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from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler |
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import gradio as gr |
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import torch |
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from PIL import Image |
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import utils |
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import datetime |
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import time |
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import psutil |
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import random |
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start_time = time.time() |
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is_colab = utils.is_google_colab() |
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state = None |
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current_steps = 25 |
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class Model: |
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def __init__(self, name, path="", prefix=""): |
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self.name = name |
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self.path = path |
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self.prefix = prefix |
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self.pipe_t2i = None |
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self.pipe_i2i = None |
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models = [ |
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Model("Protogen x5.3 (Photorealism)", "darkstorm2150/Protogen_x5.3_Official_Release") |
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] |
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custom_model = None |
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if is_colab: |
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models.insert(0, Model("Custom model")) |
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custom_model = models[0] |
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last_mode = "txt2img" |
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current_model = models[1] if is_colab else models[0] |
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current_model_path = current_model.path |
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if is_colab: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model.path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), |
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safety_checker=lambda images, clip_input: (images, False) |
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) |
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else: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model.path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") |
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) |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" |
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def error_str(error, title="Error"): |
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return f"""#### {title} |
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{error}""" if error else "" |
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def update_state(new_state): |
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global state |
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state = new_state |
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def update_state_info(old_state): |
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if state and state != old_state: |
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return gr.update(value=state) |
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def custom_model_changed(path): |
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models[0].path = path |
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global current_model |
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current_model = models[0] |
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def on_model_change(model_name): |
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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!" |
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return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix) |
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def on_steps_change(steps): |
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global current_steps |
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current_steps = steps |
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def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor): |
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update_state(f"{step}/{current_steps} steps") |
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def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""): |
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update_state(" ") |
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print(psutil.virtual_memory()) |
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global current_model |
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for model in models: |
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if model.name == model_name: |
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current_model = model |
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model_path = current_model.path |
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if seed == 0: |
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seed = random.randint(0, 2147483647) |
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generator = torch.Generator('cuda').manual_seed(seed) |
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try: |
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if img is not None: |
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return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}" |
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else: |
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return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}" |
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except Exception as e: |
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return None, error_str(e) |
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def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed): |
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print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}") |
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global last_mode |
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global pipe |
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global current_model_path |
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if model_path != current_model_path or last_mode != "txt2img": |
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current_model_path = model_path |
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update_state(f"Loading {current_model.name} text-to-image model...") |
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if is_colab or current_model == custom_model: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), |
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safety_checker=lambda images, clip_input: (images, False) |
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) |
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else: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") |
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) |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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last_mode = "txt2img" |
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prompt = current_model.prefix + prompt |
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result = pipe( |
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prompt, |
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negative_prompt = neg_prompt, |
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num_images_per_prompt=n_images, |
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num_inference_steps = int(steps), |
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guidance_scale = guidance, |
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width = width, |
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height = height, |
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generator = generator, |
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callback=pipe_callback) |
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return replace_nsfw_images(result) |
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def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed): |
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print(f"{datetime.datetime.now()} img_to_img, model: {model_path}") |
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global last_mode |
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global pipe |
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global current_model_path |
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if model_path != current_model_path or last_mode != "img2img": |
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current_model_path = model_path |
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update_state(f"Loading {current_model.name} image-to-image model...") |
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if is_colab or current_model == custom_model: |
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), |
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safety_checker=lambda images, clip_input: (images, False) |
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) |
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else: |
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=torch.float16, |
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scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") |
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) |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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last_mode = "img2img" |
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prompt = current_model.prefix + prompt |
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ratio = min(height / img.height, width / img.width) |
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img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) |
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result = pipe( |
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prompt, |
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negative_prompt = neg_prompt, |
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num_images_per_prompt=n_images, |
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image = img, |
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num_inference_steps = int(steps), |
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strength = strength, |
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guidance_scale = guidance, |
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generator = generator, |
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callback=pipe_callback) |
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return replace_nsfw_images(result) |
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def replace_nsfw_images(results): |
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if is_colab: |
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return results.images |
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for i in range(len(results.images)): |
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if results.nsfw_content_detected[i]: |
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results.images[i] = Image.open("nsfw.png") |
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return results.images |
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with gr.Blocks(css="style.css") as demo: |
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gr.HTML( |
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f""" |
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<div class="finetuned-diffusion-div"> |
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<div> |
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<h1>Finetuned Diffusion</h1> |
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</div> |
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<p> |
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Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br> |
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<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 🤗. |
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</p> |
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<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> |
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Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")} |
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</p> |
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<p>You can also duplicate this space and upgrade to gpu by going to settings:<br> |
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<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> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=55): |
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with gr.Group(): |
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model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name) |
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with gr.Box(visible=False) as custom_model_group: |
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custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True) |
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gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>") |
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with gr.Row(): |
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prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False) |
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generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) |
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") |
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state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False) |
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error_output = gr.Markdown() |
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with gr.Column(scale=45): |
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with gr.Tab("Options"): |
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with gr.Group(): |
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neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") |
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n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1) |
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with gr.Row(): |
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) |
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steps = gr.Slider(label="Steps", value=current_steps, minimum=2, maximum=75, step=1) |
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with gr.Row(): |
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width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) |
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height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) |
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) |
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with gr.Tab("Image to image"): |
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with gr.Group(): |
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image = gr.Image(label="Image", height=256, tool="editor", type="pil") |
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) |
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if is_colab: |
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model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False) |
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custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None) |
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steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False) |
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inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt] |
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outputs = [gallery, error_output] |
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prompt.submit(inference, inputs=inputs, outputs=outputs) |
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generate.click(inference, inputs=inputs, outputs=outputs) |
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ex = gr.Examples([ |
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], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False) |
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gr.HTML(""" |
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<div style="border-top: 1px solid #303030;"> |
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<br> |
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<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> |
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<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> |
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<p>Space by:<br> |
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<a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br> |
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<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> |
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<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> |
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<p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p> |
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</div> |
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""") |
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demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False) |
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print(f"Space built in {time.time() - start_time:.2f} seconds") |
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demo.queue(concurrency_count=1) |
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demo.launch(debug=is_colab, share=is_colab) |
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