import gradio as gr
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from transformers import DPTImageProcessor, DPTForDepthEstimation
import torch
import mediapy
import sa_handler
import pipeline_calls



# init models

depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
feature_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")

controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-depth-sdxl-1.0",
    variant="fp16",
    use_safetensors=True,
    torch_dtype=torch.float16,
).to("cuda")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    vae=vae,
    variant="fp16",
    use_safetensors=True,
    torch_dtype=torch.float16,
).to("cuda")
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()

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 = sa_handler.Handler(pipeline)
handler.register(sa_args, )




# run ControlNet depth with StyleAligned
def style_aligned_controlnet(ref_style_prompt, depth_map, ref_image, img_generation_prompt):
    if depth_map == True:
        image = load_image(ref_image)
        depth_image = pipeline_calls.get_depth_map(image, feature_processor, depth_estimator)
    else:
        depth_image = load_image(ref_image).resize((1024, 1024))
    #reference_prompt = ref_style_prompt #"a poster in minimalist origami style"
    #target_prompts = img_generation_prompt #["mona lisa"] #, "gal gadot"]
    controlnet_conditioning_scale = 0.8
    num_images_per_prompt = 3 # adjust according to VRAM size
    latents = torch.randn(1 + num_images_per_prompt, 4, 128, 128).to(pipeline.unet.dtype)
    latents[1:] = torch.randn(num_images_per_prompt, 4, 128, 128).to(pipeline.unet.dtype)
    images = pipeline_calls.controlnet_call(pipeline, [ref_style_prompt, img_generation_prompt],
                                            image=depth_image,
                                            num_inference_steps=50,
                                            controlnet_conditioning_scale=controlnet_conditioning_scale,
                                            num_images_per_prompt=num_images_per_prompt,
                                            latents=latents)
    #mediapy.show_images([images[0], depth_image2] +  images[1:], titles=["reference", "depth"] + [f'result {i}' for i in range(1, len(images))])
    return [images[0], depth_image] +  images[1:], gr.Image(value=images[0], visible=True)


with gr.Blocks() as demo:
    
    with gr.Row():
      
      with gr.Column(variant='panel'):
        ref_style_prompt = gr.Textbox(
          label='Reference style prompt',
          info="Enter a Prompt to generate the reference image", placeholder='a poster in <style name> style'
        )
        depth_map = gr.Checkbox(label='Depth-map',)
        ref_style_image = gr.Image(visible=False, label='Reference style image')
      
      with gr.Column(variant='panel'): 
        ref_image = gr.Image(label="Upload the reference image", 
                             type='filepath' )
        img_generation_prompt = gr.Textbox(
            label='ControlNet Prompt',
            info="Enter a Prompt to generate images using ControlNet and Style-aligned", 
            )
    
    btn = gr.Button("Generate", size='sm')
    gallery = gr.Gallery(label="Style-Aligned ControlNet - Generated images", 
                           elem_id="gallery",
                           columns=5, 
                           rows=1, 
                           object_fit="contain", 
                           height="auto",
                          )
      
    btn.click(fn=style_aligned_controlnet, 
              inputs=[ref_style_prompt, depth_map, ref_image, img_generation_prompt], 
              outputs=[gallery, ref_style_image], 
              api_name="style_aligned_controlnet")


demo.launch()