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("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "), Model("Dreamlike Photoreal 2.0", "dreamlike-art/dreamlike-photoreal-2.0", ""), Model("Eimis Anime 1.0", "flax/EimisAnimeDiffusion_1.0v", ""), Model("Eimis SemiRealistic", "eimiss/EimisSemiRealistic", ""), Model("Portrait Plus", "wavymulder/portraitplus", "portrait+ style "), Model("Protogen 5.3 (for plain realism, a bit bland)", "darkstorm2150/Protogen_v5.3_Official_Release", ""), Model("Protogen 5.8 (for realism, but toward fantasy)", "darkstorm2150/Protogen_v5.8_Official_Release", ""), Model("Protogen Dragon (for fantasy)", "darkstorm2150/Protogen_Dragon_Official_Release", ""), Model("Protogen Nova (the all in one)", "darkstorm2150/Protogen_Nova_Official_Release", ""), Model("Seek.Art Mega", "coreco/seek.art_MEGA", ""), Model("Uber Realistic Porn Merge","PrimaPramudya/uberRealisticPrnMer_urpMv11", ""), Model("Vintedois 0.1", "22h/vintedois-diffusion-v0-1", ""), Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "), Model("Anything V3", "Linaqruf/anything-v3.0", ""), Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "), Model("Archer", "nitrosocke/archer-diffusion", "archer style "), Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "), Model("Disney, modern", "nitrosocke/mo-di-diffusion", "modern disney style "), Model("Disney, Classic", "nitrosocke/classic-anim-diffusion", "classic disney style "), Model("DnD Item", "stale2000/sd-dnditem", "dnditem "), Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "), Model("f222 Zeipfher", "m4gnett/zeipher-f222", ""), Model("f222 + Anything V3", "m4gnett/anything-of-f222", ""), Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "), Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "), Model("Pokémon", "lambdalabs/sd-pokemon-diffusers"), Model("Pony Diffusion", "AstraliteHeart/pony-diffusion"), Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "), Model("Robo Diffusion", "nousr/robo-diffusion"), Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "), Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"), Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy "), Model("Waifu", "hakurei/waifu-diffusion"), Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "), Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "), Model("Anything V3 Better-Vae", "Linaqruf/anything-v3-better-vae", ""), Model("Anything V4", "andite/anything-v4.0", ""), Model("Cyberpunk Anime with Genshin Characters supported", "AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters", "cyberpunk style"), Model("Dark Souls", "Guizmus/DarkSoulsDiffusion", "dark souls style"), Model("Space Machine", "rabidgremlin/sd-db-epic-space-machine", "EpicSpaceMachine"), Model("Spacecraft", "rabidgremlin/sd-db-epic-space-machine, Guizmus/Tardisfusion", "EpicSpaceMachine, Tardis Box style"), Model("TARDIS", "Guizmus/Tardisfusion", "Tardis Box style"), Model("Modern Era TARDIS Interior", "Guizmus/Tardisfusion", "Modern Tardis style"), Model("Classic Era TARDIS Interior", "Guizmus/Tardisfusion", "Classic Tardis style"), Model("Spacecraft Interior", "Guizmus/Tardisfusion, rabidgremlin/sd-db-epic-space-machine", "Classic Tardis style, Modern Tardis style, EpicSpaceMachine"), Model("CLIP", "EleutherAI/clip-guided-diffusion", "CLIP"), Model("Genshin Waifu", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, katakana/2D-Mix, Guizmus/AnimeChanStyle", "Female, female, Woman, woman, Girl, girl"), Model("Genshin", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, katakana/2D-Mix, Guizmus/AnimeChanStyle", ""), Model("Test", "AdamOswald1/Idk", ""), Model("Test2", "AdamOswald1/Tester", ""), Model("Anime", "Guizmus/AnimeChanStyle, katakana/2D-Mix", ""), Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "), Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "), Model("Poolsuite", "prompthero/poolsuite", "poolsuite style "), Model("Epic Diffusion", "johnslegers/epic-diffusion", ""), Model("Comic Diffusion", "ogkalu/Comic-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=None ) 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 txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed): 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 update_state(f"Loading {current_model.name} text-to-image model...") if is_colab or current_model == custom_model: pipe = StableDiffusionPipeline.from_pretrained( current_model_path, torch_dtype=torch.float16, scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), safety_checker=None ) else: pipe = StableDiffusionPipeline.from_pretrained( current_model_path, torch_dtype=torch.float16, scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") ) # pipe = pipe.to("cpu") # pipe = current_model.pipe_t2i if torch.cuda.is_available(): pipe = pipe.to("cuda") pipe.enable_xformers_memory_efficient_attention() 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, callback=pipe_callback) # update_state(f"Done. Seed: {seed}") return replace_nsfw_images(result) def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed): 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 update_state(f"Loading {current_model.name} image-to-image model...") if is_colab or current_model == custom_model: pipe = StableDiffusionImg2ImgPipeline.from_pretrained( current_model_path, torch_dtype=torch.float16, scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"), safety_checker=None ) else: pipe = StableDiffusionImg2ImgPipeline.from_pretrained( current_model_path, torch_dtype=torch.float16, scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler") ) # pipe = pipe.to("cpu") # pipe = current_model.pipe_i2i if torch.cuda.is_available(): pipe = pipe.to("cuda") pipe.enable_xformers_memory_efficient_attention() 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, callback=pipe_callback) # update_state(f"Done. Seed: {seed}") return replace_nsfw_images(result) 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"""

Finetuned Diffusion

Demo for multiple fine-tuned Stable Diffusion models, trained on different styles:
Arcane, Archer, Elden Ring, Spider-Verse, Modern Disney, Classic Disney, Loving Vincent (Van Gogh), Redshift renderer (Cinema4D), Midjourney v4 style, Waifu, Pokémon, Pony Diffusion, Robo Diffusion, Cyberpunk Anime, Tron Legacy, Balloon Art + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.

You can skip the queue and load custom models in the colab: Open In Colab

Running on {device}{(" in a Google Colab." if is_colab else "")}

You can also duplicate this space and upgrade to gpu by going to settings:
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=[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=10, 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=250, step=1) with gr.Row(): width = gr.Slider(label="Width", value=512, minimum=64, maximum=2048, step=8) height = gr.Slider(label="Height", value=512, minimum=64, maximum=2048, 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("""

Models by @nitrosocke, @haruu1367, @Helixngc7293, @dal_mack, @prompthero and others. ❤️

This space uses the DPM-Solver++ sampler by Cheng Lu, et al..

Space by:
Twitter Follow
GitHub followers



Buy Me A Coffee

visitors

""") 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)