import gradio as gr import requests from pydantic import BaseModel from diffusers.utils import load_image SDXL_LORA_API_URL = 'http://127.0.0.1:8000/api/v1/product-diffusion/sdxl_v0_lora_inference' # Define the InpaintingRequest model class InpaintingRequest(BaseModel): prompt: str num_inference_steps: int guidance_scale: float negative_prompt: str num_images: int mode: str def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode): # Prepare the payload for SDXL LORA API payload = InpaintingRequest( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, num_images=num_images, mode=mode ).model_dump() response = requests.post(SDXL_LORA_API_URL, json=payload) response_json = response.json() url = response_json['url'] image = load_image(url) return image with gr.Blocks(theme='gradio/soft') as demo: with gr.Tab("SDXL LORA TEXT-TO-IMAGE"): with gr.Row(): with gr.Column(scale=1): prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here") negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here") with gr.Column(scale=1): num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps") guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale") num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images") mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode") generate_button = gr.Button("Generate Image") image_preview = gr.Image(label="Generated Image", height=512, width=512,scale=1,show_download_button=True,show_share_button=True,container=True) generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview]) demo.launch()