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from email import generator
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",
"IfanSnek/JohnDiffusion",
"nousr/robo-diffusion",
"DGSpitzer/Cyberpunk-Anime-Diffusion"
]
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]: "",
models[11]: "dgs illustration 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, guidance, steps, seed):
generator = torch.manual_seed(seed) if seed != 0 else None
if img is not None:
return img_inference(model, prompt, img, strength, guidance, steps, generator)
else:
return text_inference(model, prompt, guidance, steps, generator)
def text_inference(model, prompt, guidance, steps, 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,
num_inference_steps=int(steps),
guidance_scale=guidance,
width=512,
height=512,
generator=generator).images[0]
return image
def img_inference(model, prompt, img, strength, guidance, steps, 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
img.resize((512, 512))
image = pipe(
prompt,
init_image=img,
num_inference_steps=int(steps),
strength=strength,
guidance_scale=guidance,
width=512,
height=512,
generator=generator).images[0]
return image
css = """
<style>
.finetuned-diffusion-div {
text-align: center;
max-width: 700px;
margin: 0 auto;
}
.finetuned-diffusion-div div {
display: inline-flex;
align-items: center;
gap: 0.8rem;
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%;
}
.finetuned-diffusion-div p a {
text-decoration: underline;
}
</style>
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<div class="finetuned-diffusion-div">
<div>
<h1>Finetuned Diffusion</h1>
</div>
<p>
Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
<a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spiderverse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokemon</a>, <a href="https://huggingface.co/yuk/fuyuko-waifu-diffusion">Fuyuko Waifu</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony</a>, <a href="https://huggingface.co/IfanSnek/JohnDiffusion">John</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>
</p>
</div>
"""
)
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.Accordion("Image to image (optional)", open=False):
image = gr.Image(label="Image", height=256, tool="editor", type="pil")
strength = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=0.75)
with gr.Accordion("Advanced options", open=False):
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
run = gr.Button(value="Run")
gr.Markdown(f"Running on: {device}")
with gr.Column():
image_out = gr.Image(height=512)
prompt.submit(inference, inputs=[model, image, strength, prompt, guidance, steps, seed], outputs=image_out)
run.click(inference, inputs=[model, image, strength, prompt, guidance, steps, seed], 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, text_inference, cache_examples=False)#torch.cuda.is_available())
gr.Markdown('''
Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)
![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion)
''')
demo.queue()
demo.launch()