File size: 2,980 Bytes
2c19098
 
26063e6
2c19098
cebef55
9458c70
26063e6
 
 
2c19098
4cb8223
f581424
4cb8223
 
2c19098
 
7bd0a5a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2c19098
088c386
2c19098
4cb8223
2c19098
 
 
088c386
2c19098
4cb8223
2c19098
4cb8223
2c19098
f581424
 
 
 
 
4cb8223
40a8b65
 
 
 
 
 
 
 
 
 
2c19098
 
e7f153f
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
from diffusers import StableDiffusionPipeline
import gradio as gr
import torch

device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"

pipe = StableDiffusionPipeline.from_pretrained("nitrosocke/Arcane-Diffusion", torch_dtype=torch.float16)
if torch.cuda.is_available():
  pipe = pipe.to("cuda")

def inference(prompt, guidance, steps):
    prompt = prompt + ", arcane style"
    image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
    return image

with gr.Blocks() as demo:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Arcane Diffusion
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
               Demo for a fine-tuned Stable Diffusion model trained on images from the TV Show Arcane.
              </p>
            </div>
        """
    )
    with gr.Row():
        
        with gr.Column():
            prompt = gr.Textbox(label="prompt", placeholder="' , arcane style' is appended automatically")
            guidance = gr.Slider(label="guidance scale", value=7.5, maximum=15)
            steps = gr.Slider(label="steps", value=50, maximum=100, minimum=2)
            run = gr.Button(value="Run")
            gr.Markdown(f"Running on: {device}")
        with gr.Column():
            image_out = gr.Image(height=512)

    run.click(inference, inputs=[prompt, guidance, steps], outputs=image_out)
    gr.Examples([
        ["jason bateman disassembling the demon core", 7.5, 50],
        ["portrait of dwayne johnson", 7.0, 75],
        ["portrait of a beautiful alyx vance half life, volume lighting, concept art, by greg rutkowski!!, colorful, xray melting colors!!", 7, 50],
        ["Aloy from Horizon: Zero Dawn, half body portrait, videogame cover art, highly detailed, digital painting, artstation, concept art, smooth, detailed armor, sharp focus, beautiful face, illustration, art by Artgerm and greg rutkowski and alphonse mucha", 7, 50],
        ["fantasy portrait painting, digital art", 4, 30],
    ], [prompt, guidance, steps], image_out, inference, cache_examples=torch.cuda.is_available())
    gr.HTML('''
        <div>
            <p>Model by <a href="https://huggingface.co/nitrosocke" style="text-decoration: underline;" target="_blank">@nitrosocke</a> ❤️</p>
        </div>
        <div>Space by 
            <a href="https://twitter.com/hahahahohohe">
              <img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social">
            </a>
        </div>
        ''')

demo.queue()
demo.launch()