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import gradio as gr
import replicate
DEPLOYMENT_URIS = {
"Lora 500": "dd-ds-ai/abendblatt-lora-500",
"Lora 1000": "dd-ds-ai/lora-test-01-deployment-test",
"Lora 2000": "dd-ds-ai/abendblatt-lora-2000"
}
def generate_image(model_selection, lora_scale, guidance_scale, prompt_strength, num_steps, prompt):
deployment_uri = DEPLOYMENT_URIS[model_selection]
deployment = replicate.deployments.get(deployment_uri)
prediction = deployment.predictions.create(
input={
"model": "dev",
"lora_scale": lora_scale,
"num_outputs": 1,
"aspect_ratio": "1:1",
"output_format": "webp",
"guidance_scale": guidance_scale,
"output_quality": 90,
"prompt_strength": prompt_strength,
"extra_lora_scale": 1,
"num_inference_steps": num_steps,
"prompt": prompt
}
)
prediction.wait()
output = prediction.output
image_url = output[0] if output else None
return image_url
# Gradio-Interface erstellen
def create_gradio_interface():
model_selection = gr.Radio(choices=["Lora 500", "Lora 1000", "Lora 2000"], label="Model Selection", value="Lora 1000")
lora_scale = gr.Slider(0, 2, value=1, step=0.1, label="Lora Scale")
guidance_scale = gr.Slider(1, 10, value=3.5, step=0.1, label="Guidance Scale")
prompt_strength = gr.Slider(0, 1, value=0.8, step=0.1, label="Prompt Strength")
num_steps = gr.Slider(1, 50, value=28, step=1, label="Number of Inference Steps")
prompt = gr.Textbox(label="Prompt", value="a person reading the hamburger abendblatt newspaper")
generate_btn = gr.Button("Bild generieren")
interface = gr.Interface(
fn=generate_image,
inputs=[model_selection, lora_scale, guidance_scale, prompt_strength, num_steps, prompt],
outputs=gr.Image(label="Generated Image"),
)
interface.launch(share=True)
# Starte die Gradio-App
if __name__ == "__main__":
create_gradio_interface()
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