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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
# Load the model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
# Constants
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Style list for prompt customization
style_list = [
{"name": "D&D Art", "prompt": "dungeons & dragons style artwork {prompt}. d&d style, key visual, vibrant, studio anime, highly detailed", "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast"},
{"name": "Dark Fantasy", "prompt": "dark and moody dungeons & dragons artwork of {prompt}. gothic ruins, shadowy figures, haunting atmospheres, grim villains, muted colors, intricate textures, sinister undertones", "negative_prompt": "bright, cheerful, cartoonish, lighthearted, futuristic, deformed"},
{"name": "Epic Battle", "prompt": "dynamic dungeons & dragons artwork of {prompt}. epic battle scene, legendary heroes, fierce monsters, intense action, dramatic lighting, high-detail environment, magical effects, vibrant colors", "negative_prompt": "peaceful, mundane, low energy, modern, sci-fi, simplistic, cartoonish, low contrast"},
# Add additional styles as needed
{"name": "(No style)", "prompt": "{prompt}", "negative_prompt": ""},
]
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "D&D Art"
# Function to apply selected style
def apply_style(style_name: str, positive: str, negative: str = ""):
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
return p.replace("{prompt}", positive), n + (negative or "")
# Inference function
@spaces.GPU()
def infer(
prompt,
style,
seed=42,
randomize_seed=False,
width=1024,
height=1024,
num_inference_steps=4,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Apply style to prompt
styled_prompt, negative_prompt = apply_style(style, prompt)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=styled_prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=0.0,
negative_prompt=negative_prompt,
).images[0]
return image, seed
# Example prompts
examples = [
["A heroic adventurer wielding a flaming sword standing on a cliff", "D&D Art"],
["A mystical library with ancient scrolls and glowing runes", "Dark Fantasy"],
["A ferocious dragon breathing fire in a dark cavern", "Epic Battle"],
]
# Custom CSS for a Dungeons & Dragons theme
css = """
body {
background-color: #1b1b1b;
font-family: 'Cinzel', serif;
color: #f5f5f5;
background-image: url('https://www.transparenttextures.com/patterns/dark-matter.png');
}
#col-container {
margin: 0 auto;
max-width: 550px;
padding: 15px;
border: 4px solid #8b4513;
background: linear-gradient(145deg, #2e2b2a, #3a3433);
border-radius: 15px;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.8);
}
"""
# Interface
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
# Title and Description
gr.Markdown(
"""
# 🛡️ ChatDnD.net Dungeons & Dragons Image Generator ⚔️
**Unleash Your Imagination!** Create heroes, maps, quests, and epic scenes to bring your campaigns to life.
Tailored for adventurers seeking inspiration or Dungeon Masters constructing their next grand story. <br>
[Visit Our Website](https://chatdnd.net) | [Support Us](https://buymeacoffee.com/watchoutformike)
"""
)
# Prompt input and style selector
with gr.Row():
prompt = gr.Textbox(
label="🎲 Describe Your Vision:",
lines=3,
placeholder="Describe your hero, monster, or legendary landscape..."
)
style = gr.Dropdown(
label="🎨 Select a Style",
choices=STYLE_NAMES,
value=DEFAULT_STYLE_NAME,
)
# Run button and result display
with gr.Row():
run_button = gr.Button("Generate Image")
result = gr.Image(label="🖼️ Your Legendary Vision")
# Advanced settings
with gr.Accordion("⚙️ Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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,
)
num_inference_steps = gr.Slider(
label="Inference Steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
# Examples with styles
gr.Examples(
examples=examples,
inputs=[prompt, style],
outputs=[result],
fn=infer,
cache_examples="lazy",
)
# Interactivity
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, style, seed, randomize_seed, width, height, num_inference_steps],
outputs=[result, seed],
)
# Launch the demo
demo.launch()
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