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| import os | |
| import random | |
| import autocuda | |
| from pyabsa.utils.pyabsa_utils import fprint | |
| 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 | |
| from Waifu2x.magnify import ImageMagnifier | |
| magnifier = ImageMagnifier() | |
| start_time = time.time() | |
| is_colab = utils.is_google_colab() | |
| CUDA_VISIBLE_DEVICES = '' | |
| device = autocuda.auto_cuda() | |
| dtype = torch.float16 if device != 'cpu' else torch.float32 | |
| 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("DnD Cover Art", "sd-dreambooth-library/dndcoverart-v1", "Use the token 'dndcoverart'"), | |
| Model("Vivid Watercolors", "Evel/VividWatercolors", "watercolor style"), | |
| Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "), | |
| Model("Comic Diffusion", "ogkalu/Comic-Diffusion", "Comic Book Styles"), | |
| Model("Deliberate civitai", "XpucT/Deliberate", "none"), | |
| Model("Experience V8 civitai", "n0madic/experience-v8", "none"), | |
| Model("A-Zovya RPG Artist Tools civitai", "danbrown/A-to-Zovya-RPG-v1-5", "none"), | |
| Model("U Pron", "stablediffusionapi/urpm", "pron"), | |
| Model("U Pron2", "lilpotat/urp", "pron2"), | |
| Model("test", "Jackflack09/mrsrm1", "testing"), | |
| Model("test2", "Jackflack09/mrsrm", "testing2"), | |
| ] | |
| # 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=dtype, 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=dtype) | |
| for model in models: | |
| try: | |
| print(f"{datetime.datetime.now()} Downloading {model.name} model...") | |
| unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=dtype) | |
| model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, | |
| torch_dtype=dtype, scheduler=scheduler, | |
| safety_checker=None) | |
| model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, | |
| torch_dtype=dtype, | |
| scheduler=scheduler, safety_checker=None) | |
| 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 | |
| # model.pipe_i2i = torch.compile(model.pipe_i2i) | |
| # model.pipe_t2i = torch.compile(model.pipe_t2i) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to(device) | |
| # 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="", scale_factor=2): | |
| fprint(psutil.virtual_memory()) # print memory usage | |
| prompt = 'detailed fingers, beautiful hands,' + prompt | |
| fprint(f"Prompt: {prompt}") | |
| global current_model | |
| for model in models: | |
| if model.name == model_name: | |
| current_model = model | |
| model_path = current_model.path | |
| generator = torch.Generator(device).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, scale_factor), None | |
| else: | |
| return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, | |
| scale_factor), None | |
| except Exception as e: | |
| return None, error_str(e) | |
| # if img is not None: | |
| # return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, | |
| # generator, scale_factor), None | |
| # else: | |
| # return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor), None | |
| def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor): | |
| 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=dtype, | |
| 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(device) | |
| 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) | |
| result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor) | |
| # save image | |
| result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))) | |
| return replace_nsfw_images(result) | |
| def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator, scale_factor): | |
| fprint(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=dtype, | |
| 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(device) | |
| 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, | |
| image=img, | |
| num_inference_steps=int(steps), | |
| strength=strength, | |
| guidance_scale=guidance, | |
| # width=width, | |
| # height=height, | |
| generator=generator) | |
| result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor) | |
| # save image | |
| result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))) | |
| return replace_nsfw_images(result) | |
| def replace_nsfw_images(results): | |
| if is_colab: | |
| return results.images[0] | |
| if hasattr(results, "nsfw_content_detected") and results.nsfw_content_detected: | |
| 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: | |
| if not os.path.exists('imgs'): | |
| os.mkdir('imgs') | |
| gr.Markdown('# Super Resolution Anime Diffusion') | |
| gr.Markdown( | |
| "## Author: [yangheng95](https://github.com/yangheng95) Github:[Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion)") | |
| gr.Markdown("### This demo is running on a CPU, so it will take at least 20 minutes. " | |
| "If you have a GPU, you can clone from [Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion) and run it locally.") | |
| gr.Markdown("### FYI: to generate a 512*512 image and magnify 4x, it only takes 5~8 seconds on a RTX 2080 GPU") | |
| gr.Markdown( | |
| "### You can duplicate this demo on HuggingFace Spaces, click [here](https://huggingface.co/spaces/yangheng/Super-Resolution-Anime-Diffusion?duplicate=true)") | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| gr.Markdown("Text to image") | |
| 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) | |
| with gr.Row(): | |
| generate = gr.Button(value="Generate") | |
| with gr.Row(): | |
| with gr.Group(): | |
| neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") | |
| 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.Group(): | |
| gr.Markdown("Image to Image") | |
| with gr.Row(): | |
| 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.Row(): | |
| with gr.Group(): | |
| # 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=15, 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) | |
| with gr.Row(): | |
| scale_factor = gr.Slider(1, 8, label='Scale factor (to magnify image) (1, 2, 4, 8)', | |
| value=2, | |
| step=1) | |
| seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) | |
| 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) | |
| gr.Markdown("### based on [Anything V3](https://huggingface.co/Linaqruf/anything-v3.0)") | |
| inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, scale_factor] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs, api_name="generate") | |
| prompt_keys = [ | |
| 'girl', 'lovely', 'cute', 'beautiful eyes', 'cumulonimbus clouds', 'detailed fingers', | |
| random.choice(['dress']), | |
| random.choice(['white hair']), | |
| random.choice(['blue eyes']), | |
| random.choice(['flower meadow']), | |
| random.choice(['Elif', 'Angel']) | |
| ] | |
| prompt.value = ','.join(prompt_keys) | |
| ex = gr.Examples([ | |
| [models[0].name, prompt.value, 7.5, 15], | |
| ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False) | |
| print(f"Space built in {time.time() - start_time:.2f} seconds") | |
| if not is_colab: | |
| demo.queue(concurrency_count=2) | |
| demo.launch(debug=is_colab, enable_queue=True, share=is_colab) | |