from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler import gradio as gr import torch from PIL import Image import datetime import time import psutil start_time = time.time() 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("Cool Japanese Diffusion", "alfredplpl/cool-japan-diffusion-for-learning-2-0", "Cool Japanese 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 last_mode = "txt2img" current_model = models[0] current_model_path = current_model.path print(f"{datetime.datetime.now()} Downloading vae...") vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae") for model in models: try: print(f"{datetime.datetime.now()} Downloading {model.name} model...") unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet") model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, scheduler=scheduler) model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, 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 = "Running on GPU 🔥" if torch.cuda.is_available() else "Running on 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 = "プロンプトを入力" + 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()) 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 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 current_model == custom_model: pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, 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): 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: 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("
Model by TopdeckingLands.