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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()
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