from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import cpuinfo
import torch
from PIL import Image
from diffusers import OnnxStableDiffusionPipeline
import pipeline_openvino_stable_diffusion

model_id = 'OFA-Sys/small-stable-diffusion-v0'
prefix = ''

scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")


onnx_pipe = OnnxStableDiffusionPipeline.from_pretrained(
    "OFA-Sys/small-stable-diffusion-v0",
    revision="onnx",
    provider="CPUExecutionProvider",
)
pipe = pipeline_openvino_stable_diffusion.OpenVINOStableDiffusionPipeline.from_onnx_pipeline(onnx_pipe)

def error_str(error, title="Error"):
    return f"""#### {title}
            {error}"""  if error else ""

def inference(prompt, guidance, steps, width=512, height=512, seed=0, neg_prompt=""):

  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None

  try:
    return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
  except Exception as e:
    return None, error_str(e)

def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):

    result = pipe(
      prompt,
      negative_prompt = neg_prompt,
      num_inference_steps = int(steps),
      guidance_scale = guidance,
      width = width,
      height = height,
      generator = generator)

    return result.images[0]


css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
    gr.HTML(
        f"""
            <div class="main-div">
              <div>
                <h1>Small Stable Diffusion V0</h1>
              </div>
              <p>
               Demo for <a href="https://huggingface.co/OFA-Sys/small-stable-diffusion-v0">Small Stable Diffusion V0</a> Stable Diffusion model.<br>

              </p>
              Running on CPUs with <a href="https://github.com/OFA-Sys/diffusion-deploy">diffusion-deploy</a> to speedup the inference.

            </div>
        """
    )
    with gr.Row():

        with gr.Column(scale=55):
          with gr.Group():
              with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))

              image_out = gr.Image(height=512)
          error_output = gr.Markdown()

        with gr.Column(scale=45):
          with gr.Tab("Options"):
            with gr.Group():
              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")


              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                steps = gr.Slider(label="Steps", value=15, minimum=2, maximum=75, step=1)

              with gr.Row():
                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)

              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)




    inputs = [prompt, guidance, steps, width, height, seed, neg_prompt]
    outputs = [image_out, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs)

    gr.HTML("""
    <div style="border-top: 1px solid #303030;">
      <br>
      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
    </div>
    """)

print(cpuinfo.get_cpu_info())
demo.queue(concurrency_count=1)
demo.launch()