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Create app.py
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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from rembg import remove
import torch
# ===== εˆε§‹εŒ–ζ¨‘εž‹ =====
device = "cuda" if torch.cuda.is_available() else "cpu"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
if torch.cuda.is_available():
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/sdxl-turbo",
use_safetensors=True
)
pipe = pipe.to(device)
# ===== εŠŸθƒ½ε‡½ζ•Έ =====
def generate_anime(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=f"{prompt}, Anime",
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
return image
def remove_background(input_img):
if input_img is None:
return None
return remove(input_img)
# ===== Gradio δ»‹ι’θ¨­θ¨ˆ =====
examples = [
"A well-behaved schoolgirl with glasses",
"Astronaut in a jungle, cold color palette, 8k",
"An astronaut riding a green horse",
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("## 🧠 Anime Character Generator + Background Remover")
# Prompt row
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Describe your anime character...",
container=False,
)
run_button = gr.Button("🎨 Generate Anime")
# Output image (before and after remove background)
with gr.Row():
result_img = gr.Image(label="Generated Image")
removed_img = gr.Image(label="Background Removed")
# Advanced settings
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=True
)
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=512)
height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
with gr.Row():
guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
num_inference_steps = gr.Slider(label="Steps", minimum=1, maximum=12, step=1, value=2)
# η―„δΎ‹ζŒ‰ιˆ•ε€
gr.Markdown("#### ✨ Prompt Examples")
with gr.Row():
for example in examples:
gr.Button(example).click(lambda x=example: x, outputs=prompt)
# δΈ»ζŒ‰ιˆ• callback
run_button.click(
fn=generate_anime,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result_img]
).then(
fn=remove_background,
inputs=[result_img],
outputs=[removed_img]
)
demo.queue().launch(share=True)