from diffusers import StableDiffusionPipeline
import gradio as gr

pipe = StableDiffusionPipeline.from_pretrained("cjayic/late-stage-jerma")

def inference(prompt, guidance, steps):
    all_images = [] 
    images = pipe([prompt] * 1, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images
    all_images.extend(images)
    return all_images

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="prompt")
            guidance = gr.Slider(label="guidance scale", value=7.5, maximum=15)
            steps = gr.Slider(label="steps", value=50, maximum=100, minimum=2)
            run = gr.Button(value="Run")
        with gr.Column():
            gallery = gr.Gallery(show_label=False)

    run.click(inference, inputs=[prompt, guidance, steps], outputs=gallery)
    gr.Examples([
        ["a photo of sks jeremy elbertson disassembling the demon core", 7.5, 50],
        ["jeremy elbertson late victorian era portrait painting", 7.0, 75],
        ["realistic high-quality upper body portrait of a cyberpunk criminal in the red light district of night city, leaning back on their car, artstation front page, dramatic afternoon lighting, cityscape background", 4.4, 23],
        ["fantasy portrait painting, digital art", 4, 30],
    ], [prompt, guidance, steps], gallery, inference, cache_examples=False)

demo.queue()
demo.launch()