| from gradio_client import Client | |
| import gradio as gr | |
| client = Client("kratadata/Colabor-Image-Finetune") | |
| def generate_image( | |
| prompt, lora_model, aspect_ratio, | |
| num_inference_steps, guidance_scale, | |
| seed, lora_scale | |
| ): | |
| result = client.predict( | |
| lora_model=lora_model, | |
| prompt=prompt, | |
| aspect_ratio=aspect_ratio, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| seed=seed, | |
| lora_scale=lora_scale, | |
| api_name="/generate" | |
| ) | |
| return result | |
| iface = gr.Interface( | |
| fn=generate_image, | |
| inputs=[ | |
| gr.Textbox(label="Prompt", value="policewoman"), | |
| gr.Dropdown(["group1"], label="LoRA Model"), | |
| gr.Dropdown(["1:1", "16:9", "9:16"], label="Aspect Ratio", value="1:1"), | |
| gr.Slider(1, 100, step=1, label="Inference Steps", value=28), | |
| gr.Slider(0, 20, step=0.1, label="Guidance Scale", value=3), | |
| gr.Number(label="Seed (-1 for random)", value=-1), | |
| gr.Slider(0, 5, step=0.1, label="LoRA Scale", value=1), | |
| ], | |
| outputs=gr.Image(label="Generated Image"), | |
| title="LoRA Image Generator - kratadata/Colabor-Image-Finetune", | |
| ) | |
| iface.launch() | |