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import gradio as gr
from diffusers import StableDiffusionXLPipeline
import torch
# Lade das Modell
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.to("cuda")
# Bildgenerierungsfunktion
def generate(prompt, negative_prompt, width, height, steps, guidance, seed, progress=gr.Progress()):
generator = torch.manual_seed(seed) if seed != -1 else None
with progress.tqdm(total=steps) as pbar:
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=steps,
guidance_scale=guidance,
generator=generator,
callback=lambda i, t, s: pbar.update(1)
).images[0]
return image
# UI
with gr.Blocks() as demo:
gr.Markdown("# Stable Diffusion XL Generator")
with gr.Row():
prompt = gr.Textbox(label="Prompt", placeholder="z. B. futuristic city at sunset", lines=2)
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, low quality, distorted", lines=1)
with gr.Row():
width = gr.Slider(label="Width", minimum=512, maximum=1024, step=64, value=768)
height = gr.Slider(label="Height", minimum=512, maximum=1024, step=64, value=768)
with gr.Row():
steps = gr.Slider(label="Steps", minimum=10, maximum=100, step=1, value=30)
guidance = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.5, value=7.5)
seed = gr.Slider(label="Seed (set -1 for random)", minimum=-1, maximum=999999, step=1, value=-1)
run_button = gr.Button("Generate")
output = gr.Image(label="Output")
run_button.click(
fn=generate,
inputs=[prompt, negative_prompt, width, height, steps, guidance, seed],
outputs=output,
)
demo.launch()
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