from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
import gradio as gr
import torch
from PIL import Image
import utils
is_colab = utils.is_google_colab()
max_width = 832
max_height = 832
class Model:
def __init__(self, name, path, prefix):
self.name = name
self.path = path
self.prefix = prefix
models = [
Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
Model("Modern Disney", "nitrosocke/modern-disney-diffusion", "modern disney style "),
Model("Classic Disney", "nitrosocke/classic-anim-diffusion", ""),
Model("Waifu", "hakurei/waifu-diffusion", ""),
Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
Model("Fuyuko Waifu", "yuk/fuyuko-waifu-diffusion", ""),
Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
Model("Robo Diffusion", "nousr/robo-diffusion", ""),
Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
Model("Hergé Style", "sd-dreambooth-library/herge-style", "herge_style "),
]
current_model = models[0]
pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
if img is not None:
return img_to_img(model_name, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
else:
return txt_to_img(model_name, prompt, neg_prompt, guidance, steps, width, height, generator)
def txt_to_img(model_name, prompt, neg_prompt, guidance, steps, width, height, generator=None):
global current_model
global pipe
if model_name != current_model.name:
for model in models:
if model.name == model_name:
current_model = model
pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
prompt = current_model.prefix + prompt
results = pipe(
prompt,
negative_prompt=neg_prompt,
num_inference_steps=int(steps),
guidance_scale=guidance,
width=width,
height=height,
generator=generator)
image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png")
return image
def img_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
global current_model
global pipe
if model_name != current_model.name:
for model in models:
if model.name == model_name:
current_model = model
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
prompt = current_model.prefix + prompt
ratio = min(max_height / img.height, max_width / img.width)
img = img.resize((int(img.width * ratio), int(img.height * ratio)))
results = pipe(
prompt,
negative_prompt=neg_prompt,
init_image=img,
num_inference_steps=int(steps),
strength=strength,
guidance_scale=guidance,
width=width,
height=height,
generator=generator)
image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png")
return image
css = """
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
f"""
Finetuned Diffusion
Demo for multiple fine-tuned Stable Diffusion models, trained on different styles:
Arcane, Archer, Elden Ring, Spiderverse, Modern Disney, Waifu, Pokemon, Fuyuko Waifu, Pony, Hergé (Tintin), Robo, Cyberpunk Anime
Running on {device}
"""
)
with gr.Row():
with gr.Column():
model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=models[0].name)
prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically")
run = gr.Button(value="Run")
with gr.Tab("Options"):
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2, step=1)
width = gr.Slider(label="Width", value=512, maximum=max_width, minimum=64, step=8)
height = gr.Slider(label="Height", value=512, maximum=max_height, minimum=64, step=8)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
with gr.Tab("Image to image"):
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.Column():
image_out = gr.Image(height=512)
inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
prompt.submit(inference, inputs=inputs, outputs=image_out, scroll_to_output=True)
run.click(inference, inputs=inputs, outputs=image_out, scroll_to_output=True)
gr.Examples([
[models[0].name, "jason bateman disassembling the demon core", 7.5, 50],
[models[3].name, "portrait of dwayne johnson", 7.0, 75],
[models[4].name, "portrait of a beautiful alyx vance half life", 10, 50],
[models[5].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
[models[4].name, "fantasy portrait painting, digital art", 4.0, 30],
], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=not is_colab and torch.cuda.is_available())
gr.Markdown('''
Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️
Space by: [](https://twitter.com/hahahahohohe)
''')
if not is_colab:
demo.queue(concurrency_count=4)
demo.launch(debug=is_colab)