import gradio as gr
import torch
from diffusers import DiffusionPipeline, AutoencoderKL

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae, torch_dtype=torch.float16, variant="fp16",
    use_safetensors=True
)

# This is where you load your trained weights
pipe.load_lora_weights("victor/outicon")

pipe.to("cuda")

def infer (prompt):
    image = pipe(prompt=prompt, num_inference_steps=50).images[0]
    return image

css = """
#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
"""
with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        prompt_in = gr.Textbox(label="Prompt")
        submit_btn = gr.Button("Submit")
        image_out = gr.Image(label="Image output")

    submit_btn.click(
        fn = infer,
        inputs = [prompt_in],
        outputs = [image_out]
    )

demo.queue().launch()