import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
import torch
from typing import Tuple
style_list = [
{
"name": "(No style)",
"prompt": "{prompt}",
"negative_prompt": "",
},
{
"name": "Cinematic",
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
},
{
"name": "Photographic",
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
},
{
"name": "Anime",
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
},
{
"name": "Digital Art",
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
"negative_prompt": "photo, photorealistic, realism, ugly",
},
{
"name": "Fantasy art",
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
},
{
"name": "3D Model",
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
},
]
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"
def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
return p.replace("{prompt}", positive), n + negative
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt, negative_prompt, width, height, guidance_scale, style_name=None):
seed = random.randint(0,4294967295)
generator = torch.Generator().manual_seed(seed)
prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
image = [pipe(
prompt = prompt,
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
width = width,
height = height,
generator = generator
).images[0] for _ in range(4)]
return image
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
"A serious capybara at work, wearing a suit",
'A Squirtle fine dining with a view to the London Eye',
'a graffiti of a robot serving meals to people',
'a beautiful cabin in Attersee, Austria, 3d animation style',
]
css="""
#col-container {
margin: 0 auto;
max-width: 1000px;
padding-top: 20px;
text-align: center;
}
.header {
margin: 10px auto 10px auto;
text-align: center;
max-width: 600px;
}
#example-container {
max-width: 1000px;
margin: 0 auto;
}
.footer {
margin: 25px auto 45px auto;
text-align: center;
max-width: 600px;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(
"""
<div class="header">
<h1>Welcome to Metamorph: Your Creative Gateway</h1>
<h4>
Transform your words into stunning visuals with our advanced AI-powered Text-to-Image generator
</h4>
</div>
""")
gr.Markdown(f"""
Currently running on {power_device}.
""")
with gr.Row(elem_id="col-container"):
# Left column
with gr.Column(scale=1,elem_id="left-container"):
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Generate", scale=0)
with gr.Accordion("Advanced Settings", open=True):
negative_prompt = gr.Textbox(
label="Negative prompt",
show_label=False,
max_lines=1,
placeholder="Enter a negative prompt",
elem_id="negative-prompt-text-input",
)
style_selection = gr.Radio(
show_label=True,
container=True,
interactive=True,
choices=STYLE_NAMES,
value=DEFAULT_STYLE_NAME,
label="Image Style",
)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=50.0,
step=0.1,
value=10,
)
# Right column
with gr.Column(scale=1, elem_id="right-container"):
result = gr.Gallery(label="Results", show_label=False, format="png", show_share_button=False, height=475)
gr.Examples(
elem_id="example-container",
examples = examples,
inputs = [prompt]
)
gr.HTML(
"""
<div class="footer">
<p>
This application harnesses the cutting-edge Stable Diffusion XL (SDXL) model by <a href="https://huggingface.co/stabilityai" style="text-decoration: underline;" target="_blank">StabilityAI</a>, offering unparalleled text-to-image generation, while acknowledging potential biases and content considerations outlined in the model card.</p>
</p>
</div>
"""
)
run_button.click(
fn = infer,
inputs = [prompt, negative_prompt, width, height, guidance_scale, style_selection],
outputs = [result]
)
demo.queue().launch()