# Ref: https://huggingface.co./spaces/multimodalart/cosxl
import gradio as gr
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import spaces 
import torch 
import os

model_id = "aipicasso/emi-2"
token=os.environ["TOKEN"]

scheduler = EulerAncestralDiscreteScheduler.from_pretraind(model_id,token=token)
pipe_normal = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.bfloat16,token=token)
pipe_normal.to("cuda")

@spaces.GPU
def run_normal(prompt, negative_prompt="", guidance_scale=7, progress=gr.Progress(track_tqdm=True)):
    return pipe_normal(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=20).images[0]

normal_examples = ["portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography", "backlit photography of a dog", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece"]
with gr.Blocks(css=css) as demo:
    gr.Markdown('''# Emi 2
    Official demo for Emi 2
    ''')
    with gr.Group():
        with gr.Row():
          prompt_normal = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt, e.g.: backlit photography of a dog")
          button_normal = gr.Button("Generate", min_width=120)
        output_normal = gr.Image(label="Your result image", interactive=False)
        with gr.Accordion("Advanced Settings", open=False):
          negative_prompt_normal = gr.Textbox(label="Negative Prompt")
          guidance_scale_normal = gr.Number(label="Guidance Scale", value=7)
    gr.Examples(examples=normal_examples, fn=run_normal, inputs=[prompt_normal], outputs=[output_normal], cache_examples=True) 
    
    gr.on(
        triggers=[
            button_normal.click,
            prompt_normal.submit
        ],
        fn=run_normal,
        inputs=[prompt_normal, negative_prompt_normal, guidance_scale_normal],
        outputs=[output_normal],
    )
if __name__ == "__main__":
    demo.launch(share=True)