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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 | |
| import random | |
| start_time = time.time() | |
| is_colab = utils.is_google_colab() | |
| state = None | |
| current_steps = 25 | |
| 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("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "), | |
| Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "), | |
| Model("Archer", "nitrosocke/archer-diffusion", "archer style "), | |
| Model("Anything V4", "andite/anything-v4.0", ""), | |
| Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style "), | |
| Model("Classic Disney", "nitrosocke/classic-anim-diffusion", "classic disney style "), | |
| Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "), | |
| Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "), | |
| Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "), | |
| Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "), | |
| Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "), | |
| Model("Waifu", "hakurei/waifu-diffusion"), | |
| Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "), | |
| Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "), | |
| Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"), | |
| Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "), | |
| Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "), | |
| 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"), | |
| Model("Epic Diffusion", "johnslegers/epic-diffusion") | |
| ] | |
| 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=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), | |
| safety_checker=lambda images, clip_input: (images, False) | |
| ) | |
| else: | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| current_model.path, | |
| torch_dtype=torch.float16, | |
| scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") | |
| ) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def error_str(error, title="Error"): | |
| return f"""#### {title} | |
| {error}""" if error else "" | |
| def update_state(new_state): | |
| global state | |
| state = new_state | |
| def update_state_info(old_state): | |
| if state and state != old_state: | |
| return gr.update(value=state) | |
| 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 on_steps_change(steps): | |
| global current_steps | |
| current_steps = steps | |
| def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor): | |
| update_state(f"{step}/{current_steps} steps")#\nTime left, sec: {timestep/100:.0f}") | |
| def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""): | |
| update_state(" ") | |
| 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 | |
| if seed == 0: | |
| seed = random.randint(0, 2147483647) | |
| generator = torch.Generator('cuda').manual_seed(seed) | |
| try: | |
| if img is not None: | |
| return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}" | |
| else: | |
| return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}" | |
| except Exception as e: | |
| return None, error_str(e) | |
| def load_model(model_path, mode): | |
| global last_mode | |
| global pipe | |
| global current_model_path | |
| if model_path != current_model_path or last_mode != mode: | |
| current_model_path = model_path | |
| update_state(f"Loading {current_model.name} {mode} model...") | |
| model_class = StableDiffusionPipeline if mode == "txt2img" else StableDiffusionImg2ImgPipeline | |
| if is_colab or current_model == custom_model: | |
| pipe = model_class.from_pretrained( | |
| current_model_path, | |
| torch_dtype=torch.float16, | |
| scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), | |
| safety_checker=lambda images, clip_input: (images, False) | |
| ) | |
| else: | |
| pipe = model_class.from_pretrained( | |
| current_model_path, | |
| torch_dtype=torch.float16, | |
| scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") | |
| ) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| last_mode = mode | |
| def process_image(img, width, height): | |
| ratio = min(height / img.height, width / img.width) | |
| return img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) | |
| def inference_image(model_path, prompt, mode, *args, **kwargs): | |
| print(f"{datetime.datetime.now()} {mode}, model: {model_path}") | |
| global pipe | |
| load_model(model_path, mode) | |
| prompt = current_model.prefix + prompt3 | |
| result = pipe( | |
| prompt, | |
| *args, | |
| **kwargs, | |
| callback=pipe_callback | |
| ) | |
| return replace_nsfw_images(result) | |
| def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed): | |
| return inference_image(model_path, prompt, "txt2img", | |
| negative_prompt=neg_prompt, | |
| num_images_per_prompt=n_images, | |
| num_inference_steps=int(steps), | |
| guidance_scale=guidance, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| seed=seed | |
| ) | |
| def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed): | |
| img = process_image(img, width, height) | |
| return inference_image(model_path, prompt, "img2img", | |
| negative_prompt=neg_prompt, | |
| num_images_per_prompt=n_images, | |
| image=img, | |
| num_inference_steps=int(steps), | |
| strength=strength, | |
| guidance_scale=guidance, | |
| generator=generator, | |
| seed=seed | |
| ) | |
| def replace_nsfw_images(results): | |
| if is_colab: | |
| return results.images | |
| for i in range(len(results.images)): | |
| if results.nsfw_content_detected[i]: | |
| results.images[i] = Image.open("nsfw.png") | |
| return results.images | |
| # 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="style.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> | |
| <p>You can also duplicate this space and upgrade to gpu by going to settings:<br> | |
| <a style="display:inline-block" href="https://huggingface.co/spaces/anzorq/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></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=[2], height="auto") | |
| state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False) | |
| 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=current_steps, 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) | |
| steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False) | |
| inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt] | |
| outputs = [gallery, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| ex = gr.Examples([ | |
| [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25], | |
| [models[4].name, "portrait of dwayne johnson", 7.0, 35], | |
| [models[5].name, "portrait of a beautiful alyx vance half life", 10, 25], | |
| [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30], | |
| [models[5].name, "fantasy portrait painting, digital art", 4.0, 20], | |
| ], inputs=[model_name, prompt, guidance, steps], 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> | |
| <p>Space by:<br> | |
| <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br> | |
| <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br><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> | |
| """) | |
| demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False) | |
| 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) | |