import gradio as gr

from diffusers import StableDiffusionXLPipeline, DDIMScheduler
import torch
import sa_handler

# init models

scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
                              set_alpha_to_one=False)
pipeline = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True,
    scheduler=scheduler
).to("cuda")

pipeline.enable_model_cpu_offload() 
pipeline.enable_vae_slicing()

handler = sa_handler.Handler(pipeline)
sa_args = sa_handler.StyleAlignedArgs(share_group_norm=False,
                                      share_layer_norm=False,
                                      share_attention=True,
                                      adain_queries=True,
                                      adain_keys=True,
                                      adain_values=False,
                                     )

handler.register(sa_args, )

# run StyleAligned

def infer(prompts):
    sets_of_prompts = [
      "a toy train. macro photo. 3d game asset",
      "a toy airplane. macro photo. 3d game asset",
      "a toy bicycle. macro photo. 3d game asset",
      "a toy car. macro photo. 3d game asset",
      "a toy boat. macro photo. 3d game asset",
    ]

    images = pipeline(sets_of_prompts,).images
    return images

gr.Interface(
    fn=infer,
    inputs=[
        gr.Textbox(value="Hit submit button to test")
    ],
    outputs=[
        gr.Gallery()
    ],
    title="Style Aligned Image Generation"
).launch()