app.py

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

model_id = 'wavymulder/lomo-diffusion'
prefix = 'lomo style,'

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

pipe = StableDiffusionPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler)

pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
  model_id,
  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
  scheduler=scheduler)

if torch.cuda.is_available():
  pipe = pipe.to("cuda")
  pipe_i2i = pipe_i2i.to("cuda")

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


def _parse_args(prompt, generator):
        parser = argparse.ArgumentParser(
            description="making it work."
        )
        parser.add_argument(
            "--no-half-vae", help="no half vae"
        )

        cmdline_args = parser.parse_args()
        command = cmdline_args.command
        conf_file = cmdline_args.conf_file
        conf_args = Arguments(conf_file)
        opt = conf_args.readArguments()

        if cmdline_args.config_overrides:
            for config_override in cmdline_args.config_overrides.split(";"):
                config_override = config_override.strip()
                if config_override:
                    var_val = config_override.split("=")
                    assert (
                        len(var_val) == 2
                    ), f"Config override '{var_val}' does not have the form 'VAR=val'"
                    conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)

def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
  prompt = f"{prefix} {prompt}" if auto_prefix else prompt

  try:
    if img is not None:
      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
    else:
      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]

def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):

    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
    result = pipe_i2i(
        prompt,
        negative_prompt = neg_prompt,
        init_image = img,
        num_inference_steps = int(steps),
        strength = strength,
        guidance_scale = guidance,
        width = width,
        height = height,
        generator = generator)
        
    return result.images[0]

    def fake_safety_checker(images, **kwargs):
      return result.images[0], [False] * len(images)
    
    pipe.safety_checker = fake_safety_checker

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 style="color:yellow;">📸 Lomo Diffusion 📸</h1>
              </div>
              <p>
               Demo for <a href="https://huggingface.com/wwavymulder/lomo-diffusion">Lomo Diffusion</a> 
               Stable Diffusion model by <a href="https://huggingface.co/wavymulder/"><abbr title="Wavymulder">Wavymulder</abbr></a>. {"" if prefix else ""}  
              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. 
               </p>
             <p>Please use the prompt template below to achieve the desired result:
               </p>
               
<b>Prompt</b>:
<details><code>
lomo style photograph of <b>* subject * </b>, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, realistic, photo-realistic, full length frame, High detail RAW color art, piercing, diffused soft lighting, shallow depth of field, sharp focus, hyperrealism, cinematic lighting
<br>
<br>
<q><i>Example: lomo style photograph of Heath Ledger as Batman</i></q>
</code></details>
<q><em>Important note: Lomo Diffusion works best at a 1:1 aspect ratio, it is also successful using tall aspect ratios as well.</em></q>
<br>
<b>Negative Prompt</b>:
<details><code>
blender illustration hdr, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature 
</code></details>
<br>
Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>
<br>
            </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")
              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (portrait+ style,)", value=prefix, visible=prefix)

              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=7, maximum=15)
                steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1)

              with gr.Row():
                width = gr.Slider(label="Width", value=768, minimum=64, maximum=1024, step=8)
                height = gr.Slider(label="Height", value=768, minimum=64, maximum=1024, step=8)

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

          with gr.Tab("Image to image"):
              with gr.Group():
                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)

    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)

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

    

demo.queue(concurrency_count=1)
demo.launch()