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("EimisAnimeDiffusion_1.0v", "eimiss/EimisAnimeDiffusion_1.0v", "EimisAnimeDiffusion_1.0v 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"""

EimisAnimeDiffusion_1.0v

Demo for EimisAnimeDiffusion_1.0v

You can skip the queue by duplicating this space: Duplicate Space

""" ) 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("
Custom models have to be downloaded first, so give it some time.
") 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("""

Model by Linaqruf

""") 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)