from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
import gradio as gr
import torch
models = [
"nitrosocke/Arcane-Diffusion",
"nitrosocke/archer-diffusion",
"nitrosocke/elden-ring-diffusion",
"nitrosocke/spider-verse-diffusion",
"nitrosocke/modern-disney-diffusion",
"hakurei/waifu-diffusion",
"lambdalabs/sd-pokemon-diffusers",
"yuk/fuyuko-waifu-diffusion",
"AstraliteHeart/pony-diffusion",
"nousr/robo-diffusion",
"DGSpitzer/Cyberpunk-Anime-Diffusion",
"sd-dreambooth-library/herge-style"
]
prompt_prefixes = {
models[0]: "arcane style ",
models[1]: "archer style ",
models[2]: "elden ring style ",
models[3]: "spiderverse style ",
models[4]: "modern disney style ",
models[5]: "",
models[6]: "",
models[7]: "",
models[8]: "",
models[9]: "",
models[10]: "dgs illustration style ",
models[11]: "herge_style ",
}
current_model = models[0]
pipe = StableDiffusionPipeline.from_pretrained(current_model, 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, img, strength, prompt, neg_prompt, guidance, steps, width, height, seed):
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
if img is not None:
return txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
else:
return img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator)
def img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator=None):
global current_model
global pipe
if model != current_model:
current_model = model
pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
prompt = prompt_prefixes[current_model] + prompt
image = pipe(
prompt,
negative_prompt=neg_prompt,
num_inference_steps=int(steps),
guidance_scale=guidance,
width=width,
height=height,
generator=generator).images[0]
return image
def txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
global current_model
global pipe
if model != current_model:
current_model = model
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
prompt = prompt_prefixes[current_model] + prompt
ratio = min(height / img.height, width / img.width)
img = img.resize((int(img.width * ratio), int(img.height * ratio)))
image = 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).images[0]
return image
css = """
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
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
"""
)
with gr.Row():
with gr.Column():
model = gr.Dropdown(label="Model", choices=models, value=models[0])
prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically")
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)
width = gr.Slider(label="Width", value=512, maximum=1024, minimum=64)
height = gr.Slider(label="Height", value=512, maximum=1024, minimum=64)
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)
run = gr.Button(value="Run")
gr.Markdown(f"Running on: {device}")
inputs = [model, image, strength, prompt, neg_prompt, guidance, steps, width, height, seed]
prompt.submit(inference, inputs=inputs, outputs=image_out)
run.click(inference, inputs=inputs, outputs=image_out)
gr.Examples([
[models[0], "jason bateman disassembling the demon core", 7.5, 50],
[models[3], "portrait of dwayne johnson", 7.0, 75],
[models[4], "portrait of a beautiful alyx vance half life", 10, 50],
[models[5], "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7, 45],
[models[4], "fantasy portrait painting, digital art", 4, 30],
], [model, prompt, guidance, steps], image_out, img_to_img, cache_examples=False)
gr.Markdown('''
Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️
Space by: [](https://twitter.com/hahahahohohe)
''')
demo.queue()
demo.launch()