1> | |
| </div> | |
| <p> | |
| Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br> | |
| <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/mo-di-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/dallinmackay/Van-Gogh-diffusion">Loving Vincent (Van Gogh)</a>, <a href="https://huggingface.co/nitrosocke/redshift-diffusion">Redshift renderer (Cinema4D)</a>, <a href="https://huggingface.co/prompthero/midjourney-v4-diffusion">Midjourney v4 style</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a>, <a href="https://huggingface.co/Fictiverse/Stable_Diffusion_BalloonArt_Model">Balloon Art</a> + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗. | |
| </p> | |
| <p>You can skip the queue and load custom models 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(): | |
| model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name) | |
| with gr.Box(visible=False) as custom_model_group: | |
| custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True) | |
| gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>") | |
| 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") | |
| 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=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) | |
| 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) | |
| inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| ex = gr.Examples([ | |
| [models[0].name, "iron man", 7.5, 50], | |
| ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False) | |
| gr.HTML(""" | |
| <div style="border-top: 1px solid #303030;"> | |
| <br> | |
| <p>Models by <a href="https://huggingface.co/nitrosocke">@nitrosocke</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others. ❤️</p> | |
| <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p><br> | |
| <p>Space by: <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a></p><br> | |
| <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br> | |
| <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p> | |
| </div> | |
| """) | |
| 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=is_colab) | |