app.py

from email import generator
from diffusers import DiffusionPipeline

import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont
## VAE - Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
from diffusers import  AutoencoderKL




model = "stabilityai/stable-diffusion-xl-base-1.0"
finetuningLayer = "bbsgp/10xFWDLora"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch_dtype = torch.float16 if device.type == 'cuda' else torch.float32



import os
# HF_API_TOKEN = os.getenv("HF_API_TOKEN")

# from huggingface_hub import login
# login(token=HF_API_TOKEN)


vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype)
pipe = DiffusionPipeline.from_pretrained(
    model,
    vae=vae, 
    torch_dtype=torch_dtype, 
    use_safetensors=True
)
pipe.load_lora_weights(finetuningLayer)

pipe = pipe.to(device)




def create_error_image(message):
    # Create a blank image with white background
    width, height = 512, 512
    image = Image.new('RGB', (width, height), 'white')
    draw = ImageDraw.Draw(image)
    
    # Load a truetype or opentype font file
    font = ImageFont.load_default()
    
    # Position and message

    draw.text((127,251), message, font=font, fill="black")
    
    return image

def inference(model,finetuningLayer, prompt, guidance, steps, seed):

  

  if not prompt:
    return create_error_image("Sorry, add your text prompt and try again!!")
  else:
    generator = torch.Generator(device).manual_seed(seed)
    image = pipe(
    prompt,
    num_inference_steps=int(steps),
    guidance_scale=guidance,
    generator=generator).images[0]

    return image


css = """
  <style>
  .finetuned-diffusion-div {
      text-align: center;
      max-width: 700px;
      margin: 0 auto;
    }
    .finetuned-diffusion-div div {
      display: inline-flex;
      align-items: center;
      gap: 0.8rem;
      font-size: 1.75rem;
    }
    .finetuned-diffusion-div div h1 {
      font-weight: 900;
      margin-bottom: 7px;
    }
    .finetuned-diffusion-div p {
      margin-bottom: 10px;
      font-size: 94%;
    }
    .finetuned-diffusion-div p a {
      text-decoration: underline;
    }
  </style>
"""
with gr.Blocks(css=css) as demo:
    gr.HTML(
        """
            <div class="finetuned-diffusion-div">
              <div>
                <h1>Finetuned Diffusion</h1>
              </div>
            </div>
        """
    )
    with gr.Row():

        with gr.Column():

            model = gr.Dropdown(label="baseModel",choices=[model], default=model) 
            finetuningLayer=  gr.Dropdown(label="Finetuning Layer", choices=[finetuningLayer],  default=finetuningLayer)
            prompt = gr.Textbox(label="Prompt", placeholder="photo of 10xFWD style, 2D flat illustration - it is unique identifier need to be used to identify 10xFWD")
            

            with gr.Accordion("Advanced options", open=True):
              guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
              steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2)
              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)

            run = gr.Button(value="Run")
            gr.Markdown(f"Running on: {device}")
        with gr.Column():
            image_out = gr.Image()

    ## Add prompt and press enter to run
    ##prompt.submit(inference, inputs=[model, finetuningLayer,prompt, guidance, steps, seed], outputs=image_out)
    
    ## Click run button to run
    run.click(inference, inputs=[model,  finetuningLayer, prompt, guidance, steps, seed], outputs=image_out)


    demo.queue(default_enabled=True).launch(share=True,debug=True)

# demo.queue()
# demo.launch(auth=("FWDDNA", "10XFWD"),share=True)