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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("codermert/zehra_flux", torch_dtype=dtype).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, num_images=4, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
images = []
for _ in range(num_images):
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=0.0
).images[0]
images.append(image)
# Her görsel için farklı seed kullan
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
return images, seed
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css = """
#col-container {
margin: 0 auto;
max-width: 900px;
}
.generated-images {
display: grid;
grid-template-columns: repeat(2, 1fr);
gap: 10px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""# Zehra Flux Image Generator
4 farklı görsel üreten AI görsel oluşturucu
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Görseliniz için prompt girin",
container=False,
)
run_button = gr.Button("Oluştur", scale=0)
# 4 görsel için grid layout
with gr.Row(elem_classes="generated-images"):
results = [gr.Image(label=f"Sonuç {i+1}", show_label=True) for i in range(4)]
with gr.Accordion("Gelişmiş Ayarlar", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Rastgele seed", value=True)
with gr.Row():
width = gr.Slider(
label="Genişlik",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Yükseklik",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
num_inference_steps = gr.Slider(
label="Inference adım sayısı",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(
examples=examples,
fn=infer,
inputs=[prompt],
outputs=[*results, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs=[*results, seed]
)
demo.launch() |